Handbook of Seafood and Seafood Products Analysis [2 ed.] 1032266783, 9781032266787

Citation preview

Handbook of Seafood and ­Seafood Products Analysis Seafood and seafood products represent some of the most important foods in almost all types of societies around the world. More intensive production of fish and shellfish to meet high demand has raised some concerns related to the nutritional and sensory qualities of these cultured fish in comparison to their wild-catch counterparts. In addition, the variety in processing, preservation, and storage methods from traditional to modern is contributing to an increase in variability in consumer products. This second edition of the Handbook of Seafood and Seafood Products Analysis brings together the work of 109 experts who focus on the most recent research and development trends in analytical techniques and methodologies for the analysis of captured fresh and preserved seafood, either cultivated or wild, as well as for derived products. After providing a general introduction, this handbook provides 48 chapters distributed in six sections: • Chemistry and biochemistry focuses on the analysis of main chemical and biochemical compounds of seafood. • Processing control describes the analysis of technological quality and the use of some non-destructive techniques as well as methods to check freshness, detection of species, and geographic origin and to evaluate smoke flavoring. • Nutritional quality deals with the analysis of nutrients in seafood such as essential amino acids, bioactive peptides, antioxidants, vitamins, minerals and trace elements, and fatty acids. • Sensory quality covers the sensory quality and main analytical tools to determine color, texture, flavor and off-flavor, quality index methods as well as sensory descriptors, sensory aspects of heat-treated seafood, and sensory perception. • Biological Safety looks at tools for the detection of spoilage, pathogens, parasites, viruses, marine toxins, antibiotics, and GM ingredients. • Chemical Safety focuses on the identification of fish species, detection of adulterations, veterinary drug residues, irradiation, food contact materials, and chemical toxic compounds from the environment, generated during processing or intentionally added. Key Features: • This comprehensive handbook provides a full overview of the tools now available for the analysis of captured fresh and preserved seafood, either cultivated or wild, as well as for derived products. • This is a comprehensive and informative book that presents both the merits and limitations of analytical techniques and also gives future developments for guaranteeing the quality of seafood and seafood products. • This cutting-edge work covers processes used from all of the seven seas to ensure that consumers find safe, nutritionally beneficial, and appealing seafood products at their markets and restaurants. • This handbook covers the main types of worldwide available analytical techniques and methodologies for the analysis of seafood and seafood products.

Handbook of Seafood and ­Seafood Products Analysis Second Edition

Edited by

Fidel Toldrá and Leo M.L. Nollet

Cover credit: ©Shutterstock Second edition published 2024 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2024 selection and editorial matter, Fidel Toldrá and Leo M.L. Nollet individual chapters, the contributors First edition published by CRC Press 2009 Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. ISBN: 9781032266787 (hbk) ISBN: 9781003289401 (ebk) DOI: 10.1201/9781003289401 Typeset in Times by codeMantra

Contents Preface................................................................................................................................................x About the Editors..............................................................................................................................xii List of Contributors..........................................................................................................................xiv

PART 1  Chemistry and Biochemistry Chapter 1 Introduction: Importance of Analysis in Seafood and Seafood Products, Variability, and Basic Concepts....................................................................................3 Vikas Kumar and Anisa Mitra Chapter 2 Peptides and Proteins.................................................................................................. 18 Turid Rustad, Eva Falch, Rasa Slizyte, and Abhilash Sasidharan Chapter 3 Proteomics for Comprehensive Analysis of Seafood Products: Quality, Safety, Authentication, and Allergen Detection............................................ 27 Marco Cerqueira, Cláudia Raposo de Magalhães, Denise Schrama, Ana Paula Farinha, Raquel Carrilho, and Pedro Rodrigues Chapter 4 Endogenous Enzymes................................................................................................. 72 Ishita Ghosh, Han Liu, Benjamin K. Simpson, and Yi Zhang Chapter 5 Nucleotides and Nucleosides..................................................................................... 105 M-Concepción Aristoy, Leticia Mora, and Fidel Toldrá Chapter 6 Lipid Compounds...................................................................................................... 116 Santiago P. Aubourg Chapter 7 Lipid Oxidation......................................................................................................... 136 Turid Rustad and Eva Falch Chapter 8 Volatile Aroma Compounds in Marine Resources................................................... 145 Rósa Jónsdóttir and Guðrún Ólafsdóttir

PART 2  Processing Control Chapter 9 Microstructure........................................................................................................... 169 Isabel Hernando, Empar Llorca, and Amparo Quiles v

vi

Contents

Chapter 10 Physical Sensors and Techniques.............................................................................. 190 Daniel Cozzolino Chapter 11 Methods for Measuring Seafood Freshness Quality and Deterioration................... 198 Yesim Ozogul Chapter 12 Detection of Species and Geographic Origin of Fishery Products........................... 231 Michiaki Yamashita, Yasuharu Takashima, Jun Iguchi, Atsushi Namikoshi, and Yumiko Yamashita Chapter 13 Smoke Flavourings Technology in Seafood..............................................................240 Leo M.L. Nollet

PART 3  Nutritional Quality Chapter 14 Composition and Calories......................................................................................... 263 Eva Falch, Rasa Slizyte, Turid Rustad, and Ida-Johanne Jensen Chapter 15 Essential Amino Acids.............................................................................................. 287 M-Concepción Aristoy and Fidel Toldrá Chapter 16 Bioactive Peptides from Seafood Products...............................................................307 Ola Abdelhedi, Ali Salem, Mourad Jridi, Moncef Nasri, and Rim Nasri Chapter 17 Antioxidants.............................................................................................................. 343 Semih Otles and Vasfiye Hazal Özyurt Chapter 18 Vitamins....................................................................................................................348 Sheetal Mital and Akansha Agrwal Chapter 19 Minerals and Trace Elements.................................................................................... 370 K. S. Siddiqi, Leo M.L. Nollet, and Azamal Husen Chapter 20 Fatty Acids................................................................................................................ 387 Annalaura Lopez, Federica Bellagamba, and Vittorio Maria Moretti

vii

Contents

PART 4  Sensory Quality Chapter 21 Quality Assessment of Seafood and Seafood Products by Color Measurement.......409 Reinhard Schubring and Fidel Toldrá Chapter 22 Instrumental Texture................................................................................................. 431 Isabel Sánchez-Alonso and Mercedes Careche Chapter 23 Aroma........................................................................................................................ 447 Dimitris P. Balagiannis and J. Stephen Elmore Chapter 24 Quality Index Methods.............................................................................................. 476 Leo M.L. Nollet Chapter 25 Sensory Descriptors.................................................................................................. 496 Grethe Hyldig Chapter 26 Sensory Aspects of Heat Treated Seafood................................................................ 511 Grethe Hyldig Chapter 27 Methodologies in Sensory and Consumer Sciences for the Evaluation of Seafood Products....................................................................................................... 523 Elena Costa, Elizabeth S. Collier, and Jun Niimi

PART 5  Biological Safety Chapter 28 Detection of Seafood Spoilage Microorganisms....................................................... 545 Olumide A. Odeyemi, Deyan Stratev, Helen Onyeaka, Fera R. Dewi, and Muhamad Amin Chapter 29 Detection of Fish Spoilage........................................................................................ 560 Foteini F. Parlapani, Ioannis S. Boziaris, and Eleftherios H. Drosinos Chapter 30 Predictive Modeling of Seafood Spoilage................................................................. 586 Olumide A. Odeyemi, Deyan Stratev, Helen Onyeaka, and D. Sylvain Dabadé

viii

Contents

Chapter 31 Detection of the Principal Foodborne Pathogens in Seafood and Seafood-Related Environments................................................................................. 593 David Rodríguez-Lázaro, Nadine Yeramian, and Gislaine Fongaro Chapter 32 Seafood Parasites...................................................................................................... 615 Francisco Esteban Montero, Juan Antonio Raga, and Ana Pérez-del-Olmo Chapter 33 Detection of Enteric Viruses in Seafood................................................................... 645 Doris Sobral Marques Souza, Mariana Alves Elois, Beatriz Pereira Savi, Lucas Zanchetta, Marcel Afonso Provenzi, Marília Miotto, Gislaine Fongaro, and David Rodríguez-Lázaro Chapter 34 Analysis of Marine Biotoxins .................................................................................. 655 Arjen Gerssen Chapter 35 Biogenic Amines in Seafood Products...................................................................... 674 Claudia Ruiz-Capillas and Ana M. Herrero Chapter 36 Detection of Genetically Modified Ingredients in Fish Feed....................................690 Zahida Naseem, Shahida Naseem, Sajad Ahmad Mir, and Danish Rizwan

PART 6  Chemical Safety Chapter 37 Detection of Adulterations: Addition of Foreign Proteins........................................ 703 Véronique Verrez-Bagnis, Helena Silva, and Rogério Mendes Chapter 38 Detection of Adulteration: Identification of Seafood Species................................... 721 Negi Aditi, A. Kaarunya, and R. Meenatchi Chapter 39 DNA Barcoding Methodologies for the Identification of Fish Species in Cooked Products....................................................................................................... 748 Rosalee S. Hellberg Chapter 40 Differentiation between Fresh and Frozen–Thawed Fish......................................... 775 Leo M.L. Nollet Chapter 41 Veterinary Drugs....................................................................................................... 787 Faraat Ali, Hana Sklenářová, and Vazahat Ali

ix

Contents

Chapter 42 Spectrochemical Methods for the Determination of Metal(loid) Ions in Seafood.......................................................................................................... 796 Joseph Sneddon, Chad A. Thibodeaux, and Victor G. Mihucz Chapter 43 Food Irradiation and Its Detection............................................................................ 817 Leo M.L. Nollet Chapter 44 Analysis of Dioxins in Seafood and Seafood Products............................................. 834 Danish Rizwan, Sajad Ahmad Mir, and Zahida Naseem Chapter 45 Environmental Contaminants: Persistent Organic Pollutants................................... 852 Monia Perugini and Annamaria Iannetta Chapter 46 Innovations in Seafood Packaging Technologies...................................................... 868 Nalan Gokoglu Chapter 47 Residues of Food Contact Materials......................................................................... 890 Emma L. Bradley and Laurence Castle Chapter 48 Microplastics and Nanoplastics in Food................................................................... 915 Sadaf Jamal Gilani, Samar Zuhair Alshawwa, Mohammad Asif, Kaneez Fatima, and Najam Ali Khan Index............................................................................................................................................... 927

Preface There is a large number of seafood and seafood products. These foods represent some of the most important foods in almost all type of societies, including those in developed countries and developing countries. Intensive production of fish and shellfish has raised some concerns, some of them are related with the nutritional and sensory quality of the cultured fish in comparison to their wild catch counterpart. In addition, there is a large number of processing and preservation technologies, from traditional drying or curing to high-pressure processing and different methods of storage. This increasing variability in products attending the consumers´ demands makes necessary the use of adequate analytical methodologies focusing on the chemistry and biochemistry of post-mortem seafood, the technological, nutritional, and sensory quality, as well as safety aspects related to processing and preservation, all of them presented in this book. The first edition of the Handbook of Seafood and Seafood Products Analysis was published in 2010 and contained the main analytical techniques and methodologies and their applications. It presented a comprehensive review both in its detailing of analytical methods and its application to the compounds involved in sensory, nutritional, and technological quality as well as safety of seafood and seafood products, and its aims were to compile the top analysis techniques and methodologies from around the world into one, well-organized volume and to serve as an essential reference source to undergraduate and postgraduate students, researchers, and analysts in universities, research institutions, government agencies, and private companies. This will continue to be the purpose of this second edition. This second edition of the Handbook of Seafood and Seafood Products Analysis is bringing the most recent research and development trends in analytical techniques and methodologies. All chapters have been thoroughly revised and updated, some of them entirely renovated by different authors, and there are four new additional chapters covering the analytical techniques and methodologies for determining activity of endogenous enzymes, bioactive peptides, sensory perception of seafood, and detection of microplastics. The new edition of this book contains 48 chapters distributed in six parts: Part 1.- Chemistry and biochemistry (Chapters 1–8) focused on the analysis of main chemical and biochemical compounds of seafood; Part 2.- Processing Control (Chapters 9–13) describes the analysis of technological quality and the use of some non-destructive techniques as well as methods to check freshness, detection of species and geographic origin, and evaluation of smoke flavorings; Part 3.- Nutritional quality (Chapters 14–20) deals with the analysis of nutrients in seafood such as essential amino acids, bioactive peptides, antioxidants, vitamins, minerals and trace elements, and fatty acids; Part 4.- Sensory quality (Chapters 21–27) is related to the sensory quality and main analytical tools to determine color, texture, flavor and off-flavor, quality index methods, as well as sensory descriptors, sensory aspects of heat-treated seafood, and sensory perception; Part 5.- Biological Safety (Chapters 28–36) is devoted to analytical tools for the detection of spoilage, pathogens, parasites, viruses, marine toxins, antibiotics, and GM ingredients; and, finally, Part 6.- Chemical Safety (Chapters 37–48) is focused on the identification of fish species, detection of adulterations, veterinary drug residues, irradiation, food contact materials, and chemical toxic compounds from the environment, generated during processing, or intentionally added, which can be found in seafood either cultured or wild catched. This second edition provides a complete overview of the analytical tools available for all kinds of analyses of seafood and seafood products; the roles of these techniques and the methodologies for the analysis of technological, nutritional, and sensory quality as well as of safety aspects. In summary, readers find in this handbook the main types of worldwide available analytical techniques and methodologies for the analysis of seafood and seafood products. x

Preface

xi

The editors wish to thank all contributors for their excellent jobs and outstanding quality of the delivered manuscripts. Their work and achievements are really appreciated. The editors also wish to thank the production staff at CRC Press for their dedication and making this book possible. Fidel Toldrá & Leo Nollet Editors

About the Editors Fidel Toldrá, P  h.D. in Chemistry (University of Valencia, 1984) and Research Professor at the Department of Food Science, Instituto de Agroquímica y Tecnología de Alimentos (CSIC). He serves as Editor-in-Chief of Trends in Food Science & Technology (Elsevier), Associate Editor of Meat Science (Elsevier) and Editor of the book series Advances in Food & Nutrition Research (Academic Press/Elsevier). He has acted as Editor or Associate Editor of >50 books, being Editor with Leo Nollet of numerous books for CRC Press including the Handbook of Analysis of Active Compounds in Functional Foods (2012), the third editions of Food Analysis by HPLC (2013) and Handbook of Food Analysis (2015), and second edition of Handbook of Dairy Foods Analysis (2022). Prof. Toldrá was the Editor of the ninth edition of Lawrie’s Meat Science (2022, Elsevier) and one of the Editors-in-Chief of the Encyclopedia of Food & Health (Academic Press/Elsevier). Prof. Toldrá serves at the Editorial Board of 15 journals including Food Chemistry, Current Opinion in Food Science and Journal of Food Engineering. He has published >360 research articles (h index = 74) and >163 chapters of books, and edited/coedited >62 books. He is a Fellow of the Institute of Food Technologists (IFT), Fellow of the International Academy of Food Science & Technology (IAFOST), Fellow of the Agricultural and Food Chemistry Division of the American Chemical Society (AGFD-ACS) , and Fellow of the International Association of Advanced Materials (IAAM). He received, among others, the 2002 IMS International Prize for Meat Science and Technology, 2010 Distinguished Research Award, 2014 Meat Processing Award, and 2023 International Lectureship award, all three from the American Meat Science Association (AMSA), 2015 Dupont Science Award, the 2019 Innovation Award from the Association of Spanish Meat Industry, and the 2019 Award for Advancement of Application of Agricultural and Food Chemistry from the American Chemical Society (ACS). Leo M. L. Nollet earned an MS (1973) and PhD (1978) in biology from the Katholieke Universiteit Leuven, Belgium. He is an editor and associate editor of numerous books. He edited for M. Dekker, New York—now CRC Press of Taylor & Francis Publishing Group— the first, second, and third editions of Food Analysis by HPLC and Handbook of Food Analysis. The last edition is a two-volume book. Dr. Nollet also edited the Handbook of Water Analysis (first, second, and third editions) and Chromatographic Analysis of the Environment, third and fourth editions (CRC Press). With Fidel Toldrá, he coedited two books published in 2006, 2007, and 2017: Advanced Technologies for Meat Processing (CRC Press) and Advances in Food Diagnostics (Blackwell Publishing—now Wiley). With M. Poschl, he coedited the book Radionuclide Concentrations in Foods and the Environment, also published in 2006 (CRC Press). Dr. Nollet has also coedited with Y. H. Hui and other colleagues on several books: Handbook of Food Product Manufacturing (Wiley, 2007), Handbook of Food Science, Technology, and Engineering (CRC Press, 2005), Food Biochemistry and Food Processing (first and second editions; Blackwell Publishing—now Wiley—2006 and 2012), and the Handbook of Fruits and Vegetable Flavors (Wiley, 2010). In addition, he edited the Handbook of Meat, Poultry, and Seafood Quality, first and second editions (Blackwell Publishing—now Wiley—2007 and 2012). From 2008 to 2011, he published five volumes on xii

About the Editors

xiii

animal product-related books with Fidel Toldrá: Handbook of Muscle Foods Analysis, Handbook of Processed Meats and Poultry Analysis, Handbook of Seafood and Seafood Products Analysis, Handbook of Dairy Foods Analysis (second edition in 2021), and Handbook of Analysis of Edible Animal By-Products. Also, in 2011, with Fidel Toldrá, he coedited two volumes for CRC Press: Safety Analysis of Foods of Animal Origin and Sensory Analysis of Foods of Animal Origin. In 2012, they published the Handbook of Analysis of Active Compounds in Functional Foods. In a coedition with Hamir Rathore, Handbook of Pesticides: Methods of Pesticides Residues Analysis was marketed in 2009; Pesticides: Evaluation of Environmental Pollution in 2012; Biopesticides Handbook in 2015; and Green Pesticides Handbook: Essential Oils for Pest Control in 2017. Other finished book projects include Food Allergens: Analysis, Instrumentation, and Methods (with A. van Hengel; CRC Press, 2011) and Analysis of Endocrine Compounds in Food (Wiley-Blackwell, 2011). Dr. Nollet’s recent projects include Proteomics in Foods with Fidel Toldrá (Springer, 2013) and Transformation Products of Emerging Contaminants in the Environment: Analysis, Processes, Occurrence, Effects, and Risks with D. Lambropoulou (Wiley, 2014). In the series, Food Analysis & Properties, he edited (with C. Ruiz-Capillas) Flow Injection Analysis of Food Additives (CRC Press, 2015) and Marine Microorganisms: Extraction and Analysis of Bioactive Compounds (CRC Press, 2016). With A.S. Franca, he coedited Spectroscopic Methods in Food Analysis (CRC Press, 2017), and with Horacio Heinzen and Amadeo R. Fernandez-Alba he coedited Multiresidue Methods for the Analysis of Pesticide Residues in Food (CRC Press, 2017). Further volumes in the series Food Analysis & Properties are Phenolic Compounds in Food: Characterization and Analysis (with Janet Alejandra Gutierrez-Uribe, 2018), Testing and Analysis of GMO-containing Foods and Feed (with Salah E. O. Mahgoub, 2018), Fingerprinting Techniques in Food Authentication and Traceability (with K. S. Siddiqi, 2018), Hyperspectral Imaging Analysis and Applications for Food Quality (with N.C. Basantia, Leo M.L. Nollet, Mohammed Kamruzzaman, 2018), Ambient Mass Spectroscopy Techniques in Food and the Environment (with Basil K. Munjanja, 2019), Food Aroma Evolution: During Food Processing, Cooking, and Aging (with M. Bordiga, 2019), Mass Spectrometry Imaging in Food Analysis (2020), Proteomics in Food Authentication (with S. Ötleş, 2020), Analysis of Nanoplastics and Microplastics in Food (with K.S. Siddiqi, 2020), Chiral Organic Pollutants, Monitoring and Characterization in Food and the Environment (with Edmond Sanganyado and Basil K. Munjanja, 2020), Sequencing Technologies in Microbial Food Safety and Quality (with Devarajan Thangardurai, Saher Islam, Jeyabalan Sangeetha, 2021), Nanoemulsions in Food Technology: Development, Characterization, and Applications (with Javed Ahmad, 2021), Mass Spectrometry in Food Analysis (with Robert Winkler, 2022), Bioactive Peptides from Food: Sources, Analysis, and Functions (with Semih Ötles, 2022), and Nutriomics: Well-being through Nutrition (with Devarajan Thangadurai, Saher Islam Juliana Bunmi Adetunji, 2022). In 2023, he published with Javed Ahmad Analysis of Naturally Occurring Food Toxins of Plant Origin.

List of Contributors Ola Abdelhedi University of Sfax Sfax, Tunisia Negi Aditi Formerly Indian Institute of Food Processing Technology Thanjavur, India Akansha Agrwal Department of Applied Sciences KIET Group of institutions Ghaziabad, India Faraat Ali Charles University Hradec, Czech Republic Vazahat Ali Department of Chemistry Jamia Hamdard University New Delhi, India Samar Zuhair Alshawwa College of Pharmacy Princess Nourah bint Abdulrahman University Riyadh, Saudi Arabia Muhamad Amin University of Tasmania Launceston, Australia M-Concepción Aristoy Instituto de Agroquímica y Tecnología de Alimentos Consejo Superior de Investigaciones Científicas Paterna, Spain Mohammad Asif Faculty of Pharmacy Lachoo Memorial College of Science and Technology Jodhpur, India

xiv

Santiago P. Aubourg Instituto de Investigaciones Marinas Consejo Superior de Investigaciones Científicas Vigo, Spain Dimitris P. Balagiannis University of Reading Reading, United Kingdom Federica Bellagamba Department of Veterinary Medicine and Animal Science Università degli Studi di Milano Via dell'Università Lodi (LO), Italy Ioannis S. Boziaris University of Thessaly Volos, Greece Emma L. Bradley Fera Science Ltd., York Biotech Campus, Sand Hutton York, United Kingdom Mercedes Careche Department of Meat and Fishery Products Instituto de Ciencia y Tecnología de Alimentos y Nutrición (ICTAN-CSIC) Raquel Carrilho Universidade do Algarve Faro, Portugal Laurence Castle Fera Science Ltd., York Biotech Campus, Sand Hutton York, United Kingdom Marco Cerqueira CCMAR, Centro de Ciências do Mar Faro, Portugal

xv

List of Contributors

Elizabeth S. Collier Division of Bioeconomy and Health, Perception and Design Unit RISE Research Institutes of Sweden Gothenburg, Sweden and Division of Society and Health, Department of Health, Medicine and Caring Sciences Linköping University Gothenburg, Sweden

Eva Falch Department of Biotechnology and Food Science NTNU Norwegian University of Science and Technology Trondheim, Norway

Elena Costa Division of Bioeconomy and Health, Perception and Design Unit RISE Research Institutes of Sweden Gothenburg, Sweden and Department of Biological and Environmental Sciences Gothenburg University Gothenburg, Sweden

Kaneez Fatima Department of Pharmacology Faculty of Pharmacy Maulana Azad University Jodhpur, India

Daniel Cozzolino The University of Queensland, Brisbane Queensland, Australia D. Sylvain Dabadé Laboratory of Food Science and Technology University of Abomey-Calavi Jericho-Cotonou, Benin Fera R. Dewi University of Tasmania Launceston, Australia Eleftherios H. Drosinos Agricultural University of Athens Athens, Greece John Stephen Elmore University of Reading Reading, United Kingdom Mariana Alves Elois Federal University of Santa Catarina Florianópolis, Brazil

Ana Paula Farinha Escola Superior Agrária de Santarém Portugal

Gislaine Fongaro Federal University of Santa Catarina Florianópolis, Brazil Arjen Gerssen Wageningen Food Safety Research Wageningen University and Research Wageningen, the Netherlands Ishita Ghosh The Pennsylvania State University University Park, Pennsylvania Sadaf Jamal Gilani Department of Basic Health Sciences Princess Nourah bint Abdulrahman University Riyadh, Saudi Arabia Nalan Gokoglu Fisheries Faculty Department of Fish processing Akdeniz University Antalya, Türkiye Rosalee S. Hellberg Chapman University Schmid College of Science and Technology, Food Science Program, One University Drive Orange, California

xvi

Isabel Hernando Universitat Politècnica de València Valencia, Spain Ana M. Herrero Department of Meat and Fish Products INDMEAT group, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC) Madrid, Spain Azamal Husen Wolaita University Ethiopia Grethe Hyldig National Food Institute Technical University of Denmark Kgs. Lyngby, Denmark Annamaria Iannetta University of Teramo Teramo, Italy Jun Iguchi Food Labeling Monitoring Department Food and Agricultural Material Inspection Center Saitama, Japan Ida-Johanne Jensen Department of Biotechnology and Food Science NTNU Norwegian University of Science and Technology Trondheim, Norway Rósa Jónsdóttir Matís Reykjavik, Iceland Mourad Jridi Higher Institute of Biotechnology of Beja University of Jendouba Jendouba, Tunisia A. Kaarunya Formerly Indian Institute of Food Processing Technology Thanjavur, India

List of Contributors

Najam Ali Khan Faculty of Pharmacy GMS College of Pharmacy Amroha, India Vikas Kumar Aquaculture Research Institute Department of Animal , Veterinary and Food Sciences University of Idaho Moscow, Idaho Han Liu E2S UPPA, IPREM, CNRS Université de Pau et des Pays de l’Adour Pau, France Empar Llorca Universitat Politècnica de València Valencia, Spain Annalaura Lopez Department of Veterinary Medicine and Animal Science Università degli Studi di Milano Via dell'Università Lodi (LO), Italy Cláudia Raposo de Magalhães Universidade do Algarve Faro, Portugal R. Meenatchi National Institute of Food Technology Entrepreneurship and Management Formerly Indian Institute of Food Processing Technology Thanjavur, India Rogério Mendes Portuguese Institute for the Sea and Atmosphere Lisbon, Portugal Victor G. Mihucz Institute of Chemistry ELTE – Eötvös Loránd University Budapest, Hungary

xvii

List of Contributors

Marília Miotto Federal University of Santa Catarina Florianópolis, Brazil

Moncef Nasri University of Sfax Sfax, Tunisia

Sajad Ahmad Mir Department of Microbiology and Food Science and Technology Gitam School of Science Gitam (Deemed to be University) Visakhapatnam, India

Rim Nasri Higher Institute of Biotechnology of Monastir University of Monastir Monastir, Tunisia

Sheetal Mital Department of Applied Sciences KIET Group of institutions Ghaziabad, India Anisa Mitra Department of Zoology Sundarban Hazi Desarat College Pathankhali, India Francisco Esteban Montero Universitat de València Valencia, Spain Leticia Mora Instituto de Agroquímica y Tecnología de Alimentos Consejo Superior de Investigaciones Científicas Valencia, Spain Vittorio Maria Moretti Department of Veterinary Medicine and Animal Science Università degli Studi di Milano Via dell'Università Lodi (LO), Italy Atsushi Namikoshi Food Labeling Monitoring Department Food and Agricultural Material Inspection Center Saitama, Japan Shahida Naseem University of Kashmir Srinagar, India Zahida Naseem SKUAST-K Srinagar, India

Jun Niimi Division of Bioeconomy and Health, Perception and Design Unit RISE Research Institutes of Sweden Gothenburg, Sweden Leo M.L. Nollet University College Ghent Ghent, Belgium Olumide A. Odeyemi University of Tasmania Launceston, Australia Guðrún Ólafsdóttir University of Iceland Reykjavik, Iceland Helen Onyeaka School of Chemical Engineering University of Birmingham UK Semih Otles Ege University Izmir, Turkey Yesim Ozogul Cukurova University Balcalı/Adana, Turkey Vasfiye Hazal Özyurt Department of Gastronomy and Culinary Mugla Sıtkı Kocman University Mugla, Turkey Foteini F. Parlapani University of Thessaly Volos, Greece

xviii

Ana Pérez-del-Olmo Universitat de València Valencia, Spain Monia Perugini University of Teramo Teramo, Italy Marcel Afonso Provenzi Federal University of Santa Catarina Florianópolis, Brazil Amparo Quiles Universitat Politècnica de València Valencia, Spain Juan Antonio Raga Cavanilles Institute of Biodiversity and Evolutionary Biology Universitat de València Valencia, Spain Danish Rizwan University of Kashmir Srinagar, India Pedro Rodrigues Universidade do Algarve Faro, Portugal David Rodríguez-Lázaro University of Burgos Burgos, Spain Claudia Ruiz-Capillas Department of Meat and Fish Products INDMEAT group, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC) Madrid, Spain Turid Rustad Department of Biotechnology and Food Science NTNU Norwegian University of Science and Technology Trondheim, Norway Ali Salem Higher Institute of Biotechnology of Beja University of Jendouba Jendouba, Tunisia

List of Contributors

Isabel Sánchez-Alonso Department of Meat and Fishery Products Instituto de Ciencia y Tecnología de Alimentos y Nutrición (ICTAN-CSIC) and Department of Food Technology Universidad complutense de Madrid (UCM) Madrid, Spain Abhilash Sasidharan Norwegian University of Science and Technology Trondheim, Norway Beatriz Pereira Savi Federal University of Santa Catarina Florianópolis, Brazil Denise Schrama Universidade do Algarve Faro, Portugal Reinhard Schubring Federal Research Institute for Nutrition and Food Max Rubner-Institut Hamburg, Germany K. S. Siddiqi Aligarh Muslim University Aligarh, India Helena Silva Portuguese Institute for the Sea and Atmosphere Lisbon, Portugal Benjamin K. Simpson McGill University Quebec, Canada Hana Sklenářová Charles University Hradec, Czech Republic Rasa Slizyte Department of Fisheries and New Marine Bioresources SINTEF Ocean Trondheim, Norway

xix

List of Contributors

Joseph Sneddon McNeese State University Lake Charles, Louisiana Doris Sobral Marques Souza Federal University of Santa Catarina Florianópolis, Brazil Deyan Stratev Department of Food Quality and Safety and Veterinary Legislation Faculty of Veterinary Medicine Trakia University Bulgaria Yasuharu Takashima Food Labeling Monitoring Department Food and Agricultural Material Inspection Center Saitama, Japan Chad A. Thibodeaux Northeastern University Natchitoches, Louisiana Fidel Toldrá Instituto de Agroquímica y Tecnología de Alimentos Consejo Superior de Investigaciones Científicas Valencia, Spain

Véronique Verrez-Bagnis Ifremer, MASAE Microbiologie Aliment Santé Environnement Nantes, France Michiaki Yamashita Graduate School of Fisheries Science National Fisheries University Yamaguchi, Japan Yumiko Yamashita Environment and Fisheries Applied Techniques Research development Fisheries Technology Institute Kanagawa, Japan Nadine Yeramian University of Burgos Burgos, Spain Lucas Zanchetta Federal University of Santa Catarina Florianópolis, Brazil Yi Zhang The Pennsylvania State University University Park, Pennsylvania

Part 1 Chemistry and Biochemistry

1 Importance of Analysis in Introduction

Seafood and Seafood Products, Variability, and Basic Concepts Vikas Kumar University of Idaho

Anisa Mitra Sundarban Hazi Desarat College

1.1 INTRODUCTION Seafood, which is broadly categorized as fish (especially white and oily varieties), shellfish such as mussels, and crustaceans such as prawns, crabs, lobsters, and squid, is any form of sea life that humans consider to be food. Molluscs and crustaceans, such as mussels, oysters, and scallops, as well as prawns, prawns, crabs, and lobsters, make up the other broadclass of shellfish (Thakur, 2019). Seafood and seafood products are an important part of the human diet because they provide high-quality protein, omega-3 fatty acids, vitamins, and minerals. It is an essential component of the global food system, making significant contributions to livelihoods and nutritional well-being, particularly in countries with low or middle incomes (Hicks et al., 2019; FAO, 2022). The safety and quality of seafood products, however, can vary due to a variety of factors such as species, geography, harvesting location, processing, handling, and storage facilities. With a rapid increase in aquaculture, geographical switches in trade, and expanding commoditization and vertical integration, the seafood sector has been transforming in recent years. Global concern about fisheries and aquaculture’s ability to satisfy future seafood demand sustainably is driving advancements in management and technology (Marwaha et al., 2022). Global fish and seafood production has increased by four times in the last 50 years. The average individual currently consumes over twice as much seafood as they did 50 years ago, in addition to the fact that the world’s population has more than doubled during this time. The greatest seafood producer in the world is China that produced more than 60 million tonnes of seafood in 2019 (see Figure 1.1). Indonesia, India, Vietnam, and finally the United States come after this (Ritchie and Roser, 2020). Nearly 178 million tonnes of aquatic animals were produced globally in 2020, a tiny decrease from the record-breaking 179 million tonnes produced in 2018. Salmon, herring, cod, flatfish (halibut, sole, and turbot), redfish (ocean perch), jack mackerel, tuna, mackerel, and sardine species are the most important ocean fish for commerce. Whitebait and baby eels, which are both approximately 5 cm (2 inches) long, and bluefin tuna, which can reach lengths of 4.3 m (14 feet), are among the catch’s smaller species. In 2020, aquaculture contributed 88 million tonnes (49%) compared with the capture fishery’s 90 million tonnes (51%). In 2020, inland waterways were used to collect 37% (66 million tonnes) of the total production, with aquaculture accounting for 83% (66 million tonnes) and catch fisheries for 17% (112 million tonnes) of the overall production was gathered in maritime waters. Thirty-six million tonnes (wet weight) of algae were produced in 2020 in place of aquatic animals, with aquaculture accounting for 97% of this production (mainly marine DOI: 10.1201/9781003289401-2

3

4

Handbook of Seafood and Seafood Products Analysis

Fish and seafood production

Fish and seafood production is measured as the sum of seafood from wild catch and fish farming (aquaculture).

Our World in Data China

60 million t

50 million t

40 million t

30 million t

20 million t Indonesia India United States Peru Bangladesh Japan Brazil South Africa

10 million t

1961

1970

1980

1990

Source: Food and Agriculture Organization of the United Nations

2000

2010

2020

OurWorldInData.org/fish-and-overfishing • CC BY

FIGURE 1.1  Global seafood production (FAO, 2022).

aquaculture) (OCED FAO, 2022). A variety of factors, including environmental, economic, social, and political ones, have a role in the global capture and harvest of seafood. It has been predicted that the amount of edible food from the sea might increase by 21–44 million tonnes by 2050, a 36%–74% increase over present yields (Ainsworth et al., 2023), based on demand shifts and supply scenarios. Global fish harvesting has a significant impact on environmental sustainability, human health, and food security. Fish supplies and marine biodiversity have decreased as a result of overfishing, illegal fishing, and unsustainable fishing methods, affecting the long-term viability of the seafood business. Additionally, the seafood sector has detrimental ecological effects, such as habitat degradation, and notably contributes to the release of greenhouse gases. There have been numerous initiatives to promote ethical fishing methods, lessen overfishing, and enhance the management of marine resources in an effort to solve these issues. To increase the sustainability and profitability of the seafood business and policy decisions, research on the global catch and harvest of seafood is crucial.

1.2  VARIABILITY OF AQUATIC ANIMALS Due to differences in species, geography, harvesting locations, handling, processing, and storage, aquatic animals, such as fish and shellfish, and other marine organisms, such as seaweeds, exhibit significant variability as food. Species diversity is one of the main contributors to variation in aquatic animals used as food. Fish and shellfish come in a variety of species, each with distinctive qualities in terms of size, flavour, texture, and nutritional value. For instance, while some species of fish are prized for their firm flesh and mild flavour, others are more delicate and have a more distinctive flavour. Geographical variation in aquatic animals used as food is also highly influenced. Depending on the region and environmental factors, there can be a wide range in the kind and quality of seafood that is offered. For instance, oysters from different regions may have different flavours and textures due to

Introduction

5

variations in water temperature, salinity, and nutrient availability. Harvest location, handling, and processing can also affect the variability of aquatic animals such as seafood. The handling and processing techniques used can affect the texture, flavour, and quality of the seafood. For example, the method of catching and handling fish can affect the texture of the flesh, while the processing method used for shellfish can affect their flavour and texture. Finally, storage conditions can also contribute to variability in aquatic animals such as seafood. Factors such as temperature, humidity, and exposure to light can all affect the quality and safety of seafood products. For example, improper storage of fish can result in spoilage and the formation of harmful bacteria, while exposure to light can cause the degradation of certain nutrients in shellfish. To minimize the variability of aquatic animals such as seafood, it is essential to use standardized methods for handling, processing, and storage. Additionally, maintaining a robust quality control system and following best practices in sample collection, handling, and analysis can help to minimize variability and ensure that seafood products are safe and of high quality.

1.3  BENEFITS AND RISKS Seafood and seafood products have both benefits and risks associated with their consumption. Here are some of the main benefits and risks of seafood and seafood products:

1.3.1  Benefits Seafood is high in relevant nutrients such as n-3 PUFAs (Polyunsaturated Fatty Acid), protein, fibre, taurine, sterol, pigments, vitamins, and minerals such as iron, zinc, and selenium. These nutrients are essential for good health and lower the risk of chronic diseases such as cardiovascular disease. Seafood proteins have high levels of essential amino acids and are easily digestible. Proteins comprise 10%–25% of all seafoods and are classified into three types: sarcoplasmic, myofibrillar, and stroma. According to prior research, eating fish protein lowers serum cholesterol by blocking cholesterol and bile acid absorption and promoting cholesterol catabolism in the liver (Hosomi et al., 2012). Furthermore, dietary fish protein contains antihypertensive (Boukortt et al., 2004), fibrinolysis-stimulating (Murata et al., 2004), and antiobesity (Oishi and Dohmoto, 2009) properties. Docosahexaenoic acid (DHA) and Eicosapentaenoic acid (EPA), omega-3 fatty acids found in seafood, are essential for newborn and young child brain development. Seafood is often low in calories and high in protein, making it useful for weight management and loss. Free sterols, stanols, and sterol esters are lipid-lowering phytosterols found in seafood that have a lipid-lowering mechanism by competing with cholesterol absorption by binding to micelles in the intestine (Jones et al., 2000; Kanazawa, 2001). Phytosterols are also said to have anti-inflammatory, antioxidant, and anticancer activities (Houweling et al., 2009; Mannarino et al., 2009; Bouic, 2001). The most abundant source of carotenoids is seafood and its by-products; these compounds can be transformed into vitamin A (García-González et al., 2005) and significantly reduce oxidative stress, inflammation, and hyperlipidaemia biomarkers (Iwamoto et al., 2000; Cicero et al., 2008). In general, shellfish with muscle is quite low in fibre and carbohydrates. However, according to Jiménez-Escrig and Sánchez-Muniz (2000), water-soluble fibre makes up between 50% and 85% of the dietary fibre found in edible seaweed, which has been shown to lower plasma levels of total cholesterol, low-density lipoprotein (LDL) cholesterol, and Triglycerides (TG), and additionally acts as an antihypertensive activity (Matsushima et al., 2003; Schaffer et al., 2010).

1.3.2  Risks There are risks involved with eating seafood, even though it can have significant health benefits. When choosing seafood, it is crucial to consider the source, sustainability, and potential risks. Environmental pollutants such as pesticides, dioxins, and polychlorinated biphenyls (PCBs) can accumulate in fish flesh and be harmful to human health (Arisawa et al., 2003). These contaminants can be present in seafood. High levels of mercury contamination in some fish species can be dangerous to human health, especially for expectant mothers and young children. Exposure to methylmercury affects the delicate

6

Handbook of Seafood and Seafood Products Analysis

FIGURE 1.2  Adverse reactions to seafood upon ingestion. Reproduced from Ruethers et al. (2018) with permission of Elsevier.

nervous system. Methylmercury also has a strong effect on the developing nervous systems of foetuses and young children. Methylmercury causes damage to the central nervous system that is dependent on the amount ingested (Clarkson et al., 2003; Yoshizawa et al., 2002). Seafood may contain infective bacteria such as Vibrio and Salmonella and infective viruses such as Norovirus, which can cause foodborne illness. Shellfish taken from ­sewage-contaminated waters are frequently linked to dangerous viruses such as the Norwalk virus and hepatitis A. Oysters and clams are the main sources of bacteria, but some ready-to-eat fish products can also contain them. Eating raw or partially cooked oysters or clams poses the greatest risk because they can pick up bacteria from runoff, sewage discharges, land use, and other sources such as Vibrio vulnificus and Vibrio parahaemolyticus. These microbes, which naturally exist in warm coastal waters, have the potential to seriously ill or even kill high-risk people. In addition to bacteria and viruses, some fish, particularly those from tropical or subtropical waters, may contain toxins that make people sick. Histamine poisoning comes in two main forms: ciguatoxin and scombrotoxin. Most reef fish, including barracuda, grouper, and snapper, contain ciguatoxin. Mahimahi, fresh tuna, mackerel, and bluefish are the main sources of scombrotoxin (histamine) (Reames, 2012). Seafood allergies, which can have serious and even fatal consequences, may exist in some people. Seafood contains a prominent common allergenic protein termed tropomyosin (TPM), which is relatively resistant to peptic acidic digestion. As a result, the protein has a long-lasting effect on the immune system. For more than 80% of allergic reactions that might result in severe hypersensitivity, such as urticaria and asthma, as well as a large portion of anaphylaxis, shrimp is the most prevalent crustacean among shellfish allergens. A negative reaction to seafood can fall into one of three categories depending on the underlying mechanisms: (i) toxic reactions, which include reactions to marine biotoxins; (ii) immunological reactions, which include allergic reactions and food protein-induced enterocolitis syndrome (FPIES); and (iii) food intolerance. As a result, to protect customers from potentially harmful components, food makers are obligated to analyse, label, and emphasize shellfish allergenic substances on food packages (Woo and Bahna, 2011; Adams et al., 2019; Buyuktiryaki et al., 2021) (Figure 1.2).

1.4  SEAFOOD AND SEAFOOD PRODUCT ANALYSIS Seafood and seafood products are complex matrices that require sophisticated analytical methods to assess their quality and safety. The analysis of seafood can provide information about the nutritional value, freshness, microbial load, and chemical contaminants (heavy metals, pesticides, and antibiotics) of the product. These contaminants can accumulate in seafood and pose a risk to human health.

Introduction

7

Here are some of the key reasons why the analysis of seafood and seafood products is important: Safety: Analysis of seafood and seafood products helps identify and mitigate potential safety risks, including the presence of harmful contaminants such as bacteria, viruses, parasites, heavy metals, and toxins. Quality: Analysis of seafood and seafood products is critical to ensure their quality, including freshness, flavour, texture, and nutritional value. The analysis can help to identify the presence of spoilage organisms, monitor quality changes during storage and transportation, and assess the sensory attributes of the products. Regulatory compliance: Analysis of seafood and seafood products is necessary to ensure compliance with regulatory requirements, including food safety regulations, labelling requirements, and import or export regulations. Traceability: Analysis of seafood and seafood products is important to maintain traceability and accountability throughout the supply chain. Analytical techniques such as Deoxyribonucleic acid (DNA) barcoding can help verify the species and origin of the products, preventing mislabelling and fraud. Variability: It is a significant challenge in the analysis of seafood and seafood products. The variability can be due to the intrinsic characteristics of the products, such as differences in species, size, and age, and the extrinsic factors, such as harvest location, processing method, and storage conditions. Some of the basic concepts involved in the analysis of seafood and seafood products include chemical analysis, microbiological analysis, sensory analysis, and DNA analysis. The choice of analytical method depends on the specific attributes of the product being analysed, the regulatory requirements, and the intended use of the product to ensure its safety, quality, compliance, and traceability. Therefore, regulatory authorities have established maximum residue limits (MRLs) for these contaminants in seafood. Analytical methods such as atomic absorption spectrometry (AAS), gas chromatography (GC), polymerase chain reaction (PCR), liquid chromatography (LC), enzymelinked immunosorbent assay (ELISA), and sensory evaluation techniques are commonly used for the detection and quantification of chemical contaminants in seafood.

1.5  CHEMICAL CONTAMINANTS It is challenging to identify the precise species and allergens that are the cause of allergic reactions given the variety of seafood available. Because commercially accessible diagnostic techniques do not take into account this diversity, it is frequently advised to avoid all seafood (Lopata and Kamath, 2012). Currently, the clinical history, sensitization tests, such as allergen-specific IgE quantification and Skin prick test (SPT), and oral challenge tests, if necessary, are used to make the diagnosis of seafood allergy. In commercial preparations for the SPT, complete protein extracts with an unknown protein composition are typically sold by multiple, frequently local manufacturers. In vitro IgE tests are preferred over SPT or oral food challenges to reduce the danger of potentially fatal allergic reactions. The simplicity of testing for sensitization and the accessibility of commercial tests against a range of seafood species on various platforms are advantages of this strategy. Standardizing allergen concentrations may be challenging when using natural extracts, and detecting specific IgE to rare allergens may not be precise, producing findings that are untruthfully negative. To alleviate these issues, recent research is increasingly emphasizing the use of extracted allergens for component-resolved diagnosis (Lopata et al., 2016). Understanding the allergens’ natural abundance, location within the organism, and biological function may be important to produce a seafood allergy component-resolved diagnosis (Ruethers et al., 2018).

8

Handbook of Seafood and Seafood Products Analysis

Microplastics (MPs) have contaminated the marine environment in all compartments, including seafood (Lusher et al., 2017). An estimated 11,000 MP particles are eaten annually by seafood eaters in Europe who live in shellfish-eating nations. Fish and shellfish that are significant for commerce, both wild and farmed, have been discovered to contain MP particles (Duis and Coors, 2016). The consumption of complete organism in seafood is a big source of concern. The majority of research has been done on bivalves in terms of MP concentrations (Lusher et al., 2017). Only a small number of studies have discovered MPs in crabs or prawns (Andrady, 2011; EFAS Panel on Contaminants in the Food Chain, 2016). Surprisingly, there is little information available on seafood species other than bivalves or fish digestive tissue, leaving out the edible section, such as the muscle (Teuten et al., 2009; Murray and Cowie, 2011). The most significant dietary exposure sources for perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) in seafood are per- and poly(fluoroalkyl) substances (PFASs), a class of synthetic chemicals that have been used in a variety of industrial and consumer products. To spot packaging that might have been PFAS-coated, the majority of the seafood packaging was also looked at utilizing Fourier transform infrared-attenuated total reflectance (FTIR-ATR) (Young et al., 2022) (Figure 1.3). The most common method for detecting MPs in seafood is to first identify visible plastic particles. To solidify the scientific evidence on MP contamination and human exposures, highquality research is required using standardized methods such as particle-extraction procedures and composition identification method specifications, highlighting the varying effectiveness of research protocols (Danopoulos et al., 2020). Secondary confirmation via spectroscopic analysis, including Fourier Transform Infrared Spectroscopy (FTIR), is the most commonly used technique for confirming the polymer type present in seafood (Renner et al., 2018). Pyrolysis–gas chromatography–mass spectrometry (Py-GC/MS) is an intriguing method for studying MPs

FIGURE 1.3  Analysis of per- and poly(fluoroalkyl) substances (PFASs) in highly consumed seafood.

Introduction

9

FIGURE 1.4  Five kinds of plastic (PVC, PS, PP, PMMA, and PE) were quantified in five different organisms (squid, prawns, oysters, crabs, and sardines) at a total plastic concentration (n = 10) (in mg per g of tissue). Polyvinyl chloride (PVC), polypropylene (PP), poly(methyl methacrylate), polystyrene (PS), polyethylene (PE) and polymethyl methacrylate (PMMA). Reproduced from Ribeiro et al. (2020).

10

Handbook of Seafood and Seafood Products Analysis

FIGURE 1.5  Applications of hyperspectral imaging in the seafood sector. Reproduced from Hassoun et al. (2022).

(Mintenig et al., 2018; La Nasa et al., 2020; De Sales-Ribeiro et al., 2020) (Figures 1.4 and 1.5). It is challenging to establish methodologies to identify and quantify the most prevalent types of plastics discovered in seafood due to the inconsistent reporting of estimates of MP concentration levels in fish and shellfish. To maximize the identification and quantification of MPs by Py-GC/ MS using a mass-related quantification approach, an alkaline digestion procedure was followed by an accelerated solvent extraction (ASE) method, which used a solvent at high temperature and pressure to dissolve and extract plastics from seafood. This is an evaluation of the amount of plastic that a consumer might consume when eating seafood. The artificial preservative formaldehyde, a known carcinogen, has been found in fish products (Jinadasa et al., 2022). Fish might be purposely sprayed or dipped in formaldehyde solution to increase shelf life, stiffen, maintain freshness, and prevent rotting. However, formaldehyde has the capacity to excite the body’s dominant cells and can result in agitation, nausea, neuralgia, and skin allergies (EFSA, 2014). Numerous methods for measuring the amount of formaldehyde have been developed recently, including spectrophotometry, electrochemistry, and chromatography. These methods are accurate and reliable but call for a labour-intensive, expensive process. The substrate

FIGURE 1.6  Use of biodegradable hybrid polymer film for the detection of formaldehyde in seafood products. Reproduced from Rovina et al. (2020) with permission of Elsevier.

11

Introduction

TABLE 1.1 Summary of Pros and Cons of the Main Approaches Using DNA Barcode Markers for Seafood Authentication (Fernandes et al., 2021) Pros DNA barcoding

NGS

PCR RFLP

Real-time PCR

HRM

Well established Medium to high cost of equipment Low cost per analysis Reliable species identification High-throughput species identification Multiple species identification Reliable species identification Application to a complex and processed sample Application to a complex and processed sample Simple setup Low cost of equipment

Reliable species identification Highly sensitive Quantitative Simple setup High throughput Cost-effective Medium to low cost of equipment Application to processed food

Cons Limited application to processed samples Not applied to mixed species identification High cost of equipment High cost of analysis High complexity of data

Low reproducibility Limited application to processed samples Time-consuming Limited species identification No sequencing information

Not applied to mixed species identification Not applied to mixed species identification

for a novel biodegradable hybrid polymer film was made of starch and chitosan, which was utilized to entrap Nash colorimetric reagents for the quantitative detection of formaldehyde (Rovina et al., 2020) (Figure 1.6). As a result of an increase in seafood mislabelling over the past few years (Khaksar et al., 2015; Vandamme et al., 2016; Christiansen et al., 2018), consumers may now be exposed to hidden or undeclared species that may contain toxins, pathogens, parasites, chemicals, or allergens. DNA barcoding based on Sanger’s sequencing has been the most used method for seafood authenticity. However, recent developments in DNA barcoding techniques (Table 1.1) using next-generation sequencing (NGS) have proved noteworthy. Techniques using real-time PCR with species-specific primers and probes, or those that are followed by high-resolution melting (HRM) analysis combined with DNA barcodes, are potential alternatives for simple, inexpensive, and high-throughput species discrimination in processed seafood (Fernandes et al., 2021). DNA metabarcoding is anticipated to be employed more and more, eventually taking over, particularly for multiple species identification in intricate seafood matrices. In view of declining equipment prices and advances in bioinformatics, this trend is anticipated to continue. As dependable and economical technologies, real-time PCR-based techniques that use species-specific primers and probes to target DNA barcodes and those that are followed by High-Resolution Melting (HRM) analysis are also projected to advance in the near future. Additionally, being tested are NGS, isotopic analysis, and trace. Droplet digital PCR (ddPCR), a new technique, was used to create a very accurate quantitative method that was used for the quantitative examination of raw elements in mixed meats and highly processed blended foods (Floren et al., 2015; Noh et al., 2020).

12

Handbook of Seafood and Seafood Products Analysis

1.6  MICROBIAL DIVERSITY OF SEAFOOD Microbial deterioration and safety hazards in post-harvest handling, icing, packaging, processing, storage, and distribution can cost fish producers, processors, and distributors billions of dollars each year. Microbial analysis is also critical in seafood analysis, and different techniques such as enumeration, identification, and detection of specific pathogenic microorganisms are used. For example, quantitative PCR (qPCR) is a rapid and sensitive method to detect and quantify specific bacterial pathogens in seafood products. Seafood microbiologists create and use novel molecular techniques to describe the microbial diversity of seafood and guarantee microbial safety and quality along the seafood value chain in order to solve problems for stakeholders and contribute to sustainable development, poverty reduction, and One Health. The development of NGS technology over the past few years has allowed for paradigm-shifting discoveries regarding the microbial diversity of seafood (Villicaña et al., 2019). The ability of 16S NGS to distinguish between cultivable and non-cultivable bacterial genera from a sample has altered our knowledge of the microorganisms that predominate in or are present in seafood. However, PCR-based techniques are used to separate potential spoilers from hygiene indicators down to the strain level, such as HRM, or detect food safety-related genes specifically, such as virulence or antibiotic resistance genes, such as multiplex PCR, real-time PCR, and HRM (Parlapani et al., 2020). Bacteriological analysis can identify food spoilage brought on by bacteria, but it cannot detect the loss of freshness brought on by autolytic deterioration. Electrochemical impedance spectroscopy (EIS) is a non-destructive, very sensitive method for analysing the electrical properties of materials and systems. It works by creating alternating electrical impulses at different frequencies and measuring the related signals. To estimate the electrical impedance of an electrochemical system, Zhang et al. (2022) claim that the impedance technique fixes a tiny sinusoidal voltage at a certain frequency and detects the ensuing alternating current. Electrochemical sensor devices are new and promising analytical instruments that have the potential to be extremely beneficial as outstanding control and monitoring tools for the seafood sector since they are rapid, effective, environmentally friendly, and economically viable. However, it still seems challenging to deploy electrochemical sensors on a broad scale in the food business. Future work in this area may concentrate on creating small, portable arrays that include sensors for gas, liquid, and colour analysis, as well as enzymatic biosensors that can quickly, accurately, and selectively monitor the quality and safety of food products at the industrial level (Marx, 2023). Numerous benefits come with electrochemical biosensors, including simple miniaturization and potential integration with multiplexed analysis using straightforward formats. The time restrictions associated with traditional laboratory approaches have been successfully reduced by recent developments in the development of such electrochemical sensors (Figure 1.7) for real-time, on-site monitoring by offering high-throughput, multi-analyte, and portable biosensors (Mishra et al., 2018). One of the most novel and inventive technologies that can be employed in the food business for food production, processing, preservation, and packaging is nanotechnology (Qiu et al., 2020; Hassoun et al., 2022). Using nanoencapsulation or nanoemulsions, nanostructures may be useful in enhancing low-soluble compound accessibility, bioactivity, stability, or control of their release into the product. They can also successfully inhibit microbial growth and enhance the sensory qualities of hairtail during storage (Liu et al., 2021). Silver, zinc oxide, titanium oxide, or chitosan nanoparticles are examples of nanocomposites that can be used to inhibit microbiological development, preserve product freshness, and lengthen shelf life (Ranadheera et al., 2017). Metagenomics, proteomics, and metabolomics combined with traditional quality indices such as colour, texture, and flavour provide novel analytical tools (Table 1.2) for optimization and ensuring the quality of seafood. According to den Besten et al. (2018), omics technologies pave the way for cutting-edge monitoring tools that are essential for developing the next wave of risk assessment techniques. Current technological advancements that make use of electronic data systems, such as web platforms and applications for smartphones, will be crucial for the adoption and wide

13

Introduction

FIGURE 1.7  (a) Schematic illustration of mechanism or principle of an electrochemical biosensor. (b) Schematics for bacteria and toxin detection using an antibody-based electrochemical biosensor. Reproduced from Mishra et al. (2018).

TABLE 1.2 Examples of Omics Tools for Quality Evaluation of Aquatic Products Product Category

Aim of the Analysis

Analytical Tool

Fresh and high pressure Flesh quality assessment after high pressure processing 16S rRNA gene amplicon sequencing, treated fish LC-SWATH-MS acquisition on a triple TOF™ system Fresh fish, shrimp, and Characterization of fish flesh microbial ecology, Numerical taxonomy, SDS-PAGE roe product connection with histamine production electrophoresis, plasmid profiling, next generation sequencing (NGS), intergenic spacer region (ISR) analysis, and examination of amplified-fragment length polymorphism (AFLP) Iced-stored fish Determination of total cultivable microbiota and 16S rRNA gene amplicon sequencing freshness assessment Chilled fish Freshness assessment CG-MS analysis, 1H NMR spectroscopy SPME-GC/MS analysis, SDS-PAGE electrophoresis, and 16S rRNA gene amplicon sequencing Macroalga Lipidomics MS analysis Modified after Power et al. (2023).

14

Handbook of Seafood and Seafood Products Analysis

implementation of this molecular labelling (Ferri et al., 2015). Future improvements to universal primer sets and molecular microbiological tools should be directed at better differentiating between positive outcomes and actual pathogen contamination (Cook and Nightingle, 2018).

1.7 CONCLUSIONS The analysis of seafood and seafood products is important for ensuring their quality, safety, and authenticity. The variability of seafood requires the development of robust and adaptable analytical methods. The choice of analytical method depends on the type of parameter being measured and the properties of the seafood matrix. Analytical methods can be non-destructive or destructive, depending on the specific application. Therefore, it is important to have a comprehensive understanding of the basic concepts of seafood analysis and to validate analytical methods for different types of seafood and seafood products. It should be noted that the majority of the aforementioned applications have not yet crossed the threshold into the industrial sphere. To fully realize the potential of Industry 4.0 and further harness its technologies to address present difficulties, further collaboration between laboratory research institutes and industrial actors is essential in the future (Hassoun et al., 2022).

REFERENCES Adams S., Lopata A.L., Smuts C.M., Baatjies R., Jeebhay M.F. (2019) Relationship between serum omega-3 fatty acid and asthma endpoints. Int. J. Environ. Res. Public Health. 16:43. doi:10.3390/ijerph16010043. Ainsworth G.B., Pita P., Rodrigues J.G., Pita C., Roumbedakis K., Fonseca T., Castelo D. Longo C., Power A.M., Pierce G.J., Villasante S. (2023) Disentangling global market drivers for cephalopods to foster transformations towards sustainable seafood systems. People Nat. 5(2):508–528. Andrady A.L. (2011) Microplastics in the marine environment. Mar. Pollut. Bull. 62(8):1596–1605. doi:10.1016/j.marpolbul.2011.05.030. Arisawa K., Matsumura T., Tohyama C., Saito H., Satoh H., Nagai M., Morita M., Suzuki T. (2003) Fish intake, plasma omega-3 polyunsaturated fatty acids, and polychlorinated dibenzo-p-dioxins/polychlorinated dibenzo-furans and co-planar polychlorinated biphenyls in the blood of the Japanese population. Int. Arch. Occup. Environ. Health. 76(3):205–215. Bouic P.J. (2001) The role of phytosterols and phytosterolins in immune modulation: A review of the past 10 years. Curr. Opin. Clin. Nutr. Metab. Care. 4(6):471–475. doi:10.1097/00075197-200111000-00001. Boukortt F.O., Girard A., Prost J., Bouchenak M., Belleville J. (2004) Effect of fish protein on blood pressure, glycemia and plasma and urinary parameters in spontaneously hypertensive rats (SHR) and diabetic SHR. J. Hypertens. 22(8):A1–A2. Buyuktiryaki B., Masini M., Mori F., Barni S., Liccioli G., Sarti L., Lodi L., Giovannini M., du Toit G., Lopata A.L., Marques-Mejias M.A. (2021) IgE-Mediated fish allergy in children. Medicina (Kaunas). 57(1):76. doi:10.3390/medicina57010076. Christiansen H., Fournier N., Hellemans B., Volckaert F.A. (2018) Seafood substitution and mislabeling in Brussels’ restaurants and canteens. Food Control. 85:66–75. Cicero A.F.G., Nascetti S., López-Sabater M.C., Elosua R., Salonen J.T., Nyyssonen K., Poulsen H.E., Zunft H.-J.F., Kiesewetter H., de la Torre K., Covas M.-I., Kaikkonen J., Mursu J., Koenbick C., Bäumler H., Gaddi A.V. (2008) Changes in LDL fatty acid composition as a response to olive oil treatment are inversely related to lipid oxidative damage: The EUROLIVE study. J. Am. Coll. Nutr. 27(2):314–320. Clarkson T.W., Magos L., Myers G.J. (2003) The toxicology of mercury-current exposures and clinical manifestations. N. Engl. J. Med. 349(18):1731–1737. Cook P.W., Nightingale K.K. (2018) Use of omics methods for the advancement of food quality and food safety. Anim. Front. 8(4):33–41. Danopoulos E., Twiddy M., Rotchell J.M. (2020) Microplastic contamination of drinking water: A systematic review. PloS One. 15(7):e0236838. De Sales-Ribeiro C., Brito-Casillas Y., Fernandez A., Caballero M.J. (2020) An end to the controversy over the microscopic detection and effects of pristine microplastics in fish organs. Sci. Rep. 10(1):12434. den Besten H.M., Amézquita A., Bover-Cid S., Dagnas S., Ellouze M., Guillou S., Nychas G., O’Mahony C., Pérez-Rodriguez F., Membré J.M. (2018) Next generation of microbiological risk assessment: Potential of omics data for exposure assessment. Int. J. Food Microbiol. 287:18–27.

Introduction

15

Duis K, Coors A. (2016) Microplastics in the aquatic and terrestrial environment: Sources (with a specific focus on personal care products), fate and effects. Environ. Sci. Eur. 28(1):2. EFSA (2014) Endogenous formaldehyde turnover in humans compared with exogenous contribution from food sources. EFSA J. 12:3550. EFSA (2016) Presence of microplastics and nanoplastics in food, with particular focus on seafood. EFSA J. 14(6):4501. FAO (2022) Total seafood production by country 1961–2020. Food and Agriculture Organization of the United Nations, https://www.fao.org/faostat/en/#data/FBS (accessed on 15 May 2023). Fernandes T.J., Amaral J.S., Mafra I. (2021) DNA barcode markers applied to seafood authentication: An updated review. Crit. Rev. Food Sci. Nutr. 61(22):3904–3935. Ferri E., Galimberti A., Casiraghi M., Airoldi C., Ciaramelli C., Palmioli A., Mezzasalma V., Bruni I., Labra M. (2015) Towards a universal approach based on omics technologies for the quality control of food. BioMed Res. Int. 2015:365794. Floren C., Wiedemann I., Brenig B., Schütz E., Beck J. (2015) Species identification and quantification in meat and meat products using droplet digital PCR (ddPCR). Food Chem. 173:1054–1058. García-González M., Moreno J., Manzano J.C., Florencio F.J., Guerrero M.G. (2005) Production of Dunaliella salina biomass rich in 9-cis-beta-carotene and lutein in a closed tubular photobioreactor. J. Biotechnol. 115(1):81–90. Hassoun A., Siddiqui S.A., Smaoui S., Ucak I., Arshad R.N., Garcia-Oliveira P., Prieto M.A., Aït-Kaddour A., Perestrelo R., Câmara J.S., Bono G. (2022) Seafood processing, preservation, and analytical techniques in the age of Industry 4.0. Appl. Sci. 12:1703. doi:10.3390/app12031703. Hicks C.C., Cohen P.J., Graham N.A.J., Nash K.L., Allison E.H., D’Lima C., Mills D.J., Roscher M., Thilsted S.H., Thorne-Lyman A.L., MacNeil M.A. (2019) Harnessing global fisheries to tackle micronutrient deficiencies. Nature. 574(7776):95–98. doi:10.1038/s41586-019-1592-6. Hosomi R., Yoshida M., Fukunaga K. (2012) Seafood consumption and components for health. Glob. J. Health Sci. 4(3):72–86. doi:10.5539/gjhs.v4n3p72. Houweling A.H., Vanstone C.A., Trautwein E.A., Duchateau G.S.M.J.E., Jones P.J.H. (2009) Baseline plasma plant sterol concentrations do not predict changes in serum lipids, C-reactive protein (CRP) and plasma plant sterols following intake of a plant sterol-enriched food. Eur. J. Clin. Nutr. 63(4):543–551. doi:10.1016/j.chemosphere.2021.133499. Iwamoto T., Hosoda K., Hirano R., Kurata H., Matsumoto A., Miki W., Kamiyama M., Itakura H., Yamamoto S., Kondo K. (2000) Inhibition of low-density lipoprotein oxidation by astaxanthin. J. Atheroscler. Thromb. 7(4):216–222. Jiménez-Escrig A., Sánchez-Muniz F.J. (2000) Dietary fibre from edible seaweeds: Chemical structure, physicochemical properties and effects on cholesterol metabolism. Nutr. Res. 20(4):585–598. Jinadasa B.K.K.K., Elliott C., Jayasinghe G.D.T.M. (2022) A review of the presence of formaldehyde in fish and seafood. Food Control. 136:108882. doi:10.1016/j.foodcont.2022.108882. Jones P.J., Raeini-Sarjaz M., Ntanios F.Y., Vanstone C.A., Feng J.Y., Parsons W.E. (2000) Modulation of plasma lipid levels and cholesterol kinetics by phytosterol versus phytostanol esters. J. Lipid Res. 41(5):697–705. Kanazawa, A. (2001) Sterols in marine invertebrates. Fisheries Science 67(6): 997–1007. Khaksar R., Carlson T., Schaffner D.W., Ghorashi M., Best D., Jandhyala S., Traverso J. Amini S. (2015) Unmasking seafood mislabeling in US markets: DNA barcoding as a unique technology for food authentication and quality control. Food Control. 56:71–76. La Nasa J., Biale G., Fabbri D., Modugno F. (2020) A review on challenges and developments of analytical pyrolysis and other thermo-analytical techniques for the quali-quantitative determination of microplastics. J. Anal. Appl. Pyrolysis. 149:104841. doi:10.1016/j.jaap.2020.104841. Liu J., Shao Y., Yuan C., Takaki K., Li Y., Ying Y., Hu Y. (2021) Eugenol-chitosan nanoemulsion as an edible coating: Its impact on physicochemical, microbiological and sensorial properties of hairtail (Trichiurus haumela) during storage at 4°C. Int. J. Biol. Macromol. 183:2199–2204. Lopata A.L., Kamath S. (2012) Shellfish allergy diagnosis-gaps and needs. Curr. Opin. Allergy Clin. Immunol. 25(2):60–66. Lopata A.L., Kleine-Tebbe J., Kamath S.D. (2016) Allergens and molecular diagnostics of shellfish allergy: Part 22 of the Series Molecular Allergology. Allergo. J. 25:24–32. Lusher A., Hollman P., Mendoza-Hill J. (2017) Microplastics in fisheries and aquaculture: Status of knowledge on their occurrence and implications for aquatic organisms and food safety. FAO Fisheries and Aquaculture Technical Paper, 615.

16

Handbook of Seafood and Seafood Products Analysis

Mannarino E., Pirro M., Cortese C., Lupattelli G., Siepi D., Mezzetti A., Bertolini S., Parillo M., Fellin R., Pujia A., Averna M., Nicolle C., Notarbartolo A. (2009) Effects of a phytosterol-enriched dairy product on lipids, sterols and 8-isoprostane in hypercholesterolemic patients: A multicenter Italian study. Nutr. Metab. Cardiovasc. Dis. 19(2):84–90. Marwaha N., Beveridge Malcolm C.M., Phillips Michael J. (2022) Fad, food, or feed: Alternative seafood and its contribution to food systems. Front. Sustain. Food Syst. 6:2022. doi:10.3389/fsufs.2022.750253. Marx Í.M. (2023) Emerging trends of electrochemical sensors in food analysis. Electrochem. 4(1):42–46. Matsushima K., Schimmel H., Wyrowski F. (2003) Fast calculation method for optical diffraction on tilted planes by use of the angular spectrum of plane waves. JOSA A. 20(9):1755–1762. Mintenig S.M., Bäuerlein P.S., Koelmans A.A., Dekker S.C., van Wezel A.P. (2018) Closing the gap between small and smaller: Towards a framework to analyse nano-and microplastics in aqueous environmental samples. Environ. Sci. Nano. 5:1640–1649. doi:10.1039/C8EN00186C. Mishra G.K., Barfidokht A., Tehrani F., Mishra R.K. (2018) Food safety analysis using electrochemical biosensors. Foods. 7(9):141. Murata, M., Sano Y., Bannai S., Ishihara K., Matsushima R., Uchida M. (2004) Fish protein stimulated the fibrinolysis in rats. Annals of Nutrition and Metabolism. 48(5): 348–356. Murray F., Cowie P.R. (2011) Plastic contamination in the decapod crustacean Nephrops norvegicus (Linnaeus, 1758). Mar. Pollut. Bull. 62(6):1207–1217. doi:10.1016/j.marpolbul.2011.03.032. Noh M.F.M., Gunasegavan R.D.N., Mustafa Khalid N., Balasubramaniam V., Mustar S., Abd Rashed A. (2020) Recent techniques in nutrient analysis for food composition database. Molecules. 25(19):4567. OECD FAO. (2022) OECD-FAO Agricultural Outlook, FAO, 2022–2031. Oishi Y., Dohmoto N. (2009) Alaska pollack protein prevents the accumulation of visceral fat in rats fed a high fat diet. J. Nutr. Sci. Vitaminol. (Tokyo). 55(2):156–161. doi:10.3177/jnsv.55.156. Parlapani F.F., Ferrocino I., Michailidou S., Argiriou A., Haroutounian S.A., Kokokiris L., Rantsiou K., Boziaris I.S. (2020) Microbiota and volatilome profile of fresh and chill-stored deepwater rose shrimp (Parapenaeus longirostris). Food Res. Int. 132:109057. Qiu L., Zhang M., Bhandari B., Yang C. (2020) Shelf life extension of aquatic products by applying nanotechnology: A review. Crit. Rev. Food Sci. Nutr. 0:1–15. Ranadheera C.S., Prasanna P.H.P., Vidanarachchi J.K., McConchie R., Naumovski N., Mellor D. (2017) Nanotechnology in Microbial Food Safety. In: Nanotechnology Applications in Food: Flavor, Stability, Nutrition and Safety; Elsevier Inc.: Amsterdam, The Netherlands. pp. 245–265. ISBN 9780128119433. Reames E. (2012) Nutritional Benefits of Seafood. Southern Regional Aquaculture Center. Renner G., Schmidt T.C., Schram J. (2018) Analytical methodologies for monitoring micro(nano)plastics: Which are fit for purpose? Curr. Opin. Environ. Sci. Health. 1:55–61. doi:10.1016/j.coesh.2017.11.001. Ribeiro F., Okoffo E.D., O’Brien J.W., Fraissinet-Tachet S., O’Brien S., Gallen M., Samanipour S., Kaserzon S., Mueller J.F., Galloway T., Thomas K.V. (2020) Quantitative analysis of selected plastics in highcommercial-value Australian Seafood by pyrolysis gas chromatography mass spectrometry. Environ. Sci. Technol. 54:9408–9417. doi:10.1021/acs.est.0c02337. Ritchie, H., Roser, M. (2020). Energy—Our World in Data. https://scirp.org/reference/referencespapers?refer enceid=2943205 Rovina K., Vonnie J.M., Shaeera S.N., Yi S.X., Abd Halid N.F. (2020) Development of biodegradable hybrid polymer film for detection of formaldehyde in seafood products. Sens. Bio-Sens. Res. 27:100310. doi:10.1016/j.sbsr.2019.100310. Ruethers T., Taki A.C., Johnston E.B., Nugraha R., Le T.T.K., Kalic T., McLean T.R., Kamath S.D., Lopata A.L. (2018) Seafood allergy: A comprehensive review of fish and shellfish allergens. Mol. Immunol. 100:28–57. doi:10.1016/j.molimm.2018.04.008. Epub 2018 May 30. PMID: 29858102. Schaffer S.W, Jong C.J., Ramila K.C., Azuma J. (2010) Physiological roles of taurine in heart and muscle. J. Biomed. Sci. 17(Suppl 1):S2. Teuten E.L., Saquing J.M., Knappe D.R.U., Barlaz M.A., Jonsson S., Björn A., Rowland S.J., Thompson R.C., Galloway T.S., Yamashita R., Ochi D., Watanuki Y., Moore C., Viet P.H., Tana T.S., Prudente M., Boonyatumanond R., Zakaria M.P., Akkhavong K., Ogata Y., Hirai H., Iwasa S., Mizukawa K., Hagino Y., Imamura A., Saha M., Takada H. (2009) Transport and release of chemicals from plastics to the environment and to wildlife. Philos. Trans. R. Soc. Lond. Ser. B. Biol. Sci. 364(1526):2027–2045. doi:10.1098/ rstb.2008.0284. Thakur M. (2019) Marine Bioactive Components: Sources, Health Benefits, and Future Prospects. In: Technological Processes for Marine Foods, from Water to Fork; Apple Academic Press. pp. 61–72.

Introduction

17

Vandamme S.G., Griffiths A.M., Taylor S.A., Di Muri C., Hankard E.A., Towne J.A., Watson M., Mariani S. (2016) Sushi barcoding in the UK: Another kettle of fish. Peer J. 4:e1891. Villicaña C., Amarillas L., Soto-Castro L., Gómez-Gil B., Lizárraga-Partida M.L., León-Félix J. (2019) Occurrence and abundance of pathogenic vibrio species in raw oysters at retail seafood markets in Northwestern Mexico. J. Food Prot. 82:2094–2099. doi:10.4315/0362-028X.JFP-19-237. Woo C.K., Bahna S.L. (2011) Not all shellfish “allergy” is allergy! Clin. Transl. Allergy. 1(1):3. doi:10.1186/2045-7022-1-3. Yoshizawa K., Rimm E.B., Morris J.S., Spate V.L., Hsieh C.-C., Spiegelman D., Stampfer M.J., Willett W.C. (2002) Mercury and the risk of coronary heart disease in men. New Engl. J. Med. 347:1755–1760. Young W., Wiggins S., Limm W., Fisher C.M., DeJager L., Genualdi S. (2022) Analysis of per-and poly (fluoroalkyl) substances (PFASs) in highly consumed seafood products from US Markets. J. Agric. Food Chem. 70: 13545–13553. Zhang W., Tang Y., Han Y., Zhou W., Shi W., Teng S., Ren P., Xiao G., Li S., Liu G. (2022) Microplastics boost the accumulation of tetrabromobisphenol A in a commercial clam and elevate corresponding food safety risks. Chemosphere. 292:133499. doi: 10.1016/j.chemosphere.2021.133499. Epub 2021 Dec 31. PMID: 34979205.

2

Peptides and Proteins Turid Rustad and Eva Falch Norwegian University of Science and Technology

Rasa Slizyte SINTEF Ocean

Abhilash Sasidharan

Norwegian University of Science and Technology

2.1 INTRODUCTION Seafood contains high-value proteins, and in 2019, aquatic foods provided about 17% of animal proteins and 7% of all proteins. For 3.3 billion people, aquatic foods provide at least 20% of the average per capita intake of animal protein (Food and Agriculture Organization of the United Nations (FAO) – The State of World Fisheries and Aquaculture 2022 (fao.org)). Seafood proteins also have a high nutritional value with a high content of essential amino acids. Seafood proteins, especially fish muscle proteins, also have good functional properties such as water holding properties, emulsification, gelling, foaming, and textural properties. For products such as surimi and fish mince, the water holding capacity (WHC) and the textural properties are important for the products produced from surimi and fish mince. The functional properties of proteins are both influenced by fish species, season, etc., and influenced by the processing of the raw material. Retaining functional properties through different processing and preservation methods is therefore important to be able to make high-quality products. The proteins are also highly digestible. The amount of proteins in seafood varies with several factors, including species, size, age, sexual maturity, season, and fishing ground. Both the protein content and the properties of the proteins are highly important for the quality of the seafood raw materials. To be able to label the products and for quality control, it is therefore highly important to have good and reliable methods to analyze the content and properties of the proteins and peptides in the seafood.

2.2  TOTAL CONTENT OF PROTEINS The determination of total proteins is usually carried out by determining the nitrogen content using the Kjeldahl method or the Dumas method. In addition, the nitrogen content can also be determined using elemental analysis (Carbon Nitrogen (CN) analyzer) (Hjellnes et al., 2020). These methods are described and discussed in Chapter 16. Based on several cooperative studies taking into account the great assortment of methods, it indicates that protein determination based on nitrogen analysis for most foods overestimates the protein content independent of specific conversion factors used. The most reliable method to quantify proteins is to use quantitative amino acid analysis. The method involves hydrolysis of the food or raw material followed by analysis of the amount and composition of the amino acids. However, there is a need for improvement in the methods or conditions used to hydrolyze the proteins. This is further described in Chapter 16 and in Mæhre et al. (2018). 18

DOI: 10.1201/9781003289401-3

Peptides and Proteins

19

2.3  FUNCTIONAL PROPERTIES OF PROTEINS Functional properties have been defined as those physical and chemical properties that affect the behavior of proteins in food systems during processing, storage, preparation, and consumption (Kinsella, 1982). Functional properties of proteins include WHC, gelling properties, texture, viscosity, thickening, fat binding, emulsifying, and foaming properties. The functional properties of seafood proteins are important since they directly influence the food product including sensory and other consumer acceptability attributes. The functional properties of seafood proteins are influenced by the total physicochemical properties exhibited by the proteins while subjected to processing, consumption, and storage. Changes in functional properties such as drip loss, changes in WHC or water binding, cook loss, texture, and gelling properties can be used to determine changes in fish and seafood during storage and processing (Hultmann and Rustad, 2002; Ofstad et al., 1993). The peptide and amino acid sequences of the proteins together with parameters such as pH and ions determine the nature and intensity of the functionality parameters (Chalamaiah et al., 2012). Being able to determine or determination of the functional properties is important to determine the application potential of both extracted seafood proteins and changes in the seafood proteins during processing and storage. The methods used to determine functional properties are usually not standardized and are highly laboratory–dependent, making comparison of results from different laboratories difficult. Texture can be determined instrumentally using a texture analyzer (Sigurgisladottir et al., 1997). One of the simpler methods to measure changes in proteins is to measure changes in solubility. Proteins in fish muscle can be divided into three main groups, sarcoplasmic, myofibrillar, and connective proteins. These groups are based on the solubility of the proteins. The sarcoplasmic proteins are soluble in water or buffers with low ionic strength and mainly consist of enzymes. Myofibrillar proteins are also called salt-soluble proteins and can be extracted in buffers with ionic strength >0.3. Concentrations of 0.6 M KCl or NaCl are often used. NaCl being an ionic salt is capable of increasing protein solubility through the salting process, which creates a double ionic layer, augmenting the protein–salt interaction (Purschke et al., 2018). The connective tissue proteins are often called insoluble proteins, and to extract them, alkali or acids are needed. When comparing methods and results presented in the literature, it is clear that extraction methods and conditions (buffers, pH, ionic strength, ratio of buffer to raw material, etc.) are not standardized. Since the extraction method – as well as the determination method – are important factors influencing the result, this is important to keep in mind both when setting up experiments and when comparing results from different studies. Changes in solubility have been found to be correlated with changes in other functional properties such as water holding and textural properties. The myofibrillar proteins are susceptible to denaturation during processing, and changes in the solubility or extractability of these can reflect changes during processing. This has been used to study changes during storage (ice, frozen storage) and heat treatment (Hultmann and Rustad, 2004).

2.3.1 Water Holding Capacity (WHC) Native fish muscle proteins have good WHC. The WHC is also important for the textural properties including juiciness and tenderness that influence the mouthfeel of seafood products (Chan et al., 2021). Both WHC and drip loss (DL), which indicates a poor WHC, are representative parameters of freshness considering the association between water and fish muscle (Warner, 2014). The conventional methods used to measure WHC often rely on the application of an external force to the protein matrix. For determining DL, almost no force is applied and only the gravitational movement of water from the proteins over a period is determined where the DL is calculated as a percentage of water excluded against the original weight of the sample (Chan et al., 2020). The external application of force used to determine WHC is mechanical forces such as compression and centrifugation. Filter paper wetness (FPW) is a simple technique used in compression, where

20

Handbook of Seafood and Seafood Products Analysis

a sample is compressed between filter papers, which is highly associated with DL (Mallikarjunan, 2016). In the centrifugal method, a force of either a lower g (200–800) utilizing 2–15 g of sample or a higher g (5,000–40,000) utilizing 1–20 g of sample is used, which measures the capacity of the sample to retain the moisture contained after the process (Varmbo et al., 2000). The low g centrifugation method, which retains the structural integrity of the muscle tissue, is largely preferred for analyzing seafood WHC (Skipnes et al., 2007). Another method of using an external force is applying temperature, which measures the effect of the thermal process on the WHC of the protein matrix when subjected to denaturation (Skipnes et al., 2007). The measurement of thaw loss after freezing is also a methodology used for analyzing the WHC of the protein matrix when subjected to low-temperature preservation (Bowker, 2017). The liquid loss is generally used to calculate WHC and is ultimately expressed in percent by determining the weight difference in the sample before and after the liquid is separated through centrifugation (Sun et al., 2018).

2.3.2 Emulsifying Property Proteins can also stabilize emulsions. Emulsions are complex mixtures of two immiscible components, for example, oil in water formed under specific conditions such as temperature and presence of elements, which support the formation of such complexes called emulsifying agents. These mixtures are highly unstable thermodynamically and have different applications such as providing unique texture and flavor in food and encapsulating, safeguarding, and distributing functional components into a food medium (Walker et al., 2015). To determine the emulsifying capacity, a predetermined volume of protein with a known concentration is homogenized with vegetable oil followed by centrifugation. The volume of the emulsion is determined, and the emulsifying capacity is expressed as ml of emulsified oil/g of protein (Kinsella and Melachouris, 1976). Emulsion stability can be defined as the percentage of initial emulsion remaining after a certain time (one day at room temperature) and centrifugation (McClements, 2015).

2.3.3 Foaming Properties The foaming capacity of a food constituent such as protein finds application in the food industry regarding the preparation of texture-specific food items such as whipped cream, ice cream, and bakery items, which emphasize volume and air occupancy (Lam and Nickerson, 2013). The foaming property of proteins is attributed to their pH affinity, which results in precipitation closer to the isoelectric point (Yang and Baldwin, 2017). Moreover, the transportation, rearrangement, and penetration of protein molecules at the air–water interface determine the foaming capability properties (Elavarasan et al., 2014). Sathe and Salunkhe (1981) proposed methodologies for assessing the foaming capacity and foam stability of proteins. A predetermined mass of protein concentrate was distributed in a known volume of water, and the mixture was homogenized and transferred to a graduated beaker for measurements. The foaming capacity is defined as the percentage volume increase over the initial volume.

2.3.4 Gelling Properties The gelling property is an important functional attribute, which finds application in texture-specific food applications. The gelling feature of seafood muscle protein is attributed to the thermal-induced partial unfolding of myosin filaments in solution and further irreversible accumulation of unfolded filaments to form a three-dimensional formation trapping water, within the matrix. Fish muscle proteins can gel at low-temperature incubation for 12 h at 0°C–4°C or a mild thermal process will result in gel formation (Sasidharan and Venugopal, 2020). Gel preparation is the primary step in analyzing the gelling properties of the protein concentrates. The gel strength of the samples is generally measured using an instrumental texture analyzer

Peptides and Proteins

21

at room temperature. The samples were subjected to pressure exerted through a spherical probe or plunger with a 5 mm diameter on a 10 N load cell with a depression rate of 30–60 mm/min. The breaking force and deformation data were obtained and expressed as g and mm (Rawdkuen et al., 2009; Le et al., 2018).

2.3.5 Fat Binding Capacity The fat binding capacity indicates the ability of the protein to absorb and retain lipid components within the matrix. This property is primarily linked to emulsifying capacity and may also be further influenced by bulk density, degree of hydrolysis, and enzyme–substrate specificity depending on the extraction methodology employed (Villamil et al., 2017). The property finds application in the meat and confectionary industry as it regulates flavor characteristics (Taheri et al., 2013). Özyurt et al. (2015) proposed a method for analyzing the fat binding capacity of protein. The protein concentrates precisely weighed at 1 g were dissolved in 10 mL of vegetable oil, mixed thoroughly for 5 min, and then centrifuged at 3,000  ×  9 g for 15 min. The weight difference was later expressed as the fat binding capacity.

2.3.6 Analysis of Soluble Proteins To determine the amount of soluble proteins, they need to be extracted from different matrices in marine raw materials or in products where they may have interacted and can be bound to carbohydrates, lipids, and other nutrients. Both direct and indirect analytical methodologies can be used for protein determination. Several of the most important indirect methods for protein determination in food date from the late 1800s (Dumas, Nessler’s reagent, Biuret, Kjeldahl, Folin–Ciocalteu, and dye binding) (Owusu-Apenten, 2002). The Kjeldahl and Dumas methods are based on quantification of the total organic nitrogen followed by conversion into crude protein or a set of direct methods. The Biuret, Folin–Ciocalteu, and dye binding are methods where the proteins are chemically or physically modified for determination (colorimetric assays). These methods can be divided into two groups: dye-binding reaction and redox reaction with proteins (Owusu-Apenten, 2002). In the redox spectrophotometric methods, analyses are based on reaction with the Folin reagent, and the following methods could be mentioned: Biuret reaction (Noll et al., 1974), Lowry protein method (Lowry et al., 1951), and bicinchoninic acid (BCA) assay (Walker, 2002). In the Biuret reaction, Cu(II) with proteins in an alkaline medium is reduced to Cu(I), which binds to protein forming a Cu(I)–peptide complex with a purplish-violet color (Noll et al., 1974). The same principle is used in the BCA assay, where Cu(I) is detected by reaction with BCA, which gives an intense purple color (Liu et al., 2008). One of the most popular methods in this group is the Lowry protein method (Lowry et al., 1951), which is initially based on the Biuret reaction, where peptide bonds react with Cu(II) in an alkaline medium to produce Cu(I). Later, Cu(I) reacts with the Folin reagent. The reaction gives a strong blue color (Waterborg, 2002). The intensity of color partly depends on the amount of Tyr and Trp in samples but can also be influenced by other components such as N-containing buffer or carbohydrates (Waterborg, 2002). The amounts of proteins in sardine determined by the Lowry method were comparable to those determined by the Kjeldahl method (Noll et al., 1974). The Lowry method is suitable for protein extracts such as actomyosin, which is an important component in surimibased products (Bradford, 1976). However, the BCA assay is shorter compared with the Lowry method (where two steps are needed), is more flexible and stable in alkaline conditions, and has a broad linear range. The BSA assay can also be interfered by the usual chemical components such as ethylenediaminetetraacetic acid (EDTA), thiols, reducing sugars, hydrogen peroxide, or phospholipids (Noll et al., 1974; Liu et al., 2008). The dye-binding spectrophotometric assay is based on the reaction between acid dye and positively charged amino acid residues in proteins (Noll et al., 1974). In acidic conditions, the created insoluble complexes are removed and the unbound dye is determined by measuring its absorbance. The amount of protein is proportional to the amount of bound dye. The Coomassie dye in acidic conditions binds to proteins and creates complexes that influence a color shift from a maximum of 465 to 595 nm, using the Bradford method (Bradford, 1976). The

22

Handbook of Seafood and Seafood Products Analysis

absorbance of the Coomassie dye–protein complex is measured at 595 (575–615) nm, because the difference between the two forms of the dye is greatest in this area. Within the linear range of the assay (∼5–25 mg/mL), the protein amount is proportional to bound Coomassie (Bradford, 1976). This method is suitable for the determination of extractability of proteins and the determination of protein content in extracts (Benjakul and Bauer, 2000; Hultmann and Rustad, 2004; Søvik and Rustad, 2005; Kruger, 2002). This technique is simple and sensitive and uses a shorter analysis time compared with the Lowry method. Moreover, the dye-binding assay is less affected by reagents and nonprotein components from biological samples (Aitken and Learmonth, 2002). Proteins in solution can be quantified in a simple spectrophotometric analysis by near- or far-ultraviolet (UV) absorbance (Van Camp and Dierckx, 2004). Absorption in the near UV by proteins depends mostly on the content of Tyr and Trp and less on the amount of phenylalanine (Phe) and disulfide bonds. This absorbance measurement is simple and sensitive and needs no reagents, and the sample is recoverable (Van Camp and Dierckx, 2004). Crude protein extracts or individual fractions of proteins (Aitken and Learmonth, 2002) can be measured at 280 nm. The disadvantages of the method include interference with other components such as nucleic acid, which absorbs in the same wavelength region. Far-UV absorption can also be used for the determination of protein content: Peptide bonds absorb in the area with the maximum at about 190 nm. Different proteins give a small variation in absorbance, and the method can be considered accurate for protein determination. However, oxygen also absorbs at these wavelengths, and to avoid interference, measurements at 205 nm are used. It should also be mentioned that components such as carbohydrates, salts, lipids, amides, phosphates, and detergents interfere (Van Camp and Dierckx, 2004). Choice of buffers, extraction technique, and chemicals influence both yield and determination of proteins by spectrophotometric methods as the substances can interfere and also lead to overestimation of the protein content.

2.4  IMMUNOASSAYS/DETECTION OF ALLERGENS Seafood allergy is a global health issue related to food safety. There is no cure for seafood allergy so the most efficient way to prevent allergic reactions is to avoid eating food containing the allergens. This requires proper labeling. To achieve this, there is a need for rapid, reliable, and user-friendly methods for the detection of allergens (Li et al., 2023). The current methods can be divided into three groups, namely deoxyribonucleic acid (DNA)-based, protein-based, and aptamer-based detection methods (Faisal et al., 2019). The most common allergen in fish is parvalbumin, while tropomyosin is considered to be the most common allergen in shellfish. Most of the protein-based methods for allergen detection are based on the specific binding of antibodies to allergen epitopes. These can be divided into three main categories, namely enzyme-linked immunosorbent assay (ELISA), immunochromatography assay, and immunosensor. The most widespread methods for allergen detection are ELISA methods. Immunoassays can be used to determine the amount of a specific protein in a mixture. Immunoassays are based on specific interactions of the antibody with the target antigen (the protein that should be determined) in the sample. This can, for instance, be used to analyze the presence of allergens (Fu et al., 2019). ELISA presents advantages such as high sensitivity, large detection scale, and simple equipment design (Clark et al., 1986). There are also convenient and reliable ELISA kits for seafood allergen detection commercially (Fernandes et al., 2015). Some of the commercial kits do not have the ability to detect the allergenic peptides or proteins that have been formed during processing (heat denaturation and hydrolysis). Several studies have shown that these challenges can be solved (Li et al., 2023). Immunochromatography methods are relatively rapid and simple to carry out, but they can only be used for qualitative or semi-quantitative analysis and not for quantitative analysis. Biosensors

Peptides and Proteins

23

have several advantages, such as increased sensitivity, but they are more time-consuming than other techniques. Currently, two main types of immunosensors used for seafood allergen detection have been developed, electrochemical immunosensor and optical immunosensor. Mass Spectrometry (MS) antibody-based detection methods are sometimes inadequate for qualitative and quantitative allergen detection because of matrix or processing effects and epitope masking. However, MS-based detection methods can well resolve it (Lopez-Pedrouso et al., 2020). MS-based detection methods are usually used in the determination and detection of food allergens; nevertheless, the workflows of MS-based methods are different with diverse aims. When determining a new allergen, MS is only one tool to determine protein species, and it is often combined with other techniques to achieve the goals. Protein-based detection methods, which take a certain allergen as the direct detection object, are mostly realized based on the binding ability of antigen and antibody. However, the detection accuracy is easily affected by the sample substrate, processing, and other factors. As a protein-based detection method, mass spectrometry can overcome interference factors to some extent, but it is time-consuming and requires expensive equipment.

2.5  ELECTROPHORESIS AND CHROMATOGRAPHY-BASED METHODS During processing and storage, muscle proteins may be degraded, and this is reflected in changes in molecular weight. The molecular weight distribution of proteins can be determined by different methods. In native gel filtration chromatography, the proteins are separated based on their size and shape, which is based on their Stokes’ radii. The beads used for this are usually open, cross-linked three-dimensional polymer networks such as agarose, dextrans, cellulose, polyacrylamide, or combinations of these materials (Owusu-Apenten, 2002). For high-pressure systems, microporous silica, porous glass, or inorganic–organic composites are used. Small molecules can enter all the pores in the beads, whereas larger proteins and peptides can only enter the larger pores. This means that the proteins and peptides are separated by size and that the size can be determined by comparison with the elution volume of known standards. How the protein behaves in a column is described by the coefficient Kav, which describes the proportion of the pores available to the molecules. Kav = (Ve − V0)/ (Vt − V0), where Ve is the elution volume of the molecule, V0 is the void volume of the column, and Vt is the total volume of the column. Molecular weight and changes in molecular weight during processing and storage can also be determined by electrophoresis (Hultmann et al., 2004). Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) is one of the most commonly used methods. This method uses polyacrylamide gels, and the proteins are denatured by boiling in a solution of SDS. SDS binds to the proteins at a ratio of 1:1.4, and since SDS is charged, this means that the protein charge will be directly proportional to the molecular weight. Dithiothreitol (DTT) or mercaptoethanol is often added to reduce disulfide bonds.

2.6  PEPTIDE CHARACTERIZATION The analysis and characterization of peptides are important for several reasons. The first is because the degradation of seafood proteins can result in the formation of small peptides and free amino acids and analysis of these can be used to follow changes during processing and storage (Bauchart et al., 2007; Hultmann et al., 2004). An increase in acid-soluble peptides and free amino acids can be used to follow hydrolysis processes. Commercial protein hydrolysates show a large range of variation in the degree of hydrolysis, ranging from 50% where at least half the original peptide bonds have been broken (Johns et al., 2011). The size distribution of peptide fractions can be determined by chromatographic methods such as high-performance size exclusion chromatography (HPSEC). Different column materials and detection methods are being used, and to separate

24

Handbook of Seafood and Seafood Products Analysis

peptides in the size range from 7,000 to 100 Da, the Superdex Peptide High Resolution (HR) 10/30 column is often used (Johns et al., 2011). The amount of small peptides can be quantified by determining the content of acid-soluble peptides. This can be done by precipitation to a final concentration of 10% trichloroacetic acid (TCA) (Bergvik et al., 2012). The solubility of proteins in TCA is determined both by size and by hydrophobicity, and values from 3 to 20 amino acid residues have been suggested as peptide sizes that are soluble in 10% TCA. Precipitation in 70% ethanol has been shown to give the same concentration of soluble peptides as precipitation in 12% TCA (Rohm et al., 1996).

2.7  PROTEIN MODIFICATIONS Oxidation of proteins will lead to various changes in the proteins, both chemical changes on individual amino acids and breakdown of the peptide backbone and aggregation (Hematyar et al., 2019). Because the functions of protein are very specific, oxidative modifications can cause numerous functional consequences and lead to changes in food texture, WHC, digestibility, and juiciness (Baron et al., 2007). Protein oxidation may be caused directly by reactive oxygen species (ROS) and reactive nitrogen species or indirectly as the result of reactions with products from lipid oxidation with reducing sugars or carbohydrates (Lund et al., 2011). Protein oxidation is usually determined by the analysis of an increase in carbonyl groups and a decrease in sulfhydryl groups or the formation of dityrosine (Lund et al., 2011).

2.8 SUMMARY Seafood contains valuable proteins, and the analysis and characterization of seafood proteins are desirable both to understand the properties of the raw material and to be able to understand how the proteins will behave during storage and processing. This may enable improving the optimization of processes to make high-quality seafood products. A wide range of methods to analyze the properties of seafood proteins exist; however, many of the methods are highly empirical, especially when it comes to determining the functional properties of the proteins.

REFERENCES Aitken, A. and Learmonth, M. P. 2002. Protein Determination by UV Absorption, In The Protein Protocols Handbook, 2nd Edition, Edited by: J. M. Walker, Humana Press Inc., Totowa, NJ pp. 3–6. Baron, C. P.; Kjærsgård, I. V. H.; Jessen, F. and Jacobsen, C. 2007. Protein and lipid oxidation during frozen storage of rainbow trout (Oncorhynchus mykiss). J. Agric. Food Chem. 55, 8118–8125. Bauchart, C., Chambon, C., Mirand, P. P., Savary-Auzeloux, I., Rémond, D., and Morzel, M. 2007. Peptides in rainbow trout (Oncorhynchus mykiss) muscle subjected to ice storage and cooking. Food Chem., 100(4), 1566–1572. doi: 10.1016/j.foodchem.2005.12.023. Benjakul, S. and Bauer, F. 2000. Physicochemical and enzymatic changes of cod muscle proteins subjected to different freeze-thaw cycles. J. Sci. Food Agric. 80, 1143–1150. Bergvik, M.; Overrein, I.; Bantle, M.; Evjemo, J.O. and Turid Rustad, T. 2012. Properties of Calanus finmarchicus biomass during frozen storage after heat inactivation of autolytic enzymes, Food Chem. 132(1), 209–215. doi:10.1016/j.foodchem.2011.10.058. Bowker, B. 2017. Developments in Our understanding of water-holding capacity. In Poultry Quality Evaluation. Edited by: Petracci, M. & Berri, Woodhead Publishing. pp. 77–113. doi: 10.1016/ B978-0-08-100763-1.00004-0. Bradford, M. A. 1976. A rapid and sensitive method for the quantification of microgram quantities of protein using the principle of protein-dye binding. Anal. Biochem. 72, 248–254. Chalamaiah, M.; Dinesh kumar, B.; Hemalatha, R. and Jyothirmayiet, T. 2012. Fish protein hydrolysates: Proximate composition, amino acid composition, antioxidant activities and applications: A review. Food Chem. 135, 3020–3038. doi:10.1016/j.foodchem.2012.06.100.

Peptides and Proteins

25

Chan, S. T.; Roth, B.; Jessen, F.; Jakobsen, A. N. and Lerfall, J. 2021. Water holding properties of Atlantic salmon. Comp. Rev Food Sci Food Saf. 1–22. doi:10.1111/1541-4337.12871. Chan, S. T.; Roth, B.; Skare, M.; Hernar, M.; Jessen, F.; Løvdal, T.; Jakobsen, A. J. and Lerfall, J. 2020. Effect of chilling technologies on water holding properties and other quality parameters throughout the whole value chain: From whole fish to cold-smoked fillets of Atlantic salmon (Salmo salar). Aquaculture. 526, 735381. doi:10.1016/j.aquaculture. Elavarasan, K.; Naveen Kumar, V. and Shamasundar, B. A. 2014. Antioxidant and functional properties of (FPH) from freshwater carp (Catla catla) as influenced by the nature of enzyme. J. Food Process. Preser. 38, 1207–1214. Faisal, M.; Vasiljevic, T. and Donkor, O. N. 2019. A review on methodologies for extraction, identification and quantification of allergenic proteins in prawns. Food Res. Int. (Ottawa, ON). 121, 307–318. doi:10.1016/j. foodres.2019.03.040. Fu, L.; Cherayil, B. J.; Shi, H.; Wang, Y. and Zhu, Y. 2019. Detection and Quantification Methods for Food Allergens, In Food Allergy-From molecular mechanisms to control strategies (Edited by: Fu, L., Cherayil, B. J., Shi, H., Wang, Y. and Zhu, Y.) Springer, Singapore. pp 69–71. doi:10.1007/978-981-13-6928-5_4 Hematyar, N.; Rustad, T.; Sampels, S. and Kastrup Dalsgaard, T. 2019. Relationship between lipid and protein oxidation in fish. Aquac Res. 50, 1393-1403. doi:10.1111/are.14012 Hjellnes, V. H.; Slizyte, R.; Rustad, T.; Carvajal, A. K. and Greiff, K. 2020. Utilization of egg-laying hens (Gallus Gallus domesticus) for production of ingredients for human consumption and animal feed. BMC Biotechnol. 20, art. 22. Hultmann, L. and Rustad, T. 2002. Textural changes during iced storage of salmon (Salmo salar) and cod (Gadus morhua). J. Aquatic Food Product Technol. 11(3/4), 105–123. Hultmann, L. and Rustad, T. 2004. Iced storage of Atlantic salmon (Salmo salar) – Effects on endogenous enzymes and their impact on muscle proteins and texture. Food Chem. 87(1), 31–41. Hultmann, L.; Bencze Rørå, A. M.; Steinsland, I.; Skåra, T. and Rustad, T. 2004. Proteolytic activity and properties of proteins in smoked salmon (Salmo salar) – Effects of smoking temperature. Food Chem. 85(3), 377–387. Johns, P. W.; Jacobs, W. A.; Phillips, R. R.; McKenna, R. J.; O’Kane, K. A. and McEwen, J. W. 2011. Characterisation of peptide molecular mass distribution in commercial hydrolysates and hydrolysatebased nutritional products. Food Chem. 125(3), 1041–1050. Kinsella, J. E., & Melachouris, N. 1976. Functional properties of proteins in foods: A survey. CRC Critical Reviews in Food Science and Nutrition, 7(3), 219–280. doi: 10.1080/10408397609527208. Kinsella, J. E. 1982. Protein structure and functional properties: emulsification and flavor binding effects. In ACS Symposium Series, Food Protein deterioration, 206, Chapter 12, 301–326. doi:10.1021/bk-1982-0206. Kruger, N. J. 2002. The Bradford Method for Protein Quantitation, In The Protein Protocols Handbook, 2nd Edition, Edited by: J. M. Walker, Humana Press Inc., Totowa, NJ, 17–24. Lam, R. S. H. and Nickerson, M. T. 2013. Food proteins: A review on their emulsifying properties using a structure-function approach. Food Chem. 141, 975–984. doi:10.1016/j.foodchem.2013.04.038. Lam, A.; Can Karaca, A., Tyler, R. and Nickerson, M. 2018. Pea protein isolates: Structure, extraction, and functionality. Food Rev. Int. 34(2), 126–147. Le, H., Ting, L., Jun, C., and Weng, W. 2018. Gelling properties of myofibrillar protein from abalone (Haliotis Discus Hannai Ino) muscle. Int. J. Food Prop., 21(1), 277–288. doi: 10.1080/10942912.2018.1454463. Li, J.; Wang, H. and Cheng, J.-H. 2023. DNA, protein and aptamer-based methods for seafood allergens detection: Principles, comparisons and updated applications. Crit. Rev. Food Sci. Nutr. 63(2), 178–191. doi:1 0.1080/10408398.2021.1944977. Liu, R.; Zhao, S.-M.; Xiong, S.-B.; Qui, C.-G. and Xie, B.-J. 2008. Rheological properties of fish actomyosin and pork actomyosin solutions. J. Food Eng. 85, 173–179. Lowry, G. H.; Rosebrough, R. J.; Farr, A. L. and Randall, R. J. 1951. Protein measurements with the Folin phenol reagent. J. Biol. Chem. 193, 263–275. Lund, M. N.; Heinonen, M.; Baron, C. P. and Estévez, M. 2011. Protein oxidation in muscle foods: A review. Mol. Nutr. Food Res. 55(1), 83–95. doi:10.1002/mnfr.201000453. Mæhre, H. K., Dalheim, L., Edvinsen, G. K., Elvevoll, E. O. and Jensen, I. J. 2018. Protein determinationmethod matters. Foods. 7(1), 5. doi:10.3390/foods7010005. Mallikarjunan, P. K. 2016. Physical measurements. In Handbook of frozen food processing and packaging, Edited by : Sun D.W, 2nd edition, CRC Press, Boca Raton, FL, pp. 549–562. McClements, D. J. 2015. Food Emulsions, 3rd Edition, CRC Press, Boca Raton, FL. Noll, J. S.; Simmonds, D. H. and Bushuk, W. C. 1974. A modified biuret reagent for the determination of protein. Cereal Chem. 52, 610–616.

26

Handbook of Seafood and Seafood Products Analysis

Ofstad, R.; Kidman, S.; Myklebust, R. and Hermansson, A. M. 1993. Liquid holding capacity and structuralchanges during heating of fish muscle – Cod (gadus-morhua l) and salmon (salmo-salar). Food Struct. 12(2), 163–174. Owusu-Apenten, R. K. 2002. Food Protein Analysis, Marcel Dekker, Inc., New York. Özyurt, G., Şimşek, A., Karakaya, B. T., Aksun, E. T. and Yeşilsu, A. F. 2015. Functional, Physicochemical and Nutritional Properties of Protein from Klunzinger's Ponyfish Extracted by the pH Shifting Method. J. Food Proc. and Pres., 39(6), 1934–1943. doi: 10.1111/jfpp.12432 Purschke, B.; Tanzmeister, H.; Meinlschmidt, P.; Baumgartner, S.; Lauter, K. and Jäger, H. 2018. Recovery of soluble proteins from migratory locust (Locusta migratoria) and characterisation of their compositional and techno-functional properties. Food Res. Int. 106, 271–279. Rawdkuen, S., Sai-Ut, S., Khamsorn, S., Chaijan, M. and Benjakul, S. 2009. Biochemical and gelling properties of tilapia surimi and protein recovered using an acid-alkaline process. Food Chem., 112(1), 112–119. https://doi.org/https:/doi.org/10.1016/j.foodchem.2008.05.047 Rohm, H.; Jaros, D.; Rockenbauer, C.; Riedler-Hellrigl, M.; Uniacke-Lowe, T. and Fox, P. F. 1996. Comparison of ethanol and trichloroacetic acid fractionation for measurement of proteolysis in Emmental cheese. Int. Dairy J. 6, 1069–1077. Sasidharan, A. and Venugopal, V. 2020. Proteins and co-products from seafood processing discards: Their recovery, functional properties and applications. Waste Biomass Valorization. 11, 5647–5663. doi:10.1007/ s12649-019-00812-9. Sathe, S.K. and Salunkhe, D.K. 1981. Functional Properties of Great Northen Bean (Paeolus vulgaris L.) Protein: Emulsification Foaming, Viscocity and Gelation Properties. Journal of Food Science, 46, 71–81. doi: 10.1111/j.1365-2621.1981.tb14533.x. Sigurgisladottir, S., Torrissen, O. Ø.; Lie, Ø.; Thomassen, M. and Hafsteinsson, H. 1997. Salmon quality: Methods to determine the quality parameters. Rev. Fish. Sci. 5(3), 223–252. doi:10.1080/10641269709388599. Skipnes, D.; Østby, M. L. and Hendrickx, M. E. 2007. A method for characterising cook loss and water holding capacity in heat treated cod (Gadus morhua) muscle. J. Food Eng. 80(4), 1078–1085. doi:10.1016/j. jfoodeng.2006.08.015. Søvik, S. L. and Rustad, T. 2005. Effect of season and fishing ground on the activity of lipases in byproducts from cod (Gadus morhua). Lwt-Food Sci. Technol. 38, 867–876. Sun, Y., Ma, L., Ma, M., Zheng, H., Zhang, X., Cai, L., Li, J., and Zhang, Y. 2018. Texture characteristics of chilled prepared Mandarin fish (Siniperca chuatsi) during storage. Int. J. Food Prop. 21(1), 242–254. doi: 10.1080/10942912.2018.1451343 Taheri, A.; Anvar, S. A. A.; Ahari, H. and Fogliano, V. 2013. Comparison the functional properties of protein hydrolysates from poultry by-products and rainbow trout (Onchorhynchus mykiss) viscera. Iran. J. Fish. Sci. 12(1), 154–169. Tian, Y.; Wang, W.; Yuan, C.; Zhang, L. and Liu, J. 2017. Nutritional and digestive properties of protein isolates extracted from the muscle of the common carp using pH-shift processing. J. Food Process. Preserv. 41, e12847. Van Camp, J. and Dierckx, S. 2004. Proteins, In Handbook of Food Analysis, Physical Characterisation and Nutrient Analysis, Volume I, 2nd Edition, Revised and Expanded, Edited by: L. M. L. Nollet, Marcel Dekker, Inc., New York., pp. 167–202. Varmbo, G., Skåra, T., Olsen, S. O., and Sivertsvik, M. 2000. Modelling of water holding capacity (WHC) in ground Atlantic salmon (Salmo Salar), as affected by heat, pH and salt content. 30th WEFTA Plenary Meeting, Torshavn, Faroe Islands. Villamil, O.; Váquiro, H. and Solanilla, J. F. 2017. Fish viscera protein hydrolysates: Production, potential applications and functional and bioactive properties. Food Chem. 224, 160–171. doi:10.1016/j. foodchem.2016.12.057. Walker, J. M. 2002. The Bicinchoninic Acid (BCA) Assay for Protein Quantitation, In The Protein Protocols Handbook, 2nd Edition, Edited by: J. M. Walker, Humana Press Inc., Totowa, NJ, 11–15. Walker, R., Decker, E. A. and McClements, D. J. 2015. Development of food-grade nanoemulsions and emulsions for delivery of omega-3 fatty acids: Opportunities and obstacles in the food industry. Food Funct. 6, 41–54. doi:10.1039/C4FO00723A. Warner, R. D. 2014. Measurement of Water Holding Capacity and Colour: Objective and Subjective, In Encyclopedia of Meat Sciences, Volume 2, 2nd Edition, Edited by: Carrick Devine & Michael Dikeman, Elsevier, Academic Press, London, pp. 164–171. Waterborg, J. H. 2002. The Lowry Method for Protein Quantitation, In The Protein Protocols Handbook, 2nd Edition, Edited by: J. M. Walker, Humana Press Inc., Totowa, NJ, pp. 7–10. Yang, S. C. and Baldwin, R. E. 2017. Functional Properties of Eggs in Foods, In Egg Science and Technology, Edited by: W. J. Stadelman, D. Newkirk and L. Newby, CRC Press, Boca Raton, FL, pp. 405–463.

3

Proteomics for Comprehensive Analysis of Seafood Products Quality, Safety, Authentication, and Allergen Detection Marco Cerqueira

CCMAR, Centro de Ciências do Mar

Cláudia Raposo de Magalhães and Denise Schrama Universidade do Algarve

Ana Paula Farinha

Escola Superior Agrária de Santarém

Raquel Carrilho and Pedro Rodrigues Universidade do Algarve

3.1 INTRODUCTION 3.1.1  Background on Seafood Consumption In contemporary times, it is possible for aquatic species to be caught from the wild, farmed, processed, or produced in a far-off country or continent and subsequently imported and transported over long distances to reach consumers. When the consumption of terrestrial animal meat is projected to decline due to factors such as malnutrition, diseases related to dietary habits, and cultural or religious beliefs (Marinova and Bogueva, 2019), aquatic products (aka seafood) play an even more crucial role in global food production, nutrition, and public health. Seafood encompasses a variety of fish (e.g., salmon, tuna, sardine, trout, catfish, carp, and tilapia), shellfish (e.g., shrimp, crab, clams, and oysters), and low-trophic products (e.g., seaweed, microorganisms, and plant-based foods), which are a significant commodity in global trade, contributing to the economic growth of many countries and providing livelihoods for millions of people (FAO, 2022). There is a continuous growth in demand and consumption of seafood across the world. In 2020, worldwide seafood consumption reached 177.7 million tons of live weight (excluding 20.4 million tons used for nonfood purposes), which represents 20.2 kg per capita and accounts for 17% of animal protein intake (FAO, 2022). Still, its demand is expected to rise with the global population growth, estimated to reach 9 billion by 2037 (United Nations Department of Economic and Social Affairs, 2022). This unprecedented demographic growth has increased demand for seafood, where its availability as a food product has been facilitated by the proliferation of the worldwide seafood market, the development of the fishing industry, and the consequent surge in the variability and availability of products from aquaculture, both within and across countries (FAO, 2022). This has, however, significantly strained the environment, contributing to the current depletion of natural resources, climate change, deforestation, and biodiversity loss. These issues have far-reaching consequences for human health, livelihoods, and the survival of many species. Addressing these concerns and DOI: 10.1201/9781003289401-4

27

28

Handbook of Seafood and Seafood Products Analysis

ensuring the sustainable growth of the seafood industry are of utmost importance. It would require responsible fishing practices, which is challenging given the complexity and diversity of fisheries, regulations, and stakeholders involved. In addition, limited resources can hinder the implementation of sustainable practices and effective management measures. The aquaculture industry has the potential to be part of the solution to address these challenges in seafood production and contribute to supplementing wild-caught seafood and reducing the pressure on natural fish populations (Longo et al., 2019). Nevertheless, while efforts have been made to enhance the production of sustainable seafood, ensuring the safety and quality of seafood products that maintain consumers’ confidence remains an ongoing concern. It is essential to continue addressing and monitoring potential hazards, implementing robust safety measures, and ensuring proper quality control throughout the seafood supply chain. By doing so, we can maintain consumer confidence, promote sustainable practices, and ensure the long-term viability of seafood as a safe and reliable food source.

3.1.2 Aquaculture: A Game Changer in the Future of Seafood Consumption Aquaculture’s ascendancy in the realm of seafood production has the potential to catalyze a transition toward a more sustainable food sector and mitigate the environmental problems caused by human population growth. By producing sustainable protein sources, aquaculture can help alleviate the strain on land-based animal agriculture and contribute to global food security (Longo et al., 2019). Cultivating fish, shellfish, and other aquatic organisms provides a renewable and efficient means of protein production because, compared to land-based animal agriculture, aquaculture generally requires less feed, land, and water to produce the same amount of protein. This efficiency, combined with responsible farming practices and species diversification, can help meet the increasing demand for protein while reducing the strain on limited resources. Aquaculture seafood production, when appropriately managed, can also reduce overfishing, a significant concern for the depletion of marine resources and ecological balance. By embracing responsible aquaculture practices, including species diversification, selective breeding, and appropriate stocking densities, aquaculture can alleviate the pressure on wild fish stocks (Rocha et al., 2022). This reduction in fishing pressure allows wild fish populations to recover and maintain the ecological balance of marine ecosystems, preserving biodiversity and promoting long-term sustainability. Furthermore, the seafood industry has the opportunity to play a role in reducing greenhouse gas emissions. Compared to land-based livestock production, seafood production generally has a lower carbon footprint, given the lower environmental impacts and resource usage. Aquatic organisms have higher feed conversion efficiencies, and their farming systems can incorporate innovative practices, such as recirculating aquaculture systems, which minimize waste and energy consumption (Bianchi et al., 2022). Embracing sustainable practices as aquaculture is, therefore, crucial for the long-term viability and ecological resilience of the food industry while ensuring the availability of seafood resources for future generations. By implementing responsible farming techniques, monitoring water quality, and minimizing the use of chemicals and antibiotics, aquaculture can maintain a healthy aquatic environment, preserving the integrity of ecosystems and safeguarding the genetic diversity of farmed species. These factors have made the aquaculture industry experience substantial growth worldwide. The Food and Agriculture Organization (FAO) estimates that aquaculture production will increase by 62% by 2030, surpassing wild fisheries’ current depletion trend. This expansion provides an opportunity to meet the rising demand for seafood while minimizing the environmental impact of traditional fishing practices (FAO, 2022). This shift may facilitate the adoption of healthier, socially conscious, and environmentally responsible dietary practices. By making conscious choices regarding food consumption, individuals contribute to their own health, support ethical food systems, and contribute to the preservation of our planet for future generations. Aquaculture seafood holds a distinct advantage over fisheries seafood in terms of safety. Factors such as controlled environments, rigorous traceability and quality control systems, proactive disease prevention measures, controlled feed sources, and reduced exposure to environmental contaminants

Proteomics for Comprehensive Analysis of Seafood Products

29

make aquaculture products safer and more reliable for consumers. To fully realize the potential of aquaculture as a catalyst for sustainable and safer seafood production, ongoing scientific research, innovation, and policy development are still critical. Researchers continue to explore new technologies and methodologies to enhance aquaculture practices, improve feed efficiency, reduce environmental impacts, and enhance the nutritional composition and safety of farmed seafood (Henriksson et al., 2021). In addition, policymakers play a vital role in establishing regulations and incentives that promote sustainable aquaculture practices. This includes setting environmental standards, enforcing traceability and labeling requirements, and supporting research and development initiatives (Henriksson et al., 2021). Not less importantly, consumer education and awareness campaigns are essential to drive the adoption of sustainable seafood practices. By providing information about the environmental and health benefits of sustainable aquaculture, consumers can make informed choices and support seafood products that align with their values (Zander and Feucht, 2018). This includes seeking certifications such as the Aquaculture Stewardship Council (ASC) or Marine Stewardship Council (MSC) labels, which signify safer, sustainable, and responsibly produced seafood.

3.1.3 Trends and Concerns in the Seafood Industry Different types of fish, fish eggs, invertebrates (e.g., crustaceans and mollusks), plants, and algae are known for their role in developing innovative seafood products (such as surimi, salads, smoked fish, and carpaccio) and a sustainable way of obtaining valuable nutrients. Regarding consumer prospects, its consumption is a nourishing and beneficial source of nutrients for people of all ages, including growing children, pregnant women, active adults, and the elderly (Arshad et al., 2022). Additionally, seafood consumption has been associated with numerous health benefits, such as cardiovascular health, brain development, and overall well-being, the reason why it is currently a larger contributor to human dietary requirements worldwide. Accordingly, individuals adhering to pescatarian diets have been shown to have a lower risk of developing viral infections, including severe cases of coronavirus disease 2019 (COVID-19) (Hathaway et al., 2020; Kim et al., 2021). Furthermore, the high content of protective heart-healthy omega-3 fatty acids such as eicosapentaenoic acid and docosahexaenoic acid found in fish was shown to be beneficial in the prevention and treatment of several inflammatory disorders, as well as cardiovascular, neurological, and viral diseases (Hathaway et al., 2020). Still, seafood offers a unique combination of high-quality protein, essential vitamins (such as vitamins A, B, and D), and important minerals (including selenium, zinc, iodine, potassium, magnesium, phosphorus, and calcium). Its nutritional composition contributes to the popularity and high consumption rates of seafood among individuals seeking to meet their dietary requirements and optimize their overall health (Khalili Tilami and Sampels, 2018). Besides the nutritional content, seafood products encompass various aspects determining their desirable value (Nanou et al., 2023). These include sensory attributes such as appearance, odor, taste, texture, and freshness that should be kept throughout the supply chain. Nevertheless, the sustainability of seafood production remains a critical aspect for consumers, as evidenced by the research of Ayer et al. (2009) who brought together a series of papers covering the major drawbacks of these industries. As aforementioned, the long-term viability of seafood resources faces significant challenges due to the ecological footprints caused by factors such as overfishing, habitat destruction, and greenhouse gas emissions. Key interventions in this regard would include waste reduction, the adoption of responsible fisheries practices, and mitigating the negative environmental impacts associated with the different pescatarian industries. These measures are essential to foster consumer trust in seafood products and maintain the integrity of the seafood trade. By opting for trustful and environmental-friendly seafood choices, consumers can also reduce reliance on environmentally harmful practices, pressure for tangible welfare practices, help conserve marine ecosystems, promote biodiversity, and reduce the carbon footprint associated with food production (Ferrer et al., 2022). Apart from that, consumers also expect seafood products to be safe, wholesome, and free from bacterial and environmental contaminants that could pose health risks (Hicks, 2016). Seafood is

30

Handbook of Seafood and Seafood Products Analysis

highly perishable due to its composition and vulnerable to various factors that can degrade its valuable nutrients and compromise its quality, including improper handling, storage, and processing (Speranza et al., 2021). Other factors, including contamination and adulteration, can compromise the safety and quality of seafood products (Sheng and Wang, 2021; Haji et al., 2022). A single incident of foodborne illness outbreak linked to seafood can have severe economic repercussions, including trade restrictions and loss of consumer trust. Thus, stringent quality and safety regulations are an indispensable intervention to ensure food security and are vital for international trade, as they redundantly ensure consumer confidence and facilitate market access. Regulatory bodies and industry stakeholders, at national and international levels, have implemented rigorous standards and regulations (Paolacci et al., 2021). Within this framework, robust quality control measures are required throughout the supply chain to guarantee the safety of seafood products, protect public health, and ensure consumers receive the full benefits of this valuable food source. These measures encompass various aspects, including microbiological criteria, limits on contaminants, labeling requirements, and traceability systems (Paolacci et al., 2021). Such diligent features have prompted scientists to explore novel approaches to comply with standards and enhance productivity to meet the rising demand for aquaculture products while improving their overall quality and safety. The molecular analysis of seafood products has become indispensable in overcoming such challenges and achieving the aforementioned food security aspects. In this regard, protein analysis is of paramount importance due to the diverse roles that these biomolecules play in the composition, functionality, and potential hazards of seafood. Therefore, proteins serve as pivotal targets for obtaining valuable insights into the biological, nutritional, and safety aspects of seafood.

3.1.4 Objectives and Scope of the Chapter In the modern era of food analysis, proteomics has emerged as a powerful tool for assessing the quality and safety of seafood products. With the rapid evolution of proteomics methodologies in recent years, driven by technological advancements and emerging computational and statistical models, there is now a range of high-throughput proteomics approaches available for seafood product analysis. This chapter aimed to showcase the significant impact of high-throughput proteomics technology in the study of seafood products. The following segments of the chapter will center around the available proteomics technologies and the potential of proteomics in overcoming the various hurdles posed in seafood product analysis. This chapter aimed to furnish readers with an inclusive overview of the proteomics field, emphasizing its importance and potential in enhancing the safety, quality, and nutritional worth of seafood products. Furthermore, the integration of proteomics with other omics technologies, including genomics, transcriptomics, and metabolomics, is explored.

3.2  PROTEOMICS IN SEAFOOD SCIENCE 3.2.1 Significance of Proteins in Seafood Research Traditionally, the assessment of seafood products relies on sensory evaluation and chemical analysis. However, these methods have limitations in terms of sensitivity, specificity, and the ability to identify complex biomolecules, including proteins (Zhang et al., 2022b). Proteins are large, complex molecules composed of long chains of amino acids and fundamental components of all living organisms, including aquatic animals, microorganisms, seaweeds, and algae, as they facilitate and regulate nearly every cellular process (Aebersold and Mann, 2016). These biomolecules have a wide range of functions, including catalyzing chemical reactions, transporting molecules within cells and throughout the body, providing structural support, and serving as signaling molecules (Wijaya et al., 2014). In food products, proteins are responsible for various functional properties, such as texture, water-holding capacity, and gel-forming abilities, which directly influence the sensory attributes and overall quality of these products (Wijaya et al., 2014). Proteins can serve as indicators of

Proteomics for Comprehensive Analysis of Seafood Products

31

freshness and spoilage given that the detection and analysis of certain proteins or peptides derived from pathogens enable the identification of safety risks and the implementation of appropriate quality control measures (Li et al., 2014). In addition, changes in protein composition and degradation can occur as food products age or undergo microbial activity, providing insights into their freshness and shelf-life. Moreover, proteins can be employed for authentication purposes in food. Unique protein profiles or biomarkers, specific to certain products or geographical origins, can be utilized to verify their authenticity and traceability (Afzaal et al., 2022). Concomitantly, proteins are critical targets for seafood product analysis because they offer valuable insights into seafood’s biological, quality, safety, and authentication aspects, which are important to ensure consumer satisfaction. Examples of proteins include enzymes, antibodies, hemoglobin, and collagen. Together, they comprise a well-organized system called the proteome. The proteome of a specific biological system represents the end product of the genome and represents its biological context. Unlike the genome, which remains constant across all cells, the proteome is dynamic and varies depending on factors such as developmental stage and environmental conditions (Manzoni et al., 2016; Nissa et al., 2021) and undergoes constant changes in response to various challenges. Accordingly, proteins are the ultimate effectors of biological processes, making proteome analysis a more informative tool than genomics or gene expression studies that do not consider post-transcriptional or post-translational modifications (PTMs) (Nissa et al., 2021). By harnessing advanced proteome analysis and utilizing comprehensive databases, researchers and stakeholders can uphold rigorous standards and regulations, deliver safe and high-quality seafood products, and reinforce consumer confidence in the industry.

3.2.2 Significance of Proteomics for Food Analysis Proteomics is a rapidly advancing field that incorporates the technology to study the proteome of a given cell, tissue, organ, organism, or even at the population or multi-population level. It offers an exceptional means of comprehending biological processes, by exploring not just the structure and function of proteins, but also their modifications, interactions, and intracellular distribution. Complementarily, it detects any changes in protein expression that may occur in the proteome (Rodrigues et al., 2012, 2016; Nissa et al., 2021) and provides a quantitative analysis of the different proteins present in the sample, regardless of the complexity of the analyzed protein mixtures that can range from hundreds to several thousands of proteins. The adoption and application of Proteomics in animal, veterinary, and aquatic sciences have historically progressed at a relatively slower pace compared with other research domains. However, the past decade has witnessed a notable surge in proteomics research within these fields (Almeida et al., 2015; Marco-Ramell et al., 2016; Bilić et al., 2018; López-Pedrouso et al., 2020; Raposo de Magalhães et al., 2020b; Gajahin Gamage et al., 2022). In fact, a wealth of literature now exists, exemplifying the broad spectrum of applications for proteomics. Noteworthy examples include investigations in meat science, where proteomics has shed light on topics such as meat quality and composition (Schilling et al., 2017). Proteomics analysis has also contributed to advancements in colostrum and milk science, facilitating a deeper understanding of the molecular composition and nutritional aspects of these vital substances (Agregán et al., 2021). Additionally, proteomics has played a pivotal role in developing vaccines for tickborne diseases (Pérez-Sánchez et al., 2022), enabling the identification of immunogenic proteins and aiding in vaccine design. It has yet to establish welfare and stress biomarkers, facilitating the assessment of animal well-being (Raposo de Magalhães et al., 2020b). Furthermore, proteomics techniques have been instrumental in optimizing aquaculture rearing conditions, enhancing feed formulations, mitigating disease risks, and ultimately improving the overall efficiency and sustainability of aquaculture practices that meet consumer expectations (Moreira et al., 2017; Carrera et al., 2020b; Cerqueira et al., 2020b; Farinha et al., 2021; Raposo de Magalhães et al., 2023). In the realm of seafood product analysis, proteomics is particularly important because seafood is a rich source of high-quality protein, and the quality and safety of seafood products depend largely on the proteins present. Seafood’s detailed molecular analysis enables

32

Handbook of Seafood and Seafood Products Analysis

the identification, quantification, and characterization of a wide range of proteins that are present, including those with potential health benefits and those associated with safety concerns. Therefore, the application of advanced high-throughput proteomics technology represents a crucial opportunity to improve consumers’ health, well-being, and confidence while facilitating a transition to a healthy, socially, and environmentally responsible diet as is a seafood diet. 3.2.2.1  Advancing Aquaculture Proteomics Research for Safer Seafood Proteomics is unlocking the potential of seafood products because it is constantly evolving, assisted by advances in instrumentation for protein analysis, but it is important to recognize the broader impact of proteomics beyond seafood analysis and highlight its significance in advancing aquaculture research that concomitantly fosters the production of high-quality seafood products. Proteomics can address various aspects throughout the aquaculture rearing activity, providing valuable insights into the intricate molecular mechanisms underlying key processes such as growth, development, immune response, and stress tolerance. This is why, in recent years, there has been a growing interest in using proteomics techniques to study fish biology. This is evidenced by various studies, including those conducted by Forné et al. (2010), Nynca et al. (2017), and Dietrich and Ciereszko (2018). Additionally, several publications have reviewed the numerous applications of proteomics in aquaculture research (Rodrigues et al., 2012; Almeida et al., 2015; Rodrigues et al., 2016; Carrera et al., 2020b). A significant achievement has been made in the field as in 2020, more than 300 fish genomes were published, while, in 2022, almost 900 fish species have been sequenced at the genome level mainly in the National Center for Biotechnology Information (NCBI) repository, as stated by Cermakova et al. (2023). The Fish10K Genome Project, announced in 2020, aims to sample, sequence, assemble, and analyze the genomes of 10,000 representative fish species over the next 10 years (Fan et al., 2020). Additionally, back in 2010, Forné et al. (2010) stated that more than 45 proteome analysis studies were conducted across over 40 different fish species, predominantly focusing on zebrafish and aquaculture salmonids such as rainbow trout and Atlantic salmon. Currently, there are thousands of proteome analysis studies over more than 100 fish species. These studies have provided valuable insights into the biological processes such as evolution, physiology, immunity, development, stress, and adaptations in candidate aquaculture species (Ahmed et al., 2019; Raposo de Magalhães et al., 2020a, 2021, 2022). While proteomics has traditionally been utilized for investigating aspects of physiology and developmental biology, it has also been applied to explore the effects of contaminants on fish populations, particularly in organisms such as zebrafish, salmonids, and cyprinids (Saleh et al., 2019; Fæste et al., 2020; Kwon et al., 2023). Furthermore, there are numerous applications of proteomics in areas such as welfare, nutrition, and health of aquatic organisms, all of which are directly linked to the quality and safety of the aquaculture end product (reviewed in Rodrigues et al., 2018). For example, one area where it can have a significant impact is disease diagnostics and treatment. Identifying proteins involved in immune response and disease resistance can help develop accurate and reliable diagnostic tests for disease detection and monitoring and develop more effective treatments and management strategies for aquatic animal diseases. Moreover, by identifying proteins involved in gamete development and maturation, proteomics can improve the efficiency and effectiveness of breeding programs, leading to increased production and better-quality fish. Proteomics has still emerged as a powerful tool in biomarker discovery research, particularly in the context of improving feed development for aquaculture. Through proteomics analysis, researchers have the ability to identify specific proteins present in feed ingredients that are closely linked to enhanced growth and improved nutrient utilization in aquatic organisms. By fine-tuning feed compositions and catering to the specific dietary requirements of different aquatic species based on proteomics insights, more nutritious and sustainable feeds for aquaculture can be developed (Rodrigues et al., 2018). In addition, the field of biomarker research has contributed to the advancement of more precise and dependable methodologies for studying existing peptide markers in aquatic organisms (Afzaal et al., 2022). This progress in marker peptide discovery holds the potential to revolutionize our comprehension of various aspects related to aquaculture and enhance transparency for consumers. Accordingly, the integration

Proteomics for Comprehensive Analysis of Seafood Products

33

of biomarkers into welfare assessment protocols can significantly enhance their effectiveness, setting a crucial minimum welfare standard in sustainability certification schemes (Raposo de Magalhães et al., 2020b). The incorporation of biomarkers has the potential to drive the development of specialized devices or biosensors for convenient on-site assessments (Browning, 2023). Such advancements offer promising opportunities to improve farm productivity while prioritizing the welfare of farmed fish, thereby elevating the transparency, accountability, and ethical responsibility of the aquaculture industry in the long term. However, it is important to note that proteomics research in aquaculture is still evolving, and there is much more to explore and discover. Continued efforts in this area, including the integration of proteomics with other omics technologies (such as genomics, transcriptomics, and metabolomics) do hold great promise for further advancements in aquaculture production and sustainability (Raposo de Magalhães et al., 2020b). These advancements will collectively have a positive impact on seafood security by improving production efficiency, reducing environmental impacts, and ensuring the safety and authenticity of seafood products. 3.2.2.2  Applications of Proteomics in Seafood Analysis The suite of available tools to researchers is aiding in the understanding of protein expression and function and enabling novel insights into the design of new food products such as less allergenic seafood, or novel functional ingredients for use in the food and nutraceutical industries (Schrama et al., 2022a, b). Additionally, the expanding capabilities and diversity of analytical platforms, coupled with the application of bioinformatics, have enabled the acquisition and analysis of vast amounts of proteomics data, unraveling the intricacies of the molecular makeup of various seafood products, including shrimp (Diwan et al., 2022). Channeling this knowledge offers immense potential for seafood product analysis with significant benefits for consumers and opportunities to support responsible industry practices and ensure a secure and sustainable seafood supply (Figure 3.1). Within this scope, the burgeoning field

FIGURE 3.1  Proteomics applications toward seafood product analysis.

34

Handbook of Seafood and Seafood Products Analysis

of proteomics holds the potential to leverage toxin-free and uncontaminated products. Additionally, it can enable authenticating and tracing the origin of seafood products, a critical aspect of ensuring food safety and preventing fraudulent practices (Bi et al., 2019; Cerqueira et al., 2020a). Proteomics technology presents compelling prospects for identifying novel marker proteins and peptides that can be utilized in developing assays designed to identify adulteration and fraudulent practices. Such nefarious acts, including substituting lower-quality fish for premium species, can be effectively detected using proteomics-based approaches, as demonstrated in previous studies (Bi et al., 2019). Proteomics has also proven to be a powerful tool in researchers’ toolkits, offering valuable insights into the mechanisms that drive seafood products such as spoilage, shelf-life, and sensory attributes (Sveinsdóttir et al., 2012). Through the analysis of the proteomics changes that occur during the storage, transportation, and processing of seafood products, researchers can identify strategies to enhance the quality and extend the shelf-life of these products, therefore transforming the seafood supply chain. A comprehensive exploration of high-throughput technologies in seafood product analysis will be extensively addressed in Section 3.4. Despite its importance in seafood analysis, the significance of these technologies extends far beyond seafood production. These methodologies serve as a foundation for the implementation of ethical practices and environmentally sustainable approaches in animal husbandry across different sectors. Nevertheless, it has been in the realm of seafood production and analysis that proteomics has experienced higher progress in the past few years. This surge in interest can be attributed to the growing demands of the aquaculture industry, whose importance is increasing due to commercial overfishing and a rising consumer demand for seafood. In essence, by harnessing the power of proteomics, researchers and practitioners can pave the way for a future where animal production systems prioritize quality, safety, and sustainability, ensuring a healthier planet and meeting the demands of a conscientious consumer base.

3.3  SEAFOOD PRODUCT PROTEOMICS ANALYSIS: AN OVERVIEW 3.3.1 Introduction to Proteomics Proteomics is the study of the whole proteome of an organism, tissue, or cell, instead of single proteins. The recent past has witnessed a plethora of comprehensive reviews shedding light on the various proteomics-based technologies employed in aquaculture and its products (Rodrigues et al., 2012, 2016, 2018; Piras et al., 2016; Cerqueira et al., 2020a; Moreira et al., 2021; Nissa et al., 2021; Jaiswal et al., 2023). There has been a shift from conventional protein analysis methods, such as immunohistochemistry (IHC) staining, Western blot, and enzyme-linked immunosorbent assay (ELISA), to newer technologies, such as protein microarrays, and more advanced mass spectrometry (MS)-based high-throughput methods such as MS-based approaches, high-throughput sequence and structural analysis tools, and labeling methods (Cui et al., 2022). Protein microarrays have been established for rapid protein expression analysis, but their utilization to fully investigate the functional aspects of an entire genome presents considerable challenges (Cui et al., 2022). In this endeavor, MS has emerged to analyze complex protein mixtures with higher sensitivity and reproducibility, reducing analysis time while enhancing the accuracy and depth of proteome coverage (Rozanova et al., 2021). Notably, novel methods such as isotope-coded affinity tag (ICAT) labeling, stable isotope labeling by amino acids in cell culture (SILAC), and isobaric tag for relative and absolute quantitation (iTRAQ) have emerged as quantitative label-based proteomics techniques (Rozanova et al., 2021; Sivanich et al., 2022; Jaiswal et al., 2023). Leveraging high-throughput technologies has facilitated the collection of vast amounts of proteomics data. Numerous bioinformatics tools have been developed to enable tasks such as three-dimensional (3D) structure prediction, protein domain and motif analysis, rapid analysis of protein–protein interactions (PPIs), and data analysis of MS results (Aslam et al., 2017). Consequently, bioinformatics databases are being developed to handle and store such extensive data

Proteomics for Comprehensive Analysis of Seafood Products

35

effectively. Alignment tools have proven valuable in sequence and structure alignment, aiding the discovery of evolutionary relationships (Vihinen, 2001; Perez-Riverol et al., 2015). In this section, we briefly introduce proteomics instrumentation focusing on high-throughput methods, before delving into their applications in seafood product analysis.

3.3.2 Proteomics Workflows Proteomics workflows include a common set of steps: protein extraction and solubilization, separation, purification and digestion, MS analysis, peptide identification, protein inference and quantification, and characterization. This is schematically depicted in Figure 3.2. The first step in proteomics, i.e., preparation of the biological sample, involves various procedures such as cell lysis, total protein extraction, and purification. Subsequently, protein separation can be achieved using different approaches, such as gel-based and gel-free techniques, according to whether electrophoretic methods, e.g., sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE), are employed or not, respectively. In gel-free approaches, proteins or peptides are usually separated by liquid chromatography (LC) coupled to the MS. In bottom-up approaches, protein digestion, which can be carried out prior to or following protein separation, breaks proteins into smaller peptides using specific enzymes such as trypsin (Gevaert et al., 2011). In top-down approaches, no digestion is performed and intact proteins are analyzed, as described by Westermeier and Naven (2002). The molecules are then analyzed by MS, and the resulting spectra are then searched against known databases for protein identification. Protein quantitation can then be achieved through label-based or label-free strategies. Each step is described in further detail in the following sections. 3.3.2.1  Sample Preparation Sample preparation is a pivotal stage in the proteomics analysis of seafood products, as it directly influences the accuracy and reproducibility of results (Duong and Lee, 2023). The methods discussed in this section, including extraction, protein enrichment, sample clean-up, and protein quantification methods, are just a few examples of the many sample preparation methods available for seafood analysis. As the field continues to advance, it is likely that new sample preparation methods will be developed, further improving our ability to analyze and understand the complex protein composition of seafood products. 3.3.2.1.1  Cell Lysis/Protein Extraction In many proteomics studies, the biological sample consists of intact cells; thus, cell lysis is required. This process involves breaking down the cell membrane and releasing the intracellular proteins as they facilitate the isolation and enrichment of proteins from complex matrices. Cell lysis methods can vary depending on the cell type and the desired outcome, and they involve physical disruption (e.g., sonication and freeze–thaw cycles), chemical lysis (e.g., using detergents such as Triton X-100, NP-40, or 3-[(3-Cholamidopropyl)dimethylammonio]- 1-propanesulfonate (CHAPS), and enzymes such as lysozyme or proteinase K to solubilize proteins), and mechanical disruption (e.g., mortar and pestle or using small beads, such as glass beads or metal beads, subjected to vigorous shaking or vortexing). The choice of the method will depend on the type of sample or tissue analyzed; for example, tissues such as the brain and the liver require less mechanical homogenization than fish muscle, since muscle fibers are stronger and more difficult to destroy, while biofluids such as urine and blood plasma may only require protein solubilization. 3.3.2.1.2  Protein Enrichment Methods Protein enrichment methods can be used to concentrate the proteins of interest and reduce the complexity of the seafood sample. These methods help improve the detection and identification of lowabundance proteins, post-translationally modified proteins, or specific protein classes. Commonly used protein enrichment methods include immunoprecipitation, affinity chromatography, and

36

Handbook of Seafood and Seafood Products Analysis

FIGURE 3.2  Typical proteomics workflows in seafood product analysis: It begins with sample preparation and extraction of total protein content. This process uses a suitable extraction buffer, which varies depending on the type of seafood product or tissue being analyzed. Protein separation can be achieved before enzymatic digestion, by gel electrophoresis, or after, by liquid chromatography (LC) coupled to the MS, where peptides are separated based on their hydrophobicity. Digestion is carried out by enzymes such as trypsin, chymotrypsin, or Lys-C. In contrast, in top-down approaches, intact proteins are analyzed and no digestion is performed. Finally, the separated proteins or peptides are introduced into a mass spectrometer, where they are ionized and analyzed based on their mass-to-charge ratio (m/z). The resulting data are then searched against databases to identify and quantify the proteins present in the seafood product.

Proteomics for Comprehensive Analysis of Seafood Products

37

size-exclusion chromatography (e.g., Bio-Rad’s ProteoMiner™ enrichment) (reviewed in Liu et al., 2020). Immunoprecipitation involves using antibodies to specifically capture the protein of interest. Affinity chromatography uses a ligand that specifically binds to the protein of interest to capture and enrich it. Size-exclusion chromatography separates proteins based on their size, allowing for the  enrichment of specific size fractions. The selection of the method will be based on the nature of the seafood product (e.g., processed seafood products such as canned fish or seafood-based snacks), the nature of the proteins of interest (e.g., fish allergens such as parvalbumin), and the compatibility with downstream analysis techniques. Complex samples with a high dynamic range, such as blood plasma, often require additional enrichment steps such as high-abundant protein depletion or low-abundant protein enrichment to increase proteome coverage. These techniques were recently used for the plasma proteome profiling of rainbow trout by shotgun proteomics (Morro et al., 2020). 3.3.2.1.3  Sample Clean-Up Methods Sample clean-up methods are used to remove contaminants and unwanted substances from the protein extracts that may interfere with downstream analysis. Samples such as the liver and the intestine of vertebrates and the hepatopancreas or the digestive gland of invertebrates are examples of tissues that contain high lipid contents and impurities that may require an additional step of purification when compared to samples such as blood plasma. Commonly used sample clean-up methods include desalting, dialysis, and precipitation. Desalting and dialysis are used to remove excess salts and buffer components from the sample, which can interfere with downstream analysis. Precipitation methods, such as acetone or trichloroacetic acid (TCA) precipitation, are used to remove non-protein components from the sample. Clean-up strategies were reviewed by Tubaon et al. (2017). 3.3.2.2  Protein Separation 3.3.2.2.1  Gel-Based: Electrophoresis Within gel-based techniques, the conventional method known as two-dimensional gel electrophoresis (2-DE) has been widely utilized. Within the domain of seafood sciences, 2-DE has garnered a reputation as the quintessential technique, often referred to as “the workhorse” of proteomics research. In this method, proteins are firstly separated by isoelectric focusing (IEF) based on their isoelectric points (pI), using a specific pH range (Piñeiro et al., 2001). Subsequently, in the second dimension, the proteins are further separated by molecular weight, under denaturing conditions, using SDS–PAGE. The separated proteins are then stained using visible methods, e.g., silver staining, Coomassie blue staining, or fluorescent methods, such as SYPRO Ruby or cyanine-based dyes. Gels are then scanned and analyzed by specialized 2D software (e.g., SameSpots). These staining techniques facilitate the detection and visualization of the separated protein spots, aiding in the analysis and interpretation of the protein profile within the complex sample, allowing a more comprehensive characterization of proteins based on their pI and molecular weight. In this context, each individual spot observed on the gel corresponds to a protein or a mixture of proteins, with the intensity of the spot indicating its relative abundance. Although the 2-DE technique has progressed over time (e.g., through the quantitative two-dimensional difference in gel electrophoresis) and continues to be a powerful technique for the detection of several hundred (or even thousands) of protein spots on a single gel (Naryzhny, 2016; Lee et al., 2020; Marcus et al., 2020), it is nevertheless labor-intensive, has limited reproducibility, and poorly represents low-abundance proteins. Moreover, several proteins may co-migrate into the same gel position, leading to inaccurate protein identification. Considering these bottlenecks, the traditional 2-DE approach has been surpassed by a highly advanced technique called 2D fluorescence difference gel electrophoresis (DIGE). DIGE is used for comparative protein expression analysis between samples and incorporates the usage of cyanine dyes, specifically Cy3 and Cy5, for the purpose of differential labeling of similar proteins obtained from diverse samples. One of the key advantages of DIGE is its capability to analyze two samples simultaneously on a single 2D gel, a feat that is not achievable with other 2-DE methods.

38

Handbook of Seafood and Seafood Products Analysis

Moreover, 2D-DIGE’s ability to minimize inter-gel variability facilitates spot matching and identification in a single snapshot. Additionally, it offers enhanced quantification with statistical confidence, enabling unambiguous analysis, and the detection of PTMs (Alban et al., 2003), improving efficiency and accuracy in comparative proteomics studies (Larbi and Jefferies, 2009; Gebriel et al., 2014). This technique has been successfully used, for example, to analyze the effects of postmortem storage temperature on European sea bass muscle (Terova et al., 2011) and to identify potential stress markers in farmed gilthead seabream blood plasma (Raposo de Magalhães et al., 2020a). 3.3.2.2.2  Gel-Free: Chromatography Chromatography-based techniques have proven to be effective in separating and analyzing proteins in seafood, providing significant advantages over electrophoresis methods. High-performance LC (HPLC), or simply LC, offers the ability to separate numerous proteins from complex mixtures in a continuous manner, and when coupled with MS as LC-MS, it provides enhanced throughput capabilities (Pitt, 2009; Thomas et al., 2022). The most commonly used LC-based separation platform is reverse-phase HPLC (RP-HPLC), which involves using a hydrophobic stationary phase and a mobile phase with increasing polarity for separating proteins with varying hydrophobicity within a chromatographic column. Proteins or peptides with higher hydrophobicity exhibit stronger interactions with the stationary phase, resulting in longer retention times. In contrast, proteins with lower hydrophobicity elute earlier from the column into the MS. Another technique, ion exchange chromatography (IEX) includes strong cation exchange (SCXHPLC) or IEX methods, which are used to separate proteins with differing charges. Because this technique aids in identifying and characterizing proteins involved in important seafood attributes such as taste, texture, and functional properties, RP-HPLC is by far the most used technique in protein separation (Edelmann, 2011). Chromatography-based methods provide significant advantages over electrophoresis methods in terms of sensitivity, resolution, protein identification, quantitative analysis, compatibility with complex samples, and reproducibility. These advantages make these techniques indispensable tools in modern proteomics research, enabling further comprehensive and in-depth analysis of protein samples. Table 3.1 describes the advantages of chromatography-based proteomics methods over electrophoresis-based methods. 3.3.2.3  Protein Digestion At this step, two proteomics approaches can be distinguished based on whether the proteins are digested, and the following MS analysis is performed at the peptide level, i.e., bottom-up approaches (extensively reviewed in Dupree et al., 2020), or not digested and intact proteins are analyzed, i.e., top-down approaches. Top-down proteomics is primarily focused on the analysis of proteoforms that arise from various biological processes, including PTMs, alternative splicing, genetic variants, and proteolytic cleavage. It involves the direct analysis of intact proteins using MS techniques, without the requirement for prior proteolytic digestion (Brown et al., 2020). In bottom-up proteomics, protein digestion is an essential step, which involves breaking down complex proteins into smaller peptides that can be readily analyzed by MS techniques. Digestion greatly increases the complexity of the protein mixture, which is why most of these approaches require online coupling of LC methods, to reduce the number of peptides entering the mass spectrometer at any given time. An overview of the principles of top-down proteomics and how it differs from bottom-up proteomics is given by Campos and de Almeida (2016). Trypsin digestion is the most widely used enzyme for protein digestion, but other enzymes such as Lys-C and chymotrypsin, as well as chemical cleavage methods, offer complementary benefits. Trypsin specifically cleaves peptide bonds at the carboxyl side of arginine (R) and lysine (K) residues, except when followed by proline (P). Trypsin digestion offers several advantages, including high specificity, reliability, and compatibility with MS analysis. It generates peptides with predictable cleavage sites, facilitating protein identification and characterization (Burkhart et al., 2012).

Proteomics for Comprehensive Analysis of Seafood Products

39

TABLE 3.1 Advantages of Chromatography-Based Proteomics Methods over Electrophoresis-Based Methods Advantages Greater sensitivity

Improved resolution and separation

Enhanced protein identification

Quantitative analysis

Compatibility with complex samples

High reproducibility and robustness

Description Especially when coupled with mass spectrometry (MS) allows for the detection and identification of low-abundance proteins in complex samples. This is particularly advantageous when studying proteins present in trace amounts or in samples with limited starting material Provide better resolution and separation of proteins based on their various properties, such as hydrophobicity, charge, or size. HPLC, for instance, allows for precise control of the mobile phase, stationary phase, and gradient conditions, enabling efficient separation of complex protein mixtures. This high-resolution separation facilitates the identification and characterization of individual proteins or protein groups When coupled with mass spectrometry (MS) offers powerful protein identification capabilities. After separation, the eluted proteins can be directly analyzed by MS, enabling accurate protein identification through peptide fragmentation and database searching. This combination of chromatography and MS provides more comprehensive information about the proteins present in a sample, including post-translational modifications and sequence coverage Facilitates quantification of proteins, particularly when combined with stable isotope labeling or label-free approaches, allowing for accurate and reliable quantification of protein abundance across different samples. This quantitative analysis provides insights into changes in protein expression levels and facilitates comparative proteomics studies Well-suited for analyzing complex samples, such as biological fluids, tissue extracts, or protein mixtures, where the presence of numerous proteins can pose challenges for traditional gel-based methods. Chromatography methods can handle a wide range of sample types and accommodate different sample matrices, making them versatile tools in proteomics analysis High reproducibility and robustness. They offer consistent and reliable results, allowing for the comparison of protein profiles between different experiments or samples. This makes chromatography techniques ideal for large-scale proteomics studies and biomarker discovery

Source: Reviewed by Neverova and Van Eyk (2005).

Nowadays, this step of enzymatic digestion with trypsin is often included in sample preparation protocols specific for high-throughput approaches, which also include purification steps to wash off substances that may interfere with the MS analysis, such as the detergents needed for protein solubilization and salts. The three most common protocols, i.e., suspension trapping (S-Trap), single-pot solid-phase-enhanced (SP3), and filter-aid sample preparation (FASP), and their use in seafood samples were recently compared (Araújo et al., 2021). In addition to enzymatic digestion, chemical cleavage methods are also employed to digest proteins in seafood product analysis. For example, cyanogen bromide (CNBr) cleaves proteins, specifically at methionine (M) residues. This method can be useful when targeting specific protein regions or when analyzing samples with limited availability of suitable proteases. The choice of protein digestion method depends on various factors, including the research objectives, sample complexity, and the desired information to be obtained. Often, a combination of different enzymes or digestion strategies is employed to maximize protein sequence coverage and enhance the identification of proteins, facilitating the comprehensive characterization of the proteome of seafood products. Following protein digestion, the resulting peptides are then analyzed using various techniques enabling the separation, identification, and quantification of the digested peptides. These steps can further provide valuable insights into the protein composition, PTMs, and functional properties of seafood products.

40

Handbook of Seafood and Seafood Products Analysis

3.3.2.4  Mass Spectrometry Analysis MS is one of the most widely used techniques for protein identification, quantification, and characterization (Aebersold and Mann, 2016). MS ionizes the proteins or peptides in the sample and separates them based on their mass-to-charge ratio (m/z). It is then able to discriminate among different m/z by subjecting the ions to constant, pulsed, or periodically time-varying electric and/ or magnetic fields. The resulting mass spectra can be used to identify the proteins based on their unique and shared peptides. MS-based proteomics offers sensitivity, reproducibility, accuracy, and high throughput. The MS can be integrated with different separation techniques and prefractionation methods (e.g., electrophoresis or chromatography), leading to improved identification accuracy and yield. Advances in high-resolution MS, such as Orbitrap and Fourier transform (FT)-based MS instruments, have significantly improved the accuracy, sensitivity, and dynamic range of protein identification and quantification (Deschamps et al., 2023). Mass spectrometers are mainly composed of three fundamental components: an ion source, a mass analyzer, and a detector. Matrix-assisted laser desorption or ionization (MALDI) is a soft ionization technique that is well-suited for analyzing large biomolecules, such as proteins (Li et al., 2021). Electrospray ionization (ESI) converts peptides into gas-phase ions, and it is commonly used in combination with LC. Nowadays, it is one of the most used ionizers. Common mass analyzers are time of flight (TOF) (usually coupled with MALDI ionizers), ion trap (IT), quadrupole (Q), and Orbitrap analyzers (Li et al., 2021). In the case of tandem MS (MS/MS), more than one mass analyzer is coupled together to analyze the mass of fragment ions (MS2 spectra). These ions result from precursor ions with a specific m/z (MS1 spectra) that are first isolated and fragmented through collision with inert gases or electrons (Neagu et al., 2022). MS analysis can be performed in untargeted or discovery mode or targeted mode, depending mainly on the aim of the study. This has been reviewed in the context of seafood allergen identification and quantification (Carrera et al., 2020b). In the first, two common strategies are usually employed: data-dependent acquisition (DDA) or data-independent acquisition (DIA) analysis, while in the latter, selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and multiple reaction monitoring (MRM) are used (Carrera et al., 2020b). Discovery proteomics involves the large-scale analysis of a proteome allowing for the identification and quantification of a large number of proteins in a complex sample. In DDA, specific precursor ions are selected based on their intensity in real time during the analysis. It enables the acquisition of fragmentation spectra for a subset of the most intense precursor ions. DIA, however, involves the fragmentation of all precursor ions within a predefined mass range, providing comprehensive fragmentation spectra for subsequent peptide identification. Sequential window acquisition of all theoretical mass spectra (SWATH-MS) is a specific variation of DIA methods, which is particularly suitable for studies involving a large number of samples and necessitating the highest level of accuracy and reproducibility quantitation (Zhang et al., 2020). This technique is most employed in biomarkers or drug contamination research (Ludwig et al., 2018). However, targeted proteomics focuses on the analysis of specific proteins or peptides of interest, usually selected in the discovery or exploratory phase. It is characterized by its high sensitivity, specificity, and reproducibility, making it suitable for precise quantification of known proteins or peptides and biomarker verification (Borràs and Sabidó, 2017). In seafood sample analysis, the utilization of electrophoresis or LC-MS/MS can be employed, where ESI is frequently used. LC-MS/MS is well-suited for large-scale and systematic characterization of proteomes (Neagu et al., 2022). It is commonly used in both untargeted and targeted analysis and for the quantification of specific compounds. It is especially valuable in studies involving, e.g., seafood–pathogen interactions or seafood–contaminant interactions (such as pesticides and veterinary drugs), as it facilitates multiplex sample analysis and enables quantification of the identified proteins. However, MALDI-TOF-MS excels in rapid and qualitative analysis of large biomolecules, making it valuable for tasks such as species authentication and identification of seafood and the detection of adulteration. MALDI-TOF-MS-based strategies are particularly well-suited for

Proteomics for Comprehensive Analysis of Seafood Products

41

seafood microbial identification and diagnosis due to their advantages in terms of speed, sensitivity, and cost-effectiveness, in terms of both labor and expenses (Singhal et al., 2015). This technique allows for rapid and accurate identification of microorganisms. MALDI-TOF/TOF-MS combines the capabilities of MALDI-TOF-MS with MS/MS to provide structural information about proteins, enabling holistic characterization and PTM analysis. Table 3.2 summarizes each of these techniques and indicates a few applications in seafood product analysis. The choice of the most reliable MS strategy for seafood product analysis depends on the specific objectives of the analysis, target compounds, sample complexity, and available resources. These techniques have their strengths and limitations as reviewed elsewhere (Darie-Ion et al., 2022), and the selection should be based on the analytical requirements and the nature of the compounds being analyzed. LC-MS/MS typically requires sample preparation, chromatographic separation, and skilled data interpretation. MALDI-TOF-MS, however, offers rapid analysis but may have limitations in detecting small molecules or quantifying compounds accurately. As the latter, MALDITOF/TOF-MS offers advantages in protein characterization, speed, and database comparison, but has limitations in analyzing high-molecular-weight proteins due to the limitations of the MALDI ionization process. This can restrict the analysis of larger proteins or protein complexes, potentially missing important components of seafood samples. In many cases, a combination of techniques may be employed to complement each other’s strengths and provide a more comprehensive analysis, opening up new avenues for biomarker discovery (e.g., seafood quality biomarker), systems biology, and personalized seafood analysis. 3.3.2.4.1  Protein Identification Coupling the latest advances in proteomics and bioinformatics approaches has turned this technology into a useful tool to develop promising strategies for seafood analysis (Carrera et al., 2020b).

TABLE 3.2 Description of the Most Relevant MS Techniques for Seafood Product Analysis Method Liquid chromatography– tandem mass spectrometry (LC-MS/MS)

Description

It is a powerful technique for targeted analysis and quantification of specific compounds in complex samples. It combines liquid chromatography separation with tandem mass spectrometry detection. LC-MS/MS offers high sensitivity, selectivity, and the ability to analyze a wide range of compounds, including small molecules, peptides, and proteins. It is commonly used for the analysis of contaminants, such as toxins, pesticides, antibiotics, and other chemical residues in seafood products. LC-MS/MS allows for accurate identification and quantification of analytes. It is commonly the technology of choice for the high-throughput characterization of proteomes (Aebersold and Mann, 2016) Matrix-assisted laser It is a technique primarily used for the rapid and qualitative analysis of large desorption–ionization biomolecules, such as proteins, peptides, and lipids. It relies on the generation of ions by time-of-flight mass desorbing and ionizing analytes using a laser and then measuring the time it takes for spectrometry ions to reach the detector. MALDI-TOF-MS provides fast and reliable protein profiling (MALDI-TOF-MS) and identification, making it suitable for applications such as species authentication, identification of seafood pathogens, and detection of allergenic proteins. However, it may have limitations in analyzing small molecules and quantifying compounds Matrix-assisted laser It is an advanced variant of MALDI-TOF-MS that offers additional capabilities. In desorption–ionization addition to protein profiling and identification, MALDI-TOF/TOF-MS allows for further time-of-flight–time-of-flight analysis of selected ions. It provides an extra level of structural information by mass spectrometry (MALDI- performing MS/MS fragmentation on specific ions, enabling sequence analysis and TOF/TOF-MS) identification of post-translational modifications. This technique is valuable for in-depth protein characterization and the study of complex protein mixtures in seafood, such as identifying isoforms or analyzing protein–protein interactions

42

Handbook of Seafood and Seafood Products Analysis

The acquired MS data are processed using computational tools to match the experimental spectra and observed peptide masses to theoretical spectra and in silico-digested peptides in databases. This step is known as database search and peptide identification, and it relies on one or more search engines (e.g., Sequest HT (Eng et al., 1994), MASCOT (Perkins et al., 1999), and X!Tandem (Craig and Beavis, 2004)) with different algorithms that compare experimental data to theoretical fragment masses and attribute a score to all possible matches (peptide-to-spectrum matches (PSMs)). The advancement of novel algorithms designed to handle larger datasets with enhanced specificity and accuracy has significantly contributed to the identification and quantification of more proteins. One prominent database is UniProtKB, which comprises two platforms: Swiss-Prot, housing 569,516 expert-reviewed data entries (accessed on June 15, 2023), and TrEMBL, containing 249,308,459 (accessed on June 15, 2023). When peptides are not contained in the databases (Muth and Renard, 2017), they can be de novo sequenced either manually or by using dedicated search engines and software (e.g., Novor and PEAKS (Ma et al., 2003)). Alignment search tools, such as Protein Blast or BLASTP method, are employed to identify novel proteins by retrieving reliable and highly homologous protein sequences. Furthermore, based on the identified peptides, protein inference is then performed using specific software (e.g., Peptide Discoverer™, MaxQuant, Scaffold, and Peptide Shaker), which generally involves grouping peptides that map to the same protein. Raw and identification data should then be deposited in a public repository to enable other researchers to reproduce and reanalyze it, according to the principles of the findability, accessibility, interoperability, and reusability (FAIR) of research data. Numerous publicly accessible proteomics repositories have been established to serve the scientific community. Resources can be divided into no data reprocessing platforms, such as the PRoteomics IDEntifications database (PRIDE) archive, the MASS spectrometry Interactive Virtual Environment (MassIVE), the jPOST, and the Panorama Public, which are all part of the ProteomeXchange consortium, and data reprocessing platforms, e.g., Global Proteome Machine Database (GPMDB) and PeptideAtlas. These repositories play a vital role in facilitating the storage, sharing, and dissemination of proteomics data, thereby fostering collaboration and advancing research in the field of proteomics. 3.3.2.4.2  Peptide/Protein Quantification Protein quantification is the following step where insights into protein abundance in seafood biological systems are provided. Quantitative proteomics techniques have proven to be highly valuable in various aspects of food technology and biotechnology. By providing a quantitative assessment of protein abundance and its modifications, it facilitates the optimization of ingredient formulation, process parameters, and quality control measures. The application of these techniques not only enhances the efficiency and safety of food production but also drives innovation and the development of novel products with improved sensory attributes, nutritional profiles, and health benefits. The analysis of quantitative proteomics data requires specialized software and bioinformatics tools; however, this step is usually included in a workflow and incorporated into the same software following protein inference. MS-based quantitative proteomics can be classified into two broad categories, namely label-based and label-free methods (extensively reviewed by Anand et al., 2017). 3.3.2.4.2.1  Label-Based Quantification Label-based quantification is a robust and accurate approach that enables the comparison of samples and experimental groups by incorporating exogenous labels into protein samples to enable their differentiation during MS analysis. The versatility of label-based quantitation makes it a widely adopted approach in quantitative proteomics studies. Chemically similar but isotopically distinct labels enable both relative and absolute quantification of proteins from individual samples. Furthermore, samples can be combined, processed, and analyzed together, minimizing experimental variability, enabling accurate comparisons, and reducing analytical time. Common labeling techniques include metabolic labeling (e.g., SILAC), proteolytic labeling (e.g., ICAT and tandem mass tags (TMT)), and chemical labeling (e.g., iTRAQ) (Anand et al., 2017).

Proteomics for Comprehensive Analysis of Seafood Products

43

iTRAQ combined with MS was used to characterize the mitochondrial responses in the gills of juvenile flounder Paralichthys olivaceus, treated with two environmentally relevant concentrations of cadmium, known to be a heavy metal pollutant in the marine environment (Lu et al., 2020). With SILAC, cells are cultured in media containing isotopically labeled amino acids (e.g., heavy arginine or lysine), which are then incorporated by synthesized proteins, leading to quantification based on the relative abundance of labeled peptides. iTRAQ and TMT involve labeling peptides from different samples with isotopically labeled chemical tags. The labeled peptides are combined, and during MS analysis, the tags generate reporter ions that allow for relative or absolute quantification. ICAT uses thiol-reactive reagents to label cysteine residues in proteins. The labeled peptides can be quantified based on the relative peak intensities of light and heavy tags. 3.3.2.4.2.2  Label-Free Quantification There is a growing interest in label-free quantification due to its simplicity, since it requires minimal sample preparation, thus less prone to errors and less wet laboratory complexity, and versatility, making it suitable for large-scale studies. However, the main drawbacks are related to technical variability, machine time, and challenges in accurate comparison and normalization across samples, since seafood samples are separately prepared and subjected to individual MS runs. The different methods within label-free quantification rely on two main approaches: spectral counting (MS2-based), e.g., exponentially modified protein abundance index (emPAI), absolute protein expression (APEX), e.g., normalized spectral abundance factor (NSAF), and precursor ion intensity (MS1-based), e.g., total sum and three most intense precursor peaks (TOP3). LC-MS/MS label-free method was used to identify freshness-related proteins in seabass (Lateolabrax japonicus) filets stored in ice. The research provided several Differential abundant proteins (DAPs) as proteomics markers for monitoring filet freshness (Xiang et al., 2022). 3.3.2.4.3  Protein Characterization Following protein identification and quantification, additional characterization can be performed to ascertain their involvement in specific biological functions. Protein characterization plays a crucial role in seafood product analysis, helping in understanding the quality, nutritional value, and processing characteristics of seafood products. It involves determining various properties of proteins present in seafood and may include studying, e.g., protein PTMs, PPIs, and the structure and functions of the proteins, using various experimental techniques and bioinformatics tools to gain insights into protein functions and dynamics. PTMs are chemical modifications that occur on proteins after translation, such as phosphorylation, acetylation, methylation, glycosylation, and ubiquitination, and can significantly impact protein function. These modifications can be characterized by analyzing the mass shifts or specific fragmentation patterns observed in MS data (Doll and Burlingame, 2015). Several methods can be used for PTM analysis, including PTM-specific enrichment before the MS run and specific MS/MS ion dissociation technologies such as electron transfer dissociation (ETD) and electron capture dissociation (ECD). Others, more conventional, such as Western blotting and IHC, use antibodies that specifically recognize proteins with specific PTMs, allowing for their detection and quantification. Furthermore, studying the interactions between proteins, i.e., PPIs, is key for understanding cellular processes and signaling pathways and predicting protein functionality (Richards et al., 2021). Multiple analytical, biophysical, and computational tools are currently available to study the four levels of protein structure, being the most common Edman degradation and MS-based de novo sequencing for the primary structure, circular dichroism (CD) for the secondary structure, and X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryogenic electron microscopy (cryo-EM) for the tertiary and quaternary structure (Gupta et al., 2021). By leveraging these techniques, researchers and industry professionals gain insights into the composition, dynamics, and functional properties of proteins involved in seafood.

44

Handbook of Seafood and Seafood Products Analysis

3.4  APPLICATIONS OF PROTEOMICS IN SEAFOOD PRODUCT ANALYSIS Consumers today seek diverse and healthier food options due to increased awareness of the link between diet and health. These evolving dietary patterns observed in recent times have significantly impacted the field of food science, leading to notable transformations in how we approach food production, processing, and consumption. These changes have prompted the need for advanced and robust technologies to address the emerging challenges and demands in the food industry that go beyond traditional approaches. In the context of seafood product analysis, proteomics has emerged as a highly effective high-throughput approach, offering substantial advantages over conventional methodologies that are now deemed inadequate in meeting the rigorous regulations set forth by regulatory bodies.

3.4.1 Proteomics and Seafood Safety The European Commission has emphasized the significance of seafood safety, considering it a prominent issue in contemporary research guidelines. In the realm of food safety, proteomics tools hold immense potential in addressing health hazards, which have led to substantial economic losses in the food industry (Fernández Sánchez et al., 2022). These hazards can be of biotic nature (bacteria, allergies, parasites, viruses, or harmful algae blooms) and abiotic nature (e.g., aromatic hydrocarbons, dioxins, heavy metals, and plastics) (Carrera et al., 2020b). By employing proteomics techniques, it becomes possible to detect and identify food spoilage microorganisms through the analysis of changes in their proteomes (Carrera et al., 2020b). Notably, techniques such as MALDITOF MS, MALDI-TOF, and HPLC-ESI-MS/MS have demonstrated effectiveness in the detection, quantification, and identification of various microbes, their toxins, and different allergens in seafood (Gallardo et al., 2013b; Afzaal et al., 2022; Haider et al., 2023). Consequently, researchers and food safety authorities can effectively mitigate risks associated with contaminated seafood, develop targeted strategies for monitoring and risk assessment, safeguard public health, and comply with stringent regulatory requirements. 3.4.1.1  Enhanced Tracing of Seafoodborne Pathogens Seafoodborne pathogens are microorganisms that can cause foodborne illnesses when consumed in contaminated seafood. Accordingly, the disease burden associated with contaminated seafood has increased in the last decade (Kim and Cha, 2019). In 2021, there were 4,005 foodborne outbreaks in the European Union (EU) – a 29.8% increase compared with 2020, where crustaceans, shellfish, and mollusks were frequently implicated with such outbreaks caused by norovirus or bacterial toxins (European Food Safety Authority and European Centre for Disease Prevention and Control, 2022). Different microbiological hazards in seafood may include pathogenic bacteria (e.g., Vibrio spp., Salmonella spp., Shigella spp., Listeria monocytogenes, Staphylococcus aureus, Clostridium botulinum, or Aeromonas spp.), viruses (e.g., norovirus and hepatitis A virus), and zoonotic parasites (e.g., Anisakidae) (Elbashir et al., 2018). Moreover, marine biotoxins and histamine represent an additional threat of food poisoning associated with specific seafood products (Visciano et al., 2020). Symptoms from seafoodborne pathogens can vary from mild gastroenteritis to life-threatening infections (Bintsis, 2017). In seafood, Vibrio spp. has been responsible for several foodborne outbreaks, such as gastrointestinal illness, bloodstream infections, and wound infections, highlighting the vulnerability of individuals with compromised immune systems (Bintsis, 2017). Furthermore, sushi consumption has been associated with Salmonella outbreaks, while Listeria spp. outbreaks have been linked to the contamination of smoked mussels, salmon, and other fish. Zoonotic bacteria such as Salmonella can directly transmit diseases between aquatic species and humans, as they have the ability to cause illness in both (Bintsis, 2017). Proteomics, in this regard, offers high sensitivity, specificity, and rapidity through techniques such as sample preparation, protein identification using MS-based methods, biomarker discovery

Proteomics for Comprehensive Analysis of Seafood Products

45

for rapid and specific detection of pathogens, multiplexing capabilities for simultaneous analysis of multiple pathogens, strain differentiation, and source tracking. Commercial databases already include a large number of bacterial strains with MALDI-TOF-MS fingerprints that correspond to food spoilage pathogens. Accordingly, in a study by Mazzeo et al. (2006), researchers employed this technique to analyze intact bacterial cells of 24 different seafood spoilage bacteria and foodborne pathogens. The genera of bacteria examined included Staphylococcus, Yersinia, Proteus, Escherichia, Lactococcus, Listeria, Pseudomonas, Morganella, and Salmonella. The database can be accessed freely on the Web at http://bioinformatica.isa.cnr.it/Descr_Bact_Dbase.htm. Other authors have used this time-saving and cost-effective technique in the same endeavor to identify seafood-associated bacteria isolated from marine-origin food using MALDI-TOF-MS (Böhme et al., 2012, 2016). As a result, a recent database called Spectra Bank has been established by Santiago de Compostela University, containing mass spectral fingerprints of key spoilage and pathogenic bacteria species commonly found in seafood (Böhme et al., 2013). This comprehensive library encompasses more than 70 species that are of significant interest to the food industry (http://www.spectrabank.org). The mass spectra libraries have the capability to distinguish bacteria at the genus, species, or sub-group levels, provided that there are an adequate number of reference strains available in the database. Within the same scope, a custom Main Spectra Profile (MSP) database for rapid identification of fish bacterial pathogens was developed and validated by MALDI-TOF-MS. Piras et al. (2016) emphasized the broad applicability of MALDI-TOF-MS in characterizing microbial fingerprints, enabling the specific identification and differentiation of subspecies, strains, and serovars. This method not only facilitates understanding of the distribution and pathogenicity of microorganisms but also provides insights into their potential for causing disease. Other studied microbes, such as histamine-producing bacterial species, have been discriminated by MALDITOF-MS fingerprinting (Uehara et al., 2021). Moreover, the findings of the study conducted by Cheng et al. (2016) reinforced the use of MALDI-TOF-MS as a robust and trustworthy method for pathogen identification and discrimination. Further validation from the Food and Drug Administration (FDA) carried significant weight by providing confidence in the application of MALDI-TOF-MS in the field of food safety. MS-MS was further shown to be effectively employed as a powerful tool characterization of foodborne strains of Staphylococcus aureus. This gram-positive pathogenic microorganism is an indicator of food spoilage and is linked increasingly to major outbreaks of foodborne illness, including nausea, abdominal cramping, vomiting, and diarrhea, which can last for 2–3 days (Carrera et al., 2017). Moreover, it was suggested that target MS-based approaches, such as SRM or MRM and PRM, exhibit significant potential as robust tools for the accurate identification of foodborne pathogens (Abril et al., 2022). Proteomics has also been linked to strain differentiation and tracking seafoodborne pathogen sources (Abril et al., 2023). This information is commonly utilized to investigate outbreak origins and assess the virulence potential of specific strains, enhancing the understanding of pathogen dynamics in the seafood matrix. Though seafoodborne pathogen proteomics research has made significant strides in recent years, it is also true that there is still a gap compared with research focused on other food types. This discrepancy is particularly concerning given the prominent role of seafood in today’s food industry and the importance of maintaining its quality and safety. Nevertheless, leveraging knowledge and best practices from other food sources, researchers, regulators, and seafood industry professionals can work together to improve seafood safety measures and reduce the risks associated with seafoodborne pathogens. 3.4.1.2  Assessing Trace Exposure to Environmental Pollutants In the field of food safety, proteomics has proven to be an invaluable tool not only for detecting biological hazards but also for chemical hazards intentionally introduced or from environmental contamination, including pesticides, synthetic drugs, persistent organic pollutants, and marine toxins. The global contamination of aquatic ecosystems with these pollutants poses a significant and widespread concern. As the ultimate recipients of a diverse array of chemicals, aquatic ecosystems

46

Handbook of Seafood and Seafood Products Analysis

serve as the final destination for numerous substances carrying potential ecological hazards, which, in turn, can pose risks to public health. Indeed, pollutants have the capacity to accumulate in marine organisms and propagate through the marine food web, potentially impacting the safety of seafood (Stentiford et al., 2022). Among the organisms inhabiting these ecosystems, bivalves, particularly mussels, are widely recognized as preferred organisms for biomonitoring marine contamination and assessing the presence of chemical pollutants (López-Pedrouso et al., 2020). Research conducted on the proteome of bivalves revealed the impact of heavy metal pollution and organic chemicals on structural proteins, responsible for tissue degradation. Stress-related proteins, such as glutathione, and enzymes such as catalase, superoxide dismutase, and peroxisomes, have been found to be overexpressed in response to contaminants, indicating their potential as biomarkers (López-Pedrouso et al., 2020). Metabolic proteins have also been proposed as pollution biomarkers in bivalves. Although other marine species are used for pollution monitoring, proteomics studies of these organisms remain limited. However, stress-related proteins such as the heat shock family and proteins involved in lipid and carbohydrate metabolism have been suggested as candidate biomarkers in fish species (Roberts et al., 2010). Knowing the adverse health effects of some of these contaminants (e.g., cancer, immune system suppression, decrements in cognitive and neurobehavioral function, disruption of sex steroid and thyroid function, hypertension, cardiovascular disease, and diabetes) makes it crucial to implement effective regulatory measures alongside sustainable, effective technologies to monitor such contaminants in seafood (Carpenter, 2011; Vandermeersch et al., 2015). The legislation establishes maximum allowable levels for various contaminants, and existing monitoring programs ensure regular testing of seafood for specific environmental pollutants. Current efforts primarily focus on well-known chemical pollutants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), certain marine toxins, and toxic elements. However, there is a growing need to understand the presence and potential effects of emerging contaminants in seafood, referred to as “contaminants of emerging concern,” including pharmaceuticals, personal care products, and hormone-disrupting substances. Environmental contaminants of emerging concern in seafood were reviewed by Vandermeersch et al. (2015). This emergent increase stems from concerns about their impact on seafood safety. With the emergence of these contaminants and evolving threats, proteomics technology is crucial for systematically identifying contaminant biomarkers and gaining a deeper understanding of the molecular pathways induced by contamination at the cellular level. A research effort aiming at creating a safer and healthier environment for seafood production was conducted by Zhang and colleagues, who investigated the impact of HgCl2 contamination on oysters and identified differential protein expressions as potential biomarkers (Zhang et al., 2013). The researchers discovered 13 proteins that exhibited distinct expression patterns in response to mercury contamination, and intriguingly, four of these proteins displayed features that held promise for their application as biomarkers for detecting Hg contamination in food. Recently, a modified extraction procedure coupled with high-throughput LC/Q-TOF-MS was developed to assess multiclass chemical contaminants in seafood, including tilapia, grouper, oysters, and scallops. As a result, a comprehensive library for retrospective searching was established comprising a vast array of data, including retention times, accurate masses from MS, and MS/MS information for 524 pesticides, 182 veterinary drugs, 32 persistent organic pollutants, and 18 marine toxins (Bai et al., 2022). Recently, quantitative proteomics using SWATH-MS identified biomarkers of the pharmaceutical product fluoxetine and hormone estradiol from seabass skin, one of the most important species in Mediterranean countries (Pinto et al., 2022). In estradiol contamination, a wide range of adverse effects, including vitellogenesis, feminization, and hermaphroditism in fish, as well as breast, prostate, and testicular cancer in humans, have been extensively documented (Wojnarowski et al., 2021). The presence of pharmaceuticals in seafood can pose risks to consumers, either directly through allergic reactions and toxicity or indirectly by potentially promoting microbial resistance. A successful application of proteomics analytical techniques in the identification of these contaminants involved the utilization of HPLC in combination with a benchtop Q Exactive Orbitrap high-resolution MS. This approach enabled

Proteomics for Comprehensive Analysis of Seafood Products

47

the accurate determination of 24 specific antibiotics, including selected beta-lactams, tetracyclines, fluoroquinolones, sulfonamides, phenicols, macrolides, cephalosporins, lincosamides, and diaminopyrimidines in fish matrices (Chiesa et al., 2018). Another source of contaminants is personal care products such as disinfectants, fragrances, repellents, and preservatives (Brausch and Rand, 2011). These products can enter aquatic ecosystems through various pathways, including discharges from municipal wastewater treatment plants, runoff from agricultural areas where veterinary therapeutics are used, and releases from aquaculture sites. Preservatives such as parabens (PBs) are commonly used due to their broad-spectrum antimicrobial properties, which help against various types of microorganisms (Ocaña-González et al., 2015). Aquatic animals are more prone to accumulate such products from the environment. Accordingly, Chiesa et al. (2018) used a straightforward liquid– liquid extraction followed by HPLC, combined with a benchtop Q Exactive Orbitrap high-resolution MS to evaluate the presence of PBs in fish and bivalve samples. Results showed that PBs were more frequently present in bivalves than in fish, possibly due to their filter-feeding behavior. Bivalves have the ability to filter large volumes of water, which can lead to the accumulation of contaminants (and pathogens) present in the surrounding environment. A comprehensive review of the use of proteomics in shellfish environmental toxicity was developed by Gomes et al. (2017). Other emerging contaminants, such as microplastics and triclosan, have not been widely reported thus far as seafood contaminants, but their prevalence and increasing presence in the aquatic environment raise concerns about their potential to become a public health issue. The lack of suitable analytical methods for microplastics has limited our understanding of their distribution, abundance, and potential impacts on food safety. Indeed, it underscored the need for further research and the development of specialized techniques to address the detection and quantification challenges associated with these contaminants. Only after 2017, LC-MS/MS was validated for detecting and quantifying microplastics in oysters. The analysis involved in-gel digestion of protein spots that exhibited differential expression, which was excised from 2D electrophoresis (2-DE) gels (Sussarellu et al., 2016). Martinez et al. (2020) covered in their review about protein signatures to trace seafood contamination and the analysis of microplastics and triclosan using proteomics techniques. Triclosan is an antimicrobial agent with a wide range of activity that is not classified as an antibiotic. This agent has become a persistent pollutant in soil, air, and water due to its extensive use in over 2,000 consumer products, including toothpaste, deodorants, skin creams, and soaps. The identification of proteins from bivalves that are significantly affected by triclosan exposure was primarily carried out using analytical techniques such as 2-DE and MALDI-TOF/TOF (Riva et al., 2012). The methods employed for the identification of chemical contaminants have been demonstrated to be efficient, quick, and precise. Despite their unique characteristics, these methods can be employed for routine analysis. These findings underscore the potential of proteomics to provide valuable information regarding the presence of harmful substances in our food chain. 3.4.1.3  Identification of Allergens The global rise in food-related adverse reactions, including changes in dietary habits and food production processes, has led to an increase in the prevalence of food allergies. Allergic reactions result from an overreacting immune system against proteins (allergens). Among these allergic reactions, type I IgE-mediated allergies to food components rank fourth in importance, as stated by the World Health Organization (WHO). To prevent symptoms ranging from a runny nose (mild), conjunctivitis (moderate) to anaphylaxis (severe), avoidance of the specific allergen is still the best medical cure available for patients. Food allergies are described for more than 170 kinds of foods; however, 90% are due to the top 9, which include fish, mollusks, and crustaceans (Vincent et al., 2022). It is fair to say that food allergies pose a significant global food challenge and an increasing health problem worldwide (Ruethers et al., 2018), especially in Western countries, with a particular emphasis on children. The current prevalence worldwide is predicted to affect up to 8% of children and less than 4% of the adult population (Elghoudi and Narchi, 2022).

48

Handbook of Seafood and Seafood Products Analysis

Defining the specific trigger, which is responsible for an allergy, is important in medical diagnosis, combining patients’ history with clinical symptoms and realization of diagnostic tests (Sicherer et al., 2004). However, accurate identification and characterization of the allergenic composition of various seafood species have been stated as a challenge due to their diverse nature. Each type of seafood may contain multiple allergens, and the specific allergen-triggering reactions can vary among individuals (Matricardi et al., 2016; Jeebhay et al., 2019). Nonetheless, these allergens often fall within β-parvalbumin (β-PRVB), fructose bisphosphate aldolase, tropomyosin, enolase, and creatine kinase protein families and are known to induce clinical cross-reactivity (Matricardi et al., 2016; Jeebhay et al., 2019). β-PRVBs, which are predominantly present in the sarcoplasmic fraction of the white muscle of fish, are widely recognized as the primary allergens responsible for fish allergies (Carrera et al., 2020b). The allergenic properties of β-PRVBs are attributed to their isoform’s abundance, muscle expression, thermal stability, and resistance to certain gastrointestinal enzymes (Mukherjee et al., 2023). The remaining allergens are less resistant to heat or high-pressure food processing. Diagnostic tests such as (i) skin prick tests (SPTs) and (ii) allergen-specific serum IgE (sIgE) measurements are normally the first ones, followed by (iii) elimination diets and (iv) oral food challenges (OFCs). While these traditional tests have their own merits and are commonly used in clinical practice, proteomics has proven to be valuable. It presents several advantages over traditional diagnostic tests by providing a comprehensive analysis of the entire proteome of a specific allergenic seafood source. It is important to note that seafood often undergoes processing before consumption, which can result in protein denaturation, modification, and/or hydrolysis, potentially altering their allergenic properties. Proteomics can address this challenge by taking into consideration the unique properties of allergens. This means that all potential allergenic proteins present in the sample can be identified and characterized, providing a more holistic picture of the allergen profile and complementing and enhancing the individual allergen-specific tests. An overview of the recent advancements in this field, highlighting the innovative approaches used to analyze and understand seafood allergens, was extensively reviewed by Carrera et al. (2020a). In a nutshell, the detection of allergens in seafood typically involves the analysis of tissue aqueous extracts using Western blot (WB) with patient serum IgE. This initial approach was further enhanced by proteomics discovery and targeted workflows, which enabled the identification of proteins through techniques such as 2-DE and MS. Within this scope, quantification of parvalbumins from 22 fish species was performed using SDS–PAGE, where the authors showed that allergenicity depends on quantity, demonstrating higher quantities in white muscle and lower IgE reactivity in fish species with dark muscle (Kobayashi et al., 2016). Using solely 2-DE bottom-up proteomics methodology coupled with MS-based techniques, researchers registered the highest number of completely de novo sequenced allergens in the UniProtKB database, specifically 25 new β-PRVBs isoforms in all species of the Merlucciidae family (Carrera et al., 2010). The remaining protein allergens aforementioned have been frequently characterized by 2-DE coupled with MALDI-TOF-MS from species such as cod, salmon, tuna, and tilapia (as reviewed by Carrera et al., 2020a). Nevertheless, high-throughput techniques for the identification, detection, and quantification of seafood allergens were further validated for the rapid detection of the main fish allergen in processed and precooked foods (Carrera et al., 2012). The detection of parvalbumin occurred in less than 2 h and was performed using selected MS/MS ion monitoring (SMIM) in a linear IT (LIT) mass spectrometer. Given its high sensitivity and specificity, these methods helped to identify not only known allergens but also potential novel allergenic proteins at very low concentrations that may go undetected by other diagnostic tests. This is particularly advantageous when dealing with trace amounts of allergens or hidden allergenic sources in processed foods. A different proteomics method, based on LC-MS/MS and multiple reactions monitoring MS (LC-MRM-MS/MS), was validated more recently, which can be used to determine the main fish allergen in several foods’ matrices (Sun et al., 2019). However, it is worth noting that high-throughput technologies such as MRM can also be employed in an unbiased manner, without relying on prior knowledge of specific allergens. This is particularly significant in cases where patients experience atypical or unidentified allergens that trigger their allergic reactions.

Proteomics for Comprehensive Analysis of Seafood Products

49

Proteomics can still provide comprehensive information on cross-reactivity among different allergenic sources. It can help identify common allergens shared between different seafood species or between seafood and other allergenic sources. LC combined with MS/MS (LC-MS/MS) was used after 2-DE separation to characterize and map IgE epitopes (Di Girolamo et al., 2015). Later, the application of shotgun proteomics in samples from 15 fish species, combined with the utilization of bioinformatics tools, facilitated the identification of 35 peptides serving as B-cell epitopes for all parvalbumins present in the UniProtKB database (Carrera et al., 2019). Additionally, LC-MS/ MS analysis was employed to evaluate skipjack tuna and Nile tilapia samples, including both fresh and dried powdered fish. Protein expression profiles were compared using UniProtKB, demonstrating the efficacy of this technique for identifying allergenic proteins (Nonthawong et al., 2023). To address the limited knowledge of potential allergens associated with zoonotic nematodes that infect marine fish, a recent study conducted by Saelens et al. (2022) utilized targeted proteomics in the form of LC-MS/MS with MRM. The researchers investigated excretory or secretory (E/S) proteins and crushed nematodes as potential sources of allergens that could trigger hypersensitivity reactions in humans. These allergens are relevant to the foodborne disease known as anisakidosis, which is characterized by gastrointestinal symptoms such as acute and transient nausea, vomiting, diarrhea, and epigastric pain occurring within 1–12 h after consumption (Hochberg et al., 2010). Before, in 2016, Anisakid nematodes were detected in foodstuffs by PRM MS (Carrera et al., 2016). Traditionally, tropomyosin has been recognized as the primary allergen present in the edible portions of crustaceans (such as shrimp, crab, and lobster) and mollusks (including scallops, oysters, clams, and squid) (Lopata et al., 2016). Through discovery proteomics, tropomyosin has been identified as a significant allergen in both raw and cooked flower tail shrimp (Metapenaeus dobsoni) and white squid (Loligo edulis) (Yadzir et al., 2012; Laly et al., 2019). Furthermore, 24 previously unreported allergens from over 25,000 proteins of the Pacific Oyster (Crassostrea gigas) (Nugraha et al., 2018) were discovered by an innovative approach combining in silico bioinformatics identification based on sequence alignment, 2D immunoblotting using a serum pool from allergic patients, and shotgun proteomics. These findings, as elsewhere stated (Carrera et al., 2020a), underscore the importance of comprehensive analysis to identify previously unknown allergenic proteins, which can greatly contribute to the management of patients with concurrent reactivity to diverse allergen sources. This knowledge is further valuable for understanding cross-reactive patterns, managing allergies in patients, guiding allergen avoidance strategies, and tailoring targeted and effective treatments such as immunotherapy.

3.4.2 Seafood Authentication and Adulteration The global trade of seafood and its anticipated increase raises concerns regarding adulteration and mislabeling, emphasizing the need for seafood authentication and origin verification. The field of seafood authentication is experiencing rapid growth due to rising public awareness and concern regarding these products’ quality and safety. Consumers are becoming more conscious of the origin, composition, and authenticity of the food they consume. Food authenticity is the process of providing evidence or verification to ensure that a food product is genuine and matches the information stated on its label, including its origin and composition. It involves confirming that the food product is not adulterated or misrepresented in any way (Danezis et al., 2016). The goal of food authenticity is to establish consumer trust and confidence by ensuring that the food they purchase and consume is accurately labeled and meets their expectations in terms of quality, safety, and authenticity. On the other end of this spectrum, food fraud, including adulteration and mislabeling, refers to the illegal practice of deceiving customers by placing food products on the market that do not meet the declared nature or quality, often for financial gain. Such fraudulent activities are a serious concern in seafood safety, which has led to a growing awareness of major food fraud alerts and to common types of food fraud, as documented in a recent review (Esteki et al., 2019). Regrettably, the mislabeling of seafood is a prevalent occurrence observed in various food establishments, including markets and restaurants (Reilly, 2018). For instance, a notable case reported by Hoque et al. (2016) involved the use of formalin to make fish

50

Handbook of Seafood and Seafood Products Analysis

appear fresh, posing significant health risks due to its carcinogenic effects. Another common form of fish fraud is the intentional substitution of high-value fish species with lower-quality alternatives, as highlighted by Giusti et al. (2019). In the EU, the rate of mislabeling in a study involving 283 seafood samples amounted to 26% (Pardo et al., 2018). Similarly, in the United States and Canada, mislabeling was observed in 21% of 449 samples (Warner et al., 2019) and 32.3% of 203 samples (Shehata et al., 2019), respectively. However, in China, the mislabeling rate for roasted fish reached 75.5% out of 1,236 samples (Xiong et al., 2019), while sushi exhibited a mislabeling rate of 22% (But et al., 2019). These findings undermine consumer trust and can lead to food allergies in susceptible individuals. When incorrect or undisclosed ingredients are used, it becomes challenging for consumers to make informed choices and avoid potential health risks. Therefore, ensuring accurate labeling and ingredient information is crucial in protecting vulnerable consumers and maintaining food safety standards. Identifying, e.g., fresh fish, poses a significant challenge due to the wide variety of fish species available in the market, but this gets more challenging with the processing of seafood products, where anatomical and morphological characteristics are often lost. Anatomical features such as heads, fins, skin, or bones are crucial for authenticating fish species, but they are often removed or altered during processing. Given the problem of food fraud and the need to ensure food authenticity, there is a growing demand for reliable and effective methods to address this issue. A recent review by Danezis et al. (2016) focused on analytical advancements and novel techniques for assessing food authenticity. Conventional methods for specific authentication of food products (including electrophoretic, immunological, spectroscopy, sensory analysis, and deoxyribonucleic acid (DNA) techniques) can be time-consuming and may not detect adulteration below 5% of the product. High-throughput proteomics has gained prominence in the past two decades as an effective tool for detecting adulterants in food, offering faster, more comprehensive analysis, enhanced sensitivity, and accuracy in species identification at the protein level and even at the peptide level. These techniques are rapidly complementing or outright replacing earlier methods, especially in cases involving similar phylogenetic species and processed products. Cerqueira et al. (2020a) and Gallardo et al. (2013a, b) compiled a comprehensive collection of proteomics-based studies that have been applied to date for food-species authentication. These developments contribute to the ongoing efforts to ensure the integrity and safety of food products in the market. Discovery followed by targeted proteomics is the common pipeline used for the identification, characterization, and detection of species-specific peptide biomarkers for seafood authentication. In the discovery phase, the proteome is explored comprehensively, using reference samples to identify potential species-specific peptide biomarkers, and targeted further using SRM, or MRM techniques. This workflow, validated by Carrera et al. (2011), was stated to be rapid, sensitive, and effective for peptide biomarker monitoring. In this context, the chemical composition of seafood can serve as a valuable indicator for assessing its quality, origin, authenticity, and possible adulteration. Correspondingly, proteins play a crucial role as markers for various properties of food products throughout the entire food chain, from farm to fork. By analyzing the biomarkers of food, which reflect the variations in protein levels, one can detect changes caused by factors such as production methods, the geographical origin of raw materials, storage conditions, or adulteration practices. This makes seafood biomarkers a powerful tool for monitoring food authenticity, as highlighted in a recent review (Medina et al., 2019). The authentication of seafood has been supported by various high-throughput proteomics analytical methods, as summarized in Table 3.3. It is important to acknowledge that, while there is a wealth of research in this field, this table only provides a single example of each high-throughput proteomics technique and seafood group. For a more comprehensive understanding of the available research, it is recommended to consult Cerqueira et al. (2020a). 3.4.2.1  Species Identification Differentiation of seafood samples poses a challenge as no single extraction method or electrophoretic system has proven universally effective. When it comes to raw samples, SDS–PAGE and IEF focusing on sarcoplasmic proteins have shown better differentiation, while SDS has demonstrated better

51

Proteomics for Comprehensive Analysis of Seafood Products

TABLE 3.3 Overview of the High-Throughput Proteomics Analytical Methods Commonly Used for Authentication of Seafood Products Seafood Product

Description

Proteomics Technique

Different fish species

Distinguish between Alaska pollock, Atlantic cod, and Greenland halibut

Sea cucumber

Distinguish sea cucumber from different geographic regions Discriminate dried sea cucumber from different geographical regions

Ultrahigh-performance liquid chromatography quadrupole time-of-flight (UPLC-Q-TOF) mass spectrometry with SWATH data-independent acquisition Tandem mass tag (TMT labeling)

Sea cucumber

Different fish species Different fish species Different shrimp species European sea bass Different fish species

Trout

Different fish species Gilthead seabream Perchs

Hake species from Merlucciidae family Flatfish species Different shrimp species and families

Distinguish red snapper from Sebastes spp. (redfish) Distinguish between several Sciaenidae, tonguefish, and three other fish species Distinguish between three different species Authentication of European seabass (Dicentrarchus labrax) according to production method (wild or farmed) Identify Atlantic salmon (Salmo salar), pollock (Pollachius virens), trout (Oncorhynchus mykiss), redfish (Sebastes sp.), and Atlantic cod (Gadus morhua) Distinguish between brown trout (Salmo trutta fario) and rainbow trout (Oncorhynchus mykiss) Development of a MALDI-TOF-MSbased protein fingerprint database of fish Distinguish between farmed and wild gilthead sea bream (Sparus aurata) Distinguish between fluviatilis (European perch), Lates niloticus (Nile perch), Stizostedion lucioperca (European pikeperch), and Morone chrysops × saxatilis (sunshine bass) Distinguish between Merluccius species

Identification and discrimination of closely related flatfish species Distinguish protein from 14 different species

SWATH-MS using UPLC coupled to quadrupole time-of-flight mass spectrometer (Q-TOF-MS) LC-MS and LC-MS/MS MALDI‐TOF‐MS of fish skin proteins

UPLC-Q-TOF-MS/MS with SWATH data-independent acquisition and MRM Bidimensional liquid chromatography (2D-LC) coupled to label-free quantitative tandem mass spectrometry (MS) Desorption electrospray ionization mass spectrometry (DESI-MS)

MALDI-TOF mass spectrometry-based screening method using the vitreous fluid of fish eyes MALDI-TOF-MS

Nano-HPLC-MS/MS IEF and 2-DE

2-DE analysis of sarcoplasmic proteins, followed by matrix-assisted laser desorption–ionization–time-of-flight mass spectrometry (MALDI-TOF-MS) LC-MS/MS and spectral library matching IEF followed by tandem MS/MS

References Chien et al. (2022)

Feng et al. (2020) Zhang et al. (2019) Lasch et al. (2019) Bi et al. (2019) Hu et al. (2018) Chiozzi et al. (2018) Gerbig et al. (2017)

Ulrich et al. (2017) Stahl and Schröder (2017) Piovesana et al. (2016) Berrini et al. (2006)

Piñeiro et al. (2001)

Nessen et al. (2016) Ortea et al. (2010a) (Continued)

52

Handbook of Seafood and Seafood Products Analysis

TABLE 3.3 (Continued) Overview of the High-Throughput Proteomics Analytical Methods Commonly Used for Authentication of Seafood Products Seafood Product Thunnus sp. Different shrimp species Cod species Hake species

Different mussel species

Description Distinguish between three different species Distinguish between arginine–kinase protein from seven different species Determination of fish origin Distinguish between different species of the Merluccius and the Macruronus genera Distinguish protein from two different species

Proteomics Technique 1DE and 2-DE 2-DE followed by MALDI-TOF-MS and sequencing by ESI-IT MS/MS Tris and CHAPS-urea extracts and 2-DE 2-DE and MALDI‐TOF-MS of parvalbumin fraction from muscle 2-DE followed by MALDI-TOF

References Pepe et al. (2010) Ortea et al. (2009) Martinez et al. (2007) Carrera et al. (2006) López et al. (2002)

performance for cooked samples (Banerjee et al., 2022). By detecting specific protein markers and differentially expressed proteins, these techniques provide accurate and reliable methods for identifying and differentiating seafood species, ensuring the authenticity and quality of food products. Within this context, parvalbumin sarcoplasmic proteins found in fish and crustacean muscle were proved to be good forensic relevant markers for the identification of a high number of different species, even very closely related (Mukherjee et al., 2023). Native IEF analysis of water-soluble muscular proteins was employed to differentiate 14 shrimp and prawn species based on differences in the amino acid sequences of parvalbumins. Similarly, parvalbumins were utilized to assess intraspecies variability and geographical location in gadoid fishes, flatfishes, and hake species (Piñeiro et al., 1998, 2001). Additionally, nucleoside diphosphate kinase B (NDK B) was identified as a marker protein for the identification of hake species (Carrera et al., 2007). In the same line, potential markers for discriminating four tuna species were identified by 2-DE (Pepe et al., 2010; Tiziana et al., 2012). Nevertheless, there are only a limited number of markers identified and research is therefore needed to identify more biomarkers and assess their suitability in detecting adulteration in different seafood products. 3.4.2.2  Geographic Origin Identification Consumers are today willing to pay a premium price for products they know the origin of and whether they are labeled as environment-friendly or welfare-friendly (Eurogroup for Animals, 2018). Proteomics can play a key role in addressing both of these issues. By analyzing the protein profiles of seafood samples from different regions or different sources, proteomics can help determine the geographic origin and production practices of a product. This information can be used to ensure the accuracy of labeling and prevent fraud in the seafood industry. Accordingly, in a study conducted by Martinez et al. (2007), differentiation between wild and farmed cod was achieved using 2-DE after Tris and CHAPS extraction. The researchers identified specific spots with different electrophoretic abilities in the farmed cod samples, which were not found in the wild samples. Compared to research on meat or dairy products, the application of isotopic analysis for seafood authenticity has been relatively limited. Dempson and Power (2004) used carbon and nitrogen stable isotope ratios to differentiate between farmed and wild Atlantic salmon. Their findings revealed that aquaculture salmon exhibited a significant enrichment in 13C but depletion in 15N compared with wild fish. Complementarily, proteomics holds great promise in uncovering the effects of

Proteomics for Comprehensive Analysis of Seafood Products

53

farming and understanding the underlying physiological and pathological mechanisms (Raposo de Magalhães et al., 2020b; Moreira et al., 2021), providing information that can be exploited for tracing origin and production practices. In a study focused on gilthead sea bream, the impact of aquaculture practices was characterized using label-free shotgun proteomics of skin mucus to identify unbiased biomarkers. These peptides, representative of aquaculture rearing practices, not only help discriminate between welfare-friendly and poor welfare practices but also distinguish between farmed and wild fish (Raposo de Magalhães et al., 2023). Similarly, in species such as turbot, sea bass, cod, and gilthead sea bream, nutritional amino acids, fatty acid profiling, and chemical composition analysis were used to trace and detect variations in protein expression among genetically similar backgrounds. These findings highlight the potential of biomarkers and isotopic markers as valuable tools in seafood authentication, particularly for differentiating between wild and farmed fish. Combining isotopic analysis with other proteomics analytical approaches can assure seafood authenticity by enhancing the accuracy and reliability of species differentiation and, if coupled with, e.g., MS instrumentation, facilitating the discovery of additional biomarkers.

3.4.3 Seafood Quality The issue of seafood quality has gained significant attention throughout the entire supply chain, from farm to fork. There is growing recognition of the importance of ensuring that seafood products meet certain standards and criteria to deliver high-quality and safe products to consumers. The concept of food quality encompasses a broad spectrum of factors: 1. Nutritional value and freshness: This refers to the evaluation of various nutritional components, including vitamins, proteins, minerals, and fats, and factors such as digestibility and desirable fatty acid compositions. 2. Production method: This includes the method employed in the production of the food, whether it is sourced from wild, intensively farmed, or organically farmed sources. 3. Functionality: This relates to the presence of bioactive compounds in the food, which can confer specific health benefits. 4. Processing type: This denotes the specific processing techniques applied to the food, such as whether it is fresh, frozen, canned, smoked, or salted, which affect post-harvest product stability and shelf-life. 5. Organoleptic and sensory attributes: This encompasses the evaluation of characteristics, such as size, taste, texture, smell, and color, which contribute to the overall sensory experience of consuming food. 6. Traceability and authentication: This pertains to aspects such as determining the geographical origin and species authenticity of the food. 7. Safety: This refers to, e.g., levels of microbial pathogens, drugs, contaminants, toxins, and allergens. As outlined by Piñeiro and Martinez (2013), these aspects collectively define and contribute to the overall quality and value of food and are important from the consumers’ point of view (Verbeke et al., 2007; Rodriguez-Salvador and Calvo Dopico, 2023). Stakeholders across the seafood industry, including producers, processors, and regulators, are placing greater emphasis on maintaining and enhancing seafood quality at every stage and focusing on the differences. This attention to seafood quality is driven by the desire to deliver superior products that meet consumer expectations. Consumers are increasingly demanding high-quality seafood products that are not only safe but also delicious. They seek reassurance that the seafood they consume meets certain standards in terms of freshness, taste, texture, and overall sensory experience (Rodriguez-Salvador and

54

Handbook of Seafood and Seafood Products Analysis

Calvo Dopico, 2023). In addition, there is a growing awareness of the importance of sustainable seafood practices, which encompass responsible fishing or aquaculture practices, environmental considerations, and the preservation of marine ecosystems. Consumers are now more inclined to choose seafood products that are sourced in an environmentally friendly and socially responsible manner (Maesano et al., 2020). In addition to consumer expectations, regulatory bodies and industry organizations are implementing stricter guidelines and standards to ensure seafood quality and safety. These regulations encompass various aspects such as proper handling and storage practices, traceability of seafood products, and adherence to food safety protocols. Compliance with these regulations is crucial to prevent the occurrence of foodborne illnesses and to maintain the reputation and integrity of the seafood industry. Some technological and scientific methods to assess seafood quality include the quality index method (QIM) and instrumental methods, such as instrumental texture profile analysis, tristimulus colorimeters, and unselective chemical sensors “e-noses” (for a review of these methods, see Hassoun and Karoui (2017)). While these methods have proven to be valuable, they do not offer a comprehensive understanding of the biochemical processes occurring within the muscle. Therefore, cutting-edge proteomics techniques coupled with bioinformatics will more accurately and objectively ensure that seafood meets the desired quality standards. Within this framework, the present section provides a concise overview of significant applications of proteomics. These applications focus on assessing the food quality domains highlighted by Piñeiro and Martinez (2013) aforementioned and which overlap. They refer to antemortem and postmortem practices that affect food products and seafood included. Both safety (Section 3.4.1) and traceability and authenticity (Section 3.4.2) were already covered and will not be addressed. 3.4.3.1  Antemortem Food Quality Management Preslaughter practices that can impact food quality include the environment in which seafood species are reared, their feed composition, and the overall farming conditions. These factors directly impact the nutritional profile, growth, and overall health of the seafood. Optimal farming conditions, such as adequate water quality, proper feeding regimes, and disease prevention measures, are well recognized for promoting the well-being of seafood and, subsequently, its quality. How proteomics links to each of these issues affecting quality has been extensively highlighted in the existing literature (Sveinsdóttir et al., 2012; Carrera et al., 2013; Gallardo et al., 2013a; Di Girolamo et al., 2015; Piras et al., 2016; Rodrigues et al., 2016, 2018; Carrera et al., 2020a, 2020b; Raposo de Magalhães et al., 2020b; Nissa et al., 2021; Afzaal et al., 2022; Chien et al., 2022; Diwan et al., 2022; Jaiswal et al., 2023). 3.4.3.1.1  Nutritional Value and Freshness In broad terms, consumers generally anticipate cultured fish to exhibit organoleptic attributes that closely resemble those of wild fish where freshness, flavor, and texture are key. We can say the same for other seafood products. The attribute of “freshness” holds a special significance as it encompasses and reflects the perceived time elapsed since the fish’s slaughter. Similarly, the concept of “flavor” should be understood as a meta-attribute resulting from the interplay of “taste,” “aroma,” and “texture,” along with factors such as temperature and chemesthetic sensations. Considering the reliance of all these organoleptic characteristics on the physical and chemical properties of seafood products, it is unsurprising that their quality attributes can be influenced by a diverse array of factors. In this context, the notion that “we are what we eat” holds significant relevance in understanding and managing the quality of seafood, with dietary management playing a pivotal role. The dietary composition of seafood is a crucial determinant of its nutritional value, flavor, and texture. Understanding this factor is crucial for ensuring consistent and desirable organoleptic properties, which rely on the composition and structure of seafood proteins. Unbalanced composition and denaturation levels of these proteins play a significant role in freshness, texture, and taste of seafood products. The extent of denaturation directly impacts important properties such as gelation, emulsification, foaming, water-holding capacity, and product stability. Researchers and industry professionals strive to optimize feed management, production systems, and rearing procedures that preserve the integrity and functionality of seafood proteins.

Proteomics for Comprehensive Analysis of Seafood Products

55

The types and quality of feed provided to, e.g., farmed fish, directly influence their biochemical composition. As an example, one approach to enhance the nutritional profile and promote desirable flavor attributes is through the replacement of ingredients or the reduction in anti-nutritional elements in fish diets. Additionally, the supplementation of specific nutrients, such as essential amino acids, fatty acids, vitamins, and minerals, with functional additives is recognized as an effective method (Saleh et al., 2022; Filipe et al., 2023) and reviewed by Aragão et al. (2022). In addition, the sustainable and responsible sourcing of feed ingredients plays a crucial role in ensuring the overall sustainability and ecological impact of seafood production, while also maintaining the nutritional profile and promoting desirable flavor attributes. Moreover, the adoption of welfare-friendly practices within the industry is gaining momentum as part of this collective effort. Studies have provided evidence indicating that different farming systems, characterized by varying levels of production intensity, can lead to noticeable differences in the external appearance, fat content, and organoleptic properties of fish. In seabream, it was shown that intensive farming in cages, compared with semi-intensive earthen ponds, promotes fish that are less colorful, fattier, and overall have a poor appearance (Valente et al., 2011). A study conducted in the same species investigated the differences in muscle protein patterns between fish from natural repopulation lagoons and those from offshore cages in Italy using various proteomics techniques, including 2-DE, MALDI-MS, and LC-ESI-Q-TOF-MS. Surprisingly, the study did not find consistent variations in the protein patterns between the two groups indicating the suitability of the farming conditions and locations. However, the study did identify significant individual differences in the relative expression of parvalbumin isoforms and spots corresponding to the myosin-binding protein H (MyBP-H) isoelectric series (Addis et al., 2010). These differences did not appear to be influenced by factors such as fish length, production method, or geographical location. This highlights the complexity of the proteomics landscape and suggests that other factors beyond these variables may contribute to the observed protein variations. Interventions such as interrupting feeding before harvest have been recognized as a practice that can enhance the quality of the final seafood product through conventional methods. However, a study conducted on giant yellow croaker (Larimichthys crocea) using label-free LC-MS analysis after a 21-day fasting period revealed significant downregulation of proteins associated with muscle composition and growth, while proteins related to proteolysis were upregulated (Zhang et al., 2021). This suggests that fasting can impact muscle metabolism and the expression of growth-related proteins. When considering the findings of Orisasona et al. (2016), which demonstrated that prolonged fasting periods can negatively affect sensory attributes such as color, taste, texture, and flavor while increasing odor, it becomes evident that fasting practices need to be tailored to the specific requirements of each species. This customization is necessary to ensure the preservation of other important factors such as health, welfare, and the rate of postmortem degradation. Moreover, it is essential to consider the impact of stress experienced during rearing and during the harvesting and slaughter processes. This stress not only influences the perception of seafood product quality from a welfare standpoint but also accelerates postmortem degradation and affects muscle texture (Daskalova, 2019). Proteomics studies have successfully identified robust protein signatures associated with chronic stress in fish caused by poor handling (Raposo de Magalhães et al., 2020a). Prolonged exposure to stress leads to elevated cortisol levels, which profoundly affect the entire organism. The fish proteome exhibited an increase in the abundance of enzymes involved in protein breakdown, while the levels of structural proteins decreased, and with this, muscle development and growth are compromised. Discovery proteomics has also proven to be a valuable tool in advancing our understanding of the underlying mechanisms involved in skeletal deformities (Silva et al., 2012). To investigate the impact of preslaughter stress on postmortem processes in the gilthead seabream muscle, researchers employed 2-DE and MALDI-TOF-TOF-MS analysis. Deformities are often caused by unbalanced stocking densities and nutrition. Discovery proteomics, once more, in a study focused on grass carp gills, 2-DE followed by LC-MS/MS analysis, revealed notable changes in metabolic pathways following hypoxic stress (239). These altered pathways encompassed energy

56

Handbook of Seafood and Seafood Products Analysis

generation, metabolic processes, immune responses, oxidative processes, and proteolytic activities. During the slaughtering process, it is a common practice for fish to be exposed to air for a certain period of time. However, this practice can lead to detrimental hypoxia conditions, which can significantly impact the welfare of the fish and subsequently affect their shelf-life and overall quality. 3.4.3.2  Postmortem Food Quality Management Post-slaughter practices are equally vital in preserving and enhancing seafood quality. It is of utmost importance to implement proper handling, storage, and processing techniques to ensure the freshness of seafood, minimize bacterial growth, and address potential deterioration and safety issues for consumers. Rapid chilling or freezing immediately after harvest is particularly effective in preserving the texture, flavor, and nutritional integrity of seafood. Furthermore, the use of appropriate packaging and transportation methods is indispensable for securing and upholding the quality and safety of seafood products throughout the entire supply chain. 3.4.3.2.1  Seafood Processing and Storage Seafood products are preferred by consumers over frozen or thawed products given that thawing can decrease nutritional value and sensory properties, including color, flavor, texture, tenderness, and juiciness. Nevertheless, to increase seafood shelf-life and decrease foodborne diseases various food processing techniques are applied by the industry (Jiang and Rao, 2021; Hassoun et al., 2022; Roobab et al., 2022). Additionally, food storage and processing techniques change muscle proteins, which can be detected and identified by proteomics-based approaches (Gallardo et al., 2013b). Techniques are divided into two types, thermal and non-thermal. Blanching, boiling, steaming, smoking, canning, frying, roasting, baking, and pasteurization are examples of conventional thermal techniques, whereas lyophilization, fermentation, salting, and ultrafiltration are non-thermal (Fellows, 2009). One clear example of seafood processing objective falls within reducing the product’s allergenicity. It is acknowledged that processing might change the structure and stability of parvalbumin and consequently modify allergenicity (Jiang and Rao, 2021). With the modification of its original structure, aggregates might be formed and consequently IgE epitopes might be masked, unmasked, or damaged (Costa et al., 2022). Processing techniques such as boiling, steaming, and cooking resulted in a decreased allergenicity in several fish (Kuehn et al., 2010; Kubota et al., 2016), likewise with salted herring and dried cod (Sletten et al., 2010). In contrast, studies have shown that boiling Atlantic cod (Somkuti et al., 2012) and freezing North Pacific hake do not significantly alter their allergenicity (Dasanayaka et al., 2022). However, boiling sardine has been found to slightly increase its allergenicity (Mejrhit et al., 2017), while ultra-high heating (autoclave) and salting have been observed to decrease the detectability of parvalbumins in gilthead sea bream and European sea bass (Schrama et al., 2022c). These variations among different fish species highlight the significance of conducting research on this topic to better understand the effects of various cooking and processing methods on allergenicity. Proteomics can leverage changes in protein expression levels and structure that affect seafood quality during seafood processing and storage and has more frequently been used in this scope. Smoking of fish species such as salmon, mackerel, and haddock resulted in a novel band of parvalbumin at 30 kDa, and processed cod, salmon, trout, and pickled herring showed an altered immunogenicity (Sletten et al., 2010). The researchers used IEF gels (urea or native) in heated cooked samples and raw products. The heat treatment resulted in altered recognition of the commercial antibodies against parvalbumin (Saptarshi et al., 2014). Furthermore, 2-DE analysis characterized muscle changes during cold storage of different fish species (Verrez-Bagnis et al., 2001), while MS/MS analysis on the sarcoplasmic proteome of salmon after high-pressure treatment showed a modification in a fish allergen named phosphoglycerate mutase (Ortea et al., 2010b). Bottom-up proteomics analyzed the effects of high pressure and subsequent freezing of European hake, using ingel digestion and LC-MS/MS, which identified several allergens with modified expression (Carrera et al., 2018). In general, highly processed samples, i.e., heating, autoclaving, and high pressure by canning, might be identified using MS/MS by corresponding spectra to the high-quality reference

Proteomics for Comprehensive Analysis of Seafood Products

57

spectral libraries (Wulff et al., 2013). Besides, due to the thermostable parvalbumin, high-intensity focused ultrasound (HIFU) trypsin digestion and SMIM scanning mode in MS identified processed fish species (Carrera et al., 2011; Ortea et al., 2011). To assess spoilage factors and understand the spoilage mechanism of microorganisms in fish after storage, a comparative proteomics was used. Researchers could identify spoilage-related factors of Shewanella putrefaciens in refrigerated Bigeye tuna (Thunnus obesus) and reveal its strong spoilage potential in terms of protein degradation and tissue structure destruction (Yi and Xie, 2021). This kind of research can provide a basis for the development of novel preservatives. These are just a few examples where proteomics has been utilized to validate and improve processing and storage methods for seafood. With ongoing advancements in proteomics technology, there is immense potential to delve deeper and establish optimal and customized processing methods tailored to the unique characteristics of seafood products. This will enable us to better understand the intricate biochemical processes involved and ensure the preservation of quality attributes, nutritional value, and safety standards, ultimately delivering superior seafood products to consumers.

3.5  CHALLENGES AND FUTURE PERSPECTIVES The search for technological innovations that can enhance food production and quality, while protecting the environment, is intensifying by the reasons referred to in this chapter. The ever-evolving field of proteomics has emerged as a powerful tool to address the obstacles encountered in seafood analysis. The analysis of seafood has, however, been a complex and challenging task for the food industry due to the diverse nature of seafood matrices and the presence of various contaminants that pose a significant risk to human health. Proteomics studies have an advantage over genomic studies when it comes to understanding the function and activity of proteins, as the proteome is constantly changing due to PTMs that can significantly alter a protein’s activity and function. However, interpreting proteomics results requires information at the genomic level, which can be challenging in seafood analysis due to the limited number of seafood species with fully sequenced genomes. Consequently, protein identification is mostly made through homology searches as opposed to direct genomic information (Almeida et al., 2015). This impedes the establishment of available proteomes for seafood products, resulting in a slower progression compared with genome sequencing. The reasoning relates to (i) the high reliance on sequenced genomes, (ii) the greater complexity of protein structures, functions, and interactions compared with nucleic acids, (iii) the larger number of protein species in an organism compared with genes, and (iv) the lack of a protein amplification method, and the high cost of certain techniques, instruments, and specialized staff for maintenance, collectively still hindering proteomics development. Nonetheless, recent research consortia, such as the FA1002 – Farm Animal Proteomics COST action (http://www.cost-faproteomics.org/) and the PRIME-XS initiative (http://www.primexs.eu/), are actively tackling these challenges and driving the global adoption and dissemination of proteomics advancements in this domain. These initiatives aim to broaden the scope of species studied, develop specialized quantitative proteomics techniques, address sample complexity and variability, investigate PTMs, integrate data with other omics disciplines, establish standardized protocols, and enhance bioinformatics tools. By pursuing these endeavors, a more comprehensive understanding of seafood product quality and safety can be achieved, resulting in mutual benefits for the seafood industry and consumers.

3.5.1 Systems Biology and Proteomics The integration of proteomics data with other omics disciplines represents indeed a promising avenue for advancing our understanding of seafood products and deserves to be elaborated further. To further enhance the complex realm of seafood analysis, the adoption of a novel approach known as systems biology has gained significant traction in the last decade. Systems biology complements the insights from proteomics by integrating its information with other cutting-edge disciplines.

58

Handbook of Seafood and Seafood Products Analysis

To gain a comprehensive understanding of the intricate workings of living systems, comparable to understanding “how a car drives itself,” it is becoming evident that a thorough and quantitative comprehension of the genome, transcriptome, proteome, and metabolome is essential. This integrated approach can unravel the underlying chemical dynamics of living systems and pave the way for deeper insights into their functioning. The current escalating integration of these omics in life sciences has propelled rapid advancements in applications and their validation. The significant technological breakthroughs and reduction in the complexity analysis have catalyzed these technologies into the mainstream, making them more accessible and widely adopted in various scientific disciplines (Ferri et al., 2015; Misra et al., 2018). Comprehensively, unlike classical approaches, multiomics methodologies offer a comprehensive understanding of the entire farm-to-fork process, providing dynamic insights beyond a static snapshot. Instead of focusing on a single data type, these novel approaches integrate and analyze data from different molecular layers, such as gene variants and DNA methylation data, expressed genes and non-coding ribonucleic acid (RNA), protein abundance and PTMs, and levels of metabolites. This combination allows for a more holistic understanding of the intricate network of biological processes and their multi-level regulation within an organism, providing a more accurate picture of its physiological state (Pinu et al., 2019; Raposo de Magalhães et al., 2020b). In aquatic animal production industries such as aquaculture, this integrative approach holds immense potential in elucidating the intricate relationships between nutrition, health, and disease (Stentiford et al., 2005; Karlsen et al., 2011; Wen et al., 2019; Dale et al., 2020; Roh et al., 2020; Lazado et al., 2021; Natnan et al., 2021; Colás-Ruiz et al., 2022; Li et al., 2022; Liu et al., 2022; Zhang et al., 2022a; ColásRuiz et al., 2023). Moreover, multiomics insights into microbial communities along the food supply chain and personalized nutrition strategies and interventions tailored to species’ needs are just a few more examples with implications for human and animal health (Cook and Nightingale, 2018; Power et al., 2023). In the realm of seafood product analysis, the application of systems biology principles and proteomics techniques may allow identifying novel markers for quality assessment and microbiological spoilage fingerprints. Accordingly, Cook and Nightingale (2018) forward the development of molecular fingerprints and patterns to gain comprehensive insights into the effects of conventional and novel food processing technologies on microorganisms, product structure, color, texture, enzymatic alterations, and overall food quality. Multiomics technologies are yet highly valuable for assessing perishable food products by identifying and detecting pathogenic bacteria associated with foodborne diseases and spoilage in fish products (Tsironi et al., 2019a, b). It is stated that the nextgeneration sequencing holds promise in facilitating the identification of suitable biomarkers for the prevention of foodborne poisoning outbreaks. Assessing these biomarkers following a multi-level approach with targeted proteomics and transcriptomics can increase the accuracy of the identified biomarkers, which potentially reduce the incidence of fish product recalls and enhance consumer confidence, as suggested by Kumar et al. (2020). Additionally, the impacts of processing and storage on food components such as proteins, carbohydrates, and fats were also addressed through systems biology (Carrera et al., 2013; Ferri et al., 2015), while the effects of trace metals on field-growing oysters and important information on evaluating the ecotoxicological risks for its consumption were assessed through integrated transcriptomics and proteomics (Li and Wang, 2021). Multiomics data may promote further advancements in protein or peptide array technologies, laboratory-on-chip devices, and biosensors. These innovations enable high-throughput measurements, primarily driven by information derived from MS, facilitating the simultaneous determination of specific protein or peptide biomarkers, their presence or absence, and their quantitative levels (Lafleur et al., 2016). By harnessing these cutting-edge technologies, seafood and fish products can be more rapidly and accurately assessed, ensuring enhanced quality control in food laboratories. Apart from the challenges inherent to each single omics type, multiomics data naturally increase in complexity, mainly related to the high dimensionality of the resulting data, which consequently increases the computational requirements, and also its diversity, i.e., different data types, number

Proteomics for Comprehensive Analysis of Seafood Products

59

of features, and scaling, which often makes comparison and normalization more difficult. Such advances in bioinformatics and computational proteomics will likely be powered increasingly by machine-learning technologies while retaining their ability to generate biological insights (Aebersold and Mann, 2016). A number of different bioinformatics approaches have already been developed to facilitate the integration of different omics data and extract significant combinations of features, such as the Web-based platforms OmicsAnalyst and OmicsNet 2.0, and several algorithms within the R Environment such as the Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), Multi-Omics Factor Analysis (MOFA), and the Joint and Individual Variation Explained (JIVE). A recent review has addressed these approaches in aquatic organisms, especially in aquaculture species (Canellas et al., 2022), and another one focused on the identification and validation of novel markers for quality assessment and microbiological spoilage fingerprints (Power et al., 2023). Although the application of these technologies in the marine field is yet to mature, systems biology, bolstered by the integrative power of proteomics, holds tremendous promise in transforming our understanding of seafood products and revolutionizing the field of nutrition.

3.6  FINAL REMARKS Seafood holds a critical position in ensuring food security, and its importance is projected to grow in the future. To ensure a future where seafood is not only safe and of high quality but also beneficial for health, economy, and trade, responsible seafood production must take precedence. In light of ongoing concerns regarding food safety, the integration of proteomics approaches into risk assessment strategies for seafood analysis and processing is vital for a comprehensive evaluation of potential risks and benefits associated with seafood production. The field of proteomics, driven by remarkable advancements in genomics, possesses the necessary tools to ensure consumers’ safety and the sustainability of this food sector, as we demonstrated in this chapter. However, while significant progress has been made in human proteomics, encompassing various aspects such as PTMs, splice variants, protein interactions, and functional annotations, the study of the seafood proteome still lags behind. Efforts must be made to improve the curation and annotation of seafood proteins in publicly available reference databases. While the journey toward a comprehensive understanding of the proteome of seafood products may be challenging and time-consuming, it is also exciting. Nevertheless, ongoing advancements in high-throughput MS technology and analytical and computational methods position proteomics as an indispensable tool in seafood research. Proteomics is poised to become an even more valuable tool in seafood research, particularly when combined with other omics-based techniques and emerging technologies such as biosensors. Current endeavors strive to expand systems biology databases and enhance the representation of diverse species, enabling more comprehensive and accurate identification of genes, proteins, and metabolites in seafood research. Scientists play a vital role in translating scientific knowledge into tangible benefits for the food industry, policymakers, consumers, and the environment. As a closing remark, by harnessing the power of proteomics and fostering interdisciplinary collaborations, we can envision a future where individuals can proactively manage their health through tailored dietary approaches based on their unique molecular profiles. Ultimately, this has the potential to revolutionize our approach to human health, leading to improved outcomes and a healthier society as a whole.

REFERENCES Abril, A.G., Calo-Mata, P., Böhme, K., Villa, T.G., Barros-Velázquez, J., Pazos, M., Carrera, M., 2023. Shotgun proteomics analysis, functional networks, and peptide biomarkers for seafood-originating biogenicamine-producing bacteria. International Journal of Molecular Sciences 24, 7704.

60

Handbook of Seafood and Seafood Products Analysis

Abril, A.G., Villa, T.G., Calo-Mata, P., Barros-Velázquez, J., Carrera, M., 2022. Application of proteomics to the identification of foodborne pathogens, in: Pedrouso, M.L., Ruiz, D.F., Lorenzo, J.M. (Eds.), Food Proteomics, Academic Press, Cambridge, Massachusetts. pp. 337–362. Addis, M.F., Cappuccinelli, R., Tedde, V., Pagnozzi, D., Porcu, M.C., Bonaglini, E., Roggio, T., Uzzau, S., 2010. Proteomic analysis of muscle tissue from gilthead sea bream (Sparus aurata, L.) farmed in offshore floating cages. Aquaculture 309, 245–252. Aebersold, R., Mann, M., 2016. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355. Afzaal, M., Saeed, F., Hussain, M., Shahid, F., Siddeeg, A., Al-Farga, A., 2022. Proteomics as a promising biomarker in food authentication, quality and safety: A review. Food Science & Nutrition 10, 2333–2346. Agregán, R., Echegaray, N., López-Pedrouso, M., Kharabsheh, R., Franco, D., Lorenzo, J.M., 2021. Proteomic advances in milk and dairy products. Molecules (Basel, Switzerland) 26, 3832. Ahmed, F., Kumar, G., Soliman, F.M., Adly, M.A., Soliman, H.A.M., El-Matbouli, M., Saleh, M., 2019. Proteomics for understanding pathogenesis, immune modulation and host pathogen interactions in aquaculture. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 32, 100625. Alban, A., David, S.O., Bjorkesten, L., Andersson, C., Sloge, E., Lewis, S., Currie, I., 2003. A novel experimental design for comparative two-dimensional gel analysis: Two-dimensional difference gel electrophoresis incorporating a pooled internal standard. Proteomics 3, 36–44. Almeida, A.M., Bassols, A., Bendixen, E., Bhide, M., Ceciliani, F., Cristobal, S., Eckersall, P.D., Hollung, K., Lisacek, F., Mazzucchelli, G., McLaughlin, M., Miller, I., Nally, J.E., Plowman, J., Renaut, J., Rodrigues, P., Roncada, P., Staric, J., Turk, R., 2015. Animal board invited review: Advances in proteomics for animal and food sciences. Animal: An International Journal of Animal Bioscience 9, 1–17. Anand, S., Samuel, M., Ang, C.-S., Keerthikumar, S., Mathivanan, S., 2017. Label-based and label-free strategies for protein quantitation, in: Keerthikumar, S., Mathivanan, S. (Eds.), Proteome Bioinformatics, Springer, New York, pp. 31–43. Aragão, C., Gonçalves, A.T., Costas, B., Azeredo, R., Xavier, M.J., Engrola, S., 2022. Alternative proteins for fish diets: Implications beyond growth. Animals 12, 1211. Araújo, M.J., Sousa, M.L., Felpeto, A.B., Turkina, M.V., Fonseca, E., Martins, J.C., Vasconcelos, V., Campos, A., 2021. Comparison of sample preparation methods for shotgun proteomic studies in aquaculture species. Proteomes 9, 46. Arshad, N., Samat, N., Lee, L.K., 2022. Insight into the relation between nutritional benefits of aquaculture products and its consumption hazards: A global viewpoint. Frontiers in Marine Science 9, 925463. Aslam, B., Basit, M., Nisar, M.A., Khurshid, M., Rasool, M.H., 2017. Proteomics: Technologies and their applications. Journal of Chromatographic Science 55, 182–196. Ayer, N., Côté, R.P., Tyedmers, P.H., Martin Willison, J.H., 2009. Sustainability of seafood production and consumption: An introduction to the special issue. Journal of Cleaner Production 17, 321–324. Bai, M., Tang, R., Li, G., She, W., Chen, G., Shen, H., Zhu, S., Zhang, H., Wu, H., 2022. High-throughput screening of 756 chemical contaminants in aquaculture products using liquid chromatography/quadrupole time-of-flight mass spectrometry. Food Chemistry: X 15, 100380. Banerjee, R., Maheswarappa, N.B., Mohan, K., Biswas, S., 2022. Proteomic approaches for authentication of foods of animal origin, in: Pedrouso, M.L., Ruiz, D.F., Lorenzo, J.M. (Eds.), Food Proteomics, Academic Press, Cambridge, Massachusetts, pp. 301–336. Berrini, A., Tepedino, V., Borromeo, V., Secchi, C., 2006. Identification of freshwater fish commercially labelled “perch” by isoelectric focusing and two-dimensional electrophoresis. Food Chemistry 96, 163–168. Bi, H., Zhong, C., Shao, M., Wang, C., Yi, J., Qiao, L., Zhang, J., 2019. Differentiation and authentication of fishes at the species level through analysis of fish skin by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry 33, 1336–1343. Bianchi, M., Hallström, E., Parker, R.W.R., Mifflin, K., Tyedmers, P., Ziegler, F., 2022. Assessing seafood nutritional diversity together with climate impacts informs more comprehensive dietary advice. Communications Earth & Environment 3, 188. Bilić, P., Kuleš, J., Galan, A., Gomes de Pontes, L., Guillemin, N., Horvatić, A., Festa Sabes, A., Mrljak, V., Eckersall, P.D., 2018. Proteomics in veterinary medicine and animal science: Neglected scientific opportunities with immediate impact. Proteomics 18, 1800047. Bintsis, T., 2017. Foodborne pathogens. AIMS Microbiology 3, 529–563. Böhme, K., Antelo, S.C., Fernández-No, I.C., Quintela-Baluja, M., Barros-Velázquez, J., Cañas, B., CaloMata, P., 2016. Detection of foodborne pathogens using MALDI-TOF mass spectrometry, in: BarrosVelázquez, J. (Ed.), Antimicrobial Food Packaging, Academic Press, San Diego, pp. 203–214.

Proteomics for Comprehensive Analysis of Seafood Products

61

Böhme, K., Fernández-No, I.C., Pazos, M., Gallardo, J.M., Barros-Velázquez, J., Cañas, B., Calo-Mata, P., 2013. Identification and classification of seafood-borne pathogenic and spoilage bacteria: 16S rRNA sequencing versus MALDI-TOF MS fingerprinting. Electrophoresis 34, 877–887. Böhme, K., Morandi, S., Cremonesi, P., Fernández No, I.C., Barros-Velázquez, J., Castiglioni, B., Brasca, M., Cañas, B., Calo-Mata, P., 2012. Characterization of Staphylococcus aureus strains isolated from Italian dairy products by MALDI-TOF mass fingerprinting. Electrophoresis 33, 2355–2364. Borràs, E., Sabidó, E., 2017. What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry. Proteomics 17, 1700180. Brausch, J.M., Rand, G.M., 2011. A review of personal care products in the aquatic environment: Environmental concentrations and toxicity. Chemosphere 82, 1518–1532. Brown, K.A., Melby, J.A., Roberts, D.S., Ge, Y., 2020. Top-down proteomics: Challenges, innovations, and applications in basic and clinical research. Expert Review of Proteomics 17, 719–733. Browning, H., 2023. Improving welfare assessment in aquaculture. Frontiers in Veterinary Science 10, 1060720. Burkhart, J.M., Schumbrutzki, C., Wortelkamp, S., Sickmann, A., Zahedi, R.P., 2012. Systematic and quantitative comparison of digest efficiency and specificity reveals the impact of trypsin quality on MS-based proteomics. Journal of Proteomics 75, 1454–1462. But, G.W.-C., Wu, H.-Y., Shaw, P.-C., 2019. Identification of fish species of sushi products in Hong Kong. Food Control 98, 164–173. Campos, A.M., de Almeida, A.M., 2016. Top-down proteomics and farm animal and aquatic sciences. Proteomes 4, 38. Canellas, A.L.B., Costa, W.F., Freitas-Silva, J., Lopes, I.R., de Oliveira, B.F.R., Laport, M.S., 2022. In sickness and in health: Insights into the application of omics in aquaculture settings under a microbiological perspective. Aquaculture 554, 738132. Carpenter, D.O., 2011. Health effects of persistent organic pollutants: The challenge for the Pacific Basin and for the world. Reviews on Environmental Health 26, 61–69. Carrera, M., Böhme, K., Gallardo, J.M., Barros-Velázquez, J., Cañas, B., Calo-Mata, P., 2017. Characterization of foodborne strains of Staphylococcus aureus by shotgun proteomics: Functional networks, virulence factors and species-specific peptide biomarkers. Frontiers in Microbiology 8, 2458. Carrera, M., Cañas, B., Gallardo, J.M., 2012. Rapid direct detection of the major fish allergen, parvalbumin, by selected MS/MS ion monitoring mass spectrometry. Journal of Proteomics 75, 3211–3220. Carrera, M., Cañas, B., Gallardo, J.M., 2013. Proteomics for the assessment of quality and safety of fishery products. Food Research International 54, 972–979. Carrera, M., Cañas, B., López-Ferrer, D., Piñeiro, C., Vázquez, J., Gallardo, J.M., 2011. Fast monitoring of species-specific peptide biomarkers using high-intensity-focused-ultrasound-assisted tryptic digestion and selected MS/MS ion monitoring. Analytical Chemistry 83, 5688–5695. Carrera, M., Cañas, B., Piñeiro, C., Vázquez, J., Gallardo, J.M., 2006. Identification of commercial hake and grenadier species by proteomic analysis of the parvalbumin fraction. Proteomics 6, 5278–5287. Carrera, M., Cañas, B., Piñeiro, C., Vázquez, J., Gallardo, J.M., 2007. De novo mass spectrometry sequencing and characterization of species-specific peptides from nucleoside diphosphate kinase B for the classification of commercial fish species belonging to the family Merlucciidae. Journal of Proteome Research 6, 3070–3080. Carrera, M., Cañas, B., Vázquez, J., Gallardo, J.M., 2010. Extensive de novo sequencing of new parvalbumin isoforms using a novel combination of bottom-up proteomics, accurate molecular mass measurement by FTICR-MS, and selected MS/MS ion monitoring. Journal of Proteome Research 9, 4393–4406. Carrera, M., Fidalgo, L.G., Saraiva, J.A., Aubourg, S.P., 2018. Effects of high-pressure treatment on the muscle proteome of hake by bottom-up proteomics. Journal of Agriculture and Food Chemistry 66, 4559–4570. Carrera, M., Gallardo, J.M., Pascual, S., González Á.F., Medina, I., 2016. Protein biomarker discovery and fast monitoring for the identification and detection of Anisakids by parallel reaction monitoring (PRM) mass spectrometry. Journal of Proteomics 142, 130–137. Carrera, M., González-Fernández, Á., Magadán, S., Mateos, J., Pedrós, L., Medina, I., Gallardo, J.M., 2019. Molecular characterization of B-cell epitopes for the major fish allergen, parvalbumin, by shotgun proteomics, protein-based bioinformatics and IgE-reactive approaches. Journal of Proteomics 200, 123–133. Carrera, M., Pazos, M., Gasset, M., 2020a. Proteomics-based methodologies for the detection and quantification of seafood allergens. Foods 9, 1134. Carrera, M., Piñeiro, C., Martinez, I., 2020b. Proteomic strategies to evaluate the impact of farming conditions on food quality and safety in aquaculture products. Foods 9, 1050.

62

Handbook of Seafood and Seafood Products Analysis

Cermakova, E., Lencova, S., Mukherjee, S., Horka, P., Vobruba, S., Demnerova, K., Zdenkova, K., 2023. Identification of fish species and targeted genetic modifications based on DNA analysis: State of the art. Foods 12, 228. Cerqueira, M., Farinha, A.P., Schrama, D., Moreira, M., Raposo de Magalhães, C., Rodrigues, P., 2020a. Fish, in: Nollet, L.M.L., Ötleş, S. (Eds.), Proteomics for Food Authentication, CRC Press, Boca Raton, Florida pp. 1977–1996. Cerqueira, M., Schrama, D., Silva, T.S., Colen, R., Engrola, S.A.D., Conceição, L.E.C., Rodrigues, P.M.L., Farinha, A.P., 2020b. How tryptophan levels in plant-based aquafeeds affect fish physiology, metabolism and proteome. Journal of Proteomics 221, 103782. Cheng, K., Chui, H., Domish, L., Hernandez, D., Wang, G., 2016. Recent development of mass spectrometry and proteomics applications in identification and typing of bacteria. Proteomics. Clinical Applications 10, 346–357. Chien, H.-J., Huang, Y.-H., Zheng, Y.-F., Wang, W.-C., Kuo, C.-Y., Wei, G.-J., Lai, C.-C., 2022. Proteomics for species authentication of cod and corresponding fishery products. Food Chemistry 374, 131631. Chiesa, L., Panseri, S., Pasquale, E., Malandra, R., Pavlovic, R., Arioli, F., 2018. Validated multiclass targeted determination of antibiotics in fish with high performance liquid chromatography-benchtop quadrupole orbitrap hybrid mass spectrometry. Food Chemistry 258, 222–230. Chiozzi, R.Z., Capriotti, A.L., Cavaliere, C., La Barbera, G., Montone, C.M., Piovesana, S., Laganà, A., 2018. Label-free shotgun proteomics approach to characterize muscle tissue from farmed and wild European Sea Bass (Dicentrarchus labrax). Food Analytical Methods 11, 292–301. Colás-Ruiz, N.R., Courant, F., Gomez, E., Lara-Martín, P.A., Hampel, M., 2023. Transcriptomic and metabolomic integration to assess the response of gilthead sea bream (Sparus aurata) exposed to the most used insect repellent: DEET. Environmental Pollution 316, 120678. Colás-Ruiz, N.R., Ramirez, G., Courant, F., Gomez, E., Hampel, M., Lara-Martín, P.A., 2022. Multi-omic approach to evaluate the response of gilt-head sea bream (Sparus aurata) exposed to the UV filter sulisobenzone. Science of the Total Environment 803, 150080. Cook, P.W., Nightingale, K.K., 2018. Use of omics methods for the advancement of food quality and food safety. Animal Frontiers 8, 33–41. Costa, J., Villa, C., Verhoeckx, K., Cirkovic-Velickovic, T., Schrama, D., Roncada, P., Rodrigues, P.M., Piras, C., Martín-Pedraza, L., Monaci, L., Molina, E., Mazzucchelli, G., Mafra, I., Lupi, R., Lozano-Ojalvo, D., Larré, C., Klueber, J., Gelencser, E., Bueno-Diaz, C., Diaz-Perales, A., Benedé, S., Bavaro, S.L., Kuehn, A., Hoffmann-Sommergruber, K., Holzhauser, T., 2022. Are physicochemical properties shaping the allergenic potency of animal allergens? Clinical Reviews in Allergy & Immunology 62, 1–36. Craig, R., Beavis, R.C., 2004. TANDEM: Matching proteins with tandem mass spectra. Bioinformatics (Oxford, England) 20, 1466–1467. Cui, M., Cheng, C., Zhang, L., 2022. High-throughput proteomics: A methodological mini-review. Laboratory Investigation 102, 1170–1181. Dale, K., Yadetie, F., Müller, M.B., Pampanin, D.M., Gilabert, A., Zhang, X., Tairova, Z., Haarr, A., LilleLangøy, R., Lyche, J.L., Porte, C., Karlsen, O.A., Goksøyr, A., 2020. Proteomics and lipidomics analyses reveal modulation of lipid metabolism by perfluoroalkyl substances in liver of Atlantic cod (Gadus morhua). Aquatic Toxicology 227, 105590. Danezis, G.P., Tsagkaris, A.S., Camin, F., Brusic, V., Georgiou, C.A., 2016. Food authentication: Techniques, trends & emerging approaches. TrAC Trends in Analytical Chemistry 85, 123–132. Darie-Ion, L., Whitham, D., Jayathirtha, M., Rai, Y., Neagu, A.-N., Darie, C.C., Petre, B.A., 2022. Applications of MALDI-MS/MS-based proteomics in biomedical research. Molecules (Basel, Switzerland) 27, 6196. Dasanayaka, B.P., Li, Z., Pramod, S.N., Chen, Y., Khan, M.U., Lin, H., 2022. A review on food processing and preparation methods for altering fish allergenicity. Critical Reviews in Food Science and Nutrition 62, 1951–1970. Daskalova, A., 2019. Farmed fish welfare: Stress, post-mortem muscle metabolism, and stress-related meat quality changes. International Aquatic Research 11, 113–124. Dempson, J.B., Power, M., 2004. Use of stable isotopes to distinguish farmed from wild Atlantic salmon, Salmo salar. Ecology of Freshwater Fish 13, 176–184. Deschamps, E., Calabrese, V., Schmitz, I., Hubert-Roux, M., Castagnos, D., Afonso, C., 2023. Advances in ultra-high-resolution mass spectrometry for pharmaceutical analysis. Molecules (Basel, Switzerland) 28, 2061. Di Girolamo, F., Muraca, M., Mazzina, O., Lante, I., Dahdah, L., 2015. Proteomic applications in food allergy: Food allergenomics. Current Opinion in Allergy and Clinical Immunology 15, 259–266.

Proteomics for Comprehensive Analysis of Seafood Products

63

Dietrich, M.A., Ciereszko, A., 2018. Proteomic characterization of fresh spermatozoa and supernatant after cryopreservation in relation to freezability of carp (Cyprinus carpio L) semen. PLoS One 13, e0192972. Diwan, A.D., Harke, S.N., Panche, A.N., 2022. Application of proteomics in shrimp and shrimp aquaculture. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 43, 101015. Doll, S., Burlingame, A.L., 2015. Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chemical Biology 10, 63–71. Duong, V.A., Lee, H., 2023. Bottom-up proteomics: Advancements in sample preparation. International Journal of Molecular Sciences 24, 5350. Dupree, E.J., Jayathirtha, M., Yorkey, H., Mihasan, M., Petre, B.A., Darie, C.C., 2020. A critical review of bottom-up proteomics: The good, the bad, and the future of this field. Proteomes 8, 14. Edelmann, M.J., 2011. Strong cation exchange chromatography in analysis of posttranslational modifications: Innovations and perspectives. Journal of Biomedicine and Biotechnology 2011, 936508. Elbashir, S., Parveen, S., Schwarz, J., Rippen, T., Jahncke, M., DePaola, A., 2018. Seafood pathogens and information on antimicrobial resistance: A review. Food Microbiology 70, 85–93. Elghoudi, A., Narchi, H., 2022. Food allergy in children-the current status and the way forward. World Journal of Clinical Pediatrics 11, 253–269. Eng, J.K., McCormack, A.L., Yates, J.R., 1994. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Journal of the American Society for Mass Spectrometry 5, 976–989. Esteki, M., Regueiro, J., Simal-Gándara, J., 2019. Tackling fraudsters with global strategies to expose fraud in the food chain. Comprehensive Reviews in Food Science and Food Safety 18, 425–440. Eurogroup for Animals, 2018. European Public Perceptions on Fish Welfare, Compassion in World Farming, ComRes. European Food Safety Authority and European Centre for Disease Prevention and Control, 2022. The European Union One Health 2021 Zoonoses Report. EFSA Journal 20, e07666. Fæste, C.K., Tartor, H., Moen, A., Kristoffersen, A.B., Dhanasiri, A.K.S., Anonsen, J.H., Furmanek, T., Grove, S., 2020. Proteomic profiling of salmon skin mucus for the comparison of sampling methods. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences 1138, 121965. Fan, G., Song, Y., Yang, L., Huang, X., Zhang, S., Zhang, M., Yang, X., Chang, Y., Zhang, H., Li, Y., 2020. Initial data release and announcement of the 10,000 Fish Genomes Project (Fish10K). GigaScience 9, giaa080. FAO, 2022. The State of World Fisheries and Aquaculture 2022. Towards Blue Transformation, FAO, Rome. Farinha, A.P., Schrama, D., Silva, T., Conceição, L.E.C., Colen, R., Engrola, S., Rodrigues, P., Cerqueira, M., 2021. Evaluating the impact of methionine-enriched diets in the liver of European seabass through labelfree shotgun proteomics. Journal of Proteomics 232, 104047. Fellows, P.J., 2009. Food processing technology, in: Fellows, P.J. (Ed.), Food processing technology: Principles and practice, Third Edition, Woodhead Publishing, Sawston, Cambridge, pp. 1–8. Feng, J., Zhang, L., Xia, X., Hu, W., Zhou, P., 2020. Effect of geographic variation on the proteome of sea cucumber (Stichopus japonicus). Food Research International 136, 109498. Fernández Sánchez, J.L., Le Breton, A., Brun, E., Vendramin, N., Spiliopoulos, G., Furones, D., Basurco, B., 2022. Assessing the economic impact of diseases in Mediterranean grow-out farms culturing European sea bass. Aquaculture 547, 737530. Ferrer, E.M., Giron-Nava, A., Aburto-Oropeza, O., 2022. Overfishing increases the carbon footprint of seafood production from small-scale fisheries. Frontiers in Marine Science 9, 768784. Ferri, E., Galimberti, A., Casiraghi, M., Airoldi, C., Ciaramelli, C., Palmioli, A., Mezzasalma, V., Bruni, I., Labra, M., 2015. Towards a universal approach based on omics technologies for the quality control of food. BioMed Research International 2015, 365794. Filipe, D., Gonçalves, M., Fernandes, H., Oliva-Teles, A., Peres, H., Belo, I., Salgado, J.M., 2023. Shelf-life performance of fish feed supplemented with bioactive extracts from fermented olive mill and winery byproducts. Foods 12, 305. Forné, I., Abián, J., Cerdà, J., 2010. Fish proteome analysis: Model organisms and non-sequenced species. Proteomics 10, 858–872. Gajahin Gamage, N.T., Miyashita, R., Takahashi, K., Asakawa, S., Senevirathna, J.D.M., 2022. Proteomic applications in aquatic environment studies. Proteomes 10, 32. Gallardo, J.M., Carrera, M., Ortea, I., 2013a. Proteomics in food science, in: Cifuentes, A. (Ed.), Foodomics: Advances Mass Spectrometry in Modern Food Science and Nutrition, John Wiley & Sons, Inc., Hoboken, New Jersey, pp. 125–165.

64

Handbook of Seafood and Seafood Products Analysis

Gallardo, J.M., Ortea, I., Carrera, M., 2013b. Proteomics and its applications for food authentication and foodtechnology research. TrAC Trends in Analytical Chemistry 52, 135–141. Gebriel, M., Prabhudesai, S., Uleberg, K.E., Larssen, E., Piston, D., Bjørnstad, A.H., Møller, S.G., 2014. Zebrafish brain proteomics reveals central proteins involved in neurodegeneration. Journal of Neuroscience Research 92, 104–115. Gerbig, S., Neese, S., Penner, A., Spengler, B., Schulz, S., 2017. Real-time food authentication using a miniature mass spectrometer. Analytical Chemistry 89, 10717–10725. Gevaert, K., Vandekerckhove, J., Vandekerckhove, J., 2011. Gel-Free Proteomics. Springer. Giusti, A., Tinacci, L., Sotelo, C.G., Acutis, P.L., Ielasi, N., Armani, A., 2019. Authentication of ready-to-eat anchovy products sold on the Italian market by BLAST analysis of a highly informative cytochrome b gene fragment. Food Control 97, 50–57. Gomes, T., Albergamo, A., Costa, R., Mondello, L., Dugo, G., 2017. Potential use of proteomics in shellfish aquaculture: From assessment of environmental toxicity to evaluation of seafood quality and safety. Current Organic Chemistry 21, 402–425. Gupta, K.R., Patle, U., Kabra, U., Mishra, P., Umekar, M.J., 2021. Analytical tools in protein structure determination. International Journal of Pharmaceutical Chemistry and Analysis 8, 103–111. Haider, A., Ringer, M., Kotroczó, Z., Mohácsi-Farkas, C., Kocsis, T., 2023. The current level of MALDITOF MS applications in the detection of microorganisms: A short review of benefits and limitations. Microbiology Research 14, 80–90. Haji, M., Kerbache, L., Al-Ansari, T., 2022. Food quality, drug safety, and increasing public health measures in supply chain management. Processes 10, 1715. Hassoun, A., Karoui, R., 2017. Quality evaluation of fish and other seafood by traditional and nondestructive instrumental methods: Advantages and limitations. Critical Reviews in Food Science and Nutrition 57, 1976–1998. Hassoun, A., Siddiqui, S.A., Smaoui, S., Ucak, İ., Arshad, R.N., Garcia-Oliveira, P., Prieto, M.A., Aït-Kaddour, A., Perestrelo, R., Câmara, J.S., Bono, G., 2022. Seafood processing, preservation, and analytical techniques in the age of Industry 4.0. Applied Sciences 12, 1703. Hathaway, D., Pandav, K., Patel, M., Riva-Moscoso, A., Singh, B.M., Patel, A., Min, Z.C., Singh-Makkar, S., Sana, M.K., Sanchez-Dopazo, R., Desir, R., Fahem, M.M.M., Manella, S., Rodriguez, I., Alvarez, A., Abreu, R., 2020. Omega 3 fatty acids and COVID-19: A comprehensive review. Infection & Chemotherapy 52, 478–495. Henriksson, P.J.G., Troell, M., Banks, L.K., Belton, B., Beveridge, M.C.M., Klinger, D.H., Pelletier, N., Phillips, M.J., Tran, N., 2021. Interventions for improving the productivity and environmental performance of global aquaculture for future food security. One Earth 4, 1220–1232. Hicks, D.T., 2016. Seafood safety and quality: The consumer’s role. Foods 5, 71. Hochberg, N.S., Hamer, D.H., Hughes, J.M., Wilson, M.E., 2010. Anisakidosis: Perils of the deep. Clinical Infectious Diseases 51, 806–812. Hoque, M.S., Jacxsens, L., De Meulenaer, B., Alam, A.K.M.N., 2016. Quantitative risk assessment for formalin treatment in fish preservation: Food safety concern in local market of Bangladesh. Procedia Food Science 6, 151–158. Hu, L., Zhang, H., Zhang, X., Zhang, T., Chang, Y., Zhao, X., Xu, J., Xue, Y., Li, Z., Wang, Y., Xue, C., 2018. Identification of peptide biomarkers for discrimination of shrimp species through SWATH-MS-based proteomics and chemometrics. Journal of Agricultural and Food Chemistry 66, 10567–10574. Jaiswal, S., Rasal, K.D., Chandra, T., Prabha, R., Iquebal, M.A., Rai, A., Kumar, D., 2023. Proteomics in fish health and aquaculture productivity management: Status and future perspectives. Aquaculture 566, 739159. Jeebhay, M.F., Moscato, G., Bang, B.E., Folletti, I., Lipińska-Ojrzanowska, A., Lopata, A.L., Pala, G., Quirce, S., Raulf, M., Sastre, J., Swoboda, I., Walusiak-Skorupa, J., Siracusa, A., 2019. Food processing and occupational respiratory allergy-An EAACI position paper. Allergy 74, 1852–1871. Jiang, X., Rao, Q., 2021. Effect of processing on fish protein antigenicity and allergenicity. Foods 10, 969. Karlsen, O.A., Bjørneklett, S., Berg, K., Brattås, M., Bohne-Kjersem, A., Grøsvik, B.E., Goksøyr, A., 2011. Integrative environmental genomics of Cod (Gadus morhua): The proteomics approach. Journal of Toxicology and Environmental Health. Part A 74, 494–507. Khalili Tilami, S., Sampels, S., 2018. Nutritional value of fish: Lipids, proteins, vitamins, and minerals. Reviews in Fisheries Science & Aquaculture 26, 243–253. Kim, D.-B., Cha, S.-S., 2019. A study on microbial contamination of foods exposed to multiple environments. The Korean Journal of Food & Health Convergence 5, 35–40.

Proteomics for Comprehensive Analysis of Seafood Products

65

Kim, H., Rebholz, C.M., Hegde, S., LaFiura, C., Raghavan, M., Lloyd, J.F., Cheng, S., Seidelmann, S.B., 2021. Plant-based diets, pescatarian diets and COVID-19 severity: A population-based case-control study in six countries. BMJ Nutrition, Prevention & Health 4, 257–266. Kobayashi, Y., Yang, T., Yu, C.T., Ume, C., Kubota, H., Shimakura, K., Shiomi, K., Hamada-Sato, N., 2016. Quantification of major allergen parvalbumin in 22 species of fish by SDS-PAGE. Food Chemistry 194, 345–353. Kubota, H., Kobayashi, A., Kobayashi, Y., Shiomi, K., Hamada-Sato, N., 2016. Reduction in IgE reactivity of Pacific mackerel parvalbumin by heat treatment. Food Chemistry 206, 78–84. Kuehn, A., Scheuermann, T., Hilger, C., Hentges, F., 2010. Important variations in parvalbumin content in common fish species: A factor possibly contributing to variable allergenicity. International Archives of Allergy and Immunology 153, 359–366. Kumar, V., Sinha, A.K., Uka, A., Antonacci, A., Scognamiglio, V., Mazzaracchio, V., Cinti, S., Arduini, F., 2020. Multi-potential biomarkers for seafood quality assessment: Global wide implication for human health monitoring. TrAC Trends in Analytical Chemistry 132, 116056. Kwon, Y.S., Park, C.-B., Lee, S.-M., Zee, S., Kim, G.-E., Kim, Y.-J., Sim, H.-J., Kim, J.-H., Seo, J.-S., 2023. Proteomic analysis of zebrafish (Danio rerio) embryos exposed to benzyl benzoate. Environmental Science and Pollution Research 30, 26375–26386. Lafleur, J.P., Jönsson, A., Senkbeil, S., Kutter, J.P., 2016. Recent advances in lab-on-a-chip for biosensing applications. Biosensors and Bioelectronics 76, 213–233. Laly, S.J., Sankar, T.V., Panda, S.K., 2019. Identification of allergic proteins of Flower tail shrimp (Metapenaeus dobsonii). Journal of Food Science and Technology 56, 5415–5421. Larbi, N.B., Jefferies, C., 2009. 2D-DIGE: Comparative proteomics of cellular signalling pathways. Methods in Molecular Biology (Clifton, N.J.) 517, 105–132. Lasch, P., Uhlig, S., Uhlig, C., Wilhelm, C., Bergmann, N., Wittke, S., 2019. Development and in-house validation of an LC-MS and LC-MS/MS assay for the determination of food fraud for different fish species. Journal of AOAC International 102, 1330–1338. Lazado, C.C., Timmerhaus, G., Breiland, M.W., Pittman, K., Hytterød, S., 2021. Multiomics provide insights into the key molecules and pathways involved in the physiological adaptation of Atlantic salmon (Salmo salar) to chemotherapeutic-induced oxidative stress. Antioxidants (Basel, Switzerland) 10, 1931. Lee, P.Y., Saraygord-Afshari, N., Low, T.Y., 2020. The evolution of two-dimensional gel electrophoresis - From proteomics to emerging alternative applications. Journal of Chromatography A 1615, 460763. Li, C., Chu, S., Tan, S., Yin, X., Jiang, Y., Dai, X., Gong, X., Fang, X., Tian, D., 2021. Towards Higher sensitivity of mass spectrometry: A perspective from the mass analyzers. Frontiers in Chemistry 9, 813359. Li, J., Zhang, G., Yin, D., Li, Y., Zhang, Y., Cheng, J., Zhang, K., Ji, J., Wang, T., Jia, Y., Yin, S., 2022. Integrated application of multiomics strategies provides insights into the environmental hypoxia response in Pelteobagrus vachelli muscle. Molecular & Cellular Proteomics: MCP 21, 100196. Li, T., Li, J., Hu, W., Chen, J., Li, H., 2014. Protein changes in post mortem large yellow croaker (Pseudosciaena crocea) monitored by SDS-PAGE and proteome analysis. Food Control 41, 49–55. Li, Y., Wang, W.-X., 2021. Integrated transcriptomics and proteomics revealed the distinct toxicological effects of multi-metal contamination on oysters. Environmental Pollution 284, 117533. Liu, S., Li, Z., Yu, B., Wang, S., Shen, Y., Cong, H., 2020. Recent advances on protein separation and purification methods. Advances in Colloid and Interface Science 284, 102254. Liu, Y., Yao, M., Li, S., Wei, X., Ding, L., Han, S., Wang, P., Lv, B., Chen, Z., Sun, Y., 2022. Integrated application of multi-omics approach and biochemical assays provides insights into physiological responses to saline-alkaline stress in the gills of crucian carp (Carassius auratus). Science of the Total Environment 822, 153622. Longo, S.B., Clark, B., York, R., Jorgenson, A.K., 2019. Aquaculture and the displacement of fisheries captures. Conservation Biology 33, 832–841. Lopata, A.L., Kleine-Tebbe, J., Kamath, S.D., 2016. Allergens and molecular diagnostics of shellfish allergy. Allergo Journal International 25, 210–218. López-Pedrouso, M., Varela, Z., Franco, D., Fernández, J.A., Aboal, J.R., 2020. Can proteomics contribute to biomonitoring of aquatic pollution? A critical review. Environmental Pollution (Barking, Essex: 1987) 267, 115473. López, J., Marina, A., Vázquez, J., Alvarez, G., 2002. A proteomic approach to the study of the marine mussels Mytilus edulis and M. galloprovincialis. Marine Biology 141, 217–223. Lu, Z., Wang, S., Ji, C., Li, F., Cong, M., Shan, X., Wu, H., 2020. iTRAQ-Based proteomic analysis on the mitochondrial responses in gill tissues of juvenile olive flounder Paralichthys olivaceus exposed to cadmium. Environmental Pollution 257, 113591.

66

Handbook of Seafood and Seafood Products Analysis

Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B.C., Aebersold, R., 2018. Data-independent acquisition-based SWATH-MS for quantitative proteomics: A tutorial. Molecular Systems Biology 14, e8126. Ma, B., Zhang, K., Hendrie, C., Liang, C., Li, M., Doherty-Kirby, A., Lajoie, G., 2003. PEAKS: Powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Communications in Mass Spectrometry: RCM 17, 2337–2342. Maesano, G., Di Vita, G., Chinnici, G., Pappalardo, G., #039, Amico, M., 2020. The role of credence attributes in consumer choices of sustainable fish products: A review. Sustainability 12, 10008. Manzoni, C., Kia, D.A., Vandrovcova, J., Hardy, J., Wood, N.W., Lewis, P.A., Ferrari, R., 2016. Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences. Briefings in Bioinformatics 19, 286–302. Marco-Ramell, A., de Almeida, A.M., Cristobal, S., Rodrigues, P., Roncada, P., Bassols, A., 2016. Proteomics and the search for welfare and stress biomarkers in animal production in the one-health context. Molecular Biosystems 12, 2024–2035. Marcus, K., Lelong, C., Rabilloud, T., 2020. What room for two-dimensional gel-based proteomics in a shotgun proteomics world? Proteomes 8, 17. Marinova, D., Bogueva, D., 2019. Planetary health and reduction in meat consumption. Sustainable Earth 2, 3. Martinez, I., Sánchez-Alonso, I., Piñeiro, C., Careche, M., Carrera, M., 2020. Protein signatures to trace seafood contamination and processing. Foods 9, 1751. Martinez, I., Šližytė, R., Daukšas, E., 2007. High resolution two-dimensional electrophoresis as a tool to differentiate wild from farmed cod (Gadus morhua) and to assess the protein composition of klipfish. Food Chemistry 102, 504–510. Matricardi, P.M., Kleine-Tebbe, J., Hoffmann, H.J., Valenta, R., Hilger, C., Hofmaier, S., Aalberse, R.C., Agache, I., Asero, R., Ballmer-Weber, B., Barber, D., Beyer, K., Biedermann, T., Bilò, M.B., Blank, S., Bohle, B., Bosshard, P.P., Breiteneder, H., Brough, H.A., Caraballo, L., Caubet, J.C., Crameri, R., Davies, J.M., Douladiris, N., Ebisawa, M., PA, E.I., Fernandez-Rivas, M., Ferreira, F., Gadermaier, G., Glatz, M., Hamilton, R.G., Hawranek, T., Hellings, P., Hoffmann-Sommergruber, K., Jakob, T., Jappe, U., Jutel, M., Kamath, S.D., Knol, E.F., Korosec, P., Kuehn, A., Lack, G., Lopata, A.L., Mäkelä, M., Morisset, M., Niederberger, V., Nowak-Węgrzyn, A.H., Papadopoulos, N.G., Pastorello, E.A., Pauli, G., Platts-Mills, T., Posa, D., Poulsen, L.K., Raulf, M., Sastre, J., Scala, E., Schmid, J.M., Schmid-Grendelmeier, P., van Hage, M., van Ree, R., Vieths, S., Weber, R., Wickman, M., Muraro, A., Ollert, M., 2016. EAACI molecular allergology user’s guide. Pediatr Allergy Immunology 23, 1–250. Mazzeo, M.F., Sorrentino, A., Gaita, M., Cacace, G., Di Stasio, M., Facchiano, A., Comi, G., Malorni, A., Siciliano, R.A., 2006. Matrix-assisted laser desorption ionization-time of flight mass spectrometry for the discrimination of food-borne microorganisms. Applied and Environmental Microbiology 72, 1180–1189. Medina, S., Pereira, J.A., Silva, P., Perestrelo, R., Câmara, J.S., 2019. Food fingerprints – A valuable tool to monitor food authenticity and safety. Food Chemistry 278, 144–162. Mejrhit, N., Azdad, O., El Kabbaoui, M., Ouahidi, I., Tazi, A., Aarab, L., 2017. Sensitivity of Moroccans to sardine parvalbumin and effect of heating and enzymatic treatments. Food and Agricultural Immunology 28, 1362–1373. Misra, B.B., Langefeld, C.D., Olivier, M., Cox, L.A., 2018. Integrated omics: Tools, advances, and future approaches. Journal of Molecular Endocrinology 62, JME-18-0055. Moreira, M., Schrama, D., Farinha, A.P., Cerqueira, M., Raposo de Magalhães, C., Carrilho, R., Rodrigues, P., 2021. Fish pathology research and diagnosis in aquaculture of farmed fish; a proteomics perspective. Animals: An Open Access Journal from MDPI 11, 125. Moreira, M., Schrama, D., Soares, F., Wulff, T., Pousão-Ferreira, P., Rodrigues, P., 2017. Physiological responses of reared sea bream (Sparus aurata Linnaeus, 1758) to an Amyloodinium ocellatum outbreak. Journal of Fish Diseases 40, 1545–1560. Morro, B., Doherty, M.K., Balseiro, P., Handeland, S.O., MacKenzie, S., Sveier, H., Albalat, A., 2020. Plasma proteome profiling of freshwater and seawater life stages of rainbow trout (Oncorhynchus mykiss). PloS One 15, e0227003. Mukherjee, S., Horka, P., Zdenkova, K., Cermakova, E., 2023. Parvalbumin: A major fish allergen and a forensically relevant marker. Genes 14, 123. Muth, T., Renard, B.Y., 2017. Evaluating de novo sequencing in proteomics: Already an accurate alternative to database-driven peptide identification? Briefings in Bioinformatics 19, 954–970. Nanou, E., Kotsiri, M., Kogiannou, D., Katsouli, M., Grigorakis, K., 2023. Consumer perception of freshness and volatile composition of fresh gilthead seabream and seabass in active packaging with and without CO2-emitting pads. Foods 12, 505. Naryzhny, S., 2016. Towards the full realization of 2DE power. Proteomes 4, 33.

Proteomics for Comprehensive Analysis of Seafood Products

67

Natnan, M.E., Low, C.-F., Chong, C.-M., Bunawan, H., Baharum, S.N., 2021. Integration of omics tools for understanding the fish immune response due to microbial challenge. Frontiers in Marine Science 8, 668771. Neagu, A.-N., Jayathirtha, M., Baxter, E., Donnelly, M., Petre, B.A., Darie, C.C., 2022. Applications of tandem mass spectrometry (MS/MS) in protein analysis for biomedical research. Molecules (Basel, Switzerland) 27, 2411. Nessen, M.A., van der Zwaan, D.J., Grevers, S., Dalebout, H., Staats, M., Kok, E., Palmblad, M., 2016. Authentication of closely related fish and derived fish products using tandem mass spectrometry and spectral library matching. Journal of Agriculture and Food Chemistry 64, 3669–3677. Neverova, I., Van Eyk, J.E., 2005. Role of chromatographic techniques in proteomic analysis. Journal of Chromatography B 815, 51–63. Nissa, M.U., Pinto, N., Parkar, H., Goswami, M., Srivastava, S., 2021. Proteomics in fisheries and aquaculture: An approach for food security. Food Control 127, 108125. Nonthawong, K., Srisomsap, C., Chokchaichamnankit, D., Svasti, J., Phiriyangkul, P., 2023. Comparative proteomics and in silico allergenicity of fresh and powdered skipjack tuna and Nile tilapia. Food Control 144, 109345. Nugraha, R., Kamath, S.D., Johnston, E., Zenger, K.R., Rolland, J.M., O’Hehir, R.E., Lopata, A.L., 2018. Rapid and comprehensive discovery of unreported shellfish allergens using large-scale transcriptomic and proteomic resources. Journal of Allergy and Clinical Immunology 141, 1501–1504.e1508. Nynca, J., Arnold, G., Fröhlich, T., Ciereszko, A., 2017. Proteomic identification of rainbow trout blood plasma proteins and their relationship to seminal plasma proteins. Proteomics 17, 1600460. Ocaña-González, J.A., Villar-Navarro, M., Ramos-Payán, M., Fernández-Torres, R., Bello-López, M.A., 2015. New developments in the extraction and determination of parabens in cosmetics and environmental samples. A review. Analytica Chimica Acta 858, 1–15. Orisasona, O., Ajani, E.K., Jenyo-Oni, Olanrewaju, N.A., 2016. Effects of fasting period on post-harvest flesh quality of Clarias gariepinus. Tropical Animal Production Investigation 19, 26–32. Ortea, I., Cañas, B., Calo-Mata, P., Barros-Velázquez, J., Gallardo, J.M., 2010a. Identification of commercial prawn and shrimp species of food interest by native isoelectric focusing. Food Chemistry 121, 569–574. Ortea, I., Cañas, B., Gallardo, J.M., 2009. Mass spectrometry characterization of species-specific peptides from arginine kinase for the identification of commercially relevant shrimp species. Journal of Proteome Research 8, 5356–5362. Ortea, I., Cañas, B., Gallardo, J.M., 2011. Selected tandem mass spectrometry ion monitoring for the fast identification of seafood species. Journal of Chromatography A 1218, 4445–4451. Ortea, I., Rodríguez, A., Tabilo-Munizaga, G., Pérez-Won, M., Aubourg, S.P., 2010b. Effect of hydrostatic high-pressure treatment on proteins, lipids and nucleotides in chilled farmed salmon (Oncorhynchus kisutch) muscle. European Food Research and Technology 230, 925–934. Paolacci, S., Mendes, R., Klapper, R., Velasco, A., Ramilo-Fernandez, G., Muñoz-Colmenero, M., Potts, T., Martins, S., Avignon, S., Maguire, J., De Paz, E., Johnson, M., Denis, F., Pardo, M.A., McElligott, D., Sotelo, C.G., 2021. Labels on seafood products in different European countries and their compliance to EU legislation. Marine Policy 134, 104810. Pardo, M.Á., Jiménez, E., Viðarsson, J.R., Ólafsson, K., Ólafsdóttir, G., Daníelsdóttir, A.K., Pérez-Villareal, B., 2018. DNA barcoding revealing mislabeling of seafood in European mass caterings. Food Control 92, 7–16. Pepe, T., Ceruso, M., Carpentieri, A., Ventrone, I., Amoresano, A., Anastasio, A., 2010. Proteomics analysis for the identification of three species of Thunnus. Veterinary Research Communications 34(Suppl 1), S153–S155. Pérez-Sánchez, R., Carnero-Morán, A., Luz Valero, M., Oleaga, A., 2022. A proteomics informed by transcriptomics insight into the proteome of Ornithodoros erraticus adult tick saliva. Parasites & Vectors 15, 1. Perez‐Riverol, Y., Alpi, E., Wang, R., Hermjakob, H., Vizcaíno, J.A., 2015. Making proteomics data accessible and reusable: Current state of proteomics databases and repositories. Proteomics 15, 930–950. Perkins, D.N., Pappin, D.J., Creasy, D.M., Cottrell, J.S., 1999. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567. Piñeiro, C., Barros-Velázquez, J., Sotelo, C.G., Pérez-Martín, R.I., Gallardo, J.M., 1998. Two-Dimensional electrophoretic study of the water-soluble protein fraction in white muscle of gadoid fish species. Journal of Agricultural and Food Chemistry 46, 3991–3997. Piñeiro, C., Martinez, I., 2013. Evaluation of fish quality and safety by proteomics techniques, in: Toldrá, F., Nollet, L.M.L. (Eds.), Proteomics in Foods: Principles and Applications, Springer US, Boston, MA, pp. 161–180.

68

Handbook of Seafood and Seafood Products Analysis

Piñeiro, C., Vázquez, J., Marina, A.I., Barros-Velázquez, J., Gallardo, J.M., 2001. Characterization and partial sequencing of species-specific sarcoplasmic polypeptides from commercial hake species by mass spectrometry following two-dimensional electrophoresis. Electrophoresis 22, 1545–1552. Pinto, P.I., Anjos, L., Estêvão, M.D., Santos, S., Santa, C., Manadas, B., Monsinjon, T., Canário, A.V.M., Power, D.M., 2022. Proteomics of sea bass skin-scales exposed to the emerging pollutant fluoxetine compared to estradiol. Science of the Total Environment 814, 152671. Pinu, F.R., Beale, D.J., Paten, A.M., Kouremenos, K., Swarup, S., Schirra, H.J., Wishart, D., 2019. Systems biology and multi-omics integration: Viewpoints from the metabolomics research community. Metabolites 9, 76. Piovesana, S., Capriotti, A.L., Caruso, G., Cavaliere, C., La Barbera, G., Zenezini Chiozzi, R., Laganà, A., 2016. Labeling and label free shotgun proteomics approaches to characterize muscle tissue from farmed and wild gilthead sea bream (Sparus aurata). Journal of Chromatography A 1428, 193–201. Piras, C., Roncada, P., Rodrigues, P.M., Bonizzi, L., Soggiu, A., 2016. Proteomics in food: Quality, safety, microbes, and allergens. Proteomics 16, 799–815. Pitt, J.J., 2009. Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. The Clinical Biochemist. Reviews 30, 19–34. Power, D.M., Taoukis, P., Houhoula, D., Tsironi, T., Flemetakis, E., 2023. Integrating omics technologies for improved quality and safety of seafood products. Aquaculture and Fisheries 8, 457–462. Raposo de Magalhães, C., Farinha, A.P., Blackburn, G., Whitfield, P.D., Carrilho, R., Schrama, D., Cerqueira, M., Rodrigues, P.M., 2022. Gilthead seabream liver integrative proteomics and metabolomics analysis reveals regulation by different prosurvival pathways in the metabolic adaptation to stress. International Journal of Molecular Sciences 23, 15395. Raposo de Magalhães, C., Farinha, A.P., Carrilho, R., Schrama, D., Cerqueira, M., Rodrigues, P.M., 2023. A new window into fish welfare: A proteomic discovery study of stress biomarkers in the skin mucus of gilthead seabream (Sparus aurata). Journal of Proteomics 281, 104904. Raposo de Magalhães, C., Schrama, D., Farinha, A.P., Revets, D., Kuehn, A., Planchon, S., Rodrigues, P.M., Cerqueira, M., 2020a. Protein changes as robust signatures of fish chronic stress: A proteomics approach to fish welfare research. BMC Genomics 21, 309. Raposo de Magalhães, C., Schrama, D., Nakharuthai, C., Boonanuntanasarn, S., Revets, D., Planchon, S., Kuehn, A., Cerqueira, M., Carrilho, R., Farinha, A.P., Rodrigues, P.M., 2021. Metabolic plasticity of gilthead seabream under different stressors: Analysis of the stress responsive hepatic proteome and gene expression. Frontiers in Marine Science 8, 676189. Raposo de Magalhães, C.S.F., Cerqueira, M.A.C., Schrama, D., Moreira, M.J.V., Boonanuntanasarn, S., Rodrigues, P.M.L., 2020b. A proteomics and other omics approach in the context of farmed fish welfare and biomarker discovery. Reviews in Aquaculture 12, 122–144. Reilly, A., 2018. Overview of Food Fraud in the Fisheries Sector. FAO Fisheries and Aquaculture Circular I-21. FAO, Rome. Richards, A.L., Eckhardt, M., Krogan, N.J., 2021. Mass spectrometry-based protein-protein interaction networks for the study of human diseases. Molecular Systems Biology 17, e8792. Riva, C., Cristoni, S., Binelli, A., 2012. Effects of triclosan in the freshwater mussel Dreissena polymorpha: A proteomic investigation. Aquatic Toxicology (Amsterdam, Netherlands) 118–119, 62–71. Roberts, R.J., Agius, C., Saliba, C., Bossier, P., Sung, Y.Y., 2010. Heat shock proteins (chaperones) in fish and shellfish and their potential role in relation to fish health: A review. Journal of Fish Diseases 33, 789–801. Rocha, C.P., Cabral, H.N., Marques, J.C., Gonçalves, A.M.M., 2022. A global overview of aquaculture food production with a focus on the activity’s development in transitional systems—The case study of a South European Country (Portugal). Journal of Marine Science and Engineering 10, 417. Rodrigues, P.M., Martin, S.A.M., Silva, T.S., Boonanuntanasarn, S., Schrama, D., Moreira, M., Raposo, C., 2018 Proteomics in fish and aquaculture research, in: de Almeida, A.M., Eckersall, D., Miller, I. (Eds.), Proteomics in Domestic Animals: From Farm to Systems Biology, Springer International Publishing AG, Midtown Manhattan, New York City, pp. 311–338. Rodrigues, P.M., Schrama, D., Campos, A., Osório, H., Freitas, M., 2016. Applications of proteomics in aquaculture, in: Salekdeh, G.H. (Ed.), Agricultural Proteomics Volume 1: Crops, Horticulture, Farm Animals, Food, Insect and Microorganisms, Springer International Publishing, Cham, pp. 175–209. Rodrigues, P.M., Silva, T.S., Dias, J., Jessen, F., 2012. Proteomics in aquaculture: Applications and trends. Journal of Proteomics 75, 4325–4345. Rodriguez-Salvador, B., Calvo Dopico, D., 2023. Differentiating fish products: Consumers’ preferences for origin and traceability. Fisheries Research 262, 106682.

Proteomics for Comprehensive Analysis of Seafood Products

69

Roh, H., Kim, A., Kim, N., Lee, Y., Kim, D.H., 2020. Multi-omics analysis provides novel insight into immunophysiological pathways and development of thermal resistance in rainbow trout exposed to acute thermal stress. International Journal of Molecular Sciences 21, 9198. Roobab, U., Fidalgo, L.G., Arshad, R.N., Khan, A.W., Zeng, X.-A., Bhat, Z.F., Bekhit, A.E.-D.A., Batool, Z., Aadil, R.M., 2022. High-pressure processing of fish and shellfish products: Safety, quality, and research prospects. Comprehensive Reviews in Food Science and Food Safety 21, 3297–3325. Rozanova, S., Barkovits, K., Nikolov, M., Schmidt, C., Urlaub, H., Marcus, K., 2021. Quantitative mass spectrometry-based proteomics: An overview, in: Marcus, K., Eisenacher, M., Sitek, B. (Eds.), Quantitative Methods in Proteomics, Springer US, New York, pp. 85–116. Ruethers, T., Taki, A.C., Johnston, E.B., Nugraha, R., Le, T.T.K., Kalic, T., McLean, T.R., Kamath, S.D., Lopata, A.L., 2018. Seafood allergy: A comprehensive review of fish and shellfish allergens. Molecular Immunology 100, 28–57. Saelens, G., Planckaert, S., Martínez-Sernández, V., Ubeira, F.M., Devreese, B., Gabriël, S., 2022. Targeted proteomics and specific immunoassays reveal the presence of shared allergens between the zoonotic nematodes Anisakis simplex and Pseudoterranova decipiens. Scientific Reports 12, 4127. Saleh, E.S.E., Tawfeek, S.S., Abdel-Fadeel, A.A.A., Abdel-Daim, A.S.A., Abdel-Razik, A.H., Youssef, I.M.I., 2022. Effect of dietary protease supplementation on growth performance, water quality, blood parameters and intestinal morphology of Nile tilapia (Oreochromis niloticus). Journal of Animal Physiology and Animal Nutrition 106, 419–428. Saleh, M., Kumar, G., Abdel-Baki, A.-A.S., Dkhil, M.A., El-Matbouli, M., Al-Quraishy, S., 2019. Quantitative proteomic profiling of immune responses to Ichthyophthirius multifiliis in common carp skin mucus. Fish & Shellfish Immunology 84, 834–842. Saptarshi, S.R., Sharp, M.F., Kamath, S.D., Lopata, A.L., 2014. Antibody reactivity to the major fish allergen parvalbumin is determined by isoforms and impact of thermal processing. Food Chemistry 148, 321–328. Schilling, M.W., Suman, S.P., Zhang, X., Nair, M.N., Desai, M.A., Cai, K., Ciaramella, M.A., Allen, P.J., 2017. Proteomic approach to characterize biochemistry of meat quality defects. Meat Science 132, 131–138. Schrama, D., Czolk, R., Raposo de Magalhães, C., Kuehn, A., Rodrigues, P.M., 2022a. Fish allergenicity modulation using tailored enriched diets–Where are we? Frontiers in Physiology 13, 897168. Schrama, D., Raposo de Magalhães, C., Cerqueira, M., Carrilho, R., Farinha, A.P., Rosa da Costa, A.M., Gonçalves, A., Kuehn, A., Revets, D., Planchon, S., Engrola, S., Rodrigues, P.M., 2022b. Effect of creatine and EDTA supplemented diets on European seabass (Dicentrarchus labrax) allergenicity, fish muscle quality and omics fingerprint. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 41, 100941. Schrama, D., Raposo de Magalhães, C., Cerqueira, M., Carrilho, R., Revets, D., Kuehn, A., Engrola, S., Rodrigues, P.M., 2022c. Fish processing and digestion affect parvalbumins detectability in gilthead seabream and European seabass. Animals 12, 3022. Shehata, H.R., Bourque, D., Steinke, D., Chen, S., Hanner, R., 2019. Survey of mislabelling across finfish supply chain reveals mislabelling both outside and within Canada. Food Research International (Ottawa, Ont.) 121, 723–729. Sheng, L., Wang, L., 2021. The microbial safety of fish and fish products: Recent advances in understanding its significance, contamination sources, and control strategies. Comprehensive Reviews in Food Science and Food Safety 20, 738–786. Sicherer, S.H., Muñoz-Furlong, A., Sampson, H.A., 2004. Prevalence of seafood allergy in the United States determined by a random telephone survey. The Journal of Allergy and Clinical Immunology 114, 159–165. Silva, T.S., Cordeiro, O.D., Matos, E.D., Wulff, T., Dias, J.P., Jessen, F., Rodrigues, P.M., 2012. Effects of preslaughter stress levels on the post-mortem sarcoplasmic proteomic profile of gilthead seabream muscle. Journal of Agricultural and Food Chemistry 60, 9443–9453. Singhal, N., Kumar, M., Kanaujia, P.K., Virdi, J.S., 2015. MALDI-TOF mass spectrometry: An emerging technology for microbial identification and diagnosis. Frontiers in Microbiology 6, 791. Sivanich, M.K., Gu, T.-J., Tabang, D.N., Li, L., 2022. Recent advances in isobaric labeling and applications in quantitative proteomics. Proteomics 22, 2100256. Sletten, G., Van Do, T., Lindvik, H., Egaas, E., Florvaag, E., 2010. Effects of industrial processing on the immunogenicity of commonly ingested fish species. International Archives of Allergy and Immunology 151, 223–236. Somkuti, J., Bublin, M., Breiteneder, H., Smeller, L., 2012. Pressure-temperature stability, Ca2+ binding, and pressure-temperature phase diagram of cod parvalbumin: Gad m 1. Biochemistry 51, 5903–5911.

70

Handbook of Seafood and Seafood Products Analysis

Speranza, B., Racioppo, A., Bevilacqua, A., Buzzo, V., Marigliano, P., Mocerino, E., Scognamiglio, R., Corbo, M.R., Scognamiglio, G., Sinigaglia, M., 2021. Innovative preservation methods improving the quality and safety of fish products: Beneficial effects and limits. Foods 10, 2854. Stahl, A., Schröder, U., 2017. Development of a MALDI-TOF MS-based protein fingerprint database of common food fish allowing fast and reliable identification of fraud and substitution. Journal of Agricultural and Food Chemistry 65, 7519–7527. Stentiford, G.D., Peeler, E.J., Tyler, C.R., Bickley, L.K., Holt, C.C., Bass, D., Turner, A.D., Baker-Austin, C., Ellis, T., Lowther, J.A., Posen, P.E., Bateman, K.S., Verner-Jeffreys, D.W., van Aerle, R., Stone, D.M., Paley, R., Trent, A., Katsiadaki, I., Higman, W.A., Maskrey, B.H., Devlin, M.J., Lyons, B.P., Hartnell, D.M., Younger, A.D., Bersuder, P., Warford, L., Losada, S., Clarke, K., Hynes, C., Dewar, A., Greenhill, B., Huk, M., Franks, J., Dal-Molin, F., Hartnell, R.E., 2022. A seafood risk tool for assessing and mitigating chemical and pathogen hazards in the aquaculture supply chain. Nature Food 3, 169–178. Stentiford, G.D., Viant, M.R., Ward, D.G., Johnson, P.J., Martin, A., Wenbin, W., Cooper, H.J., Lyons, B.P., Feist, S.W., 2005. Liver tumors in wild flatfish: A histopathological, proteomic, and metabolomic study. Omics: A Journal of Integrative Biology 9, 281–299. Sun, L., Lin, H., Li, Z., Sun, W., Wang, J., Wu, H., Ge, M., Ahmed, I., Pavase, T.R., 2019. Development of a method for the quantification of fish major allergen parvalbumin in food matrix via liquid chromatography-tandem mass spectrometry with multiple reaction monitoring. Food Chemistry 276, 358–365. Sussarellu, R., Suquet, M., Thomas, Y., Lambert, C., Fabioux, C., Pernet, M.E.J., Le Goïc, N., Quillien, V., Mingant, C., Epelboin, Y., 2016. Oyster reproduction is affected by exposure to polystyrene microplastics. Proceedings of the National Academy of Sciences 113, 2430–2435. Sveinsdóttir, H., Martin, S.A.M., Vilhelmsson, O.T., 2012. Application of proteomics to fish processing and quality, in: Simpson, B.K. (Ed.), Food Biochemistry and Food Processing, John Wiley & Sons, Inc., Hoboken, New Jersey, pp. 406–424. Terova, G., Addis, M.F., Preziosa, E., Pisanu, S., Pagnozzi, D., Biosa, G., Gornati, R., Bernardini, G., Roggio, T., Saroglia, M., 2011. Effects of postmortem storage temperature on sea bass (Dicentrarchus labrax) muscle protein degradation: Analysis by 2-D DIGE and MS. Proteomics 11, 2901–2910. Thomas, S.N., French, D., Jannetto, P.J., Rappold, B.A., Clarke, W.A., 2022. Liquid chromatography-tandem mass spectrometry for clinical diagnostics. Nature Reviews. Methods Primers 2, 96. Tiziana, P., Marina, C., Andrea, C., Iole, V., Angela, A., Aniello, A., Maria Luisa, C., 2012. Differentiation of four tuna species by two-dimensional electrophoresis and mass spectrometric analysis, in: Joshua, L.H., Christopher, J.P., Subject editors: Tsz-Kwong, M., Ricardo, J.F. (Eds.), Proteomic Applications in Biology, IntechOpen, Rijeka, Ch. 9, pp. 191–208. Tsironi, T., Anjos, L., Pinto, P.I., Dimopoulos, G., Santos, S., Santa, C., Manadas, B., Canario, A., Taoukis, P., Power, D., 2019a. High pressure processing of European sea bass (Dicentrarchus labrax) fillets and tools for flesh quality and shelf life monitoring. Journal of Food Engineering 262, 83–91. Tsironi, T., Lougovois, V., Simou, V.N., Mexi, A., Koussissis, S., Tsakali, E., Papatheodorou, S.A., Stefanou, V., Van Impe, J., Houhoula, D., 2019b. Next generation sequencing (NGS) for the determination of fish flesh microbiota. Journal of Food Research 8, 101–110. Tubaon, R.M., Haddad, P.R., Quirino, J.P., 2017. Sample clean-up strategies for ESI mass spectrometry applications in bottom-up proteomics: Trends from 2012 to 2016. Proteomics 17, 1700011. Uehara, S., Kobayashi, M., Kimura, K., Suzuki, J., Sadamasu, K., 2021. Rapid identification of histamineproducing bacteria isolated from fish using MALDI-TOF MS. Journal of Food Science and Technology 58, 4055–4061. Ulrich, S., Beindorf, P.M., Biermaier, B., Schwaiger, K., Gareis, M., Gottschalk, C., 2017. A novel approach for the determination of freshness and identity of trouts by MALDI-TOF mass spectrometry. Food Control 80, 281–289. United Nations Department of Economic and Social Affairs, P.D., 2022. World Population Prospects 2022: Summary of Results, UN DESA/POP/2022/TR/NO. 3. Valente, L.M.P., Cornet, J., Donnay-Moreno, C., Gouygou, J.P., Bergé, J.P., Bacelar, M., Escórcio, C., Rocha, E., Malhão, F., Cardinal, M., 2011. Quality differences of gilthead sea bream from distinct production systems in Southern Europe: Intensive, integrated, semi-intensive or extensive systems. Food Control 22, 708–717. Vandermeersch, G., Lourenço, H.M., Alvarez-Muñoz, D., Cunha, S., Diogène, J., Cano-Sancho, G., Sloth, J.J., Kwadijk, C., Barcelo, D., Allegaert, W., Bekaert, K., Fernandes, J.O., Marques, A., Robbens, J., 2015. Environmental contaminants of emerging concern in seafood – European database on contaminant levels. Environmental Research 143, 29–45.

Proteomics for Comprehensive Analysis of Seafood Products

71

Verbeke, W., Sioen, I., Brunsø, K., De Henauw, S., Van Camp, J., 2007. Consumer perception versus scientific evidence of farmed and wild fish: Exploratory insights from Belgium. Aquaculture International 15, 121–136. Verrez-Bagnis, V., Ladrat, C., Morzel, M., Noël, J., Fleurence, J., 2001. Protein changes in post mortem sea bass (Dicentrarchus labrax) muscle monitored by one- and two-dimensional gel electrophoresis. Electrophoresis 22, 1539–1544. Vihinen, M., 2001. Bioinformatics in proteomics. Biomolecular Engineering 18, 241–248. Vincent, E., Bilaver, L.A., Fierstein, J.L., Thivalapill, N., Pappalardo, A.A., Coleman, A., Robinson, A., Sharma, H.P., Brewer, A., Assa’ad, A.H., Jiang, J., Hultquist, H.W., Kulkarni, A.J., Choi, J., Mahdavinia, M., Pongracic, J., Tobin, M.C., Warren, C., Gupta, R.S., 2022. Associations of food allergy-related dietary knowledge, attitudes, and behaviors among caregivers of black and white children with food allergy. Journal of the Academy of Nutrition and Dietetics 122, 797–810. Visciano, P., Schirone, M., Paparella, A., 2020. An overview of histamine and other biogenic amines in fish and fish products. Foods 9, 1795. Warner, O.K., Roberts, W., Mustain, P., Lowell, B., Swain, M., 2019. Casting a wider net: More action needed to stop seafood fraud in the United States. doi:10.31230/osf.io/sbm8h. Wen, X., Hu, Y., Zhang, X., Wei, X., Wang, T., Yin, S., 2019. Integrated application of multi-omics provides insights into cold stress responses in pufferfish Takifugu fasciatus. BMC Genomics 20, 563. Westermeier, R., Naven, T., 2002. Proteomics in Practice: A Laboratory Manual of Proteome Analysis. Wileyvch, Weinheim, Germany. Wijaya, C.H., Wijaya, W., Mehta, B.M., 2014. General properties of major food components, in: Cheung, P.C.K., Mehta, B.M. (Eds.), Handbook of Food Chemistry, Springer, Berlin, Heidelberg, pp. 1–32. Wojnarowski, K., Podobiński, P., Cholewińska, P., Smoliński, J., Dorobisz, K., 2021. Impact of estrogens present in environment on health and welfare of animals. Animals 11, 2152. Wulff, T., Nielsen, M.E., Deelder, A.M., Jessen, F., Palmblad, M., 2013. Authentication of fish products by large-scale comparison of tandem mass spectra. Journal of Proteome Research 12, 5253–5259. Xiang, Y., Sun, C., Zhao, Y., Li, L., Yang, X., Wu, Y., Chen, S., Wei, Y., Li, C., Wang, Y., 2022. Label-free proteomic analysis reveals freshness-related proteins in sea bass (Lateolabrax japonicus) fillets stored on ice. LWT 155, 112885. Xiong, X., Yuan, F., Huang, M., Lu, L., Xiong, X., Wen, J., 2019. DNA barcoding revealed mislabeling and potential health concerns with roasted fish products sold across China. Journal of Food Protection 82, 1200–1209. Yadzir, Z.H., Misnan, R., Murad, S., 2012. Identification of tropomyosin as major allergen of white squid (Loligo edulis) by two-dimensional immunoblotting and mass spectrometry. The Southeast Asian Journal of Tropical Medicine and Public Health 43, 185–191. Yi, Z., Xie, J., 2021. Comparative proteomics reveals the spoilage-related factors of Shewanella putrefaciens under refrigerated condition. Frontiers in Microbiology 12, 740482. Zander, K., Feucht, Y., 2018. Consumers’ willingness to pay for sustainable seafood made in Europe. Journal of International Food & Agribusiness Marketing 30, 251–275. Zhang, F., Ge, W., Ruan, G., Cai, X., Guo, T., 2020. Data-independent acquisition mass spectrometry-based proteomics and software tools: A glimpse in 2020. Proteomics 20, e1900276. Zhang, H., Zhang, X., Zhao, X., Xu, J., Lin, C., Jing, P., Hu, L., Zhao, S., Wang, X., Li, B., 2019. Discrimination of dried sea cucumber (Apostichopus japonicus) products from different geographical origins by sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS)-based proteomic analysis and chemometrics. Food Chemistry 274, 592–602. Zhang, Q.H., Huang, L., Zhang, Y., Ke, C.H., Huang, H.Q., 2013. Proteomic approach for identifying gonad differential proteins in the oyster (Crassostrea angulata) following food-chain contamination with HgCl2. Journal of Proteomics 94, 37–53. Zhang, Y., Liu, C., Liu, J., Liu, X., Tu, Z., Zheng, Y., Xu, J., Fan, H., Wang, Y., Hu, M., 2022a. Multi-omics reveals response mechanism of liver metabolism of hybrid sturgeon under ship noise stress. The Science of the Total Environment 851, 158348. Zhang, Y., Shen, W., Li, J., Zheng, B., Lou, Z., Hosain, M.A., Qian, B., Xue, L., 2021. Effect of fasting on protein metabolism in muscle tissue of Larimichthys crocea revealed by transcriptome and proteome. Journal of Oceanology and Limnology 39, 1972–1984. Zhang, Z., Sun, Y., Sang, S., Jia, L., Ou, C., 2022b. Emerging approach for fish freshness evaluation: Principle, application and challenges. Foods 11, 1897.

4

Endogenous Enzymes Ishita Ghosh The Pennsylvania State University

Han Liu Université de Pau et des Pays de l’Adour

Benjamin K. Simpson McGill University

Yi Zhang The Pennsylvania State University

4.1 INTRODUCTION Globally, the supply and demand chain for fish production and consumption continue to grow, with an increase of about 122% in fish consumption from 1990 to 2018 (FAO, 2020). Global fish production reached about 179 million tonnes in 2018, of which 156 million tonnes were for human consumption, while the remaining 22 million tonnes were destined to produce fish oil and fish meal (FAO, 2020). It inspires the requirement of maintaining seafood quality during post-mortem handling, storage, and processing. In fish and shellfish, the neutral pH, high water activity, and endogenous autolytic enzymes make them highly susceptible to developing rancid flavors and undesirable sensory changes during frozen or refrigerated storage (Oliveira et al., 2017). The endogenous enzymes in fish/shellfish are associated with the deterioration of seafood quality via enzyme-­ catalyzed autolysis, oxidation, etc. (Roobab et al., 2022). On the other hand, some reactions catalyzed by endogenous enzymes contribute to the pleasant seafood flavor and taste. Macromolecules, such as proteins, lipids, nucleic acids, and carbohydrates, are the substance matrices for the quality and nutrition of fish and shellfish. In this chapter, we introduce endogenous protein-degrading, lipid-degrading, carbohydrate-degrading, and nucleic acid–degrading enzymes and other enzymes, including transglutaminase and polyphenol oxidases. The major enzymes present in fish and shellfish include trypsins, chymotrypsins, calpains, cathepsins, pepsins, lipases, phospholipases, lipoxygenases (LOXs), myokinase, AMP deaminase, transglutaminase, trimethylamine N-oxide (TMAO) reductase, and demethylase (Chandrakumar et al., 2010; Chung & Chan, 2009; Hong et al., 2015). Polyphenol oxidase is present primarily in crustaceans (Sae-leaw & Benjakul, 2019). These enzymes perform various functions in fish and shellfish in vivo and during postharvest handling, storage, or processing. For instance, chymotrypsins, cathepsins, metalloproteases, nucleoside phosphorylase, and transglutaminases (TG) seem to play a role in fish immunity against bacterial and viral infections (Dai et al., 2016, 2017; Danwattananusorn et al., 2009; Li et al., 2015; Podok et al., 2014; Xu et al., 2012; Yeh et al., 2013). After harvest, some endogenous enzymes influence the quality of postmortem fish, which has been studied since the early 1900s (Almy, 1926). Some endogenous enzymes positively affect fish/shellfish quality. For example, ATP degradation products inosine monophosphate (IMP) and AMP could impart an umami taste in fish (Hong et al., 2015). However, most enzymatic reactions bring undesirable muscle softening, unpleasant flavors and odors, short shelf life, discoloration, and low consumer acceptability. For example, the softening of postmortem fish during storage can be induced 72

DOI: 10.1201/9781003289401-5

Endogenous Enzymes

73

by the action of serine and cysteine proteases (trypsin, chymotrypsin, calpain, and cathepsin) and matrix metalloproteases (Singh & Benjakul, 2018). Unpleasant flavors and odors in spoiled fish, i.e., the rancid, putrid, ammonia-like, and sulfurous odors, are partially produced due to lipid oxidation by LOXs and from the ammonia generated from nucleic acid degradation by AMP deaminase, 5′-nucleotidase, nucleoside phosphorylase, inosine nucleosidase, and xanthine oxidase (XO) enzymes (Hong et al., 2015; Świeca et al., 2014). The visual appeal of fish and shellfish is crucial for consumer satisfaction. Discoloration can be induced by the oxidation of lipids and phenols catalyzed by endogenous lipoxygenases and polyphenol oxidases, reducing seafood product quality (Li et al., 2020; Sae-leaw & Benjakul, 2019). Utilization and value addition to fish and shellfish waste has raised attention to achieving a sustainable food chain. Discards and by-products from fish processing are good sources to obtain these endogenous enzymes, such as trypsin and pepsin, which usually possess special enzymological properties and can be used in the processing of fish and fish products such as fish gelatin production (Balti et al., 2009; Ktari et al., 2012; Sholeh et al., 2020). The main endogenous enzymes and their properties and role in fish/shellfish growth, postmortem handling, lowtemperature storage, and seafood processing are described in this chapter. The knowledge helps better understand the role of these enzymes, retard the loss of seafood quality, and maximize the economic value of seafood products.

4.2  PROTEIN-DEGRADING ENZYMES Proteolytic enzymes, or proteases, hydrolyze peptide bonds in a polypeptide chain to break down the backbone of proteins into low-molecular-weight peptides and free amino acids. They are the enzyme category that has received considerable traction among the various enzymes in fish and shellfish. Depending on the cleavage site, proteases can be grouped into exopeptidases and endopeptidases. Exopeptidases cleave peptide bonds at the terminal (amino or carboxyl) end of the polypeptide chain, while endopeptidases randomly hydrolyze internal (nonterminal amino acids) peptide bonds (Sriket, 2014). Proteases are ubiquitous in nature and are associated with physiological, biochemical, and regulatory functions in cells and organisms. For animals, including fish/shellfish, proteases play a significant role in food digestion. Proteases can be classified by the active site associated with the catalysis mechanism as serine, cysteine, aspartate, and metalloproteases. The four groups show different catalytic sites, substrate specificity, sensitivity to inhibitors, and activity in acid or alkaline environments. In fish and shellfish, the properties of proteases are diverse based on fish species, organs, and other environmental factors. Proteases are mainly located in aquatic animals’ muscle tissue and/or organs, particularly digestive organs (Singh & Benjakul, 2018), and include calpain, trypsin, chymotrypsin, collagenase, pepsin, elastase, carboxypeptidase, and carboxylesterase.

4.2.1 Serine Proteases Serine proteases (EC 3.4.21) are endopeptidases with a serine residue that serves as the nucleophilic residue in the catalytic site. The catalytic triad contains a serine, a histidine, and an aspartic acid residue. Serine proteases are generally active at a neutral and alkaline pH, with an optimal pH range of 7–11 (Singh & Benjakul, 2018). Their molecular weights range between 18 and 35 kDa (Klomklao, 2008). The reactions catalyzed by serine proteases lead to gel weakening of bigeye snapper surimi (Benjakul et al., 2003), autolysis of bigeye snapper (Priacanthus macracanthus) skin (Intarasirisawat et al., 2007), and the degradation of gelatin and native type I collagen in red sea bream (Wu et al., 2010b), suggesting their role in the postmortem tenderization as well as undesirable textural changes in fish flesh. The two main members of the serine protease family are trypsin and chymotrypsin. They cleave fish/shellfish muscles, such as connective tissues, leading to mushiness, softening, and gaping during postmortem storage (Felberg et al., 2010; Lødemel & Olsen, 2003; Sriket et al., 2011).

74

Handbook of Seafood and Seafood Products Analysis

4.2.1.1 Trypsin Trypsin (EC 3.4.21.4) catalyzes the hydrolysis of proteins by cleaving the peptide bond between arginine or lysine residues and the adjacent residue. Trypsin is naturally synthesized in fish as inactive zymogen or pro-enzyme, which could be activated by proteolytic cleavage (Sainz et al., 2004). During the storage of fish and shellfish, trypsin plays a role in the softening of the ­postmortem muscle. In white shrimp, a trypsin-like protease purified from the hepatopancreas degraded myofibrillar protein, deteriorated muscle texture, and eventually led to meat softening (Peng et al., 2019). Similarly, trypsin from freshwater prawn hepatopancreas showed high collagenolytic activity toward prawn, shrimp, and fish collagens, suggesting its possible role in muscle softening (Sriket et al., 2012). Trypsin enzymes have been purified and characterized from a variety of fish and shellfish species, including bigeye snapper (P. macracanthus) (van Hau & Benjakul, 2006), Amazonian fish tambaqui (Colossoma macropomum) (Marcuschi et al., 2010), Pacific white shrimp (Litopenaeus vannamei) (Senphan et al., 2015), cuttlefish (Sepia officinalis) (Balti et al., 2009), brownstripe red snapper (Lutjanus vitta) (Khantaphant & Benjakul, 2010), Tunisian barbel (Barbus callensis) (Sila et al., 2012), common smooth hound (Mustelus mustelus) (Bougatef et al., 2010), and unicorn leatherjacket (Aluterus monoceros) (Zamani & Benjakul, 2016). The commonly used purification techniques are ammonium sulfate precipitation followed by a series of chromatographic purification using ion exchange chromatography, gel filtration, and affinity columns. The purified trypsin from different fish and shellfish showed different optimum activities. For example, trypsin from the pyloric ceca of bigeye snapper exhibited optimal activity at 55°C and a pH of 8–11 (van Hau & Benjakul, 2006). Trypsin from Pacific white shrimp exhibited the highest activity at 60°C and pH 8.0, while trypsin from cuttlefish had an optimum pH and temperature at pH 8.0 and 70°C, respectively (Balti et al., 2009; Senphan et al., 2015). It is worth noting that fish-sourced trypsin is mainly extracted from the fish waste, including skipjack tuna (Katsuwonus pelamis) spleens (Klomklao et al., 2006), Tunisian barbel (Barbus callensis) viscera (Sila et al., 2012), pyloric ceca of unicorn leatherjacket (Zamani & Benjakul, 2016) and Amazonian fish pirarucu (Arapaima gigas) (Freitas-Júnior et al., 2012), and viscera of zebra blenny (Salaria basilisca) (Ktari et al., 2012). Thus, producing fish trypsin from seafood is an economically beneficial strategy to obtain the profitable use of seafood. Furthermore, the obtained trypsin can be utilized in fish processing. For example, barbel trypsin efficiently assisted in the recovery of carotenoprotein complexes from shrimp shells (Sila et al., 2012). Similarly, purified trypsin from unicorn leatherjacket hydrolyzed Indian mackerel protein isolate to produce fish protein hydrolysates. Except for seafood processing, fish/shellfish trypsin can be used in nonfood industries. For example, zebra blenny trypsin was studied as a potential laundry detergent additive (Ktari et al., 2012). 4.2.1.2 Chymotrypsin Chymotrypsin (EC 3.4.21.1) has a broader specificity than trypsin as it cleaves peptide bonds involving amino acids such as tyrosine, phenylalanine, tryptophan, and leucine. Chymotrypsinogen, an inactive form of chymotrypsin existing in the fish pancreas, is activated by proteolytic cleavage. Chymotrypsin has been purified from various fish and shellfish species such as sailfin catfish (Pterygoplichthys disjunctivus) (Villalba-Villalba et al., 2013), cod (Gadus morhua L.) (Raae & Walther, 1989), cuttlefish (Sepia officinalis) (Balti et al., 2012), striped sea bream (Lithognathus mormyrus) (el Hadj Ali et al., 2010), Monterey sardine (Sardinops sagax caeruleus) (Castillo-Yáñez et al., 2006), Nile tilapia (Oreochromis niloticus Linneaus) (Hinsui et al., 2006), and rainbow trout (Oncorhynchus mykiss) (Kristjánsson & Nielsen, 1992), using similar purification techniques to trypsin purification. Their optimal activity is usually in the pH range of 7.5–11 (Zhou et al., 2011). For instance, chymotrypsin from sailfin catfish had maximal activity at pH 9 and 50°C, while its counterpart from striped sea bream was highly active at pH 7.0–12.0, with an optimum pH of 10.0−11.0 (Villalba-Villalba et al., 2013). The optimum pH and temperature for chymotrypsin from the hepatopancreas of cuttlefish, viscera of Monterey sardine, and Nile tilapia were 8.5, 8.0, and

Endogenous Enzymes

75

9.0 and 55°C, 50°C, and 60°C, respectively (Balti et al., 2012; Castillo-Yáñez et al., 2006; Hinsui et al., 2006). Chymotrypsin is reported to have immune-related functions in fish and shellfish. In kuruma shrimp, the gene encoding chymotrypsin-like serine protease increased in expression in hemocytes, lymphoid organs, and hepatopancreas after peptidoglycan stimulation, suggesting its immunological role (Danwattananusorn et al., 2009). Similarly, Chinese shrimp, after bacterial (Vibrio anguillarum) and viral (white spot syndrome virus) infection, had upregulation of serine protease chymotrypsin levels (Shi et al., 2008).

4.2.2  Cysteine Proteases Cysteine proteases (EC 3.4.22), a group of endopeptidases, have cysteine and histidine as the key residues in the catalytic site. They require a reducing and acidic environment to be active (Grzonka et al., 2007). In the digestive organs of marine animals, cysteine proteases are most active at acidic pH and inactive at alkaline pH (Klomklao, 2008). The most important cysteine proteases in fish are cytoplasmic calpains and lysosomal cathepsins (B, H, and L) (Cheret et al., 2007), which contribute to the myofibrillar proteolysis during the postmortem storage of fish muscle. 4.2.2.1 Calpain Calpain (EC 3.4.22.17) belongs to cytosolic Ca2+-dependent cysteine proteases. The calpain system comprises two calpains (μ-calpain and m-calpain) and their specific endogenous inhibitor, calpastatin (Goll et al., 2003). The Ca2+ concentration in vivo is essential in the activation mechanism of calpains (Cheret et al., 2007), and the two calpains significantly differ in sensitivity to calcium ions (Ahmed et al., 2013). The Ca2+ concentration required for μ-calpain and m-calpain to reach 50% of the maximal activity was 3–50 and 50–150 μmol/L, respectively (Goll et al., 2003). Other cations, such as Sr2+ and Ba2+, can also activate calpains in vitro (Ladrat et al., 2002). Calpains have been identified in various fish species such as tilapia (Tilapia nilotica) (Wang et al., 1993), grass shrimp (Penaeus monodon) (Wang et al., 1993), carp (Cyprinus carpio) (Toyohara et al., 1985), sea bass (Dicentrarchus labrax L.) (Delbarre-Ladrat et al., 2004), Atlantic salmon (Salmo salar L.) (Gaarder et al., 2011), sea bream (Sparus aurata) (Caballero et al., 2009), and rainbow trout (Oncorhynchus mykiss) (Saito et al., 2007). The calpains from tilapia and grass shrimp muscles showed half-maximal activation at calcium concentrations of 0.23 and 2.4 mM, respectively, with optimal temperature at 30°C and pH around 7.0–7.5 (Wang et al., 1993). The two calpains derived from fish/shellfish exhibit differences in enzymological properties. In Atlantic salmon, the proteolytic activity of m-calpain (optimum conditions of 15°C, 0.2 mM Ca2+ concentration) was 15 times higher than that of μ-calpain (optimum conditions of 25°C, 0.6 mM Ca2+ concentration), and there were two forms of calpastatin identified as well (Gaarder et al., 2011). In addition, the calpain requirement for calcium differs between fish species. Approximately 0.1, 0.5, and 2.5 mM of Ca2+ was required for half-maximal activation of m-calpain in tilapia (Jiang et al., 1991), salmon (Geesink et al., 2000), and trout (Salem et al., 2005), respectively. Calpain in postmortem fish leads to the weakening and destabilization of the myofibrillar structure. It was reported that m-calpain in European sea bass contributed to the release of myofibrillar proteins (tropomyosin, α-actinin, etc.) and the degradation of myosin heavy chain, α-actinin, and desmin (Verrez-Bagnis et al., 2002). The softening of fish muscle was also accelerated by calpains with exogenous calcium (Salem et al., 2005). 4.2.2.2 Cathepsins Cathepsins are acid proteases located in lysosomes. They may be liberated into the cytoplasm and intracellular spaces postmortem due to muscle temperature and pH change (Dutson, 1983). Cathepsins B (EC 3.4.22.1), D (EC 3.4.23.5), and L (EC 3.4.22.15) have been purified from fish, and cathepsins B, D, L, and H (EC 3.4.22.16) are the major cathepsins within the fish muscle lysosomes (Aoki et al., 2000). They are inhibited by the in vivo inhibitor cystatin (Turk & Bode, 1991). Cathepsins B and L have been identified in a variety of fish and shellfish, including turbot

76

Handbook of Seafood and Seafood Products Analysis

(Scophthalmus maximus L.) (Chen et al., 2020; Zhou et al., 2015), carp (C. carpio) (Aranishi et al., 1997a, 1997b), Pacific abalone (Haliotis discus hannai) (Qiu et al., 2013; Shen et al., 2015), red crayfish (Procambarus clarkii) (Dai et al., 2016, 2017), black tiger shrimp (P. monodon) (Pongsomboon et al., 2008), silver carp (Hypophthalmichthys molitrix) (Liu et al., 2006, 2008), and rock bream (Oplegnathus fasciatus) (Whang et al., 2011). The standard purification techniques included ammonium sulfate precipitation and chromatography techniques such as affinity and ion exchange chromatography (Liu et al., 2006, 2008). In silver carp, cathepsin B showed maximal activity at pH 5.5 and 35°C, while cathepsin L had an optimal pH and temperature of 5.0 and 55°C, respectively (Liu et al., 2006, 2008). Cathepsins in fish in vivo have immunological functions against bacterial, fungal, and viral infections. The expression of cathepsin B increased in red crayfish after a lipopolysaccharide challenge and in channel catfish following Edwardsiella ictaluri and Flavobacterium columnare challenge (Dai et al., 2016; Li et al., 2015). Cathepsin L activity was also significantly up-regulated in the hepatopancreas of abalone after 8 h following Vibrio parahaemolyticus infection (Shen et al., 2015), and a similar result was found in striped murrel after fungus (Aphanomyces invadans) and bacteria (Aeromonas hydrophila) infection (Kumaresan et al., 2014). After fish/shellfish harvest, the roles of cathepsins in the proteolysis of myofibrillar components and tenderization of postmortem muscle have been widely studied (Ahmed et al., 2015). A correlation was found between textural changes and cathepsin activity in grass carp fillets during storage in chilling (−1.5°C ± 0.2°C) and ice (0.2°C ± 0.1°C) conditions, indicating a high cathepsin activity in sarcoplasm, myofibrillar, and heavy mitochondrial fractions during the first three days in the postmortem state, which led to textural deterioration (Ge et al., 2015). A similar study found a higher cathepsin B and B + L activity in lysosomes, mitochondria, and myofibrils of Northern pike (Esox lucius) fillets stored under refrigeration (4°C ± 1°C) compared to partial freezing (−3°C ± 0.5°C) (Qiu et al., 2020). In addition, preslaughter long-term stress seems to accelerate cathepsin activity, resulting in faster muscle degradation (Bahuaud et al., 2010). In seafood processing, cathepsins have undesirable effects on seafood products. For example, cathepsins B and L could destroy the silver carp surimi gel network, causing gel softening (Liu et al., 2008). Similarly, mackerel cathepsins L and L-like enzymes decreased the strength of mackerel surimi (Ho et al., 2000).

4.2.3 Aspartate Proteases Aspartate proteases (EC 3.4.23) are a class of endopeptidases that contain two aspartic acid residues in the center of a deep cleft covered by a hairpin loop (flap) at the active site (Coates et al., 2008). Aspartic proteases are generally active at acidic pH and are specifically inhibited by pepstatin A (Dı ́az-López et al., 1998). For instance, an aspartic protease purified from the viscera of sardine (Sardinella aurita) exhibited stability at pH 2.0–5.0 and retained >50% activity after heating at 50°C for 30 min (Khaled et al., 2011). Common aspartic proteases in fish include pepsin, cathepsin D, and chymosin. 4.2.3.1 Pepsin Pepsin (EC 3.4.23.1) is found in the gastric juice and membrane of the stomach lumen and has been isolated from a wide variety of species, including fish/shellfish, humans, and various other terrestrial animals. In the gastric membrane, pepsin is synthesized and secreted as the inactive pepsinogen. Apart from the stomach, pepsin is also found in limited amounts in blood, muscle, and urine (Effront et al., 2007). Pepsin is the major protease in fish stomach, and it has been purified and characterized in various fish species, including Atlantic salmon (Einarsson et al., 1996), albacore tuna, skipjack tuna, tongol tuna (Nalinanon et al., 2008), tilapia (Yamada et al., 1993), true sardine (Klomklao et al., 2008), Atlantic cod (Gildberg, 2004b), sea bream (Zhou et al., 2007), smooth hound (Bougatef et al., 2008), orange-spotted grouper (Feng et al., 2008), and European eel (Wu et al., 2009). In true sardine, the optimal activity of pepsin was found at pH 3.5 and 55°C (Klomklao

Endogenous Enzymes

77

et al., 2008), while smooth hound pepsin exhibited maximum activity at 40°C and pH 2.0 (Bougatef et al., 2008). Pepsinogens isolated from fish can be converted in vitro to pepsins at pH 2.0. For example, four pepsinogens (PG-I, PG-II, PG-III, and PG-IV) from sea bream stomach were turned into pepsiwns within a few minutes at pH 2.0, and the optimum conditions of pepsins were pH 3.0–3.5 and 45°C–50°C (Zhou et al., 2007). Similarly, three pepsinogens (PG-I, PG-II, and PG-III) from European eel stomach were converted into three pepsins at pH 2.0 exhibiting maximal activity at pH 3.5, 2.5, and 2.5 and temperatures of 40°C, 40°C, and 35°C, respectively (Wu et al., 2009). The techniques to isolate pepsinogens from fish include ammonium sulfate fractionation (Bougatef et al., 2008; Wu et al., 2009), ion exchange chromatography (Zhou et al., 2007, 2008), gel filtration chromatography (El-Beltagy et al., 2004; Nalinanon et al., 2010), and aqueous two-phase system (Nalinanon et al., 2009). Fish stomach, which is considered a by-product or waste, is the source for producing pepsin. Sholeh et al. obtained pepsin from the viscera of tuna, Hemigaleus balfouri Day, skipjack, and cod using the ammonium sulfate fractionation technique (Sholeh et al., 2020). Purified pepsins from fish can be utilized for seafood product processing, such as fish collagen extraction, fish descaling, and the production of surimi. For example, pepsin isolated from rainbow trout stomachs was used to recover collagen (Heidari & Rezaei, 2022). Similarly, pepsins from albacore tuna, skipjack tuna, and tongol tuna stomachs helped in the collagen extraction from threadfin bream skin, and the highest collagen yield was obtained from the albacore tuna pepsin (Nalinanon et al., 2008). Besides, Atlantic cod pepsin is often used for industrial descaling (Gildberg, 2004a). Pepsin extracted from tuna fish stomach was used to improve the quality of red tilapia surimi paste (Nurhayati et al., 2022). 4.2.3.2  Cathepsin D Cathepsin D (EC 3.4.23.5) is a lysosomal aspartic protease involved in the cellular degradation of proteins. In fish, cathepsin D is highly active in the spleen and liver and abundant in the muscle (Gildberg, 1988). Cathepsin D has been purified and characterized from Atlantic cod (G. morhua L.) (Wang et al., 2007), cuttlefish (Sepia officinalis) (Balti et al., 2010), Tilapia mossambica (Doke et al., 1980), cod (McLay, 1980), herring (Clupea harengus) (Nielsen & Nielsen, 2001), C. carpio (GoldmanLevkovitz et al., 1995), and Antarctic icefish (Chionodraco hamatus) (Capasso et al., 1999). Cathepsin D has functions in the immune responses of bacteria-infected fish. When channel catfish and grass carp were challenged with Edwardsiella ictaluri and A. hydrophila, respectively, the expression of the cathepsin D genes was induced (Dong et al., 2012; Feng et al., 2011). The purification of cathepsin D traditionally uses ammonium sulfate precipitation, gel filtration, ion exchange chromatography, and affinity chromatography (Balti et al., 2010; Capasso et al., 1999; Doke et al., 1980). Purified cathepsin D from cuttlefish and Antarctic icefish had an optimal pH of 3.0 (Balti et al., 2010; Capasso et al., 1999). Tilapia cathepsin D exhibited a dual optimal pH at pH 2.8 and 3.8 (Doke et al., 1980). Cathepsin D from herring and common carp was active at pH 2.7–3.7 and pH 2.5, respectively, with pepstatin as an inhibitor (Goldman-Levkovitz et al., 1995; Nielsen & Nielsen, 2001). Cathepsin D participates in the postmortem softening of fish tissue. In rainbow trout, the degradation of muscle structure was found to be caused by proteases calpain and cathepsin D, B, and L, and cathepsin D alone had a significant effect on the texture of fish (Godiksen et al., 2009). In sea bass muscle, cathepsin D was involved in the postmortem proteolysis of myofibrillar and sarcoplasmic proteins (Ladrat et al., 2003). In grass carp, a positive correlation between cathepsin D activity and myofibril fragmentation was observed, contributing to its postmortem tenderization (Wang et al., 2016).

4.2.4 Matrix Metalloproteinases Matrix metalloproteinases (EC 3.4.24) (MMPs) are a family of zinc- and calcium-dependent proteases that degrade extracellular matrix (ECM) proteins. MMPs are secreted as zymogens which need to be activated (Maskos & Bode, 2003). MMPs have been identified in fish species such as grass

78

Handbook of Seafood and Seafood Products Analysis

carp (Ctenopharyngodon idellus) (Wu et al., 2014), common carp (C. carpio) (Xu et al., 2015), silver carp (Hypophthalmichthys molitrix) (Wu et al., 2016), Pacific rockfish (Sebastes sp.) (Bracho & Haard, 1995), Japanese flounder (Paralichthys olivaceus) (Kubota et al., 2001), Atlantic cod (G. morhua), spotted wolffish (Anarhichas minor), and Atlantic salmon (Salmo salar) (Lødemel & Olsen, 2003). In the muscles of grass carp, silver carp, and red sea bream, the highest MMP activity was observed at a pH of 8 and optimal temperatures of 30°C–50°C, 30°C–60°C, and 40°C, respectively (Wu et al., 2010a, 2016, 2014). Metalloproteases have also been identified in fish pathogens such as Flavobacterium psychrophilum (Secades et al., 2003), and A. hydrophila strain B32 isolated from rainbow trout (Rodriguez et al., 1992). MMPs play a role in embryogenesis, reproduction, and immune response in fish. Studies on zebrafish (Danio rerio) revealed that the development of embryos requires MMP expression (Hillegass et al., 2007; Zhang et al., 2003). MMPs also protect against bacterial infections, such as grass carp from A. hydrophila infection (Xu et al., 2012), zebrafish from lethal infection with Listeria monocytogenes (Shan et al., 2016), and Nile tilapia from S. agalactiae invasion (Liang et al., 2020). In low-temperature fish storage, MMPs participate in degradation and texture softening of fish muscle via the breakdown of connective tissues. For example, two MMPs were found in the skeletal muscle of Pacific rockfish, which readily hydrolyzed collagen and gelatin (Bracho & Haard, 1995). Another MMP was identified in the Japanese flounder muscle, which showed the ability to solubilize type I collagen from a crude connective tissue at 4°C (Kubota et al., 2003). Purified MMPs from red sea bream, common carp, grass carp, and silver carp skeletal muscles could also effectively hydrolyze type I collagen at 4°C (Wu et al., 2010a, 2016, 2014; Xu et al., 2015).

4.3  LIPID-DEGRADING ENZYMES Lipids are the major energy source for fish/shellfish, but they also participate in their growth, mechanism, and reproduction (Leaver et al., 2008). Fish lipids contain plenty of saturated and unsaturated fatty acids and large amounts of triacylglycerols (TAGs) and phospholipids (Shahidi & Wanasundara, 1998). Long-chain unsaturated fatty acids (14–22 carbon atoms) account for approximately 40% of fish lipids (Secci & Parisi, 2016). Fish lipids differ from mammalian lipids in composition. Fatty acids in mammalian lipids usually possess less than two double bonds, while fish lipids can have fatty acids with five or six double bonds (Secci & Parisi, 2016). It is well known that unsaturated fatty acids are important for human health and nutrition such as anti-inflammation (Lu et al., 2019). On the other hand, the substantial long-chain unsaturated fatty acids in fish/shellfish are highly susceptible to lipolysis and lipid oxidation, and the degradation involved is potentiated by two endogenous enzymes, i.e., lipases and LOXs, respectively.

4.3.1 Lipase Lipase (EC 3.1.1.3) catalyzes hydrolysis of ester bonds in TAGs, phospholipids, wax esters, cholesterol esters, and vitamin esters. It belongs to the α/β hydrolase-fold family, composed of two distinct domains: a catalytic domain with α/β hydrolase structure in the N-terminus and a C-terminal auxiliary domain with β-sandwich structure (Kurtovic et al., 2009). The N-terminal domain has several functional areas, such as the active site with the oxyanion hole, lid domain, Ca2+-binding site, and glycosylation site. The C-terminal domain assists in binding with substrates and cofactors (colipase, heparin, and apolipoprotein C-II) (Wong & Schotz, 2002). Lipase in fish/shellfish has specific differences from its counterparts in mammals, plants, and microorganisms. The reactivity of fish lipase prefers long-chain PUFAs, while mammalian lipase mainly catalyzes the hydrolysis of a low degree of unsaturated fatty acids within 20 carbons (Kurtovic et al., 2009). Its unique substrate preference makes fish lipase useful in various applications, especially in the production of fish oil. Shellfish lipase is relatively less studied and has mainly focused on lipases in crustaceans, such as lipase characterized in lobster larvae, playing a role in the growth and development

Endogenous Enzymes

79

of larvae (Goncalves et al., 2022). Lipases in fish species, such as anchovy, rainbow trout, and Atlantic salmon (Salmo salar) (Albalat et al., 2006; Goncalves et al., 2022; Krogdahl et al., 2015), can be found in their pancreas, intestinal wall, stomach, and intestinal mucosa. The main types of lipolytic enzymes in fish/shellfish responsible for lipolysis are triacylglycerol lipase and phospholipase, which regulate the adipose tissue, thus affecting the flesh yield and meat quality of fish/shellfish products (Weil et al., 2013). Fish/shellfish contain a high level of endogenous triacylglycerol lipases since TAGs are the predominant lipids. Triacylglycerol lipase catalyzes the hydrolysis of mono-, di-, and TAGs to glycerol and fatty acids. It shows catalytic activity at a wide range of temperatures from –20°C to 65°C (Jensen, 1983). The catalytic mechanism of endogenous phospholipases in fish is probably similar to that in mammals, regarding the degradation of phospholipids, especially phosphoglycerides. Phospholipase assists in the hydrolysis of phospholipids into fatty acids and other lipophilic substances. It can be divided into several categories based on the cleavage position of ester bonds, i.e., phospholipase A1 (PLA1), phospholipase A2 (PLA2), phospholipase B (PLB), lysophospholipase A1/2 (LysoPLA1/2), phospholipase C (PLC), and phospholipase D (PLD) (Richmond & Smith, 2011), of which PLA2 and PLB are the ones that mainly participate in the hydrolysis of fish lipids (Ghaly, 2010). Besides, triacylglycerol lipases seem to have high activity in the hydrolysis of water-insoluble long- and medium-chain TAGs, whereas phospholipases generally work on the amphipathic phosphoglyceride ester bonds instead of neutral lipids (Richmond & Smith, 2011). Lipases from fish/shellfish have a wide range of potential applications in food production, ­biomedical engineering, and the detergent industry. Immobilized microbial lipase has been wellstudied in the production of high-quality fish oil (Hosseini et al., 2018) that contains eicosapentaenoic acid, docosapentaenoic acid, and docosahexaenoic acid with preventive effects on coronary heart and cardiovascular and neurological diseases (Segato et al., 2005). However, the purified fish/ shellfish lipase has not been well investigated and commercially utilized, probably due to the low yield and low availability of raw materials (Kurtovic et al., 2009).

4.3.2 Lipoxygenase Lipid oxidation plays an essential role in the quality and safety of seafood products. Off-flavor in seafood (i.e., rancid, putrid, ammonia-like, and sulfurous odors) is the predominant cause for low sales. Off-flavor is produced by lipid oxidation, microbial actions, and some thermally or environmentally derived reactions (Świeca et al., 2014). The fresh color of fish appeals to customers, but lipid oxidation could turn fish color to dark red and further to red-brown. Moreover, lipid oxidation reduces the nutritional values of fish/shellfish because of the toxins generated from the co-oxidation of fatty acids with vitamins and cholesterol. Oxidation of cholesterol can generate various compounds, which can lead to atherogenicity (Vicente et al., 2012). Unsaturated fatty acids present at high levels in fish and shellfish are prone to be oxidized to generate peroxides, free fatty acids, carbonyls, and alcohols, leading to poor seafood quality, including deteriorated odor, color, and potential toxicity (Shahidi & Zhong, 2010). Fish/shellfish lipids can also interact with molecular oxygen that generates highly active hydroperoxides (Vieira et al., 2017), which can be transformed into unwanted free radicals, e.g., peroxyl radicals and alkoxy radicals. The accumulation and decomposition of these compounds would, in turn, form hydrocarbons, alcohols, aldehydes, acids, ketones, and other compounds through various pathways (Chaiyasit, 2007; Mariutti & Bragagnolo, 2017). Membrane phospholipids are the primary substrates for lipid oxidation due to the substantial PUFAs and considerable exposure to oxygen (Cui & Decker, 2016), so, in general, fish skin releases more fishy odors than fish muscle (Mansur et al., 2003). Thiobarbituric acid (TBA) value is a common parameter used for the assessment of the degree of lipid oxidation, which measures the concentration of malondialdehyde (MDA) formed by hydroperoxides. A TBA value between 2 and 4 mg MDA/kg indicates that the seafood is of good quality (Kostaki et al., 2009). There are two types of lipid oxidations in fish/shellfish: nonenzymatic and enzymatic oxidations. Enzymatic oxidation requires lipoxygenase (LOX, EC 1.13.11.12) that catalyzes the

80

Handbook of Seafood and Seafood Products Analysis

oxidation of a variety of unsaturated fatty acids, particularly those with a structure of (1Z,4Z)-1,4pentadiene, by adding oxygen molecules. Active LOX has been reported in the eggs of sea urchin (Strongylocentrotus purpuratus), skin of sardine (Sardinops melanosticta), gill of freshwater mussel (Ligumia subrostrata), and skin and gill of trout (German & Creveling, 1990; Hagar et al., 1989; Hawkins & Brash, 1987; Mohri et al., 1990). LOX in the skin and gill is found to be related to seafood flavor development. LOX-catalyzed oxidation is the dominant contributor to the off-odors due to some produced compounds, including 1-octen-3-ol, 1-penten-3-ol, (E)-2-pentenal, (E)-2nonenal, and (E,Z)-2,6-nonadienal (Prost et al., 2006). The odors developed in the postharvest of sea bass (Lates calcarifer), red tilapia (Oreochromis mossambicus and O. niloticus), and silver carp (Hypophthalmichthys molitrix) suggested that they were mediated by endogenous lipoxygenase (Fu et al., 2009; Thiansilakul et al., 2010). Various strategies are developed to prevent lipid oxidation catalyzed by LOXs, which include freezing, salting, drying, boiling, and proper packaging. Freezing, a commonly used seafood processing method, can decrease the activity of LOXs in fish. The optimal temperature for the longterm preservation of high-quality fish is –35°C (Hermes & Melo, 2008). Reducing water activity in fish by salting and drying is another effective method that inhibits enzymatic reactions, including LOX. High-temperature processing such as boiling denatures enzymes in fish, therefore alleviating the deterioration of fish lipids. Proper packaging methods such as vacuum packaging and controlled atmosphere packaging are also efficient in preventing LOX-catalyzed oxidation. Unlike LOX-catalyzed oxidation, nonenzymatic lipid oxidation occurs via uncontrolled oxidation by free radicals such as hydroxy, peroxy, and alkoxy radicals. This oxidation involves the initial generation of lipid radicals by the free radicals and entails propagation and termination steps. Various metabolites of nonenzymatic lipid oxidation have been reported, which include oxidized products of PUFAs (e.g., ω-3) such as isoprostanes, neuroprostanes, dihomo-isoprostanes, and phytoprostanes. These compounds have become important biomarkers of lipid products.

4.4  CARBOHYDRATE-DEGRADING ENZYMES Carbohydrates participate in many metabolic processes in fish and shellfish, such as the tricarboxylic acid cycle, gluconeogenesis, and chitin synthesis (Hemre et al., 2002). Compared to terrestrial vertebrates, fish/shellfish seem less efficient in carbohydrate utilization (Leaver et al., 2008). Still, they have endogenous carbohydrate-degrading enzymes to digest and absorb simple and complex carbohydrates.

4.4.1 Alginate Lyase Algae, especially brown algae, are a typical diet for fish and shellfish in marine habitats. Alginate exists in the cell wall of brown algae (Phaeophyceae) as a gelling polysaccharide. α-l-Guluronate (G) and β-d-mannuronate (M) are the monomeric units of alginate (Zhu & Yin, 2015). Alginate lyases (EC 4.2.2.3), belong to polysaccharide lyases, catalyze the depolymerization reaction on 4-O-glycosidic bond through β-elimination and form a double bond between C-4 and C-5, therefore producing unsaturated oligosaccharides (Zhu & Yin, 2015). There are three types of alginate lyases according to the substrate specificity, which are PolyMlyases (EC 4.2.2.3), PolyGlyases (EC 4.2.2.11), and PolyMGlyases (EC 4.2.2.-) (Xu et al., 2017). Alginate lyases have been found in many fish/shellfish species, e.g., black mussels, marine abalones, and surf clams (Jacober et al., 1980; Suzuki et al., 2006). Heta et al. studied the properties of alginate lyases purified from a few marine gastropod mollusks, i.e., Haliotis discus hannai, H. iris, Omphalius rusticus, and Littorina brevicula and found that they showed the favorable poly(M) lyase activity (Hata et al., 2009). Alginate lyases seem to be involved in the degradation of alginate in their dietary algae. Alginate is widely utilized in the food and pharmaceutical industries. Low-molecular-weight degradation products from alginate display potential for more versatile applications because they

Endogenous Enzymes

81

have increased degradability and hydrophobicity. Generation of low-molecular-weight degradation products can be achieved using alginate lyase in an environmentally friendly and economical way. For example, alginate oligosaccharides prepared via alginate lyase–catalyzed depolymerization were found as an excellent natural antioxidant (Falkeborg et al., 2014). Alginate lyase also has promising applications in material areas (drug delivery) and biomedical areas (gene delivery, gene therapy, and cartilage transplant and repair) (Kianersi et al., 2021; Liu et al., 2022a). Nowadays, the focus on alginate lyases is those derived from microorganisms.

4.4.2  Chitinase Chitin (C8H13O5N)n is a mucopolysaccharide distributed in various organisms, and it is the second most abundant biopolymer in the world. Chitinases hydrolyze the β-1,4-glycoside linkages of chitin and chitooligomers. Chitinase enzymes can be divided into endochitinase and exochitinase. Endochitinase (EC 3.2.1.14) degrades chitin to low-molecular-weight multimers, such as diacetylchitobiose, chitotetraose, and chitotriose. Exochitinase can further be subdivided into chitobiosidases (EC 3.2.1.29) and β-(1,4)-N-acetylglucosaminidases (NAGase) (E.C. 3.2.1.30). Chitobiosidases act on the nonreducing positions of chitin to generate diacetyl chitobiose, and diacetyl chitobiose is subsequently cleaved by NAGase to form monomers of N-acetylglucosamine (Wang et al., 2001b). Chitinase in fish was first reported by Jeuniaux (1961), and several subsequent studies showed that fish chitinase mainly existed in the digestive tract of fish (Gutowska et al., 2004). A research team compared the activities of chitinases from different organs in several fish species and found that chitinase in the stomach has the highest activity than its counterparts in the intestine and liver (Matsumiya & Mochizuki, 1996). To confirm the endogenous origin of chitinase in fish, studies have been carried out using starvation or selective antibiotics, and the experimental results suggested that chitinase is endogenous, not exogenous induced by bacteria (Fines & Holt, 2010). However, the exact role of chitinase in fish/shellfish is still not clear. Based on the difference in its distribution location, chitinase may display different roles. For example, chitinase in lymphoid myeloid organs probably relates to immune function, while chitinase in the digestive tract is likely to protect fish/ shellfish from the chitinous pathogens, e.g., nematodes, bacteria, or fungi (Vervaet, 2019).

4.4.3 Amylase Starch is not the common dietary constituent for fish, so endogenous amylases in fish may play a role in the hydrolysis of glycogen in prey. Amylases degrade starch (and glycogen) to produce maltose and branched oligosaccharides, etc., via the cleavage of O-glucosidic bonds. There are three classes of amylases: α-amylases (EC 3.2.1.1) existing in animals, plants, and microorganisms, β-amylase (EC 3.2.1.2) found in microorganisms and plants, and γ-amylase (EC 3.2.1.3) identified in animals and plants (Vigneswaran et al., 2014). Among them, α-amylase has been extracted from a variety of fish species, e.g., Pagrus pagrus, Pagellus erythrinus, P. bogaraveo, Boops boops, and Diplodus annularis (Fernández et al., 2001). Amylases in fish/shellfish are synthesized and secreted by the exocrine pancreas, and the activity of amylases in herbivorous and omnivorous fish species is higher than the carnivorous species (Bakke et al., 2010). For example, the amylase activities of herbivorous carp, omnivorous goldfish, and carnivorous eel were found to be 107.96 ± 7.32, 23.80 ± 4.19, and 0.76 ± 0.08 U/mg, respectively (Hidalgo et al., 1999). More research needs to be conducted to understand the biosynthesis, functions, and regulations of amylases in fish/shellfish.

4.5  NUCLEIC ACID–DEGRADING ENZYMES The degradation of nucleic acids (RNA and DNA) by ribonucleases (RNases) and deoxyribonucleases (DNases) could enhance the general flavor of fish due to the generation of flavor-related nucleotides. DNA in food materials, including fish and shellfish, is relatively stable during the processing

82

Handbook of Seafood and Seafood Products Analysis

FIGURE 4.1  ATP degradation patterns and the enzymes involved.

along the food chain. Based on this, DNA-based techniques have been developed for authentication and traceability applications in the fish industry (Nimbkar et al., 2021), in which case degradation of nucleic acids is not favorable. ATP is a multifunctional nucleotide that drives many physiological processes, such as energy storage and transportation in living organisms. ATP-related compounds are highly associated with the freshness and flavor of fish and shellfish and other muscle foods (Hong et al., 2015). Figure 4.1 shows the degradation process of ATP and the related enzymes. Both ATP and adenosine diphosphate (ADP) act as plasticizing agents for actin and myosin, preventing their interaction and relaxing the muscle. Their levels in fish and shellfish are rapidly depleted when the rigor mortis process is initiated, which causes fish proteins to interact and fish muscles to stiffen in the process (Borgstrom, 2012). The sequential conversion of ATP to ADP, AMP, and IMP usually takes place within 24 h. IMP is the major nucleotide that exists in postmortem fish and contributes to the umami taste of fish and shellfish; thus, strategies to monitor, assess, and maintain the level of IMP have been widely reported (Chang et al., 2020; Prabhakar et al., 2020; Yoshioka et al., 2019). For crustaceans, the main nucleotide is AMP instead (Mendes et al., 2001). IMP is further degraded into inosine (Ino) and then transformed into hypoxanthine (Hx). Hx is converted to xanthine (Xa), uric acid (UA), and other ring cleavage products. These breakdown products of IMP have objectionable flavors, and their formation connotes spoilage of products.

4.5.1 ATPase ATPases (EC 3.6.1.3) are a class of enzymes that catalyze the decomposition of ATP into ADP and phosphate ions. Among the ATPases, Na+/K+ -ATPase is an enzyme that plays a significant role in Na+ and K+ transfer across the cell membrane, ion regulation, and cellular water balance in fish and shellfish (Ajima et al., 2021). Thus, the activity of Na+/K+ -ATPase is an excellent indicator of fish/shellfish physiological function to evaluate their performance in various aquatic environments such as climate change and plastic pollution (Chaudhuri et al., 2021; Rangasamy et al., 2022). The ATPase activity in muscle is regulated by the Ca 2+ ions in the sarcoplasm, which is responsible for ATP breakdown, resulting in the release of energy used in muscle contraction. Thus, its activity is a parameter used to estimate the quality and deterioration of protein in fish and shellfish (Likhar & Chudasama, 2021).

Endogenous Enzymes

83

4.5.2 Myokinase or Adenylate Kinase Myokinase (EC 2.7.4.3), also known as adenylate kinase (AK), is responsible for the reversible conversion of adenine nucleotides, leading to the production of AMP that is one of the main flavoring substances in fish and shellfish. A study on Chinese mitten crab (Eriocheir sinensis) meat found that AMP (75.3 mg/100 g), together with IMP (34.4 mg/100 g) and guanosine monophosphate (GMP) (2.3 mg/100 g), contributed to its umami taste (Chen & Zhang, 2007). The occurrence of GMP in fish during storage could be attributed to the degradation of guanosine triphosphate, and GMP eventually degrades to guanosine which undergoes cleavage to guanine and then is immediately catabolized to Xa. ATP + AMP ⇌ 2ADP 2ADP → AMP + Pi Myokinases are found in many fish species, including bastard halibut (Paralichthys olivaceus), Antarctic fish (Notothenia coriiceps), medaka fish (Oryzias latipes), silver catfish (Rhamdia quelen), Pacific white shrimp, and Antarctic krill (Arai et al., 2020; Baldissera et al., 2020; Maruyama et al., 2022; Moon et al., 2017; Zhang et al., 2018). This enzyme plays an essential role in cellular energy metabolism. A study found that medaka fish expressing major AK1 isoforms in humans exhibited higher locomotor activity compared to the wild-type medaka (Maruyama et al., 2022). Purified myokinase from the halibut muscle showed high stability over a wide pH range of 4.5–10.5, with an optimal pH of 7 and temperature of 40°C (Arai et al., 2020). In fish, the effect of microbial infection on AK activity has no clear conclusion. The silver catfish that were naturally infected with Ichthyophthirius multifiliis showed enhanced splenic AK activity (Baldissera et al., 2018a). However, A. hydrophila-infected grass carp exhibited lower AK activity in the gills compared to the uninfected group (Morselli et al., 2020), and no significant difference in AK activity was observed in silver catfish experimentally infected with F. columnare (Baldissera et al., 2020). Thymol supplementation to the infected fish may prevent the inhibition of AK activity to increase longevity, but high concentrations could cause side effects to fish (Morselli et al., 2020). Various cooking methods, such as steaming of crab muscle for 10 min, could generate high concentrations of AMP that contribute to the pleasant taste of seafood (Chen et al., 2022; Shi et al., 2020). Thus, the processing technique influences the breakdown of ATP, but the effect on AK activity is not clear.

4.5.3 AMP Deaminase AMP deaminase (EC 3.5.4.6) is an enzyme that catalyzes the generation of IMP and ammonia from the degradation of AMP, as shown below. AMP → IMP + NH3 IMP is responsible for the desirable umami taste of fish and shellfish, while ammonia is an important index for the detrimental quality of fish and shellfish. A research study on fish and crustaceans from the Portuguese coast during their ice storage period of 72 h showed that during ice storage, different fish and shellfish species, i.e., North Atlantic hake, monkfish, rockfish, Norway lobster, red shrimp, showed different transformation extents of AMP into IMP (Mendes et al., 2001). Purified AMP deaminase from the white skeletal muscle of common carp C. carpio showed high stability, non-Michaelis-Menten kinetics with an S 0.5 value for AMP of 2.52 ± 0.16 mM, a specific activity of 1,000–1,200 U/mg protein, optimal pH at 6.3, and inhibition by phosphate and fluoride (Lushchak et al., 2008). Microorganisms seem to play no significant role in the degradation of ATP-related compounds. A research team reported that the AMP deaminase activity was less affected by the abundance of spoilage bacteria that caused no effect on the transformation of ATP to IMP in common carp fillets during ice storage at 4°C. It was reported that AMP deaminase activity was less

84

Handbook of Seafood and Seafood Products Analysis

affected by the abundance of spoilage bacteria, causing no effect on the transformation of ATP to IMP (Li et al., 2017). A study on IMP generation in Japanese scallops (Patinopecten yessoensis) suggested that endogenous enzymes in scallops stored at both 4°C and 20°C caused the accumulation of IMP, and varying metal ion chelators such as EDTA possibly affected the generation of IMP for umami, a pleasant savory taste in scallops (Wei et al., 2020).

4.5.4  5′-Nucleotidase 5′-Nucleotidase (EC 3.1.3.35) catalyzes the hydrolysis of IMP to produce Ino and phosphate ions. IMP → Inosine + Pi In fish and shellfish, IMP is degraded during chilled storage due to the combined action of multiple enzymes, including acid phosphatase, alkaline phosphatase hydrolase, and 5′-nucleotidase (Likhar & Chudasama, 2021). 5′-Nucleotidase has been found in the muscles of different fish species such as cod, carp, snapper, black rockfish, and medaka (Itoh, 2013; Murakami et al., 2022). In fish, 5′-nucleotidase exists in two forms, membrane-bound and soluble in cytosol (Marseno et al., 1994), and its activity varies depending on the form and fish species. In a study on 11 different fish species, blackrock fish (Sebastes inermis) exhibited the highest activity of 5′-nucleotidase (Marseno et al., 1992). In blackrock fish (S. inermis) muscle, 5′-nucleotidase was found in the nuclear, microsomal, and cytosolic fractions, and its specific activity in the microsomal fraction was the highest with an optimal pH and temperature of 8.3°C and 45°C, respectively, strong inhibition by ADP and ATP, and activation by a high concentration (5–10 mM) of phosphocreatine (Marseno et al., 1992). Cytosolic 5′-nucleotidase had lower activity than the membrane-bound counterpart, which may be due to their structural differences (Marseno et al., 1993a, 1993b, 1994). In cod muscle, the optimum pH of 5′-nucleotidase was 7.6, which was inhibited by Zn2+, Cu2+, ADP, ATP, and EDTA (Yamamoto et al., 1986). Purified 5′-nucleotidase from snapper muscle exhibited an optimal pH of 8.5 in the presence of MgC12 and was activated by Mn2+ and Co2+ but inhibited by Zn2+, Cu2+, and EDTA (Nedachi & Hirota, 1992).

4.5.5 Nucleoside Phosphorylase & Inosine Nucleosidase The degradation of Ino to Hx is one of the critical steps, as shown below, in the overall ATP breakdown pathway, since Hx contributes to the off-flavor in spoiled fish, thus used as a significant indicator of spoilage in fish (Mathew et al., 2021). Hx imparts a bitter-like taste to fish that adversely affects the sensory quality of fish. Thus, the formation of Hx often serves as the bottom line for edible fish quality (Mørkøre et al., 2010). The reaction is catalyzed by two enzymes, nucleoside phosphorylase (EC 2.4.2.1) and inosine nucleosidase (EC 2.4.2.2), and both have been found in Pacific lingcod and Atlantic cod (Pandey & Parhi, 2021). Ino + Pi → Hx + Ribose-1-phosphate Ino + H2O → Hx + d-ribose Interestingly, the spoilage bacteria, such as Proteus vulgaris, isolated from the spoiling cod, also exhibited high activity of inosine nucleosidase that had an isoelectric pH of 6.8 and a Michaelis constant (Km) for the substrate inosine of 3.9 × 10−5 M (Surette et al., 1990). Thus, the presence of spoilage bacteria also enhances the conversion of Ino to Hx. For example, various bacteria such as Plasmodium falciparum and Trichomonas vaginalis have been reported with nucleoside phosphorylase and inosine nucleosidase activities (Lewandowicz & Schramm, 2004; Munagala & Wang, 2002). A study reported that the inosine nucleosidase involved in the degradation of HxR during the

Endogenous Enzymes

85

cold storage of bighead carp was mainly derived from bacterial secretions of Aeromonas sobria, Pseudomonas helmanticensis, and Shewanella putrefaciens (Liu et al., 2018). In addition, purine nucleoside phosphorylase plays an important role in the innate immunity of fish and shellfish. Research has been conducted on the role of this enzyme in virus- and bacteriainfected crucian carp (Carassius auratus gibelio) (Podok et al., 2014). This enzyme was also found to be helpful for Pacific oysters Crassostrea gigas to defend against Vibrio mediterranei pathogenesis (Liu et al., 2022b).

4.5.6 Xanthine Oxidase XO (EC 1.1.3.22) catalyzes the conversion of Hx to Xa and UA via a two-step process, as shown below, which is the final in the ATP breakdown pathway. Hypoxanthine + H2O + O2 → Xanthine + H2O2 Xanthine + H2O + O2 → Uric acid + H2O2 XO has been detected in a wide variety of fish species and is present in both fish muscle and the bacteria that cause fish spoilage. A study found that the XO activity was increased in fish infected with Pseudomonas aeruginosa, while the nano-encapsulated tea tree oil could prevent the infection by inhibiting the activity of XO (Baldissera et al., 2018b). Another study investigated the grass carp-borne microorganisms in the chilled fish juice and found that the Pseudomonas putida and S. ­putrefaciens produced XO that catalyzed the degradation of Hx into UA, but Aeromonas rivipollensis produced almost no XO (Li et al., 2022). It is worth noting that the H2O2 produced in the reaction could further participate in the Fenton reaction to react with Fe+2, which generates highly reactive hydroxyl radical (OH·). Such hydroxyl radicals in turn bring undesirable flavors from lipid oxidation in fish and shellfish (Shahidi & Hossain, 2022). These reactions can be controlled via oxygen elimination or freezing processing (Baron et al., 2007; Gonçalves et al., 2004).

4.5.7 Influence on Seafood Quality Nucleic acid–degrading enzymes, especially the ones involved in the ATP degradation process, essentially affect fish quality because of their contribution to off-odors and spoiled flavors. Ammonia released from the AMP deaminase-catalyzed AMP → IMP reaction is associated with off-odors and seafood spoilage. Ammonia is one of the total volatile basic amines that appears as the most common chemical indicator of fish spoilage (Jinadasa, 2014). On the other hand, the IMP produced from this reaction imparts the umami taste in seafood in synergy with other compounds such as GMP, and AMP (Hong et al., 2015). Enzymic dephosphorylation of IMP via Ino to Hx leads to the loss of desirable fresh flavors and the development of bitter off-flavor in staling fish. The accumulated Hx, together with some amino acids and peptides may also bring a bitter taste to seafood (Fatima et al., 1981; Sarower et al., 2012). The pattern of ATP degradation and its metabolites was first reported in 1959 (IMP, 1959), and a formula on K-value was developed for indicating fish freshness based on nucleotide changes, shown as K(%) = ([Ino] + [Hx])/([ATP] + [ADP] + [AMP] + [IMP] + [Ino] + [Hx]) × 100, which represents the relative degree of decomposition in the spoilage process, so a fresh fish has a lower K-value (C18) PUFAs, while it is still mandatory for the quantification of medium- and short-chain ( 0.5, only slight browning. When b*/a* > 0.8, product becomes not merchantable L* and b* of fish fed CO diet were lower than the others, and a* of fish-fed PO diet was different from those of fish fed RO and SO diets in raw fillet. Important increases in L*, a*, b*, or C* were observed during frozen storage

[93]

Fillets were lighter, less red, and yellower at the anterior compared to the posterior end. They tended to become darker and redder with progress of season Only during storage at −10°C and somewhat lower at −20°C, a noticeable increase in L* was observed. At −30°C, L* did not change. b* behaved comparable to L* After six months of frozen storage, control and sodium lactatetreated products were lighter and yellower than carbohydratetreated products. Samples did not differ in a* after 6 months. After each freeze/thaw cycle, control and sodium lactate-treated raw products were lighter than carbohydrate-treated. a* did not change with increasing number of FT cycles. Carbohydratetreated products were generally less red and yellow compared to control and lactate-treated products DE 18 maltodextrin or the combination of sucrose/sorbitol slowed down color changes. b and a were, respectively, lower and higher in treated samples than in control. Only slight differences between samples were found in L

[94]

[95]

[93,94, 96–120]

[100,101]

[102]

(Continued)

421

Color Measurement

TABLE 21.4 (Continued) Color Measurements on Frozen Products Task

Color Effects

WS of NAM extracted from frozen fillets and was less than that of NAM extracted from fresh fish. When whole frozen fish was used for NAM extraction WS was less compared with NAM from frozen fillet. WS decreased in the presence of aldehydes Impact of freezing temperature (−10°C, Regression analysis showed curvilinear relationship between freezing temperature and L*. Fillets frozen at −10°C had higher −25°C, −40°C, −55°C, or −70°C) on L* than those frozen at −70°C. L* were similar for fillets frozen color of farmed Atlantic cod fillet at −25°C to −55°C Fading phenomenon of farmed steelhead The increases in expressible fluid correlated positively with fading fillet during frozen storage (L*) and negatively with a* Influence on whiteness (WS) of natural actomyosin (NAM)

References [83]

[97]

[103]

TABLE 21.5 Color Measurements on Heat-Treated Products Task Influence of species and formulation on color of AP, catfish, and redfish during thermal processing Discoloration in thermally processed blue crab meat Effect of heating of Pacific chum salmon in water (60°C–100°C) for 0–40 min Effect of cooking of aquacultured fish fillets (pacu, rainbow trout, hybrid striped bass, catfish, and tilapia) Relation of color and color stability of smoked fillets during chill storage to duration of frozen storage prior to smoking of fillets

Color changes in smoked rainbow trout fed diet supplemented with canthaxanthin in combination with different lipid levels and the effect of different packaging conditions Investigation of effects of salting (injection salting vs. dry salting), smoking temperature (20°C vs. 30°C), and storage (chilled storage vs. no storage) on surface color of cold smoked salmon fillets

Color Effects

References

L increased for catfish but decreased for redfish and AP after thermal processing, a and b increased in general during canning, formulation of canned products had no effect on color Blue crab meat became darker with increasing heating process. Meat at bottom of a can was darker than that on top Increase in processing temperature or time increased L* but decreased a* and b* of muscle Cooking increased L and decreased a and b. Strongest change in L observed in catfish and least change in hybrid striped bass

[107]

Smoked fillets from fish fed lower level of astaxanthin had significantly higher L and lower a and b compared to smoked products from fish fed higher level. Smoked products from fish fed high fat level had higher L and lower a and b than smoked fillets from fish fed diet with the lowest fat level. Only small changes in color during chill storage Smoke-curing lead to a decrease of L* and an increase of h* more marked in fish fed the diet with high lipid level. Use of MAP for the packaging of fillets leads to maintaining the color of the flesh in comparison with packaging under vacuum or under air

[109]

Higher increase in b* and C* and higher h* and ΔE* of dry-salted than injection-salted fillets. No influence on L* and a* by salting method. Drop in a* was higher after smoking fillets at 20°C than at 30°C; AE* was higher when fillets were smoked at 30°C than at 20°C

[111]

[106] [105] [104]

[110]

(Continued)

422

Handbook of Seafood and Seafood Products Analysis

TABLE 21.5 (Continued) Color Measurements on Heat-Treated Products Task Comparison of color of sliced salmon sampled in a French hypermarket from fish originally grown in Norway, Scotland, and Ireland Effects of cold-smoking temperature (range 21.5°C–29.9°C) and dietary oil sources (pure Peruvian fish oil (FO) or pure SO supplements) on color characteristics Effects of tripolyphosphate (STPP) in brine on smoke adsorption and color of cold smoked mullet Comparison of steam blanching, water blanching, and MW-heating applied to desalted cod Color analysis of skinless catfish fillets from steam-treated catfish and control Effects of sous vide cooking on color of fish/sauce packs Influence of irradiation on color of rainbow trout muscle Assessment of influence of heating on rainbow trout muscle color by heated fish cutlets at temperatures in the range 30°C–70°C Evaluation of kinetics of reactions leading to changes in salmon color during thermal processing

Color Effects

References

There was a significant effect of country of origin on color, with the Irish having higher values for a*, b*, C*, and h* than the Norwegian with the Scottish in-between

[112,113]

Only b* values exhibited correlations with temperature. Dietary oil source only had significant effects on a* and C* during storage. Salmon-fed diets with FO were slightly redder than salmon-fed SO. FO group had C* values higher than SO group

[114]

L, reflecting smoke adsorption, was lightest for control fillets, followed by 5% salt treatment, and darkest for 5% and 10% STPP both with 5% salt Thermally treated cod was lighter and little yellower. Heatinduced changes in cod were a little smaller than in MW-heated cod No differences in L, a, b, and whiteness were found between steam-treated and control fillets Sous vide cooking caused a loss of L, a, and b. Sauce color lightened as indicated by a rise in L/b values Significant difference in L* at the time before and after exposure. a* was identical and b* decreased L* most influenced by heating increased linearly up to 60°C without further changes. a* did not change markedly, whereas b* slightly increased at higher temperatures

[115]

When heating, muscle color is characterized by rapid whitening followed by slow browning. Whitening occurred within the first 10 min with L* increased to a maximum, and a* and b* decreased to a minimum. During browning, changes of L*, b*, and AE* followed a zero-order reaction Effect of fat content and fillet shape on Smoked salmon had lower L* and a* and higher b* compared color of smoked Atlantic salmon with raw salmon. Ice-stored salmon displayed higher change due to smoking in L* and lower change of a* and b* with increasing fat content. The change in L*, a*, and b* of frozen stored salmon showed no correlation with raw material characteristics. Changes in color from raw to smoked products are affected by variations in fat content only when fresh material is used Color changes in skinless mahimahi fillet Treating mahimahi fillets with FS increased a* in muscle and portions either treated with filtered stabilized it during frozen storage. Redness did decay rapidly on smoke (FS) or left untreated for 24 h, cold storage for both defrosted and fresh filtered-smoke-treated followed by either aerobic storage at products, and reached initial (presmoking) redness levels in 4°C for 8 day or freezing for 30 days 2 days (−25°C) followed by thawing and aerobic storage at 4°C for 8 days

[121]

[116]

[117] [118] [119] [120]

[122]

[123]

423

Color Measurement

TABLE 21.6 Color Measurements on High-Pressure-Treated Products Task High-pressure (HP) treatment hammour fillets at 375 MPa for 20 min followed by refrigerated storage for 30 days Pressure treatment of pelagic and demersal species from different fishing grounds using same conditions

High pressure (100–300 MPa) applied (for 0–30 min) to fresh seafood to control enzyme-related texture Changes in the color of turbot fillets during frozen storage at −20°C after pressure shift freezing (PSF) and air blast freezing (ABF)

Color Effects L* increased with pressure giving a lighter product, a* was significantly reduced, while b* was slightly reduced Color changes measured on demersal fishes caused by pressure treatment resulted in marked increase of L* and in decreases of both a* and b*. High-pressure treatments (higher than 150–200 MPa, 5 min) resulted in a cooked appearance of pollock, cod, tuna, mackerel, salmon trout, carp, plaice, and anglerfish. Only octopus retained a raw appearance till 400–800 MPa L and b increased progressively with amount of pressure applied and duration of pressure application, while a was reduced. ΔE indicated no color difference between sample treated up to 200 MPa for 10 min

PSF resulted in overall increase in L* and b* and decrease in a*. Frozen storage did not particularly modify color parameters of PSF fillets. ABF did not give a cooked aspect after thawing to the turbot fillets. During the storage of ABF fillets, b* increased significantly Influence of high-pressure-assisted In raw fillets, color changes (high AE*) were mainly caused by a thawing (PAT) on color of fillets from strong increase in L*. Smaller changes were monitored for a* redfish, cod, rainbow trout, whiting, (decrease) and b* (increase). After heat treatment, the influence haddock, and salmon of high pressure on color was much smaller. ΔE* between cooked fillets previously thawed either by high-pressure treatment or conventionally varied from negligible to significant Color changes in carp muscle exposed to Carp muscles lost their transparency; L increased parallel with an increase of pressurization at room temperature high pressures of 50–500 MPa/10 min Effect of high-pressure treatment (up to Nonpressurized fillets showed an increase in L* for refrigerated 500 MPa, 5 min) on color of sea bass storage time of 7 days, followed by decrease in L* after 14 days fillets after 0, 7, and 14 days of of storage. a* and b* remained constant during storage. refrigerated storage Regardless of pressure level, application of pressure on fillet increased L* High pressure to obtain cod sausage with After pressure treatment, L* increased markedly, whereas b* added chitosan decreased slightly Effect of high-pressure treatments at 400 Thermal treatments of pressure-treated and control samples were assigned to kamaboko (90°C), setting (40°C/90°C) and modori and 600 MPa (1 and 5 min) on color of heat-induced fish gels obtained from (60°C/90°C). L* ranged from 76.6 to 81.7, a* varied from −1.37 arrowtooth flounder fish paste to −0.69, and b* varied from 6.0 to 10.9. Modori samples had lowest L* Pressure treatment improved whiteness (WS) of surimi gels as Comparison of AP and Pacific whiting compared with heat-treated surimi gels, whereas additives did surimi gels containing potato starch and/or egg white (400 and 650 MPa for not. At 400 MPa, WS was 10% higher than heated gels. At 10 min at 20°C) with heat-induced gels 650 MPa, WS increased 8% (90°C, 40 min) Effect on color of minced albacore L* increased with pressure giving a lighter product, a* decreased, muscle treated with high hydrostatic and b* increased giving a light yellow/grayish hue, cooked pressure at 275 and 310 MPa for appearance product 2–6 min

References [103]

[124,125]

[126]

[127]

[128]

[129] [130]

[131] [132]

[133]

[134]

(Continued)

424

Handbook of Seafood and Seafood Products Analysis

TABLE 21.6 (Continued) Color Measurements on High-Pressure-Treated Products Task Effect of combined application of high-pressure treatment and modified atmospheres on the color of fresh Atlantic salmon Changes in whiteness of tilapia gels obtained by combined hydrostatic pressure (200 MPa) and setting (50°C) treatments Effects of PSF and/or PAT on color of sea bass muscle were evaluated and compared with conventional (air-blast) frozen (AF) and thawed (AT) samples

Color Effects L* of salmon increased with increasing intensity and time of pressurization. High-pressure treatment (150 MPa, 60 min or 200 MPa, 10 min) resulted in an opaque product (L* > 70). A product with L* > 70 or a* 22

Cod (G. morhua) 0–4 5–13 14–16 >17

Sole (S. vulgaris) 0–5 6–19 20–27 >28

Turbot (S. maximus) 0–5 6–19 20–26 >27

24.3 CONCLUSION The QIM has been proven in the marketplace to be an easy-to-use, practical tool. It is widely used in research as a reference method and can be applied to a wide variety of species including crustacea and cephalopods. It is versatile and can integrate time and temperature effects since it is designed to mimic the kinetics of microbial, and enzymic action, and the changes in bulk properties. Consequently, the range of schemes becoming available is quite extensive. Further development of handheld devices containing these schemes and the ability to link the results with photos, traceability, authenticity, catch, and market information is required to make fuller use of the available information and to enhance electronic marketing, safety, and quality assurance procedures.

REFERENCES





1. Bremner A. A convenient easy-to-use system for estimating the quality of chilled seafood. Fish Process. Bull., 7, 59–70, 1985. 2. Larsen EP, Hyldig G, and Frederiksen M. Fersk fisk-Kvalitet i detailhandlen. DFU Rapport udgivet af Danmarks Fiskeriundersøgelser, Afdeling for Fiskeindustriel Forskning, Lyngby, Denmark, 1998–1999. 3. Bonilla AC, Sveinsdottir K, and Martinsdottir E. Development of quality index method (QIM) scheme for fresh cod (Gadus morhua) fillets and application in shelf life study. Food Control, 18, 352–358, 2007. 4. Hyldig G and Nielsen J. A rapid sensory method for quality management. In Ólafsdóttir G and others (eds.). Action, C. Methods to Determine the Freshness of Fish in Research and Industry; Proceedings of the Final Meeting of the Concerted Action “Evaluation of Fish Freshness” AIR3CT942283, Nantes Conference, Nov. 12–14, International Institute of Refrigeration, Paris, France, p. 297, 1997. 5. Warm K, Bøknæs N, and Nielsen J. Development of quality index methods for evaluation of frozen cod (Gadus morhua) and cod fillets. J. Aquat. Food Prod. Technol., 7, 45–59, 1998. 6. Herrero AM, Huidobro A, and Careche M. Development of quality index method for frozen hake (M. capensis and M. paradoxus). J. Food Sci., 68, 1086–1092, 2003. 7. Warm K. Sensory Quality Criteria for New and Traditional Fish Species of Relevance to Consumer Needs. Danish Institute for Fisheries Research, Lyngby, The Royal Veterinary and Agricultural University, Copenhagen, Denmark, p. 1, 2000. 8. Larsen E, Hyldig G, Dalgaard P, Bech AC, and Østerberg C. Consumers and experts responses to fresh cod fillets. In Luten JB, Oehlenschlager J, and Ólafsdóttir G (eds.). Quality of Fish from Catch to Consumer: Labelling, Monitoring and Traceability. Wageningen Academic Publ., Wageningen, the Netherlands, p. 345, 2003. 9. Nielsen J, Hyldig G, and Larsen E. Eating quality of fish-A review. J. Aquat. Food Prod. Technol., 11, 125–141, 2002. 10. Larsen EP, Heldbo J, Jespersen CM, and Nielsen J. Development of a standard for quality assessment on fish for human consumption. In Huss HH, Jacobsen M, and Liston J (eds.). Quality Assurance in the Fish Industry. Elsevier, Amsterdam, the Netherlands, p. 351, 1992. 11. Huidobro A, Pastor A, and Tejada M. Quality index method developed for raw gilthead seabream (Sparus aurata). J. Food Sci., 65, 1202–1205, 2000.

492

Handbook of Seafood and Seafood Products Analysis

12. Sveinsdóttir K, Martinsdóttir E, Hyldig G, Jørgensen B, and Kristbergsson K. Application of quality index method (QIM) scheme in shelf life study of farmed Atlantic Salmon (Salmo salar). J. Food Sci., 67(4), 1570–1579, 2002. 13. Sveinsdóttir K, Hyldig G, Martinsdóttir E, Jorgensen B, and Kristbergsson K. Development of quality index method (QIM) scheme for farmed Atlantic Salmon (Salmo salar). Food Qual. Prefer., 14, 237–245, 2003. 14. Nielsen D, Green D, Bolton G, Hodson R, and Nielsen J. Hybrid Striped Bass-A QIM Scheme for a Major U.S. Aquaculture Species; Poster at First Joint Trans Atlantic Fisheries Technology Conference. 15. Hyldig G and Nielsen J. QIM-A tool for determination of fish freshness. In Shahidi F and Simpson BK (eds.). Seafood Quality and Safety. Advances in the New Millennium. ScienceTech Publishing Company, St. John’s, NL, p. 81, 2004. 16. Hyldig G and Green-Petersen DMB. Quality index method-An objective tool for determination of sensory quality. J. Aquat. Food Prod. Technol., 13(4), 71–80, 2005. 17. Bremner HA, Olley J, and Vail AMA. Estimating time-temperature effects by a rapid systematic sensory method. In : Donald E. Kramer, John Liston (eds.), Advances in Food Research: Seafood Quality Determination. Elsevier Press, Amsterdam, the Netherlands, p. 413, 1987. 18. Anonymous. Council regulation (EC) No. 2406/96 of November 26, 1996, laying down common marketing standards for certain fishery products. Off. J. Eur. Commun., L334, 1–14, 1996. 19. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A31996R2406. 20. Martinsdóttir E, Sveinsdóttir K, Luten J, Schelvis-Smit R, and Hyldig G. Sensory Evaluation of Fish Freshness. A Reference Manual for the Fish Industry. QIM-Eurofish, the Netherlands, 2001, 2004. https://dl-manual.com/doc/qim-manual-english-version-mnop66r14jzq. 21. Boulter M, Poole S, and Bremner A. Australian Quality Index Manual. Fisheries Research and Development Corporation, Canberra, ACT, 2006. 22. https://www.sydneyfishmarket.com.au/Seafood-Trading/Quality/Australian-Seafood-Quality-Index. 23. Portela CDG. Tecnologia pós-despesca dos camarões de água doce Macrobrachium rosenbergii e Macrobrachium amazonicum [Doc Thesis]. Jaboticabal, Brazil: Universidade Estadual Paulista; 2009. 24. Cyprian OO, Sveinsdóttir K, Magnússon H, and Martinsdóttir E. Application of quality index method (QIM) scheme and effects of short-time temperature abuse in shelf life study of fresh water artic char (Salvelinus alpinus). J. Food Prod. Technol., 17, 303–321, 2008. 25. Guillerm-Regost C, Haugen T, Nortvedt R, Carlehög M, Lunestad BT, Kiessling A, and Rørár AMB. Quality characterization of farmed Atlantic halibut during ice storage. J. Food Sci., 71(2), 83–90, 2006. 26. Musgrove R, Carragher J, Mathews, and Slattery S. Value adding Australian sardines: Factors affecting rates of deterioration in sardine (Sardinops sagax) quality during post-harvest handling. Food Control, 18, 1372–1382, 2007. 27. Silva SC. Validade comercial de sardinhas inteiras e refrigeradas avaliadas por análises físico-químicas, bacteriológicas e sensorial. Niterói, Brazil: Universidade Federal Fluminense; 2010. 28. Nunes ML, Batista I, and Cardoso C. Aplicação do índice de qualidade (QIM) na avaliação da frescura do pescado. Lisboa: Publicações Avulsas do IPIMAR; 2007. 29. Sant’ana LS, Soares SM, and Vaz-Pires P. Development of a quality index method (QIM) sensory scheme and study of shelf-life of ice-stored blackspot seabream (Pagellus bogaraveo). LWT/Food Sci. Tech., 44, 2253–2259, 2011. 30. Bogdanovic T, Simat V, Frka-Roic A, and Markovic K. Development and application of quality index method scheme in a shelf life study of wild and fish farm affected bogue (Boops boops, L.). J. Food Sci., 77, S99–S106, 2012. 31. Song Y, Liu L, Shen H, You J, and Luo Y. Effect of sodium alginate-based edible coating containing different anti-oxidants on quality and shelf life of refrigerated bream (Megalobrama amblycephala). Food Control, 22, 608–615, 2011. 32. Martinsdóttir E, Sveinsdottir K, Luten JB, Schelvis-Smit R, and Hyldig G. Reference manual for the fish sector: Sensory evaluation of fish freshness. Ijmuiden: QIM Eurofish; 2001. 33. Vaz-Pires P and Seixas P. Development of new quality index method (QIM) schemes for cuttlefish (Sepia officinalis) and broadtail shortfin squid (Illex coindetii). Food Control, 17(12), 942–949, 2006. 34. Barbosa A and Vaz-Pires P. Quality index method (QIM): Development of a sensorial scheme for commo octopus (Octopus vulgaris). Food Control, 15, 161–168, 2004. 35. Sykes AV, Oliveira AR, Domingues PM, Cardoso CM, Andrade JP, and Nunes ML. Assessment of European cuttlefish (Sepia officinalis, L.) nutritional value and freshness under ice storage using a developed quality index method (QIM) and biochemical methods. LWT/Food Sci. Tech., 42, 424–432, 2009.

Quality Index Methods

493

36. Özogul Y, Özyurt G, Özogul F, Kuley E, and Polat A. Freshness assessment of European eel (Anguilla anguilla) by sensory, chemical and microbiological methods. Food Chem., 92, 745–751, 2005. 37. Massa AE, Palacios DL, Paredi ME, and Crupkin M. Postmortem changes in quality indices of icestored flounder (Paralichtys patagonicus). J. Food Biochem., 29, 570–590, 2005. 38. Alasalvar C, Taylor KDA, Öksüz A, Garthwaite T, Alexis, MN, and Grigorakis K. Freshness assessment of cultured sea bream (Sparus aurata) by chemical, physical and sensory methods. Food Chem., 72, 33–40, 2001. 39. Campus M, Bonaglini E, Cappuccinelli R, Porcu MC, Tonelli R, and Roggio T. Effect of modified atmosphere packaging on quality index method (QIM) scores o farmed gilthead seabream (Sparus aurata L.) at low and abused temperatures. J. Food Sci., 76(3), 185–191, 2011. 40. Özyurt G, Kuley E, Özkütük S, and Özogul F. Sensory, microbiological and chemical assessment of the freshness of red mullet (Mullus barbatus) and goldband goatfish (Upeneus moluccensis) during storage in ice. Food Chem., 114, 505–510, 2009. 41. Silva KB. Avaliação do frescor e vida útil da lagosta (Panulirus argus), pré- cozida e armazenada sobrefrigeração [Msc Thesis]. Recife, Brazil: Universidade Federal Rural do Semi-Árido; 2009. 42. Lyhs U and Schelvis-Smit R. Development of a quality index method (QIM) for maatjes herring stored in air and under modified atmosphere. J. Aquat. Food Prod. Technol., 14, 63–76, 2005. 43. Triqui R and Bouchriti N. Freshness assessment of Moroccan Sardine (Scardina pilchardus): Comparison of overall sensory changes to instrumentally determined volatiles. J. Agric. Food Chem., 51, 7540, 2003. 44. Noojuy N and Boonprab K. Quality index method (QIM) and its related indexes for meder’s mangrove crab (Neoepisesarma Mederi, H. Milne Edwards 1853) stored in ice. Kmitl Sci. J., 8(2), 52–59, 2008. 45. Pons-Sánchez-Cascado S, Vidal-Carou MC, Nunes ML, and Veciana-Nogués MT. Sensory analysis to assess the freshness of mediterranean anchovies (Engraulis encrasicholus) stored in ice. Food Control, 17, 564–569, 2006. 46. Baixas-Nogueras S, Bover-Cid S, Veciana-Nogués T, Nunes ML, and Vidal-Carou MC. Development of a quality index method to evaluate freshness in mediterranean hake (Merluccius merluccius). J. Food Sci., 68(3), 1067–1071, 2003. 47. Wünnenburg A and Oehlenschläger J. Untersuchungen zur saisonalen Abhängigkeit der Haltbarkeit von Zuchtforellen (Onchorhynchus mykiss) während der Eislagerung mittels der Qualitäts-Index-Method (QIM) an Ganzfisch und Sensorik gegarter Filetproben. Archiv für Lebensmittelhyg, 59, 221–226, 2008. 48. Alasalvar C, Taylor KDA, Öksüz A, Shahidi F, and Alexis M. Comparison of freshness quality of cultured and wild sea bass (Dicentrarchus labrax). J. Food Sci., 67, 3220–3226, 2002. 49. Gonçalves AC. Qualidade e valorização em aquacultura: Propriedades sensoriais e período de conservação útil de peixe e bivalves [Doc Thesis]. Lisboa, Portugal: Universidade de Lisboa; 2010. 50. Oliveira VM, Freitas MQ, Clemente SCS, and Mársico ET. Método do Índice de Qualidade (MIQ) desenvolvido para camarão (Litopeneaus vannamei) cultivado. Rev. Univ. Rur. Série Ciências da Vida., 29, 60–71, 2009. 51. Rodrigues TP. Estudo de critérios para avaliação da qualidade da tilápia do Nilo (Oreochromis niloticus) cultivada; eviscerada e estocada em gelo [Msc Thesis]. Niterói, Brazil: Universidade Federal Fluminense; 2008. 52. Bekaert K. Development of quality index method scheme to evaluate freshness of tub gurnard (Chelidonichthys lucernus). In Luten JB, Jacobsen C, Bekaert K, Sæbo A, and Oehlenschlager J (eds.). Seafood Research from Fish to Dish. Wageningen Academic Publishers, Wageningen, pp. 289–296, 2006. 53. Teixeira MS, Borges A, Franco RM, Clemente SCS, and Freitas MQ. Método de Índice de Quaidade (MIQ): protocolo sensorial para corvina (Micropogonias furnieri). Rev. Bras. Ciênc Vet., 16, 83–88, 2009. 54. Ritter DO, Lanzarin M, Novaes SF, Monteiro MLG, Almeida Filho ES, Mársico ET, Franco RM, Conte-Junior CA, and Freitas MQ. Quality index method (QIM) for gutted ice-stored hybrid tambatinga (Colossoma macropomum×Piaractus brachypomum) and study of shelf life. LWT Food Sci. Technol., 67, 55–61, 2016. 55. Lanzarin M., Ritter DO, Novaes SF, Monteiro MLG, Almeida Filho ES, Mársico ET, Franco RM, Conte CA, and Freitas MQ. Quality index method (QIM) for ice stored gutted Amazonian Pintado (Pseudoplatystoma fasciatum × Leiarius marmoratus) and estimation of shelf life. LWT Food Sci. Technol., 65, 363–370, 2016, 56. Agüeria D, Sanzano P, Vaz-Pires P, Rodríguez E, and Yeannes MI. Development of quality index method scheme for common carp (Cyprinus carpio) stored in ice: shelf-life assessment by physicochemical, microbiological, and sensory quality indices. J. Aquat. Food Prod. Technol., 25(5), 708–723, 2016.

494

Handbook of Seafood and Seafood Products Analysis

57. Gutiérrez GN, Amorocho CC, Sandoval AA, and Ruíz OY. Quality index method developed for gutted and ungutted red tilapia (Oreochromis ssp). Revista MVZ Córdoba, 20(1), 4461–4471, 2015. 58. López-García MM, Ramil-Novo LA, Vázquez-Odériz ML, and Romero-Rodríguez MA. Development of a quality index method for freshness assessment of thawed Greenland halibut (Reinhardtius hippoglossoides) stored at chilling temperature. Food Bioproc. Technol., 7(6), 1847–1852, 2014. 59. Cyprian OO, Sveinsdóttir K, Magnússon H, Arason S, Jóhannsson R, and Martinsdóttir E. Development of quality index method (QIM) scheme for farmed tilapia fillets and its application in shelf life study. J. Aquat. Food Prod. Technol., 23(3), 278–290, 2014. 60. Borges A, Conte-Junior CA, Franco RM, Mársico ET, and Freitas MQ. Quality index method (QIM) for the hybrid tambacu (Colossoma macropomum × Piaractus mesopotamicus) and the correlation among its quality parameters. LWT Food Sci. Technol., 56(2), 432–439, 2014. 61. Massa AE, Manca E, and Yeannes MI. Development of quality index method for anchovy (Engraulis anchoita) stored in ice: Assessment of its shelf-life by chemical and sensory methods. Food Sci. Technol. Int., 18(4), 339–351, 2012. 62. Bogdanović T, Šimat V, Frka‐Roić A, and Marković K. Development and application of quality index method scheme in a shelf‐life study of wild and fish farm affected bogue (Boops boops, L.). J. Food Sci., 77(2), S99–S106, 2012. 63. Roiha IS, Jonsson A, Backi CJ, Lunestad BT, and Karlsdottir MG. A comparative study of quality and safety of Atlantic cod (Gadusmorhua) fillets during cold storage, as affected by different thawing methods of pre-rigor frozen headed and gutted fish: Quality and safety of chilled cod fillets as affected by thawing methods. J. Sci. Food Agric., 98(1), 400–409, 2018. 64. Le NT, Doan NK, Nguyen Ba T, and Tran TVT. Towards improved quality benchmarking and shelf life evaluation of black tiger shrimp (Penaeus monodon). Food Chem., 235, 220–226, 2017. 65. Goncalves AA and Soares KMDP. Quality index method scheme for whole fresh carapeba (Eucinostomus gula, Quoy & Gaimard, 1824) stored in ice. Brazilian J. Food Technol., 20, 66–72, 2017. 66. Fogaca FHDS, Gonzaga Junior MA, Vieira SGA, Araujo TDS, Farias EA, Ferreira-Bravo IA, Alves Silva TF, Calvet RM, Moura P, Alitiene L, and Prentice-Hernandez C. Appraising the shelf life of farmed cobia, Rachycentron canadum, by application of a quality index method: Shelf life of farmed cobia. J. World Aquac. Soc., 48(1), 70–82, 2017. 67. Araujo WSC, De Lima CLS, Peixoto Joele MRS, and Lourenco LDFH. Development and application of the quality index method (QIM) for farmed tambaqui (Colossoma macropomum) stored under refrigeration: Application of the quality index method to establish fish shelf life. J. Food Saf., 37(1), e12288, 2017. 68. Li X, Chen Y, Cai L, Xu Y, Yi S, Zhu W, ... Lin H. Freshness assessment of turbot (Scophthalmus maximus) by quality index method (QIM), biochemical, and proteomic methods. LWT Food Sci. Technol., 78, 172–180, 2017. 69. Ahmadi Shalhe M, Khodanazary A, and Hosseini SM. Development of a quality index method (QIM) scheme for whole goldlined seabream Rhabdosargus sarba stored in ice. Int. J. Food Prop., 21(1), 2539–2549, 2018. 70. Diler A, and Genc İY. Apractical quality index method (QIM) developed for aquacultured rainbow trout (Oncorhynchus mykiss). Int. J. Food Prop., 21(1), 858–867, 2018. 71. Freitas J, Vaz-Pires P, and Camara JS. Freshness assessment and shelf-life prediction for seriola dumerili from aquaculture based on the quality index method. Molecules, 24(19), 3530, 2019. 72. Ghani Kuvei F, Khodanazary A, and Zamani I. Quality index method (QIM) sensory scheme for gutted greenback grey mullet Chelon subviridis and its shelf life determination. Int. J. Food Prop., 22(1), 618–629, 2019. 73. Khodanazary A. Freshness assessment of shrimp Metapenaeus affinis by quality index method and estimation of its shelf life. Int. J. Food Prop., 22(1), 309–319, 2019. 74. Vazquez-Sanchez D, Garcia EES, Galvao JA, and Oetterer M. Quality index method (QIM) scheme developed for whole Nile tilapias (Oreochromis niloticus) ice stored under refrigeration and correlation with physicochemical and microbiological quality parameters. J. Aquat. Food Prod. Technol., 29(3), 1–13, 2020. 75. Bernardi DC, Mársico ET, and Freitas MQD. Quality index method (QIM) to assess the freshness and shelf life of fish. Brazilian Arch. Biol. Technol., 56, 587–598, 2013. 76. Ndraha N. Fish quality evaluation using quality index method (QIM), correlating with physical, chemical and bacteriological changes during the ice-storage period: A review. In Isnansetyo A and Nuringtyas TR (eds.). Proceeding of the 1st International Conference on Tropical Agriculture. Springer International Publishing, NewYork City. pp. 185–196, 2017.

Quality Index Methods

495

77. Bernardo YA, Rosario DK, Delgado IF, and Conte‐Junior CA. Fish quality index method: Principles, ­weaknesses, validation, and alternatives-A review. Comp. Rev. Food Sci. Food Saf., 19(5), 2657–2676, 2020. 78. ISO 8589. Sensory Analysis-General Guidance for the Design of Test Rooms, 1st edn., International Standard, Geneva, Switzerland, 1988. 79. NMKL procedure No. 12. Guiding on Sampling. Nordic Committee on Food Analysis, Copenhagen, Denmark, 2002. Available at https://www.nmkl.org. 80. Codex: Recommended International code of Practice for Fresh Fish CAC/RCP 9-1976Codex standards for methods of analysis and sampling, “Sampling Plans for Prepackaged Foods (AQL 6.5),” XOT 13-1969, Rome, FAO/WHO Codex. Alimentarius. Codex XOT 13-1969, 1976. 81. Nielsen D and Hyldig G. Influence of handling procedures and biological factors on the QIM evaluation of whole herring (Clupea harengus L.). Food Res. Int., 37(10), 975, 2004. 82. Huidobro A, Pastor A, Lopez-Caballero ME, and Tejada M. Washing effect on the quality index method (QIM) developed for raw gilthead seabream (Sparus auratus). Eur. Food Res. Technol., 212, 408, 2001. 83. Howgate P. Approaches to the Definition and Measurement of Storage Life of Chilled and Frozen Fish. Torry Research Station, Aberdeen, UK, 1985. 84. Martinsdóttir E and Blomsterberg F. Sjálfvirk ferskleikameling með RT-geðaflokkara 12. RIT Icelandic Fisheries Laboratories, 1987. 85. Magnússon H, Martinsdóttir E, and Steinpórsson P. Áhrif frystingar og frystigeymslu porsks eftir píðingu. 26. Icelandic Fisheries Laboratories REPORT no. 26, 1990. 86. Rehbein H, Martinsdóttir E, Blomsterberg F, Valdimarsson G, and Öehlenschlager J. Shelf life of icestored redfish, Sebastes marinus and S. mentella. Int. J. Food Sci. Technol., 29, 303, 1994. 87. Martinsdóttir E, Sveinsdóttir K, and Olafsdóttir G. Development and implementation of a computerised sensory system (QIM) for fish freshness. Icelandic Fisheries Laboratories Project Report 11, 2000. 88. Bremner HA, Statham JA, and Sykes SJ. Tropical species from the North-West Shelf of Australia. Sensory assessment and acceptability of fish stored on ice. Proceedings of the Sixth Session IPFC Working Party on Fish Technology and Marketing, Melbourne 1984 FAO. Fish Rep., 317(Suppl.), 41, 1984.

25

Sensory Descriptors Grethe Hyldig Technical University of Denmark

25.1 INTRODUCTION The sensory quality of seafood is influenced by its treatment and processing from harvest through transportation, storage, and processing. Sensory analysis of seafood has therefore, for years, played a natural part of the seafood chain.

25.1.1 Sensory Analysis In sensory analysis, appearance, odor, flavor, and texture are evaluated using the human senses of vision, smell, taste, touch, and hearing. Scientifically, the process can be divided into three steps: (i) detection of a stimulus by the human sense organs; (ii) evaluation and interpretation by a mental process; and (iii) the response of the assessor to the stimuli.

25.1.2 Objective and Subjective Sensory Analysis What is Special about sensory analysis is that it can be both objective and subjective. The objective tests include discriminative (triangle test and forced choice) and descriptive (profiling and structured scaling) sensory tests. Both groups of tests are analytical measurements of the intrinsic quality of the product. Subjective tests are used for consumer testing and measure the attitude and emotional response of the consumer toward the product. Subjective tests can be applied in fields like market research and product development where the reaction of the consumer is needed. Objective tests use a sensory panel consisting of human beings that are tested in the use of their senses, and the sensory panel objectively describes attributes of products using defined sensory descriptors (ISO 3972, 2011; ISO 8586, 2012; and ISO 13299, 2016). Whereas subjective tests use untrained human beings (consumers) that answer subjectively, for example, how much they like/dislike a product. The objective sensory analytical methods do not depend on whether assessors like or dislike a certain item. Instead, it operates by determining the intensities of sensory descriptors such as sweetness, amine, sourness, softness, etc. In the following, the focus will be on sensory descriptors that are used in objective sensory tests.

25.2  THE HUMAN SENSES In a sensory test, the first sense that the assessor uses is the vision. The vision is used to measure the appearance, such as color and texture properties. The next sense is the olfactory, which is used for detecting odors. Then the assessor touches the sample, with the fingers, directly or indirectly by using a tool (ex. a spoon or fork), and in the mouth, where the tongue and lips are used to measure the texture properties. In the mouth, the sense of gestation and the trigeminal sense are used to measure the taste, pain, and cooling effects. During the whole assessment, the sense of hearing is used to measure the sound both outside and inside the mouth, like when a cracker is broken or during chewing.

496

DOI: 10.1201/9781003289401-29

Sensory Descriptors

497

25.2.1 The Sense of Vision The visual sense is often equated with only color but also provides input on many appearance attributes such as size, shape, and surface structure. In particular, the visual senses can provide an early and strong expectation of the flavor and textural properties of food. The visual receptors, the rods and cones, are located in the retina of the eye. These receptors contain light-sensitive pigments that change shape when stimulated by light energy, leading to the generation of electrical nerve impulses that travel along the optic nerves to the brain. There are approx. 120 million rods in the retina. The maximum rod concentration is approx. 20° from the foveal area; this area is the parafovea. The six million cones operate at higher light intensities (levels of illumination) and provide chromatic information (color), allowing photopic vision. The light reflected from an object, or the light passing through an object, falls on the corona, travels through the lens, and from there to the retina, where most of the light falls on a small hallow in the retina, the fovea, a small depression located in a yellow-colored spot (macula lute) on the retina. The rods are capable of operating at extremely low light intensities (less than 1 lux). The rods yield only achromatic (black/white) information, and under low light conditions, this gives a scotopic vision with no color perception. The cones are concentrated on the fovea, where the highest color resolution occurs. When viewing an object, the unconscious movement of the eyes serves to bring the image of the object onto the foveal areas. The cones contain three color-sensitive pigments, each responding to red (two polymorphic variations at 552 and 557 nm), green (at 530 nm), or blue light (at 426 nm) most sensitively (Merbs and Nathans, 1993, 1995). People lacking one of these pigments are color-blind categories, and depending on which pigments they are missing can be classified into different groups. The genes for the more common forms of color-blindness are recessive and carried on the X chromosome. Thus, the trait is seen much more frequently in men than in women.

25.2.2 The Olfactory Sense The odor response is much more complex. The olfactory receptors are located very high in the nasal cavity. There are several million receptors on each side of the nose and they are highly ciliated. The function of the cilia is to greatly increase the surface area by exploring the receptors to chemical stimuli. Odors are detected as volatiles entering the nasal passage, either directly via the nose or indirectly through the retronasal path via the mouth. Some 150–200 odor qualities have been recognized (Meilgaard, Civille, and Carr, 2006). The odor receptors are easily saturated and specific anosmia (blindness to specific odors) is common. It is thought that the wide range of possible odor responses contribute to variety in flavor perception.

25.2.3 The Sense of Touch Texture is perceived by the sense of touch and comprises two components: somesthesis, a tactile surface response from skin, and kinesthesis (or proprioception), a deep response from muscles and tendons. The touch stimuli themselves can arise from tactile manipulation of the food with hands and fingers, either directly or through the intermediary of utensils such as a knife or spoon. Oral contact with food can occur through the lips, tongue, palate, and teeth, all of which provide textural information. For many foods, visual stimuli will generate an expectation of textural properties. As food enters the mouth and is either bitten or manipulated between the tongue and palate, changes occur to the structure of the food that strongly influences the way in which tastants and odorants are released from the food. Of particular importance are temperature increases (hot foods) or decreases (cold foods) and dilution by saliva.

498

Handbook of Seafood and Seafood Products Analysis

25.2.4 The Gustatory Sense Taste is defined as the response by the sense organs on the tongue and the soft palate contains the taste receptors. These have been defined as four primary basic taste sensations – salt, sweet, sour, bitter, and umami. The taste receptors are organized into groups of cells, known as tastebuds, located within specialized structures called papillae. These are located mainly on the tip, sides, and rear upper surface of the tongue. In a study by Correa et al. (2013), they found that adults have 140.20 ± 5.50 papillae. Taste buds are onion-like structures; each taste bud contains approximately 50–100 taste cells. A single-taste cell may be multimodal, with several types of receptors; one type may be more active than others on that cell. Each taste receptor cell is connected through a synapse to a sensory nerve ending, which is a peripheral afferent fiber, sending the taste-coding information to the brain. On the surface of the tongue, the taste buds are located within the taste papillae. On the front and edges of the tongue, there are slightly larger mushroom-shaped fungiform papillae (reddish spots). There are over a hundred on each side. These papillae contain from two to four taste buds each (Arvidson, 1979) (Miller and Bartoshuk, 1991). Along the sides of the tongue, there are several parallel grooves about two-thirds of the way back from the tip to the root. These are the foliate papillae; each contains several hundred taste buds. At the back of the tongue, there are large button-shaped bumps arranged in an inverted V. These are circumvallate papillae, which also contain several hundred taste buds. Frequency counts of taste buds show that people with higher taste sensitivity tend to possess more taste buds (Bartoshuk, Duffy, and Miller, 1994). Saliva plays an important part in taste function, both as a carrier of sapid molecules to the receptors and because it contains substances capable of modulating taste receptors. Saliva contains sodium and other cations, bicarbonate capable of buffering acids, and a range of proteins and mucopolysaccharides that give it its slippery and coating properties.

25.2.5 The Chemical/Trigeminal Sense The chemical sense corresponds to a pain response through stimulation of the trigeminal nerve. This effect is produced by chemical irritants such as ginger and capsaicin (from chili), both of which give a heat response, and chemicals such as menthol and sorbitol, which give a cooling response. Astringency is a chemically induced complex of tactile sensations.

25.2.6 The Sense of Hearing Sound emission from crisp and crunchy foods has been shown to be of great importance in the perception of their texture and to form a basis for discrimination of crisp and crunchy foods.

25.3  THE USE OF SENSORY DESCRIPTORS In analytical sensory studies, a trained panel objectively describes the attributes of products (ISO 11035, 1994; ISO 5492, 2008). The criteria for specific sensory descriptors, the key aspects of the words, which should be considered are: (i) that the descriptors should be orthogonal; (ii) that they should be based on underlying structure if it is known; (iii) that the descriptors should be based on a broad reference set; (iv) that they should be precisely defined; and finally, that they should be “primary” rather than “integrated”. When developing a set of sensory descriptors that can be used for the sensory characterization of a product, the process is split up into a qualitative and a quantitative part.

499

Sensory Descriptors

The qualitative part consists of collecting descriptive words for appearance, odor, taste, and texture. First, assessors suggest words describing the sensory attributes and then words from literature are added to the vocabulary. The main point of the qualitative part is to group similar words in a mental map by the assessors with help from the panel leader. A verbal explanation of the descriptors is developed in cooperation with both panel leaders and the panel. References of the different attributes are found. If it is necessary to reduce the vocabulary, further multivariate data analysis can be used. In the quantitative part, the panel is trained in the use of the scale that is chosen for the sensory analysis. In a study of Green-Petersen, Nielsen, and Hyldig (2006), they performed a sensory profile of the most common chilled and frozen salmon products available to consumers on the Danish market. The sensory profiling was made on 12 salmon products, varying in salmon species, origin, storage method, and time. Samples stored in ice between 7 and 16 days, frozen for one month, or stored in a modified atmosphere for five days all had sensory profiles dominated by sea/seaweed odor, juicy and oily texture, fresh fish oil, sweetness, and mushroom flavor. The list of words from this study in the qualitative part of developing the set of sensory descriptors is shown in Table 25.1. As it can be seen, the first list of words consisted of 20 words for odor, 12 for appearance, 24 for taste/flavor, and 12 for texture. Based on the criteria that the words should be relevant and discriminate clearly between the salmon samples, they should be non-redundant, and they should be cognitively clear to the assessors, the number of words was reduced, and the selected descriptors for the sensory profiling are shown in Table 25.2.

TABLE 25.1 Words from the First Session in the Qualitative Part in Developing a Set of Sensory Descriptors Used for Sensory Profiling of Salmon Odor Green grass Amine/farmyard Sickly sweet Caramel Sweetness/ fermented grass Appearance Bright/dull Discoloration Oily Taste/flavor Warm milk Mushroom Hey Green Metal Cream Texture Fiber Fatty Sticky

Cucumber Sour/sour socks Warm milk Burnt Bear and bread

Sea Rancid Sweet Hay/dry grass Mushroom

Seaweed Sourish Wet dog Pleasant farmyard Forest floor/earthy

Slimy surface Coagulated protein Salmon

Shiny Moisture in the bowl Orange

Perl-shiny Orange oil droplets Redish

Sweetness

Fish oil/fresh nuts Sourish Rancid Bitter Mealy taste Brown soap

Nutty

Soft/firm Loos flakes Grainy

Dry/juicy Flaky (firm) Mealy

Sea Amine/farmyard Sour Salty Fermented green grass Watery Oily Fibrous

Sickly sweet Cooked potato Chemical Cooked egg white Cabbage/broccoli

500

Handbook of Seafood and Seafood Products Analysis

TABLE 25.2 Sensory Descriptors Used for Sensory Profiling of Salmon Descriptor

Description

Odor Sea/seaweed Sourish Sweet Rancid Sour Appearance

Fresh seaweed, fresh sea Acidic, fresh citric acid Sweet Rancid fish, paint, varnish Sour dishcloth/sour sock

Discolored Salmon color

Brown or yellow spots, dark areas Evaluated with a SalmoFan ruler (Roche)a

Texture Juicy Firm Oily Flavor Fresh fish oil Sweet Sourish Cooked potatoes Mushroom Rancid Salt

The samples ability to hold water after 2–3 chews Force required to compress the sample between the molars Amount of fat coating in the mouth Fresh oil, fresh green hazelnut Sweet, warm milk Acidic, fresh citric acid Cooked peel potatoes Mushroom flavor Rancid fish, paint, varnish Salt

Evaluated with a SalmoFan ruler (Roche) on an interval scale from 20 to 34, where 20 is more light salmon color (pink) than 34.

a

25.4  SENSORY DESCRIPTORS IN SEAFOOD Criteria for the selection of attributes, which can discriminate between samples have to be relevant for the specific seafood, discriminate clearly between the samples, be non-redundant, and be cognitively clear to the assessors. To see if these demands are fulfilled, the sensory data can be analyzed with regards to the signal-to-noise relation for each assessor and attribute. The signal to noise analysis can be evaluated with multivariate data analysis (Thybo and Martens, 2000; Tomic et al., 2010). To be cognitively clear and to make reference samples it is good to have a definition of the sensory attribute. Reference samples for the different attributes can be found not only in seafood samples but also in other commodities such as vegetables, e.g., cucumber and boiled potato. Another example is the sensory attribute warm milk, where only heated and not boiled milk can be used because when boiling milk, a sulfurous odor is developed.

25.4.1 Appearance Appearance can be color/discolor and texture properties such as flaky or gaping. Flaky is assessed by pressing on the sample with a fork; if the muscle separates into flakes, the sample has a high intensity of flakiness. The fish is gaping when the muscle splits up into factions or chaps. Gaping

501

Sensory Descriptors

can be seen both in raw and heat-treated fish muscle. For assessing color, it is helpful to use standards such as the natural color system NCS® or, for salmon, the SalmoFanTM lineal from Roche. A description of the attributes is given in Table 25.3.

25.4.2 Odor The odor often has a higher intensity than the flavor of seafood. A description of the odor attributes is given in Table 25.4.

25.4.3 Taste/Flavor A description of the attributes for taste/flavor and aftertaste is given in Table 25.5.

25.4.4 Texture Texture is by definition a sensory parameter and only a human being can perceive, describe, and quantify texture adequately. However, texture is very difficult to evaluate due to its complexity. In many cases, several terms are used to describe the same characteristics. In other cases, the same term is used to describe several characteristics. In addition, the same word may have different meanings to different people (Szczesniak 1963). The use of sensory texture profiling therefore requires highly trained assessors (Meilgaard, Civille, and Carr, 2006; Barroso, Careche, and Borderías, 1998). Sensory perception of texture has been thoroughly reviewed by Jack, Paterson, and Piggott (1995). The best way to get a “full picture” of the texture is by using the Texture Profile Method (Johnson et al., 1981; Bourne, 1978; Breene, 1975; Friedman, Whitney, and Szczesniak, 1963). A texture profile is defined as the sensory analysis of the texture complex of a food in terms of its mechanical, geometrical, and compositional (fat and moisture) characteristics, the degree of each trait present, and the order in which they appear from the first bite through complete mastication (Brandt, Skinner, and Coleman, 1963). In Table 25.6, there is a long list of words used in the literature for describing texture properties.

TABLE 25.3 Sensory Descriptors for Appearance (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

Beige

Intensity of beige color

Brownish Brown

Light brown The brownness of the meat near the head (cross-section at cut end) of cooked shrimp Whitish form on top of and beside sample Amount of coagulation that appears on the surface of the salmon steak 1 = white/yellow, 7 = strong red Crumbliness of steak when cut with a knife Color distribution uneven

Coagulated protein

Color intensity and shade Crumbly Discoloration

References Farmer, McConnell, and Kilpatrick (2000) Warm, Nielsen, and Hyldig (2000)

Warm, Nielsen, and Hyldig (2000) Farmer, McConnell, and Kilpatrick (2000) Sivertsvik et al. (1999) Farmer, McConnell, and Kilpatrick (2000) Ginés et al. (2004) (Continued)

502

Handbook of Seafood and Seafood Products Analysis

TABLE 25.3 (Continued ) Sensory Descriptors for Appearance (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

Discolored

Brown or yellow spots, dark areas

Discolored Fat droplets in water Flacky

Not the natural color Quantity and size of fat droplets in the liquid Visible flakiness of steak when cut with a knife Tissue parts into flakes by pressing with fork Tissue parts into flakes by press with fork Light gray The amount of light reflected from the meat, from dull to glossy From red = 1 to yellow = 9

Grayish Glossiness Hue

Yellow = 1, red = 9 Juicy appearance Moist appearance Orange Paleness Peach Pink Red/orange Separation Shiny Whiteness Whitish Yellow water

Amount of juice that has seeped onto the plate from the salmon Visible moistness of steak when cut with a knife Intensity of orange color in the uncut steak Intensity of paleness in the uncut steak Intensity of peach color in the uncut steak Intensity of pink color in the uncut steak The redness of the surface Oiliness of the juice seeping out of the salmon Gloss of tissue caused by oil Intensity of white color in the uncut steak Not totally white Degree of yellow water liquid present

References Green-Petersen, Nielsen, and Hyldig (2006) Larsen et al. (2003) Ginés et al. (2004) Farmer, McConnell, and Kilpatrick (2000) Warm, Nielsen, and Hyldig (2000) Larsen et al. (2003) Warm, Nielsen, and Hyldig (2000) Erickson et al. (2007) Einen and Skrede (1998); Hemre et al. (2004) Rørå et al. (1998) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Erickson et al. (2007) Farmer, McConnell, and Kilpatrick (2000) Warm, Nielsen, and Hyldig (2000) Farmer, McConnell, and Kilpatrick (2000) Warm, Nielsen, and Hyldig (2000) Ginés et al. (2004)

TABLE 25.4 Sensory Descriptors for Odor (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute Acid Amine

Definition

References

Vinegar A solution of trimethylamine Urine

Stohr et al. (2001) Cardinal et al. (2004) Stohr et al. (2001)

Fishy/trimethylamine

Larsen et al. (2003) (Continued)

503

Sensory Descriptors

TABLE 25.4 (Continued ) Sensory Descriptors for Odor (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

Bacon Blue cheese Boiled corn Boiled egg Boiled prawn Butter Cabbage Canned tuna

Odor corresponding to the product Musty Sweet aroma in boiled corn Smell of boiled egg Typical aroma in boiled prawn Caramel Gas/garlic Aroma of canned tuna

Cardboardy Cheese Cold ash Cooked fish

Marine fish off-odor notes Sour feet Odor of ash once the fire is out Aroma of cooked fish Typical aroma of cooked fish

Cooked potato Cucumber-like Earthy

As newly cooked potato Grated cucumber Intensity of any earthy/peaty odor

Farmyard

Intensity of manure/cow-dung odor

Fat Fish oil Fish oil

Fresh fish oil, unripe hazelnut Smell of canned mackerel or sardine “Fatty” odor, herring oil, typical smell of fresh herring Intensity of oily (fish oil) odor

Fish oil Fish/herring oil Fishy Fishy Fresh odor

Fried chicken Green Green aroma Grilled fish Ham Herring Hydrogen sulfide Irritate Marine/seaweed Metallic

“Fatty” odor, herring oil, typical smell of fresh herring Smell of raw not fresh fish Trimethylamine crystals (Marine fish off-odor notes) The typical odor of rainbow trout, an element of fresh, slightly acidulous odor intensifies the freshness Aroma of fried chicken Fresh green odor of soybean milk Freshly cut grass Aroma of grilled fish, roasted fish oil Cooked meat Odor corresponding to the product Egg Ammonia-like Fresh marine Warm metal, blood

References Cardinal et al. (2004) Stohr et al. (2001) Morita, Kubota, and Aishima (2001) Morita, Kubota, and Aishima (2001) Morita, Kubota, and Aishima (2001) Stohr et al. (2001) Stohr et al. (2001) Nielsen et al. (2004); Nielsen et al. (2005) Hong et al. (1996) Stohr et al. (2001) Cardinal et al. (2004) Morita, Kubota and Aishima (2001, 2003) Nielsen et al. (2004, 2005) Larsen et al. (2003) Hong et al. (1996) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Vogel et al. (2006) Morita, Kubota, and Aishima (2003) Nielsen et al. (2004, 2005) Farmer, McConnell, and Kilpatrick (2000) Nielsen et al. (2004, 2005) Nielsen et al. (2004, 2005) Hong et al. (1996) Johansson et al. (2000)

Morita, Kubota, and Aishima (2003) Morita, Kubota, and Aishima (2003) Stohr et al. (2001) Morita, Kubota, and Aishima (2003) Stohr et al. (2001) Cardinal et al. (2004) Stohr et al. (2001) Morita, Kubota, and Aishima (2001) Larsen et al. (2003) Nielsen et al. (2004, 2005) (Continued)

504

Handbook of Seafood and Seafood Products Analysis

TABLE 25.4 (Continued ) Sensory Descriptors for Odor (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

Rancid

Rancid fish, paint, varnish

Rotten seaweed Sea/seaweed

Putrefied seaweed Fresh seaweed, fresh sea

Sickly sweet Smokiness Sour

Sickly sweetness Intensity of smoky odor Sour caused by putrifaction Sour dishcloth/sour sock Sour milk

Sourish

Acidic, acetic acid, citric acid

Sweet

Sucrose-like Sweet

Wet dog

As a wet dog

References Nielsen et al. (2004, 2005); Green-Petersen, Nielsen, and Hyldig (2006); Fu, Xu, and Wanga (2009); Fan et al. (2021) Larsen et al. (2003) Green-Petersen, Nielsen, and Hyldig (2006) Larsen et al. (2003) Vogel et al. (2006) Larsen et al. (2003) Green-Petersen, Nielsen, and Hyldig (2006) Jaffrès et al. (2011); Fan et al. (2021) Green-Petersen, Nielsen, and Hyldig (2006); Nielsen et al. (2005); Vogel et al. (2006) Vogel et al. (2006) Nielsen et al. (2004, 2005); Green-Petersen, Nielsen, and Hyldig (2006) Larsen et al. (2003)

TABLE 25.5 Sensory Descriptors for Flavor and Aftertaste (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

References

Acidulous Amine Bitter

Fruit-acid like flavor Fishy/trimethylamine Quinine-/or caffeine-like

Chicken-like aftertaste Chemical

Intensity of chicken-like aftertaste

Einen and Thomassen (1998) Larsen et al. (2003) Erickson et al. (2007); Larsen et al. (2003); Vogel et al. (2006); Warm, Nielsen, and Hyldig (2000) Farmer, McConnell, and Kilpatrick (2000) Kang et al. (2007)

Cooked potato Cooked shrimp Dried sea mussel Dried shrimp

Aromatic associated with lotion and cleanser As newly cooked peeled potatoes The flavor associated with cooked shrimp Aromatics associated with dried sea mussel Aromatics associated with crustacean such as dried shrimp

Green-Petersen, Nielsen, and Hyldig (2006); Larsen et al. (2003) Erickson et al. (2007) Kang et al. (2007) Kang et al. (2007) (Continued)

505

Sensory Descriptors

TABLE 25.5 (Continued ) Sensory Descriptors for Flavor and Aftertaste (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

References

Earthy aftertaste

Intensity of earthy aftertaste

Earthy flavor

Intensity of any earthy/peaty flavor Intensity of manure/cow-dung flavor Impression of fatness perceived as aromatic notes and mouth coating film Intensity of any other fish-like flavors Fresh oil, fresh green hazelnut

Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Farmer, McConnell, and Kilpatrick (2000) Turchini et al. (2007)

Farmyard flavor Fatty flavor

Fishy flavor Fresh fish oil Fresh taste

Herring Iodine aftertaste Marine Metallic

Metallic aftertaste “MSG”

The typical taste of rainbow trout. In this investigation, the typical taste was scored after the sample had been masticated five times. As element of fresh, slightly acidulous taste intensifies the freshness Typical taste of fresh herring Aftertaste associated with the chemical iodine Fresh marine Flavor associated with metal Warm metal, blood Intensity of metallic aftertaste

Mushroom

Fundamental taste sensation of which monosodium glutamate or other nucleotides are typical Raw mushroom flavor

Musty Oily

Flavor associated with damp earth Intensity of fish oil flavor Fresh fish oil, unripe hazelnut

Oily aftertaste

Intensity of fish oil aftertaste

Rancid

Paint, varnish The atypical taste associated with oxidized fat Rancid fish, paint, varnish

Salmon-like flavor

Intensity of distinctive salmonlike flavor

Farmer, McConnell, and Kilpatrick (2000) Green-Petersen, Nielsen, and Hyldig (2006) Johansson et al. (2000)

Nielsen et al. (2005, 2004) Erickson et al. (2007) Warm, Nielsen, and Hyldig (2000) Ginés et al. (2004) Nielsen et al. (2005, 2004); Vogel et al. (2006); Larsen et al. (2003) Farmer, McConnell, and Kilpatrick (2000) Kang et al. (2007)

Green-Petersen, Nielsen, and Hyldig (2006); Larsen et al. (2003) Ginés et al. (2004) Farmer, McConnell, and Kilpatrick (20008) Vogel et al. (2006) Farmer, McConnell, and Kilpatrick (2000) Nielsen et al. (2004) Johansson et al. (2000); Fu, Xu, and Wanga (2009); Fan et al. (2021) Green-Petersen, Nielsen, and Hyldig (2006); Nielsen et al. (2005) Farmer, McConnell, and Kilpatrick (2000) (Continued)

506

Handbook of Seafood and Seafood Products Analysis

TABLE 25.5 (Continued ) Sensory Descriptors for Flavor and Aftertaste (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

References

Salty

Intensity of salt-like flavor

Erickson et al. (2007), Farmer, McConnell and Kilpatrick (2000) Nielsen et al. (2005, 2004); Vogel et al. (2006); Mall and Schieberle (2017); Fan et al. (2021) Kang et al. (2007); Turchini et al. (2007) Farmer, McConnell, and Kilpatrick (2000); Vogel et al. (2006) Jaffrès et al. (2011); Fan et al. (2021) Nielsen et al. (2004) Green-Petersen, Nielsen, and Hyldig (2006); Nielsen et al. (2005) Larsen et al. (2003); Vogel et al. (2006) Green-Petersen, Nielsen, and Hyldig (2006); Nielsen et al. (2005) Erickson et al. (2007); Kang et al. (2007); Larsen et al. (2003); Vogel et al. (2006); Warm, Nielsen, and Hyldig (2000) Ginés et al. (2004)

Typical taste of salt, seawater

Smokiness

The taste on the tongue associated with sodium ions Intensity of smoky flavor

Sour

Sour milk

Sourish

Acidic, acetic acid, citric acid Acidic, acetic acid, citric acid

Sweet

Sourish like in fruit or as fresh fruit acids Sweet as in warm milk Sucrose-like

Sweet/fresh Time

Characteristic flavor of cooked Artic charr fillets Time when aftertaste starts

Farmer, McConnell, and Kilpatrick (2000)

TABLE 25.6 Sensory Descriptors for Texture (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute Adhesiveness

Astringency Chewiness Cohesiveness

Definition Degree to which the sample sticks to the mouth surface Force required to remove the material that adheres to the mouth during the normal eating process The feeling which shrivels the tongue associated with tannin or alum The time required to masticate sample to a consistency acceptable for swallowing The extent to which a material can be deformed before it ruptures Degree to which the sample deforms before it ruptures during a bite between the molar teeth Degree to which a substance is compressed between the teeth before it breaks

References Hamann and Webb (1979) Sánchez (1996) Kang et al. (2007) Erickson et al. (2007) Borderias, Lamua, and Tejada (1983) Hamann and Webb (1979) Sánchez (1996) (Continued)

507

Sensory Descriptors

TABLE 25.6 (Continued ) Sensory Descriptors for Texture (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

Crispness

The amount of force exerted during first incisor bite that generates a high-pitched sound The ability of the material to return to its original shape after deformation. Judged by compressing the substance slightly between the molars, or between the tongue and palate, and noting to what extent the material returns to its original shape The ability of the sample to regain its form after the first compression by the molars Amount of fat coating the mouth surfaces after three chews The presence of individual muscle fibers in the shrimp meat The force required to compress between tongue and palate (first bite/first chew) Force required to compress the sample between the molars The effort to bite through the fish sample with the front teeth The force required to compress the sample between the molar teeth The force required to compress the material between the molars or between the tongue and the palate The amount of force needed to deform the head-end of the shrimp meat by first biting through skin with incisors, then chewing with molars (skin toward molars). The amount of small, rounded particles Heterogeneity. The amount of particles in the sample after three chews Resistance to breakdown on chewing to a state suitable for swallowing Force required to compress a substance between molar teeth (in the case of solids) or between tongue and palate (semisolids) The perceived force required to compress the sample using the molar teeth Chewing with molars

Elasticity

Elasticity Fatty mouth feel Fibrous Firm

Firmness

Grainy Gritty Hardness

Juicy

Juiciness

Force required to bite the shrimp between second and third segment with incisors Compress sample between molar teeth and release pressure Amount of wetness The sample ability to hold water after two to three chews The amount of moisture to masticate sample to a consistency acceptable for swallowing

References Erickson et al. (2007) Borderias, Lamua, and Tejada (1983)

Nielsen et al. (2004, 2005) Nielsen et al. (2004, 2005) Erickson et al. (2007) Larsen et al. (2003) Green-Petersen, Nielsen, and Hyldig (2006) Hurling, Rodell, and Hunt (1996); Iseya, Sugiura, and Sarki (1996) Hamann and Webb (1979), Nielsen et al. (2004), Nielsen et al. (2005) Borderias, Lamua, and Tejada (1983) Erickson et al. (2007)

Larsen et al. (2003) Nielsen et al. (2004, 2005) Borderias, Lamua, and Tejada (1983) Sánchez (1996)

Cardello et al. (1982) Iseya, Sugiura, and Sarki (1996) Gundavarapu, Hung, and Reynolds (1998) Leblanc and Leblanc (1990) Larsen et al. (2003) Green-Petersen, Nielsen, and Hyldig (2006); Nielsen et al. (2004, 2005) Erickson et al. (2007) (Continued)

508

Handbook of Seafood and Seafood Products Analysis

TABLE 25.6 (Continued ) Sensory Descriptors for Texture (Only References Where the Evaluation of the Property Is Described Are Included in the Table) Attribute

Definition

References

Water release during chewing

Turchini et al. (2007)

Oiliness Oily

Degree to which oil is perceived after chewing Amount of fat coating in the mouth

Sliminess Soft

The feeling of a slimy film in the mouth Resistance to a very slight opening and shutting of the jaws Force required to compress samples

Vogel et al. (2006) Green-Petersen, Nielsen, and Hyldig (2006) Erickson et al. (2007) Schubring and Oehlenschläger (1997) Warm, Nielsen, and Hyldig (2000)

Springiness

Degree to which a product returns to its original shape once it has been compressed between the teeth Degree to which the sample rapidly returns to its original shape after a partial deformation between the molar teeth Resistance to breakdown in substructures when compressing between tongue and palate

Tenderness

Sánchez (1996)

Hamann and Webb (1979)

Schubring and Oehlenschläger (1997)

REFERENCES Arvidson, K. 1979. Location and variation in number of taste buds in human fungiform papillae. Scandinavian Journal of Dental Research, 87: 435–442. Barroso, M., Careche, M. and Borderías, AJ. 1998. Quality control of frozen fish using rheological techniques. Trends in Food Science and Technology, 9: 223–229. Bartoshuk, L.M., Duffy, V.B. and Miller, I.J. 1994. PTC/PROP tasting: Anatomy, psychophysics and sex effects. Physiology and Behavior, 56: 1165–1171. Borderias, A.J., Lamua, M. and Tejada, M. 1983. Texture analysis of fish fillets and minced fish by both sensory and instrumental methods. Journal of Food Technology, 18: 85–95. Bourne, M.C. 1978. Texture profile analysis. Food Technology, 22: 62–66, 72. Brandt, M.A., Skinner, E.Z. and Coleman, J.A. 1963. Texture profile method. Journal of Food Science, 28: 404–409. Breene, W.M. 1975. Application of texture profile analysis to instrumental food texture evaluation. Journal of Texture Studies, 6: 53–82. Cardello, A.V., Sawyer, F.M., Maller, O. and Digman, L. 1982. Sensory evaluation of the texture and appearance of 17 species of North Atlantic fish. Journal of Food Science, 47: 1818–1823. Cardinal, M., Gunnlaugsdottir, H., Bjoernevik, M., Ouisse, A., Vallet, J.L. and Leroi, F. 2004. Sensory characteristic of cold-smoked Atlantic salmon (Salmo salar) from European market and relationships with chemical, physical and microbiological measurements. Food Research International, 37: 181–193. Correa, M., Hutchinson, I., Laing, D.G. and Jinks, A.L. 2013. Changes in fungiform papillae density during development in humans. Chemical Senses, 38: 519–527. Einen, O. and Skrede, G. 1998. Quality characteristics in raw and smoked fillets of Atlantic salmon, Salmo salar, fed high-energy diets. Aquaculture Nutrition, 4: 99–108. Einen, O. and Thomassen, M.S. 1998. Starvation prior to slaughter in Atlantic salmon (Salmo salar). II. Muscle composition and evaluation of freshness, texture and colour characteristics in raw and cooked fillets. Aquaculture, 169: 37–53. Erickson, M.C., Bulgarelli, M.A., Resurreccion, A.V.A., Vendetti, R.A. and Gates, K.A. 2007. Sensory differentiation of shrimp using a trained descriptive analysis panel. LWT 40: 1774–1783. Fan, Y., Odabasi, A., Sims, C.A., Schneider, K.R., Gao, Z. and Sarnoski, P.J. 2021. Determination of aquacultured whiteleg shrimp (Litopanaeus vannemei) quality using a sensory method with chemical standard references. Journal of the Science of Food and Agriculture, 101: 5236–5244.

Sensory Descriptors

509

Farmer, L.J., McConnell, J.M. and Kilpatrick, D.J. 2000. Sensory characteristic of farmed and wild Atlantic salmon. Aquaculture, 187: 105–125. Friedman, H.H., Whitney, J.E. and Szczesniak, A.S. 1963. The texturometer - A new instrument for objective texture measurement. Journal of Food Science, 28: 390–396. Fu, X., Xu, S. and Wanga, Z. 2009. Kinetics of lipid oxidation and off-odor formation in silver carp mince: The effect of lipoxygenase and hemoglobin. Food Research International, 42: 85–90. Ginés, R., Valdimarsdottir, T., Sveinsdottir, K. and Thorarensen, H. 2004. Effect of rearing temperature and strain on sensory characteristics, texture, colour and fat of Arctic charr (Salvelinus alpinus). Food Quality and Preferences, 15: 177–185. Green-Petersen, D.M.B., Nielsen, J. and Hyldig, G. 2006. Sensory profiling of the most common salmon products on the Danish market. Journal of Sensory Studies, 21: 415–427. Gundavarapu, S., Hung, Y.-C. and Reynolds, A.E. 1998. Consumer acceptance and quality of microwavecooked shrimp. Journal of Food Quality, 21: 71–84. Hamann, D.D. and Webb, N.B. 1979. Sensory and instrumental evaluation of material properties of fish gels. Journal of Texture Studies, 10: 117–130. Hemre, G.-I., Karlsen, Ø., Eckhoff, K., Tveit, K., Mangor-Jensen, A. and Rosenlund, G. 2004. Effect of season, light and diet on muscle composition and selected quality parameters in farmed Atlantic cod, Gadus morhuaL. Aquaculture Research, 35: 683–697. Hong, L.C., Leblanc, E.L., Hawrysh, Z.J. and Hardin, R.T. 1996. Quality of Atlantic mackerel (Scomber scombrus L.) fillets during modified atmosphere storage. Journal of Food Science, 61: 646–651. Hurling, R., Rodell, J.B. and Hunt, H.D. 1996. Research note: Fiber diameter and fish texture. Journal of Texture Studies, 27: 679–685. Iseya, Z., Sugiura, S. and Sarki, H. 1996. Procedure for mechanical assessment of texture change in dried fish meat. Fisheries Science, 62: 772–775. ISO 11035. 1994. Sensory Analysis - Identification and Selection of Descriptors for Establishing a Sensory Profile by a Multidimensional Approach. International Organization for Standardization. Switzerland. ISO 13299. 2016. Sensory Analysis - Methodology - Guidance for Establishing a Sensory Profile. International Organization for Standardization, Geneva, Switzerland. ISO 3972. 2011. Sensory Analysis - Methodology - Method of Investigating Sensitivity of Taste. Reference number ISO 3972: 2011. International Organization for Standardization, Geneva, Switzerland. ISO 5492. 2008. Sensory Analysis - Vocabulary. International Organization for Standardization, Geneva, Switzerland. ISO 8586. 2012. Sensory Analysis - General Guidance for the Selection, Training and Monitoring of Assessors. International Organization for Standardization, Geneva, Switzerland. Jack, F.R., Paterson, A. and Piggott, J.R. 1995. Percieved texture: Direct and indirect methods for use in product development. International Journal of Food Science and Technology, 30: 1–12. Jaffrès, E., Lalanne, V., Macé, S., Cornet, J., Cardinal, M., Sérot, T., Dousset, X. and Joffraud, J.-J. 2011. Sensory characteristics of spoilage and volatile compounds associated with bacteria isolated from cooked and peeled tropical shrimps using SPME-GC-MS analysis. International Journal of Food Microbiology, 147: 195–202. Johansson, L, Kiessling, A, Kiessling, K.-H. and Berglund, L. 2000. Effects of altered ration levels on sensory characteristics, lipid content and fatty acid composition of rainbow trout (oncorhynchus mykiss). Food Quality and Preference, 11: 247–254. Johnson, E.A., Peleg, M., Sawyer, F.M., Segars, R.A. and Cardello, A. 1981. Mechanical methods of measuring textural characteristics of fish flesh. Refrigeration Science and Technology, 4: 93–103. Kang, M.-W., Chung, S.-J., Lee, H.-S., Kim, Y. and Kim, K.-O. 2007. The sensory interactions of organic acids and various flavors in Ramen soup systems. Journal of Food Science, 72(9): 639–647. Larsen, E., Hyldig, G., Dalgaard, P., Bech, A.C. and Østerberg, C. 2003. Consumers and experts responses to fresh cod fillets. In Luten, J.B., Oehlenschlager, J. and Ólafsdóttir, G. (eds.) Quality of Fish from Catch to Consumer: Labelling, Monitoring and Traceability. pp. 345–360. Wageningen Academic Publ., Wageningen, The Netherlands. Leblanc, R.J. and Leblanc, E.L. 1990. Effect of retort process time on the physical and sensory quality of canned lobster (Homarus americanus) meat. Journal of Food Processing and Preservation, 14: 345–368. Mall, V. and Schieberle, P. 2017. Evaluation of key aroma compounds in processed prawns (whiteleg shrimp) by quantitation and aroma recombination experiments. Journal of Agricultural Food Chemistry, 65: 2776−2783. Meilgaard, M., Civille, G.V. and Carr, B.T. 2006. Sensory Evaluation Techniques, 4th edition. CRC Press Ltd., London.

510

Handbook of Seafood and Seafood Products Analysis

Merbs, S.L. and Nathans, J. 1992. Absorbtion spectra of human cone pigments. Nature, 356: 433–435. Merbs, S.L. and Nathans, J. 1993. Role of hydroxyl-bearing amino acids in differently tuning the absorpion spectra of the human red and green cone pigments. Photochemistry and Photobiology, 58: 706–710. Miller, I.J. and Bartoshuk, L.M. 1991. Taste perseption, taste bud distribution and spatial relationships. In Getchell, T.V., Doty, R.L., Bartoshuk, L.M. and Snow, J.B. (eds.) Smell and Taste in Health and Disease. pp. 205–255. Raven, New York. Morita, K., Kubota, K. and Aishima, T. 2001. Sensory characteristics and volatile components in aroma of boiled prawns prepared according to experimental designs. Food Research International, 34: 473–481. Morita, K., Kubota, K. and Aishima, T. 2003. Comparison of aroma characteristics of 16 fish species by sensory evaluation and gas chromatographic analysis. Journal of the Science of Food and Agriculture, 83: 289–297. Nielsen, D., Hyldig, G., Nielsen, H.H. and Nielsen, J. 2004. Sensory properties of marinated herring (Clupea harengus) - Influence of fishing ground and season. Journal of Aquatic Food Product Technology, 13: 3–24. Nielsen, D., Hyldig, G., Nielsen, J. and Nielsen, H.H. 2005. Sensory properties of marinated herring (Clupea harengus) processed from raw material from commercial landings. Journal of the Science of Food and Agriculture, 85(1): 127–134. Rørå, A.M.B., Kvåle, A., Mørkøre, T., Rørvik, K.A., Stein, S.S. and Thomassen, M.S. 1998. Process yield, colour and sensory quality of smoked Atlantic salmon (Salmo salar) in relation to raw material characteristics. Food Research International, 31: 601–609. Sánchez, M.T. 1996. Food texture: Concept and measurement. Alimentaria, 272: 29–34. Schubring, R. and Oehlenschläger, J. 1997. Comparison of the ripening process in salted Baltic and North Sea herring as measured by instrumental and sensory methods. Zeitschrift für Lebensmittel Untersuchung und Forschung A, 205: 89–92. Sivertsvik, M., Rosnes, J.T., Vorre, A., Randell, K., Ahvenainen, R. and Bergslien, H. 1999. Quality of whole gutted salmon in various bulk packages. Journal of Food Quality, 22: 387–402. Stohr, V., Joffraud, J.J., Cardinal, M. and Leroi, F. 2001. Spoilage potential and sensory profile associated with bacteria isolated from cold-smoked salmon. Food Research International, 34: 797–806. Szczesniak, A.S. 1963. Classification of textural characteristics. Journal of Food Science, 28: 385–389. Thybo, A.K. and Martens, M. 2000. Analysis of sensory assessors in texture profiling of potatoes by multivariate modelling. Food Quality and Preference, 11: 283–288. Tomic, O., Luciano, G., Nielsen, A., Hyldig, G., Lorensen, K. and Næs, T. 2010. Analysing sensory panel performance in a proficiency test using the PanelCheck software. European Food Research Technology, 230(3): 497–511. Turchini, G.M., Moretti, V.M., Mentasi, T., Orban, E. and Valfré, F. 2007. Effects of dietary lipid source on fillet chemical composition, flavour volatile compounds and sensory characteristics in the freshwater fish tench (Tinca tinca L.). Food Chemistry, 102: 1144–1155. Vogel, B.F., Ng, Y.Y., Hyldig, G., Mohr, M. and Gram, L. 2006. Potassium lactate combined with sodium diacetate can inhibit growth of Listeria monocytogenes in vacuum-packed cold-smoked salmon and has no adverse sensory effects. Journal of Food Protection, 69: 2134–2142. Warm, K., Nielsen, J. and Hyldig, G. 2000. Sensory quality criteria for five fish species. Journal of Food Quality, 23: 583–601.

26

Sensory Aspects of Heat Treated Seafood Grethe Hyldig Technical University of Denmark

26.1 INTRODUCTION Sensory analysis plays an important role in quality control and quality assurance in the fish sector. To a certain extent, sensory analysis is also used in product development and optimization. Both objective and subjective sensory testing are used. The objective tests include discriminative (triangle test, forced choice) and descriptive (profiling, structured scaling) sensory tests. Both groups of tests are analytical measurements of the intrinsic quality of the product, whereas affective (subjective test) methods are used for consumer testing and measure the attitude and emotional response of the consumer toward the product. All these methods are used in the seafood chain. Descriptive sensory analysis such as profiling can be used to characterize post mortem changes of all kinds of seafood. Descriptive testing provides quantitative data and can either be very simple and used for the assessment of a single attribute or more complex and give a total characterization of the sensory quality. Several conditions are unique for the objective sensory profile of seafood and will be described in the following.

26.2 SEAFOOD 26.2.1 The Anatomy of Shellfish Shellfish is a broad term for all aquatic animals that have a shell of some kind. Both saltwater and freshwater invertebrates are considered shellfish. Shellfish is a misnomer because these invertebrates are definitely not fish. The term finfish is sometimes used to distinguish ordinary (vertebrate) fish from shellfish. Shellfish are separated into two basic categories – crustaceans and molluscs. Crustaceans have elongated bodies and jointed soft (crust-like) shells. The crustacean family includes crabs, crayfish, barnacles, lobsters, prawns, and shrimps. Molluscs are invertebrates with soft bodies covered by a shell of one or more pieces. Molluscs are further divided into gastropods, bivalves, and cephalopods. Gastropod often referred to as a univalve, can be any of several molluscs with a single (univalve) shell and single muscle. Among the more common gastropods are the abalone, limpet, periwinkle, snail, and whelk. A bivalve is any soft-bodied mollusc, such as a clam, scallop, oyster, or mussel, which has two shells hinged together by a strong muscle. Cephalopods are a class of mollusc that includes the octopus, squid, and cuttlefish. It is the most biologically advanced of the molluscs. All cephalopods share two common characteristics — tentacles attached to the head and ink sacs, which they use to evade their predators. The sensory quality of shellfish can vary considerably according to season, fishing grounds, farming conditions, stress, and harvesting conditions.

DOI: 10.1201/9781003289401-30

511

512

Handbook of Seafood and Seafood Products Analysis

26.2.2 The Anatomy of Fish Fish (vertebrates) have a backbone composed of segments (vertebrae) - and a cranium covering the brain. The backbone runs from the head to the tail. The vertebrates are extended dorsally to form neural spines, and in the trunk region, they have lateral processes that bear ribs. The ribs are cartilaginous or bony structures in the connective tissue between the muscle segments. Usually, there are also a corresponding number of false ribs or “pin bones”, extending more or less horizontally into the muscle tissue. These bones have to be removed if a bone-free fillet is the goal. Fish have muscle cells running in parallel and connected to sheaths of connective tissue (myocommata) anchored to the skeleton and the skin. The bundles of parallel muscle cells are called myotomes. The myocommata run in an oblique pattern perpendicular to the long axis of the fish, from the skin to the spine. This anatomy is ideally suited for the flexing muscle movements necessary for swimming through the water. All muscle cells extend the full length between two myocommata and run parallel with the longitudinal direction of the fish. The muscle mass on each side of the fish makes up the fillet, of which the upper part is termed the dorsal muscle and the lower part the ventral muscle.

26.2.3 Difference between Fish Species and between Individuals The variation in the chemical composition of fish is closely related to feed intake, migratory swimming, and sexual changes in connection with spawning. Fish will have starvation periods for natural or physiological reasons (such as migration and spawning) or due to external factors such as a shortage of food. Fish raised in aquaculture may also show variation in chemical composition, but in this case, several factors can be controlled, and the chemical composition may be predicted. To a certain extent, the fish farmer is able to design the fish by selecting the farming conditions. It has been reported that factors such as feed composition, environment, fish size, and genetic traits all have an impact on the composition and quality (Reinitz et al., 1979; Einen and Skrede, 1998; Nortvedt and Tuene, 1998; Baron et al., 2009; Green-Petersen and Hyldig, 2010; Green-Petersen et al., 2014). There is not only a difference between species but also a considerable variation between individuals. These variations must be taken into account when setting up experiments using sensory analysis to characterize fish and fish products. In farmed salmon (Salmo salar), e.g., the lipid content can differ from 10% to 19% lipid in salmon from the same farm. Also, for herring, there is a wide variation in lipid content within catches. A single catch can contain herring (Clupea harengus) with a lipid content ranging from 1% to 25%. This variation is due to heterogeneity caused by mixing between stocks (Nielsen et al., 2005b; Hyldig et al., 2012). The lipid fraction is the component showing the greatest variation. Often, the variation within a certain species will display a characteristic seasonal curve with a minimum around the time of spawning. Although the protein fraction is rather constant in most species, variations have been observed, such as protein reduction occurring in salmon during long spawning migrations (Ando et al., 1985; Ando and Hatano, 1986) and in Baltic cod during the spawning season (Borresen, 1992). Fish is often divided into lean and fatty fish. A possible method for discriminating between lean and fatty fish species is to term fish that store lipids only in the liver as lean and fish storing lipids in fat cells distributed in other body tissues as fatty fish. Typical lean species are bottom-dwelling groundfish like codfish and flatfish species. The lipid content of fillets from lean fish is low and stable, whereas the lipid content in fillets from fatty species varies considerably. In many species, most of the muscle is white or has a light color, but many fish species have a certain amount of dark tissue of a brown or reddish color. The dark muscle is located just under the skin along the body side. A typical pelagic fish, such as herring, will contain approximately 25% of the dark muscle required for prolonged aerobic muscle activity. The proportion of dark muscle varies with the activity of the fish. The more active, the larger the dark muscle. There are many differences in the chemical composition of the two muscle types, e.g., higher levels of lipids and myoglobin in the dark muscle.

Sensory Aspects of Heat Treated Seafood

513

From a technological point of view, the high lipid content of dark muscle is important and can give high intensities of desirable sensory notes, but it also has a shorter shelf life due to lipid oxidation, resulting in rancid and sour notes, among others. Demersal species, such as cod, have a white, flaky, and tender muscle; textural changes, such as toughness and chewiness, are the most noticeable problems during storage. The white muscle is used for quick attacks or escapes – not long destinations. Whether a fish is lean or fatty, the actual fat content is important for the technological characteristics post mortem. The changes taking place in fresh lean fish may be predicted from knowledge of biochemical reactions in the protein fraction, whereas in fatty species, the changes in the lipid fractions have to be taken into account. In consequence, the storage time is reduced due to lipid oxidation, or special precautions have to be taken to avoid this. The reddish meat color found in salmon and sea trout does not originate from myoglobin but is due to the red carotenoid astaxanthin. The function of this pigment has not been clearly established, but it has been proposed that the carotenoid may play a role as an antioxidant (Toshiki and Geert 2020).

26.2.4  Handling of Seafood Rigor mortis begins immediately or shortly after death if the fish is starved and the glycogen reserves are depleted or if the fish is stressed. The technological significance of rigor mortis is of major importance when the fish is filleted before or in rigor. In rigor the fish body will be completely stiff, the filleting yield will drop significantly, and rough handling can cause gasp in the fillets. If the fillets are removed from the bone pre-rigor, the muscle can contract freely and the fillets will shorten following the onset of rigor. Dark muscle may shrink up to 52% and white muscle up to 15% of the original length (Buttkus 1963). If the fish is cooked pre-rigor the texture will be very soft and pasty. In contrast, the texture is tough but not dry when the fish is cooked in rigor. Post-rigor the flesh will become firm, succulent, and elastic. In some fisheries, the bleeding of the fish is very important as a uniform white fillet is desirable. Discoloration of the fillet may also be a result of rough handling during catch and catch handling while the fish is still alive. Physical rough handling in the net (long trawling time, very large catches) or on the deck (stepping on the fish or throwing boxes, containers, and other items on top of the fish) may cause bruises, rupture of blood vessels, and blood oozing into the muscle tissue (hematoma). Shellfish are harvested and stored alive or shortly after harvesting processed and frozen. Shellfish act as a filter and contain therefore mud and bacteria; they should therefore be washed in clear seawater as soon as they are harvested. After being washed, shellfish should be packed and kept cool under storage and transport.

26.3  SENSORY ANALYSIS OF SEAFOOD Objective descriptive sensory requires a tested and trained panel together with a test room (ISO 8586 2012; ISO 8589 2007). In the sensory method, profiling a series of attributes will be developed to describe the sensory characteristics describing appearance, smell, taste, and texture (see Chapter 25). The panel must be trained in the identification of the attributes and furthermore, the panel must be trained in using a scale for describing the intensity of the attributes. In a session, samples will be served in random order, and between the samples there will be served something that can be used to clean the mouth cavity.

26.3.1 Assessors When selecting and training assessors for sensory analysis of seafood, it is very important to be aware of that some people cannot taste the rancid flavor, iodine, and geosmin and some have a very low response to cold-storage flavor and rancidity. In addition, some people are allergic or hypersensitive to different fish proteins, shellfish, or histamine.

514

Handbook of Seafood and Seafood Products Analysis

Special care must be taken to a number of relations: the assessors must not be afraid of fish bones; some flavors are very special-like iodine (from bromophenols) and muddy (from geosmin or 2-methyl-iso-borneol) and must be known to the assessors; there might be considerable differences between the individual seafood; and it can be a challenge to have homogeneous samples and even more complicated to get replicates for a panel of 12. The assessors require in all cases intensive training and a detailed briefing before each session. The interpretation of the stimulus and response must be trained very carefully in order to receive objective responses. It is very easy – as an example – to give an objective answer to the question: is the fish in rigor (completely stiff)? But more training is needed if the assessor has to decide whether the fish is post- or pre-rigor. Training is essential as it provides all assessors with a common vocabulary, thus decreasing the risk of different quantitative and qualitative interpretations of descriptors (ISO 8586, ISO 11035). Furthermore, continuous training of the sensory panel during long-term projects, preferably using reference material, can reduce the risk of drifting. If the training is interrupted, the assessors might forget descriptor meanings and/or rating levels with time. Just as with any other instrument in the laboratory, the performance of a sensory panel has to be evaluated and calibrated in order to give reliable results. If this is not the case, the risk of drawing wrong conclusions from the experiment is high. By serving the same product as a reference before each session, assessors can recapitulate the descriptors easily and can recalibrate their evaluations to the same scale. The additional use of coded reference samples served together with the actual samples makes it possible to monitor the performance of the panel. Multivariate data analysis allows for quick calculations and results are easily interpreted with the visual layout (Nielsen et al., 2005c).

26.3.2 Sessions It is important to serve the heated samples hot and in containers with lids. In this way, the assessors get the full impression of the odor just after opening the lid. When samples are cooked in plastic bags, odor attributes are assessed immediately after opening the vacuum bags, while flavor and texture are evaluated after removing the samples from the bag (Bjerkeng et al., 1997; Nortvedt and Tuene, 1998). Each assessor evaluates samples in replicate and all samples must be served to each assessor in a randomized order to minimize possible carry-over effects between samples. The number of samples for each session depends on the number of attributes and the type of sample. For cooked samples without any spices added, the assessors can evaluate approximately 10 attributes in six samples within a session. If there are many attributes, it is necessary to cut down the number of samples in each session. It is important to give the assessors a short break - 1–3 min between each sample. 26.3.2.1  Test Room There are international and national standards and guidelines for the design and construction of sensory assessment rooms (ISO 8589 2007; ISO 8586 2012; ISO 11035 1994; NMKL Procedure No. 6 2022; NMKL Procedure No. 21 2022; Meilgaard et al. 2006). In quality control procedures, special facilities or rooms are preferred for sensory evaluation, but it is not always possible. In industry and auctions, the testing area should adhere to the ISO 8589 standard as closely as possible. • The test room must be a separate room and it must be easy to access for the assessors. There must be a booth for each assessor. • The temperature must be around 20°C–24°C and kept constant. • The noise level shall be kept to a minimum; during a session, the assessors must be able to work without any interruption.

Sensory Aspects of Heat Treated Seafood

515

• Free of any foreign odor. As a minimum, there must not be any waste, other matter, or operation with a strong smell nearby. It must be possible to change the air in the test room. • The testing area must be easy to clean and disinfect. Regular cleaning and disinfecting shall take place. It must be ensured that the cleaning agents used do not leave any odors in the testing area. The colors in the test room must be light gray such as NCS-S1002Y-S3002Y. • Lighting is very important. It is preferable that the light correspond to real daylight according to ISO standards (ISO 8589 2007) and as a minimum have an intensity of 600–1,500 LUX/m2. • No eating, drinking, or smoking shall ever be allowed in the testing area. 26.3.2.2 Scales Different types of scales can be used, but the most common are the line scale and the nine-point scale. For assessing color, it is helpful if standards are used, such as the natural color system NCS®. The most common scale (Figure 26.1a) is the 15 cm unstructured line scale, with two anchor points: “little” and “much” of attribute intensity, e.g., placed 1.5 and 13.5 cm from the end point of the scale (Papadopoulos and Finne 1986; Hong et al. 1996; Warm et al. 2000; Bøkness et al. 2002; Sveinsdóttir et al. 2003; Nielsen et al. 2004, 2005a; Green-Petersen et al. 2006). An intensity scale from 0 to 100 (Figure 26.1b) is also suggested (Schubring and Oehlenschläger 1997; Rasmussen et al. 2000; Ginés et al. 2004). Here, 0 is defined as the minimum mark or nothing of the attribute and 100 as the maximum mark of the attribute. Others are using a nonstructured continuous line scale of different lengths: 8 cm (Stohr et al. 2001), 10 cm (Morita et al. 2001; Richards and Hultin 2001; Cardinal et al. 2004), or 15 cm (Whitfield et al. 1997; Rørå et al. 1998; Whitfield et al. 2002; Morita et al. 2003). The nine-point scale (Figure 26.1c) can be either a continuous intensity scale going from 0 (low intensity) to 9 (high intensity) (Johansson et al. 2000; Einen and Thomassen, 1998) or a line scale where the responses are transformed into numbers after assessment, where 1 equals no intensity and 9 equals high intensity (Edmunds and Lillard 1979; Bjerkeng et al. 1997; Einen and Skrede 1998; Nortvedt and Tuene 1998; Rørå et al. 1998). 26.3.2.3  Between Samples Depending on the samples, different cleaning items can be used to dissipate residual flavors and particles between evaluations. Tap water can be used if it is not too bitter and it should be served at room temperature together with unsalted crackers or crisp bread. Tap water can be filtered through a domestic water filter to remove any extraneous flavors, but also distilled water combined with unsalted crackers can be used (Cardello et al., 1983; Farmer et al., 2000; and NMKL Procedure No. 21 2022).

FIGURE 26.1  Intensity scales. (a): unstructured line scale with two anchor points; (b): scale from 0 to 100; (c): nine points going from 0 to 9.

516

Handbook of Seafood and Seafood Products Analysis

If the samples have off-flavor or taint, some acidic solution can be used in combination with a longer break between samples (Petersen et al., 2011). Bett et al. (2000) used unsalted soda crackers and citric acid (0.03%) solution as “palate cleansers”. Peeled cucumber in slices can be used together or instead of crackers.

26.3.3 Preparation of Heat Treated Seafood Samples Sampling for sensory analysis must be as representative as possible and therefore consideration must be given not only to how and where the sample is cut but also to the effect of the chosen method for heat treatment. The preparation must have a minimal sensory impact on the “innate” characteristics of the samples. Different types of seafood products demand different sample preparations. The optimal amount of seafood prepared for each assessor is a portion size of 30–100 g. All samples must be marked individually with three-digit or four-digit codes. The samples must be placed in individual containers, for instance, porcelain bowls and covered with porcelain lids. For fatfish species, it is important to consider whether the samples should be with or without skin. As the dark muscle is right under the skin and if the fillets are skinned, some of the dark muscle will be removed. This can have an influence on rancid flavor due to the high lipid content in the dark muscle. If the samples for replicates for each assessor are taken from the same position of each fillet, it is possible to have a real replicate and thereby identify if there are any differences between assessors or between fillets from different parts of the fish. If the texture is of no importance, it is possible to make minced fish samples to eliminate the differences between individuals. For shellfish, there can be some special conditions due to the sample size. For example, shrimp can be very different in size; some are large enough to be assessed in one “mouthful”, but others are so small that several shrimp are needed for one “mouthful”. Concerning the variation between individuals, it can be a solution to ask the assessors to take three or four shrimp, depending of the size, at a time. 26.3.3.1  Fish Samples Heat Treated in Oven Samples from fish species such as cod (Gadus morhua), salmon (Salmo salar), and saithe (Pollachius virens) are cut from the loin part in 8 × 4 × 2 cm pieces, corresponding to approximately 75 g. Smaller fish species such as rainbow trout (Oncorhynchus mykiss), herring (Clupea harengus), and plaice (Pleuronectes platessa) require a whole fillet, which gives samples of approximately 40–50 g for plaice and herring and approximately 75–100 g for rainbow trout. The samples are heated, e.g., in a convection oven in their own juice at 100°C to an internal temperature of 70°C. 26.3.3.2  Fish Samples Cooked in Water Bath Fish can also be cooked in a water bath as suggested by Bjerkeng et al. (1997), Nortvedt and Tuene (1998), Sivertsvik et al. (1999), and Hemre et al. (2004). 1.5 cm thick cutlets of salmon (Salmo salar), cod (Gadus morhua), or loin part of Atlantic halibut (Hippoglossus hippoglossus L.) fillets were packaged in diffusion-tight plastic bags under vacuum and cooked in a water bath at 85°C for 15 min or at 70°C–72°C for 1 h without salt or other spices. 26.3.3.3  Fish Broth For very different fish species, it can be an advantage to have a sample preparation where the fish are cut into small pieces and boiled to a broth. This was done in the study of Morita et al. (2003). All the species except loach were gutted, and tuna and swordfish were also skinned. Small fish species such as banded blue-sprat, sardine, loach, pond smelt, and goby were boiled whole, while the others were cut into 3–4 cm lengths before being boiled as in ordinary domestic cooking. Each sample (200 g) was placed in a three-necked separable 31-round flask along with 200 mL of boiling water, then heated with refluxing for 30 min by a mantle heater. The fish broth (350 mL) obtained after filtering the boiled mixture through three layers of cotton gauze was divided into 35 mL aliquots in 50 mL

Sensory Aspects of Heat Treated Seafood

517

glass vials. Fish broth (35 mL) prepared from 20 g of fish was placed in a 260 mL disposable plastic cup, covered with a plastic Petri dish, and served to the panellists at 40°C. 26.3.3.4  Prawns and Shrimps Frozen prawns and shrimps must either be thawed for 1 h at room temperature before cooking or added directly to the boiling water, depending on the size. Cooking time in boiling fresh water is 3 min. After cooking, the prawns/shrimps are drained, cooled with cold water, and then peeled and held at 7°C–10°C until evaluation (Edmunds and Lillard 1979; Papadopoulos and Finne 1986; Whitfield et al. 1997). 26.3.3.5  Prawns Broth If a sensory profiling of whole prawn is wanted, it is possible to make a broth where both shell and the meat are assessed at the same time. Morita et al. (2001) thawed frozen prawns (Kuruma prawns (Penaeus japonicus) and black-tiger prawns (Penaeus monodon)) with running water. One hundred and ninety grams of meat and/or 30 g of shell were cut into 1 cm2 pieces, and after adding 400 mL of deionized distilled water, the mixture was boiled for 30 min. Broth was obtained by filtering the boiled mixture through three layers of cotton gauze. The broth was divided into portions of 35 mL and preserved in 50 mL glass vials at −50°C until performing sensory evaluation.

26.3.4 Sensory Attributes The criteria for the selection of sensory attributes are that they can discriminate between samples, are relevant for the specific seafood, arenon-redundant, and are cognitively clear to the assessors. In Chapter 25, there are tables with attributes and the definition used in the literature. Thise tables can be used as basis or inspiration for developing a vocabulary for objective sensory profiling.

26.4  EXAMPLES OF SENSORY CHARACTERIZATION OF SEAFOOD Green-Petersen et al. (2006) gave an overview of both the sensory properties and differences between the most common salmonid products available on the Danish market. Twelve salmon samples, which differed in storage conditions, packaging, and species, were used in the experiment. All the samples were obtained from local shops or companies and bought as consumer products. There were nine samples of Salmo salar, two were modified atmosphere packed (MAP), four were stored in ice, and three were frozen. There were also two frozen samples of Oncohynchus kisutch and one frozen sample of Oncohynchus keta. The four samples stored in ice had a very similar sensory profile as described by the attribute sea/seaweed odor, the flavors fresh fish oil, sweet and mushroom, and the texture juicy and oily. Even though the ice storage samples represented different fish farms, different batches and different storage times in ice (7 or 16 days), they did not observe any clear sensory differences, which were in agreement with Sveinsdóttir et al. (2003). The MAP samples were stored for five days, and the ice storage samples had a relatively similar sensory profile. Whereas the MAP packed that was stored for seven days was rather different, it was rancid and sour. For the frozen samples, the results show that a longer freezing time gave a more soft texture, an increase in discolor, and a decrease in sea/seaweed odor. However, they found that freezing only had little influence on flavor. The results indicate that, in general, the sensory properties of Oncohynchus keta and Salmo salar are fairly similar. Furthermore, the results indicate that the sensory properties of Oncohynchus kisutch are very different from the sensory properties of Oncohynchus keta and Salmo salar. The two samples of Oncohynchus kisutch had a high intensity of firm texture, rancid flavor, and were discolored, combined with a low intensity of juicy and oily texture, sea/seaweed odor and the flavors sweet, fresh fish oil, and mushroom.

518

Handbook of Seafood and Seafood Products Analysis

The length of starvation before slaughtering can have a considerable influence on the sensory attributes. In farmed Atlantic salmon (Salmo salar), fresh flavor was significantly reduced in groups starved from 30 to 86 days, whereas no significant differences were found among groups starved for 0 to 30 days. The sensory test was performed on days 13–16 post-mortem fish (Einen and Thomassen 1998). The content of the feed can influence not only the growth rate but also the sensory characteristics of the fish. For Atlantic halibut (Hippoglossus hippoglossus) fed with a diet containing 20% or 39% fat, the sensory test showed that larger fish (2.1–2.7 kg) were characterized by a fresher and more acidic flavor and a more juicy consistency compared to smaller fish (1.4 kg). The growth of the 20 halibut used in the sensory test showed that high growth was associated with small fish, which achieved higher scores for off-flavor and rancid flavor than the larger halibut with a lower growth rate. This does not imply that a high growth rate is a contributing factor to the off-flavor or the rancid flavor of the small fish, but shows that these characteristics are typical for the small fish (Nortvedt and Tuene 1998). The odor for turbot (Psetta maxima) fed with three experimental diets (with 9% (w/w) added lipid – fish oil, soybean oil or linseed oil) was described as having a fatty fish note, even though the lipid content of turbot muscle was very low. Grass and hay notes were also found to characterize the odor of flesh (Sérot et al. 2001). Sveinsdóttir et al. (2009, 2010) studied farmed and wild cod and they found that farmed cod samples were clearly different from the wild cod samples. The farmed cod had different sensory characteristics as compared with wild cod and kept odor and flavor characteristics for fresh cod longer compared with wild cod. The farmed cod samples were significantly more described with meat flavor and meaty, rubbery, and clammy texture and less with mushy and tender texture compared with most of the wild cod samples. There can be large variation in the sensory characteristics of different fish species, but also similarities. Cardello et al. (1983) characterized 17 fish species by sensory profiling and by using cluster analysis of the data, they found that the 17 species could be grouped into three major groups. The first group is characterized by primarily low-fat, low flavor intensity, and white-fleshed fish and consists of tilefish (Lopholatilus chamaeleonticeps), pollock (Pollachius virens), haddock (Malanogrammus aeglefinus), wolfish (Anarhichas lups), Atlantic cod (Gadus morhua), cusk (Brosme brosme), white hake (Urophycis tenuis), whiting (Merluccius bilinearis), blackback flounder (Pseudopleuronectes americanus), Atlantic halibut (Hippoglossus hippoglossus), monkfish (Lophius americanus), and grouper (Mycteroperca microlepis). The second major group consists of the high-fat, high-flavor intensity, dark-fleshed fish, including bluefish (Pomotomus saltatrix), Atlantic mackerel (Scomber scombrus), weakfish (Cynoscion regalis), and striped bass (Morone saxatilis). The third group consists solely of swordfish (Xiphias gladius), but none of the sensory parameters describe the swordfish very well. The swordfish was included in another study by Morita et al. (2003). In this study, different saltwater fish, migratory coastal fish, coastal bottom fish, pelagic fish, deep-sea fish, freshwater fish, anadromous fish, and brackish water fish, in total, 16 species were sensory characterized. By multivariate data analysis, four groups were observed in a biplot using PCA (principal component analysis). Red sea bream snapper (Pagrus major), common Japanese conger (Conger myiaster), Japanese eel (Anguilla japonica), and freshwater species, i.e., loach (Misgurmus anguillicaudatus), pond smelt (Hypomesus nipponensis), and carp (Cyprinus carpio), were grouped around the flavor “green”. Migratory coastal fish species, i.e., sardine (Sardinops melanosticta), banded blue-sprat (Spratelloides gracilis), and chub mackerel (Scomber japonicus), were located close to “fish oil”, “grilled fish”, “sea breeze”, and “fishy”. Swordfish (Xiphas gladius), sablefish (Anoplopoma fimbria), and chum salmon (Oncorhynchus keta) were located close to “fried chicken”. Slime flounder (Microstmus achne), pacific cod (Gadus macrocephalus), blue-fin tune (Thunnus thynnus), and yellowfin goby (Acanthogobius flavimanus) were grouped around “cooked fish”, “sweet”, “canned tuna”, and “roasted soy sauce”. During the ice storage of fish, the intensity of the sensory attributes will change. Sensory profiling of ice storage-farmed salmon showed that sensory attributes characterizing the salmon (Salmo

Sensory Aspects of Heat Treated Seafood

519

salar) on day one were seaweed, cucumber, and sourish odor, sweetish, sourish, fish oil, and mushroom flavor and after 22–24 days of storage, the salmon were characterized by rancid, sour, amine odor and rancid flavor. The sensory attributes used in another storage experiment with farmed salmon stored in ice were grouped into “positive sensory parameters” and “negative sensory parameters”. Samples from every second storage day were analyzed. The changes in the sensory attributes indicate that the salmon was approaching the end of acceptable flavor after 20–21 days, when the salmon was characterized by increasing intensity of sour, amine, and rancid odor, and flavor. All positive attributes had a high intensity and were very characteristic for the salmon at the beginning of the storage time, but after 21–22 days of storage, they were hardly detectable. It was concluded from the result of the sensory profiling that the shelf-life for salmon, where the fish is no longer fit for human consumption, was 20 days in ice (Sveinsdóttir et al. 2003). In storage studies with MAP, the development of sensory attributes is different than for ice storage of the same fish species. Hong et al. (1996) found that the odor of Atlantic mackerel (Scomber scombrus L.) during MAP storage changed from day 0 (seaweed, fishy, and rancid) to 7 days of storage (seaweed, cucumber-like, sour, fishy, painty, and rancid) and again from 14 days of storage (seaweed, sour, fishy, and rancid) to 21 days of storage (seaweed, sour, fishy, metallic, and rancid). The rapid development of rancidity in stored muscle from fish such as mackerel, herring, capelin, and bluefish is often attributed to the fact that these species contain high levels of lipid. However, studies by Richards and Hultin (2001) indicate that blood-mediated oxidation of washed cod (Gadus morhua) lipids requires ≤0.1% phospholipid to cause rancidity. They also found that with long-term, frozen storage of mackerel (Scomber scombrus) muscle, sensory quality deterioration occurred in spite of no measurable change in fatty acid composition. Manipulation of the environmental salinity affected the flavor of frozen white shrimp (Penaeus vannamei). Since free amino acids are major osmo-effectors in shrimp and also primary flavor producers in marine products, more flavorful shrimp can be produced by acclimation to high environmental salinities (Papadopoulos and Finne 1986). Bak et al. (1999) investigated the effect of packaging atmosphere, temperature fluctuation, and light exposure on frost formation, lipid oxidation, discoloration, and meat toughness of shell-oncold-water shrimps (Pandalus borealis) during 12 months of frozen storage. They found that the single most important handling factor affecting shrimp quality was exposure to oxygen during storage. Rancid flavor was significantly higher for samples packed in atmospheric air compared to samples packed in modified air. The effect of light on the rancid flavor of atmospheric air packed shrimps was significant. The sensory score for rancid flavor for modified air-packed samples did not increase significantly between 9 and 12 months of frozen storage. The sensory evaluation of shrimp meat toughness revealed a clear effect of the packaging method and storage conditions. They found a significantly higher score for toughness for samples packed in atmospheric air compared to samples packed in modified air. In addition, storage in light resulted in significantly tougher shrimp meat. The sensory score for toughness was almost constant for samples packed in modified air for the first 9 months of storage, whereas a significant increase was observed between 9 and 12 months of storage. Erikson et al. (2007) investigated different frozen shrimps that were obtained from commercial distributors. The samples were identified as Gulf brown shrimp (Penaecus aztecus), Gulf white shrimp (Penaecus setiferus), Gulf pink shrimp (Penaecus duorarum), Georgia brown shrimp (Penaecus aztecus), Georgia white shrimp (Penaecus setiferus), Burma black tiger shrimp (Penaecus monodon), Belise white shrimp (Penaecus vannamei or Penaecus stylirostris), Colombia white shrimp (Penaecus vannamei or Penaecus stylirostris), Honduras white shrimp (Penaecus vannamei or Penaecus stylirostris), and Mexican white shrimp (Penaecus vannamei or Penaecus stylirostris). Sensory attributes that distinguish the frozen shrimp were associated with appearance. In the case of Burma black tiger shrimp, distinguishing features were the shell darkness and stripe darkness of the raw products and the red/orange color of the cooked meat product. Belise white shrimp was found to have a muted shell color and significantly lower shell and stripe darkness than the other

520

Handbook of Seafood and Seafood Products Analysis

shrimp samples. While the high intensity of brown meat color differentiated the Colombian white shrimp from other shrimp, Georgia white shrimp were unique from other samples in the high iridescence displayed in the tail of the raw product. They found that the iridescence of Georgia white shrimp was nearly twice as much than the other shrimp samples, including Gulf white shrimp (the same species of shrimp but harvested from a different location). Commercial claims stated that Georgian white shrimps are sweet, but Erikson et al. (2007) found the highest sweetness intensity in Burma black tiger shrimp. Georgia white shrimp were characterised as having the highest cooked shrimp flavor, but the intensity was not significantly different from Gulf brown, Georgia brown, Gulf pink, or Mexican white shrimp. They also found that notable differences in aroma or basic taste attributes occurred, which could be ascribed to variations in processing applied to the commercially available shrimp samples. For example, the aroma of old shrimp in the raw product was significantly higher in Mexican white shrimp than in other shrimp samples. At the same time, blotchiness ratings were also significantly higher in Mexican white raw and cooked shrimps, and they concluded that the shrimp could have been held for some period of time prior to freezing. Another variation in processing that was also perceived by the trained panel was the addition of the additives, sodium tripolyphosphate and sodium bisulfite, in Gulf shrimp. In fact, the salty intensity in Gulf pink shrimp were nearly twice that of other shrimp, while their sweetness scores were 20% lower than other shrimps. In the investigation, the trained panel found significant differences between fresh and commercially available frozen shrimps (Georgia white shrimp (Penaeus setiferus) and Gulf pink shrimp (Penaeus monodon)) for 18 of the 30 sensory attributes. In terms of appearance, fresh shrimp were glossier than frozen shrimp for both the raw and cooked products. The most notable change in appearance for the raw product, however, was a loss in tail iridescence. While for cooked products, the most notable changes in appearance were the loss of red/orange color on the shrimp surface. Frozen shrimp had a higher intensity of cooked shrimp flavor, cooked shrimp aroma, and ocean/ seawater aroma than fresh shrimp, whereas no difference in the old shrimp aroma was found. They also found changes in texture, shrimp that had been frozen were firmer and less juicy than fresh shrimps. The science of frozen shrimp was commercially obtained in this study, the period of time they had been in frozen storage is unknown.

REFERENCES Ando S and Hatano M. 1986. Biochemical characteristics of chum salmon muscle during spawning migration. Bulletin of the Japanese Society of Fisheries 52: 1229–1235. Ando S, Hatano M and Zama K. 1985. Deterioration of chum salmon (Oncorhynchus keta) muscle during spawning migration - 1. Changes in proximate composition of chum salmon muscle during spawning migration. Comparative Physiology and Biochemistry 80B: 303–307. Bak LS, Andersen AB, Andersen EM and Bertelsen G. 1999. Effect of modified atmosphere packeaging on oxidative changes in frozen stored cold water shrimp (Pandalus borealis). Food Chemistry 64: 169–175. Baron CP, Hyldig G and Jacobsen C. 2009. Does feed composition affect oxidation of rainbow trout (Oncorhynchus mykiss) during frozen storage? Journal of Agricultural and Food Chemistry 57: 4185–4194. Bett KL, Ingram DA, Grimm CC, Vinyard BT, Boyette KDC and Dionigi CP. 2000. Alteration of the sensory perception of the muddy/earthy odorant 2-methylisoborneol in channel catfish Ictalurus punctatus fillet tissues by addition of seasonings. Journal of Sensory Studies 15: 459–472. Bjerkeng B, Refstie, Fjalestad KT, Storebakken T, Røbotten M and Roem AJ. 1997. Quality parameters of the flesh of Atlantic salmon (Salmo salar) as affected by dietary fat content and full-fat soybean meal as a partial substitute for fish meal in the diet. Aquaculture 157: 297–309. Bøkness N, Jensen KN, Guldager HS, Østerberg C, Nielsen J and Dalgaard P. 2002. Thawed chilled Barents Sea cod fillets in modified atmosphere packaging-appæication of multivariate data analysis to select key parameters in good manufacturing practice. Lebensmittel-Wissenschaft & Technologie 35: 436–443. Borresen T. 1992. Quality aspects of wild and reared fish. In Huss HH, Jacobsen M and Liston J (eds.) Quality Assurance in the Fish Industry. Proceedings of an International Conference, Copenhagen, Denmark August 1991. Elsevier, Amsterdam: pp. 1–17.

Sensory Aspects of Heat Treated Seafood

521

Buttkus HJ. 1963. Red and white muscle of fish in relation to rigor mortis. Journal of the Fisheries Board of Canada 20: 45–58. Cardello AV, Sawyer MF, Prell P, Maller O and Kapsalis J. 1983. Sensory methodology for the classification of fish according to “edibility characteristics”. Lebensmittel-Wissenschaft & Technologie 16: 190–194. Cardinal M, Gunnlaugsdottir H, Bjoernevik M, Ouisse A, Vallet JL and Leroi F. 2004. Sensory characteristic of cold-smoked Atlantic salmon (Salmo salar) from European market and relationships with chemical, physical and microbiological measurements. Food Research International 37: 181–193. Edmunds WJ and Lillard DA. 1979. Sensory characteristics of oysters, clams and cultured and wild shrimp. Journal of Food Science 44: 368–373. Einen O and Skrede G. 1998. Quality characteristics in raw and smoked fillets of Atlantic salmon, Salmo salar, fed high-energy diets. Aquaculture Nutrition 4: 99–108. Einen O and Thomassen MS. 1998. Starvation prior to slaughter in Atlantic salmon (Salmo salar) II. White muscle composition and evaluation of freshness, texture and colour characteristics in raw and cooked fillets. Aquaculture 169: 37–53. Erikson, MC, Bulgarelli, MA, Resurreccion, AVA, Vendetti, RA and Gates KA, 2007. Sensory differentiation of shrimp using a trained descriptive analysis panel. Food Science and Technology 40: 1774–1783. Farmer LJ, McConnell JM and Kilpatrick, DJ. 2000. Sensory characteristic of farmed and wild Atlantic salmon. Aquaculture 187: 105–125. Ginés R, Valdimarsdottir T, Sveinsdottir K and Thorarensen H. 2004. Effect of rearing temperature and strain on sensory characteristics, texture, colour and fat of Arctic charr (Salvelinus alpinus). Food Quality and Preferences 15: 177–185. Green-Petersen DMB and Hyldig G. 2010. Variation in sensory profile between individual rainbow trout (Oncorhynchus mykiss) from the same production batch. Journal of Food Science 75(9): S499–S505. Green-Petersen D, Hyldig G, Jacobsen C, Baron CP, Lund L, Nielsen HH and Jokumsen A. 2014. Influence of dietary lipid and protein sources on the sensory quality of organic rainbow trout (Oncorhynchus mykiss) after ice storage. Journal of Aquatic Food Product Technology, 23(4): 333–346. Green-Petersen DMB, Nielsen J and Hyldig G. 2006. Sensory profiling of the most common salmon products on the Danish market. Journal of Sensory Studies 21: 415–427. Hemre G-I, Karlsen Ø, Eckhoff K, Tveit K, Mangor-Jensen A and Rosenlund G. 2004. Effect of season, light and diet on muscle composition and selected quality parameters in farmed Atlantic cod, Gadus morhua L. Aquaculture Research 35: 683–697. Hong LC, Leblanc EL, Hawrysh ZJ and Hardin RT. 1996. Quality of Atlantic Mackerel (Scomber scombrus L.) fillets during modified atmosphere storage. Journal of Food Science 61: 646–651. Hyldig G, Jørgensen BM, Undeland I, Olsen RE, Jónsson Á and Nielsen HH. 2012. Sensory properties of frozen herring (Clupea harengus) from different catch seasons and locations. Journal of Food Science 77(9): S288–S293. ISO 8586. 2012. Sensory Analysis - General Guidance for the Selection, Training and Monitoring of Assessors Part 1: Selected Assessors. International Organization for Standardization, Switzerland. ISO 8589. 2007. Sensory Analysis - General Guidance for the Design of Test Rooms. Reference number ISO 8589:2007. International Organization for Standardization, Switzerland. ISO 11035. 1994. Sensory Analysis - Identification and Selection of Descriptors for Establishing a Sensory Profile by a Multidimensional Approach. International Organization for Standardization, Switzerland. Johansson L, Kiessling A, Kiessling K-H and Berglund. 2000. Effects of altered ration levels on sensory characteristics, lipid content and fatty acid composition of rainbow trout (oncorhynchus mykiss). Food Quality and preference 11: 247–254. Meilgaard M, Civille GV and Carr BT. 2006. Sensory Evaluation Techniques. 4th Ed., CRC Press. Morita K, Kubota K and Aishima T. 2001. Sensory characteristics and volatile components in aroma of boiled prawns prepared according to experimental designs. Food Research International 34: 473–481. Morita K, Kubota, K and Aishima T. 2003. Comparison of aroma characteristics of 16 fish species by sensory evaluation and gas chromatographic analysis. Journal of the Science of Food and Agriculture 83: 289–297. Nielsen D, Hyldig G, Nielsen HH and Nielsen J. 2004. Sensory properties of marinated herring (Clupea harengus) - Influence of fishing ground and season. Journal of Aquatic Food Product Technology 13: 3–24. Nielsen D, Hyldig G, Nielsen HH and Nielsen J. 2005b. Lipid content in herring (Clupea harengus L) - Influence of biological factors and comparison of different methods of analyses: Solvent extraction, fatmeter, NIR and NMR. Lebensmittel-Wissenschaft & Technologie 38: 537–548.

522

Handbook of Seafood and Seafood Products Analysis

Nielsen D, Hyldig G, Nielsen J and Nielsen HH. 2005a. Sensory properties of marinated herring (Clupea harengus) processed from raw material from commercial landings. Journal of the Science of Food and Agriculture 85(1): 127–134. Nielsen D, Hyldig G and Sørensen R. 2005c. An effective way to minimize drifting and monitor the performance of a sensory panel during long-term projects - A case study from a project on herring quality. Journal of Sensory Studies 20: 35–47. NMKL Procedure NR. 6. 2022. General guidelines for quality assuring sensory laboratories. Nordisk Metodikkomité for Levnedsmidler. https://www.nmkl.org. NMKL Procedure NR. 21. 2022. Guide for sensory analysis of fish and shelfish. Nordisk Metodikkomité for Levnedsmidler. https://www.nmkl.org. Nortvedt R and Tuene S. 1998. Body composition and sensory assessment of three weight groups of Atlantic halibut (Hippoglossus hippoglossus) fed three pellet sizes and three dietary fat levels. Aquaculture 161: 295–313. Papadopoulos LS and Finne G. 1986. Effect of enviromental salinity on sensory characteristics of Penaeid shrimp. Journal of Food Science 51: 812–814. Petersen MA, Hyldig G, Strobel BW, Henriksen NH and Jørgensen NOG. 2011. Chemical and sensory quantification of geosmin and 2-methylisoborneol in rainbow trout (Oncorhynchus mykiss) from recirculated aquacultures in relation to concentrations in basin water. Journal of Agricultural and Food Chemistry 59(23): 12561–12568. Rasmussen RS, Ostenfeld TH, Rønsholdt B and McLean E. 2000. Manipulation of end-product quality of rainbow trout with finishing diets. Aquaculture Nutrition 6: 17–23. Reinitz GL, Orme LE and Hitzel FN. 1979. Variations of body composition and growth among strains of rainbow trout (Slamo gairdneri). Transactions of the American Fisheries Society 108: 204–207. Richards M and Hultin HO. 2001. Rancidity development in a fish model system as affected by phospholipids. Journal of Food Lipids 8: 215–230. Rørå AMB, Kvåle A, Mørkøre T, Rørvik KA, Stein SS and Thomassen MS. 1998. Process yield, colour and sensory quality of smoked Atlantic salmon (Salmo salar) in relation to raw material characteristics. Food Research International 31: 601–609. Schubring R and Oehlenschläger J. 1997. Comparison of the ripening process in salted Baltic and North Sea herring as measured by instrumental and sensory methods. Zeitschrift für Lebensmitteluntersuchung und -Forschung A 205: 89–92. Sérot T, Regot C, Prost C, Robin J and Arzel J. 2001. Effect of dietary lipid sources on odour-active compounds in muscle of turbot (Psetta maxima). Journal of the Science of Food and Agriculture 81: 1339–1346. Sivertsvik M, Rosnes JT, Vorre A, Randell K, Ahvenainen R and Bergslien H. 1999. Quality of whole gutted salmon in various bulk packages. Journal of Food Quality 22: 387–402. Stohr V, Joffraud JJ, Cardinal M and Leroi F. 2001. Spoilage potential and sensory profile associated with bacteria isolated from cold-smoked salmon. Food Research International 34: 797–806. Sveinsdóttir K, Hyldig G, Martinsdóttir E, Jørgensen B and Kristbergsson K. 2003. Development of quality index method (QIM) scheme for farmed Atlantic salmon (Salmo salar). Food Quality and Preferences 14: 237–245. Sveinsdóttir K, Martinsdóttir E, Green-Petersen D, Hyldig G, Schelvis R and Delahunty C. 2009. Sensory characteristics of different cod products and consumer preferences. Food Quality and Preference 10: 120–132. Sveinsdóttir K, Martinsdóttir E, Hyldig G and Sigurgísladóttir S. 2010. Sensory characteristics of different cod products. Journal of Sensory Studies 25(2): 294–314. Toshiki N and Geert W. 2020. Properties of carotenoids in fish fitness: A review. Marine Drugs 18(11): 0568. Warm K, Nielsen J and Hyldig G. 2000. Sensory quality criteria for five fish species. Journal of Food Quality 23: 583–601. Whitfield FB, Helidoniotis F, Shaw KJ and Svoronos D. 1997. Distribution of bromophenols in Australian wildharvested and cultivated prawns (shrimp). Journal of Agriculture and Food Chemistry 45: 4398–4405. Whitfield FB, Helidoniotis F and Smith D. 2002. Role of feed ingredients in the bromophenol content of cultured prawns. Food Chemistry 79: 355–365.

27

Methodologies in Sensory and Consumer Sciences for the Evaluation of Seafood Products Elena Costa RISE Research Institutes of Sweden Gothenburg University

Elizabeth S. Collier RISE Research Institutes of Sweden Linköping University

Jun Niimi RISE Research Institutes of Sweden

27.1 INTRODUCTION Food provides more than just sustenance and nutrition; it offers sensory pleasure. Although we rely on our senses to evaluate foods in terms of safety and quality (e.g., the ripeness of an apple, whether the milk has gone sour etc.), our senses also guide our choices in terms of preferences and enjoyment. The palatability and overall sensory experience offered by a given product matter for its continued success among consumers. As such, the intrinsic sensory attributes of foods, such as appearance and texture, play a critical role in consumers’ decision to purchase. Characterizing and quantifying sensory experiences is a rather complex process that interacts with the needs and prior expectations of consumers. Given the importance of sensory experience in product acceptability and repeat purchase, sensory analysis has expanded rapidly as a scientific discipline over the past decades, supported by the latest knowledge in human psychology and behavior. At the same time, instrumental methods have been increasingly developed to mimic human sensory perception and are occasionally touted as replacements for human sensory testing. Although instrumental sensory analysis offers benefits for standardized testing, cost reduction, and time required, there are occasions where only humans can provide a comprehensive evaluation of the sensory characteristics with the appropriate level of sensitivity. Because variability is inherent to human experiences, the methodology used to evaluate them should consider such variabilities. A combined approach using both human sensory perception and consumer behavior data can yield a more indepth understanding, and these can be used iteratively during seafood product development. For new food products, integrating sensory and consumer analysis throughout the stages of development could strongly affect its chances of success on the market. Besides providing safe and nutritious food products, it is necessary to be responsive to consumers’ needs and expectations while anticipating future trends. For instance, in the seafood industry, an increased focus on sustainability has triggered adjustments in the processing methods as well as introduced changes in feed formulations. As the global population increases and with it the demand for seafood, the production and consumption of a wider variety of species is crucial for the seafood industry to remain sustainable. This opportunity to develop new seafood products will require a better understanding of their sensory characteristics and consumer acceptance, as well as how these are DOI: 10.1201/9781003289401-31

523

524

Handbook of Seafood and Seafood Products Analysis

related. Sensory analysis and consumer research provide the necessary tools to guide this innovation process (at times co-creative) both toward more sustainable versions of familiar products – targeting an increasingly environmentally aware consumer base – as well as innovative, novel concepts – challenging consumers to expand their understanding of what seafood can offer. Sensory and consumer sciences facilitate the exploration of market opportunities, from the very early stages of product development to commercialization. In turn, this could lead to increased consumer uptake. This chapter aims to provide an overview of a range of methodologies typically used in sensory and consumer sciences. Given the richness and breadth of the topic, not all aspects can be addressed in this chapter alone. Instead, the utilities and relevance of a range of measurement methods that are applicable to the seafood industry will be described with examples that illustrate their application in seafood products.

27.2  SENSORY AND CONSUMER RESEARCH METHODS A broad range of measurement methods are available to investigate the sensory properties and consumer responses to seafood and its derivative products. Notably, over the past two decades, there has been a substantial growth of new techniques, aimed at shortening and simplifying the evaluation to reduce the resources involved, with minimal impact on data reliability and resolution. Although some methods are more frequently used than others, particularly when applied to seafood, it is important to be aware of the existence of alternative methods and to understand when their use is most suitable. Moreover, each technique has strengths and weaknesses that should be considered both when designing the study and interpreting the data. No single method will provide a solution to all research needs, and the first stage must be to determine the specific research objectives for the study, keeping in mind any possible restrictions in terms of resources and time available. Setting a specific purpose with a clear scope in mind will allow the analyst to choose the appropriate methodology and achieve the desired results. The sensory and consumer research methods discussed here have been categorized into four groups: analytical tests, affective tests, temporal tests, and other methods.

27.2.1 Analytical Tests Analytical tests are intended to measure the detection or description of perceived sensory characteristics among products. These tests are aimed at evaluating the sensory characteristics in the most objective way possible, are product focused, and can be subdivided into two groups: discrimination and descriptive tests. 27.2.1.1  Discrimination Tests Discrimination tests are used to determine whether differences between products can be detected. They are rapid and commonly used techniques in the industry when comparing several samples and are particularly appropriate when changes which may have an impact on the sensory experience are introduced in either the processing or the ingredients of a given product. Additionally, discrimination tests are a valuable tool to determine sensory thresholds such as detection, recognition, and difference thresholds, as well as when screening and training assessors. These tests are especially suitable to determine if changes in components or ingredients in products can be noticed by assessors. Either trained sensory panelists or consumers can be utilized for these methods. More commonly, panels of 28–30 assessors are used, especially when the differences between samples are expected to be subtle (Kemp et al., 2009). Although the exact number of assessors of course will vary and may be bound by practical limitations, it should be determined wherever possible through power calculations (which will not be covered here). Assessors should be screened out for any

Evaluation Methodologies in Sensory and Consumer Sciences

525

seafood allergies, and if they cannot perceive key components that are important for evaluation, e.g., rancid flavor, iodine, and geosmin (Hyldig et al., 2007). Samples can be compared in terms of overall sensory differences, where assessors can base their decision without any sensory modality restrictions, or for specific modalities of interest (e.g., flavor only). If the latter is used, disguising other sample attributes may be required. For example, using nose clips to avoid the detection of aroma, colored lights to remove any possible influence of appearance, or processing such as minced fish if texture should not be a part of the assessment. An overview of common discrimination tests can be found in Table 27.1. Although there are a variety of tests, the most common techniques are the paired-comparison test (also known as 2-alternative forced choice; 2AFC), duo-trio, and triangle test. As highlighted in Table 27.1, discrimination tests mainly differ in the number of samples to be compared and the specific task given to the assessors. It should be noted that using a test that presents a higher number of samples may increase data discrimination and reduce the chances of false inferences but will hasten the onset of sensory fatigue and increase task difficulty as a result. Simple set-ups are recommended wherever possible. As an example, a triangle test was used to select seven panelists based on their ability to detect off-flavors, which followed a sensory assessment of raw chub mackerel fillet samples (Goulas and Kontominas, 2007). The main advantage of discrimination tests is their ability to reveal subtle differences in the samples that can be difficult to capture or describe through other methods. All in all, these tests offer simplicity and intuitiveness to determine whether differences between products can be perceptually detected.

TABLE 27.1 Overview of Commonly Used Discrimination Tests Test

No. of Samples

Directional paired comparison Duo trio

2

Triangle

3 (2 are the same) 2

Same/different (a.k.a. simple difference) A-not-A

2 + reference

2 + reference

n-Alternative Forced Choice (n-AFC)

2, 3, 4, … n

Tetrad

4

Ranking

Multiple

Task

References

Indicate which sample has a higher intensity concerning a sensory attribute of interest One sample is identical to the reference and the other is different. Identify the sample that is different to the reference Two of the samples are identical and one is different. Identify the odd sample Indicate if samples are the same or different from each other, without needing to explain or describe the difference(s) One of the samples is identical to the reference. Identify the sample that is identical (A) Several samples are used. Indicate the odd sample given a sensory attribute of interest, or the sample in a set with the higher/lower perceived intensity of a given attribute Four samples are presented, two of each sample (2 of A and 2 of B). Separate the samples into two groups according to which samples are identical Place samples in order (e.g., from least to most) based on the intensity of a single or several attributes

ISO (2005, standard 5495) ASTM (2018a, E2610-18) ASTM (2018b, E1885-18) ASTM (2018c, E2139-05) ISO (2017, standard 8588) ASTM (2016, E2164-16)

ASTM (2018d, E3009-15e1)

ISO (2006, standard 8587)

526

Handbook of Seafood and Seafood Products Analysis

27.2.1.2  Descriptive Tests Descriptive tests – also known as sensory characterization methods – facilitate obtaining detailed and accurate information about the sensory profile of food products. They are used to describe and quantify human perception in response to a product, indicating the magnitude of the perceived sensory characteristics based on specific criteria. Descriptive methods are particularly valuable to guide and optimize product formulation as well as when determining product shelf life. Sensory characterization has traditionally been performed by a highly trained group of assessors, who have been selected based on their ability to detect and measure attributes for the relevant products of interest in a precise and reliable way. These panels provide highly detailed and consistent results, and therefore a small number of panelists is sufficient for the assessment (typically from 6 to 18 assessors). Although there are several methods that can be used to describe the sensory characteristics of products, here we describe the general stages of how a generic descriptive analysis test is conducted (Kemp et al., 2009): Screening and training of assessors: a sensory panel must be first selected (if one does not exist yet) based on their sensory ability to identify certain attributes and generally trained in the use of a scale to measure their degree of intensity. This is followed by more in-depth training, which will depend on the specific descriptive method. Regardless of the method used, panelists’ performance must be evaluated and monitored several times per year. The training aims to adequately calibrate assessors to achieve the most reliable data with low variability. The panel training is a crucial step of the process that will determine the quality, accuracy, and consistency of the data obtained. Attribute generation: assessors are exposed to the samples to familiarize themselves with the products, and they generate attributes to describe their sensory qualities. This list is then discussed among assessors for consensus. Synonyms and antonyms are grouped, and the list is narrowed down to achieve a common vocabulary with unambiguous and independent terms that are relevant to describe the products. Each attribute is named and defined clearly to minimize overlap, and reference standards are allocated to enhance alignment between assessors. Generally, a list of up to 15–20 attributes is considered adequate but this may depend on the complexity of the samples (see Section 27.3 for considerations). Determining assessment protocol: the evaluation of samples must be executed following an evaluation procedure. Samples are presented in a random order to minimize carry-over effects, but they might be phased and thus specified by the protocol (e.g., smell, bite, chew, swallow, and aftertaste). The protocol will also define how to reset the senses back to a baseline (e.g., using a palate cleanser such as water and crackers), the time required for interstimulus breaks, scale types, and scale anchors. Figure 27.1 shows an example protocol to evaluate the firmness of a surimi product. Following a protocol is not only important for internal consistency, but also improves reproducibility if the same or a similar assessment is to be repeated later or with a different group of assessors. Performance monitoring: to ensure that the data will be as accurate as possible, panel performance needs to be monitored prior to data collection. Several aspects are monitored, assuring that samples are discriminated by sensory characteristics in the same way by all assessors. Steps include determining if there remains any confusion in the understanding of attributes across the assessors, identifying any redundant/missing attributes, adjusting reference standards and scale anchors, verifying the usage of scales, checking repeatability and reproducibility, and agreement among the assessors. All of this is monitored by analyzing data collected from mock tests and providing specific feedback to the assessors. Should any changes in the attribute list be required, this is achieved through discussion and consensus within the panel.

Evaluation Methodologies in Sensory and Consumer Sciences

527

FIGURE 27.1  Example of a descriptive sensory assessment of a surimi product, in relation to its texture (firmness). The assessment protocol is defined by a standard procedure that is provided to all assessors. Standard procedure: Hold the product between your fingers (forefinger on the colored part, thumb on the white part) and apply gentle pressure until resistance is felt. Evaluate the overall resistance of the product to the pressure. Reconstruction from an example described in Sieffermann et al. (2005).

Evaluation of samples: each sample is scored for each attribute in terms of intensity according to the protocol. The type of scale used in the evaluation will depend on the selected descriptive method. Often data is collected in replicates and samples are served blind in a randomized order. An overview of common descriptive tests can be found in Table 27.2. Among the first standardized methods developed to evaluate the profile of products are the Flavor Profile Method and Texture Profile Method (see Table 27.2), which rely on group discussions and consensus to generate the vocabulary and rate the samples. Both methods are focused on the assessment of a single modality (e.g., Flavor Profile Method for profiling flavor attributes), whereas other descriptive methods comprise the entire sensory profile. Quantitative Descriptive Analysis (QDA®) and Spectrum™ method include all sensory modalities and use instead an individual approach to assess the samples. The level of training required varies between the two. In the QDA® method, the training is focused on assessor consistency, agreement across assessors, discrimination ability, repeatability, and reliability. The Spectrum™ method is even more rigorous and could require up to six months of training, as each attribute is rated on standardized references that allow comparisons across products, categories, studies, and over time. Moreover, as QDA® is focused on the relative difference between products, it uses line scales to indicate the strength of the attribute and may include intensity references (i.e., anchors) to illustrate the endpoints. When using Spectrum™ method, attributes are rated on a standardized scale that includes references. Descriptive methods have been widely used to evaluate the sensory attributes of seafood products. For example, the freshness of shucked mussels was evaluated using descriptive analysis (Gökoglu, 2002). A group of trained panelists generated a list of attributes that was later narrowed down by consensus to 16 terms (related to odor, color, texture, and appearance). Each term included a definition, and assessors provided intensity scores on a 7-point category scale. The assessment was repeated every 12 h, leading to a total of six evaluations (from freshly harvested to 120 h after). In another study focused on mussels, the sensory and nutritional profile of raw/cooked mussels from different production sites was evaluated, comparing mussels produced with traditional rafts in Spain and mussels from new offshore production in Portugal (Oliveira et al., 2015). The study enriched the sensory characterization by including additional biometric and nutritional data, with results indicating that offshore mussels produced in Portugal could compete with other mussels in the market given their characteristic flavor and nutritional profile.

528

Handbook of Seafood and Seafood Products Analysis

TABLE 27.2 Overview of Descriptive Tests Test

Assessors

Task

References

Quantitative descriptive analysis® (QDA®)

Trained

Meilgaard et al. (2016)

Spectrum™

Trained

Flavor profile and texture profile

Trained

Free choice profile (FCP)

Trained/ untrained

Flash profile (FP), variant of FCP with ranking

Trained/ untrained

Ideal profile (IPM)

Untrained

Pivot profile© (PP)

Trained/ untrained

Generate a precise sensory description of a product, using consumer-like attributes from multiple sensory modalities. Assessors are screened based on their performance on discriminative tests, and trained/calibrated by a panel leader (who acts as facilitator) Evaluate the perceived intensity across an array of lexicons of sensory properties using a reference standard, on a scale that allows to compare across a variety of products and over time. It relies on meticulous training and maintenance, to achieve high consistency and repeatability with the rest of the panel. The panel leader acts as an instructor Assess specifically the flavor or textural properties using a standardized lexicon for different product categories. The vocabulary generation and ratings are performed through group discussions. Generate individual attributes to describe the product, as many terms as they want without consensus, and score the perceived intensity of those attributes. Faster to generate data, but more sophisticated data analysis Generate individual attribute lists and rank the products in order of intensity for each of the sensory attributes previously elicited. Quick method that requires limited training and is intuitive because it compares products Rate perceived attribute intensities for a product (sensory profile) as well as the ideal intensities for each of the attributes scored (ideal profile) Compare products (one at a time) against a single reference product or pivot. Assessors write down the attributes that the product has less or more of in comparison to the reference. It allows to compile data across multiple sessions if the reference product is kept constant.

Meilgaard et al. (2016)

Cairncross and Sjostrom (1950); Brandt et al. (1963)

Lazo et al. (2016)

Dairou and Sieffermann (2002)

Worch et al. (2013)

Thuillier et al. (2015)

Another example application of descriptive analysis was an evaluation of raw sea urchin roe (Evechinus chloroticus). A panel comprised of 8–11 assessors initially generated 250 sensory terms, which were distilled down to 35 attributes to describe the most meaningful attributes that varied between sea urchin roe samples (Phillips et al., 2009). The authors compiled a comprehensive lexicon table including attribute definitions, descriptive words associated with each attribute, and a reference standard to enhance the understanding of the attributes by the panel. Using this lexicon, the effect of gender, feed diet and storage were investigated. The outcomes indicated that male sea urchin roe was characterized by dairy flavor, freshness odor, and a sweet taste, while female roe showed astringent flavor, sour and bitter taste, and sulphur flavor. A diet rich in glycine

Evaluation Methodologies in Sensory and Consumer Sciences

529

and glutamate also had an impact on the sweetness, while valine and methionine on sulphur odor. Storage across 10 days had reduced the intensity of attributes such as sulphur flavor, marine odor, dairy flavor, and metallic flavor, amongst others. Further, the sensory characteristics of sea urchin roe were also affected by the location in which they were harvested, in this example southern vs. northern islands of New Zealand (Phillips et al., 2010). Southern sea urchins were characteristic of positive attributes such as sweet taste, dairy flavor, and odor, while northern sea urchins were characteristic of sharp odor, bitter and sour tastes, and metallic flavor. QDA® was also applied in combination with a consumer appreciation test to evaluate the sensorial characteristics of fish croquettes made with different aquatic species (Berik and Çankırılıgil 2018). In another study, the Spectrum™ method was used instead to assess blue crab meat samples and to identify indicators of spoilage (Sarnoski et al., 2010). Six panelists received training for 8–12 months, followed by evaluating an array of spoilage characteristics related to odor, taste, and texture. As much as obtaining data from conventional profiling could be beneficial, time and budget constraints can pose a barrier to such comprehensive tests, especially when applying them routinely as part of the development process. Traditional descriptive profiling methods impose high demands, both in terms of training and the need to create a customized vocabulary adapted for each product or set of products under evaluation. Thus, rapid descriptive methods have emerged as quicker alternatives to characterize food products. These are more flexible in terms of training requirements and can be performed by either trained or untrained assessors. Rapid profiling methods such as Free Choice Profiling (FCP) and Flash Profile (FP) substantially reduce the time commitment required in the initial training phase, making the descriptive evaluation less expensive and less time consuming than QDA®. For example, FP was used to characterize the sensory attributes of smoked shrimp and to identify the differences between formulations, allowing for quick measurement and optimization (Ramírez Rivera et al., 2009). FP and Napping® (a qualitative method described in Section 27.2.4) have also been used to profile complex products with contrasting textural layers such as breaded fish nuggets, cooked using different procedures (Albert et al., 2011). The results obtained in this study were comparable to QDA® and provided additional advantages in terms of efficiency and flexibility. Rapid profiling methods with consumers are particularly suitable when there are large sensory differences between samples. There are however also limitations to rapid profiling. When using these techniques, the interpretation of the terms can be more ambiguous and, since there is no assessment protocol, the product description is less accurate than in rigorous profiling methods. Especially if the sensory differences between samples are very subtle, conventional profiling methods are preferred as they require trained assessors to capture these differences in detail.

27.2.2 Affective Tests Affective tests are used to evaluate consumers’ preference, acceptance, liking or emotional response to a product. Respondents are ideally recruited based on certain screening criteria, for example, current users of a product or potential users that fit the desired target group. As they represent a wider population of interest for whom the product is intended, it is necessary to recruit a minimum of one hundred consumers to be able to extrapolate the results obtained. If possible, it is recommended to incorporate additional participants if customer segmentation is of interest. There are three main affective tests: preference tests, acceptance tests, and emotion evaluation. 27.2.2.1  Preference Tests Preference tests are techniques used to establish whether there are differences between two or more products in terms of subjective preference. Traditionally, products are compared in pairs (i.e., paired preference test) through discrete choice tests, and when using more than two products it is possible

530

Handbook of Seafood and Seafood Products Analysis

to rank samples in order of preference (i.e., ranking test). Providing a “no preference” option or allowing options to be equally preferred, is sometimes more suitable than a forced choice approach. Preference tests provide important information when, for instance, launching a new product to the market, benchmarking a product against the competition, or when modifying its formulation. Nevertheless, although this approach indicates which products are preferred, it does not allow to determine the magnitude of this preference nor the underlying factors driving it, which needs to be determined through other methods (such as conjoint analysis, see Table 27.3). 27.2.2.2  Acceptance Tests Acceptance tests are used to evaluate the degree of liking, which can be measured in several ways. Just About Right (JAR) is a method used to determine how a particular perception of a sensory attribute (e.g., saltiness) based on intensity reflects their acceptance by determining if the perception is “just about right” (ASTM, 2009, MNL63-EB). The level is represented in an ordinal scale with “just about right” in the middle, with the endpoints indicating preferential extremes (not enough and too much). Often this method is coupled with liking measures to monitor how much liking changes with any deviation from “just about right”. For example, a sensory assessment using JAR scales was conducted to compare cured Irish pollock roe with commercial mullet and cod products, with the aim of potentially adding value to under-utilized roe (Furey et al., 2020). The perception of key attributes such as fishiness, saltiness, savouriness, bitterness, and acidity, was evaluated using a 5-point ordinal scale (1 = not nearly enough, 3 = JAR, 5 = too much), showing that overall liking was negatively impacted when the flavor perception was considered “too fishy”. Hedonic ratings are used to assess the degree of liking through ordered categorical scales (e.g., 9-point hedonic scale). Responses are transformed into numerical values, and this evaluation provides an indication of the potential market success. A consumer test conducted in Denmark (Budhathoki et al., 2021) evaluated the role of production method information on the liking of smoked salmon (wild-caught, conventional, and organic), among 92 participants using a 7-point hedonic scale before and after receiving the information. The results showed a significant effect of production information on consumer liking of smoked salmon, increasing the liking of wild-caught for the informed condition in comparison to the blind condition. 27.2.2.3  Emotion Evaluation Emotions play a critical role in consumers’ food choices. Over the past decade and a half, the study of emotions in sensory and consumer research has rapidly gained popularity. This growing interest in evaluating the affective component has been fueled by the need to obtain a more comprehensive understanding of consumers’ experience with food, beyond acceptability and sensory measures. It has also raised some methodological challenges and debates related to how emotional experiences are measured. Two main perspectives can be distinguished when measuring emotions elicited by food products, namely explicit and implicit methods. Explicit methods rely on self-reported measurements, most commonly questionnaires which include a list of emotional terms that consumers can select or rate (e.g., Check-all-that-apply or CATA, Rate-all-that-apply or RATA, see Section 27.2.4). While a wide variety of standardized verbal-based methods have been developed to evaluate emotional responses (e.g., EsSense Profile ®, GEOS, EmoSemio), other non-verbal methods have been proposed as alternatives (e.g., emoji or image-based questionnaires). For example, a comparison study utilized different variants of emoji questionnaires to evaluate consumers’ emotional associations with seafood and found emoji RATA as a particularly suitable method variant to increase sample discrimination and frequency of use, in comparison to emoji CATA and forced yes/no questions (Ares and Jaeger, 2017). Product involvement and emotional response towards seafood, among other food categories, were also explored using emoji questionnaires in a study carried out in New Zealand and China (Jaeger et al., 2018). Emoji surveys provide higher ecological validity compared to word-based questionnaires and facilitate cross-cultural research as it does not require translation (Jaeger et al., 2021).

Evaluation Methodologies in Sensory and Consumer Sciences

531

Explicit methods are the most prominent approach to measure food-related emotions due to ease of implementation and analysis. However, in search for a more indirect assessment of emotions, implicit methods have been increasingly incorporated into consumer research as they allow to register the emotional response without consumers’ cognitive interpretation. This can be measured physiologically (e.g., eye tracking, heart rate, skin conductance, or brain responses), through facial expression or implicit behavioral tasks. Both explicit and implicit approaches are relevant and, even though explicit methods (in particular word-based questionnaires) are still the most common approach, a combination of implicit and explicit methods may be beneficial (Schouteten, 2021). When combining both approaches, researchers should consider that most implicit measures provide a continuous measurement (e.g., facial expression) of the emotional response while explicit measures capture information from a certain point in time, usually after sensory perception or consumption (Lagast et al., 2017). Moreover, it is expected that newer methods, such as text mining (Chen et al., 2021, see Table 27.2) will continue to be developed in sensory and consumer research to identify emotional associations and trends in written text, which can even be sourced from nonlaboratory settings (e.g., social media). For further reading on this topic, the reader is referred to Meiselman’s Emotion Measurement (2021).

27.2.3 Temporal Tests The experience of consuming food products is not a static but rather a dynamic process. As the food is processed and transformed in the mouth, these dynamics can impact on its perceived sensory attributes. The sensory methods described so far in this chapter approach the evaluation from a static perspective by collecting data at a specific point in time. However, in some cases, it is relevant to capture the evolution in the sensory perception of products. Thus, temporal methods have been developed to understand relevant changes that may occur during consumption. Time-intensity was among the first temporal methods to be introduced, mainly focused on quantifying the intensity of one attribute (Cliff and Heymann, 1993) and in some cases two attributes (Dual Attribute Time Intensity or DATI). Although time-intensity provides high-resolution data, this comes at a cost of higher task complexity and intense training of assessors. Further, the method is limited to a maximum of two attributes which can be evaluated at a time. Alternative methods have been developed to capture the perceptual changes of multiple attributes, such as Progressive profiling (PP) and more recently the Temporal Dominance of Sensations (TDS). PP is a method originally developed to monitor changes in multiple texture attribute intensities with every chewing stroke until there are no further changes (Jack et al., 1994). However, it has been adapted to measure changes in texture attribute intensities across set time points to yield low resolution yet quantitative data on changes in texture perception (Kang et al., 2019). TDS (Pineau et al., 2009) introduces a simplified approach to assess several attributes simultaneously, by focusing on the identification of a single dominant attribute during evaluation. For example, TDS has been applied to evaluate complex seafood products such as fish sticks with contrasting textural layers (Albert et al., 2012). In TDS, assessors are provided with a list of attributes and continuously select single attributes that are “dominant” during consumption/evaluation of the sample (i.e., the attribute that catches their attention and stands out, but not absolute intensity per se), reducing the task complexity. Assessors are free to change the dominant attribute during this time, which yields data in the form of line plots (TDS curves) that reveal the attributes that were dominant as a function of time per sample. In recent years, a temporal extension of CATA (see Section 27.2.4) was developed: Temporal CheckAll-That-Apply or TCATA (Castura et al., 2016). In contrast with the TDS method, TCATA does not measure dominant attributes but rather shows which attributes were present throughout the duration of consumption. It therefore allows assessors to select multiple relevant attributes at once, as opposed to indicating one single attribute. Lastly, it is worth mentioning that temporal methods can be used to measure affect, in addition to analytical evaluation. For instance, there are temporal methods related to emotion evaluation, to

532

Handbook of Seafood and Seafood Products Analysis

capture the changes in emotional response over time (e.g., Temporal Dominance of Emotions; TDE, (Jager et al., 2014)).

27.2.4 Other Methods In this section, another group of methods that can be used to assess the response of individuals to seafood products are provided. Some of these methods listed in Table 27.3 are designed to collect qualitative or semi-qualitative data and generally do not require the use of scales to evaluate differences between samples (except Rate-All-That-Apply (RATA) and polarized sensory positioning (PSP)). Sorting and mapping techniques (such as projective mapping and Napping®) are qualitative methods that are used to identify the overall qualitative differences between samples to determine

TABLE 27.3 Overview of Other Methods Used in Sensory and Consumer Research Test

Vocabulary

Task

References

Free sorting

Check all that apply (CATA) Rate all that apply (RATA) Focus groups

Provided by researcher Provided by researcher N/A

Interviews

N/A

Choice-based conjoint analysis

N/A

Ethnographic methods

N/A

Open-ended questions and text mining Conceptual profiling

Provided by assessors

Classify the samples into groups based on similarities or differences in sensory properties, indicating qualitative differences between the samples Place the samples onto a two-dimensional sensory map (represented in a large sheet of paper or computer screen), where the proximity between samples is determined by the X and Y coordinates and indicates the perceived degree of similarity/difference. Subsequently, assessors write down the attributes that describe the samples. Select the terms from a list based on which attributes are appropriate to describe the samples (multiple choice) Rate the intensity of the attributes on a scale to describe the samples. Values do not indicate absolute intensity Discuss a topic of interest amongst a group of participants (8–12), guided by a trained moderator who typically asks open-ended questions One-to-one interviews, usually unstructured or semi-structured, where a trained interviewer asks a set of broad questions related to a topic of interest Simulated product choices from a set of alternatives, to identify the underlying attribute preferences without asking consumers directly Observation of consumers in their natural environment, as they would normally behave, with as little interference as possible from the researcher Gather and analyze large amounts of unstructured text data to identify patterns and consumer trends

Lawless et al. (1995)

Projective mapping (PM) or Napping®

Provided by assessors or researcher Provided by assessors

Polarized sensory positioning (PSP)

N/A

Provided by researcher

Identify which of the conceptual words presented, which may have emotional or functional connotations, are associated with a specific product Compare products (one at a time) against a set of reference products and indicate the degree of dissimilarity. It allows to compile data across multiple sessions if the set of references is kept constant.

Pagès (2005)

Ares et al. (2010) Ares et al. (2014) Hackett et al. (2016) Hackett et al. (2016) Lockshin et al. (2006) Hackett et al. (2016) Chen et al. (2021) Thomson et al. (2010) Teillet et al. (2010)

Evaluation Methodologies in Sensory and Consumer Sciences

533

the sensory characteristics within a relatively short timeframe. When evaluating multiple samples, it may require physically positioning them on a space such as a large sheet of paper to indicate the degree of similarity or difference. Two variants of methods that have been incorporated recently but are increasingly used as alternatives to conventional descriptive analysis are CATA and RATA, mainly due to their simplicity and cost-effectiveness. These two rapid methods typically use a large sample size of 80–120 consumers, where participants are required to select any responses that apply to the sample (with an additional rating sub-task in the case of RATA). For example, a recent study (Silva et al., 2020) utilized CATA as a method to evaluate the sensory profile of five under-utilized fish species using 16 members of a semi-trained panel. The results found CATA to be a suitable method for sensory characterization, allowing to discriminate fish species based on appearance, odor, and flavor, particularly when evaluated by semi-trained panelists. The application of CATA extends beyond sensory characterization and can also be used to evaluate emotions or situational appropriateness. However, one of the limitations of CATA is that the binary responses it provides do not allow to directly measure the perceived intensity of sensory attributes. This could hinder the discrimination between products that have similar sensory properties but with different intensities, particularly when used with untrained consumers. To overcome this limitation and quantify attribute intensity, RATA was introduced as a variant of CATA. This method provides respondents with a list of terms that can be selected if applicable and, in those cases, these are rated on an intensity scale. Although RATA provides better product discrimination than CATA, some authors have found discrepancies when comparing it to descriptive analysis with trained assessors (Ares et al., 2018) in particular when samples are complex or have similar sensory characteristics. Another qualitative method often used within consumer research is focus groups. Focus groups are particularly valuable to obtain a deeper understanding of a subject, for example, to gain knowledge of consumers’ experience with a type of product. They allow to obtain rich qualitative information through a dynamic discussion among participants, focused on current or new products under development, and an experienced moderator is necessary to facilitate the discussion. In a study carried out across different regions in Spain, nine focus group discussions were held to gain an indepth understanding of consumers’ perceptions and beliefs about wild vs farmed fish (Claret et al., 2014). Although focus groups have traditionally been conducted physically, gathering a small group of consumers in a room (in a central location test or CLT, see Section 27.3.2.), current technology enables digital focus groups as well. As an example, digital focus groups were combined with cooking sessions to explore consumers’ barriers to reducing meat consumption (Collier et al., 2022). In another study, digital focus groups divided into positive/neutral/negative focus groups were conducted to gain insights into the beliefs and attitudes of Norwegian consumers toward smoke flavoring of salmon (Waldenstrøm et al., 2022). In-depth interviews are an alternative method to obtain detailed qualitative information about how consumers perceive or use products. As opposed to focus groups, in-depth interviews are usually conducted on a one-to-one basis, and the interest is not to create a social dynamic where opinions are exchanged but to obtain information about individuals’ experiences and perceptions regarding a topic of interest. Interviews have a very flexible format (e.g., task-led, semi-structured, or structured) and can be conducted physically, online, or by phone. In-depth interviews also minimize the risk of biases that could arise in more social settings, although social desirability bias is still present in both methodologies (i.e., respondents may steer their answers to present a more favorable image of themselves). Other research methods analyze consumers’ preferences and choices indirectly, limiting social desirability bias by providing tasks to participants. The choice-based conjoint analysis presents consumers with a set of alternatives from which they can choose their preferred option, to investigate the relative importance of different product attributes (Lockshin et al., 2006). Several studies have applied this method to examine seafood preferences, for example, to assess the importance of packaging and preparation convenience on consumers’ choice of oysters (Mueller Loose et al.,

534

Handbook of Seafood and Seafood Products Analysis

2013) and to evaluate the potential market opportunity for sea urchins (Grisolía et al., 2012). On the other hand, ethnographic methods gather information through the observation of consumers in the natural environment (everyday settings such as cooking at home or purchasing products in the supermarket). This approach aims at capturing the natural behavior of consumers in a non-intrusive way, and benefits from large flexibility in terms of format (e.g., self-recorded video, photographic documentation, notes of participant observation, or wearable eye tracking). For instance, one study conducted in China combined ethnographic methods with in-depth interviews with stakeholders to gain an understanding of the social context and changing consumption patterns in the Beijing seafood market (Fabinyi and Liu, 2016). Lastly, text mining is the process of extracting meaningful patterns from vast quantities of unstructured information, for instance from discussions on social media. This allows us to obtain insights about consumers’ opinions about a topic, or their experiences with a product, as well as to identify market trends. For example, a recent study that used social media to analyze food-related content identified patterns of fish and seafood consumption, with more frequent mentions of fish/ seafood in dinner and lunch eating situations (Dondokova et al., 2019).

27.3 CONSIDERATIONS WHEN PLANNING AND CONDUCTING SENSORY AND CONSUMER RESEARCH Unlike instrumental measurements, sensory and consumer research involve human participants. Whether the data is collected from trained panelists, semi-trained, or naïve (untrained) consumers, human perception entails a certain degree of variability by nature (e.g., due to participants’ previous experience, product familiarity, psychological traits, or environmental circumstances). Nevertheless, considerations should be made to control and minimize bias with the goal of obtaining reliable and accurate information. In this section, we discuss some of the main factors that the researcher should consider when planning and conducting sensory and consumer research.

27.3.1 Participants’ Level of Experience Sensory analysis methodologies have traditionally been classified and divided into “objective” (analytical) and “subjective” (affective) tests, to indicate whether a sensory panelist or a consumer was performing the assessment. However, the distinction between trained and consumer panels has begun to merge over the last decades (Ares and Varela, 2017). Initially, sensory characterization of foods was performed exclusively by either experts or trained sensory panelists. Naïve consumers were not considered suitable to evaluate sensory properties of food due to their lack of knowledge in descriptive profiling until later comparative studies with experts and consumer panels found that consumers could provide useful sensory data. Since then, and enhanced by the development of rapid sensory methods, the focus has been shifted to consumers as they are often the intended target users of products. Although sensory evaluation with trained panelists still provides valuable information, for example in quality control or when differences between samples are subtle, consumers’ experience is nowadays at the core of product development (Cardello, 2020). It should be noted that the decision to employ trained panels or consumers for the assessment will ultimately depend on the research objectives, the type of products, and the degree of product differentiation required. Whether to use trained sensory assessors, experts, or consumers should be decided on a case-by-case basis and will entail advantages and limitations with regard to each expertise (see Table 27.4). Thus, trained panelists are sometimes necessary to obtain rigorous and detailed product characterizations that allow detection of small variations between similar and/ or complex products (e.g., detection of off-flavors). In addition, trained panels are able to provide accurate intensity information that is particularly useful when modifying the product formulation

535

Evaluation Methodologies in Sensory and Consumer Sciences

TABLE 27.4 Advantages and Disadvantages of Using Assessors with Different Experience Levels Assessor

Advantages

Disadvantages

Expert (e.g., chef, sommelier, competition judge)

Product expertise, rich description

Sensory panelist

Familiar with evaluation, high precision to detect small variations, low variability (common vocabulary and strong analytical ability), ability to use scales, ability to describe sensory characters High representativeness, does not require training, fast data acquisition

Very low representativeness of population, personal evaluation methodology and vocabulary Low representativeness, does not necessarily translate into consumers’ sensitivity, requires training, requires maintenance, time intensive and expensive

Consumer

High variability, low ability to articulate sensory characters, low precision, requires large sample sizes

(Ares and Varela, 2017). When such a level of detail is not required in the study, naïve consumers may be sufficient.

27.3.2 Test Locations Food choices do not happen in isolation, they are tied to environmental and contextual factors. In addition to the macro and local contexts, the environmental settings in which food is consumed also play a key role (Poelman and Steenhuis, 2019). When conducting sensory and consumer studies, researchers should be aware of how the results may be influenced by the location in which the test takes place. Sensory research has been traditionally conducted in controlled settings such as central location tests (CLT), where participants are invited to laboratory booths to evaluate the samples. These contexts seek to standardize the test conditions, providing a neutral and sterile context that allows us to focus on isolating the variables of interest. Moreover, it makes it easier to compare the results over time or when testing in separate locations (e.g., across different countries). Sterile booths under a controlled environment are typically required for analytical tests, to determine how changes in composition may impact perception. While controlled settings tend to have high internal validity, the disadvantage is that the consumption context does not represent the actual food consumption environment, thus leading to a lack of ecological validity (depending on the research question). More naturalistic test locations are necessary to understand whether the results can be generalized to real-life situations (e.g., at a restaurant, supermarket, or at home). Non-laboratory settings, despite being more uncontrolled and difficult to predict, provide invaluable information about how consumers interact with food products in natural environments. Some authors have stressed the importance of performing consumer research in the field to minimize the risk of new product failure (Payne and Wansink, 2010). For example, home-use tests (HUT) conducted in real settings have been linked to higher liking scores in comparison to CLT. A study compared consumer liking between HUT and CLT when evaluating two farmed cod samples and found that consumers were able to discriminate between the two products only in CLT settings, where they appeared to be more analytical (Sveinsdóttir et al., 2010). Moreover, the results showed that consumers in CLT rated the samples lower in terms of liking, whereas in HUT they could decide their cooking method. Interest in conducting research in HUT has increased over the years, along with online testing, especially since strict restrictions were imposed on CLT during the COVID-19 pandemic from

536

Handbook of Seafood and Seafood Products Analysis

2020–2022. In particular, sensory and consumer tests conducted online allow to reach a wide sample size with lower costs and high flexibility, although precautions should be followed as the uncontrolled nature of online data collection introduces elements that can potentially affect its quality (Jaeger and Cardello, 2022). The choice of test location also depends on the type of product to be tested, as products that require more preparation might introduce noise when evaluated using HUT (Niimi et al., 2022). A compromise between laboratory and non-laboratory settings would be ideal: the testing situation should resemble as much as possible the real consumption context while allowing to control the variables of interest. Virtual environments present a promising alternative that combines the advantages of both laboratory and non-laboratory tests (Porcherot et al., 2018; Lichters et al., 2021). Although they are still relatively uncommon in sensory and consumer research, technological advancements will enable more tests to be performed in immersive environments.

27.3.3  Holistic Versus Analytic Processes Human perception is multisensory and multifaceted, capable of representing products as a unified whole by integrating a flow of diverse sensory input. Consumer experiences and responses can also be impacted by cognitive and emotional information. Measurement of perceptions can be broadly separated into two types of processes, which are method dependent: holistic and analytic processes. Methodologies where the assessment is based on understanding the overall perception of food products, such as sorting or projective mapping, are referred to as holistic methodologies. Researchers should consider the use of holistic methodologies in studies aiming to determine global similarities and differences among samples, as these methods are particularly suitable to identify the most relevant characteristics according to assessors’ own criteria (Varela and Ares, 2012). By contrast, analytic methodologies are better suited when the aim is to tease apart and profile samples for specific attributes. For example, CATA is a method where assessors can select from a predefined list if certain descriptors are applicable. Analytical process methods yield data on how the samples differ and, depending on the method, the magnitude of differences can also be reported.

27.3.4 Information Effects Despite sensory attributes being an important driver of food liking and product success, extrinsic factors, such as information (e.g., brand, price, label claims, country of origin, convenience, or production method) also have a substantial impact on consumers’ responses and choices (i.e., top-down effects). In some instances, these information effects may undesirably influence the experiment and, in such cases, any provision of information about the samples needs to be excluded. On the other hand, the effect of this information on consumers’ expectations and product acceptance can be tested as a factor in itself, if it is relevant to the research question. In these situations, the inclusion of a blind condition (without any additional product information) is ideal, as well as an informed condition with the attributes of interest as recommended by Li et al. (2015). Considering the added layer of complexity when examining the effect of information, the cost of such studies tends to be higher. Several studies have investigated the effect of production method information on consumers’ liking of fish. In one study four different species with two methods of production (wild vs farmed) were presented to consumers for a hedonic assessment, under blind and informed conditions (Claret et al., 2016). The results indicated a preference for wild fish in the informed condition, while farmed fish was preferred in the blind condition. This indicates that the sensory characteristics of farmed fish are satisfactory to consumers and do not need to be further improved. Instead, efforts to enhance the image of farmed fish are necessary, as the preference for wild fish is related to a generalized positive attitude when information about the production method is provided. This study demonstrates

Evaluation Methodologies in Sensory and Consumer Sciences

537

that information can exert a strong effect on responses, to the extent that consumer hedonics can be reversed. Similarly, the influence of production method information was evaluated under blind and informed conditions (Budhathoki et al., 2021). In this study, overall liking of wild-caught smoked salmon was higher than conventional and organic salmon when information was provided. These results further illustrate how information can steer consumers’ preferences and choices, and the importance of sensory and consumer research in providing recommendations to the industry.

27.3.5 Number of Attributes With the increased interest in using rapid methods for sensory product characterizations (e.g., using CATA and RATA and consumers for the assessment), best practices and methodological recommendations are fundamental to guide the research design. Among the important decisions a researcher should make is the selection of terms to include when incorporating CATA questions in a study. The influence of lexicon length on the elicited sensory characterization was examined in a series of studies (Jaeger et al., 2015). Although an optimal number of terms has not been suggested, mainly as it depends on the research objectives and the products evaluated, including shorter attribute lists (comprised of 10–20 terms) is preferable to longer lists (30 terms or higher). Additional terms could lead to a dilution effect, spreading consumers’ responses across attributes, especially when using synonyms. Lastly, the order in which these attributes are presented to consumers should be randomized to avoid bias toward the terms that appear first in the list; and grouped within sensory modalities based on the order in which consumers will evaluate them (e.g., appearance, aroma, flavor, and texture) (Ares and Jaeger, 2013). For example, a consumer study investigated the sensory profiles of emerging fish species through a CATA list comprised of 25 attributes, which were presented according to the sensory modalities to reduce cognitive load and randomized within modalities to minimize order-related biases (Alexi et al., 2018).

27.3.6  Cognitive Load When assessors complete sensory and consumer tests, a critical component that can interfere with data quality is cognitive load. Participants’ ability to maintain their attention on the instructed task will be affected by different factors and could impact the quality of the responses. In sensory tests, discrimination tests that include too many repetitions within a session could lead to fatigue and even boredom. The same number of tests from a directional paired-comparison test may not be feasible with a tetrad test for example. The number of samples to be characterized in a profiling task should also be considered to avoid overstimulation, and interferences between samples can be prevented by providing sufficient inter-stimulus breaks with palate cleansing (e.g., carry-over effect) and limiting the number of samples to be evaluated per session. The degree of task complexity should be as optimized as possible – not too high so that it outweighs the effort required to measure sensory perceptions (generating fatigue) nor too low as it could lead to task disengagement and boredom. In consumer tests, the number of samples to be assessed at a time should not be too excessive as too many samples may cause fatigue. Although a maximum number is not strictly set, no more than six to seven samples could be a rough guide. The degree of task difficulty may also interfere with the results, as well as the length of the tests. Ideally, tasks provided to consumers should be kept simple, albeit engaging and motivating. Lastly, unlike experienced sensory assessors, consumers are not trained to focus on sensory perception for long periods of time and the time to task completion needs to be considered.

538

Handbook of Seafood and Seafood Products Analysis

27.4  CONCLUDING REMARKS Sensory analysis and consumer research provide valuable tools to guide the innovation and development of new seafood products, maximizing the chances of market success. Consumer choices are complex, but a better understanding of the sensory expectations and experiences of potential consumers will help reduce the uncertainties related to product acceptance. New seafood product developers need to satisfy consumers not only in terms of intrinsic product characteristics (e.g., appearance or texture) and extrinsic product characteristics (e.g., brand or packaging) but also account for psychological and contextual factors that may influence consumer choice. Both traditional and alternative sensory methodologies offer a wide range of possibilities to obtain insights about the characteristics of seafood products and, when integrated with knowledge about the consumer, facilitate development of products and communication strategies that meet customer needs and expectations. Being aware of these methodologies and when they are most suitable is valuable, but most importantly the researcher should start by defining a specific purpose and scope for the study. Only by defining the objectives as early as possible will the analyst be able to select the appropriate methodology and obtain useful results. For further details on the execution of the methodology and the general principles of sensory evaluation, the readers are recommended to read Kemp, Hollowood, and Hort (2009) and Lawless and Heymann (2010).

REFERENCES Albert, A., Salvador, A., Schlich, P., & Fiszman, S. (2012). Comparison between temporal dominance of sensations (TDS) and key-attribute sensory profiling for evaluating solid food with contrasting textural layers: Fish sticks. Food Quality and Preference, 24(1), 111–118. doi:10.1016/j.foodqual.2011.10.003. Albert, A., Varela, P., Salvador, A., Hough, G., & Fiszman, S. (2011). Overcoming the issues in the sensory description of hot served food with a complex texture. Application of QDA(r), flash profiling and projective mapping using panels with different degrees of training. Food Quality and Preference, 22(5), 463–473. doi:10.1016/j.foodqual.2011.02.010. Alexi, N., Byrne, D. V., Nanou, E., & Grigorakis, K. (2018). Investigation of sensory profiles and hedonic drivers of emerging aquaculture fish species. Journal of the Science of Food and Agriculture, 98(3), 1179–1187. doi:10.1002/jsfa.8571. Ares, G., Barreiro, C., Deliza, R., Giménez, A., & Gámbaro, A. (2010). Application of a check-all-thatapply question to the development of chocolate milk desserts. Journal of Sensory Studies, 25, 67–86. doi:10.1111/j.1745-459X.2010.00290.x. Ares, G., Bruzzone, F., Vidal, L., Cadena, R. S., Giménez, A., Pineau, B., Hunter, D. C., Paisley, A. G., & Jaeger, S. R. (2014). Evaluation of a rating-based variant of check-all-that-apply questions: Rate-all-thatapply (RATA). Food Quality and Preference, 36, 87–95. doi:10.1016/j.foodqual.2014.03.006. Ares, G., & Jaeger, S. R. (2013). Check-all-that-apply questions: Influence of attribute order on sensory product characterization. Food Quality and Preference, 28(1), 141–153. doi:10.1016/j.foodqual.2012.08.016. Ares, G., & Jaeger, S. R. (2017). A comparison of five methodological variants of emoji questionnaires for measuring product elicited emotional associations: An application with seafood among Chinese consumers. Food Research International, 99, 216–228. doi:10.1016/j.foodres.2017.04.028. Ares, G., Picallo, A., Coste, B., Antúnez, L., Vidal, L., Giménez, A., & Jaeger, S. R. (2018). A comparison of RATA questions with descriptive analysis: Insights from three studies with complex/similar products. Journal of Sensory Studies, 33(5), e12458. doi:10.1111/joss.12458. Ares, G., & Varela, P. (2017). Trained vs. consumer panels for analytical testing: Fueling a long lasting debate in the field. Food Quality and Preference, 61, 79–86. doi:10.1016/j.foodqual.2016.10.006. ASTM. (2009). MNL63-EB Just-about-Right (JAR) Scales: Design, Usage, Benefits, and Risks. West Conshohocken, PA: ASTM International. ASTM. (2016). E2164-16, Standard Test Method for Directional Difference Test. West Conschohocken, PA: ASTM International. ASTM. (2018a). E2610-18, Standard Test Method for Sensory Analysis - Duo-trio test. West Conschohocken, PA: ASTM International. ASTM. (2018b). E1885-18, Standard Test Method for Sensory Analysis - Triangle Test. West Conschohocken, PA: ASTM International.

Evaluation Methodologies in Sensory and Consumer Sciences

539

ASTM. (2018c). E2139-05, Standard Test Method for Same-Different Test. West Conschohocken, PA: ASTM International. ASTM. (2018d). E3009-15e1, Standard Test Method for Sensory Analysis - Tetrad Test. West Conschohocken, PA: ASTM International. Berik, N., & Çankırılıgil, E. (2018). Sensorial evaluation of fish croquettes produced from different seafood. Aquatic Sciences and Engineering, 33(3), 96–101. doi:10.26650/ASE201818. Brandt, M. A., Skinner, E. Z., and Coleman, J. A. (1963). Texture profile method. Journal of Food Science 28:404–409. Budhathoki, M., Zolner, A., Nielsen, T., & Reinbach, H. C. (2021). The role of production method information on sensory perception of smoked salmon: A mixed-method study from Denmark. Food Quality and Preference, 94, 104325. doi:10.1016/j.foodqual.2021.104325. Cairncross, S. E. and Sjostrom, L. B. (1950). Flavor Profiles - A new approach to flavor problems. Food Technology 4:308–311. Cardello, A. V. (2020). Human experience of eating and drinking: Perspectives on 50 years of measurement progress. H.L. Meiselman (Ed.), In H.L. Meiselman (Ed.), Handbook of Eating and Drinking: Interdisciplinary Perspectives, Springer International Publishing (pp. 1599–1625). Castura, J. C., Antúnez, L., Giménez, A., & Ares, G. (2016). Temporal check-all-that-apply (TCATA): A novel dynamic method for characterizing products. Food Quality and Preference, 47, 79–90. doi:10.1016/j. foodqual.2015.06.017. Chen, Z., Gurdian, C., Sharma, C., Prinyawiwatkul, W., & Torrico, D. D. (2021). Exploring text mining for recent consumer and sensory studies about alternative proteins. Foods, 10(2537), 14. doi:10.3390/ foods10112537. Claret, A., Guerrero, L., Gartzia, I., Garcia-Quiroga, M., & Ginés, R. (2016). Does information affect consumer liking of farmed and wild fish? Aquaculture, 454, 157–162. doi:10.1016/j.aquaculture.2015.12.024. Claret, A., Guerrero, L., Ginés, R., Grau, A., Hernández, M. D., Aguirre, E., Peleteiro, J. B., Fernández-Pato, C., & Rodríguez-Rodríguez, C. (2014). Consumer beliefs regarding farmed versus wild fish. Appetite, 79, 25–31. doi:10.1016/j.appet.2014.03.031. Cliff, M., & Heymann, H. (1993). Development and use of time-intensity methodology for sensory evaluation: A review. Food Research International, 26(5), 375–385. doi:10.1016/0963-9969(93)90081-S. Collier, E. S., Normann, A., Harris, K. L., Oberrauter, L. M., & Bergman, P. (2022). Making more sustainable food choices one meal at a time: Psychological and practical aspects of meat reduction and substitution. Foods, 11(9), 1182. doi:10.3390/foods11091182. Dairou, V., & Sieffermann, J.-M. (2002). A comparison of 14 jams characterized by conventional profile and a quick original method, the flash profile. Journal of Food Science, 67(2), 826–834. doi:10.1111/j.1365-2621.2002.tb10685.x. Dondokova, A., Aich, S., Kim, H.-C., & Huh, G. H. (2019). A text mining approach to study individuals’ food choices and eating behavior using twitter feeds. In J. C. Hung, N. Y. Yen, & L. Hui (Eds.), Frontier Computing (pp. 520–527). Springer. doi:10.1007/978-981-13-3648-5_60. Fabinyi, M., & Liu, N. (2016). The social context of the Chinese food system: An ethnographic study of the Beijing seafood market. Sustainability, 8(3), 244. doi:10.3390/su8030244. Furey, A. E., Hoeche, U., & Noci, F. (2020). Adding value to under-utilised Irish fish roe: A physico-chemical and sensory comparison of cured Irish pollock (Pollachius pollachius) roe with commercial mullet (Mugil cephalus) and cod (Gadus morhua) products. Irish Journal of Agricultural and Food Research, 59(1). doi:10.15212/ijafr-2020-0114. Gökoglu, N. (2002). A descriptive method for sensory evaluation of mussels. LWT - Food Science and Technology, 35(7), 563–567. doi:10.1006/fstl.2001.0868. Goulas, A. E., & Kontominas, M. G. (2007). Effect of modified atmosphere packaging and vacuum packaging on the shelf-life of refrigerated chub mackerel (Scomber japonicus): Biochemical and sensory attributes. European Food Research and Technology, 224(5), 545–553. doi:10.1007/s00217-006-0316-y. Grisolía, J. M., López, F., & Ortúzar, J. de D. (2012). Sea urchin: From plague to market opportunity. Food Quality and Preference, 25(1), 46–56. doi:10.1016/j.foodqual.2012.01.004. Hackett, P. M. W., Schwarzenbach, J. B., & Jürgens, U. M. (2016). Consumer Psychology: A Study Guide to Qualitative Research Methods. Berlin: Verlag Barbara Budrich. doi:10.3224/84740772. Hyldig, G., Larsen, E., & Green-Petersen, D. (2007). Fish and sensory analysis in the fish chain. In Nollet, L.M.L. , Boylston, T. , Chen, F. , Coggins, P.C. , Gloria, M.B. , Hyldig, G. , Kerth, C.R. , Mckee, L.H. and Hui, Y.H. (Eds.), Handbook of Meat, Poultry and Seafood Quality, Wiley-Blackwell, Oxford (pp. 499–510). doi:10.1002/9780470277829.ch39.

540

Handbook of Seafood and Seafood Products Analysis

ISO. (2005). Standard 5495: Sensory Analysis - Methodology - Paired Comparison Test. Geneva, Switzerland: ISO. ISO. (2006). Standard 8587: Sensory Analysis - Methodology - Ranking. Geneva, Switzerland: ISO. ISO. (2017). Standard 8588: Sensory Analysis - Methodology - “A” - “Not A” Test. Geneva, Switzerland: ISO. Jack, F. R., Piggott, J. R., & Paterson, A. (1994). Analysis of textural changes in hard cheese during mastication by progressive profiling. Journal of Food Science, 59(3), 539–543. doi:10.1111/j.1365-2621.1994. tb05557.x. Jaeger, S. R., Beresford, M. K., Paisley, A. G., Antúnez, L., Vidal, L., Cadena, R. S., Giménez, A., & Ares, G. (2015). Check-all-that-apply (CATA) questions for sensory product characterization by consumers: Investigations into the number of terms used in CATA questions. Food Quality and Preference, 42, 154–164. doi:10.1016/j.foodqual.2015.02.003. Jaeger, S. R., & Cardello, A. V. (2022). Factors affecting data quality of online questionnaires: Issues and metrics for sensory and consumer research. Food Quality and Preference, 102, 104676. doi:10.1016/j. foodqual.2022.104676. Jaeger, S. R., Lee, P.-Y., & Ares, G. (2018). Product involvement and consumer food-elicited emotional associations: Insights from emoji questionnaires. Food Research International, 106, 999–1011. doi:10.1016/j. foodres.2018.01.024. Jaeger, S. R., Vidal, L., & Ares, G. (2021). Should emoji replace emotion words in questionnairebased food-related consumer research? Food Quality and Preference, 92, 104121. doi:10.1016/j. foodqual.2020.104121. Jager, G., Schlich, P., Tijssen, I., Yao, J., Visalli, M., de Graaf, C., & Stieger, M. (2014). Temporal dominance of emotions: Measuring dynamics of food-related emotions during consumption. Food Quality and Preference, 37, 87–99. doi:10.1016/j.foodqual.2014.04.010. Kang, W., Niimi, J., Muhlack, R. A., Smith, P. A., & Bastian, S. E. P. (2019). Dynamic characterization of wine astringency profiles using modified progressive profiling. Food Research International, 120, 244–254. doi:10.1016/j.foodres.2019.02.041. Kemp, S. E., Hollowood, T., & Hort, J. (2009). Sensory Evaluation: A Practical Handbook. Hoboken, NJ: John Wiley & Sons Ltd. Lagast, S., Gellynck, X., Schouteten, J. J., De Herdt, V., & De Steur, H. (2017). Consumers’ emotions elicited by food: A systematic review of explicit and implicit methods. Trends in Food Science & Technology, 69, 172–189. doi:10.1016/j.tifs.2017.09.006. Lazo, O., Claret, A., & Guerrero, L. (2016). A comparison of two methods for generating descriptive attributes with trained assessors: Check-All-That-Apply (CATA) vs. Free Choice Profiling (FCP). Journal of Sensory Studies, 31(2), 163–176. doi:10.1111/joss.12202. Lawless, H. T., Sheng, N., and Knoops, S. S. C. P. (1995). Multidimensional scaling of sorting data applied to cheese perception. Food Quality and Preference 6:91–98. Lawless, H. T., & Heymann, H. (2010). Sensory Evaluation of Food: Principles and Practices (2nd ed.). New York: Springer. Li, X. E., Jervis, S. M., & Drake, M. A. (2015). Examining extrinsic factors that influence product acceptance: A review. Journal of Food Science, 80(5), R901–R909. doi:10.1111/1750-3841.12852. Lichters, M., Möslein, R., Sarstedt, M., & Scharf, A. (2021). Segmenting consumers based on sensory acceptance tests in sensory labs, immersive environments, and natural consumption settings. Food Quality and Preference, 89, 104138. doi:10.1016/j.foodqual.2020.104138. Lockshin, L., Jarvis, W., d’Hauteville, F., & Perrouty, J.-P. (2006). Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice. Food Quality and Preference, 17(3–4), 166–178. doi:10.1016/j.foodqual.2005.03.009. Meilgaard, M. C., Civille, G. V., & Carr, B. T. (2016). Sensory Evaluation Techniques (5th ed.). CRC Press, Taylor & Francis Group, Boca Raton. Meiselman, H. L. (2021). Emotion Measurement (2nd ed.). Woodhead Publishing, Cambridge, UK Mueller Loose, S., Peschel, A., & Grebitus, C. (2013). Quantifying effects of convenience and product packaging on consumer preferences and market share of seafood products: The case of oysters. Food Quality and Preference, 28(2), 492–504. doi:10.1016/j.foodqual.2012.11.004. Niimi, J., Collier, E. S., Oberrauter, L.-M., Sörensen, V., Norman, C., Normann, A., Bendtsen, M., & Bergman, P. (2022). Sample discrimination through profiling with rate all that apply (RATA) using consumers is similar between home use test (HUT) and central location test (CLT). Food Quality and Preference, 95, 104377. doi:10.1016/j.foodqual.2021.104377.

Evaluation Methodologies in Sensory and Consumer Sciences

541

Oliveira, A. R., Sykes, A. V., Hachero-Cruzado, I., Azeiteiro, U. M., & Esteves, E. (2015). A sensory and nutritional comparison of mussels (Mytilus sp.) produced in NW Iberia and in the armona offshore production area (Algarve, Portugal). Food Chemistry, 168, 520–528. doi:10.1016/j.foodchem.2014.07.082. Pagès, J. (2005). Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley. Food Quality and Preference, 16(7), 642– 649. Woodhead Publishing, Cambridge, UK. Enhanced Reader. doi:10.1016/j.foodqual.2005.01.006. Payne, C. R., & Wansink, B. (2010). Doing consumer research in the field. In Jaeger SR, MacFie H. (Eds.), Consumer-Driven Innovation in Food and Personal Care Products (pp. 358–385). Woodhead Publishing, Cambridge, UK. doi:10.1533/9781845699970.3.358. Phillips, K., Bremer, P., Silcock, P., Hamid, N., Delahunty, C., Barker, M., & Kissick, J. (2009). Effect of gender, diet and storage time on the physical properties and sensory quality of sea urchin (Evechinus chloroticus) gonads. Aquaculture, 288(3–4), 205–215. doi:10.1016/j.aquaculture.2008.11.026. Phillips, K., Niimi, J., Hamid, N., Silcock, P., Delahunty, C., Barker, M., Sewell, M., & Bremer, P. (2010). Sensory and volatile analysis of sea urchin roe from different geographical regions in New Zealand. LWT Food Science and Technology, 43(2), 202–213. doi:10.1016/j.lwt.2009.08.008. Pineau, N., Schlich, P., Cordelle, S., Mathonnière, C., Issanchou, S., Imbert, A., Rogeaux, M., Etiévant, P., & Köster, E. (2009). Temporal dominance of sensations: Construction of the TDS curves and comparison with time-intensity. Food Quality and Preference, 20(6), 450–455. doi:10.1016/j.foodqual.2009.04.005. Poelman, M. P., and Steenhuis, I. H. (2019). Food choices in context. In H. L. Meiselman (Ed.), Context: The Effects of Environment on Product Design and Evaluation (pp. 143–168). United Kingdom: Woodhead Publishing. Porcherot, C., Delplanque, S., Gaudreau, N., Ischer, M., Marles, A., & Cayeux, I. (2018). Immersive Techniques and Virtual Reality. Volume 2: Alternative Approaches and Special Applications (pp. 69–83). Elsevier, Duxford, United Kingdom. doi:10.1016/B978-0-08-101743-2.00003-0. Ramírez Rivera, E. D. J., Guadalupe Ramón Canul, L., Huante González, Y., Shaín Mercado, A. J., Rafael Bravo Delgado, H., & Martínez Liébana, C. (2009). Caracterización sensorial del camarón ahumado (Litopenaeus vannamei) mediante la técnica perfil flash. Ciencia y Mar 2009, XIII(38), 27–34. Sarnoski, P. J., O’Keefe, S. F., Jahncke, M. L., Mallikarjunan, P., & Flick, G. J. (2010). Analysis of crab meat volatiles as possible spoilage indicators for blue crab (Callinectes sapidus) meat by gas chromatographymass spectrometry. Food Chemistry, 122(3), 930–935. doi:10.1016/j.foodchem.2010.03.069. Schouteten, J. J. (2021). Are questionnaires the best way to measure emotions for food products and beverages? Food Quality and Preference, 92, 104122. doi:10.1016/j.foodqual.2020.104122. Sieffermann, J. M., Lopetcharat, K., & Pipatsattayanuwong, S. (2005). Application of sensory science to surimi seafood. In J. W. Park (Ed.), Surimi and Surimi Seafood (pp. 803–845). Boca Raton: CRC. Silva, F., Duarte, A. M., Mendes, S., Pinto, F. R., Barroso, S., Ganhão, R., & Gil, M. M. (2020). CATA vs. FCP for a rapid descriptive analysis in sensory characterization of fish. Journal of Sensory Studies, 35(6), e12605. doi:10.1111/joss.12605. Sveinsdóttir, K., Martinsdóttir, E., Thórsdóttir, F., Schelvis, R., Kole, A., & Thórsdóttir, I. (2010). Evaluation of farmed cod products by a trained sensory panel and consumers in different test settings. Journal of Sensory Studies, 25(2), 280–293. doi:10.1111/j.1745-459X.2009.00257.x. Teillet, E., Schlich, P., Urbano, C., Cordelle, S., & Guichard, E. (2010). Sensory methodologies and the taste of water. Food Quality and Preference, 21(8), 967–976. doi:10.1016/j.foodqual.2010.04.012. Thomson, D. M. H., Crocker, C., & Marketo, C. G. (2010). Linking sensory characteristics to emotions: An example using dark chocolate. Food Quality and Preference, 21(8), 1117–1125. doi:10.1016/j. foodqual.2010.04.011. Thuillier, B., Valentin, D., Marchal, R., & Dacremont, C. (2015). Pivot(c) profile: A new descriptive method based on free description. Food Quality and Preference, 42, 66–77. doi:10.1016/j.foodqual.2015.01.012. Varela, P., & Ares, G. (2012). Sensory profiling, the blurred line between sensory and consumer science. A review of novel methods for product characterization. Food Research International, 48(2), 893–908. doi:10.1016/j.foodres.2012.06.037. Waldenstrøm, L., Wahlgren, M. B., Strand, Å., Lerfall, J., & Gaarder, M. Ø. (2022). Norwegian consumers’ skepticism towards smoke-flavoring of salmon-is it for real? Foods, 11(14), 2170. doi:10.3390/ foods11142170. Worch, T., Lê, S., Punter, P., & Pagès, J. (2013). Ideal profile method (IPM): The ins and outs. Food Quality and Preference, 28(1), 45–59. doi:10.1016/j.foodqual.2012.08.001.

Part 5 Biological Safety

28

Detection of Seafood Spoilage Microorganisms Olumide A. Odeyemi University of Tasmania

Deyan Stratev Trakia University

Helen Onyeaka University of Birmingham

Fera R. Dewi and Muhamad Amin University of Tasmania

28.1  SEAFOOD SPOILAGE MICROORGANISMS Over the years, the dominant microorganisms of various fresh and raw seafood storage at chilled temperatures have been investigated (Table 28.1). These investigations comprised shrimps (Jia et al., 2019; Parlapani et al., 2020a), crabs (Parlapani et al., 2019b; Ramachandran et al., 2018), mussels (Parlapani et al., 2020b), oysters (Chen et al., 2019a), cuttlefish (Parlapani et al., 2018), and fish fillets (Huang et al., 2018; Kuuliala et al., 2018; Liu et al., 2018; Silbande et al., 2018; Zhang et al., 2019). Most of the studies have used universal primers to extract and sequence DNA or RNA molecules from the hypervariable region of the 16S rRNA gene (V1–V4) to identify the vast majority of bacteria, both culturable and non-cultivable. Photobacterium, Acinetobacter, Psychrobacter, Pseudomonas, Janthinobacterium, Flavobacterium, Comamonas, Rhodococcus, Blastococcus, Brevundimonas, Brochothrix, Arthrobacter, Lactobacillus, Photobacterium, Aeromonas, Shewanella, and Serratia were the most identified bacteria found in fish fillets (Jääskeläinen et al., 2019; Li et al., 2022a; Sørensen et al., 2020; Syropoulou et al., 2020; Zotta et al., 2019). However, Pseudomonas, Photobacterium, Aeromonas, and Shewanella were the dominant bacteria. Shewanella and Exiguobacterium were dominant in Maryland blue crabs (Ramachandran et al., 2018), while uncultured Vibrio spp. and Pseudoalteromonas were dominant in blue crab originating from Greece water. Crab’s origin seems to affect the microbial community and the dominants (Parlapani et al., 2019b). To prevent food spoiling, it is necessary to manage and restrict the growth of microorganisms within the product. In the fisheries industry, refrigeration is the most significant preservation technology. Pre- and post-processing temperatures must be low to preserve the freshness of the fish products. In an industrial setting, however, it is difficult always to maintain the low temperature necessary to ensure freshness from harvest through manufacture and distribution. Besides using low temperatures, it is desirable to take additional measures to prevent the growth of microorganisms in the items, known as hurdle technology. Thus, a combination of hurdles such as water activity, pH, oxygen content, and redox potential can be utilized to prevent the growth of microorganisms in food products (Dewi et al., 2021; Qian et al., 2022; Sørensen et al., 2020). Over the years, many investigations have been carried out to inhibit and maintain the quality of seafood products using various methods of hurdles (Table 28.2). The methods include modified atmosphere packaging (MAP) (Antunes-Rohling et al., 2019b; Kuuliala et al., 2018; DOI: 10.1201/9781003289401-33

545

546

Handbook of Seafood and Seafood Products Analysis

TABLE 28.1 Spoilage Microorganisms in Fresh and Raw Seafood Storage at Chilled Temperatures Seafood Atlantic Cod (Gadus morhua) Cuttlefish (Sepia officinalis) Aqua-cultured Carp (Cyprinus carpio) Bighead carp (Aristichthys nobilis) Tropical red drum (Sciaenops ocelatus) Blue Crab (Callinectes sapidus) Grass carp (Ctenopharyngodon idellus) fillet Pasific Oyters (Crassostrea gigas) Eastern Oyster (C. virginica) Gilthead seabream (Sparus aurata) Yellow Fin Tuna (Thunnus albacares) Atlantic Salmon (Salmo salar) Hake (Meluccius capensis and M. paradoxus) Plaice (Pleuronectes platessa) fillets Blue Crab (Callinectes sapidus) White shrimp (Litopenaeus vannamei) Atlantic Cod fillets (Gadus morhua L.) Grouper Epinephelus; Tilapia - Oreochromis Pacific Saury (Cololabis saira) Tilapia (skin off) (Oreochromis niloticus)

Origin

Storage Temperature

Atlantic Ocean Greece

4°C

Beijing Market Chinese market Atlantic coast of Martinique Chesapeake Bay

4°C

Method

Dominant

V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing

Photobacterium Acinetobacter Psychrobacter

Fresh

16S rRNA amplicon sequencing

Chinese market

4°C

V3–V4 region, 16S rRNA Pseudomonas amplicon sequencing

Fanny Bay

4°C

New Brunswick Greece Maldives Norway farm Southern Atlantic

2°C

4°C 4°C

V4–V5 region, 16S rRNA amplicon sequencing V4–V5 region, 16S rRNA 4°C amplicon sequencing 0°C and 5°C V3–V4 region, 16S rRNA 545-pryrosequencinng V3–V4 region, 16S rRNA 3°C amplicon sequencing V3–V4 region, 16S rRNA 3°C amplicon sequencing 0°C and 10°C V3–V4 region, 16S rRNA amplicon sequencing

Source

Pseudomonas

Kuuliala et al. (2018) Parlapani et al. (2018) Li et al. (2018)

Aeromonas, Pseudomonas Arthrobacter, Chryseobacterium

Liu et al. (2018) Silbande et al. (2018)

Shewanella, Exiguobacterium

Ramachandran et al. (2018)

Areobacter Sprichaeta, Psychrobacter Pseudomonas Pseudomonas, Lactobacillus Photobacterium Pseudomonas

Zhang et al. (2019) and Huang et al. (2018) Chen et al. (2019a)

Parlapani et al. (2019a) Jääskeläinen et al. (2019)

Zotta et al. (2019)

North-Eastern 0°C and 10°C V3–V4 region, 16S rRNA Acinetobacter Atlantic amplicon sequencing Chalastra 4°C and 10°C V3–V4 region, 16S rRNA Uncultured Vibrio Parlapani et al. Greece amplicon sequencing spp. and (2019b) Pseudoalteromonas Beijing 0°C V3–V4 region, 16S rRNA Candindatus Jia et al. market amplicon sequencing Bacilloplasma (2019) Greenland

0.4°C and −1.7°C 4°C

V3–V4 region, 16S rRNA Pseudomonas spp. amplicon sequencing V3–V4 region, 16S rRNA P. azotoformans amplicon sequencing P. faecalis

Sørensen et al. (2020) Huang and Xie (2020)

Pacific Ocean

2°C

Hainan

4°C

V3–V4 region, 16S rRNA Pseudomonadaceae amplicon sequencing V3–V4 region, 16S rRNA Enterobacter, amplicon sequencing Aeromonas

Cao et al. (2020b) Cao et al. (2020a)

Shanghai market

(Continued)

547

Detection of Seafood Spoilage Microorganisms

TABLE 28.1 (Continued ) Spoilage Microorganisms in Fresh and Raw Seafood Storage at Chilled Temperatures Seafood

Origin

Storage Temperature

Method

Dominant

Source

Rose Shrimp (Parpenaeus longirostris) Sardine (Sadinella brasiliensis)

Strymonikos Gulf

0°C and 4°C

V3–V4 region, 16S rRNA Psychrobacter, amplicon sequencing Carnobacterium

Parlapani et al. (2020a)

Rio Grande do Sul market

Fresh

V4 region, 16S rRNA amplicon sequencing

de Lira et al. (2020)

Mussels (Mytilus galloprovincialis) Cultured Sea bass (Dicentrarchus) Tilapia (Oreochromis niloticus) Northern whitefish (Coregonus peled) fillets

Halastra Greece Greece

4°C 0°C

Tianjin

4°C

Xinjiang

4°C

Macrococcus spp., Acinetobacter spp., and Pseudomonas spp., V3–V4 region, 16S rRNA Psychrobacter HRM alimentaris V3–V4 region, 16S rRNA Psychrobacter HRM glacincola V4 region, 16S rRNA Pseudomonas amplicon sequencing V4 region, 16S rRNA Aeromonas, amplicon sequencing Pseudomonas

Parlapani et al. (2020b) Syropoulou et al. (2020) Zhao et al. (2021) Li et al. (2022a)

TABLE 28.2 Spoilage Microorganisms in Seafood Products with Hurdles Technology and Storage at Various Temperature Seafood

Origin

Preservation

Method V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing

Photobacterium

V3–V4 region, 16S rRNA amplicon sequencing V1–V3 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing

Photobacterium, Psychrobacter

Atlantic Cod (Gadus morhua)

Atlantic ocean

MAP (4°C–8°C)

Grass carp (Ctenopharyngodon idellus) fillet

Chinese market

Hake fillets (Merluccius merluccius) Live mussel (Mytilus galloprovincialis)

Bay of Biscay

Essential oil (oregano, thyme and star anise) stored at 4°C MAP (1°C, 4°C and 7°C)

White shrimp (Litopenaeus vannamei) Yellow Fin Tuna (Thunnus albacares) Atlantic Cod fillets (Gadus morhua L.)

Chinese market

Tasmania

Maldives

Greenland

MAP (80% O2; 20% N2, stored at 4°C 0.1% e-Polylysine, stored at 0°C VP, 3°C

MAP (0.4°C and −1.7°C)

Dominant

Aeromonas, Pseudomonas

Acinetobacter Psychrobacter Candindatus Bacilloplasma Pseudomonas, Lactobacillus Photobacterium spp.

Source Kuuliala et al. (2018) Huang et al. (2018)

Antunesrohling et al. (2019b) Odeyemi et al. (2019) Jia et al. (2019) Jääskeläinen et al. (2019) Sørensen et al. (2020) (Continued)

548

Handbook of Seafood and Seafood Products Analysis

TABLE 28.2 (Continued ) Spoilage Microorganisms in Seafood Products with Hurdles Technology and Storage at Various Temperature Seafood

Origin

Preservation

Method

EGCG-gelatin (EGT), VP stored at 4°C 0.1% LAE solution, stored at 4°C 0.2% Rosemary extract and 1% e-polylysine, stored at 4°C Fish gelatin/grape seed extract, VP 4°C Gaseous ozone ((1–3 mg/m3) for 5–10 min) Hot smoke, VP, 2°C and 7.9°C

V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing

Enterobacter, Aeromonas

Cao et al. (2020a)

Aeromonas

Zhuang et al. (2020)

Pseudomonas, Carnobacterium

Lan et al. (2021)

V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing Culture- dependent and MALDI TOF

Pseudomonas

Zhao et al. (2021)

Photobacterium

Qian et al. (2022)

C. zeylanoides, E. faecalis Acinobacter, Pseudomonas

Ekonomou et al. (2022) Liu et al. (2022a)

Alivibrio, Pseudoalteromonas

Li et al. (2022b)

Shewanella, Bronchothrix

Lan et al. (2022)

Tilapia (skin off) (Oreochromis niloticus) Largemouth bass (Micropterus samonides) Yellow croaker (Pseudosciaena crocea)

Hainan

Tilapia (Oreochromis niloticus)

Tianjin

Atlantic Salmon (Salmo salar)

Shanghai

Rainbow trout fillet (Oncorhynchus mykiss) Shrimp (Solenocera melantho)

Greece

Zhejiang

HVEF −20°C

Pacific White Shrimp (Litopenaeus vannamei) Pompano (Trachinotus ovatus)

Beijing Market

Freeze-Chill (−20°C folowed by 4°C) Ultrasoundassisted chitosan grafted caffeic acid coating, ice storage

Guangzhou

Shanghai Market

Shanghai Market

V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing V3–V4 region, 16S rRNA amplicon sequencing

Dominant

Source

Note:    MAP: Modified atmosphere packaging VP: Vacuum Pack EGCG: Epigallocatechin gallate EGT: gelatin biofilm treatment LAE: ethyl lauroyl arginate hydrochloride MALDI-TOFMS: Matrix-assisted laser desorption ionization–time of flight mass spectrometry HVEF: High-voltage electrostatic field

Odeyemi et al., 2019; Sørensen et al., 2020), vacuum packaging (VP) (Ekonomou et al., 2022; Jääskeläinen et al., 2019), essential oils (Huang et al., 2018), active substance (Cao et al., 2020a; Jia et al., 2019), novel freezing technique (Li et al., 2022a; Liu et al., 2022a), and ultrasound (Lan et al., 2022). The dominants of seafood products that went through hurdles are dynamic based on the hurdle and the product itself. Photobacterium is dominant for a fish product packed in MAP (Antunes-Rohling et al., 2019b; Kuuliala et al., 2018; Sørensen et al., 2020), while Pseudomonas was the dominant of seafood packed in VP (Sørensen et al., 2020) and essential oils and other active substance (Lan et al., 2021; Zhao et al., 2021). The most abundant bacteria in mussels were Psychrobacter pulmonis, Ps. celer, and Oceanimonas smirnovii, with Ps. alimentarius as

Detection of Seafood Spoilage Microorganisms

549

dominant (Parlapani et al., 2020b). In live mussels packed in MAP, Psychrobacter was dominant in mussels that went through the depuration, while Acinetobacter was dominant for undepurated mussels (Odeyemi et al., 2019). Although many studies have been carried out on spoilage microorganisms, investigations on specific spoilage organism (SSO) in seafood products are limited. The term “specific spoilage organisms” or SSO refers to particular microorganisms that are capable of contributing to undesirable alterations and causing the sensory rejection (spoilage) of the items (Gram & Dalgaard, 2002). An organism needs to satisfy both of the following criteria in order to be considered a possible SSO: (i) it should be able to thrive around the same environment as the product, processing or storage; (ii) the derivatives that are created by those organisms should lead to significant changes to the colors, flavors, odors, and textures of the fish (Castell & Anderson, 1981; Odeyemi et al., 2019; Parlapani et al., 2019b). Hence, not all spoilage bacteria can be categorized as SSO.

28.2 EMERGING METHODS FOR DETECTION OF SEAFOOD SPOILAGE BACTERIA Traditional culture-dependent methods for seafood spoilage bacteria isolation and identification include a wide range of nonselective and selective enrichment media, followed by biochemical and molecular identification. This process is time-consuming and generally lasts up to 5 days. Moreover, a shortcoming of culture-dependent methods is producing false results because of the vast microbial diversity of seafood. Otherwise, rapid identification of spoilage bacteria is essential to ensure seafood quality and consumer safety. Considering that seafood has a short shelf life, applying fast and reliable methods for detecting seafood spoilage bacteria is recommended (Figure 28.1) (Ferone et al., 2020; Odeyemi et al., 2018a).

28.3  SPECIES-SPECIFIC POLYMERASE CHAIN REACTION (SSPCR) Polymerase chain reaction is routinely used for rapid detection, identification, and differentiation of bacteria. It is the preferred method because it is rapid and accurate. It avoids situations where biochemical identification is ambiguous or wrong (Adzitey et al., 2013). Each PCR

–

FIGURE 28.1  Methods for identification of seafood spoilage bacteria.

550

Handbook of Seafood and Seafood Products Analysis

reaction amplifies target sections of DNA via three steps: denaturation, annealing, and extension (Fletcher et al., 2018). The main advantages of classic PCR are high sensitivity, high specificity, automation, and reliability, while the requirement for the DNA purification step and the impossibility of distinguishing between viable and not viable bacterial cells are considered disadvantages (Ferone et al., 2020). Taking into account that seafood becomes spoiled at bacterial count >7 log CFU/g (Yavuzer & Köse, 2022), Lee and Levin (2007) and Lee and Levin (2006b, a) set a PCR method for quantification of total bacteria associated with fish fillets. Fernández-No et al. (2012) recovered two Streptococcus parauberis isolates from a spoiled vacuum-packaged refrigerated surimi-based product. A pair of highly conserved 16S rDNA primers, p8FPL (50-AGTTT-GATCCTGGCTCAG-30) and p806R (50-GGACTACCAGGGTATCTAAT-30) was used to amplify 16S rRNA gene, followed by its sequencing. Jaffrès et al. (2010) collected five isolates from a batch of spoiled modified atmosphere packaged cooked, peeled, brined, and drained shrimp (Penaeus vannamei). A 16S rRNA gene sequence analysis was performed, and a novel species, Vagococcus penaei, was proposed. In another study, Photobacterium phosphoreum, Brochothrix thermosphacta, Serratia proteamaculans, and Carnobacterium divergens were isolated from spoiled vacuum-packed cold-smoked salmon by amplification and pyrosequencing of 16S rRNA V1-V3 gene region (Leroi et al., 2015). Parlapani et al. (2013) investigated the initial and spoilage microbiota of iced stored sea bream using16S rRNA gene amplification, cloning and sequencing of DNA extracted directly from the fish tissue. This approach revealed that the initial microflora consisted mainly of Acinetobacter, while Pseudomonas, Aeromonas salmonicida, and Shewanella dominated at the end of shelf life.

28.4 POLYMERASE CHAIN REACTION-DENATURING GRADIENT GEL ELECTROPHORESIS (PCR-DGGE) PCR-DGGE is an electrophoretic method detecting differences between amplicons of the same size but with different sequences. These amplicons are separated in a denaturing gradient gel due to their differential denaturation (melting) profile (Ercolini, 2004). This method is usually employed to establish the genetic diversity of microbial populations (Diaz et al., 2016). According to Meays et al. (2004), the main advantage of the method is that it works on isolates, but it possesses several disadvantages, such as being technically demanding, time-consuming, limited simultaneous processing, not good on environmental isolates, and database required. Several researchers have employed PCR-DGGE for the identification of seafood spoilage bacteria. Bekaert et al. (2015) identified Psychrobacter and Pseudomonas on both whole Norway lobster and tails at the end of the storage period. At the same time, Luteimonas was recovered only on tails and Pseudoalteromonas and Microbacterium arborescens only on whole Norway lobster. Broekaert et al. (2013) established using PCR-DGGE that Psychrobacter and Pseudoalteromonas were dominant microflora of brown shrimp (Crangon crangon) stored on ice, while Carnobacterium, Shewanella, and Psychrobacter were most abundant in brown shrimp (Crangon crangon) stored under modified atmosphere packaging at 4°C (Calliauw et al., 2016). Hovda et al. (2007a) found out by PCR-DGGE that spoilage bacteria Photobacterium, Shewanella putrefaciens, and Pseudomonas dominated in modified atmosphere packaging (50% CO2 and 50% N2) and air-packaged Atlantic cod (Gadus morhua) stored at 0°C. Farmed cod (Gadus morhua) stored in a modified atmosphere (60% CO2 and 40% N2) at 4°C and analyzed by PCR-DGGE revealed that Photobacterium phosphoreum, Pseudomonas, Shewanella baltica, and S. putrefaciens were predominant specific spoilage bacteria (Hovda et al., 2007b).

28.5  NEXT-GENERATION SEQUENCING (NGS) Although Whole-Genome Sequencing was developed by Sanger et al. (1977), Next-Generation Sequencing has only been commercially available for about 10 years. Unlike the Sanger approach, NGS does not decouple enzymatic nucleotide incorporation from sequence ladder separation and data

551

Detection of Seafood Spoilage Microorganisms

• • • • • • • • • • • • • • • •

–

–

–

–

–

FIGURE 28.2  Commonly used Whole Genome Sequencing platforms.

acquisition (McCombie et al., 2019). NGS does not require specific targeting as PCR does. It is cheaper than WGS, but there is a lack of competent personnel working in this area. In food microbiology, NGS is beneficial in two ways: (i) whole genome sequence of a single cultured isolate and (ii) sequences of multiple microorganisms in a sample, i.e. metagenomics (Ferone et al., 2020). Jagadeesan et al. (2019) outlined the commonly used Whole Genome Sequencing platforms (Figure 28.2). NGS does not require the isolation of bacteria, and it is used to characterize microbial diversity in seafood (Ohshima et al., 2019). Tsironi et al. (2019) confirmed that NGS is a valuable tool for the determination of bacterial flora associated with seafood spoilage. Pseudomonas, Acinetobacter, Moraxella, Shewanella, Psychrobacter, Lactobacillus, Brochothrix, and Photobacterium were identified as initial spoilage microbiota of fish purchased from local food stores in Greece. Recent research by Syropoulou et al. (2022) revealed that at the end of the shelf life of MAP-stored fillets of European sea bass (Dicentrarchus labrax) at 12°C dominant microbiota was constituted by the genus Serratia, but Pseudomonas and Brochothrix were also detected by 16S rRNA Gene HighThroughput Sequencing approach. Antunes-Rohling et al. (2019a) applied High-throughput 16S rRNA gene sequencing to determine microbial diversity of hake fillets stored under MAP at different temperatures. It was found that Photobacterium and Psychrobacter dominated at the time of spoilage, followed by Moritella, Carnobacterium, Shewanella, and Vibrio.

28.6  AMPLIFIED FRAGMENT LENGTH POLYMORPHISM (AFLP) AFLP is a highly specific and discriminatory method that employs restriction enzymes to fragment genomic DNA and amplifies a subset of restriction fragments using ligated adaptors. This reaction uses primers complementary to the adaptor sequences and adjacent nucleotides (Franco-Duarte et al., 2019). This method was successfully applied by Jérôme et al. (2016) for discrimination of Photobacterium phosphoreum, Photobacterium iliopiscarium, and Photobacterium kishitanii isolated from smoked salmon, salmon steak, spoiled haddock fillet, herring, and other marine fish using three primer sets for the selective amplification step.

28.7 MATRIX-ASSISTED LASER DESORPTION/IONIZATION TIMEOF-FLIGHT MASS SPECTROMETRY (MALDI-TOF MS) MALDI-TOF MS has changed the routine microbiology, ensuring timely and cost-effective identification of bacteria. It is a reliable and highly sensitive tool for identification of bacteria, which usually

552

Handbook of Seafood and Seafood Products Analysis

FIGURE 28.3  MALDI-TOF MS steps.

takes only a few minutes Rodríguez-Sánchez et al. (2019), but the method is culture-dependent with database limitations, poor reproducibility and limited results for mixed populations (Ferone et al., 2020). MALDI-TOF MS consists of the following steps as shown in Figure 28.3 (Santos et al., 2016). MALDI-TOF MS has been applied for the determination of microbial diversity of spoiled seafood. Böhme et al. (2010) successfully identified 26 species of seafood spoilage and pathogenic bacteria, including Aeromonas hydrophila, Acinetobacter baumanii, Enterobacter, Proteus, Providencia, Pseudomonas, Serratia, Shewanella, and Vibrio. In another study, Fernández-No et al. (2012) recovered two Streptococcus parauberis isolates responsible for the spoilage of vacuum-packaged refrigerated surimi-based products.

28.8 SODIUM DODECYL SULFATE-POLYACRYLAMIDE GEL ELECTROPHORESIS (SDS-PAGE) SDS-PAGE or Western blotting separates specific proteins by their molecular weight via gel electrophoresis. The method comprises the following steps: (i) sample preparation; (ii) gel electrophoresis; (iii) blotting; (iv) washing; (v) blocking; (vi) antibody incubation and (vii) signal detection from enzyme-bound antibody (Dolan et al., 2018). Microbial diversity in spoiled green-lipped mussels (Perna viridis) was investigated by SDS-PAGE, and Photobacterium, Vibrio, and Shewanella were predominant bacteria (Tan et al., 2017). In earlier studies via SDS-PAGE, Enterococcus faecalis, Carnobacterium divergens, and Lactobacillus curvatus from spoiled cooked and brined shrimps stored under modified atmosphere (Dalgaard et al., 2003) and Pseudomonas lundensis, Ps. fluorescens, Ps. fragi, and Ps. putida from spoiled gilt-head sea bream (Sparus aurata) stored under air-packaging and MAP (Tryfinopoulou et al., 2002) were recovered.

28.9  BIOFILM FORMATION AND QUORUM SENSING IN SPOILAGE BACTERIA Consumers’ demand for fresh, safe seafood has been exponentially increasing over the past few decades. The seafood industry is one of the most booming food sectors, since both global production

Detection of Seafood Spoilage Microorganisms

553

and consumption are increasing exponentially year. However, seafood is also an extremely perishable product due to spoilage microorganisms (Odeyemi et al., 2018b). Food spoilage is a metabolic process that renders food and becomes unfit for human consumption due to undesirable alterations in sensory characteristics such as color, texture, odor, and flavor (Johansson et al., 2020). Three mechanisms set the basis for this phenomenon include autolysis by endogenous enzymes, chemical oxidation of lipids, and spoilage bacteria which are the key causes of food spoilage. According to previous studies, the spoilage associated organisms (SAOs) in seafood are caused by several bacterial genera such as Acitenobacter, Aeromonas, Bacillus, Brochthrix, Chyseobacterium, Flavobacterium, Photobacterium, Pseudoalteromonas, Pseudomonas, Psychrobater, Serratia, Shewanella, Vibrio, and Enterobacteriaceae. The ability of these bacteria to spoil food is closely related to their high adaptability, versatility, and replication ability at refrigeration temperatures. One of the most important mechanisms of spoilage bacteria is its ability to form biofilms in food processing environments (Yi et al., 2022). The formation of biofilm on the other hand is a sort of self-protection in bacteria, resulting in enhanced resistance to antimicrobial agents. Other authors explained that biofilm formation is frequently observed on food and food contact surfaces due to having high nutrients.

28.10  BIOFILM FORMATION STAGES BY SPOILAGE BACTERIA Biofilm formation in seafood or seafood contact surfaces starts with (i) the formation of a conditioning film; (ii) cellular attachment; (iii) the formation of microcolonies to mature biofilms, and (iv) dispersion and recolonization (Mizan et al., 2015). At first, spoilage bacteria attach actively or passively onto the surface of food or food contact surfaces and start to produce a small quantity of extracellular polymeric substances (EPS). The attached bacteria start to produce more EPS and enter the initial stage of biofilm development. The bacterial cells form a microcolony, in which EPS helps stabilize the colony. In addition, cell-cell signaling molecules are produced. The bacterial biofilm then gradually matures, and the EPS is transported to and within the biofilm. During the whole bacterial biofilm formation, the final step is a dispersion of the biofilm, in which the bacteria revert to a planktonic form. Thus, it is indispensable to develop effective methods to control and eliminate the formation of biofilms. Molecularly, a study of RNA sequencing on Pseudomonas fragi, using qRT-PCR revealed 332 significantly upregulated genes and 37 downregulated genes in the initial stage of biofilm formation. While during biofilm maturation and dispersal, the study found that genes coding for flp family type IVb pilin, creatininase, and ribosome modulation factor were the most upregulated genes. On the contrary, among the 37 downregulated genes, genes encoding iron uptake systems such as TonB-dependent siderophore receptor and taurine transport were significantly down-regulated (Wickramasinghe et al., 2021). Another study by Machado et al. (2020) found that spoilage bacteria such as B. cereus, S. putrefaciens used quorum sensing (QS) involved in biofilm formation to communicate with biofilm community interspecies and intraspecies. In addition, many members of the Vibrionaceae have been reported to regulate biofilm formation by quorum sensing. For instance, quorum sensing regulates the secretion of EPS required for biofilm formation in V. cholera (Waters et al., 2008). A study dealt with the effect of signaling molecules on initial adherence and EPS production in biofilms, which are the two major steps required for the initiation of biofilms. Since the biofilm cells have a barrier preventing or lessening contact with environmental stresses, antimicrobial agents, and the host immune system it is difficult to eliminate the biofilm in the food industries. It has become common knowledge that microbial biofilms are difficult to control or eradicate.

28.11 STRATEGIES FOR CONTROLLING BIOFILM FORMATION IN THE SEAFOOD INDUSTRY There are several approaches to inhibit biofilm formation including physical treatment, chemical, and biochemical treatment, or contamination of physical and chemical treatment in the seafood

554

Handbook of Seafood and Seafood Products Analysis

and seafood contact surfaces. Previous studies viewed that mechanical treatment such as cleaning was unable to remove all spoilage bacterial cells, thus combining with chemical treatment may eliminate the bacterial cells and inhibit biofilm formation (Zhu et al., 2022). The use of sodium hypochlorite (NaClO), for instance, is a potential disinfectant capable of inhibiting biofilm formation from spoilage bacteria (Lim et al., 2023). The biochemical agents including essential oils (EOs), enzymes, biosurfactants, and quorum sensing inhibition have been discussed based on safe and “green” approaches to control biofilm formation.

28.12  ESSENTIAL OILS (EOS) Essential oils (EOs), which are a kind of plant-derived secondary metabolites, are known as natural broad-spectrum antimicrobials that exhibit certain bacteriostatic activity for various spoilage bacteria (Liu et al., 2022b). The main active ingredients in EOs are terpenes, oxygenated derivatives, and terpenoids (aromatic compounds, aliphatic acid esters, and phenolic compounds). Among the derivatives, the compound phenol is considered to be the most antimicrobial active, followed by aldehydes, ketones, alcohols, ethers, and hydrocarbons. With the deepening research on the antimicrobial activity of EOs, the study also has found that some EOs have a certain effect on biofilm formation. EOs have been proven to reduce the formation of Pseudomonas aeruginosa PAO1 biofilm by 80%; this is accompanied by a decrease in EPS (Sankar Ganesh & Rai Vittal, 2015). VázquezSánchez et al. (2018) also found that thyme and patchouli oils are the most effective EOs in selected EOs because of the significant reduction of biofilm viability.

28.13 ENZYMES Enzymes such as proteases or other degrading enzymes have shown the ability to inhibit biofilm formation in food-processing equipment. An enzyme produced by Aspergillus niger has been reported to eradicate/disperse the biofilms of selected pathogens (Kaur et al., 2021). In addition, polysaccharidases have been proven to eliminate biofilm formation by Pseudomonas fluorescens (Oliveira et al., 2014). The enzymes decrease the adhesion of the EPS in the bacterial biofilm. The use of enzymes can disintegrate the substances of bacterial biofilms and promote the sensitivity of biofilms to hydrodynamic stresses, giving enzymes potential use in the food industries of clean-inplace procedures. Therefore, destroying the physical integrity of the biofilm matrix would be an attractive alternative for both medical and industrial applications.

28.14 BIOSURFACTANTS Biosurfactant is a metabolite secreted by microorganisms with surface activity and amphipathicity. Previous studies found that biosurfactants showed antimicrobial ability and may prevent the attachment of bacterial cells during biofilm formation on food-contact surfaces. Some examples of biosurfactants are trehalolipid, rhamnolipids, and surfactin. De Araujo et al. (2011) reported rhamnolipids and surfactin had been reported to reduce the adhesion of P. fluorescens and L. monocytogenes on polystyrene by up to 54% and 79% respectively. The biosurfactants have also been reported to decrease biofilm formation on stainless-steel surfaces by up to 73% and 83% respectively. In addition, Chen et al. (2019b) documented rhamnolipid and phospholipids were able to inhabit the adherence and eliminate the EPS which later disrupts biofilm formation in Helicobacter pylori.

28.15  ANTI-QUORUM SENSING Anti-quorum sensing has also been proven to inhibit biofilm formation by interfering with the swarming motilities of spoilage bacteria. Few studies reported food plant-derived phytochemicals might be great sources of QS inhibitors. Soybean isoflavones (SI), for instance, can be a quorum

Detection of Seafood Spoilage Microorganisms

555

sensing (QS) inhibitor against P. aeruginosa and significantly reduce violacein production, biofilm formation, motility, and virulence factor production, AHLs production, and expression levels of QS-related genes in P. aeruginosa (Yin et al., 2022). Thus, the study provided a deep insight into SI as a QS inhibitor against P. aeruginosa.

REFERENCES Adzitey, F., Huda, N., & Ali, G. R. R. (2013). Molecular techniques for detecting and typing of bacteria, advantages and application to foodborne pathogens isolated from ducks. 3 Biotech, 3(2), 97–107. doi:10.1007/ s13205-012-0074-4 Antunes-Rohling, A., Calero, S., Halaihel, N., Marquina, P., Raso, J., Calanche, J., Beltrán, J. A., Álvarez, I., & Cebrián, G. (2019a). Characterization of the spoilage microbiota of hake fillets packaged under a modified atmosphere (MAP) rich in CO2 (50% CO2/50% N2) and stored at different temperatures. Foods, 8(10), 489. doi:10.3390/foods8100489 Antunes-rohling, A., Calero, S., Halaihel, N., Marquina, P., Raso, J., Calanche, J., Beltr, A., Ignacio, Á., & Cebri, G. (2019b). Characterization of the spoilage microbiota of hake different temperatures. Foods, 8(489), 1–14. Bekaert, K., Devriese, L., Maes, S., & Robbens, J. (2015). Characterization of the dominant bacterial communities during storage of Norway lobster and Norway lobster tails (Nephrops norvegicus) based on 16S rDNA analysis by PCR-DGGE. Food Microbiology, 46, 132–138. doi:10.1016/j.fm.2014.06.022 Böhme, K., Fernández-No, I. C., Barros-Velázquez, J., Gallardo, J. M., Calo-Mata, P., & Cañas, B. (2010). Species differentiation of seafood spoilage and pathogenic gram-negative bacteria by MALDI-TOF mass fingerprinting. Journal of Proteome Research, 9(6), 3169–3183. doi:10.1021/pr100047q Broekaert, K., Heyndrickx, M., Herman, L., Devlieghere, F., & Vlaemynck, G. (2013). Molecular identification of the microbiota of peeled and unpeeled brown shrimp (Crangon crangon) during storage on ice and at 7.5°C. Food Microbiology, 36(2), 123–134. doi:10.1016/j.fm.2013.04.009 Calliauw, F., De Mulder, T., Broekaert, K., Vlaemynck, G., Michiels, C., & Heyndrickx, M. (2016). Assessment throughout a whole fishing year of the dominant microbiota of peeled brown shrimp (Crangon crangon) stored for 7 days under modified atmosphere packaging at 4°C without preservatives. Food Microbiology, 54, 60–71. doi:10.1016/j.fm.2015.10.016 Cao, J., Wang, Q., Ma, T., Bao, K., Yu, X., Duan, Z., Shen, X., & Li, C. (2020a). Effect of EGCG-gelatin biofilm on the quality and microbial composition of tilapia fillets during chilled storage. Food Chemistry, 305(August 2019), 125454. doi:10.1016/j.foodchem.2019.125454 Cao, R., Lin, R., Sun, H., & Liu, Q. (2020b). Microbiota and shelf life of whole and gutted Pacific saury (Cololabis saira) during refrigerated storage. Journal of Ocean University of China, 19(2), 473–478. doi:10.1007/s11802-020-4165-2 Castell, C. Y., & Anderson, G. W. (1981). Bacteria associated with spoilage of cod fillets. Journal of Food Science, 46(2), 468–470. doi:10.1111/j.1365-2621.1981.tb04887.x Chen, H., Wang, M., Yang, C., Wan, X., Ding, H. H., Shi, Y., & Zhao, C. (2019a). Bacterial spoilage profiles in the gills of Pacific oysters (Crassostrea gigas) and Eastern oysters (C. virginica) during refrigerated storage. Food Microbiology, 82(February), 209–217. doi:10.1016/j.fm.2019.02.008 Chen, X., Li, P., Shen, Y., Zou, Y., Yuan, G., & Hu, H. (2019b). Rhamnolipid-involved antibiotics combinations improve the eradication of Helicobacter pylori biofilm in vitro: A comparison with conventional triple therapy. Microbial Pathogenesis, 131, 112–119. Dalgaard, P., Vancanneyt, M., Euras Vilalta, N., Swings, J., Fruekilde, P., & Leisner, J. J. (2003). Identification of lactic acid bacteria from spoilage associations of cooked and brined shrimps stored under modified atmosphere between 0°C and 25°C. Journal of Applied Microbiology, 94(1), 80–89. doi:10.1046/j.1365-2672.2003.01806.x De Araujo, L. V., Abreu, F., Lins, U., Santa Anna, L. M. D. M., Nitschke, M., & Freire, D. M. G. (2011). Rhamnolipid and surfactin inhibit Listeria monocytogenes adhesion. Food Research International, 44(1), 481–488. de Lira, A. D., de Castro, I. M. S., Mann, M. B., Mallmann, L. P., Kothe, C. I., Varela, A. P. M., Frazzon, A. P. G., & Frazzon, J. (2020). Evaluating Sardinella brasiliensis quality indicators through the quantification of histamine and bacterial communities. Heliyon, 6(8), e04461. doi:10.1016/j.heliyon.2020.e04461 Dewi, F. R., Powell, S. M., & Stanley, R. A. (2021). The effects of pre-processing sanitation and modified atmosphere packaging on microbial growth in bulk packs of Atlantic salmon (Salmo salar) fillets. IOP Conference Series: Earth and Environmental Science, 733(1), 012081. doi:10.1088/17551315/733/1/012081

556

Handbook of Seafood and Seafood Products Analysis

Diaz, M., Ladero, V., Redruello, B., Sanchez-Llana, E., del Rio, B., Fernandez, M., Martin, M. C., & Alvarez, M. A. (2016). A PCR-DGGE method for the identification of histamine-producing bacteria in cheese. Food Control, 63, 216–223. doi:10.1016/j.foodcont.2015.11.035 Dolan, E. B., Verbruggen, S. W., & Rolfe, R. A. (2018). Techniques for studying mechanobiology. In: Stefaan W. Verbruggen (Ed.) Mechanobiology in Health and Disease (pp. 1–53). Elsevier. doi:10.1016/ B978-0-12-812952-4.00001-5 Ekonomou, S. I., Parlapani, F. F., Kyritsi, M., Hadjichristodoulou, C., & Boziaris, I. S. (2022). Preservation status and microbial communities of vacuum-packed hot smoked rainbow trout fillets. Food Microbiology, 103(November 2021), 103959. doi:10.1016/j.fm.2021.103959 Ercolini, D. (2004). PCR-DGGE fingerprinting: Novel strategies for detection of microbes in food. Journal of Microbiological Methods, 56(3), 297–314. doi:10.1016/j.mimet.2003.11.006 Fernández-No, I. C., Böhme, K., Calo-Mata, P., Cañas, B., Gallardo, J. M., & Barros-Velázquez, J. (2012). Isolation and characterization of Streptococcus parauberis from vacuum-packaging refrigerated seafood products. Food Microbiology, 30(1), 91–97. doi:10.1016/j.fm.2011.10.012 Ferone, M., Gowen, A., Fanning, S., & Scannell, A. G. M. (2020). Microbial detection and identification methods: Bench top assays to omics approaches. Comprehensive Reviews in Food Science and Food Safety, 19(6), 3106–3129. doi:10.1111/1541-4337.12618 Fletcher, B., Mullane, K., Platts, P., Todd, E., Power, A., Roberts, J., Chapman, J., Cozzolino, D., & Chandra, S. (2018). Advances in meat spoilage detection: A short focus on rapid methods and technologies. CyTA - Journal of Food, 16(1), 1037–1044. doi:10.1080/19476337.2018.1525432 Franco-Duarte, R., Černáková, L., Kadam, S., Kaushik, K. S., Salehi, B., Bevilacqua, A., Corbo, M. R., Antolak, H., Dybka-Stępień, K., Leszczewicz, M., Relison Tintino, S., Alexandrino de Souza, V. C., Sharifi-Rad, J., Melo Coutinho, H. D., Martins, N., & Rodrigues, C. F. (2019). Advances in chemical and biological methods to identify microorganisms-From past to present. Microorganisms, 7(5), 130. doi:10.3390/ microorganisms7050130 Gram, L., & Dalgaard, P. (2002). Fish spoilage bacteria - Problems and solutions. Current Opinion in Biotechnology, 13(3), 262–266. doi:10.1016/S0958-1669(02)00309-9 Hovda, M. B., Lunestad, B. T., Sivertsvik, M., & Rosnes, J. T. (2007a). Characterisation of the bacterial flora of modified atmosphere packaged farmed Atlantic cod (Gadus morhua) by PCR-DGGE of conserved 16S rRNA gene regions. International Journal of Food Microbiology, 117(1), 68–75. doi:10.1016/j. ijfoodmicro.2007.02.022 Hovda, M. B., Sivertsvik, M., Lunestad, B. T., & Rosnes, J. T. (2007b). Microflora assessments using PCRdenaturing gradient gel electrophoresis of ozone-treated and modified-atmosphere-packaged farmed cod fillets. Journal of Food Protection, 70(11), 2460–2465. doi:10.4315/0362-028X-70.11.2460 Huang, W., & Xie, J. (2020). Characterization of the volatiles and quality of hybrid grouper and their relationship to changes of microbial community during storage at 4°C. Molecules, 25(4), 1–16. doi:10.3390/ molecules25040818 Huang, Z., Liu, X., Jia, S., Zhang, L., & Luo, Y. (2018). The effect of essential oils on microbial composition and quality of grass carp (Ctenopharyngodon idellus) fillets during chilled storage. International Journal of Food Microbiology, 266(July 2017), 52–59. doi:10.1016/j.ijfoodmicro.2017.11.003 Jääskeläinen, E., Jakobsen, L. M. A., Hultman, J., Eggers, N., Bertram, H. C., & Björkroth, J. (2019). Metabolomics and bacterial diversity of packaged yellowfin tuna (Thunnus albacares) and salmon (Salmo salar) show fish species-specific spoilage development during chilled storage. International Journal of Food Microbiology, 293(August 2018), 44–52. doi:10.1016/j.ijfoodmicro.2018.12.021 Jaffrès, E., Prévost, H., Rossero, A., Joffraud, J.-J., & Dousset, X. (2010). Vagococcus penaei sp. Nov., isolated from spoilage microbiota of cooked shrimp (Penaeus vannamei). International Journal of Systematic and Evolutionary Microbiology, 60(9), 2159–2164. doi:10.1099/ijs.0.012872-0 Jagadeesan, B., Gerner-Smidt, P., Allard, M. W., Leuillet, S., Winkler, A., Xiao, Y., Chaffron, S., Van Der Vossen, J., Tang, S., Katase, M., McClure, P., Kimura, B., Ching Chai, L., Chapman, J., & Grant, K. (2019). The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiology, 79, 96–115. doi:10.1016/j.fm.2018.11.005 Jérôme, M., Macé, S., Dousset, X., Pot, B., & Joffraud, J.-J. (2016). Genetic diversity analysis of isolates belonging to the Photobacterium phosphoreum species group collected from salmon products using AFLP fingerprinting. International Journal of Food Microbiology, 217, 101–109. doi:10.1016/j. ijfoodmicro.2015.10.018

Detection of Seafood Spoilage Microorganisms

557

Jia, S., Liu, Y., Zhuang, S., Sun, X., Li, Y., Hong, H., Lv, Y., & Luo, Y. (2019). Effect of ε-polylysine and ice storage on microbiota composition and quality of Pacific white shrimp (Litopenaeus vannamei) stored at 0°C. Food Microbiology, 83(April), 27–35. doi:10.1016/j.fm.2019.04.007 Johansson, P., Jääskeläinen, E., Nieminen, T., Hultman, J., Auvinen, P., & Björkroth, K. J. (2020). Microbiomes in the context of refrigerated raw meat spoilage. Meat and Muscle Biology, 4(2), 10369. Kaur, A., Soni, S. K., Vij, S., & Rishi, P. (2021). Cocktail of carbohydrases from Aspergillus niger: An economical and eco-friendly option for biofilm clearance from biopolymer surfaces. AMB Express, 11(1), 22. doi:10.1186/s13568-021-01183-y Kuuliala, L., Al Hage, Y., Ioannidis, A. G., Sader, M., Kerckhof, F. M., Vanderroost, M., Boon, N., De Baets, B., De Meulenaer, B., Ragaert, P., & Devlieghere, F. (2018). Microbiological, chemical and sensory spoilage analysis of raw Atlantic cod (Gadus morhua) stored under modified atmospheres. Food Microbiology, 70, 232–244. doi:10.1016/j.fm.2017.10.011 Lan, W., Sun, Y., Liu, S., Guan, Y., Zhu, S., & Xie, J. (2022). Effects of ultrasound-assisted chitosan grafted caffeic acid coating on the quality and microbial composition of pompano during ice storage. Ultrasonics Sonochemistry, 86(April), 106032. doi:10.1016/j.ultsonch.2022.106032 Lan, W., Sun, Y., Zhang, N., & Xie, J. (2021). Effects of ε-polylysine and rosemary extract on quality attributes and microbial communities in vacuum-packaged large yellow croaker (Pseudosciaena crocea) during ice storage. Food Science and Biotechnology, 30(3), 465–474. doi:10.1007/s10068-021-00880-5 Lee, J. L., & Levin, R. (2007). Rapid quantification of total bacteria on cod fillets by using real-time PCR. Journal of Fisheries Sciences.Com, 1(2), 58–67. doi:10.3153/jfscom.2007008 Lee, J.-L., & Levin, R. (2006a). Direct application of the polymerase chain reaction for quantification of total bacteria on fish fillets. Food Biotechnology, 20(3), 287–298. doi:10.1080/08905430600904500 Lee, J.-L., & Levin, R. (2006b). Selection of universal primers for PCR quantification of total bacteria associated with fish fillets. Food Biotechnology, 20(3), 275–285. doi:10.1080/08905430600904492 Leroi, F., Cornet, J., Chevalier, F., Cardinal, M., Coeuret, G., Chaillou, S., & Joffraud, J.-J. (2015). Selection of bioprotective cultures for preventing cold-smoked salmon spoilage. International Journal of Food Microbiology, 213, 79–87. doi:10.1016/j.ijfoodmicro.2015.05.005 Li, M., Shi, J.-N., You, G., Ma, Y., & Zaho, Q. (2022). Characterization of bacterial communities in Coregonus peled fillets during chilled storage and interactions between selected bacterial strains. Journal of Applied Microbiology, 132(6), 4359–4370. Li, Q., Zhang, L., & Luo, Y. (2018). Changes in microbial communities and quality attributes of white muscle and dark muscle from common carp (Cyprinus carpio) during chilled and freeze-chilled storage. Food Microbiology, 73, 237–244. doi:10.1016/j.fm.2018.01.011 Li, Y., Lei, Y., Tan, Y., Zhang, J., Hong, H., & Luo, Y. (2022). Efficacy of freeze-chilled storage combined with tea polyphenol for controlling melanosis, quality deterioration, and spoilage bacterial growth of Pacific white shrimp (Litopenaeus vannamei). Food Chemistry, 370(August 2021), 130924. doi:10.1016/j. foodchem.2021.130924 Lim, E. S., Nam, S. J., Koo, O. K., & Kim, J.-S. (2023). Protective role of Acinetobacter and Bacillus for Escherichia coli O157:H7 in biofilms against sodium hypochlorite and extracellular matrix-degrading enzymes. Food Microbiology, 109, 104125. doi:10.1016/j.fm.2022.104125 Liu, J., Wang, Y., Zhu, F., Yang, J., Ma, X., Lou, Y., & Li, Y. (2022a). The effects of freezing under a high-voltage electrostatic field on ice crystals formation, physicochemical indices, and bacterial communities of shrimp (Solenocera melantho). Food Control, 142(July), 109238. doi:10.1016/j. foodcont.2022.109238 Liu, X., Li, D., Li, K., & Luo, Y. (2018). Monitoring bacterial communities in ε-polylysine-treated bighead carp (Aristichthys nobilis) fillets using culture-dependent and culture-independent techniques. Food Microbiology, 76(October 2017), 257–266. doi:10.1016/j.fm.2018.06.001 Liu, Y., Yan, Y., Dong, P., Ni, L., Luo, X., Zhang, Y., & Zhu, L. (2022b). Inhibitory effects of clove and oregano essential oils on biofilm formation of Salmonella Derby isolated from beef processing plant. LWT, 162, 113486. doi:10.1016/j.lwt.2022.113486 Machado, I., Silva, L. R., Giaouris, E. D., Melo, L. F., & Simões, M. (2020). Quorum sensing in food spoilage and natural-based strategies for its inhibition. Food Research International, 127, 108754. doi:10.1016/j. foodres.2019.108754 McCombie, W. R., McPherson, J. D., & Mardis, E. R. (2019). Next-generation sequencing technologies. Cold Spring Harbor Perspectives in Medicine, 9(11), a036798. doi:10.1101/cshperspect.a036798

558

Handbook of Seafood and Seafood Products Analysis

Meays, C. L., Broersma, K., Nordin, R., & Mazumder, A. (2004). Source tracking fecal bacteria in water: A critical review of current methods. Journal of Environmental Management, 73(1), 71–79. doi:10.1016/j. jenvman.2004.06.001 Mizan, M. F. R., Jahid, I. K., & Ha, S.-D. (2015). Microbial biofilms in seafood: A food-hygiene challenge. Food Microbiology, 49, 41–55. doi:10.1016/j.fm.2015.01.009 Odeyemi, O. A., Burke, C. M., Bolch, C. C. J., & Stanley, R. (2018a). Seafood spoilage microbiota and associated volatile organic compounds at different storage temperatures and packaging conditions. International Journal of Food Microbiology, 280, 87–99. doi:10.1016/j.ijfoodmicro.2017.12.029 Odeyemi, O. A., Burke, C. M., Bolch, C. J., & Stanley, R. (2018b). Evaluation of spoilage potential and volatile metabolites production by Shewanella baltica isolated from modified atmosphere packaged live mussels. Food Research International, 103, 415–425. Odeyemi, O. A., Burke, C. M., Bolch, C. C. J., & Stanley, R. (2019). Spoilage microbial community profiling by 16S rRNA amplicon sequencing of modified atmosphere packaged live mussels stored at 4°C. Food Research International, 121(June 2018), 568–576. doi:10.1016/j.foodres.2018.12.017 Ohshima, C., Takahashi, H., Insang, S., Phraephaisarn, C., Techaruvichit, P., Khumthong, R., Haraguchi, H., Lopetcharat, K., & Keeratipibul, S. (2019). Next-generation sequencing reveals predominant bacterial communities during fermentation of Thai fish sauce in large manufacturing plants. LWT, 114, 108375. doi:10.1016/j.lwt.2019.108375 Oliveira, H. L. D. C. D. D., Fleming, M. E. C. K., Silva, P. V., Paula, G. R. D., Futuro, D. O., Velarde, G. C., Esper, L. M. R., & Teixeira, L. A. (2014). Influence of papain in biofilm formed by methicillin-resistant Staphylococcus epidermidis and methicillin-resistant Staphylococcus haemolyticus isolates. Brazilian Journal of Pharmaceutical Sciences, 50, 261–267. Parlapani, F. F., Boziaris, I. S., Meziti, A., Michailidou, S., Haroutounian, S. A., Argiriou, A., & Karapanagiotidis, I. T. (2019a). Microbiological status based on 454-pyrosequencing and volatilome analysis of gilthead seabream (Sparus aurata) fed on diets with hydrolyzed feather meal and poultry by-product meal as fishmeal replacers. European Food Research and Technology, 245(7), 1409–1420. doi:10.1007/ s00217-019-03270-8 Parlapani, F. F., Ferrocino, I., Michailidou, S., Argiriou, A., Haroutounian, S. A., Kokokiris, L., Rantsiou, K., & Boziaris, I. S. (2020a). Microbiota and volatilome profile of fresh and chill-stored deepwater rose shrimp (Parapenaeus longirostris). Food Research International, 132, 109057. doi:10.1016/j. foodres.2020.109057 Parlapani, F. F., Meziti, A., Kormas, K. A., & Boziaris, I. S. (2013). Indigenous and spoilage microbiota of farmed sea bream stored in ice identified by phenotypic and 16S rRNA gene analysis. Food Microbiology, 33(1), 85–89. doi:10.1016/j.fm.2012.09.001 Parlapani, F. F., Michailidou, S., Anagnostopoulos, D. A., Koromilas, S., Kios, K., Pasentsis, K., Psomopoulos, F., Argiriou, A., Haroutounian, S. A., & Boziaris, I. S. (2019b). Bacterial communities and potential spoilage markers of whole blue crab (Callinectes sapidus) stored under commercial simulated conditions. Food Microbiology, 82(March), 325–333. doi:10.1016/j.fm.2019.03.011 Parlapani, F. F., Michailidou, S., Anagnostopoulos, D. A., Sakellariou, A. K., Pasentsis, K., Psomopoulos, F., Argiriou, A., Haroutounian, S. A., & Boziaris, I. S. (2018). Microbial spoilage investigation of thawed common cuttlefish (Sepia officinalis) stored at 2°C using next generation sequencing and volatilome analysis. Food Microbiology, 76(March), 518–525. doi:10.1016/j.fm.2018.08.004 Parlapani, F. F., Syropoulou, F., Tsiartsafis, A., Ekonomou, S., Madesis, P., Exadactylos, A., & Boziaris, I. S. (2020b). HRM analysis as a tool to facilitate identification of bacteria from mussels during storage at 4°C. Food Microbiology, 85, 103304. doi:10.1016/j.fm.2019.103304 Qian, Y. F., Zhang, J. J., Liu, C. C., Ertbjerg, P., & Yang, S. P. (2022). Effects of gaseous ozone treatment on the quality and microbial community of salmon (Salmo salar) during cold storage. Food Control, 142(June), 109217. doi:10.1016/j.foodcont.2022.109217 Ramachandran, P., Reed, E., & Ottesen, A. (2018). Exploring the microbiome of Callinectes sapidus (Maryland Blue Crab). Genome Announcement, 6(22), e00466-18. doi:10.1128/genomeA.00466-18 Rodríguez-Sánchez, B., Cercenado, E., Coste, A. T., & Greub, G. (2019). Review of the impact of MALDI-TOF MS in public health and hospital hygiene, 2018. Eurosurveillance, 24(4), 1800193. doi:10.2807/15607917.ES.2019.24.4.1800193 Sanger, F., Nicklen, S., & Coulson, A. R. (1977). DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences, 74(12), 5463–5467. doi:10.1073/pnas.74.12.5463 Sankar Ganesh, P., & Rai Vittal, R. (2015). In vitro antibiofilm activity of Murraya koenigii essential oil extracted using supercritical fluid CO2 method against Pseudomonas aeruginosa PAO1. Natural Product Research, 29(24), 2295–2298. doi:10.1080/14786419.2015.1004673

Detection of Seafood Spoilage Microorganisms

559

Santos, I. C., Hildenbrand, Z. L., & Schug, K. A. (2016). Applications of MALDI-TOF MS in environmental microbiology. The Analyst, 141(10), 2827–2837. doi:10.1039/C6AN00131A Silbande, A., Adenet, S., Chopin, C., Cornet, J., Smith-Ravin, J., Rochefort, K., & Leroi, F. (2018). Effect of vacuum and modified atmosphere packaging on the microbiological, chemical and sensory properties of tropical red drum (Sciaenops ocellatus) fillets stored at 4°C. International Journal of Food Microbiology, 266(May 2017), 31–41. doi:10.1016/j.ijfoodmicro.2017.10.015 Sørensen, J. S., Bøknæs, N., Mejlholm, O., & Dalgaard, P. (2020). Superchilling in combination with modified atmosphere packaging resulted in long shelf-life and limited microbial growth in Atlantic cod (Gadus morhua L.) from capture-based-aquaculture in Greenland. Food Microbiology, 88(December 2019), 103405. doi:10.1016/j.fm.2019.103405 Syropoulou, F., Anagnostopoulos, D. A., Parlapani, F. F., Karamani, E., Stamatiou, A., Tzokas, K., Nychas, G.-J. E., & Boziaris, I. S. (2022). Microbiota succession of whole and filleted European Sea Bass (Dicentrarchus labrax) during storage under aerobic and MAP conditions via 16S rRNA gene highthroughput sequencing approach. Microorganisms, 10(9), 1870. doi:10.3390/microorganisms10091870 Syropoulou, F., Parlapani, F. F., Bosmali, I., Madesis, P., & Boziaris, I. S. (2020). HRM and 16S rRNA gene sequencing reveal the cultivable microbiota of the European sea bass during ice storage. International Journal of Food Microbiology, 327(February), 108658. doi:10.1016/j.ijfoodmicro.2020.108658 Tan, T., Wu, J., Wang, Y., & Teng, J. (2017). Spoilage profiles of green-lipped mussel Perna viridis: Autolysis of mussel. Journal of Food Processing and Preservation, 41(5), e13106. doi:10.1111/jfpp.13106 Tryfinopoulou, P., Tsakalidou, E., & Nychas, G.-J. E. (2002). Characterization of Pseudomonas spp. associated with spoilage of gilt-head sea bream stored under various conditions. Applied and Environmental Microbiology, 68(1), 65–72. doi:10.1128/AEM.68.1.65-72.2002 Tsironi, T., Lougovois, V., Simou, V. N., Mexi, A., Koussissis, S., Tsakali, E., Papatheodorou, S. A., Stefanou, V., Impe, J. V., & Houhoula, D. (2019). Next generation sequencing (NGS) for the determination of fish flesh microbiota. Journal of Food Research, 8(4), 101. doi:10.5539/jfr.v8n4p101 Vázquez-Sánchez, D., Galvão, J. A., Ambrosio, C. M. S., Gloria, E. M., & Oetterer, M. (2018). Single and binary applications of essential oils effectively control Listeria monocytogenes biofilms. Industrial Crops and Products, 121, 452–460. doi:10.1016/j.indcrop.2018.05.045 Waters, C. M., Lu, W., Rabinowitz, J. D., & Bassler, B. L. (2008). Quorum sensing controls biofilm formation in Vibrio cholerae through modulation of cyclic di-GMP levels and repression of vpsT. Journal of Bacteriology, 190(7), 2527–2536. Wickramasinghe, N. N., Ravensdale, J., Coorey, R., Dykes, G. A., & Chandry, P. S. (2021). Transcriptional profiling of biofilms formed on chilled beef by psychrotrophic meat spoilage bacterium, Pseudomonas fragi 1793. Biofilm, 3, 100045. doi:10.1016/j.bioflm.2021.100045 Yavuzer, E., & Köse, M. (2022). Prediction of fish quality level with machine learning. International Journal of Food Science & Technology, 57(8), 5250–5255. doi:10.1111/ijfs.15853 Yi, Z., Yan, J., Ding, Z., & Xie, J. (2022). The HD-GYP domain protein of Shewanella putrefaciens YZ08 regulates biofilm formation and spoilage activities. Food Research International, 157, 111466. doi:10.1016/j. foodres.2022.111466 Yin, L., Zhang, Y., Azi, F., Zhou, J., Liu, X., Dai, Y., Wang, Z., Dong, M., & Xia, X. (2022). Inhibition of biofilm formation and quorum sensing by soy isoflavones in Pseudomonas aeruginosa. Food Control, 133, 108629. doi:10.1016/j.foodcont.2021.108629 Zhang, J., Li, Y., Liu, X., Lei, Y., Regenstein, J. M., & Luo, Y. (2019). Characterization of the microbial composition and quality of lightly salted grass carp (Ctenopharyngodon idellus) fillets with vacuum or modified atmosphere packaging. International Journal of Food Microbiology, 293(December 2018), 87–93. doi:10.1016/j.ijfoodmicro.2018.12.022 Zhao, X., Chen, L., Wongmaneepratip, W., He, Y., Zhao, L., & Yang, H. (2021). Effect of vacuum impregnated fish gelatin and grape seed extract on moisture state, microbiota composition, and quality of chilled seabass fillets. Food Chemistry, 354(November 2020), 129581. doi:10.1016/j.foodchem.2021.129581 Zhu, T., Yang, C., Bao, X., Chen, F., & Guo, X. (2022). Strategies for controlling biofilm formation in food industry. Grain & Oil Science and Technology, 5(4), 179–186. doi:10.1016/j.gaost.2022.06.003 Zhuang, S., Li, Y., Hong, H., Liu, Y., Shu, R., & Luo, Y. (2020). Effects of ethyl lauroyl arginate hydrochloride on microbiota, quality and biochemical changes of container-cultured largemouth bass (Micropterus salmonides) fillets during storage at 4°C. Food Chemistry, 324(April), 126886. doi:10.1016/j. foodchem.2020.126886 Zotta, T., Parente, E., Ianniello, R. G., De Filippis, F., & Ricciardi, A. (2019). Dynamics of bacterial communities and interaction networks in thawed fish fillets during chilled storage in air. International Journal of Food Microbiology, 293(January), 102–113. doi:10.1016/j.ijfoodmicro.2019.01.008

29

Detection of Fish Spoilage Foteini F. Parlapani and Ioannis S. Boziaris University of Thessaly

Eleftherios H. Drosinos Agricultural University of Athens

29.1 INTRODUCTION Spoilage is a natural phenomenon that leads to the decomposition of a food substratum. In particular, the spoilage of fish can be considered an ecological phenomenon that encompasses a series of changes in the available components (e.g., low molecular weight compounds) through (i) natural processes (e.g., enzymatic activity or autolytic process), (ii) chemical activity (e.g., lipid oxidation), and (iii) microbial proliferation. A general feature of microbial spoilage is its relatively sudden onset, i.e., it does not appear to develop gradually, but more often as an unexpected and unpleasant revelation. This is a reflection of the exponential nature of microbial growth and its consequence that microbial metabolism can also proceed at an exponentially increasing rate. If a microbial product associated with spoilage, for example, an off odor, has a certain detection threshold, the level will be well below this threshold for most of the product’s acceptable shelf life. Many efforts to delay this natural process encompass certain strategies that are determined by general factors of the society. Nowadays the mild preservation of foods and the effort to extend the food shelf life have changed the status of the food enterprises. Quality and food safety management systems are planned, established, applied, operated, and maintained to satisfy the consumers’ needs, expectations, and requirements. Monitoring and validation of the control measures applied are essential parts of the applied systems. Detection of spoilage and estimation of shelf life is of paramount importance during the operation of these systems [1]. Fish spoilage is a complex process in which physical, chemical, biochemical, and microbiological mechanisms are implicated. Upon fish harvesting, the fish regulatory mechanisms, which prevent invasion of the tissues by bacteria, cease to function—bacteria or the enzymes invade the flesh of fish. This process produces metabolites in the fish, and the fish becomes spoiled. The initial stages of fish spoilage, which are characterized by the loss of characteristic fresh odor and taste, are mainly due to autolytic degradation (fish endogenous enzymes), while the final stages of fish quality deterioration are characterized by softening or toughening of flesh texture along with the production of unpleasant odors and flavors that are mainly due to microbial activity [2,3]. It is important to determine the nature of the specific spoilage organisms (SSO) or ephemeral spoilage organisms (ESO) growth and dominance in fish and fish products. Based on this information, it is more convenient to establish indices that reflect the microbiological quality of fish. In addition, microbiological quality will be interpreted in terms of spoilage [4–9]. Another cause of fish spoilage is lipid oxidation and hydrolysis that leads to the development of rancidity, even with storage at subzero temperatures. This is due to the large amount of polyunsaturated fatty acid moieties found in fish lipids. In fact, this is a major cause of frozen fish spoilage [10]. Characteristic changes in sensory attributes related to appearance, taste, odor and texture, occur during spoilage [11]. Since fish is a very perishable commodity, it has drawn special attention. As most raw materials, fish consist of a large number of species of widely differing appearance and flavor so that customers are often unsure if particular species of products made from them are suitable and safe to eat. The public 560

DOI: 10.1201/9781003289401-34

Detection of Fish Spoilage

561

also becomes more demanding with respect to freshness, microbiological safety, free from pollutants, protection from damage, and convenience. Quality control and labeling of fish products depend on defining the appropriate criteria, which may be of different importance to the various parts of the supply chain in the fish sector. Freshness of fish is the key factor determining quality, but seasonal variations, catching methods, handling, processing, and storage techniques also influence quality [12,13].

29.2  TYPES OF SPOILAGE Spoilage of fish and seafood is caused by microbial, oxidative, and enzymatic activity which results in undesirable changes in odor, flavor, and texture [14]. Evisceration plays an important role in spoilage, e.g., small pelagics are not gutted; hence, proteolytic enzymes along with microorganisms rapidly invade the fish’s tissues. Oily fish are susceptible to oxidative rancidity. Cold water species of fish, due to their psychrophilic microbiota, are predisposed to earlier spoilage under chill conditions. Type and rate of spoilage is multifactorial [15].

29.2.1 Microbial Spoilage Microbial spoilage begins after fish death. As fish is alive, microorganisms are normally found on skin, gills, and intestines. After death, they begin to colonize the flesh where with others from various sources of contamination in harvesting, handling, processing, packaging, distribution, etc., they build a complex multi-species microbial ecosystem (endogenous and exogenous microbiota or initial microbiota). Therefore, the composition of the initial microbiota greatly depends on the microbiota of the water of catching or growing areas and on the hygiene conditions of fisheries or aquaculture equipment and facilities [16–18]. Fish, as a poikilothermic organism, possess a microflora influenced by the temperature of the water and by the microbiota dominating the bottom sediment of the catch area. Fish caught on a line may have lower bacterial counts than fish that are trawled by dragging a net along the bottom; the trawl net drags through the bottom sediment, which usually has high counts of microorganisms. Unlike other crustacean shellfish (i.e., lobster, crab, and crayfish) that are kept alive until preparation for consumption, shrimp die soon after harvesting. Decomposition of shrimp involves bacteria on the surface that originate from the aquatic environment or are introduced during handling and washing. Molluscan shellfish (i.e., oysters, clams, scallops, and mussels) are sessile and filter feeders, and, as such, their microbiota depends greatly on the quality of water in which they reside, the quality of wash water, and other factors [9]. Microorganisms require a combination of physical (e.g., temperature and pH) and chemical factors (e.g., macro- and micro-molecules) to grow. Time and temperature conditions of fish in post-harvest, packaging and distribution conditions (temperature and atmosphere) throughout value chain directly affect the selection and growth of spoilage microorganisms. These microorganisms, known as SSOs, are usually consortium of only a few microbial genera or species of the initial microbiota of fish which grow faster than the rest and produce metabolites responsible for the development of off-odors and off-flavors during storage (microbial spoilage), resulting the organoleptic rejection  of the product [16]. To give emphasis that microbial spoilage of fish occurs due to the prevailing of an ephemeral fraction of the initial microbial association, Nychas et al. [9] introduced the term “Ephemeral Spoilage Organisms (ESO)”. To explore the microbiota of fish and fish products during storage and to reveal the potential SSOs or ESOs, culture-dependent (plate-based method) and culture-independent (biomolecules/ targets derived directly from the sample) approaches have been used by various researchers around the world. Culture-dependent approach has been used for isolation and identification of microorganisms in order to describe the microbial diversity of seafood and/or study the spoilage potential and activity of the final microbiota. For several decades, the identification of the isolates was carried out

562

Handbook of Seafood and Seafood Products Analysis

using biochemical assays (e.g., Gram-reaction, catalase and oxidase tests, Hugh and Leifson reaction, production of H2S or other gases, growth at several temperatures, NaCl concentrations or pH, etc.) and morphological or immunological tests [19,20]. Based on this approach, the initial microbiota of fish and fish products was found to consist of Gram-negative genera such as Pseudomonas, Shewanella, Psychrobacter, Moraxella, Acinetobacter, Flavobacterium, Vibrio, Photobacterium, and Aeromonas and Gram-positive genera such as Micrococcus, Corynebacterium, Bacillus, and Clostridium [16,21]. The selection of the final or spoilage microbiota, which usually belongs to the genera of Pseudomonas, Shewanella, Photobacterium and lactic acid bacteria (LAB), is mainly depended on the applied storage conditions (e.g. temperature and atmosphere). In the first decade of the 21st century, this knowledge has been enriched due to the introduction of more specific identification approaches such as genomics (e.g., full-length or partial 16S rRNA gene sequencing analysis) and proteomics. The use of 16S rRNA gene is currently the most common approach for microbial community analysis due to the phylogenetic properties and the large amount of both variable and conserved regions available. The sequence of the 16S rRNA gene has been widely used to determine phylogenetic relationships among bacteria as well as to identify unknown bacteria to the genus or species level. Therefore, using such approaches, other bacteria such as Psychrobacter spp., Pseudoalteromonas spp., Aeromonas spp., Carnobacterium spp., and Vagococcus spp. were also found to compose the cultivable microbiota at the end of shelf life of fish and fish products from the cold [22–25] and/or the warmer [26,27] temperate sea waters. However, the evolution in microbiology came with the discovery of next generation sequencing (NGS) technologies such as 454-pyrosequencing and Illumina sequencing which do not depend on the cultivation of microorganisms on culture media (culture independent approach). In seafood microbiology, NGS technologies have provided us with theorychanging breakthroughs since they reveal cultivable and non-cultivable bacterial genera directly from the sample by extracting and sequencing either DNA or RNA molecules. Several microorganisms that were never found in seafood before have been now revealed by NGS, mainly by Illumina protocols and platforms [17,28,29]. However, such technologies have not changed dramatically our knowledge about the dynamics of SSOs or ESOs in seafood compared to culture-dependent molecular approach. Pseudomonas; Shewanella; Photobacterium; Psychrobacter; Pseudoalteromonas; Aeromonas; Brochothrix; and LAB such as Lactobacillus, Lactococcus, and Carnobacterium remain as the most frequently found SSOs or ESOs in fish and fish products even using NGS technology. However, it has to be mentioned that using these tools, Shewanella was found at very low abundances in fish caught from the waters of Southern Atlantic and Mediterranean [28,30], indicating a possible overestimation of its population in culture media for fish from the Southern Europe. 29.2.1.1  Specific Spoilage Organisms/Ephemeral Spoilage Organisms The SSO or specifically ESO concept has contributed significantly to our understanding of meat and seafood spoilage. It is now established that the prevailing of particular microorganisms during storage depends on a variety of factors or ecological determinants (e.g., intrinsic, processing, extrinsic, implicit, and the emergent effects) which may differ from batch to batch and lead to the development of a different microbial association each time [31]. Biological variability (e.g., fish species, fish origin, lot-to-lot) and the type of product (e.g., whole, gutted, filleted) are among the most important factors that lead to the selection of different microbial association in fresh and thawed chill-stored fish [27,28,30,32]. The microbial associations developing on muscle tissues stored aerobically at cold temperatures are characterized by an oxidative metabolism. The Gram-negative bacteria that spoil fish are either aerobes or facultative anaerobes. In the past, Pseudomonas spp., Shewanella putrefaciens, and Photobacterium phosphoreum were found to be the dominant species on fish muscle stored at cold temperatures. It has also referred that Brochothrix thermosphacta also occurs on chilled fish stored mainly under modified atmospheres in significant numbers that contribute to the microbial associations [4], and LAB are considered to be important in spoilage when fish is stored under vacuum or

563

Detection of Fish Spoilage

modified atmosphere packaging (MAP). Although the aforementioned information is well documented by various researchers [7,8,20,21,33–37], the development of cutting-edge methodologies and technologies e.g., PCR and NGS over the last years has also revealed or confirmed other bacteria such as Psychrobacter spp., Pseudoalteromonas spp., Aeromonas spp., Carnobacterium spp., and Vagococcus spp. composing the spoilage microbiota of finfish and shellfish (Table 29.1). Of them, Psychrobacter (mainly in air-stored fish) and Carnobacterium (mainly in MAP-stored fish) are the most frequently found genera in finfish and shellfish. In ice- or chill-stored fish under air, apart from Pseudomonas [26,44–46] and/or Shewanella [26,47–49], Psychrobacter has been frequently found to dominate in European seabass [27], gilthead seabream [28], farmed mussels [50], deepwater rose shrimp [51], thawed common cuttlefish [52], Eastern oysters [53], tilapia [47], brown shrimp [54], Norway lobster [55], and so on. In MAP, Carnobacterium has been mainly found to dominate in fish fillets e.g., salmon [24], Atlantic horse mackerel [22], and gilthead seabream [26]. Moreover, this genus was found to dominate against others (8°C) or co-dominate with others e.g., Psychrobacter (4°C), in gilthead seabream stored aerobically at higher chill temperatures [28]. In aforementioned cases, one bacterial genus dominated against others in fish tissue and might be responsible for microbial spoilage. However, different species or strains of Psychrobacter (e.g., Ps. cibarius, Ps. cibarius-like species, Ps. cryohalolentis, Ps. glacincola, Ps. immobilis, Ps. TABLE 29.1 Specific/Ephemeral Spoilage Organisms in Seafood Seafood Fresh and chilled, stored in air

Typical SSOs/ESOs Pseudomonas spp.a,b S. putrefaciens-likeb,c Psychrobacter spp.b Pseudoalteromonas spp.b Aeromonas spp.a

Fresh, chilled, and stored in vacuum or modified atmosphere packaging

P. phosphoreumb,g Lactic acid bacteriaa,h B. thermosphactaa Pseudomonas spp.d

Sous-vide cooked and chill-stored Lightly preservede and chill stored

Gram-positive spore formers Lactic acid bacteriaf Enterobacteriaceae P. phosphoreum Vibrio spp. Halobacterium spp., Halococcus spp., and osmotolerant molds and yeasts Molds and lactic acid bacteria

Semi-preserved, salt-cured, and chilled Fermented and chilled

Source: [9,17,29,38–43]. a Typical of fish from freshwater and fish from warmer waters. b Typical of fish from marine and temperate waters. c Refer to S. putrefaciens, Shewanella baltica, and other closely related H2Sproducing, Gram-negative bacteria. d In packages containing oxygen or using film of high permeability. e Include, for example, cold-smoked salmon, cooked and brined shrimps, and brined roe products. f Include, for example, Lactobacillus curvatus and Lactobacillus sakei. g Not dominating in fish from the Mediterranean Sea. h Include also Carnobacterium spp. and Vagococcus spp.

564

Handbook of Seafood and Seafood Products Analysis

namhaensis, Ps. alimentarius, Ps. glacincola, Ps. fozii, Ps. maritimus, Ps. proteolyticus, Ps. glacialis, Ps. halophilus, etc.), Pseudomonas (e.g., P. fragi, P. putida, P. fluorescens, P. lundensis, P. gessardii, P. veronii, P. vranovensis), Shewanella (e.g., S. putrefaciens, S. baltica, S. morhuae), or Carnobacterium (e.g., Carnobacterium maltaromaticum, C. divergens, C. piscicola), etc., were found alternatively to dominate in each case depending on factors that differed or varied from case to case, thus confirming the ESO concept [9,31]. 29.2.1.2  Metabolic Activity In general, the metabolic activity of the ephemeral microbial association, which prevails in a muscle ecosystem, leads to the manifestation of changes that are characterized as spoilage of animal origin food. These undesirable spoilage changes are related to the type, composition, and population of the microbial association and the type and availability of substrates for energy production in fish (Table 29.2). Indeed, the type and the extent of spoilage is governed by the availability of low-molecular-weight compounds (e.g., glucose, lactate) existing in fish; eventual muscle food changes and subsequent overt spoilage are due to catabolism of nitrogenous compounds as well as secondary metabolic reactions. The growth and metabolism of microorganisms results in the formation of amines, sulfides, alcohols, aldehydes, ketones, and organic acids with unpleasant and unacceptable off-flavors [58]. Ephemeral spoilage organisms (ESOs) give rise to the offensive off-flavors. Although they might be a minor part of the total microbiota, they are classified as active spoilers. However, others that can reach higher numbers, but their spoilage activity is limited, are not considered SSO/ESO. Thus, SSO/ESO is considered the microorganism that has (i) the potential to produce unpleasant metabolites (spoilage potential) and (ii) the activity to produce a considerable amount (spoilage activity) enough to deteriorate sensory properties of the product [12,59,60]. TABLE 29.2 Main Low-Molecular-Weight Components of Animal Origin Foods and Fish Pre- and Post-Rigor Mortis (mg/100 g) Component

Pre

Post

Creatine phosphate Creatine Betaine ATP IMP Glycogen Glucose Glucose-6-phosphate Lactic acid pH Free amino acids TMAO Carnosine and anserine

9.3 ndb nadc 6.5a nd 220 220 21 100 7.3 nad nd nd

0.2 nd 100d 0.2a nd 40 40 32 400 6.5 250 350–1,000 100e

Source: [9,10,56,57]. a μmol/g. b nd: not determined. c nad: no available data. d In some fish. e Cod.

a

a

565

Detection of Fish Spoilage

A plethora of studies have been dealt with the metabolism of spoilage microorganisms in seafood. Some of them monitored the spoilage potential and/or the spoilage activity of the potential seafood spoilers in inoculated sterile fish model substrates such as black rockfish, prawn extract, black rockfish, shrimp, cold-smoked salmon, and gilthead seabream juice agar under various storage conditions [23,61–66]. Metabolite such as acetaldehyde was linked with the activity of P. fragi and Psychrobacter; methyl mercaptan and dimethyl disulfide were linked with P. fragi, Pseudoalteromonas spp., and/or P. fluorescens; dimethyl sulfide with P. fragi and S. putrefaciens; ethanol with P. fragi and LAB; acetic acid with P. fragi, Pseudoalteromonas spp., and LAB; sulfides e.g., dimethyl disulfide and dimethyl sulfide with P. fragi, P. fluorescens, Pseudoalteromonas spp., and S. putrefaciens; and ethyl and methyl esters with P. fragi and S. putrefaciens (the latter mainly with methyl esters), while particular aldehydes e.g., 2- and 3-methylbutanal; ketones e.g., 3-hydroxy-2-butanone, 3-heptanone, 2-heptanone; and alcohols e.g., 2-ethyl-1-hexanol, 3-methyl1-butanol were linked with the activity of LAB particularly C. divergens and C. maltaromaticum [23,61–66]. The majority of these compounds is produced by the catabolism of amino acids or other compounds of substrate by SSOs or ESOs, for example, sulfur compounds through the catabolism of sulfur-containing amino acids e.g., cysteine and methionine by Shewanella spp., Pseudomonas spp., etc.; esters through esterification of alcohols and carboxylic acids; or other microbial esterase activity, etc. Indeed, the aforementioned compounds have been also found, and increased in some cases, in seafood carrying the natural microbiota when such microorganisms dominated during chilled storage under air-, MAP-, or vacuum-packaged conditions [67–73]. Another fish spoilage aspect that must be presented separately, due to the fact that safety issues also arise, is the production of biogenic amines. Biogenic amine accumulation in fish flesh occurs because of enzymatic decarboxylation of some specific amino acids which exist in higher amounts in scombroid fish (Table 29.3). The enzymes are mostly of microbial origin and to a lesser extent are endogenous to fish tissue. Main biogenic amines usually found in fresh and processed fish are putrescine (PUT), cadaverine (CAD), histamine (HI), and tyramine (TY), while natural polyamines

TABLE 29.3 Production of Biogenic Amines by Muscle Microbial Biota in Muscle Foods and Broths Storage Condition Biogenic Amine PUT CAD HI

Bacteria Hafnia alvei, Serratia liquefaciens, Shewanella putrefaciens H. alvei, S. liquefaciens, S. putrefaciens Proteus morganii, Kebsiella pneumoniae, H. alvei, Aeromonas hydrophila, Moraxella morganii, Photobacterim phosphoreum, S. putrefaciens

T (°C) 1 1 1 1 1.7 2.1

Medium/Pack vpa Fish broth vp Fish in vp Fish in vp, fish broth

SPM

Temperature, pH, histidine utilization

pH, SPD

SPD TY

Factors pH, ornithine (arginine) utilization pH, lysine utilization

pH, agmatine, arginine Lactobacillus sp., L. carnis, L. divergens, Enterococcus feacalis

Tryptamine Source: [9,74–81]. a Vacuum pack. b Aerobic storage.

1 20

vp Airb

pH, tyrosine pH

566

Handbook of Seafood and Seafood Products Analysis

(PAs), spermidine (SPD), and spermine (SPM)contents slightly change during storage or processing [82]. It is well-documented that PUT is produced by decarboxylation of arginine, HI by decarboxylation of histidine, CAD by decarboxylation of lysine, and TY by decarboxylation of tyrosine [83]. Even though all the aforementioned are synthesized in one pathway, it should be mentioned that PUT is formed via two different pathways with arginine being the startup [84,85]. Higher polyamines SPD and SPM are synthesized from putrescine spermidine synthase and spermine synthase, respectively [86]. Several bacteria (both Gram-positive and negative) that are commonly linked to fish spoilage, as well as to fish safety, are responsible for Biogenic Amines (BAs) production. For instance, a variety of species belonging to the genera Pseudomonas, Aeromonas, Acinetobacter, and Serratia, as well as potential food-borne pathogens such as Klebsiella, Staphylococcus, Vibrio, and Escherichia, are able to produce histamine [87–89]. Additionally, PUT is closely related to Aeromonas spp., Serratia spp., Shewanella putrefaciens, and LAB (mainly Lactobacillus species) [90,91]. The latter is also strongly connected with tyramine production [92], while Pseudomonas and several bacteria belonging to the family of Enterobacteriaceae are responsible for cadaverine synthesis [93,94]. The appearance and physical properties of the fish flesh change due to action of the microorganisms. Besides odor and flavor, the slime on skin and gills, initially watery and clear, becomes cloudy, clotted, and discolored. Skin loses its bright iridescent appearance; bloom and smooth feel become dull, bleached, and rough to the touch [2,12].

29.2.2  Chemical Spoilage—Fat Oxidation Oxygen promotes several types of deteriorative reactions in foods including fat oxidation, browning reactions, and pigment oxidation. There is a wide surface area exposed to oxygen on fresh fish. Fish lipids are rich in the polyunsaturated fatty acids such as omega-3 fatty acids. The lipids in oily fish are concentrated in a layer beneath the skin in contiguity with the dark muscle bands which contain powerful oxidizing enzymes such as cytochrome c. Oxygen reacts with this fat to produce rancidity, and undesirable strong odoriferous and flavorous compounds are produced. Fish spoil rapidly in air due to moisture loss or uptake, reaction with oxygen, and the growth of aerobic microorganisms i.e., bacteria and molds. Fat oxidation can be a serious problem for frozen fish stored for very long. This is one reason why fatty fish like bluefish do not remain in good condition during frozen storage unlike leaner fish like flounder [42]. Fat oxidation causes degradation of main compounds present in seafood to secondary compounds such as ketones, alcohols, hydrocarbons, and alkanals and are responsible for unpleasant odor and loss of nutritive value, making in parallel the product unacceptable for consumption [95–98]. This phenomenon starts at the postmortem stage, since the releasing free fatty acids and enzymes start with the production of alkyl radicals that react with oxygen, forming peroxy-free radicals [98].

29.2.3 Enzymatic Spoilage A lot of enzymatic activities take place after fish death. In postmortem muscle, energy is produced from glycogen through the anaerobic path since the supply of oxygen to the muscle tissue has been interrupted. The depletion of adenosine triphosphate (ATP) causes the rigor mortis and pH drop of the flesh with a concomitant decrease of water-holding capacity. ATP degrades to compounds such as diphosphate (ADP), adenosine monophosphate (AMP), inosine monophosphate (IMP), inosine (Ino), and hypoxanthine (Hx). The end of rigor mortis occurs due to the action of various digestive enzymes (autolytic activities), mostly proteases such as cathepsins, calpains, and collagenases, which affect mainly the texture of fish and the softening of flesh at post rigor [10]. Digestive enzymes may begin to digest the fish itself, causing belly burn or softening of the flesh around the gut. Such autolytic enzymes can soften the product at early stages without producing any unpleasant odors/flavors, indicating that their activity shortens the shelf life of the

Detection of Fish Spoilage

567

product prior to SSOs prevalence [99]. While the quantity of the enzymes rises, autolytic changes occur, and flavor components make the flesh tasteless. This is especially likely if a fish is caught while feeding, since its digestive enzymes are already active. Other enzymes in fish muscle can also begin to affect the flavor and texture of the fillet. At warmer temperatures, the enzymes act more rapidly. But even at low temperatures, enzymatic processes may lead to a product showing a high degree of proteolysis. Furthermore, enzymatic lipid degradation caused by lipase and phospholipase activity can also take place at post-mortem point. Such activities are strongly related to the oxidation of unsaturated fatty acids, leading to the production of several unpleasant odors and tastes as well [100]. Another kind of enzymatic spoilage is the enzymatic browning of crustaceans [101]. In crustaceans, like lobsters, not only odor but also color are important characteristics that determine shelf life [102]. Melanosis occurs from the action of polyphenol oxidase on tyrosine and its derivatives such as tyramine [103].

29.3  DETERMINATION METHODS There are various methods to detect fish spoilage, or the other way around fish freshness. Oehlenschläger and Sörensen [104] stated that freshness of fish means that a fish in its entire properties is not far away from those properties it had in the living state, or that only a short period of time has passed since the fish has been caught or harvested. The methods are categorized as sensory, microbiological, chemical, and physical. The activity of microorganisms is the main factor limiting the shelf life of fresh fish resulting in the degradation of the fish, the development of off-flavors, and other unfavorable changes such as gills color changes, slime on skin and gills, etc. Thus, well-established method for the evaluation of fish freshness is based on the evaluation of sensorial characteristics of fish. The estimation of the total viable counts or the population of spoilage microorganisms and measurement of chemical indicators are also used as acceptability indices in guidelines and specifications. Sensory and microbial analyses are lengthy procedures, so rapid chemical, biochemical, and physical methods are better solutions to measure the freshness of fish. These methods are based on e.g., nucleotide catabolism; production of amines or other nitrogenous compounds; production of volatile organic compounds or physical properties e.g., electrical properties by handheld devices. However, none of these methods are widely used in fish industry [11]. In recent years, research has focused on developing new, rapid instrumental methods to detect the freshness of fish. Several new promising techniques have been developed, and some of them have shown good correlation with traditional methods evaluating quality of freshness [105].

29.3.1 Sensory Analysis Sensorial analysis employs several criteria, including the appearance of the skin, eyes, mucus and gills, the firmness of the flesh, and odor. These criteria are checked by trained specialists with evaluation equipment. They determine the samples using appropriate common, universal food sensory analysis. These are wholly dependent upon the human senses: sight, touch, odor, and flavor. Measure of a particular aspect of quality is usually compared to a standard, having gradations of quality (a well-known scale). It is always possible to construct a scale showing change or incidence using value words: slight, trace, medium, moderate, very highly. A further development of scales is to denote the different steps by numbered scores from 0 or 1 upwards. Human senses are better at recognizing complexities and are more discriminatory than instruments, but such determination of freshness is in part subjective, because of personal expressions. In addition, for transformed products, such as fillets, sensory analysis is less reliable because the number of assessment criteria diminishes [2,106].

568

Handbook of Seafood and Seafood Products Analysis

Well-established method for the evaluation of fish freshness is the European E-A-B fish freshness scheme or Multilingual Guide to EU Freshness Grades for Fishery Products [107], in which various attributes skin, outer slime, eyes, gills appearance, and odor are described, and according to their characteristics, the samples are classified into four grades: E (extra) A, B, and C (unfit for consumption). The quality index method (QIM) is also used as a reference method [108]. QIM is based on well-defined characteristic changes of quality-related attributes (eyes, skin, gills, and smell) and corresponding score system of index points. However, sensory evaluation schemes have some limitation in order to be used for routine analysis and continuous effort for alternative methods for rapid and reliable estimation of spoilage/freshness is required [109].

29.3.2 Microbiological Analyses 29.3.2.1  Quantification of Spoilage Microorganisms 29.3.2.1.1  Classical Approach Microbiological analysis is traditionally based on the growth and enumeration of colonies on general or selective culture media. Bacterial population is determined by inoculating dilutions of microbial suspension on the surface of solid dry agar media (spread plates) or mixing the test suspension with the liquefied agar media (pour plates). In fish and fish products, enumeration of the total cultivable microbial population (TCMP thereafter) is the most common way to assess freshness or the level of spoilage. Plate count agar (PCA); iron agar Lyngby (IA) [110]; and Long and Hammer Agar (LH), with or without added NaCl [111], have been used to determine TCMP. According to the standard ISO (2003)/ISO 4833:2003 [112], TCMP should be counted on pour plates of PCA after aerobic incubation at 30°C for 72 h. However, other general culture media have been proposed as more suitable for the TCMP enumeration based on qualitative and quantitative results. For fish from the Mediterranean Sea, Tryptone Soy Agar (TSA) is used compared to PCA and Iron Agar (IA) because TCMP was found at much higher population levels compared to PCA or IA pour plates, with or without added NaCl [113]. For fish from the North Sea, IA gives significantly higher counts than PCA, after 3–4 days incubation at 25°C [114]. In many studies, IA with 1% w/v NaCl has been used [110,115]. Moreover, other researchers suggest the use of LH for the psychotropic plate counts (PPC), especially for fish and fish products in which Photobacterium dominates (LH has no use for fish from the Mediterranean where Photobacterium is not present/increased in fish storage). For shellfish, especially bivalves, marine agar (MA) has been proposed as more suitable than PCA or PCA with 1% NaCl [116]. Therefore, which general culture medium must be used might depend on fish origin, mainly on the waters where fish live (fish from different waters usually present different initial microbiota). Additionally, almost all commonly used general culture media in seafood present disadvantages. For example, Broekaert et al. [117], using molecular identification, found that some spoilage-related bacteria, such as Pseudomonas fragi and Acinetobacter spp., are unable to grow on various non-selective culture media. The organoleptic rejection in fish and fish products usually occurs when the population of SSOs or ESOs reaches the level of 107–109 cfu/g [16]. Using the spread plate method, Pseudomonas spp. are enumerated on cetrimide-fucidin-cephaloridine agar (CFC) after incubation at 25°C for 48 h; Brochothrix thermosphacta population in seafood is enumerated on streptomycin sulfate thallous acetate agar (STAA) spread plates after incubation at 20°C for 72 h; yeast and molds, mostly found in processed seafood, are enumerated on chloramphenicol-based media, e.g., rose Bengal chloramphenicol (RBC) and Tryptose-Glucose-Yeast Extract with chloramphenicol, or oxytetracycline-glucose-yeast extract agar after incubation at 25–30°C for 3–5 days; and Vibrio spp. on thiosulfate-citrate-bile salts-sucrose agar (TCBS), incubated at 37°C for 24 h, while using the pour plate method, H 2S producing bacteria are enumerated on IA by counting only black colonies after incubation at 25°C for 72 h; Enterobacteriaceae on violet red bile glucose

Detection of Fish Spoilage

569

agar (VRBGA) after incubation at 37°C for 24 h; and LAB are enumerated on De Man, Rogosa, Sharpe agar (MRS) after incubation at 25°C for 72 h. 29.3.2.1.2  PCR-Based Approach Alternatively, TCMP and SSOs or ESOs have been quantified in seafood using PCR-based techniques such as real-time PCR. This method is based on the quantification of genes present in prokaryotes e.g., 16S rRNA for the TCMP assessment in cod fillets [118]; whole, gutted, and filleted European seabass; and gilthead seabream [119], or genes present in particular microbial genus or species in seafood, e.g., the carbamoyl phosphate synthase gene (carA) to quantify Pseudomonas spp. in cod [120], torA gene coding one of the major bacterial enzymes in fish spoilage, the trimethylamine N-oxide reductase in whiting and plaice [121], and gyrB to quantify P. phosphoreum in salmon packed under MAP [122] for the quantification of SSOs or ESOs. This method is more promising to be used versus the traditional microbiological analysis, but there are weaknesses, mainly regarding the DNA extraction process (difficulties in extracting the whole prokaryotic DNA from seafood) to be solved.

29.3.3  Chemical Spoilage Indices Microorganisms produce a plethora of volatile organic compounds such as alcohols, aldehydes, ketones, organic acids, esters, and ammoniac and sulfur compounds during storage of fish. The nature of microorganisms (e.g., physical and chemical requirements; competitive behavior; spoilage potential and activity) and the availability of nutrients in fish tissue (e.g., glucose), in conjunction with the applied storage conditions, mainly determine the type of metabolites produced and the development of off-odors and off-flavors in the product [9,41]. For instance, a significant production of ammonia and various nitrogenous volatile compounds and ethyl esters is observed in chilled-stored fish dominated by Pseudomonas. Volatile sulfides (e.g., dimethylsulfide, hydrogen sulfide, methanethiol etc) and amines, such as trimethylamine, are produced in fish dominated by Shewanella. Ammonia, malodorous amines and volatile sulfides including H2S are produced in fish dominated by Enterobacteriaceae. Finally, L-lactic acid, acetic acid, acetoin (3-hydroxy2-butanone), formic acid and ethanol are the main metabolic products of lactic acid bacteria e.g., Lactobacillus spp., Leuconostoc spp., Carnobacterium spp., and B. thermosphacta [9]. In all these cases, the role of glucose (first preferred substrate), lactate (second preferred substrate), and amino acids (third preferred substrate) as the major energy sources is fundamental for bacterial growth and metabolism. As these molecules are used by microorganisms, their chemical structure changes resulted, in many cases, in the formation of volatile compounds with the characteristic spoilage related odors e.g., sulfur, sulfide-like, fishy, putrid. Glucose is consumed first by the microorganisms since it is the simpler monosaccharide (meaning lower energy—ATP cost). Pseudomonas is the most frequently found spoiler of animal origin foods, including seafood; this might be because of a greater catabolism of glucose (faster growth rates and greater affinity for oxygen) over other spoilage-related bacteria of muscle [123]. To assess freshness in fish and fish products, researchers use many analytical approaches for determining volatile compounds e.g., total volatile bases nitrogen (TVB-N), trimethylamine (TMA), biogenic amines, volatile organic compounds (VOCs), etc., produced by microorganisms during storage. Of them, traditional approaches such TVB-N, TMA, and biogenic amines present weaknesses; in particular, TVB-N and TMA increase in fish only at the late stages of storage, and biogenic amines are not produced in considerable amounts in non-scombroids. Moreover, considering the SSOs or ESOs concepts, it is uncertain if such compounds can be produced in seafood of different species, batches, lots, etc. Therefore, these traditional compounds cannot be used as markers to assess spoilage/freshness [71,124,125]. On the other hand, the VOCs approach is a better choice for the determination of microbial metabolites and the assessment of freshness in any seafood since it identifies/quantifies the metabolites of the microorganisms dominating in

570

Handbook of Seafood and Seafood Products Analysis

the sample (mainly those of any SSOs or ESOs), allowing us to know which of them are produced by a particular microbiota, increase during storage, show good correlation with microbial growth, sensory score, and remaining shelf life. For instance, volatile compounds such as ethanol, acetaldehyde, dimethyl disulfide, 2-ethyl-1-hexanol, 2-heptanone, 2-methyl-1-butanol, 3-methyl-1-butanol, 2-methylbutanal, 3-methylbutanal, 3-hydroxy-2-butanone, 2-butanone, acetic acid, 3-methylbutanoic acid, ethyl acetate, ethyl isobutyrate, ethyl-2-methylbutyrate, ethyl isovalerate, etc. have been proposed as potential spoilage markers of seafood because they are associated with the activity of microorganisms and increase during storage [41,67,126,127]. Additionally, other compounds, such as 1-octen-3-ol, trans-2-octenal, cis-4-heptenal, and trans-2,cis-6-nonadienal, which are associated with chemical reactions e.g., lipid oxidation, have been also proposed as potential markers to assess spoilage/freshness of seafood. 29.3.3.1  Total Volatile Bases (TVB) and Trimethylamine (TMA) Total volatile bases nitrogen (TVB-N) and trimethylamine nitrogen (TMA-N) are commonly used as chemical spoilage markers of seafood [126]. TVB-N includes compounds such as TMA, ammonia, etc. [16]. P. phosphoreum and S. putrefaciens can produce high amounts of TMA through the reduction of TMAO to TMA [59]. The psychotropic nature and the ability of the S. putrefaciens to reduce TMAO to TMA explains its importance in spoilage of fish stored at low temperatures, where the “fishy” off-odor of spoiling fish is caused by the production of TMA. The bacterium also degrades sulfur-containing amino acids and produces volatile sulfides including H2S [111]. H2S-producing bacteria constitute only a minor fraction of the initial microbiota on newly caught fish, but Gram-negative, psychotropic species become dominant during iced storage, and H2S-producing bacteria will typically grow to levels of 107 colony forming units (CFU)/g. However, P. phosphoreum reduces TMAO to TMA at 10–100 times the amount per cell than S. putrefaciens [60] and produces 30 times more TMA than cells of S. putrefaciens in spoiled fish such as cod [59]. In such fish from cold waters, where these microorganisms are the main spoilers, high amounts of TVB-N and TMA have been reported [59,128–130]. TVB-N and TMA exceed 35 and 15 mg N/l00 g, respectively, at spoilage level according to EC 2074/2005 and Connell and Shewan [128]. On the other hand, in fish from the Mediterranean Sea, TVB-N values do not exceed 30 mg N/100 g at the time of rejection [8,44,70,71,125]. However, large amounts of TVB-N have been reported for crustaceans such as pink shrimp [131], white shrimp [132], tropical brackish water shrimp [133], and Norway lobster [102,134]. Regarding TMA also, low amounts have been reported in fish from the Mediterranean Sea waters under air [8,44,71,135,136] and MAP [137]. This can be explained by the lower content of TMAO in fish from the Hellenic seawaters [7]. Similarly, low amounts of TMA have been also reported for fish originating from warm waters [138–140]. The procedure for the determination of TVB-N is to extract nitrogenous compounds, the increase of which is one of the causes of fish spoilage, and then express the result as mg of nitrogen in 100 g of sample. For the extraction, the sample must be homogenized and then trichloroacetic acid or perchloric acid is added and then filtered. After that, the filtrate undergoes steam distillation, where the volatile nitrogenous compounds are bound by boric acid and finally titration with acid take place [141,142]. Alternatively, the extract can also be placed in a Conway plate and total volatile bases (TVB) are determined according to Conway’s microdiffusion method [143]. Trimethylamine can be determined photometrically using the reaction with picric acid [144] or can be determined with HPLC using similar protocol like the one used for the analysis of biogenic amines (see below). 29.3.3.2  Biogenic Amines The determination of biogenic amines in fresh and processed food is gaining great interest not only for their potential risk to human health, but also because they could have a role as chemical indicators of unwanted microbial contamination and processing conditions. Biogenic amines are

Detection of Fish Spoilage

571

indicators of fish spoilage because their precursor amino acids are decarboxylated by bacterial enzymes (Table 29.3). We can monitor the presence of diacetyl by exposing an aromatic orthodiamine at acidic pH to an environment containing, or possibly containing, diacetyl and detecting any change in the absorption or reflection of electromagnetic radiation due to the ortho-diamine. The change may suitably, though not necessarily, be in the UV or visible region or both. At first, a previous confirmation is necessary and then an extraction with hydrochloric acid [107]. With high-performance liquid chromatography (HPLC), analytes are carried through a column filled with packing material (stationary phase) via a mobile phase (e.g., organic solvent). Individual analytes have varying affinities for the stationary phase and have characteristic retention times that dictate when each component will elute. Components coming off the column are visualized with a detector, e.g., fluorescence. This monitors eluent based on the ability of the sample compound to absorb light at a specific wavelength. Unfortunately, there are drawbacks of these methods that are related to precolumn, postcolumn, or on-column derivative process leading to an overall long analysis time and low reproducibility owing to the stability of the derivatization products. Pre derivatization consists of a series of manual time-consuming steps that may introduce imprecision but offers certain selectivity. Postcolumn derivatization has the advantage that it is in line, but it adds complexity to the instrumentation, and system must be set up in order to reduce the contributions to band widening. Changing of pH with a postcolumn system is simple, easy, and quick. The example of the chromatographic technique is ion-pair chromatographic method on a C18 reversed-phase column involving a postcolumn reaction with o-phthaldialdehyde (OPA) to form fluorescent derivatives with amines. For HI, European Community and Spanish regulations have fixed a maximum average value of 100 mg/kg in a group of nine samples of fresh or canned fish and lower than twice this value for ripened fish products. The U.S. Food and Drug Administration has lowered the HI defect action level from 100 to 50 mg/kg. 29.3.3.3  Volatile Organic Compounds (VOCs) The determination of Volatile Organic Compounds (VOCs) is a new trend of 21st century to evaluate the course of microbiological spoilage of seafood. The production of such compounds has been studied in inoculated sterile fish model substrates and in real fish by various researchers in the world using analytical methods for VOCs collection e.g., solid-phase microextraction (SPME); purge-andtrap sampling [68,145], and VOCs analysis e.g., gas chromatography-mass spectrometry (GC–MS); selected ion flow tube mass spectrometry (SIFT–MS) [73]. Of them, solid phase microextraction coupled with gas chromatography/mass spectrometry (SPME-GC/MS) is the most used method for VOCs determination [45,51,52,66,67,70–72,127,146–151]. SPME is a proven tool in volatile analysis and has previously been used for the analysis of flavor volatiles in seafood as well as in a variety of other foods. In general, SPME is both simple and cost-effective to use and can be used to analyze the levels of a wide range of volatile compounds. The method can be used manually with any GC or GC/MS. GC can separate volatile and semivolatile compounds with great resolution, but it cannot identify them. MS can provide detailed structural information on most compounds and identify them, but it cannot readily separate them. This contributed to an idea of the combination of the two techniques. In both techniques, the sample is in the vapor phase, and both techniques deal with about the same amount of sample (usually less than 1 ng). The vapor-phased sample is carried by the gas that is almost always a small molecule such as helium or hydrogen with a high diffusion coefficient. In MS method, organic molecules have much lower diffusion coefficients. GC effluent (the carrier gas with the organic analytes) is sprayed through a small nozzle because of the high diffusion coefficient; the helium is sprayed over a wide solid angle. And in MS spectrometry, the heavier organic molecules are sprayed over a much narrower angle and tend to go straight across the vacuum region. Then the gas passes it to packed columns of the mass spectrometer. The higher-molecular-weight organic compounds are separated from the carrier gas, which is removed by the vacuum pump. The compounds of the sample are separated by the special separating material inside (e.g., separators made from

572

Handbook of Seafood and Seafood Products Analysis

glass drawing down a glass capillary). The particles escape from the column and are lodged in the spray orifice and stop (or at least severely reduce) the gas flow out of the GC column and into the mass spectrometer. All modern GC–MS data systems are capable of displaying the mass spectrum on the computer screen as a bar plot of normalized ion abundance versus mass-to-charge (m/z) ratio (often called mass). Like the other parts of the GC–MS instrument, the data system must be calibrated [68].

29.3.4 Non-invasive Technologies Fish industry and food authorities require fast, accurate, and cost-effective methods to ensure safety and quality of seafood. Invasive methodologies such as microbiological, molecular, chemical, and biochemical described herein are reliable; however, they are also costly, laborious, and time-consuming. To meet stakeholders’ demands, food scientists are now developing intelligent research-led tools and attempting to establish rapid, reliable, and user-friendly non-invasive technologies for the evaluation of seafood quality and safety in an extremely short time. Such non-invasive technologies include (i) arrays of biomimetic sensors, which are natureinspired arrays of sensors such as electronic noses and tongues, designed to imitate the olfactory (electronic nose or e-nose) and gustatory (e-tongue) systems of humans; (ii) vibrational spectroscopy that includes two analytical methods: the infrared and Raman Spectroscopy, which are nondestructive, non-invasive tools that measure vibrational energy levels (associated with chemical bonds) in the sample and give a characteristic spectrum for the sample, like a fingerprint, providing information about the molecular composition, structure, and interactions within the sample; and (iii) hyperspectral and multispectral imaging (HSI-MSI) which combines vibrational spectroscopy and computer vision in which the latter mimics the human vision [152]. The obtained datasets from these sensors are too difficultly handled; therefore, chemometrics and artificial intelligence-based technologies, particularly machine learning and evolutionary computational methods, are extensively used to model and visualize the derived data [153–156]. Machine learning, including unsupervised, semi-supervised, reinforced, or supervised learning, creates models that use algorithms for recognition, classification, and prediction of the data feedback [154,155]. Spectroscopic and multispectral imaging technologies coupled or not with machine learning were applied for the assessment of the quality/microbiological spoilage of ready-to-eat pineapple [157], ready-to-eat leafy vegetables [158], edible seaweeds [159], minced pork [160], minced meat [161], European sea bass fillets [162], gilthead sea bream fillets [163], shrimp [164] as well as the assessment of microbial contamination of ready-to-eat green salads [165]. The establishment of such intelligent technologies is now needed as never before, since we have to tackle the new challenges of the 21st century e.g., climate change that can affect safety and quality of seafood.

29.3.5 Monitoring of Fish Spoilage by TTIs and Colorimetric Sensors The advances in food packaging, especially smart or intelligent packaging, where suitable sensors or other devices can be placed on the package, can provide information about spoilage/freshness status of seafood. Such applications are the use of TTIs (time-temperatureindicators or Integrators) or colorimetric sensors. Both approaches have the advantage of non-destructive, easy, and instantaneous assessment of freshness/spoilage status. TTI is a device that depicts time-temperature dependent change, usually as irreversible color change, that corresponds to the time-temperature history of the product. The mechanism is based on mechanical, chemical, electrochemical, enzymatic, or microbiological change [166]. The change rate has to match the spoilage rate of the product in the corresponding time-temperature profile. There are many scientific papers and quite few reviews in the literature that deal with the implementation and applicability of various types of TTIs, while some TTIs can be found in the

Detection of Fish Spoilage

573

market [167–169]. However, there are still a few problems with TTIs and their applicability in the food distribution chain. Temperature abuses prior to the packaging will not be taken into account by the TTIs, since they provide indication of freshness/spoilage status and the remaining shelf life based on the temperature fluctuation and do not provide an indication of the real quality of the product. Another approach is the application of chemo-sensitive compounds for the development of colorimetric sensors that can detect spoilage compounds and hence provide an indication of actual freshness/spoilage status. Those compounds react with some of the volatile compounds that are chemical spoilage indices, and this reaction can be detected usually as color change. The simplest way is the detection of basic volatiles such as ammonia (NH3), trimethylamine (TMA), dimethylamine (DMA), and other amines by using pH-sensitive dyes. If amines and ammonia are produced during fish spoilage, the concentration TVB-N in the headspace is likely to be sufficiently high so that it can be detected by various types of colorimetric sensors [170–172]. The advances of such approach have been enhanced in last years with various chemo-sensitive compounds to be used embedded in various materials [173–176].

29.3.6 Predicting Fish Spoilage The basic question to be addressed among scientists dealing with predicting modeling is “how wrong do they have to not to be useful?” Mathematical models have been developed for the quantification of parameters, e.g., temperature, aw, pH, and carbon dioxide that affect growth and survival of spoilage bacteria such as B. thermosphacta, lactic acid bacteria, P. phosphoreum, pseudomonads, S. putrefaciens, Listeria monocytogenes, and Salmonella in muscle foods [177–186]. However, the application of these models has not been focused on monitoring muscle food quality per se, but mainly as a management tool for shelf life and safety prediction and as a scientific tool to gain insight into muscle food spoilage. Different models (kinetic or stochastic) have been developed for various muscle products, but an accurate prediction of shelf life is not attainable yet [187–193]. There are limited successfully validated models for predicting the growth of ESOs that have been included in application software, and this has facilitated the prediction of food shelf-life under constant and dynamic temperature

TABLE 29.4 Available Shareware Software for Fish Spoilage Seafood spoilage and safety predictor (shelf life of seafoods and growth of ESOs; Listeria monocytogenes in coldsmoked salmon) Dalgaard et al. [194]. Safety monitoring and assurance system (Koutsoumanis et al. [189,195]) (Greek predictive microbiology application software under development); software is based on kinetic data of spoilage bacteria derived from fish, meat, and milk in situ. Gri-Gri*; shelf life modeling of fish stored under various storage conditions (*Greek for the fishermen setting sail in the evening) Software produced by K. Koutsoumanis (kkkoutsou@ auth.agr.gr) within G Nychas’s group ([email protected]) of the Agricultural University of Athens, Greece. Fish Shelf Life Prediction Program (FSLP) and “FISHMAP” program: These programs are property of AZTI and had been developed by the Institute of Food Research, Norwich, UK in collaboration with AZTI. https://www.azti.es/en/ servicios/fishmap-fslp-fish-shelf life-prediction-program/ Other available software not specifically focused on fish GrowthPredictor (UK): www.ifr.ac.uk/Safety/GrowthPredictor/(based on data previously used in the FoodMicromodel software; 18 models for growth of pathogenic bacteria; available free of charge). Sym’Previus: www.symprevius.net (French predictive microbiology application software under development). (Continued)

574

Handbook of Seafood and Seafood Products Analysis

TABLE 29.4 (Continued) Available Shareware Software for Fish Spoilage Pathogen Modelling Program (USA): www.arserrc.gov/mfs/pathogen.htm (37 models of growth, survival, and inactivation; frequently updated (version 7.0); Available free of charge during the last 15 years; ~5,000 downloads per year. ComBase: Combined database for predictive food microbiology (a collaboration between the University of Tasmania and the USDA Agricultural Research Service (USDA-ARS)) - the world’s largest and freely-accessible database that describes how pathogens and spoilage organisms respond to diverse food environments. Its main features are the ComBase database and ComBase models. Link to tool https://www.combase.cc/index.php/en/ [196] Other available software focused on quality and safety in general Histamine sampling tool: FAO and WHO have developed a tool to support decision-making related to the establishment and/or use of sampling plans for detection of histamine. Link to tool http://tools.fstools.org/histamine/ [196] Database for flavoring specifications: This database provides the most recent specifications for flavorings evaluated by JECFA permits searches for specific flavorings using six different criteria (flavoring name or synonym, alphabetical browsing, JECFA number, CAS number, FEMA number, and structural group). Link to tool http://www.fao.org/food/food-safety-quality/scientific-advice/jecfa/jecfa-flav/en/ [196] Database for food additives specifications: This database provides the most recent specifications for food additives evaluated by JECFA and permits searches for specific additives using five criteria (additive name, browsing an alphabetical, INS number, CAS number, functional use). Link to tool http://www.fao.org/food/food-safety-quality/scientific-advice/jecfa/jecfa-additives/en/ [196] Database for residues of veterinary drugs: This database contains the most recent information on veterinary drugs and their residues in foods evaluated by JECFA and allows searches using four criteria (Keyword, Functional Class, Acceptable daily intake (ADI) status, Maximum residue level (MRL) status). Link to tool http://www.fao.org/food/food-safety-quality/scientific-advice/jecfa/jecfa-vetdrugs/en/ [196] Microbiological sampling plan analysis tool: This tool focuses on the elimination of lots deemed unacceptable in accordance with the specified sampling plan; estimates the risk reduction that is obtained as a result of this removal of unacceptable product from the exposure pathway; explore the impact of two- and three-class sampling plans (including both presence/absence and concentration-based) in terms of the likelihood to detect and therefore reject product not meeting the microbiological criterion; estimate the risk reduction associated with this removal of unacceptable product from the exposure pathway. Link to tool http://www.fstools.org/sampling/ [196] Mycotoxin sampling tool: This tool provides support in analyzing performance of sampling plans and determining the most appropriate mycotoxin sampling plan to minimize risk of misclassifying lots. The mycotoxin sampling tool is based on a model developed by Dr. Thomas Whitaker and Mr. Andrew Slate during 46 years of mycotoxin sampling research with USDA-ARS and North Carolina State University. Link to tool http://tools.fstools.org/mycotoxins/ [196] Risk assessment for Cronobacter sakazakii in powdered infant formula: This tool describes the effect that each of the preparation and storage stages has upon the intrinsic microbiological quality of the PIF in terms of C. sakazakii. Assesses the impact of different end product sampling regimes on risk reduction and product rejection Assesses the impact of control measures taken during reconstitution of powdered infant formula on risk reduction. Link to tool http://tools.fstools.org/esakmodel/ESAKRAModelWizard.aspx [196] Risk management tool for the control of Campylobacter and Salmonella in chicken meat: This tool is based on the Codex Guidelines (CAC/GL 78-2011) for the control of Campylobacter and Salmonella in chicken meat link to tool http:// tools.fstools.org/poultryRMTool/ [196] Risk ranger: a simple food safety risk calculation tool: This is a simple and accessible food safety risk calculation tool intended to help determine relative risks from various product/pathogen/processing combinations and is presented in Microsoft® Excel spreadsheet software. The tool is available free of charge from Australia’s food safety information portal. Link to tool http://www.foodsafetycentre.com.au/riskranger.php [196] (Continued)

Detection of Fish Spoilage

575

TABLE 29.4 (Continued) Available Shareware Software for Fish Spoilage Statistical tool for data analysis and elaboration of draft maximum residue limits for veterinary drugs (MRLVDs) in edible tissues: This is a statistical tool for experts who prepare working papers for the FAO/WHO joint expert committee on food additives (JECFA). It may be used in cases where a statistical approach appears to be the method of first choice when elaborating MRLs. Tool: www.fao.org/fileadmin/user_upload/agns/zip/tool_1.1551.zip Examples: www.fao.org/fileadmin/user_upload/agns/zip/examples.zip Documentation: www.fao.org/fileadmin/user_upload/agns/zip/handbook.zip [196] WiseFish supports the entire value chain from sourcing to plate. It offers product traceability, implementation of quality standards, and automated flow of information. https://www.wisefish.com/ iNECTA Seafood is an ERP solution designed specifically for the seafood industry. iNECTA Seafood leverages state-ofthe-art technology from Microsoft. https://www.inecta.com/seafood Sources: [194–198].

storage conditions (Table 29.4). Measurement of enzyme synthesis and activity could be used to estimate shelf-life, offering a completely different modeling approach [199]. The quality of the fish depends on how far the normal spoilage processes have progressed. These processes cannot be stopped, but the rate at which they occur can be controlled. The most important thing is to control the storage temperature—fresh, unfrozen fish should be kept as close as possible to 0°C, so the best way to do that is to pack fish in ice or ice water. The prediction can mean acidification or the addition of preservatives like sorbate and benzoate. Also, drying or heavy drysalting of fish eliminates bacterial growth. Recently, preservation of fish shelf-life has relied on the elimination or growth inhibition of spoilage organisms because the growth of bacteria is exponential. Respiratory Gram-negative bacteria are typically inhibited in fish products preserved by the addition of low levels of NaCl, a slight acidification, and chill storage in vacuum packs. Under these conditions, the microflora typically becomes dominated by Lactobacillus spp. and Carnobacterium spp. with an association of Gram-negative fermentative bacteria such as P. phosphoreum and psychotropic Enterobacteriaceae. Growth of the ESOs can help in prediction from fish spoilage, but the technique that identifies the bacteria must be capable of detecting as little as 102 ESO/g of product. Shelf-life can be predicted as the time ESO required to multiply from initial number to the minimal spoilage level (MLS). An example of the ESO’s technique is the CO2 packing of fresh fish. The respiratory spoilage bacteria (Shewanella and Pseudomonas) are inhibited, and the shelf life is markedly extended [12,197].

29.4 CONCLUSIONS The major cause of seafood spoilage is microbial growth and metabolism resulting in the formation of a plethora of volatile organic compounds such as amines, sulfides, alcohols, aldehydes, ketones, esters, and organic acids with unpleasant and unacceptable off-flavors. To determine the level of fish spoilage, sensory and microbiological analyses are most widely used. Sensory analysis is appropriate for product development but is costly and therefore not an attractive proposition for routine analyses. Microbiological methods give retrospective information which is satisfactory for product safety validation but unsuitable for product monitoring. Even when the SSOs/ESOs are well established, there are instances where their enumeration is of limited value. Combining microbiological parameters, molecular biology techniques, analytical chemistry, sensory analysis, and mathematical modeling, food scientists develop methods to determine, predict, and extend the shelf life of products. However, stakeholders now require faster, more accurate,

576

Handbook of Seafood and Seafood Products Analysis

and cost-effective methods to assess quality of seafood. For this reason, the costly, laborious, and time-consuming invasive methodologies need to be replaced by others that will give feedback on the seafood quality status in an extremely short time. Chemo-sensitive compounds for use in colorimetric sensors or TTIs are an option. Furthermore, biomimetic sensors, vibrational spectroscopy (Infrared and Raman Spectroscopy), and hyperspectral and multispectral imaging followed by datasets handling using chemometrics and artificial intelligence-based technologies, particularly machine learning and evolutionary computational methods, are now used to meet stakeholders’ demands. The establishment of such intelligent technologies is now needed as never before, since we have to tackle the new challenges of the 21st century e.g., climate change, that can affect quality and safety of seafood.

REFERENCES

1. Hernández Palacios, M.R. 2001. Study of the Quality Management System and Product Traceability in a Fish Processing Company. The United Nations University, Fisheries Training Programme, Iceland. 2. Connell, J.J. 1995. Control of Fish Quality, 4th edn. Fishing News Books, Oxford, U.K. 3. Gill, T.A. 1992. Biochemical and chemical indices of seafood quality. In Quality Assurance in the Fish Industry. H.H. Huss, M. Jakobsen, and J. Liston (eds.), pp. 377–398, Elsevier, Amsterdam, London, New York, and Tokyo. 4. Drosinos, E.H. and Nychas, G.-J.E. 1996. Brochothrix thermosphacta, a dominant microorganism in Mediterranean fresh fish (Sparus aurata) stored under modified atmosphere. Italian Journal of Food Science 4: 323. 5. Herbert, R.A., Hendrie, M.S., Gibson, D.M., and Shewan, J.M. 1971. Bacteria active in the spoilage of certain sea foods. Journal of Applied Bacteriology 34: 41. 6. Hozbor, M.C., Saiz, A.I., Yeannes, M.I., and Fritz, R. 2006. Microbiological changes and its correlation with quality indices during aerobic iced storage of sea salmon (Pseudopercis semifasciata). LWT - Food Science and Technology 39(2): 99–104. 7. Koutsoumanis, K. and Nychas, G.-J.E. 1999. Chemical and sensory changes associated with microbial flora of Mediterranean boque (Boops boops) stored aerobically at 0, 3, 7 and 10°C. Applied and Environmental Microbiology 65: 698. 8. Koutsoumanis, K. and Nychas, G.-J.E. 2000. Application of a systematic experimental procedure in order to develop a microbial model for rapid fish shelf life predictions. International Journal of Food Microbiology 60: 171. 9. Nychas, G.-J.E., Marshall, D., and Sofos, J. 2007. Meat poultry and seafood. In Food Microbiology Fundamentals and Frontiers, 3rd edn. M.P. Doyle, L.R. Beuchat, and T.J. Montville (eds.), pp. 105–140, ASM Press, Washington, DC. 10. Huss, H.H. 1995. Quality and Quality Changes in Fresh Fish. FAO Fisheries Technical Paper, No 348. FAO, Rome, Italy. 11. Olafsdóttir, G., Martinsdottir, E., Oehlenschlager, J., Dalgaard, P., Jensen, B., Undeland, I., Mackie, I.M., Henehan, G., Nielsen, J., and Nielsen, H. 1997. Methods to evaluate fish freshness in research and industry. Trends in Food Science and Technology 8: 258. 12. Gram, L. and Dalgaard, P. 2002. Fish spoilage bacteria - Problems and solutions. Current Opinion in Biotechnology 13: 262. 13. Gram, L. and Huss, H.H. 2000. Fresh and processed fish and shellfish. In The Microbiological Safety and Quality of Food. B.M. Lund, T.C. Baird-Parker, and G.W. Gould (eds.), pp. 472–502, Aspen Publishers, Gaithersburg, MD. 14. Olafsdóttir, G., Lauzon, H.L., Martinsdottir, E., and Kristbergsson, K. 2006. Influence of storage temperature on microbial spoilage characteristics of haddock fillets (Melanogrammus aeglefinus) evaluated by multivariate quality prediction. International Journal of Food Microbiology 111: 112. 15. Luten, J.B., Oehlenschlager, J., and Olafsdóttir, G. 2003. Quality of Fish from Catch to Consumer; Labelling, Monitoring and Traceability. Wageningen Academic Publishers, Wageningen, the Netherlands. 16. Gram, L. and Huss, H.H. 1996. Microbiological spoilage of fish and fish products. International Journal of Food Microbiology 33: 121–137. 17. Parlapani, F.F. 2021. Microbial diversity of seafood. Current Opinion in Food Science 37: 45–51.

Detection of Fish Spoilage

577

18. Parlapani, F.F., Boziaris, I.S., and DeWitt, C.Μ. 2022. Pathogens and their sources in freshwater fish, sea finfish, shellfish and algae. In Present Knowledge in Food Safety: A Risk-Based Approach Through the Food Chain, 1st edn. M. Knowles, L. Anelich, A. Boobis, and B. Popping (eds.), Elsevier, UK, pp. 471–492. 19. Boziaris, I.S. and Parlapani, F.F. 2014. Microbiological examination of seafood. In Seafood Processing. Technology, Quality and Safety. I.S. Boziaris (ed.), Wiley-Blackwell, IFST Advances in Food Science Series, pp. 387–418. 20. Tryfinopoulou, P., Tsakalidou, E., and Nychas, G.-J.E. 2002. Characterization of Pseudomonas spp. associated with spoilage of gilt-head sea bream stored under various conditions. Applied and Environmental Microbiology 68: 65–72. 21. Gram, L., Wedell-Neergaard, C., and Huss, H.H. 1990. The bacteriology of fresh and spoiling Lake Victorian Nile perch (Lates niloticus). International Journal of Food Microbiology 10: 303–316. 22. Alfaro, B. and Hernandez, I. 2013. Evolution of the indigenous microbiota in modified atmosphere packaged Atlantic horse mackerel (Trachurus trachurus) identified by conventional and molecular methods. International Journal of Food Microbiology 167: 117–123. 23. Broekaert, K., Heyndrickx, M., Herman, L., Devlieghere, F., and Vlaemynck, G. 2013. Molecular identification of the microbiota of peeled and unpeeled brown shrimp (Crangon crangon) during storage on ice and at 7.5°C. Food Microbiology 36(2): 123–134. doi:10.1016/j.fm.2013.04.009. 24. Rudi, K., Maugesten, T., Hannevik, S.E., and Nissen, H. 2004. Explorative multivariate analyses of 16S rRNA gene data from microbial communities in modified-atmosphere-packed salmon and coalfish. Applied and Environmental Microbiology 70: 5010–5018. 25. Svanevik, C.S. and Lunestad, B.T. 2011. Characterisation of the microbiota of Atlantic mackerel (Scomber scombrus). International Journal of Food Microbiology 151(2): 164–170. doi:10.1016/j. ijfoodmicro.2011.08.016. 26. Parlapani, F.F., Kormas, K.A., and Boziaris, I.S. 2015. Microbiological changes, shelf life and identification of initial and spoilage microbiota of sea bream fillets stored under various conditions using 16S rRNA gene analysis. Journal of the Science of Food and Agriculture 95: 2386–2394. 27. Syropoulou, F., Parlapani, F.F., Bosmali, I., Madesis, P., and Boziaris, I.S. 2020. HRM and 16S rRNA gene sequencing reveal the cultivable microbiota of the European sea bass during ice storage. International Journal of Food Microbiology 327: 108658. 28. Parlapani, F.F., Michailidou, S., Pasentsis, K., Argiriou, A., Krey, G., and Boziaris, Ι.S. 2018. A metabarcoding approach to assess and compare the storage temperature-dependent bacterial diversity of gilt-head sea bream (Sparus aurata) originating from fish farms from two geographically distinct areas of Greece. International Journal of Food Microbiology 278: 36–43. 29. Anagnostopoulos, D.A., Parlapani, F.F., and Boziaris, I.S. 2022. The evolution of knowledge on seafood spoilage microbiota from the 20st to the 21st century: Have we finished or just begun? Trends in Food Science and Technology 120: 236–247. 30. Zotta, T., Parente, E., Ianniello, R.G., De Filippis, F., and Ricciardi, A. 2019. Dynamics of bacterial communities and interaction networks in thawed fish fillets during chilled storage in air. International Journal of Food Microbiology 293: 102–113. 31. Nychas, G.E., Skandamis, P.N., Tassou, C.C., and Koutsoumanis, K.P. 2008. Meat spoilage during distribution. Meat Science 78: 77–89. 32. Syropoulou, F., Parlapani, F.F., Kakasis, S., Nychas, G.-J.E., and Boziaris, I.S. 2021. Primary processing and storage affect the dominant microbiota of fresh and chill-stored sea bass products. Foods 10: 671 33. Pantazi, D., Papavergou, A., Pournis, N., Kontominas, M.G., and Savvaidis, I.N. 2008. Shelf-life of chilled fresh Mediterranean swordfish (Xiphias gladius) stored under various packaging conditions: Microbiological, biochemical and sensory attributes. Food Microbiology 25(1): 136–143. 34. Dalgaard, P. 2000. Fresh and lightly preserved seafood. In Shelf-Life Evaluation of Foods. C.M.D. Man and A.A. Jones (eds.), pp. 110–139, Aspen Publishers, London. 35. Dalgaard, P., Gram, L., and Huss, H.H. 1993. Spoilage and shelf life of cod fillets packed in vacuum or modified atmospheres. International Journal of Food Microbiology 19: 283–294. 36. Emborg, J., Laursen, B.G., Rathjen, T., and Dalgaard, P. 2002. Microbial spoilage and formation of biogenic amines in fresh and thawed modified atmosphere-packed salmon (Salmo salar) at 2°C. Journal of Applied Microbiology 92: 790–799. 37. Gennari, M., Tomaselli, S., and Cotrona, V. 1999. The microflora of fresh and spoiled sardines (Sardina pilchardus) caught in Adriatic (Mediterranean) Sea and stored in ice. Food Microbiology 16: 15–28.

578

Handbook of Seafood and Seafood Products Analysis

38. Gram, L. and Huss, H.H. 2000. Fresh and processed fish and shellfish. In The Microbiological Safety and Quality of Food. B.M. Lund, T. C. Baird-Parker and G. W. Gould (eds.), pp. 472–506, Aspen Publishers, Gaithersburg, MD. 39. Gram, L. and Dalgaard, P. 2005. Fish spoilage bacteria--Problems and solutions. Current Opinion in Biotechnology 13: 262–262; Vogel, B.F. Venkateswaran, K., Satomi, M., Gram, L. 2005. Applied and Environmental Microbiology 71: 6689. 40. Ekonomou, S.I., Parlapani, F.F, Kyritsi, M., Hadjichristodoulou, C., and Boziaris, I.S. 2022. Preservation status and microbial communities of vacuum-packed hot smoked rainbow trout fillets. Food Microbiology 103: 103959. doi:10.1016/j.fm.2021.103959. 41. Boziaris, I.S. and Parlapani, F.F. 2017. Specific spoilage organisms (SSO) in fish. In Microbiological Quality of Food: Foodborne Spoilers. A. Bevilacqua, M.R. Corbo, M. Sinigaglia, and R. Sykes (eds.), pp. 60–98, Elsevier, Woodhead Publishing, UK. 42. Huss, H.H. 1995. Assurance of Seafood Quality, FAO Fisheries Technical Paper, No. 334. United Nations Food and Agriculture Organization, Rome, Italy. 43. Leroi, F. 2010. Occurrence and role of lactic acid bacteria in seafood products. Food Microbiology 27(6): 698–709. doi:10.1016/j.fm.2010.05.016. 44. Parlapani, F.F. and Boziaris, I.S. 2016. Monitoring of spoilage status and determination of microbial communities based on 16S rRNA gene sequence analysis of whole sea bream stored at various temperatures. LWT - Food Science and Technology 66: 553–559. 45. Parlapani, F.F., Verdos, G.I., Haroutounian, S.A., and Boziaris, I.S. 2015. The dynamics of pseudomonas and volatilome during the spoilage of gutted sea bream stored at 2°C. Food Control 55: 257–265. 46. Syropoulou, F., Parlapani, F.F., Anagnostopoulos, D.A., Stamatiou, A., Mallouchos, A., and Boziaris, I.S. 2021. Spoilage investigation of chill stored meagre (Argyrosomus regius) using modern microbiological and analytical techniques. Foods 10: 3109. 47. Duan, S., Zhou, X., Xiao, H., Miao, J., and Zhao, L. 2019. Characterization of bacterial microbiota in tilapia fillets under different storage temperatures. Journal of Food Science 84: 1487–1493. 48. Paarup, T., Sanchez, J.A., Moral, A., Christensen, H., Bisgaard, M., and Gram, L. 2002. Sensory, chemical and bacteriological changes during storage of iced squid (Todaropsis eblanae). Journal of Applied Microbiology 92: 941–950. 49. Tryfinopoulou, P., Tsakalidou, E., Vancanneyt, M., Hoste, B., Swings, J., and Nychas, G.-J.E. 2007. Diversity of Shewanella population in fish Sparus aurata harvested in the Aegean Sea. Journal of Applied Microbiology 103: 711–721. 50. Parlapani, F.F., Syropoulou, F., Tsiartsafis, A., Ekonomou, S., Madesis, P., Exadactylos, A., and Boziaris, I.S. 2020. HRM analysis as a tool to facilitate identification of bacteria from mussels during storage at 4°C. Food Microbiology 85: 103304. 51. Parlapani, F.F., Ferrocino, I., Michailidou, S., Argiriou, A., Haroutounian, S.A., Kokokiris, L., Rantsiou, K., and Boziaris, I.S. 2020. Microbiota and volatilome profile of fresh and chill-stored deepwater rose shrimp (Parapenaeus longirostris). Food Research International 132: 109057. 52. Parlapani, F.F., Michailidou, S., Anagnostopoulos, D.A., Sakellariou, A.K., Pasentsis, K., Psomopoulos, F., Argiriou, A., Haroutounian, S.A., and Boziaris, I.S. 2018. Microbial spoilage investigation of thawed common cuttlefish (Sepia officinalis) stored at 2°C using next generation sequencing and volatilome analysis. Food Microbiology 76: 518–525. 53. Chen, H., Wang, M., Yang, C., Wan, X., Ding, H.H., Shi, Y., and Zhao, C. 2019. Bacterial spoilage profiles in the gills of Pacific oysters (Crassostrea gigas) and Eastern oysters (C. virginica) during refrigerated storage. Food Microbiology 82: 209–217. 54. Broekaert, K., Noseda, B., Heyndrickx, M., Vlaemynck, G., and Devlieghere, F. 2013. Volatile compounds associated with Psychrobacter spp. and Pseudoalteromonas spp., the dominant microbiota of brown shrimp (Crangon crangon) during aerobic storage. International Journal of Food Microbiology 166: 487–493. 55. Bekaert, K., Devriese, L., Maes, S., and Robbens, J. 2015. Characterization of the dominant bacterial communities during storage of Norway lobster and Norway lobster tails (Nephrops norvegicus) based on 16S rDNA analysis by PCR-DGGE. Food Microbiology 46: 132–138. 56. Lambert, A.D., Smith, J.P., and Dodds, K.L. 1991. Shelf life extension and microbiological safety of fresh meat - A review. Food Microbiology 8: 267–297. 57. Jay, J.M. 2000. Modern Food Microbiology. Aspen Publishers, Gaithersburg, MD. 58. Olafsdóttir, G., Jonsdottir, R., Lauzon, H.L., Luten, J., and Kristbergsson, K. 2005. Characterization of volatile compounds in chilled cod (Cadus morhua) fillets by gas chromatography and detection of quality indicators by an electronic nose. Journal of Agricultural and Food Chemistry 53: 10140.

Detection of Fish Spoilage

579

59. Dalgaard, P. 1995. Qualitative and quantitative characterization of spoilage bacteria from packed fish. International Journal of Food Microbiology 26: 319–333. 60. Dalgaard, P., Mejlholm, O., and Huss, H.H. 1996. Conductance method for quantitative determination of photobacterium phosphoreum in fish products. Journal of Applied Bacteriology 81: 57–64. 61. Chinivasagam, H.N., Bremner, H.A., Wood, A.F., and Nottingham, S.M. 1998. Volatile components associated with bacterial spoilage of tropical prawns. International Journal of Food Microbiology 42: 45–55. 62. Joffraud, J.J., Leroi, F., Roy, C., and Berdague, J.L. 2001. Characterisation of volatile compounds produced by bacteria isolated from the spoilage flora of cold-smoked salmon. International Journal of Food Microbiology 66: 175–181. 63. Laursen, B.G., Leisner, J.J., and Dalgaard, P. 2006. Carnobacterium species: Effect of metabolic activity and interaction with Brochothrix thermosphacta on sensory characteristics of modified atmosphere packed shrimp. Journal of Agricultural and Food Chemistry 54, 3604–3611. 64. Miller, III, A., Scanlan, R.A., Lee, J.S., and Libbey, L.M. 1973. Identification of the volatile compounds produced in sterile fish muscle (Sebastes melanops) by Pseudomonas fragi. Applied Microbiology 25: 952–955. 65. Miller, III, A., Scanlan, R.A., Lee, J.S., and Libbey, L.M. 1973. Volatile compounds produced in sterile fish muscle (Sebastes melanops) by Pseudomonas putrefaciens, Pseudomonas fluorescens, and an Achromobacter species. Applied Microbiology 26: 18–21. 66. Parlapani, F.F., Malouchos, A., Haroutounian, S.A., and Boziaris, I.S. 2017. Microbial and non-microbial origin volatile organic compounds produced on model fish substrate inoculated or not with gilt-head sea bream spoilage bacteria. LWT - Food Science and Technology 78: 54–62. 67. Anagnostopoulos, D.A., Parlapani, F.F., Mallouchos, A., Angelidou, A., Syropoulou, F., Minos, G., and Boziaris, I.S. 2022. Volatile organic compounds and 16S metabarcoding in ice-stored red seabream Pagrus major. Foods 11: 666. 68. Jonsdottir, R., Olafsdottir, G., Chanie, E., and Haugen, J.E. 2008. Volatile compounds suitable for rapid detection as quality indicators of cold smoked salmon (Salmo salar). Food Chemistry 109: 184–195. 69. Jørgensen, B.R. and Huss, H.H. 1989. Growth and activity of Shewanella putrefaciens isolated from spoiling fish. International Journal of Food Microbiology 9: 51–62. 70. Parlapani, F.F., Malouchos, A., Haroutounian, S.A., and Boziaris, I.S. 2014. Microbiological spoilage and investigation of volatile profile during storage of sea bream fillets under various conditions. International Journal of Food Microbiology 189: 153–163. 71. Parlapani, F.F., Haroutounian, S.A., Nychas, G.-J.E., and Boziaris, I.S. 2015. Microbiological spoilage and volatiles production of gutted European sea bass stored under air and commercial modified atmosphere package at 2°C. Food Microbiology 50: 44–53. 72. Parlapani, F.F., Anagnostopoulos, D.A., Koromilas, S., Kios, K., Michailidou, S., Pasentsis, K., Psomopoulos, F., Argiriou, A., Haroutounian, S.A., and Boziaris, I.S. 2019. Bacterial communities and potential spoilage markers of whole Blue Crab (Callinectes sapidus) stored under commercial simulated conditions. Food Microbiology 82: 325–333. 73. Kuuliala, L., Al Hage, Y., Ioannidis, A.-G., Sader, M., Kerckhof, F.-M., Vanderroost, M., Boon, N., De Baets, B., De Meulenaer, B., Ragaert, P. and Devlieghere, F. 2018. Microbiological, chemical and sensory spoilage analysis of raw Atlantic cod (Gadus morhua) stored under modified atmospheres. Food Microbiology 70: 232–244. 74. Bover-Cid, S., Hernández-Jover, T., Miguélez-Arrizado, M.J., and Carmen Vidal-Carou, M. 2003. Contribution of contaminant enterobacteria and lactic acid bacteria to biogenic amine accumulation in spontaneous fermentation of pork sausages. European Food Research and Technology 216: 477–482. doi:10.1007/s00217-003-0691-6. 75. Chaouqy, N.E., El Marrakchi, A., and Zekhnini, A. 2005. Bacteria active in the spoilage of anchovy (engraulis encrasicholus) stored in ice and at ambient temperature. [Bactéries actives dans l’altération de l’anchois (Engraulis encrasicholus) entreposé sous glace et à température ambiante]. Sciences Des Aliments 25(2): 129–146. doi:10.3166/sda.25.129-146. 76. Edwards, R., Dainty, R., Hibbard, C., and Ramantanis, S. 1987. Amines in fresh beef of normal pH and the role of bacteria in changes in concentration observed during storage in vacuum packs at chill temperatures. Journal of Applied Bacteriology 63: 427–434. doi:10.1111/j.1365-2672.1987.tb04864.x. 77. Emborg, J., Laursen, B.G., and Dalgaard, P. 2005. Significant histamine formation in tuna (Thunnus albacares) at 2°C--Effect of vacuum- and modified atmosphere-packaging on psychrotolerant bacteria. International Journal of Food Microbiology 101(3): 263–279. doi:10.1016/j.ijfoodmicro.2004.12.001. PMID: 15925710.

580

Handbook of Seafood and Seafood Products Analysis

78. Karpas, Z., Tilman, B., Gdalevsky, R., and Lorber, A. 2002. Determination of volatile biogenic amines in muscle food products by ion mobility spectrometry. Analytica Chimica Acta 463(2): 155–163. doi:10.1016/S0003-2670(02)00378-1. 79. López-Caballero, M.E., Álvarez Torres, M.D., Sánchez-Fernández, J.A., and Moral, A. (2002). Photobacterium phosphoreum isolated as a luminescent colony from spoiled fish, cultured in model system under controlled atmospheres. European Food Research and Technology 215(5): 390–395. doi:10.1007/s00217-002-0575-1. 80. Luten, J.B., Bouquet, W., Seuren, L.A.J., Burggraaf, M.M., Riekwel-Booy, G., Durand, P., Etienne, M., Gouyou, J.P., Landrein, A., Ritchie, A., Leclerq, M. and Guinet, R. 1992. Biogenic amines in fishery products: standardization methods within E.C. In: Quality Assurance in the Fish Industry, H.H. Huss, M. Jakobsen and J. Liston (eds.). Elsevier Science Publishers B.V., Amsterdam, pp. 427–439 81. Veciana-Nogués, M.T., Mariné-Font, A., and Vidal-Carou, M.C. 1997. Biogenic amines as hygienic quality indicators of tuna. Relationships with microbial counts, ATP-related compounds, volatile amines, and organoleptic changes. Journal of Agricultural and Food Chemistry 45: 2036. 82. Hernández-Jover, T., Izquierdo-Pulido, M., Veciana-Nogués, M.T., Mariné-Font, A., and Vidal-Carou, M.C. 1997. Biogenic amine and polyamine contents in meat and meat products. Journal of Agricultural and Food Chemistry 45: 2780–2784. 83. Basavakumar, K., Karunasagar, I., and Karunasagar, I. 1992. Ability of fish associated bacteria to produce amines suspected to be involved in scombroid poisoning. Indian Journal of Microbiology 32: 75–79. 84. Moinard, C., Cynober, L., and Bandt, J.P. 2005. Polyamines: Metabolism and implications in human diseases. Clinical Nutrition 24: 184–197. 85. Prester, L. 2011. Biogenic amines in fish, fish products and shellfish: A review. Food Additives and Contaminants: Part A 28(11): 1547–1560. 86. Larqué, E, Sabater-Molina, M., and Zamora, S. 2007. Biological significance of dietary polyamines. Nutrition 23: 87–95. 87. Biji, K.B., Ravishankar, C.N., Venkateswarlu, R., Mohan, C.O., and Srinivasa Gopal, T.K. 2016. Biogenic amines in seafood: A review. Journal of Food Science and Technology 53: 2210–2218. 88. Doeun, D., Davaatseren, M., and Chung, M.S. 2017. Biogenic amines in foods. Food Science and Biotechnology 26: 1463–1474. 89. Xu, Y., Zang, J., Regenstein, J.M., and Xia, W. 2020. Technological roles of microorganisms in fish fermentation: A review. Critical Reviews in Food Science and Nutrition 61: 1–13. 90. Wunderlichová, L., Buňková, L., Koutný, M., Jančová, P., and Buňka, F. 2014. Formation, degradation, and detoxification of putrescine by foodborne bacteria: A review. Comprehensive Reviews in Food Science and Food Safety 13: 1012–1030. 91. Wang, H., Luo, Y., Huang, H., and Xu, Q. 2014. Microbial succession of grass carp (Ctenopharyngodon idellus) filets during storage at 4 C and its contribution to biogenic amines’ formation. International Journal of Food Microbiology 190: 66–71. 92. Marcobal, A., De Las Rivas, B., Landete, J.M., Tabera, L., and Muñoz, R. 2012. Tyramine and phenylethylamine biosynthesis by food bacteria. Critical Reviews in Food Science and Nutrition 52: 448–467. 93. Paleologos, E.K., Savvaidis, I.N., and Kontominas, M.G. 2004. Biogenic amines formation and its relation to microbiological and sensory attributes in ice-stored whole, gutted and filleted Mediterranean Sea bass (Dicentrarchus labrax). Food Microbiology 21: 549–557. 94. Kuley, E., Durmus, M., Balikci, E., Ucar, Y., Regenstein, J.M., and Özogul, F. 2017. Fish spoilage bacterial growth and their biogenic amine accumulation: Inhibitory effect of olive by-products. Intrenational Journal of Food Properties 20: 1029–1043. 95. Iglesias, J. and Medina, I. 2008. Solid-phase microextraction method for the determination of volatile compounds associated to oxidation of fish muscle. Journal of Chromatography A 1192: 9–16. 96. Kunyaboon, S., Thumanu, K., Park, J.W., Khongla, C., and Yongsawatdigul, J. 2021. Evaluation of lipid oxidation, volatile compounds and vibrational spectroscopy of silver carp (Hypophthalmichthys molitrix) during ice storage as related to the quality of its washed mince. Foods 10: 495. doi:10.3390/ foods10030495. 97. Belitz HD, Grosch W, Schieberle P. Lipids. In: Belitz HD, Grosch W, Schieberle P, (eds.). Food Chemistry. Berlin: Springer; 2009. pp. 158–247. 98. Rathod, N.B., Ranveer, R.C., Benjakul, S., Kim, S.K., Pagarkar, A.U., Patange, S., and Ozogul, F. 2021. Recent developments of natural antimicrobials and antioxidants on fish and fishery food products. Comprehensive Reviews in Food Science and Food Safety 20(4): 4182–4210.

Detection of Fish Spoilage

581

99. Ghaly, A.E., Dave, D., Budge, S., and Brooks, M.S. 2010. Fish spoilage mechanisms and preservation techniques. American Journal of Applied Sciences 7(7): 859. 100. Nie, X., Zhang, R., Cheng, L., Zhu, W., Li, S., and Chen, X. 2022. Mechanisms underlying the deterioration of fish quality after harvest and methods of preservation. Food Control 135: 108805. 101. Ashie, I.N.A., Smith, J.P., and Simpson, B.K. 1996. Spoilage and shelf-life extension of fresh fish and shellfish. Critical Reviews in Food Science and Nutrition 36: 87–121. 102. Boziaris, I.S., Kordila, A., and Neofitou, C. 2011. Microbial spoilage analysis and its effect on chemical changes and shelf-life of Norway lobster (Nephrops norvegicus) stored in air at various temperatures. International Journal of Food Science and Technology 46: 887–895. 103. Martinez-Alvarez, O., Lopez-Caballero, M.E., Montero, P., and Gomez-Guillen, M.C. 2007. Spraying of 4-hexylresorcinol based formulations to prevent enzymatic browning in Norway lobsters (Nephrops norvegicus) during chilled storage. Food Chemistry 100: 147–155. 104. Oehlenschläger, J. and Sörensen, N.-K. 1997. Criteria of seafish freshness and quality aspects in methods to determine the freshness of fish in research and industry. Proceedings of the Final Meeting of the Concerted Action “Evaluation of Fish Freshness”. International Institute of Refrigeration, 1997/11/12 Paris, France. 105. Haugen, J.E., Chanie, E., Westad, F., Jonsdottir, R., Bazzo, S., Labreche, S., Marcq, P., Lundby, F., and Olafsdóttir, G. 2006. Rapid control of smoked Atlantic salmon (Salmo salar) quality by electronic nose: Correlation with classical evaluation methods. Sensors and Actuators B 116: 72. 106. Dainty, R.H. 1996. Chemical biochemical detection of spoilage. International Journal of Food Microbiology 33: 19. 107. Howgate, P., Johnston, A., and Whittle, K.J. 1992. Multilingual Guide to EC Freshness Grades for Fishery Products. Marine Laboratory, Scottish Office of Agriculture, Environment and Fisheries Department, Aberdeen. 108. Olafsdóttir, G., Nesvadba, P., Di Natale, C., Careche, M., Oehlenschlager, J., Tryggvadóttir, S.V., Schubring, R., Kroeger, M., Heia, H., Esaiassen, M., Macagnano, A., and Jørgensen, B.M. 2004. Multisensor for fish quality determination. Trends in Food Science and Technology 15: 86. 109. Bernardo, Y.A.A., Rosario, D.K.A., Delgado, I.F., and Conte-Junior, C.A. 2020. Fish quality index method: Principles, weaknesses, validation, and alternatives-A review. Comprehensive Reviews in Food Science and Food Safety 19(5): 2657–2676. doi:10.1111/1541-4337.12600. 110. Gram, L., Trolle, G., and Huss, H.H. 1987. Detection of specific spoilage bacteria from fish stored at low (0°C) and high (20°C) temperatures. International Journal of Food Microbiology 4: 65. 111. Van Spreekens, K.J.A. 1974. The suitability of a modification of Long and Hammer’s medium for the enumeration of more fastidious bacteria from fresh fishery products. Archiv für Lebensmittelhygiene 25: 213–219. 112. ISO 2003. ISO 4833. Microbiology of Food and Animal Feeding Stuffs. Horizontal Method for the Enumeration of Microorganisms - Colony-Count Technique at 30°C. International Organization for Standardization, Geneva, Switzerland. 113. Kakasis, S., Parlapani, F.F., and Boziaris, I.S. 2011. Performance and selectivity of media used for the enumeration of bacterial populations on seafood. 33rd Scientific Conference of Hellenic Association for Biological Sciences, Edessa, 19–21 May. 114. Gram, L. 1992. Evaluation of the bacteriological quality of seafood. International Journal of Food Microbiology 16: 25–39. 115. Lopez-Caballero, M.E., Martinez-Alvarez, O., Gomez-Guillen, M.C., and Montero, P. 2006. Effect of natural compounds alternative to commercial antimelanosisc on polyphenol oxidase activity and microbial growth in cultured prawns (Marsupenaeous tiger) during chill storage. European Food Research Technology 223: 7–15. 116. Khan, M.A., Parrish, C.C., and Shahidi, F. 2005. Enumeration of total heterotrophic and psychrotrophic bacteria using different types of agar to evaluate the microbial quality of blue mussels (Mytilus edulis) and sea scallops (Placopecten magellanicus). Research International 38: 751–758. 117. Broekaert, K., Heyndrickx, M., Herman, L., Devlieghere, F., and Vlaemynck, G. 2011. Seafood quality analysis: Molecular identification of dominant microbiota after ice storage on several general growth media. Food Microbiology 28: 1162–1169. 118. Lee, J.-L. and Levin, R.E. 2007. Quantification of total viable bacteria on fish fillets by using ethidium bromide monoazide real-time polymerase chain reaction. International Journal of Food Microbiology 118: 312–317.

582

Handbook of Seafood and Seafood Products Analysis

119. Anagnostopoulos, D., Syropoulou, F., Kokioumi, D., Parlapani, F., and Boziaris, I. 2022. The use of qPCR for the rapid assessment of seafood freshness. FoodMicro2022, 27th International ICFMH Conference, Athens, Greece, 28–31 August, p. 216. 120. Reynisson, E., Lauzon, H.L., Magnusson, H., Hreggvidsson, G.O., and Marteinsson, V.T. 2008. Rapid quantitative monitoring method for the fish spoilage bacteria Pseudomonas. Journal of Environmental Monitoring 10: 1357–1362. 121. Duflos, G., Theraulaz, L., Giordano, G., Mejean, V., and Malle, P. 2010. Quantitative PCR method for evaluating freshness of whiting (Merlangius merlangus) and plaice (Pleuronectes platessa). Journal of Food Protection 73: 1344–1347. 122. Macé, S., Mamlouk, K., Chipchakova, S., Prévost, H., Joffraud, J.-J., Dalgaard, P., Pilet, M.-F., and Dousset, X. 2013b. Development of a rapid real-time PCR method as a tool to quantify viable photobacterium phosphoreum bacteria in salmon (Salmo salar) steaks. Applied and Environmental Microbiology 79: 2612–2619. 123. Gill, C.O. and Newton, K.G. 1977. The development of aerobic spoilage flora on meat stored at chill temperatures. Journal of Applied Microbiology 43: 189–195. 124. Castro, P., Padron, J.C.P., Cansino, M.J.C., Velazquez, E.S., and De Larriva, R.M. 2006. Total volatile base nitrogen and its use to assess freshness in European sea bass stored in ice. Food Control 17: 245–248. 125. Oehlenschläger, J. 2014. Seafood quality assessment. In Seafood Processing, IFST Advances in Food Science Series. Technology, Quality and Safety. I.S. Boziaris (ed.), pp. 361–386, Wiley-Blackwell, West Sussex, UK. 126. Kritikos, A., Aska, I., Economou, S., Mallouchos, A., Parlapani, F.F., Haroutounian, S.A., and Boziaris, I.S. 2020. Volatilome of chill-stored European seabass and Atlantic salmon products under modified atmosphere packaging. Molecules 25: 1981. 127. Jia, S., Li, Y., Zhuang, S., Sun, X., Zhang, L., Shi, J., Hong, H., and Luo, Y. 2019. Biochemical changes induced by dominant bacteria in chill-stored silver carp (Hypophthalmichthys molitrix) and GC-IMS identification of volatile organic compounds. Food Microbiology 84: 103248. doi:10.1016/j.fm.2019.103248. 128. Connell, J.J. and Shewan, J.M. 1980. Sensory and non-sensory assessment of fish. In Advances in Fish Science and Technology. J. Connell (ed.), p. 55556, Fishing News Books, Farnham. 129. Huss, H.H. 1988. Fresh Fish - Quality and Quality Changes. FAO, Danish International Development Agency, Rome, Sagraf. 130. Jørgensen, B.R., Gibson, D.M., and Huss, H.H. 1988. Microbiological quality and shelf life prediction of chilled fish. International Journal of Food Microbiology 6: 295–307. 131. Mendes, R., Huidobro, A., and Lopez-Caballero, M.E. 2002. Indole levels in deepwater pink shrimp (Parapenaeus longirostris) from the Portuguese coast. Effects of temperature abuse. European Food Research and Technology 214: 125–130. 132. Huang, Y.-R., Guevara Zelaya, M.F., and Shiau, C.-Y. 2016. Changes in biochemical compositions and quality of white shrimp (Litopenaeus vannamei) during storage. Journal of Qquatic Food Product Technology 25: 35–45. 133. Dabadé, D.S., den Besten, H.M.W., Azokpota, P., Nout, M.J.R., Hounhouigan, D.J., and Zwietering, M.H. 2015. Spoilage evaluation, shelf-life prediction, and potential spoilage organisms of tropical brackish water shrimp (Penaeus notialis) at different storage temperatures. Food Microbiology 48: 8–16. 134. Losada, V., Rodriguez, O., Miranda, J.M., Barros-Velazquez, J., and Aubourg, S.P. 2006. Development of different damage pathways in Norway lobster (Nephrops norvegicus) stored under different chilling systems. Journal of the Science of Food and Agriculture 86: 1552–1558. 135. Erkan, N. and Özden, Ö. 2006. Gutted and un-gutted sea bass (Dicentrarchus labrax) stored in ice: Influence on fish quality and shelf-life. International Journal of Food Properties 9: 331–345. 136. Erkan, N. and Özden, Ö. 2008. Quality assessment of whole and gutted sardines (Sardina pilchardus) stored in ice. International Journal of Food Science and Technology 43: 1549–1559. 137. Drosinos, E.H., Lambropoulou, K., Mitre, E., and Nychas, G.-J.E. 1997. Attributes of fresh gilt seabream (Sparus aurata) fillets treated with potassium sorbate, sodium gluconate and stored under a modified atmosphere at 0±1°C. Journal of Applied Microbiology 83: 569–575. 138. Reddy, N.R., Schreiber, C.L., Buzard, K.S., Skinner, G.E., and Armstrong, D.J. 1994. Shelf life of fresh tilapia fillets packaged in high barrier film with modified atmospheres. Journal of Food Science 59: 260–264. 139. Gonçalves, A.C., Antas, S.E., and Nunes, M.L. 2007. Freshness and quality criteria of iced farmed Senegalese sole (Solea senegalensis). Journal of Agriculture and Food Chemistry 55: 345–346.

Detection of Fish Spoilage

583

140. Tejada, M., Heras, C.D., and Kent, M. 2007. Changes in the quality indices during ice storage of farmed Senegalese sole (Solea senegalensis). European Food Research and Technology 225: 225–232. 141. Antonacopoulos, N., and Vyncke, W. 1989 Determination of volatile basic nitrogen in fish: A third collaborative study by the West European Fish Technologists' Association (WEFTA). Zeitschrift für Lebensmittel-Untersuchung und -Forschung, 189(4), pp. 309–316 142. Vyncke, W., Luten, J., Brünner, K., and Moermans, R. 1987. Determination of total volatile bases in fish: A collaborative study by the West European Fish Technologists’ Association (WEFTA). Zeitschrift für Lebensmittel-Untersuchung und Forschung 184: 110–114. 143. Conway, E.J. and O’malley, E. 1942. Microdiffusion methods. Ammonia and urea using buffered absorbents (revised methods for ranges greater than 10 μg. N). Biochemical Journal 36(7–9): 655–661. doi:10.1042/bj0360655. 144. Dyer, W.J. 1945. Amines in fish muscle. I. Calorimetric determination of trimethylamine as the picrate salt. Journal of the Fisheries Board of Canada 8: 314. 145. Olafsdottir, G., Steinke, J.A., and Lindsay, R.C. 1985. Quantitative performance of a simple tenax-GC adsorption method for use in the analysis of aroma volatiles. Journal of Food Science 50: 1431–1436. doi:10.1111/j.1365-2621.1985.tb10493.x. 146. Soncin, S., Chiesa, L.M., Panseri, S., Biondi, P., and Cantoni, C. 2009. Determination of volatile compounds of precooked prawn (Penaeus vannamei) and cultured gilthead sea bream (Sparus aurata) stored in ice as possible spoilage markers using solid phase microextraction and gas chromatography/mass spectrometry. Journal of the Science of Food and Agriculture 89: 436–442. doi:10.1002/jsfa.3466. 147. Duflos, G., Coin, V.M., Cornu, M., Antinelli, J.F., and Malle, P. 2006. Determination of volatile compounds to characterize fish spoilage using headspace/mass spectrometry and solid-phase microextraction/ gas chromatography/mass spectrometry. Journal of the Science of Food and Agriculture 86: 600–611. 148. Edirisinghe, R.K.B., Graham, A.J., and Taylor, S.J. 2007. Characterisation of the volatiles of yellowfin tuna (Thunnus albacares) during storage by solid phase microextraction and GC-MS and their relationship to fish quality parameters. International Journal of Food Science and Technology 42: 1139–1147. 149. Fratini, G., Lois, S., Pazos, M., Parisi, G., and Medina, I. 2012. Volatile profile of Atlantic shellfish species by HS-SPME GC/MS. Food Research International 48: 856–865. 150. Miks-Krajnik, M., Yoon, Y.-J., Ukuku, D.O., and Yuk, H.-G. 2016. Volatile chemical spoilage indexes of raw Atlantic salmon (Salmo salar) stored under aerobic condition in relation to microbiological and sensory shelf lives. Food Microbiology 53: 182–191. 151. Tuckey, N.P.L., Day, J.R., and Miller, M.R. 2013. Determination of volatile compounds in New Zealand Greenshell(tm) mussels (Perna canaliculus) during chilled storage using solid phase microextraction gas chromatography-mass spectrometry. Food Chemistry 136: 218–223. 152. Ropodi, A., Panagou, E.Z., and Nychas, G.-J.E. 2016. Data mining derived from food analyses using non-invasive/non-destructive analytical techniques: Determination of food authenticity, quality and safety in tandem with computer science disciplines. Trends in Food Science and Technology 50: 11–25. 153. Soni, A., Dixit, Y., Reis, M.M., and Brightwell, G. 2022. Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants. Comprehensive Reviews in Food Science and Food Safety 21: 3717–3745. doi:10.1111/1541-4337.12983. 154. Sathya, R. and Abraham, A. 2013. Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence 2(2): 34–38. 155. Ghannam, R.B. and Techtmann, S.M. 2021. Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring. Computational and Structural Biotechnology Journal 19: 1092–1107. doi:10.1016/j.csbj.2021.01.028. 156. Çetin, N., Karaman, K., Kavuncuoğlu, E., Yıldırım, B., and Jahanbakhshi, A. 2022. Using hyperspectral imaging technology and machine learning algorithms for assessing internal quality parameters of apple fruits. Chemometrics and Intelligent Laboratory Systems 230: 104650. doi:10.1016/j. chemolab.2022.104650. 157. Manthou, E., Lago, S.L., Darges, E., Lianou, A., Tsakanikas, P., Panagou, E.Z., Anastasiadi, M., Mohareb, F., and Nychas, G.-J.E. 2020. Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools. Computers and Electronics in Agriculture 175: 105529. 158. Manthou, E., Karnavas, A., Fengou, L.C., Bakali, A., Lianou, A., Tsakanikas, P., and Nychas, G.-J.E. 2022. Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables. International Journal of Food Microbiology 361: 109458.

584

Handbook of Seafood and Seafood Products Analysis

159. Lytou, A.E., Tsakanikas, P., Lymperi, D., and Nychas, G.-J.E. 2022. Rapid assessment of microbial quality in edible seaweeds using sensor techniques based on spectroscopy, imaging analysis and sensors mimicking human senses. Sensors 22(18): 7018. doi:10.3390/s22187018. 160. Fengou, L.C., Spyrelli, E., Lianou, A., Tsakanikas, P., Panagou, E.Z., and Nychas, G.E. 2019. Estimation of minced pork microbiological spoilage through Fourier transform infrared and visible spectroscopy and multispectral vision technology. Foods 8(7): 238. 161. Estelles-Lopez, L., Ropodi, A., Pavlidis, D., Fotopoulou, J., Gkousari, C., Peyrodie, A., Panagou, E., Nychas, G.J., and Mohareb, F. 2017. An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling. Food Research International 99(Pt 1): 206–215. doi:10.1016/j.foodres.2017.05.013. PMID: 28784477. 162. Govari, M., Tryfinopoulou, P., Parlapani, F.F., Boziaris, I.S., Panagou, E.Z., and Nychas, G.E. 2021. Quest of intelligent research tools for rapid evaluation of fish quality: FTIR spectroscopy and multispectral imaging versus microbiological analysis. Foods 10(2): 264. doi:10.3390/foods10020264. 163. Govari, M., Tryfinopoulou, P., Panagou, E.Z., and Nychas, G.-J.E. 2022. Application of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI) and electronic nose (E-nose) for the rapid evaluation of the microbiological quality of gilthead sea bream fillets. Foods 11: 2356. doi:10.3390/ foods11152356. 164. Yu, X., Tang, L., Wu, X., Lu, H. 2018. Nondestructive freshness discriminating of shrimp using visible/ near-infrared hyperspectral imaging technique and deep learning algorithm. Food Analytical Methods 11: 768–780. doi:10.1007/s12161-017-1050-8. 165. Tsakanikas, P., Fengou, L.-C., Manthou, E., Lianou, A., Panagou, E.Z., and Nychas, G.-J.E. 2018. A unified spectra analysis workflow for the assessment of microbial contamination of ready-to-eat green salads: Comparative study and application of non-invasive sensors. Computers and Electronics in Agriculture 155: 212–219. 166. Taoukis, P.S. and Labuza, T.P. 2003. Time-temperature indicators (TTIs). In Novel Food Packaging Techniques, pp. 103–126, Elsevier Ltd. doi:10.1016/B978-1-85573-675-7.50010-9. 167. Wang, S., Liu, X., Yang, M., Zhang, Y., Xiang, K., and Tang, R. 2015. Review of time temperature indicators as quality monitors in food packaging. Packaging Technology and Science 28(10): 839–867. doi:10.1002/pts.2148. 168. Mercier, S., Villeneuve, S., Mondor, M., and Uysal, I. 2017. Time-temperature management along the food cold chain: A review of recent developments. Comprehensive Reviews in Food Science and Food Safety 16(4): 647–667. doi:10.1111/1541-4337.12269. 169. Pacquit, A., Crowley, K. and Diamond, D. (2008). Smart Packaging Technologies for Fish and Seafood Products. In : J. Kerry and P. Butler (eds.). Smart Packaging Technologies for Fast Moving Consumer Goods, J. Kerry and P. Butler (eds.). John Wiley and Sons, Inc., USA, pp. 75-98. https://doi. org/10.1002/9780470753699.ch5 170. Pacquit, A., Frisby, J., Diamond, D., Lau, K.T., Farrell, A., Quilty, B., and Diamond, D. 2007. Development of a smart packaging for the monitoring of fish spoilage. Food Chemistry 102: 466. 171. Pacquit, A., Lau, K.T., and Diamond, D. 2004. Development of a colorimetric sensor for monitoring of fish spoilage amines in packaging headspace. Sensors 1: 365. 172. Pacquit, A., Lau, K.T., McLaughlin, H., Frisby, J., Quilty, B., and Diamond, D. 2006. Development of a volatile amine sensor for the monitoring of fish spoilage. Talanta 69(2 SPEC. ISS.): 515–520. doi:10.1016/j.talanta.2005.10.046. 173. Morsy, M.K., Zór, K., Kostesha, N., Alstrøm, T.S., Heiskanen, A., El-Tanahi, H., Sharoba, A., Papkovsky, D., Larsen, J., Khalaf, H., Jakobsen, M.H., and Emnéus, J. 2016. Development and validation of a colorimetric sensor array for fish spoilage monitoring. Food Control 60: 346–352. doi:10.1016/j.foodcont.2015.07.038. 174. Wells, N., Yusufu, D., and Mills, A. 2019. Colourimetric plastic film indicator for the detection of the volatile basic nitrogen compounds associated with fish spoilage. Talanta 194: 830–836. doi:10.1016/j. talanta.2018.11.020. 175. Ma, Q., Lu, X., Wang, W., Hubbe, M.A., Liu, Y., Mu, J., Wang, J., Sun, J., and Rojas, O.J. 2021. Recent developments in colorimetric and optical indicators stimulated by volatile base nitrogen to monitor seafood freshness. Food Packaging and Shelf Life 28: 100634. doi:10.1016/j.fpsl.2021.100634. 176. Tavakoli, S., Mubango, E., Tian, L., Bohoussou ŃDri, Y., Tan, Y., Hong, H., and Luo, Y. 2023. Novel intelligent films containing anthocyanin and phycocyanin for nondestructively tracing fish spoilage. Food Chemistry 402: 134203. doi:10.1016/j.foodchem.2022.134203. 177. Bovill, R.A., Bew, J., and Baranyi, J. 2001. Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature II. Rapidly changing temperatures. International Journal of Food Microbiology 67: 131–137.

Detection of Fish Spoilage

585

178. Devlieghere, F., Geeraerd, A.H., Versyck, K.J., Vandewaetere, B., Van Impe, J., and Debevere, J. 2001. Growth of Listeria monocytogenes in modified atmosphere packed cooked meat products: A predictive model. Food Microbiology 18: 53–66. 179. Duffy, L.L., Vanderlinde, P.B., and Grau, F.H. 1994. Growth of Listeria monocytogenes on vacuumpacked cooked meats: Effect of pH, aw, nitrite and ascorbate. InternationalJournal of Food Microbiology 23: 377–390. 180. Farber, J.M., Cai, Y., and Ross, W.H. 1996. Predictive modeling of the growth of Listeria monocytogenes in CO2 environments. International Journal of Food Microbiology 32: 133–144. 181. Fernandez, P.S., George, S.M., Sills, C.S., and Peck, M.W. 1997. Predictive model of the effect of CO2, pH, temperature, and NaCl on the growth and survival of foodborne pathogenic bacteria. International Journal of Food Microbiology 37: 37–45. 182. Koutsoumanis, K.P. and Taoukis, P. 2005. Meat safety, refrigerated storage and transport: Modeling and management. In Improving the Safety of Fresh Meat. J.N. Sofos (ed.), pp. 503–561, Woodhead/ Publishing, Ltd., CRC Press, Cambridge, U.K. 183. McClure, P.J., Blackburn, C., Cole, M.B., Curtis, P.S., Jones, J.E., Legan, J.D., Ogden, I.D., Peck, M.W., Roberts, T.A., Sutherland, J.P., and Walker, S.J. 1994. Modeling the growth, survival and death of microorganisms in foods: The UK food micromodel approach. International Journal of Food Microbiology 34: 265–275. 184. McMeekin, T.A., Olley, J.N., Ross, T., and Ratkowsky, D.R. 1993. Predictive Microbiology - Theory and Application. Research Studies Press Ltd., Taunton, U.K., p. 339. 185. Ross, T. 1996. Indices for performance evaluation of predictive models in food microbiology. Journal of Applied Bacteriology 81: 501–508. 186. Taoukis, P.S., Koutsoumanis, K., and Nychas, G.-J.E. 1999. Use of time-temperature intergrator and predictive modelling for shelf life control of chilled fish under dynamic storage conditions. International Journal of Food Microbiology 53: 21. 187. Giannakourou, M., Koutsoumanis, K., Nychas, G.-J.E., and Taoukis, P.S. 2001. Development and assessment of an intelligent shelf life desicion system (SLDS) for quality optimization of the food chill chain. Journal of Food Protection 64: 1051. 188. Koutsoumanis, K.P. 2001. Predictive modeling of the shelf life of fish under non isothermal conditions. Applied and Environmental Microbiology 76: 1821–1825. 189. Koutsoumanis, K., Giannakourou, M., Taoukis, P.S., and Nychas, G.-J.E. 2002. Application of SLDs (shelf life decision system) to marine cultured fish quality. International Journal of Food Microbiology 73: 375. 190. Rasmussen, S.K.J., Ross, T., and McMeekin, T. 2002. A process risk model for the shelf life of Atlantic salmon fillets. International Journal of Food Microbiology 73: 47–60. 191. Taoukis, P.S., Koutsoumanis, K., and Nychas, G.-J.E. 1999. Use of time-temperature intergrator and predictive modelling for shelf life control of chilled fish under dynamic storage conditions. International Journal of Food Microbiology 53: 21. 192. Van Impe, J.F.M. and Bernaerts, K. 2000. Predictive Modelling in Foods - Conference Proceedings. KULeuven/BioTec, Belgium. 193. Vanderzant, C., Savell, J.W., Hanna, M.O., and Potluri, V. 1986. A comparison of growth of individual meat bacteria on the lean and fatty tissue of beef, pork and lamb. Journal of Food Science 51: 5–8, 11. 194. Dalgaard, P., Buch, P., and Silberg, S. 2002. Seafood spoilage predictor - Development and distribution of a product specific application software. International Journal of Food Microbiology 73: 343–349. 195. Koutsoumanis, K., Stamatiou, A., Skandamis, P., and Nychas, G.-J.E. 2006. Development of a microbial model for the combined effect of temperature and pH on spoilage of ground meat and validation of the model under dynamic temperature conditions. Applied and Environmental Microbiology 72: 124–134. 196. FAO 2022. Tools on chemical and microbiological hazards. https://www.fao.org/food-safety/resources/ tools/en/. 197. Fraser, O. and Sumar, S. 1998. Compositional changes and spoilage in fish. Nutrition and Food Science 98: 275. 198. Palleroni, N.J. (1992). Introduction to the family Pseudomonadaceae. In: The Prokaryotes, Balows, A., Truper, H.G., Dworkin, M., Harder, W. and Schleifer K-H. (eds.). Springer-Verlag, New York, USA, pp. 3071–3085. 199. Sutherland, J. 2003. Modelling food spoilage. In Food Preservations Techniques. P. Zeuthen and L. Bogh-Sorensen (eds.), pp. 451–474, CRC Woodhead Publishing Limited, Cambridge, U.K.

30

Predictive Modeling of Seafood Spoilage Olumide A. Odeyemi University of Tasmania

Deyan Stratev Trakia University

Helen Onyeaka University of Birmingham

D. Sylvain Dabadé University of Abomey-Calavi

30.1  SEAFOOD PRODUCTS Seafood products are widely acknowledged as some of the most perishable foods, with a relatively short shelf life. Maintaining the quality of seafood is a key concern for food businesses that deal with seafood products, given the varying shelf-life of seafood products, which can be affected by factors such as initial microbiological quality, seasonality, feeding, and handling (Bernardi et al., 2013). Existing methods for detecting seafood spoilage, such as traditional microbiological testing and sensory analysis, have limitations in their ability to predict the shelf-life and safety of seafood products accurately and efficiently. Microbiological testing, while useful in identifying the presence of spoilage microorganisms, can be time-consuming and may not always accurately reflect the overall quality of the product. Sensory analysis, while able to provide a subjective evaluation of the product’s quality, can be affected by the personal biases of the evaluator and may not always correlate with the presence of spoilage microorganisms. Additionally, these methods do not consider the complex interactions between intrinsic factors such as the type of seafood and packaging materials, and extrinsic factors such as storage temperature and handling practices that can affect spoilage (Koutsoumanis et al., 2001). Predictive microbiology involves the use of mathematical models to describe microbial responses to intrinsic and extrinsic conditions such as pH, and storage temperature. It is used to predict the shelf-life and spoilage of different foods including seafood. For example, predictive microbiology was used to predict spoilage based on the shelf-life of stored tropical shrimp (Penaeus notialis) due to the presence of Pseudomonas psychrophila and Carnobacterium maltaromaticum (Dabadé et al., 2015). Similarly, the spoilage activity of H2S spoilage bacteria was modeled in tropical brackish water shrimp as a function of storage temperature modeling the growth of H2S-producing bacteria in tropical brackish water shrimp. The growth rate of Shewanella putrefaciens in Bigeye tuna (Thunnus obesus) was predicted. Predictive modeling is an alternative approach that can be used to predict the shelf life and quality of seafood products, without the need for direct product testing (Fadiji et al., 2021). Predictive microbiology can help to simulatethe growth and inactivation of microorganisms in seafood products under different storage conditions, considering the physical, chemical, and microbiological properties of seafood, as well as environmental factors such as temperature, pH, and oxygen levels. It is also a powerful tool for understanding the factors that contribute to seafood spoilage and determining the shelflife of seafood products. This approach combines mathematical equations and statistical techniques 586

DOI: 10.1201/9781003289401-35

Predictive Modeling of Seafood Spoilage

587

with information about the physical, chemical, and microbiological properties of seafood to simulate the growth and inactivation of microorganisms under different storage conditions (Stavropoulou and Bezirtzoglou, 2019). By understanding how different environmental factors such as temperature, pH, and oxygen levels affect the behavior of spoilage microbes, predictive modeling can help seafood processors and manufacturers optimize storage conditions and identify potential risks for spoilage. Additionally, predictive modeling can aid in the development of new preservation methods, such as modified atmosphere packaging, high-pressure processing, and other non-thermal technologies, that can improve the shelf life of seafood products and enhance food safety. Extensive research has been performed to develop microbiological, sensory and biochemical methods for the evaluation of seafood spoilage. These methods are time-consuming and indicate sensitivity limitations. Hence, their alternative for seafood spoilage testing is predictive microbiology. Mathematical modeling of shelf-life demands knowledge of seafood spoilage mechanism. In fact, the temperature is one of the most important factors influencing modeling the growth of specific spoilage organisms (SSO), and therefore it must be considered in the shelf-life prediction of seafood (Koutsoumanis, 2001). It is a well-known fact that bacterial growth limits the shelf-life of seafood. Kinetic models of specific spoilage organisms’ growth can be applied to predict shelf-life. Identification of SSO and growth kinetics parameters estimation is required for the accurate performance of the kinetic model (Dalgaard, 1995). Primary models, such as Gompertz and Logistic, were used to simulate the growth curves of SSO in seafood (Yi and Xie, 2021) as shown in Figure 30.1. The 3- and 4-parameter logistic models were fitted to bacterial growth curves, where the data were log-transformed to stabilize their variance. The following integrated and log-transformed form of the 3- and 4-parameter logistic models were suggested by Dalgaard (1995):



  N max log ( N ( t )) = log    1 + exp ( − µmax ( t − ti )) 



  N max − N min log ( N ( t )) = log  N min + 1 + exp ( − µmax ( t − ti ))  

FIGURE 30.1  Evolution of Pseudomonas spp. in fresh shrimp collected from Lagoon of Porto-Novo during storage at 0°C (diamond), 7°C (square), 15°C (triangle), and 28°C (circle). The reparameterized Gompertz model (solid line) was fitted to the growth data.

588

Handbook of Seafood and Seafood Products Analysis

SQRT (mu) lnCFU/h

1.2

y = 0.0325x + 0.1725 R² = 0.9871

1

0.8 0.6 0.4

0.2 0

0

5

10

15

20

25

30

Temperature (°C)

FIGURE 30.2  Maximum specific growth rate of Pseudomonas spp. in fresh shrimp at square.

where N(t) is the cell concentration at time t (hours), Nmin and Nmax is the minimum and maximum asymptotic cell concentration (cfu/mL or cfu/g), µmax is the maximum specific growth rate (per hours), and ti is the time (hours) when half the maximum cell concentration is reached. Secondary models (square root models) were used to describe the temperature effect on the maximum specific growth rate (µmax) of SSO in seafood (Yi and Xie, 2021) as shown in Figure 30.2. A linear relationship between the square root of the growth rate constant (r) and the absolute temperature (T) was proposed by Ratkowsky et al. (1982):

r = b ( T − To )

where b is the slope of the regression line, T is temperature and To is a conceptual temperature of no metabolic significance. Koutsoumanis (2001) presented this type equation as follow:

µmax =   bµ ⋅ ( T − Tmin )

where T is the temperature, b is a constant, and Tmin is the nominal minimum temperature for growth estimated by extrapolation of the regression line to µmax = 0. According to (Dalgaard, 1995), shelf life can be calculated as the time, which SSO requires for multiplication from the initial load N(0) to a minimal spoilage level (MSL):



 log ( MSL ) − log ( N ( 0 ))  × ln (10 ) Shelf life  ( days ) =  µmax h −1 × 24

( )

Seafood Spoilage Predictor (SSP) software was developed to predict the effect of temperatures on the growth of SSO and the shelf-life of seafood. The first version of SSP contained only a few models (Figure 30.3) (Dalgaard et al., 2002). A new and expanded version of this software called Food Spoilage and Safety Predictor (FSSP™) v. 4.0 was launched in 2014. It contains new predictive models and facilities in addition to all models already available: (i) Relative rate of spoilage (RRS) models; (ii) Microbial spoilage (MS) models; (iii) Psychrotolerant Lactobacillus spp. (LAB); (iv) Histamine formation models; (v) Listeria monocytogenes growth and growth boundary models; (vi) Listeria monocytogenes and lactic acid bacteria (LAB); (vii) Generic growth models (Dalgaard, 2014).

Predictive Modeling of Seafood Spoilage

589

FIGURE 30.3  Seafood Spoilage Predictor software v. 1.1.

The use of mathematical modeling in predicting the shelf-life of foods requires a thorough understanding of the mechanisms of spoilage (Koutsoumanis, 2001). In the case of seafood and seafood products, spoilage is caused by specific spoilage organisms (SSOs) that are a subset of the overall microflora (Macé et al., 2014). Temperature is one of the most critical factors controlling the growth of these SSOs, so predicting the growth of SSOs as a function of temperature is essential for predicting the shelf-life of seafood products. Several models for predicting the growth of spoilage microorganisms at different temperature ranges have been developed, such as the models proposed by Dalgaard et al. (1997) and Neumeyer et al. (1997) and more recently. In a study by Giarratana et al. (2022). However, the majority of these investigations were conducted under constant conditions, which may not reflect the actual conditions of food products during storage and distribution. Temperature, unlike other parameters influencing microbial growth such as water activity and pH, can fluctuate greatly across the whole chain of production and distribution. Food is frequently subjected to severe temperature changes during transit and storage prior to delivery to the final consumer. These changing temperatures make predicting the spoilage of seafood difficult, as temperature changes near the minimum, growth of spoilage microorganisms have resulted in quite substantial departures from the model. Several other factors can affect the predictive modeling of seafood spoilage, including: 1. Type of seafood: Different types of seafood have varying susceptibilities to spoilage, and this must be considered when developing predictive models. 2. Packaging materials: The type of packaging used to store seafood can also affect spoilage. Models must consider the effect of different packaging materials on the shelf-life of the product. 3. Handling practices: Proper handling and storage practices are essential for maintaining the safety and quality of seafood products. Predictive models must consider the impact of various handling practices on spoilage rates. 4. Microorganisms: The presence of specific microorganisms can affect the spoilage rate of seafood products, and this must be considered in predictive models. 5. Environmental factors: Factors such as humidity, water activity, and pH can also play a role in the spoilage of seafood products and must be considered in predictive models. 6. Storage conditions: The conditions under which seafood is stored, such as temperature, light, and humidity, can affect the rate of spoilage and must be considered in predictive models.

590

Handbook of Seafood and Seafood Products Analysis

Predictive models that forecast the bacterial growth in foods based on storage conditions have gained popularity in the food industry as a means of assessing the safety and quality of products (Membré and Lambert, 2008). These models are built on an understanding of which microorganisms are of concern in a specific product, either because they cause off-odors or off-flavors or because they can lead to illness in humans (Koutsoumanis, 2001; Macé et al., 2014). Predictive models that forecast the growth of specific bacterial species in seafood products based on the concentration of carbon dioxide and storage temperature have become valuable tools for the food industry to make decisions about the safety and quality of their products (Dalgaard et al., 2002). One such model, the Seafood Spoilage and Safety Predictor (SSSP) version 2.0, includes models for the spoilage of different types of seafood products in the air, modified atmosphere packaging (MAP), and vacuum packs, as well as models for the growth of P. phosphoreum (MAP), Shewanella putrefasciens (air) and Listeria monocytogenes. This software allows the food industry to determine the shelf-life of their seafood products based on the storage conditions and the growth of specific microorganisms. The Food Spoilage and Safety Predictor (FSSP) has been developed as an improvement to the Seafood Spoilage and Safety Predictor (SSSP). The FSSP includes new predictive models and additional capabilities that enhance the functionality of the previous version. These new models allow for the prediction of the impact of temperature storage conditions on the product shelf-life, forecasting the growth of specific spoilage microorganisms to predict the shelflife of fresh fish, and predicting food safety concerns such as histamine formation in marine finfish. These advancements further improve the ability of the software to assist the food industry in making decisions about the safety and quality of their seafood products (Dalgaard, 2014). However, despite the usefulness of these predictive models, they are not without limitations. One issue is that the models are based on assumptions about the growth conditions of the microorganisms and may not consider variations in real-world conditions. In addition, the models may not account for the presence of other microorganisms that could interact with the target microorganisms and affect their growth. Furthermore, these models may not be able to predict the growth of new or emerging strains of microorganisms. Another limitation is that these models are based on laboratory experiments, which may not accurately reflect the conditions found in the real world. For example, laboratory experiments are often conducted under controlled conditions, while in the real world, storage conditions may vary greatly, such as temperature fluctuations and exposure to light, which can affect the growth of microorganisms. Additionally, the use of predictive models for seafood spoilage detection is also limited by the availability of data. The models require a large and diverse dataset that includes information about the seafood products, such as the type of seafood, the storage temperature, and the packaging materials. However, data collection for seafood spoilage is often difficult and time-consuming, and the data may not be representative of the entire population of seafood products.

30.2  OTHER LIMITATIONS Complexity of the system: The spoilage of seafood products is a complex process that is influenced by multiple factors, and it can be difficult to accurately model all of these factors in a single predictive model. Limited accuracy: Even with extensive data and sophisticated models, predictions of seafood spoilage may not always be accurate due to factors that cannot be measured or accounted for in the model. Validation: Validation of predictive models is essential to ensure their performance and robustness, limited validation data and resources can be a limitation. Model limitations: Predictive models are based on assumptions and simplifications of realworld systems, and they are not able to take into account all the factors that may affect spoilage, this can be a limitation.

Predictive Modeling of Seafood Spoilage

591

Adaptation to changing conditions: Predictive models may not be able to adapt to changing conditions, such as changes in storage temperature, packaging materials, or handling practices, which can affect the accuracy of predictions over time. Future studies on predictive modeling of seafood spoilage could focus on the following areas: Development of more sophisticated models: With advancements in machine learning and artificial intelligence, it will be possible to develop more sophisticated models that can better account for the complex interactions between multiple factors that affect seafood spoilage. Incorporation of new data sources: With the increasing use of sensors and Internet of Things (IoT) technology, it will be possible to collect more detailed and real-time data on seafood storage conditions, which can be used to improve the accuracy of predictive models. Combining multiple models: Combining different models such as growth, quality, and safety models will provide a more comprehensive understanding of the seafood spoilage process and improve the accuracy of predictions. Validation of the models: There is a need for more validation studies to evaluate the performance of predictive models in different contexts and to ensure their robustness. Application of predictive models in different contexts: Predictive models can be applied in different contexts such as different types of seafood products and different storage conditions; this can help to understand how the models behave in different scenarios. Incorporating external factors: Predictive models can be enhanced by incorporating external factors such as weather, transportation, and storage conditions in order to improve the accuracy of predictions. In conclusion, predictive modeling of seafood spoilage is an important tool for the food industry to ensure the safety and quality of seafood products. These models use mathematical equations to simulate the growth of spoilage microorganisms in response to different environmental parameters such as temperature, pH, and salt concentration. The use of predictive modeling can help food businesses make better decisions about the storage and handling of seafood products to reduce spoilage and improve food safety. However, there are limitations to predictive models, including the availability of data, the complexity of the system, and the limited accuracy of predictions. Although it is a promising alternative to these traditional methods it can provide an accurate prediction of the shelflife of seafood products based on the conditions in which they are stored. However, these models are still being developed, and research is needed to improve their accuracy and applicability for a wider range of seafood products. Future studies should focus on developing more sophisticated models, incorporating new data sources, and validating the models in different contexts. The incorporation of external factors such as weather, transportation and storage conditions can also be improved to enhance the predictions. Overall, predictive modeling is a valuable tool for managing seafood spoilage and improving food safety, but it must be used in conjunction with other techniques.

REFERENCES Bernardi, D. C., Mársico, E. T., and Freitas, M. Q. D. (2013). Quality index method (QIM) to assess the freshness and shelf life of fish. Brazilian Archives of Biology and Technology, 56, 587–598. Dabadé, D. S., Azokpota, P., Nout, M. R., Hounhouigan, D. J., Zwietering, M. H., and den Besten, H. M. (2015). Prediction of spoilage of tropical shrimp (Penaeus notialis) under dynamic temperature regimes. International Journal of Food Microbiology, 210, 121–130. Dalgaard, P. (1995). Modelling of microbial activity and prediction of shelf life for packed fresh fish. International Journal of Food Microbiology, 26(3), 305–317. doi:10.1016/0168-1605(94)00136-T.

592

Handbook of Seafood and Seafood Products Analysis

Dalgaard, P. (2014). Food Spoilage and Safety Predictor (FSSP) Software. In Annual Report on Zoonoses in Denmark 2013 (pp. 31–31). DTU Food. Annual Report on Zoonoses in Denmark https://backend. orbit.dtu.dk/ws/portalfiles/portal/96685008/Dalgaard_2014_FSSP_Annual_Report_2_.pdf, accessed 7/01/2023. Dalgaard, P., Buch, P., and Silberg, S. (2002). Seafood spoilage predictor-development and distribution of a product specific application software. International Journal of Food Microbiology, 73(2–3), 343–349. doi:10.1016/S0168-1605(01)00670-5. Dalgaard, P., Mejlholm, O., and Huss, H. H. (1997). Application of an iterative approach for development of a microbial model predicting the shelf-life of packed fish. International Journal of Food Microbiology, 38(2–3), 169–179. Fadiji, T., Ashtiani, S. H. M., Onwude, D. I., Li, Z., and Opara, U. L. (2021). Finite element method for freezing and thawing industrial food processes. Foods, 10(4), 869. doi: 10.3390/foods10040869. Giarratana, F., Panebianco, F., Nalbone, L., Ziino, G., Valenti, D., and Giuffrida, A. (2022). Development of a predictive model for the shelf-life of Atlantic mackerel. Italian Journal of Food Safety, 11(1), 10019. Koutsoumanis, K. (2001). Predictive modeling of the shelf life of fish under nonisothermal conditions. Applied and Environmental Microbiology, 67(4), 1821–1829. doi:10.1128/AEM.67.4.1821-1829.2001. Macé, S., Cardinal, M., Jaffrès, E., Cornet, J., Lalanne, V., Chevalier, F., Sérot, T., Pilet, M.-F., Dousset, X., and Joffraud, J. J. (2014). Evaluation of the spoilage potential of bacteria isolated from spoiled cooked whole tropical shrimp (Penaeus vannamei) stored under modified atmosphere packaging. Food Microbiology, 40, 9–17. Membré, J. M., and Lambert, R. J. (2008). Application of predictive modelling techniques in industry: from food design up to risk assessment. International Journal of Food Microbiology, 128(1), 10-15. doi: 10.1016/j.ijfoodmicro.2008.07.006. Neumeyer, K., Ross, T., and McMeekin, T. A. (1997). Development of a predictive model to describe the effects of temperature and water activity on the growth of spoilage pseudomonads. International journal of food microbiology, 38(1), 45-54. doi: 10.1016/S0168-1605(97)00089-5. Ratkowsky, D. A., Olley, J., McMeekin, T. A., and Ball, A. (1982). Relationship between temperature and growth rate of bacterial cultures. Journal of Bacteriology, 149(1), 1–5. doi:10.1128/jb.149.1.1-5.1982. Stavropoulou, E., and Bezirtzoglou, E. (2019). Predictive modeling of microbial behavior in food. Foods, 8(12), 654. doi: 10.3390/foods8120654. Yi, Z., and Xie, J. (2021). Prediction in the dynamics and spoilage of Shewanella putrefaciens in bigeye tuna (Thunnus obesus) by gas sensors stored at different refrigeration temperatures. Foods, 10(9), 2132. doi:10.3390/foods10092132.

31

Detection of the Principal Foodborne Pathogens in Seafood and SeafoodRelated Environments David Rodríguez-Lázaro and Nadine Yeramian University of Burgos

Gislaine Fongaro Federal University of Santa Catarina

31.1 INTRODUCTION The importance of foodborne pathogens in Public Health is substantial. They cause more than 14 million illnesses, 60,000 hospitalizations, and 1,800 deaths per year in the United States [1] with annual medical and productivity losses above 6,500 million dollars [2]. In England and Wales, the figures are similar, and they cause 1.3 million illnesses, 20,759 hospitalizations, and 480 deaths each year [3]. The number of bacterial gastroenteritis associated with seafood products has increased considerably during last decades due to the rapid globalization of the food market, the increase in personal and food transportation, and profound changes in food consumption habits [1,4]. Among the bacterial pathogens that can produce gastroenteritis associated with seafood products, three can be considered a primary threat: the enteropathogenic Vibrio, Listeria monocytogenes and Salmonella spp. Three Vibrio species, V. parahaemolyticus, V. vulnificus, and V. cholerae, are well-documented human pathogens, especially associated with the consumption of raw or undercooked seafood products [5–7]. V. parahaemolyticus is an important seafood-borne pathogen worldwide [8]. It was first identified as a cause of a foodborne illness in Japan in 1950 [9], and it has been reported to account for 20%–30% of foodborne illnesses in Japan [10] and a common cause of seafood-borne gastroenteritis in Asian countries [11,12]. In contrast, infections are occasional in Europe, and only sporadic outbreaks have been reported in Spain and France [13]. In the Unites States, V. parahaemolyticus is the leading cause of gastroenteritis associated with seafood consumption, and between 1973 and 1998, approximately 40 outbreaks were reported [14]. Consumption of raw or undercooked seafood, particularly shellfish, contaminated with V. parahaemolyticus, may produce self-limiting gastroenteritis involving symptoms such as vomiting, nausea, diarrhea with abdominal cramps, headache, and low-grade fever. V. parahaemolyticus is disseminated worldwide in estuarine, marine, and coastal water environments [15]. Some environmental factors such as water temperature, salinity, zooplankton blooms, tidal flushing, and dissolved oxygen modulate its spatial and temporal distribution [16]. The increase in the prevalence of V. parahaemolyticus in raw shellfish is also correlated to warm seawaters. Loads of V. parahaemolyticus in oysters are usually lower than 103 cfu/g [17], but they can increase notably when the shellfish is cultivated in warmer seawater [18]. There is V. parahaemolyticus pandemic clone, the O3:K6 clone, which is distributed worldwide since its emergence in 1996 in India [19], and it has been involved in seafood-related outbreaks in 1998 DOI: 10.1201/9781003289401-36

593

594

Handbook of Seafood and Seafood Products Analysis

in Japan [20] and in the United States [14]. In Chile, the O3:K6 clone has also been responsible for most of the gastroenteritis cases since 2004 [21]. However, other serotypes have also been involved in V. parahaemolyticus outbreaks: serotype O6:K18 in Alaska after the consumption of oysters [22] and serotypes O4:K12 and O4:K on the US Atlantic coast [23] after the consumption of shellfish and seafood. In Spain, a massive V. parahaemolyticus outbreak (serotypes O4:K12 and O4:KUT) was reported linked to the consumption of shrimp during a food banquet on a cruise [24]. Recently, a new emerging serotype, O4:K8, has been reported in southern China linked to seafood diarrheal cases [25]. V. vulnificus produces one of the most severe foodborne infections, with a case-fatality rate greater than 50% [26]. It can cause fatal septicemia, wound infections, and gastroenteritis, especially in immunocompromised individuals [27]. It was first isolated by the Center for Disease Control in 1964 [28]. This organism is also disseminated worldwide in waters of different temperatures and salinities [29]. Environmental conditions such as water temperature and salinity modulate the variation in its prevalence [30]. Most of the V. vulnificus cases have been reported in Japan [31–34], the United States [35–40], New Caledonia [41], Korea [42], and the Gulf of Mexico [43] during summer generally associated with the consumption of raw seafood. V. cholerae is the causative agent of cholera outbreaks and epidemics. The World Health Organization reported 172,454 cases of cholera in 2015 including 1,304 deaths in a total of 42 countries including six European countries [44]. There is a direct relationship between the consumption of raw, undercooked, contaminated, or re-contaminated seafood and outbreaks produced by V. cholerae [5,45,46]. Foodstuff can be contaminated by this pathogen through contaminated irrigation water or human-origin fertilizer [45,47]. The O1 serogroup is the group predominantly isolated in cholera epidemics [46], and a new pathogenic serogroup, O139, has been also identified [48]. V. cholerae O1 was reported as being responsible for an outbreak in Haiti in October 2010 [49,50]. Another V. cholerae O1 outbreak occurred at a wedding in the Dominican Republic in January 2011 linked to the consumption of shrimps contaminated via the ice on which the seafood was stored [51]. Non-O1/O139 serogroups are sporadically involved in cholera-like diarrheal episodes but infrequently in outbreaks [52–56]. Toxigenic V. cholerae O1 is rarely isolated and no isolations of serogroup O139 have been reported in western countries. In contrast, non-O1/O139 isolates are commonly found in estuarine water and shellfish [57]. Various O1 strains have become endemic in many regions of the world, including Australia and the US Gulf Coast [58,59]. Listeria monocytogenes is an important foodborne pathogen, which usually (20%–50% of the cases) produces a fatal infection. It has been isolated from a wide range of sources, and seafood and seafood-related environments have been reported as important niches for this bacterium [60]. Cao et al. [61] reported the recurrent presence of this pathogen in shrimp samples and a frozen shrimp processing line environment, without a positive correlation between its presence and the accompanying environmental microbiota. Farber [62] reported a low incidence of L. monocytogenes in imported seafood products between 1996 and 1998 (below 1%), and a complete absence in Canadian seafood products. Van Coillie et al. [63] studied the prevalence of L. monocytogenes in different ready-to-eat (RTE) seafood products in the Belgian market. The occurrence of L. monocytogenes was 23.9%, and the contamination levels were low in most cases (84% below 100 cfu/g). The most prevalent serotype was 1/2a and serotypes 1/2b, 1/2c, and 4b were also present. In a longitudinal study of seafood between 2001 and 2005 in France, Midelet-Bourdin et al. [64] observed similar findings (a prevalence of 28% with a low level of contamination). The presence of L. monocytogenes in tropical fish and shellfish in Mangalore, India, was 17% and 12%, respectively [65]. Similar results were obtained by Nakamura et al. [66,67] in RTE seafood products commercially available or in a cold-smoked fish processing plant in Osaka, Japan (13% and 7%, respectively). Its incidence was mainly in the summer and autumn, and it was only isolated in cold-smoked fish samples and in low numbers (below 100 cfu/g). The serotype 1/2a was the most prevalent in both studies, and serotypes 1/2b, 3b, 4b, and 3a were also present. In a survey conducted in Thailand, a total of 595 samples were collected from raw materials, seafood products, and related environments, and L. monocytogenes was found in 22 (3.7%) samples [68]. In another study conducted in Spain, 250 refrigerated RTE seafood products were tested for the presence of L. monocytogenes, and 4.8% of

Detection of the Principal Foodborne Pathogens in Seafood and Related Environment

595

smoked salmon samples were positive with low levels (33 days) (Graczyk et al., 2006). Therefore, current depuration protocols are not enough to completely eliminate the risk of human protist infections. The chances of human infections can be reduced by avoiding fishing and harvesting in particular areas and avoiding certain species or body sizes. In addition, the control of feed in farmed species is highly relevant since parasitic infections are almost non-existent in species fed exclusively with artificial feed (López-Verdejo et al., 2022). In the case of anisakids, the occurrence of infective larvae in fish muscle and skin is due to migration from the visceral cavity, and it happens mostly post-mortem when the fish are captured and the parasites excapsulate and migrate to muscle. Evisceration on board reduces but does not completely eliminate the risk of human infections (Adroher et al., 1996). Moreover, evisceration is not feasible in small species, such as sardines or anchovies. In addition, it has been suggested that viscera discarded overboard can enhance the transmission of parasites (Abollo et al., 2001). However, this assumption should be tested, as when the viscera are thrown into the sea, the parasites are released into the water column or stay within the remains of the fish and are then eaten by scavengers and small invertebrates, which means a restart of the parasite’s life cycle. In addition, seabirds degrade Anisakis spp. during digestion, which in this case would be a cul-de-sac for the parasites (i.e., no restart of the life cycle). Trimming away the belly flaps of fish, candling, and physically removing parasites detected visually can also reduce the number of parasites; however, these measures do not eliminate the hazards (Center for Food Safety and Applied Nutrition, 2001).

32.7.1 Antiparasitic Processing Thermal processing (either cooking or freezing) is the most effective way to eliminate the risk of parasitic diseases from seafood. Other procedures (radiation, high pressure, acidification, salting, etc.) are in their experimental phases or can affect the organoleptic properties of the product or do not reduce the risks to acceptable levels (Scientific Committee, Spanish Agency of Food Security and Nutrition, 2005). Parasites are inactivated by heating until the inner part of the product reaches at least 63°C for 15 s or longer (Butt et al., 2004). So, fully cooked, hot-smoked, and pasteurized seafood are safe

638

Handbook of Seafood and Seafood Products Analysis

from a parasitological point of view. When using conventional cooking, heating the food to 65°C for 10 min is recommended. Microwaving can be problematic because microwave heating only acts on the outermost part of the product, and heat is only further transmitted inside the product by conduction. Therefore, microwave cooking requires a higher safety temperature (74°C) (Center for Food Safety and Applied Nutrition, 2005). In the European Union, products to be consumed raw, cold-smoked, marinated, or salted must be frozen at a temperature of not more than −20°C, in all parts of the product for no less than 24 h (European Parliament and Council of the European Union, 2023). This regulation is open to some misinterpretation since the time needed to reach −20°C depends greatly on the type of freezer. For instance, a recent study (Adams et al., 2005) has shown that it can take about 10 h to attain a temperature of −20°C inside fish placed in household freezers. Moreover, the same investigation showed that 50.5 h were required to kill all A. simplex larvae in fish kept at an internal temperature of −20°C (Adams et al., 2005). This result suggests that the European regulation may not be stringent enough. The U.S. Food and Drug Administration recommendations (Center for Food Safety and Applied Nutrition, 2005), namely, freezing and storing at −20°C or below for 7 days or −35°C or below for 15 h, seem more in line with the above scientific evidence. The efficacy of proper freezing in preventing parasite infections in humans is unquestionable. Indeed, freezing is the only critical control point considered reliable in Hazard Analysis and Critical Control Points (HACCP) protocols for seafood production (Scientific Committee, Spanish Agency of Food Security and Nutrition, 2005). In recent years, however, a concern has been raised about the efficacy of thermal processing in preventing allergic reactions to A. simplex. This issue is currently unsettled. Several studies suggest that allergic responses are only elicited by live larvae (Scientific Committee, Spanish Agency of Food Security and Nutrition, 2005), at least in most patients (Baeza et al., 2004). However, other investigations have shown that A. simplex allergens are thermostable and, thus, cooking and freezing may not be protective enough (Audicana and Kennedy, 2008; Moneo et al., 2007). The latter opinion is currently subscribed by the European Food Safety Authority (2010).

32.7.2 Recommendations for Consumers and Restaurateurs Informing consumers is of the utmost importance to minimize the risks associated with parasites found in seafood. In order to avoid excessive public alarm, consumers should be aware that only a small portion of the parasite species found in seafood are hazardous to public health. Most recommendations are oriented to anisakid prevention for their frequency; however, measures can be extended to other parasites, particularly helminths. See, for example the information provided by the European Food Safety Authority (2010), the Spanish Agency of Food Safety and Nutrition (Scientific Committee, 2005), and the U.S. Food and Drug Administration (Center for Food Safety and Applied Nutrition, 2001, 2005). Fish and seafood should be purchased as fresh as possible. Medium and large fish should be bought eviscerated or eviscerated immediately after purchase. The abdominal cavity should be washed and examined for parasites. Raw, cold-smoked, marinated, or lightly cooked seafood should be consumed with care unless proof is given that the product has been kept frozen at −20°C or less, for at least 1 week. Note that some legislations indicate storage for either 24 or 48 h at −20°C for Anisakis control, which will kill most parasites. Moreover, most freezers can only reach minimum temperatures of −18°C, and these are constantly opened and closed during catering hours, thereby increasing the temperature. Seafood dishes should be properly cooked. Baking, boiling, and frying are safer than broiling and microwaving. In the latter case, setting the oven at 14°C higher than the safety temperature (i.e., 74°C + 14°C) is recommended. The use of cooking thermometers placed in the thickest part of the food is encouraged to guarantee that safety temperatures are attained. In

Seafood Parasites

639

addition, consumers and restaurateurs can check that the dish is cooked throughout before serving or eating.

32.7.3 Recommendations for Allergic and Immunosuppressed Persons Persons suffering from immunological deficiency or allergies to A. simplex s.l. should take special care. Infections of waterborne protists can be fatal for immunosuppressed patients. Thus, people with this ailment should by all means avoid consumption of raw or undercooked seafood (Graczyk et al., 2006). The same type of precautions should be taken to avoid parasitism by flatworms (trematodes and cestodes). Particular care should be taken in the geographical risk areas of each continent, and undercooked seafood from recreational or local fisheries in these regions should be avoided. Evidence suggests that patients allergic to A. simplex s.l. react differently after the ingestion of frozen fish (Baeza et al., 2004; Moneo et al., 2007). Therefore, only a doctor can provide adequate dietary advice to each individual. However, most patients [between 80% and 90% (Moneo et al., 2007)] show good tolerance to frozen fish. Fish eviscerated and blast-frozen on factory vessels are to be preferred since the risk of larval emigration from the visceral cavity to the muscle is greatly reduced. Fish tails as well as farmed and freshwater fish are safer. Allergic patients should always avoid eating the hypaxial musculature of fish (belly flaps), as well as small whole fish, as parasites mostly infect the areas close to viscera (Scientific Committee, Spanish Agency of Food Security and Nutrition, 2005). In the event of symptoms arising after the recent consumption of raw or undercooked seafood, seek medical attention and indicate whether fish or shellfish has been eaten, especially in the case of vulnerable patients.

ACKNOWLEDGEMENTS The authors thank Dr. Jesús S. Hernández-Orts (Natural History Museum of London), Alejandro López-Verdejo (Zoologic Station Anton Dohrn and University of Valencia), and the Institute of Parasitology of the Czech Academy of Science for kindly contributing to the photographs and comments for this review. The authors also acknowledge Dr. Rachel Pool (University of Valencia) for revising English.

REFERENCES Abollo, E., Gestal, C., and Pascual, S. Anisakis in marine fish and cephalopods from Galician waters: An updated perspective. Parasitol. Res., 87, 492, 2001. doi:10.1007/s004360100389. Adams, A.M., Ton, M.N., Weckell, M.M., MacKenzie, A.P. and Dong, F.M. Survival of Anisakis simplex in arrowtooth flounder (Atheresthes stomia) during frozen storage. J. Food Prot. 68, 1441-1446, 2005. doi: 10.4315/0362-028X-68.7.1441. Adams, A.M. Foodborne trematodes, in Foodborne Parasites. Ortega, Y.R., Ed., Springer, New York, 2006, Chapter 7. doi:10.1007/0-387-31197-1_7. Adroher, F.J. et al. Larval anisakids (Nematoda:Ascaridoidea) in horse mackerel (Trachurus trachurus) from the fish market in Granada (Spain). Parasitol. Res., 82, 253, 1996. doi:10.1007/s004360050105. Adroher-Auroux, F.J., and Benítez-Rodríguez, R. Anisakiasis and Anisakis: An underdiagnosed emerging disease and its main etiological agents. Res. Vet. Sci., 132, 535, 2020. doi:10.1016/j.rvsc.2020.08.003. Aibinu, I.E., Smooker, P.M., and Lopata, A.L. Anisakis nematodes in fish and shellfish-from infection to allergies. Int. J. Parasitol. Parasites Wildl., 9, 384, 2019. doi:10.1016/j.ijppaw.2019.04.007. Alarcos, A.J., and Suby, J.A. Potential parasitic infections transmitted by seafood in ancient populations from Patagonia. Quat. Int., 568, 43, 2020. doi:10.1016/j.quaint.2020.06.037. Alonso-Gómez, A. et  al. Anisakis simplex only provokes allergic symptoms when the worm parasitizes the gastrointestinal tract. J. Parasitol. Res., 93, 378, 2004. doi:10.1007/s00436-004-1085-9. Ando Y., Ono, Y., and Ono, S. Diphyllobothriasis from eating Sushi. Am. J. Trop. Med. Hyg., 104, 1953, 2021. doi:10.4269/ajtmh.21-0105.

640

Handbook of Seafood and Seafood Products Analysis

APROMAR. Evaluación de la presencia de nematodos del género Anisakis en los pescados de acuicultura marina españoles. Ministerio de Agricultura, Alimentación y Medio Ambiente, 2012. https://www. mapa.gob.es/es/pesca/temas/fondos-europeos/APROMAR%20-%20Informe%20ANISAKIS%202012_ tcm30-73680.pdf. Araújo, A., Reinhard, K., and Ferreira, L.F. Palaeoparasitology - Human parasites in ancient material. Adv. Parasitol., 90, 349, 2015. doi:10.1016/bs.apar.2015.03.003. Arlian, L.G. et  al. Characterization of allergens of Anisakis simplex. Allergy, 58, 1299, 2003. doi: 10.1046/j.1398-9995.2003.00344.x. Audicana, M.T. et al. Anisakis simplex: Dangerous-dead and alive? Trends Parasitol., 18, 20, 2002. doi:10.1016/ s1471-4922(01)02152-3. Audicana, M.T., and Kennedy, M.W. Anisakis simplex: From obscure infectious worm to inducer of immune hypersensitivity. Clin. Microbiol. Rev., 21, 360, 2008. doi:10.1128/CMR.00012-07. Baeza, M.L. et al. Characterization of allergens secreted by Anisakis simplex parasite: Clinical relevance in comparison with somatic allergens. Clin. Exp. Allergy, 34, 296, 2004. doi:10.1111/j.1365-2222.2004.01883.x. Bao, M. et al. Assessing the risk of an emerging zoonosis of worldwide concern: Anisakiasis. Sci. Rep., 7, 2017. doi:10.1038/srep43699. Bao, M., Pierce, G.J., Strachan, N.J.C., Pascual, S., González-Muñoz, M. and Levsen, A. Human health, legislative and socioeconomic issues caused by the fish-borne zoonotic parasite Anisakis: Challenges in risk assessment. Trends Food Sci. Technol., 86, 298-310, 2019. doi: 10.1016/j.tifs.2019.02.013. Bathurst, R.R. Archaeological evidence of intestinal parasites from coastal shell middens. J. Archaeol. Sci., 32, 115, 2005. doi:10.1016/j.jas.2004.08.001. Besprozvannykh, V.V., Yulia V. Tatonova, Y.V. and Shumenko, P.G. Life cycle, morphology of developmental stages of Metorchis ussuriensis sp. nov. (Trematoda: Opisthorchiidae), and phylogenetic relationships with other opisthorchiids. J Zool Syst Evol Res. 57, 24–40. 2019. doi: 10.1111/jzs.12230. Bhargava, D. et al. Anisakiasis of the tonsils. J. Laryngol. Otol., 110, 387, 1996. doi:10.1017/s0022215100133717. Blair, D. Lung flukes of the genus Paragonimus: Ancient and re-emerging pathogens. Parasitology, 149, 1286, 2022. doi:10.1017/S0031182022000300. Blair, D., Nagasawa, K., Freeman, M., Forbes, L.B., Walker, J., Overstreet, R.M. and Robin M. Medical importance. In: Rohde, K. (Ed.) Marine Parasitology, CSIRO Publishing, Collingwood, Victoria, Australia, 427–446, 2005. Bouchet, F. et  al. Identification of parasitoses in a child burial from Adak Island (Central Aleutian Islands, Alaska). C. R. Acad. Sci. III, 324, 123, 2021. doi:10.1016/S0764-4469(00)01287-7. Butt, A.A., Aldridge, K.E., and Sanders, C.V. Infections related to the ingestion of seafood. Part II: Parasitic infections and food safety. Lancet Infect. Dis., 4, 294, 2004. doi:10.1016/S1473-3099(04)01005-9. Cabrera, R., and Trillo-Altamirano, M.P. Anisakidosis: ¿Una zoonosis parasitaria marina desconocida o emergente en el Perú? Rev. Gastroenterol. Perú, 24, 335, 2004. doi:10.47892/rgp.2004.244.703. Cameron, J.J., Chanad, S.H., and Andrew, J.F. Gnathostomiasis in remote northern Western Australia: The first confirmed cases acquired in Australia. Med. J. Aust., 195, 42–44, 2011. doi:10.5694/j.1326-5377.2011. tb03188.x. Cammilleri, G. et al. Presence of Anisakis pegreffii in farmed sea bass (Dicentrarchus labrax L.) commercialized in Southern Italy: A first report. Vet. Parasitol., 259, 13, 2018. doi:10.1016/j.vetpar.2018.06.021. Center for Food Safety and Applied Nutrition. Fish and Fisheries Products Hazards and Controls Guidance (3rd edn.). U.S. Food and Drug Administration, Washington, DC, Chapter 5, 2001. https://www.cfsan. fda.gov/∼comm/haccp4.html. Center for Food Safety and Applied Nutrition. Food Code Report PB 2005-102200. U.S. Food and Drug Administration, College Park, MD, 2005, p. 75. https://www.cfsan.fda.gov/∼dms/fc05-toc.html. Chai, J.Y. Human Intestinal Flukes. From Discovery to Treatment and Control. Springer Nature, Dordrecht, The Netherlands, 2019. doi:10.1007/978-94-024-1704-3. Chai, J.Y., and Jung, B.K. Fishborne zoonotic heterophyid infections: An update. Food Waterborne Parasitol., 8–9, 33, 2017. doi:10.1016/j.fawpar.2017.09.001. Chai, J.Y., Murrell, K.D., and Lymbery, A.J. Fish-borne parasitic zoonoses: Status and issues. Int. J. Parasitol., 35, 1233, 2005. doi:10.1016/j.ijpara.2005.07.013. Chai, J.Y. et  al. Stictodora lari (Digenea: Heterophyidae): The discovery of the first human infections. J. Parasitol., 88, 627, 2002. doi:10.1645/0022-3395(2002)088(0627:SLDHTD)2.0.CO;2. Chai, J.Y. et al. Gymnophalloides seoi: A new human intestinal trematode. Trends Parasitol., 19, 109, 2003. doi:10.1016/s1471-4922(02)00068-5.

Seafood Parasites

641

Chai, J.Y. et al. Foodborne intestinal flukes in Southeast Asia. Korean J. Parasitol., 47, S69, 2009. doi:10.3347/ kjp.2009.47.S.S69. Chai, J.Y. and Jung, B.K. Epidemiology of Trematode Infections: An Update. In Digenetic Trematodes, Toledo, R. and Fried, B. Eds., Springer Nature, New York, Adv. Exp. Med. Biol., 1154, 359-409, 2019. doi: 10.1007/978-3-030-18616-6_12. Cleveland, C.A. et al. Possible role of fish as transport hosts for Dracunculus spp. Larvae. Emerg. Infect. Dis., 23, 1590, 2017. doi:10.3201/eid2309.161931. Cross, J.H. Fish and invertebrate-borne helminths, in Foodborne Disease Handbook (2nd ed.). Hui, Y.H. et al., Eds., Marcel Dekker Inc., New York, 2001, Chapter 12. doi:10.1201/9781351072083. David, T.J., and Petri, W.A. The blood- and tissue-dwelling nematodes, in Markell and Voge´s Medical Parasitology (9th ed.). Elsevier, Saunders, London, 480 pages, 2006. ISBN 978-0-7216-4793-7. Dias, M.L.G.G., Eiras, J,C, Eiras, J.C., Machado, M.H., Souza, G.T.R. and Pavanelli, G.C. The life cycle of Clinostomum complanatum Rudolphi, 1814 (Digenea, Clinostomidae) on the floodplain of the high Parani river, Brazil. Parasitol. Res. 89, 506–508, 2003. doi:10.1007/s00436-002-0796-z. Eastburn, R.L., Treitschke, T.R., and Terhune, C.A., Jr. Human intestinal infection with Nanophyetus salmincola from salmonid fishes. Am. J. Trop. Med. Hyg., 36, 586, 1987. doi:10.4269/ajtmh.1987.36.586. Ebert, M.B., Fernández, M., Valente, A.L.S., Cremer, M.J., de Castilho, P.V., and da Silva, R.J. Ascocotyle longa (Digenea: Heterophyidae) infecting dolphins from the Atlantic Ocean. Parasitol. Res., 120(1), 347–353. 2021. doi: 10.1007/s00436-020-06956-1. Esteban, J.G. et  al. Diagnosis of human trematode infections, in Digenetic Trematodes (2nd ed.). Toledo, R., and Fried, B., Eds., Springer Nature, Cham, Switzerland, 2019, Chapter 14, 437–471. doi:10.1007/978-3-030-18616-6. European Food Safety Authority. Scientific opinion on risk assessment of parasites in fishery products. EFSA J., 8, 91, 2010. doi:10.2903/j.efsa.2010.1543. European Parliament and Council of the European Union. Regulation (EC) No. 853/2004 of 29 April 2004 laying down specific hygiene rules for on the hygiene of foodstuffs. Off. J. Eur. Union, L139/55, 2023, 30 April 2004, section VIII, Current consolidated version: 15/02/2023. https://eur-lex.europa.eu/ legal-content/EN/AUTO/?uri=CELEX:02004R0853–20230215. Fernández de Corres, L. et  al. Prevalencia de la sensibilización a Anisakis simplex en tres áreas españolas, en relación a las diferentes tasas de consumo de pescado. Relevancia de la alergia a Anisakis simplex. Alergol. Immunol. Clín., 16, 337, 2001. Fioravanti, M.L. et  al. Negligible risk of zoonotic anisakid nematodes in farmed fish from European mariculture, 2016 to 2018. Eurosurveillance, 26, 611, 2021. doi:10.2807/1560-7917.ES.2021.26.2. 1900717. Fiorenza, E.A. et al. It’s a wormy world: Meta‐analysis reveals several decades of change in the global abundance of the parasitic nematodes Anisakis spp. and Pseudoterranova spp. in marine fishes and invertebrates. Glo. Chang. Biol., 26, 2854–2866, 2020. doi:10.1111/gcb.15048. Freire-Santos, A.M. et al. Detection of Cryptosporidium oocysts in bivalbe molluscs destined for human consumption. J. Parasitol., 86, 853, 2000. doi:10.1645/0022-3395(2000)086[0853:DOCOIB]2.0.CO;2. Fritsche, T.R. et al. Praziquantel for treatment of human Nanophyetus salmincola (Troglotrema salmincola) infection. J. Infect. Dis., 160, 896, 1989. doi:10.1093/infdis/160.5.896. Fürst, T., Keiser, J., and Utzinger, J. Global burden of human food-borne trematodiasis: A systematic review and meta-analysis. Lancet Infect. Dis., 12, 210, 2012. doi:10.1016/S1473-3099(11)70294-8. Garcia, L.S. Diagnostic Medical Parasitology (6th edn.). ASM Press, Washington, DC, 2016. doi:10.1128/9781555819002. García, M.L., Esteban, M., Rodríguez, F., and Rodríguez, E. La alergia por Anisakis y medidas de prevención. Rev. Com. Cient. AESAN, 1, 19, 2005. Gómez-Couso, H. et al. Contamination of bivalve molluscs by Crystosporidium oocysts: The need for new quality control standards. Int. J. Food Microbiol., 87, 97, 2003. doi:10.1016/s0168-1605(03)00057-6. Gómez-Couso, H. et  al. Detection of Crystosporidium and Giardia in molluscan shellfish by multiplexed nested-PCR. Int. J. Food Microbiol., 91, 279, 2004. doi:10.1016/j.ijfoodmicro.2003.07.003. Graczyk, T.K. et  al. Recovery, bioaccumulation and inactivation of human waterborne pathogens by the Chesapeake Bay nonnative oyster, Crassotrea ariakensis. Appl. Environ. Microbiol., 72, 3390, 2006. doi:10.1128/AEM.72.5.3390-3395.2006. Guardone, L. et al. Ascaridoid nematode larvae in wild gilthead seabream (Sparus aurata) and European seabass (Dicentrarchus labrax) caught in the Tyrrhenian Sea (Western Mediterranean Sea): A contribute towards the parasitological risk assessment on two commercially important fish species. Food Control, 118, 107377, 2020. doi:10.1016/j.foodcont.2020.107377.

642

Handbook of Seafood and Seafood Products Analysis

Guarneri, F., Guarneri, C., and Benvenga, S. Cross-reactivity of Anisakis simplex: Possible role of Ani s 2 and Ani s 3. Int. J. Dermatol., 46, 146, 2007. doi:10.1111/j.1365-4632.2006.03091.x. Harrell, L.W., and Deardorff, T.L. Human nanophyetiasis: Transmission by handling naturally infected coho salmon (Oncorhynchus kisutch). J. Infect. Dis., 161, 146, 1990. doi:10.1093/infdis/161.1.146. Herreras, M.V. et al. Anisakid larvae in the musculature of the Argentinean Hake, Merluccius hubbsi. J. Food Prot., 63, 1141, 2000. doi:10.4315/0362-028x-63.8.1141. Hwang, K.P. et al. Clinical features of human intestinal capillariasis in Taiwan. World J. Gastroenterol., 10, 2391–2393, 2004. doi:10.3748/wjg.v10.i16.2391. Ibarrola, I. et  al. Expression of a recombinant protein immunochemically equivalent to the major Anisakis simplex allergen Ani s 1. J. Investig. Allergol. Clin. Immunol., 18, 78, 2008. Kasuya, S., and Koga, K. Significance of detection of specific IgE in Anisakis-related diseases. Arerugi, 41, 106, 1992. [In Japanese]. Kawai, T. et al. Identification of Kudoa septempunctata as the causative agent of novel food poisoning outbreaks in Japan by consumption of Paralichthys olivaceus in raw fish. Clin. Infect. Dis., 54(8), 1046– 1052, 2012. doi:10.1093/cid/cir1040. Keiser, J., and Utzinger, J. Food-bornetrematodiases. Clin. Microbiol. Rev., 22, 466, 2009. doi:10.1128/ CMR.00012-09. Kimura, S., Takagi, Y., and Gomi, K. IgE response to Anisakis simplex compared to seafood. Allergy, 54, 1224, 1999. doi:10.1034/j.1398-9995.1999.00329.x. Klimpel, S. et al. Life cycle of Anisakis simplex in the Norwegian Deep (northern North Sea). Parasitol. Res., 94, 1, 2004. doi:10.1007/s00436-004-1154-0. Ko, R.C. Fish-borne parasitic zoonoses, in Fish Diseases and Disorders. Vol. I. Protozoan and Metazoan Infections. Woo, P.T.K., Ed., CAB International, Oxon, p. 631, 1995. Ko, R.C. Fish-borne parasitic zoonoses, in Fish Diseases and Disorders (1st edn.). Woo, P.T.K., Ed., CAB International, Wallingford, p. 592, 2006. Køie, M. Experimental infections of copepods and sticklebacks Gasterosteus aculeatus with small ensheathed and large third-stage larvae of Anisakis simplex (Nematoda, Ascaridoidea, Anisakidae). Parasitol. Res., 87, 32, 2001. doi:10.1007/s004360000288. Králová-Hromadová, I. et al. Ups and downs of infections with the broad fish tapeworm Dibothriocephalus latus in Europe from 1900 to 2020: Part I. Adv. Parasitol., 114, 75, 2021. doi:10.1016/bs.apar.2021.08.008. Kuchta, R., Serrano-Martínez, M., and Scholz, T. Pacific broad tapeworm Adenocephalus pacificus as a causative agent of globally reemerging Diphyllobothriosis. Emerging Infect. Dis., 21, 1697, 2015. doi:10.3201/eid2110.150516. Lee, S.H. and Chai, J.Y. A review of Gymnophalloides seoi (Digenea: Gymnophallidae) and human infections in the Republic of Korea. Korean J. Parasitol., 39, 85–118, 2001. doi: 10.3347/kjp.2001.39.2.85. Le Bailly, M., and Bouchet, F. Diphyllobothrium in the past: Review and new records. Int. J. Paleopathol., 3, 182, 2013. doi:10.1016/j.ijpp.2013.05.004. Lima dos Santos, C.A.M., and Howgate, P. Fishborne zoonotic parasites and aquaculture: A review. Aquaculture, 318, 253, 2011. doi:10.1016/j.aquaculture.2011.05.046. Liu, Q. et al. Paragonimiasis: An important food-borne zoonosis in China. Trends Parasitol., 24, 318, 2008. doi:10.1016/j.pt.2008.03.014. López-Verdejo, A. et al. Infection process, viability and establishment of Anisakis simplex s.l. L3 in farmed fish; A histopathological study in gilthead seabream. Vet. Parasitol., 311, 109805, 2022. doi:10.1016/j. vetpar.2022.109805. Marino, A.F.P. et al. Toxoplasma gondii in edible fishes captured in the Mediterranean basin. Zoonoses Public Health, 66, 826, 2019. doi:10.1111/zph.12630. Marino, F. et al. Experimental susceptibility of gilthead sea bream, Sparus aurata, via challenge with Anisakis pegreffii larvae. Biomed. Res. Int., 2013, 701828, 2013. doi:10.1155/2013/701828. Mattiucci, S. et al. Genetic and ecological data on the Anisakis simplex complex with evidence for a new species (Nematoda, Ascaridoidea, Anisakidae). J. Parasitol., 83, 401, 1997. doi:10.2307/3284402. McClelland, G. The trouble with sealworms (Pseudoterranova decipiens species complex, Nematoda): A review. Parasitology, 124, S183, 2002. doi:10.1017/s0031182002001658. Millemann, R.E., and Knapp, S.E. Biology of Nanophyetus salmincola and “salmon poisoning” disease. Adv. Parasitol., 8, 1, 1970. doi:10.1016/S0065-308X(08)60250-X. Moneo, I. et al. Sensitization of the fish parasite Anisakis simplex: Clinical and laboratory aspects. Parasitol. Res., 101, 1051, 2007. doi:10.1007/s00436-007-0587-7. Montero, E. et al. Human gnathostomosis in Spain: First report in humans. Acta Trop., 78, 59, 2001. doi:10.1016/ S0001-706X(00)00168-6.

Seafood Parasites

643

Mordvinov, V.A., Yurlova, N.I., Ogorodova, L.M. and Katokhin, A.V. Opisthorchis felineus and Metorchis bilis are the main agents of liver fluke infection of humans in Russia. Parasitol. Int., 61, 25–31, 2012. doi: 10.1016/j.parint.2011.07.021. Nagasawa, K. Anisakiasis, in Marine Parasitology. Rhode, K., Ed., CSIRO Publishing, Collingwood, Victoria, Australia, 2005, p. 430. OECD/FAO. OECD-FAO Agricultural Outlook 2022–2031. OECD Publishing, Paris, 2022. doi:10.1787/ f1b0b29c-en. Peñalver, J., Dolores, E.M., and Muñoz, P. Absence of anisakid larvae in farmed European sea bass (Dicentrarchus labrax L.) and gilthead sea bream (Sparus aurata L.) in southeast Spain. J. Food Prot., 73, 1332–1334, 2010. doi:10.4315/0362-028x-73.7.1332. Perri, A.R. et al. Detecting hidden diets and disease: Zoonotic parasites and fish consumption in Mesolithic Ireland. J. Archaeol. Sci., 97, 137, 2018. doi:10.1016/j.jas.2018.07.010. Puente, P. et al. Anisakis simplex: The high prevalence in Madrid (Spain) and its relation with fish consumption. Exp. Parasitol., 118, 271, 2008. doi:10.1016/j.exppara.2007.07.002. Radačovská, A., Čisovská Bazsalovicsová, E., and Králová-Hromadová, I. Mitochondrial and microsatellite data show close genetic relationships between Dibothriocephalus latus from South America (Argentina) and Europe (the Alpine lakes region). Parasitol. Res., 121, 3635, 2022. doi:10.1007/s00436-022-07690-6. Rahmati, A.R., Kiani, B., Afshari, A., Moghaddas, E., Williams, M. and Shamsi, S. World-wide prevalence of Anisakislarvae in fish and its relationship to human allergic anisakiasis: a systematic review. Parasitol. Res., 119, 3585–3594, 2020. doi:10.1007/s00436-020-06892-0. Ramirez, D.A. Parasites in the Atacama Desert: New insights into the lifestyles of ancient human populations (3000-500 BP). J. Archaeol. Sci., 39, 103171, 2021. doi:10.1016/j.jasrep.2021.103171. Rodríguez-Pérez, R. et al. Cloning and expression of Ani s 9, a new Anisakis simplex allergen. Mol. Biochem. Parasitol., 159, 92, 2008. doi:10.1016/j.molbiopara.2008.02.008. Salati, F. et al. Presence of Contracaecum spp. in teleosts cultured and fished in Sardinia. Vet. Parasitol., 196, 382, 2013. doi:10.1016/j.vetpar.2013.03.014. Sánchez-Velasco, P. et  al. Association of hypersensitivity to the nematode Anisakis simplex with HLA class II DRB1*1502-DQB1*0601 haplotype. Hum. Immunol., 61, 314, 2000. doi:10.1016/ s0198-8859(99)00167-6. Schmidt, G.D. Acanthocephalan infection of man, with two new records. J. Parasitol., 57, 582, 1971. doi:10.2307/3277920. Scholz, T., and Kuchta, R. Fish-borne, zoonotic cestodes (Diphyllobothrium and relatives) in cold climates: A never-ending story of neglected and (re)-emergent parasites. Food Waterborne Parasitol., 4, 23, 2016. doi:10.1016/j.fawpar.2016.07.002. Scholz, T., Kuchta, R., and Brabec, J. Broad tapeworms (Diphyllobothriidae), parasites of wildlife and humans: Recent progress and future challenges. Int. J. Parasitol. Parasites Wildl., 9, 359, 2019. doi:10.1016/j. ijppaw.2019.02.001. Scientific Committee, Spanish Agency of Food Security and Nutrition. La alergia por Anisakis y medidas de prevención. Rev. Com. Cient. AESAN, 1, 19, 2005. https://www.informacionconsumidor.org/ Documentacioacuten/tabid/57/Default.aspx?xspc=anisakis. Seo, M. et al. The changing pattern of parasitic infection among Korean populations by paleoparasitological study of Joseon Dynasty mummies. J. Parasitol., 100, 147, 2014a. doi:10.1645/12-60.1. Seo, M. et al. Paleoparasitological studies on mummies of the Joseon Dynasty, Korea. Korean J. Parasitol., 52, 235, 2014b. doi:10.3347/kjp.2014.52.3.235. Shamsi, S. Seafood-borne parasitic diseases: A ‘One-Health’ approach is needed. Fishes, 4, 9, 2019. doi:10.3390/fishes4010009. Sharp, G.J.E., Pike, A.W., and Secombes, C.J. The immune response of wild rainbow trout, Salmo gairdneri Richardson, to naturally acquired plerocercoid infections of Diphyllobothrium dendriticum (Nitzsch, 1824) and D. ditremum (Creplin, 1825). J. Fish Biol., 35, 781, 1989. doi:10.1111/j.1095-8649.1989. tb03029.x. Skírnisson, K. Hringormar berast í folk á Íslandi við neyslu á lítið elduðum fi ski. Læknablaðið, 92, 21, 2006. Slifko, T.R., Smith, H.V., and Rose, J.B. Emerging parasite zoonoses associated with water and food. Int. J. Parasitol., 30, 1379, 2000. doi:10.1016/s0020-7519(00)00128-4. Sotillo, J. et  al. Trematode genomics and proteomics, in Digenetic Trematodes (2nd edn.). Toledo, R., and Fried, B., Eds., Springer Nature, Cham, 2019, Chapter 13. doi:10.1007/978-3-030-18616-6. Søe, M.J. et al. Ancient DNA from latrines in Northern Europe and the Middle East (500 BC-1700 AD) reveals past parasites and diet. PLoS One, 13, e0195481, 2018. doi:10.1371/journal.pone.0195481. Sugiyama, H. et al., Anisakiasis annual incidence and causative species, Japan, 2018–2019. Emerg. Infect. Dis., 28, 2105–2108, 2022. doi:10.3201/eid2810.220627.

644

Handbook of Seafood and Seafood Products Analysis

Sulaiman, I.M., and Cama, V. The biology of Giardia parasites, in Foodborne Parasites. Ortega, Y.R., Ed., Springer, New York, 2006, Chapter 2. doi:10.1007/0-387-31197-1_2. Sunnotel, O. et al. Effectiveness of standard UV depuration at inactivating Cryptosporidium parvum recovered from spiked Pacific Oysters (Crassostrea gigas). Appl. Environ. Microbiol., 73, 5083, 2007. doi:10.1128/ AEM.00375-07. Suzuki, J. et al. Detection and identification of Diphyllobothrium nihonkaiense plerocercoids from wild Pacific salmon (Oncorhynchus spp.) in Japan. J. Helminthol., 84, 434, 2010. doi:10.1017/S0022149X10000155. Tantaleán, M.V., and Cárdenas, J. Profilicollis altmani (Perry, 1942) Van Cleave, 1947 (Acanthocephala) en el Perú. Con notas sobre la infección experimental de mamiferos terrestres. Rev. Peruana Biol., 9, 1, 2013. doi:10.15381/rpb.v9i1.2522. Toledo, R., Bernal, M.D., and Marcilla, A. Proteomics of foodborne trematodes. J. Proteomics, 74, 1485, 2011. doi:10.1016/j.jprot.2011.03.029. Toledo, R., and Fried, B. Digenetic Trematodes (2nd edn.). Springer Nature, Cham, 2019. doi:10.1007/978-3-030-18616-6. Toro, C. et  al. Seropositivity to a major allergen of Anisakis simplex, Ani s 1 in dyspeptic patients with Helicobacter pylori infection: Histological and laboratory findings and clinical significance. Clin. Microbiol. Infect., 12, 453, 2006. doi:10.1111/j.1469-0691.2006.01376.x. USA Food and Drug Administration. Fish and Fishery Products Hazards and Controls Guidance (FDA Hazards Guide) (4th edn.). US Department of Health and Human Services, Food and Drug Administration, Maryland, 2022. https://www.fda.gov/food/seafood-guidance-documents-regulatory-information/ fish-and-fishery-products-hazards-and-controls. Valls, A., Pascual, C.Y., and Esteban, M.M. Anisakis allergy: An update. Rev. Fr. Allergol. Immunol. Clin., 45, 108, 2005. doi:10.1016/j.allerg.2005.01.005. Wang, Y., Li, X., Sun, Q., Gong, P., Zhang, N., Zhang, X., Wang, X., Li, G., and Li, J. First case report of Metorchis orientalis from Black Swan. Int J Parasitol Parasites Wildl., 13, 7–12. 2020. doi: 10.1016/j. ijppaw.2020.07.011. Waeschenbach, A. et al. The catholic taste of broad tapeworms - Multiple routes to human infection. Int. J. Parasitol., 47, 831, 2017. doi:10.1016/j.ijpara.2017.06.004. Wen-yuan, Y. et al. Parasitologische Untersuchung einer alten Leiche aus der Chu-Dynastie der Streitenden Reiche aus dem Mazhuan-Grab Nr. 1, Kreis Jiangling, Provinz Hubei. Acta Acad. Med. Wuhan, 4, 23, 1984. Wicht, B., de Marval, F., and Peduzzi, R. Diphyllobothrium nihonkaiense (Yamane et al., 1986) in Switzerland: First molecular evidence and case reports. Parasitol. Int., 56, 195, 2007. doi:10.1016/j.parint.2007.02.002. Wicht, B. et al. Multiplex PCR for differential identification of broad tapeworms (Cestoda: Diphyllobothrium) infecting humans. J. Clin. Microbiol., 48, 3111, 2010. doi:10.1128/JCM.00445-10. Williams, H., and Jones, A. Parasitic Worms of Fish. Taylor & Francis, London, 1994. doi:10.1201/b12595. Williams, M., Hernandez-Jover, M., and Shamsi, S. Fish substitutions which may increase human health risks from zoonotic seafood borne parasites: A review. Food Control, 118, 107429, 2020. doi:10.1016/j. foodcont.2020.107429. Wood, C.L. et al. A reconstruction of parasite burden reveals one century of climate-associated parasite decline. Proc. Natl. Acad. Sci. U.S.A., 120, e2211903120, 2023. doi:10.1073/pnas.2211903120. World Health Organization. Integrating neglected tropical diseases into global health and development: fourth WHO report on neglected tropical diseases, 2017. ISBN 978-92-4-156544-8 https://apps.who.int/iris/ handle/10665/255011. Xiao, L., and Cama, V. Cryptosporidium and cryptosporidiosis, in Foodborne Parasites. Ortega, Y.R., Ed., Springer, New York, 2006, Chapter 4, 57–108, doi:10.1007/0-387-31197-1_2. Yera, H. et al. Putative Diphyllobothrium nihonkaiense acquired from a Pacific salmon (Oncorhynchus keta) eaten in France; genomic identification and case report. Parasitol. Int., 55, 45, 2006. doi:10.1016/j. parint.2005.09.004. Zhou, P. et al. Food-borne parasitic zoonoses in China: Perspective for control. Trends Parasitol., 24, 190, 2008. doi:10.1016/j.pt.2008.01.001. Zhu, X.Q. et al. SSCP-based identification of members within the Pseudoterranova decipiens complex (Nem atoda:Ascaridoidea:Anisakidae) using genetic markers in the internal transcribed spacers of ribosomal DNA. Parasitol., 124, 615, 2002. doi:10.1017/s0031182002001579.

33

Detection of Enteric Viruses in Seafood Doris Sobral Marques Souza, Mariana Alves Elois, Beatriz Pereira Savi, Lucas Zanchetta, Marcel Afonso Provenzi, Marília Miotto, and Gislaine Fongaro Federal University of Santa Catarina

David Rodríguez-Lázaro University of Burgos

33.1 INTRODUCTION Bivalve shellfish including oysters, clams, and cockles have high nutritional value and great economic importance (Fiorito et al. 2021). They are filter-feeding and may filter about 40 L of seawater per hour, becoming vectors for viral pathogens (Loosanoff 1958) (Figure  33.1). The number of viruses found in some bivalves can exceed one hundred times that found in the seawater where these animals are growing (Keller et al. 2013; Butt et al. 2004). The viruses most frequently implicated in outbreaks are human noroviruses (HNoVs) and Hepatitis A virus (HAV) (Yu et al. 2015; Maalouf et al. 2010a). However, hepatitis E virus (HEV), astroviruses (AsVs), adenoviruses (AdVs), rotaviruses (RVs), aichiviruses (AiVs), and sapoviruses (SaVs) are also found in seafood and involved in foodborne diseases (Nagarajan et al. 2022; Macaluso et al. 2021; Varela et al. 2016; Le Guyader et al. 2008).

33.1.1 HNoVs and HAV The HNoVs are non-enveloped viruses, with a single-stranded positive-sense RNA genome. They belong to the Caliciviridae family, and are clustered in five genogroups within the norovirus genus; I, II, IV, XVIII, and XIX (Chhabra et al. 2019). They are associated to acute gastroenteritis worldwide (Cardemil and Hall 2020) after shellfish consumption, mainly oysters (Sun et al. 2022; Macaluso et al. 2021). This occurs because some oyster species have ligands in their digestive gland (DT) cells that are similar to the carbohydrate-based antigens present in humans, called the histo-blood group antigens (HBGA) (Le Guyader et al. 2012). In humans, HBGA is distributed among some cells and body fluids. HBGA is used for the HNoVs attachment to the host cells at the beginning of viral infection (Heggelund et al. 2017). Although these viruses are unable to infect bivalve mollusks, similar ligands (called HBGA-like) are present in some oysters increasing the persistence of HNoVs in these animals (Le Guyader et al. 2012). Some authors reported that HNoVs persisted in oysters even after depuration or relaying processes by four weeks (Ueki et al. 2021; McLeod et al. 2017; Ueki et al. 2007). Several studies have shown that among bivalve mollusks, oysters are the most frequently related to HNoV gastroenteritis (Baker et al. 2011; Le Guyader et al. 2006). This is due to, besides the greater persistence of these viruses in these animals, the habit of eating raw or lightly cooked oysters (Provost et al. 2011; Maalouf et al. 2010b). Besides the oysters, other shellfish such as clams and mussels also can carry hNoVs (Tian et al. 2007; Souza et al. 2018).

DOI: 10.1201/9781003289401-38

645

646

Handbook of Seafood and Seafood Products Analysis

FIGURE 33.1  (a) Viruses associated to foodborne diseases and outbreaks are resistant to sewage treatment. Consequently, (b) these viruses achieve the marine environment and can contaminate shellfish. (c) The consumption of raw or lightly cooked shellfish exposes the consumers to the pathogenic viruses bioaccumulated by these animals and leads to the occurrence of foodborne diseases. Moreover, the feces of the contaminated person will go to the sewage treatment, restarting the cycle.

HAV is also bioaccumulated by shellfish and raises serious concerns regarding the possible occurrence of foodborne outbreaks (Souza et  al. 2018). HAV are non-enveloped viruses, single-stranded positive-sense RNA genomes. They belong to the Picornaviridae family and Hepatovirus genus (McKnight and Lemon 2018). Similarly to HNoVs, HAV can also attach to shellfish tissues (e.g., oysters) via carbohydrate moieties (Araud et al. 2016; Ko et al. 2011). Therefore, a standard depuration process is not effective for decreasing HAV (Polo et al. 2014; Nappier et al. 2010).

33.1.2 New Challenges Posed by COVID-19 Pandemic: SARS-CoV-2 in Food It is important to highlight that most protocols for the detection of viruses in environmental samples target non-enveloped viruses (Bosch et al. 2018). However, the emergence and worldwide spread of Severe-Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), raise a new challenge for environmental virologists, the development of protocols targeting enveloped viruses. Moreover, the SARS-CoV-2 also raises new concerns regarding possible coastal environmental contamination through human sewage (Desdouits et al. 2021; La Rosa et al. 2020). Very recently a research group showed that heat-inactivated SARS-CoV-2 can be bioaccumulated in oysters (Desdouits et al. 2021). Other researchers used zebra mussels (Dreissena polymorpha)

Detection of Enteric Viruses in Seafood

647

exposed to raw and treated urban wastewater and detected the SARS-CoV-2 genome in their digestive tissues (Le Guernic et al. 2022). Similarly, another study used two bivalve molluscan species (Ruditapes philippinarum and Ruditapes decussatus) to investigate the presence of SARS-CoV-2 in the marine coastal environment. The results showed that SARS-CoV-2 RNA was detected in 9/12 clams. However, the SARS-CoV-2 RNA presented a high degree of degradation and therefore a noninfective viral state (Polo et al. 2021).

33.2 CONCENTRATION AND DETECTION METHODS CURRENTLY APPLIED TO VIRAL DETECTION IN SHELLFISH Enteric viruses are predominantly accumulated in the bivalve mollusk DT (McLeod et al. 2009; Maalouf et al. 2010b). This bioaccumulation behavior and lesser amount of inhibitors such as lipids in DT make them the sites of choice for viral investigation in these animals (Grodzki et al. 2014; EFSA 2011). The process for virus analyzing consists of a first viral elution from the DT, the subsequent virus concentration and the removal of most of the animal tissue, which contains inhibitors for the molecular analysis and, finally, the extraction of the genetic virus material for further molecular analysis (Schrader et al. 2012). The inhibition caused by animal tissues is a problem in viral extraction from animal products, so methods are developed to maximize the viral recovery and minimize other organic compounds in the sample (Schrader et al. 2012). There are currently two main methods for viral isolation from bivalve tissues; their main steps are shown in Figure 33.2. The first main detection technique is based on adding a solution containing polyethylene glycol and has been widely used since the late 1980s (Atmar et al. 1995; Le Guyader et al. 2009; Souza et al. 2018). This technique, commonly known as PEG, had already been used in environmental samples of water, sewage, and macerated plant species since Lewis and Metcalf (1988) suggested

FIGURE 33.2  The two main methods for viral isolation from bivalve tissues.

648

Handbook of Seafood and Seafood Products Analysis

its use for the concentration of enteric viruses in bivalve tissues (Nupen 1970; Leberman 1966; Lund and Hedström 1966). Although the technique has undergone modifications and improvements throughout its application, the principle based in the isolation of viral particles from an environmental matrix using PEG remains the same. It is a method of relative speed in its application, which does not require high investments and still maintains the viability of the viral particles (Albertsson 1970). Briefly, the technique starts with the maceration of 2 g of DT (Figure  33.2), followed by homogenization by 30 min in Tryptose Phosphate Broth (TPB) diluted in 0.05 M glycine to promote de viral particles elution from the molluscan tissues. Since the publication and consolidation of this method, researchers in the field have made changes to the originally published protocol in order to optimize the quality and processing of viral concentration. Among the main adaptations implemented, Chloroform:Butanol solution (1:1 v/v) clarification steps were added to improve the separation of viral particles from the digestive tissue of the bivalves, as well as to separate the lipid material and remnants of the solutions which can act as inhibitors, harmful to the steps of extracting the genetic material and the molecular quantitative analysis of the researched virus (Atmar et al. 1995). After centrifuging, the supernatant is collected and added to PEG/NaCl solution (6,000 or 8,000) and incubated for at least 2 h (Rigotto et al. 2010; Atmar et al. 1995; Lewis and Metcalf 1988). Since its implementation, the PEG viral recovery has underwent modifications in the choice of elution solvent used. Applying this technique to oyster tissue collected in a non-contaminated area, good virus recovery rates have been observed: 97% for HAV, 40% for human RVs, 97% for simian RVs, and 105% for poliovirus type I. When compared to organic flocculation, the standard technique for viral recovery from environmental samples at that time, the method developed by Lewis and Metcalf surpasses both the concentration and the variety of types of viruses recovered. Consequently, the method commonly known as PEG emerges as a technique capable of recovering a wide variety of different types of viruses (Lewis and Metcalf 1988). PEG precipitation usually preserves the integrity of unenveloped viruses and may be used as a concentration method before cell culture assays. The second technique has been standardized for assessing the presence of norovirus and hepatitis A virus in bivalve mollusks using a solution containing proteinase K and is described in ISO 15216-1:2017 (ISO/TS 15216-1:2017 2017). This detection approach has been recommended for the quantitative analysis of hepatitis A virus (HAV) and human norovirus (NoV) genogroup I and II in food samples (Figure 33.2). The principle of the method is the use of a solution containing proteinase K in the preparation of the eluate. Proteinase K (PK) is an enzyme that was first isolated from the broth culture of the fungus Engyodontium album, currently known for its degradation activity against enzymes and proteins that can contaminate nucleic acids (Silva et al. 2015). Similar to PEG precipitation, the PK digestion method processes 2 g of dissected DT per sample to which is added the proteinase K solution (30 U/mg). This mixture is kept under agitation at 37°C for 1 h to maximize the efficiency of the enzyme. Afterward, the mixture of digestive tissue and proteinase K is kept stirred for 15 minutes at 60°C for enzyme inactivation. This step may impact the viral viability through the increase in temperature. After this step, the solution is centrifuged and the supernatant is collected to proceed with the extraction of viral genetic material and molecular quantitative analysis (ISO/TS 15216-1:2017 2017). PK digestion allows results of viral detection up to 1 day, having fewer steps during processing. Proteinase K is used to lyse the digestive tissue of bivalves and release the viruses into the solution. However, this method does not have a step where another reagent is used to optimize the quality of the viral concentration. In addition, the lack of clarification steps to remove PCR inhibitors in the method is one of the disadvantages that can affect the effectiveness of virus quantification in this matrix (Li et al. 2023; Sincero et al. 2006). To solve this problem, research was recently published with a modified ISO method to increase viral recovery rates in bivalves (Li et al. 2023). The main modification implemented in relation to the original standard method is the addition of a viral concentration step using PEG, implemented after the proteinase K inactivation phase. The treatment of the supernatant with PEG shows higher viral recovery rates when compared to the standard ISO method for samples of oysters and mussels

Detection of Enteric Viruses in Seafood

649

contaminated with NoV, as well as samples of clams contaminated with NoV and HAV (Li et al. 2023; Wang et al. 2022; Zhang et al. 2020; Zhou et al. 2013). Nowadays, there are several approaches to virus detection. However, the detection of viruses in food matrices faces some challenges such as (i) the low number of virus particles needed to cause disease; (ii) the high variability of the virus genome; (iii) the presence of inhibitory substances in such a complex and variable matrix; (iv) the long incubation period of some foodborne infections (e.g., hepatitis E) and the lack of an efficient monitoring system for the detection of viral contamination of food (Widén et al. 2011; Newell et al. 2010). One of the approaches used for virus detection in shellfish is cell culture. This method allows the detection of viable viral particles and can be used to establish the dose and time of infection and the mechanisms of the life cycle. However, as with any approach, cell culture has its limitations such as the long time period for results, susceptibility to bacterial contamination and toxic substances, the underestimation of the virus burden, different serotypes are indistinguishable in a single sample, and lack of standardized cell cultures of host cells to support the replication of most enteric viruses (Hrdy and Vasickova 2022; Dolskiy et al. 2020; Julian and Schwab 2012). Another widespread method for virus detection in shellfish is based on the amplification of the viral genome. The polymerase chain reaction (PCR), for DNA genomes, and PCR preceded by reverse transcription (RT-PCR) for RNA genomes, allows the detection of viral nucleic acids (DNA or RNA). Among its advantages, its standouts are its high specificity and sensitivity and rapid detection (Hrdy and Vasickova 2022). The main challenge associated with this technique is the presence of inhibitors in the matrices (e.g., humic, fulvic, and tannic acids, complex polysaccharides, bacterial debris, nucleases, and some metals) that directly affect the PCR assays and can interfere with the results (Julian and Schwab 2012). The international standard ISO 15216-1:2017 recommends Real-time RT-PCR (also called Quantitative RT-PCR, or RT-qPCR) using fluorescents probes for HNoV and HAV detections and quantifications. RT-qPCR is also used for monitoring of several enteric viruses in food samples (Hernroth and Allard 2007; Le Guyader et al. 2008). It is more sensitive than PCR, however, in addition to the limitations found in the PCR, a correct viral quantification is dependent on the calibration standard curve (usually made using a serial dilution of plasmids cloned with a viral genome sequence; or viral RNA/DNA). Both PCR and qPCR are not able to assess the viral infectivity. Techniques variations utilizing molecular methods coupled with cell culture-based techniques are also used to infer the viral viability in shellfish samples. The most frequently used are Integrated Cell Culture followed by PCR or qPCR (ICC-(RT)-PCR and ICC-(RT)-qPCR, respectively). Some studies added an additional step using enzyme treatment for DNA viruses (DNAse I) (Pilotto et al. 2019). Enzyme pretreatments (DNAse/RNAse), photoactivatable dyes (such as Propidium Monoazide, PMA), and other protocols have been coupled to (RT)-PCR/(RT)-qPCR assays to infer viral viability based on the capsid integrity (Rigotto et al. 2010; Razafimahefa et al. 2021). The virus and the seafood to be analyzed, before the selection of the method for viral analysis or detection, it is important to evaluate whether or not the selected concentration method preserves the integrity of the viral particles.

33.3 NEW APPROACHES USED FOR VIRUS DETECTION IN SHELLFISH Next-generation sequencing (NGS) techniques can also be applied for virus detection in shellfish. The principle of this technique is similar to the capillary electrophoresis of the Sanger sequencing method. The process involves DNA/RNA fragmentation into multiple pieces, the addition of adapters, sequencing of the libraries, and reassembling of them to form a genomic sequence (Tan et al. 2021). Recently, the portable and real-time genome sequencing MinION platform, by Oxford Nanopore Technologies, allowed sequencing to be used in a field. Nanopore sequencing is based on two ionic solution-filled chambers (cis and trans sides of the membrane) separated by a voltagebiased membrane containing nanopores, which serve as biosensors providing ssDNA or ssRNA

650

Handbook of Seafood and Seafood Products Analysis

translocation between those two chambers (usually 30 bases per second). This mover is controlled by an enzyme, and the ionic current produced by translocation is measured by a sensitive ammeter (Deamer et al. 2016). Virome sequencing delivers rapid diagnostics, with precise viral detection and characterization; generates a large amount of genomic data; and is a feasible method for novel virus discovery. Because bivalve mollusks are filter-feeding and concentrate viral particles from the water where they are immersed, their virome sequencing may prevent viral foodborne diseases, and provide information about the sanitary conditions of the environment (Table  33.1). Unfortunately, some issues are impairing the effective implementation of NGS for virus monitoring in foods, and in particular in seafood: the lack of common regions in the viral genomes for genotyping; the sample collection and storage greatly affect the quality of the results, and the accuracy of sequencing; and finally, the enrichment processes and nucleic acid extraction protocols may select some particular viruses. Biosensor-based detection converts a biological response into an electrical signal. It has a bioreceptor (utilize biochemical mechanisms, it can be tissues, cells, microorganisms, antibody, or other) (Table 33.1), which are able to recognize the target analyte nucleic acid, and a transducer (thermometric, optical, magnetic, other) responsible transform the recognition in an electric signal (Velusamy et al. 2010). Digital (RT)-PCR (RT-dPCR) is an end-point nucleic acid quantitative method that does not require a standard curve for its calibration and analysis of results. It is based on the Poison statistic, dividing one sample into thousands of fractions (or drops) for quantification of their positive and negative results. This technique shows more accurate quantification combined to be less impacted by inhibitors of molecular reactions present in environmental samples (Pinheiro et al. 2012). Loop-mediated isothermal amplification (LAMP) is another molecular method that was applied to investigate HNoVs in shellfish samples. It is considered a sensitive method, showing faster and cheaper protocols than qPCR. Fukuda et al. (2007) used 13 oligonucleotide primers with sequences of the RNA-dependent RNA polymerase region (RdRp) in a Two-Step Iso-Thermal Amplification assay (NASBA-RT-LAMP assay) to investigate two genogroups of HNoVs (I and II) in oysters. Some other detection methodologies have been developed and tested for (enveloped or undeveloped) virus detection in several matrices, such as strand displacement amplification (SDA) (Li et al. 2017), self-sustained sequence replication (3SR) (Höfler et  al. 1995), rolling circle amplification (RCA) (Kumari et al. 2022), helicase-dependent amplification (HDA) (Zasada et al. 2022), and multiplex oligonucleotide ligation-PCR in combination with xMAP® technology (xMAP®-MOL-PCR) (Luminex Corporation, Austin, TX) (Hamza et al. 2014). There are no studies of these methods used in shellfish, however, is important to know that other possibilities are available and could be implemented.

TABLE 33.1 New Detection Approaches Applied to Clinical and Environmental Samples for Virus Investigation Method Illumina sequencing Short-read paired-end Illumina MiSeq sequencing and MinION platform Droplet digital RT-PCR (dd RT-PCR) NASBA-RT-LAMP assay Biosensor of magnetosome-coupled VP28 antibody

Viruses

Sample Matrix

References

HNoV HNoV

Oysters Clinical samples

Strubbia et al. (2019) Flint et al. (2021)

SARS-CoV-2 HNoV White spot syndrome virus

Mytilus galloprovincialis Crassostrea gigas Freshwater crabs (Paratelphusa hydrodomous)

Mancusi et al. (2022) Fukuda et al. (2007) Sannigrahi et al. (2021)

Detection of Enteric Viruses in Seafood

651

33.4 CONCLUSIONS All methodologies presented in this chapter constitute a great contribution to the knowledge about the circulation, diversity of shellfish viruses and the data obtained can be used to strengthen environmental surveillance. Moreover, despite challenges to viral diagnosis, novel and improved methods show to be promising for detecting total virus and virus nucleic acids while improving assay sensitivity and reducing sample inhibition.

REFERENCES Albertsson, P.-Å. (1970). Partition of cell particles and macromolecules in polymer two-phase systems. Advances in Protein Chemistry, 24, 309–341. doi:10.1016/S0065-3233(08)60244-2. Araud, E., DiCaprio, E., Ma, Y., Lou, F., Gao, Y., Kingsley, D., et al. (2016). Thermal inactivation of enteric viruses and bioaccumulation of enteric foodborne viruses in live oysters (Crassostrea virginica). Applied and Environmental Microbiology, 82(7), 2086–2099. doi:10.1128/AEM.03573-15. Atmar, R. L., Neill, F. H., Romalde, J. L., Le Guyader, F., Woodley, C. M., Metcalf, T. G., & Estes, M. K. (1995). Detection of Norwalk virus and hepatitis A virus in shellfish tissues with the PCR. Applied and Environmental Microbiology, 61(8), 3014–3018. Baker, K., Morris, J., McCarthy, N., Saldana, L., Lowther, J., Collinson, A., & Young, M. (2011). An outbreak of norovirus infection linked to oyster consumption at a UK restaurant, February 2010. Journal of Public Health, 33(2), 205–211. doi:10.1093/pubmed/fdq089. Bosch, A., Gkogka, E., Le Guyader, F. S., Loisy-Hamon, F., Lee, A., van Lieshout, L., et al. (2018). Foodborne viruses: Detection, risk assessment, and control options in food processing. International Journal of Food Microbiology, 285(November), 110–128. doi:10.1016/j.ijfoodmicro.2018.06.001. Butt, A. A., Aldridge, K. E., & Sanders, C. V. (2004). Infections related to the ingestion of seafood part I: Viral and bacterial infections. The Lancet Intectious Diseases, 4(4), 201–212. doi:10.1016/ S1473-3099(04)00969-7. Cardemil, C. V., & Hall, A. J. (2020). Chapter 4 Travel-Related Infectious Diseases. Norovirus. In: Brunette, G.W. and Nemhauser, B. (Eds.), CDC Yellow Book (p. 78). Centre for Disease Control and Prevention. New York: Oxford University Press. https://wwwnc.cdc.gov/travel/yellowbook/2020/ travel-related-infectious-diseases/norovirus. Chhabra, P., de Graaf, M., Parra, G. I., Chan, M. C. W., Green, K., Martella, V., et al. (2019). Updated classification of norovirus genogroups and genotypes. Journal of General Virology, 100(10), 1393–1406. doi:10.1099/JGV.0.001318. Deamer, D., Akeson, M., & Branton, D. (2016). Three decades of nanopore sequencing. Nature Biotechnology, 34(5), 518–524. doi:10.1038/nbt.3423. Desdouits, M., Piquet, J. C., Wacrenier, C., Le Mennec, C., Parnaudeau, S., Jousse, S., et  al. (2021). Can shellfish be used to monitor SARS-CoV-2 in the coastal environment? Science of the Total Environment, 778(March). doi:10.1016/j.scitotenv.2021.146270. Dolskiy, A. A., Grishchenko, I. V., & Yudkin, D. V. (2020). Cell cultures for virology: Usability, advantages, and prospects. International Journal of Molecular Sciences, 21(21), 7978. doi:10.3390/ijms21217978. EFSA. (2011). Scientific opinion on an update on the present knowledge on the occurrence and control of foodborne viruses. EFSA Journal, 9(7), 1–96. doi:10.2903/j.efsa.2011.2190. Fiorito, F., Di Concilio, D., Lambiase, S., Amoroso, M. G., Langellotti, A. L., Martello, A., et al. (2021). Oyster Crassostrea gigas, a good model for correlating viral and chemical contamination in the marine environment. Marine Pollution Bulletin, 172, 112825. doi:10.1016/j.marpolbul.2021.112825. Flint, A., Reaume, S., Harlow, J., Hoover, E., Weedmark, K., & Nasheri, N. (2021). Genomic analysis of human noroviruses using combined Illumina-Nanopore data. Virus Evolution, 7(2), 1–11. doi:10.1093/ve/veab079. Fukuda, S., Sasaki, Y., Kuwayama, M., & Miyazaki, K. (2007). Simultaneous detection and genogroup-screening test for norovirus genogroups I and II from fecal specimens in single tube by reverse transcription-loop-mediated isothermal amplification assay. Microbiology and Immunology, 51(5), 547–550. doi:10.1111/j.1348-0421.2007.tb03932.x. Grodzki, M., Schaeffer, J., Piquet, J. C., Le Saux, J. C., Chevé, J., Ollivier, J., et al. (2014). Bioaccumulation efficiency, tissue distribution, and environmental occurrence of hepatitis E virus in bivalve shellfish from France. Applied and Environmental Microbiology, 80(14), 4269–4276. doi:10.1128/AEM.00978-14.

652

Handbook of Seafood and Seafood Products Analysis

Hamza, I. A., Jurzik, L., & Wilhelm, M. (2014). Development of a Luminex assay for the simultaneous detection of human enteric viruses in sewage and river water. Journal of Virological Methods, 204, 65–72. doi:10.1016/j.jviromet.2014.04.002. Heggelund, J. E., Varrot, A., Imberty, A., & Krengel, U. (2017). Histo-blood group antigens as mediators of infections. Current Opinion in Structural Biology, 44, 190–200. doi:10.1016/j.sbi.2017.04.001. Hernroth, B., & Allard, A. (2007). The persistence of infectious adenovirus (type 35) in mussels (Mytilus edulis) and oysters (Ostrea edulis). International Journal of Food Microbiology, 113(3), 296–302. doi:10.1016/j.ijfoodmicro.2006.08.009. Höfler, H., Pütz, B., Mueller, J. D., Neubert, W., Sutter, G., & Gais, P. (1995). In situ amplification of measles virus RNA by the self-sustained sequence replication reaction. Laboratory Investigation; A Journal of Technical Methods and Pathology, 73(4), 577–585. https://www.ncbi.nlm.nih.gov/pubmed/7474930. Hrdy, J., & Vasickova, P. (2022). Virus detection methods for different kinds of food and water samples - The importance of molecular techniques. Food Control, 134, 108764. doi:10.1016/j.foodcont.2021.108764. ISO /TS 15216-1:2017. (2017). ISO 15216-1:2017 Microbiology of the food chain - Horizontal method for determination of hepatitis A virus and norovirus using real-time RT-PCR - Part 1: Method for quantification. https://www.iso.org/standard/65681.html. Julian, T. R., & Schwab, K. J. (2012). Challenges in environmental detection of human viral pathogens. Current Opinion in Virology, 2(1), 78–83. doi:10.1016/j.coviro.2011.10.027. Keller, R., Justino, J. F., & Cassini, S. T. (2013). Assessment of water and seafood microbiology quality in a mangrove region in Vitória, Brazil. Journal of Water and Health, 11(3), 573–580. doi:10.2166/ wh.2013.245. Ko, S.-M., Kim, J.-O., Oh, M.-J., & Kim, D. (2011). Effects of an oxidative agent and lectins on the binding inhibition of recombinant hepatitis A virus proteins to oyster digestive tissues. Journal of Food Protection, 74(1), 157–160. doi:10.4315/0362-028X.JFP-10-211. Kumari, R., Lim, J. W., Sullivan, M. R., Malampy, R., Baush, C., Smolina, I., et al. (2022). A novel rolling circle amplification-based detection of SARS-CoV-2 with multi-region padlock hybridization. Diagnostics, 12(9), 2252. doi:10.3390/diagnostics12092252. La Rosa, G., Bonadonna, L., Lucentini, L., Kenmoe, S., & Suffredini, E. (2020). Coronavirus in water environments: Occurrence, persistence and concentration methods - A scoping review. Water Research, 179(January), 115899. doi:10.1016/j.watres.2020.115899. Le Guernic, A., Palos Ladeiro, M., Boudaud, N., Do Nascimento, J., Gantzer, C., Inglard, J.-C., et al. (2022). First evidence of SARS-CoV-2 genome detection in zebra mussel (Dreissena polymorpha). Journal of Environmental Management, 301, 113866. doi:10.1016/j.jenvman.2021.113866. Le Guyader, F. S., Atmar, R. L., & Le Pendu, J. (2012). Transmission of viruses through shellfish: When specific ligands come into play. Current Opinion in Virology, 2(1), 103–110. doi:10.1016/j.coviro.2011.10.029. Le Guyader, F. S., Le Saux, J. C., Ambert-Balay, K., Krol, J., Serais, O., Parnaudeau, S., et al. (2008). Aichi virus, norovirus, astrovirus, enterovirus, and rotavirus involved in clinical cases from a French oysterrelated gastroenteritis outbreak. Journal of Clinical Microbiology, 46(12), 4011–4017. doi:10.1128/ JCM.01044-08. Le Guyader, F. S., Loisy, F., Atmar, R. L., Hutson, A. M., Estes, M. K., Ruvoën-Clouet, N., et  al. (2006). Norwalk virus-specific binding to oyster digestive tissues. Emerging Infectious Diseases, 12(6), 931–936. Le Guyader, F. S., Pommepuy, M., & Atmar, R. L. (2009). Monitoring Viral Contamination in Shellfish Growing Areas. In G. Burnel (Ed.), New Technologies in Aquaculture: Improving Production Efficiency, Quality and Environmental Management (178th ed., p. 1232). Australia: Woodhead Publishing Limited. https://archimer.ifremer.fr. Leberman, R. (1966). The isolation of plant viruses by means of “simple” coacervates. Virology, 30(3), 341– 347. doi:10.1016/0042-6822(66)90112-7. Lewis, G. D., & Metcalf, T. G. (1988). Polyethylene glycol precipitation for recovery of pathogenic viruses, including hepatitis A virus and human rotavirus, from oyster, water, and sediment samples. Applied and Environmental Microbiology, 54(8), 1983–1988. doi:0099-2240/88/081983-06$02.00/0. Li, Y., Li, R., Zou, L., Zhang, M., & Ling, L. (2017). Fluorometric determination of Simian virus 40 based on strand displacement amplification and triplex DNA using a molecular beacon probe with a guanine-rich fragment of the stem region. Microchimica Acta, 184(2), 557–562. doi:10.1007/s00604-016-2041-y. Li, Y., Xue, L., Gao, J., Cai, W., Liang, Y., Zhang, Z., et  al. (2023). Differences in the effectiveness of the high-efficient concentrated pretreatment method on the norovirus detection in oysters and mussels. International Journal of Food Microbiology, 384, 109957. doi:10.1016/j.ijfoodmicro.2022.109957. Loosanoff, V. L. (1958). Some aspects of behavior of oysters at different temperatures. The Biological Bulletin, 114(1), 57–70. doi:10.2307/1538965.

Detection of Enteric Viruses in Seafood

653

Lund, E., & Hedström, C. E. (1966). The use of an aqueous polymer phase system for enterovirus isolations from sewage. American Journal of Epidemiology, 84(2), 287–291. doi:10.1093/oxfordjournals.aje. a120642. Maalouf, H., Pommepuy, M., & Le Guyader, F. S. (2010a). Environmental conditions leading to shellfish contamination and related outbreaks. Food and Environmental Virology, 2(3), 136–145. doi:10.1007/ s12560-010-9043-4. Maalouf, H., Zakhour, M., Le Pendu, J., Le Saux, J. C., Atmar, R. L., & Le Guyader, F. S. (2010b). Distribution in tissue and seasonal variation of norovirus genogroup I and II ligands in oysters. Applied and Environmental Microbiology, 76(16), 5621–5630. doi:10.1128/AEM.00148-10. Macaluso, G., Guercio, A., Gucciardi, F., Di Bella, S., La Rosa, G., Suffredini, E., et al. (2021). Occurrence of human enteric viruses in shellfish along the production and distribution chain in Sicily, Italy. Foods, 10(6), 1384. doi:10.3390/foods10061384. Mancusi, A., Capuano, F., Girardi, S., Di Maro, O., Suffredini, E., Di Concilio, D., et al. (2022). Detection of SARS-CoV-2 RNA in bivalve mollusks by droplet digital RT-PCR (dd RT-PCR). International Journal of Environmental Research and Public Health, 19(2), 1–13. doi:10.3390/ijerph19020943. McKnight, K. L., & Lemon, S. M. (2018). Hepatitis A virus genome organization and replication strategy. Cold Spring Harbor Perspectives in Medicine, 8(12), a033480. doi:10.1101/cshperspect.a033480. McLeod, C., Hay, B., Grant, C., Greening, G., & Day, D. (2009). Localization of norovirus and poliovirus in Pacific oysters. Journal of Applied Microbiology, 106(4), 1220–1230. doi:10.1111/j.1365-2672.2008.04091.x. McLeod, C., Polo, D., Le Saux, J. C., & Le Guyader, F. S. (2017). Depuration and relaying: A review on potential removal of norovirus from oysters. Comprehensive Reviews in Food Science and Food Safety, 16(4), 692–706. doi:10.1111/1541-4337.12271. Nagarajan, V., Chen, J.-S., Hsu, G.-J., Chen, H.-P., Chao, H.-C., Huang, S.-W., et al. (2022). Surveillance of adenovirus and norovirus contaminants in the water and shellfish of major oyster breeding farms and fishing ports in Taiwan. Pathogens, 11(3), 316. doi:10.3390/pathogens11030316. Nappier, S. P., Graczyk, T. K., Tamang, L., & Schwab, K. J. (2010). Co-localized Crassostrea virginica and Crassostrea ariakensis oysters differ in bioaccumulation, retention and depuration of microbial indicators and human enteropathogens. Journal of Applied Microbiology, 108(2), 736–744. doi:10.1111/j.1365-2672.2009.04480.x. Newell, D. G., Koopmans, M., Verhoef, L., Duizer, E., Aidara-Kane, A., Sprong, H., et al. (2010). Food-borne diseases - The challenges of 20 years ago still persist while new ones continue to emerge. International Journal of Food Microbiology, 139, S3–S15. doi:10.1016/j.ijfoodmicro.2010.01.021. Nupen, E. M. (1970). Virus studies on the Windhoek waste-water reclamation plant (South-West Africa). Water Research, 4(10), 661–672. doi:10.1016/0043-1354(70)90028-X. Pilotto, M. R., Souza, D. S. M., & Barardi, C. R. M. (2019). Viral uptake and stability in Crassostrea gigas oysters during depuration, storage and steaming. Marine Pollution Bulletin, 149, 110524. doi:10.1016/j. marpolbul.2019.110524. Pinheiro, L. B., Coleman, V. A., Hindson, C. M., Herrmann, J., Hindson, B. J., Bhat, S., & Emslie, K. R. (2012). Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Analytical Chemistry, 84(2), 1003–1011. doi:10.1021/ac202578x. Polo, D., Álvarez, C., Vilariño, M. L., Longa, Á., & Romalde, J. L. (2014). Depuration kinetics of hepatitis A virus in clams. Food Microbiology, 39, 103–107. doi:10.1016/j.fm.2013.11.011. Polo, D., Lois, M., Fernández-Núñez, M. T., & Romalde, J. L. (2021). Detection of SARS-CoV-2 RNA in bivalve mollusks and marine sediments. Science of the Total Environment, 786, 147534. doi:10.1016/j. scitotenv.2021.147534. Provost, K., Dancho, B. A., Ozbay, G., Anderson, R. S., Richards, G. P., & Kingsley, D. H. (2011). Hemocytes are sites of enteric virus persistence within oysters. Applied and Environmental Microbiology, 77(23), 8360–8369. doi:10.1128/AEM.06887-11. Razafimahefa, R. M., Ludwig-Begall, L. F., Le Guyader, F. S., Farnir, F., Mauroy, A., & Thiry, E. (2021). Optimisation of a PMAxxTM-RT-qPCR assay and the preceding extraction method to selectively detect infectious murine norovirus particles in mussels. Food and Environmental Virology, 13(1), 93–106. doi:10.1007/s12560-020-09454-w. Rigotto, C., Victoria, M., Moresco, V., Kolesnikovas, C. K. K., Corrêa, A., Souza, D. S. M. S. M., et  al. (2010). Assessment of adenovirus, hepatitis A virus and rotavirus presence in environmental samples in Florianopolis, South Brazil. Journal of Applied Microbiology, 109(6), 1979–1987. doi:10.1111/j.1365-2672.2010.04827.x.

654

Handbook of Seafood and Seafood Products Analysis

Sannigrahi, S., Arumugasamy, S. K., Mathiyarasu, J., Sudhakaran, R., & Suthindhiran, K. (2021). Detection of white spot syndrome virus in seafood samples using a magnetosome-based impedimetric biosensor. Archives of Virology, 166(10), 2763–2778. doi:10.1007/s00705-021-05187-8. Schrader, C., Schielke, A., Ellerbroek, L., & Johne, R. (2012). PCR inhibitors - Occurrence, properties and removal. Journal of Applied Microbiology, 113(5), 1014–1026. doi:10.1111/j.1365-2672.2012.05384.x. Silva, C. J., Vázquez-Fernández, E., Onisko, B., & Requena, J. R. (2015). Proteinase K and the structure of PrPSc: The good, the bad and the ugly. Virus Research, 207, 120–126. doi:10.1016/j.virusres.2015.03.008. Sincero, T. C. M., Levin, D. B., Simões, C. M. O., & Barardi, C. R. M. (2006). Detection of hepatitis A virus (HAV) in oysters (Crassostrea gigas). Water Research, 40(5), 895–902. doi:10.1016/j.watres.2005.12.005. Souza, D. S. M., Dominot, A. F. Á., Moresco, V., & Barardi, C. R. M. (2018). Presence of enteric viruses, bioaccumulation and stability in Anomalocardia brasiliana clams (Gmelin, 1791). International Journal of Food Microbiology, 266(August 2017), 363–371. doi:10.1016/j.ijfoodmicro.2017.08.004. Strubbia, S., Schaeffer, J., Oude Munnink, B. B., Besnard, A., Phan, M. V. T., Nieuwenhuijse, D. F., et  al. (2019). Metavirome sequencing to evaluate norovirus diversity in sewage and related bioaccumulated oysters. Frontiers in Microbiology, 10(OCT), 2394. doi:10.3389/fmicb.2019.02394. Sun, Z., Niu, P., Jin, M., Gao, Q., Wang, J., & Ma, X. (2022). Detection and genetic correlation analysis of diarrhea cases and norovirus in oysters in Yantai, China. Frontiers in Public Health, 10, 819890. doi:10.3389/ fpubh.2022.819890. Tan, M. T. H., Ho, S. X., Chu, J. J. H., & Li, D. (2021). Application of virome capture sequencing in shellfish sold at retail level in Singapore. Letters in Applied Microbiology, 73(4), 486–494. doi:10.1111/ lam.13540. Tian, P., Engelbrektson, A. L., Jiang, X., Zhong, W., & Mandrell, R. E. (2007). Norovirus recognizes histoblood group antigens on gastrointestinal cells of clams, mussels, and oysters: A possible mechanism of bioaccumulation. Journal of Food Protection, 70(9), 2140–2147. doi:10.4315/0362-028X-70.9.2140. Ueki, Y., Amarasiri, M., Kamio, S., Sakagami, A., Ito, H., Uprety, S., et al. (2021). Human norovirus disease burden of consuming Crassostrea gigas oysters: A case-study from Japan. Food Control, 121, 107556. doi:10.1016/j.foodcont.2020.107556. Ueki, Y., Shoji, M., Suto, A., Tanabe, T., Okimura, Y., Kikuchi, Y., et al. (2007). Persistence of caliciviruses in artificially contaminated oysters during depuration. Applied and Environmental Microbiology, 73(17), 5698–5701. doi:10.1128/AEM.00290-07. Varela, M. F., Hooper, A. S., Rivadulla, E., & Romalde, J. L. (2016). Human sapovirus in mussels from Ría do Burgo, A Coruña (Spain). Food and Environmental Virology, 8(3), 187–193. doi:10.1007/ s12560-016-9242-8. Velusamy, V., Arshak, K., Korostynska, O., Oliwa, K., & Adley, C. (2010). An overview of foodborne pathogen detection: In the perspective of biosensors. Biotechnology Advances, 28(2), 232–254. doi:10.1016/j. biotechadv.2009.12.004. Wang, D., Cao, J., Tian, Z., Fang, B., Qi, X., Lei, Z., et al. (2022). Development of a new concentration method for hepatitis A virus detection (ISO 15216-2:2019) in Manila clams (Ruditapes philippinarum). LWT, 172, 114172. doi:10.1016/j.lwt.2022.114172. Widén, F., Vågsholm, I., Belák, S., & Muradrasoli, S. (2011). Achievement V - Methods for breaking the transmission of pathogens along the food chain. Detection of viruses in food. Trends in Food Science and Technology, 22(SUPPL. 1), 49–57. doi:10.1016/j.tifs.2011.05.008. Yu, Y., Cai, H., Hu, L., Lei, R., Pan, Y., Yan, S., & Wang, Y. (2015). Molecular epidemiology of oyster-related human noroviruses and their global genetic diversity and temporal-geographical distribution from 1983 to 2014. Applied and Environmental Microbiology, 81(21), 7615–7624. doi:10.1128/AEM.01729-15. Zasada, A. A., Mosiej, E., Prygiel, M., Polak, M., Wdowiak, K., Formińska, K., et al. (2022). Detection of SARS-CoV-2 using reverse transcription helicase dependent amplification and reverse transcription loop-mediated amplification combined with lateral flow assay. Biomedicines, 10(9), 2329. doi:10.3390/ biomedicines10092329. Zhang, L., Xue, L., Gao, J., Cai, W., Jiang, Y., Zuo, Y., et al. (2020). Development of a high-efficient concentrated pretreatment method for noroviruses detection in independent oysters: An extension of the ISO/ TS 15216-2:2013 standard method. Food Control, 111, 107032. doi:10.1016/j.foodcont.2019.107032. Zhou, D., Ma, L., Zhao, F., Yao, L., Su, L., & Li, X. (2013). An efficient method of noroviruses recovery from oysters and clams. Journal of Ocean University of China, 12(1), 85–90. doi:10.1007/s11802-013-2133-9.

34

Analysis of Marine Biotoxins Arjen Gerssen Wageningen University and Research

34.1 INTRODUCTION In recent years, there is a definite increase in the use of liquid chromatography (LC) coupled to mass spectrometry for the analysis of marine biotoxins. When performing a search in the Scopus database on the search terms such as mass spectrometry and marine biotoxins, it becomes clear that over the last 20 years, a total of 492 publications are present in the database. The majority of these references (85%; n = 418) were published in the last 10 years, and in the last 5 years, more than 50% (n = 268) were published. This increase in popularity of mass spectrometric detection is present in all analytical research fields. For the liquid chromatographic mass spectrometric detection of marine biotoxins, we can distinguish between various types of methods. First, we have methods that are intended to be used for research purposes such as the detection and structure elucidation of new toxins or toxin metabolites. Secondly, LC-MS methods intended for use in routine applications or surveys of known toxins in various commodities. Within this chapter, both types of application fields will be discussed. Also, the recent developments in the field of LC mass spectrometric detection will be discussed, which are the use of high-resolution chromatography (ultra-performance or very-high-pressure LC) and high-mass accuracy, high-resolution mass spectrometric detection such as time-of-flight or orbitrap MS. The overview given in this chapter will focus on the two groups of marine biotoxins. Based on their chemical properties, marine biotoxins can be divided into two different classes: hydrophilic and lipophilic marine biotoxins. Toxins associated with the syndromes amnesic shellfish poisoning (ASP) and paralytic shellfish poisoning (PSP) are more or less hydrophilic and have a molecular weight below 500 Da. Toxins responsible for diarrhetic shellfish poisoning (DSP), azaspiracid shellfish poisoning (AZP), neurologic shellfish poisoning, ciguatera fish poisoning, and other toxins such as pectenotoxins (PTXs), yessotoxins (YTXs), cyclic imines (CI), and palytoxins all have as common denominator a molecular weight above 500 Da (up to 3,000 Da). These toxins have certain lipophilic properties and therefore will be discussed in this chapter as lipophilic marine biotoxins.

34.2 HYDROPHILIC MARINE BIOTOXINS 34.2.1 Domoic Acid In this section, the various LC-MS methods for hydrophilic marine biotoxins are discussed. The toxin responsible for ASP is domoic acid (DA). After the discovery of this toxin responsible for ASP intoxication, several LC-MS methods were developed [1]. The first LC-MS papers published on this topic focused on the detection of DA and several isomers (A–G). Electrospray ionization (ESI) was and is still used as the preferred ionization technique not only for DA but in general for all marine biotoxins. Pineiro et al. optimized as one of the first a LC-MS method for DA and various analogs. Using isocratic HPLC under reversed-phase conditions followed by single-ion monitoring (SIM) DA could be detected as well as other compounds present in the shellfish extract. DA with a molecular ion of m/z of 312 [M + H]+ and possible masses that could be related to hydroxylated DA (m/z 327) were also detected [2]. Also, fragmentation studies were performed for DA by using techniques such as ion trap MS. With ion trap MS, it is possible to perform multiple fragmentation DOI: 10.1201/9781003289401-39

655

656

Handbook of Seafood and Seafood Products Analysis

steps and after each fragmentation step isolating the produced fragment for the next fragmentation step; this is also called MSn experiments. This type of fragmentation is called fragmentation in time as each step occurs at a different time. Furey et al. studied and elucidated the fragmentation pathway of DA using these LC-MSn experiments [3]. With five fragmentation steps (MS5), they were able to propose a fragmentation pathway. The other applied MS technique is tandem mass spectrometry using a triple quadrupole MS, which is currently by far the most applied MS technique for marine biotoxins. Fragmentation within a triple quadrupole is done by first selecting the ion of interest in the first quadrupole, often the protonated molecular ion (i.e., m/z 312 for [M + H]+). In the second quadrupole, an inert gas and energy is applied to induce fragmentation of the ion, so-called collision-induced dissociation (CID). In the third quadrupole, the produced fragment ion can be filtered and subsequently measured by the detector (i.e., m/z 266 [M + H–HCOOH]+) (Figure 34.1). These techniques are so-called tandem in space techniques where the ion selection and fragmentation occur in different parts of the instruments. Being a single compound, DA is often incorporated in multitoxin methods like including the toxin within the LC-MS/MS analysis of DSP or PSP toxins. Both in reversed-phase conditions (used for DSP) and in hydrophilic interaction chromatography (HILIC) conditions (used for PSP), it is possible to incorporate DA [4,5]. In 2003, an in-house validated method was published using reversed-phase conditions and ESI-MS/MS. This method showed a low measurement uncertainty at levels between 5 and 50 mg/kg (RSDr ∼ 8%), and the estimated limit of detection was 0.15 mg DA/kg. The method was developed using conventional LC with a total analysis time of 10 min [6]. In 2011, an LC-MS/MS method using the latest generation equipment was developed and validated according to EU criteria (657/2002/EC) [7]. By applying ultra-performance chromatography, de la Iglesia et al. were able to separate DA and epi-DA within less than 3 min. Also, the limits of detection reported in this study were far below the regulatory limit of 20 mg DA/kg edible shellfish between 0.05 and 0.09 mg DA/ kg. In literature, till today, one interlaboratory validation study has been performed that included DA. Unfortunately, the study performed by McNabb et al. was partly using shellfish extracts instead of shellfish homogenates [5]. Hence, till now no formal inter-laboratory study has been organized for DA in shellfish products. Therefore, the EU legislation is still referring to reference methods such as LC-UV while LC-MS/MS has proven to be more selective, specific, and sensitive.

34.2.2 Paralytic Shellfish Poisoning Toxins For PSP toxins, more than 50 different analogs are described in literature [8]. For each of these analogs, the toxicity differs and worldwide different toxicity factors have been applied [9,10]. These variations complicate the analysis of PSP toxins in shellfish. Also, the applied chemical detection methods such as the HPLC-FLD methods have pros and cons in the separation and detection of certain analogs [11–14]. Till today, these HPLC-FLD methods are the only chemical methods for PSP toxins that were successfully validated through inter-laboratory validation studies [11,12]. The complexity of the PSPs is mainly due to the polarity of the toxins as well as their structural similarities. Mass spectrometric analysis methods for PSP toxins and the application in shellfish monitoring programs are still in their infancy. In literature, a few different LC-MS/MS methods or derivatives of these methods have been described [15–17]. As mentioned earlier, the PSP toxins are polar, and therefore, a different retention mechanism is required compared to other marine biotoxins COOH H3C

COOH

CH3 +

NH2

FIGURE 34.1  Fragmentation pathway of DA.

COOH m/z 266

[M+H]+ m/z 312

657

Analysis of Marine Biotoxins

applications. Furthermore, also important, the mobile-phase composition should be compatible with the mass spectrometer. Therefore, the HPLC-FLD methods that are using reversed-phase conditions are not compatible with mass spectrometric detection. The so-called HPLC-FLD Lawrence method [11] is using precolumn oxidation, which results in the same but multiple reaction products for some isomeric analogues, for example, gonyautoxin-1 and -4 (GTX1, 4). For mass spectrometric detection, this is not desirable as multiple oxidation reaction products should then be analyzed making data analysis more complex. The post-column oxidation HPLC-FLD method described by van de Riet et al. [12] has the advantage that the toxins are separated before the oxidation occurs; therefore, analogs that differ in toxicity factors are separated, and data analysis is more simple. Unfortunately, the mobile-phase conditions used in this method, a low percentage of organic solvent and ion-pair reagents, are not preferred within LC-MS analysis. Therefore, the use of the reversedphase conditions for PSP toxins, which generally consists of a low-percentage organic solvent and nonvolatile salts, is not suitable for LC-MS. One of the first mass spectrometric applications for the detection of PSPs was using capillary electrophoresis (CE) as a separation tool [18,19]. Although this approach seemed promising, the use of CE-MS was never applied on a large scale. This was due to the effect of salts that originate from the sample extract, and the multiple injections that were needed to analyze the different toxins having different charge states. Applications developed more recently used ion-exchange chromatography (IEC) mass spectrometry [17]. A problem with both CE and IEC is the use of relatively MS unfavorable salts that are nonvolatile or causing major signal suppression. IEC, for example, can be applied using heptanesulfonic acid that will cause problems with ionization efficiencies. Therefore, the most promising approach is the use of HILIC chromatography. This type of chromatography is using comparable mobile-phase compositions as the MS applications using reversed-phase chromatography. In general, HILIC can be applied for the separation of polar and charged compounds. The first HILIC-MS/MS method for a wide variety of PSP toxins was developed by Quilliam et al., and studied in depth by Dell’ Aversano et al. [15,20]. The developed method uses a TSK-gel Amide-80 column and LC conditions containing MS favorable mobile-phase compositions such as a high-percentage acetonitrile (>60%), ammonium formate, and formic acid. From this paper, it also becomes clear that although the tandem mass spectrometry is a powerful and promising tool for PSP toxins analysis and can be more selective than the HPLC-FLD methods, there are also some drawbacks. These drawbacks are described by the authors and are in general applicable for all PSP toxin analyses with LC-MS/MS. The first drawback is the in-source fragmentation resulting in the loss of SO3 for the PSP toxins with a sulfonic acid in the α-orientation on the C-11 group (Figure 34.2). This can be a problem for identification as, for example, gonyautoxin-2 (GTX2) gives in ESI predominantly a precursor ion of m/z 316 [M + H–SO3]+, which is the same m/z as the [M + H]+ for neo-saxitoxin. As the various structures show large similarities, GTX2 O 3 m/z 220

H2N

[M+H]+ m/z 396

NeoSTX

O

O H H N

HN HN

O 2 m/z 220

H 2N

N

HO NH+2

NH OH 2 m/z 298 OH OSO3–

1 [M+H–SO3]+ m/z 316 Partly insource

HN

H H N

N N

NH OH OH

NH2+

1 [M+H–H2O]+ m/z 298

FIGURE 34.2  Fragmentation of GTX2 and neo-saxitoxin under ESI+.

[M+H]+ m/z 316

658

Handbook of Seafood and Seafood Products Analysis

the product ions also are similar; this results in, for example, multiple peaks (up to 4) in the MRM transition m/z 316 → 298. So, it can be concluded that for PSP toxins, chromatographic separation is of key importance as separation by selecting different precursors and product ions will not give the selectivity needed. The second general problem the authors recognized is that HILIC is very sensitive to the presence of salts in the extract. Due to the relatively high amount of salts present in the shellfish, retention times tend to shift under HILIC conditions up to minutes. Furthermore, quite a high number of interfering peaks appear in the chromatogram when analyzing shellfish extracts with LC-MS/MS. This suggests that there is a need for extensive sample cleanup in order to remove salts (avoid retention time shifts) and interfering peaks. A second HILIC approach, studied in detail by Diener et al., uses a zwitterionic HILIC stationary phase [16]. With this method, the separation of the various PSP toxin analogues (e.g., GTX1 and GTX4) is superior to the method published by Dell’Aversano et al. Furthermore, this method also produced stable retention times for the toxins in various matrices such as mussels and algae (Alexandrium catenella). Although the authors suggest that it is feasible to apply this method on a routine basis, still a 40-min run is required for the analysis of these toxins. This will hamper the implementation into routine monitoring programs that require results within a short time frame. According to Turrell et al. with both methods mentioned earlier, the sensitivity for some of the PSP toxins is poor, and matrix effects occur that have an effect on sensitivity and retention time stability. These issues hampered the implementation of these applications into official control programs [21]. Therefore, Turrell et al. and Sayfritz et al. improved the application by investigating different types of cleanup such as solid-phase extraction [22]. These improvements in the methodologies will contribute to make these applications at the end fit-for-purpose for routine monitoring programs. Till today, no full inter-laboratory studies for LC-MS/MS methods for PSP toxins have been performed. Therefore, till now within the field of PSP toxins, the LC-MS/MS techniques are mainly used for research applications [23–25].

34.3 REGULATED LIPOPHILIC MARINE BIOTOXINS In what follows, various toxin classes will be discussed. First, the traditional lipophilic marine biotoxins will be discussed, such as diarrheic shellfish poisoning (DSP) toxins, AZP toxins, PTXs, and YTXs. All these toxins show a similar retentive behavior in LC systems, and therefore, these toxins are often analyzed in a single multitoxin analysis. The multitoxin methods are often only focusing on the regulated lipophilic marine biotoxins [5,26,27]. Thirteen different lipophilic marine biotoxins are currently stated in EU legislation. The EU is more stringent than other international bodies such as the US Food and Drug Administration (FDA) and CODEX. These bodies do not mention the PTXs and YTXs; therefore, there is continuous discussion on the compliance of shellfish with legislation. PTXs and YTXs are included in EU legislation due to their intraperitoneal (i.p.) toxicity in the mouse bioassay (MBA), which was the official reference method for lipophilic marine biotoxins. Within the EU legislation, a multitoxin method for lipophilic marine biotoxins is now established and is applicable for monitoring programs [28]. From a research perspective, it is interesting to have dedicated methods for certain toxin classes, for example, to investigate the formation of metabolites within shellfish, production of certain toxins by algae or effects of treatments such as heat or radiation [29,30]. The validation of multitoxin methods used for official control is discussed later; first, the individual toxin groups will be discussed and specific dedicated LC-MS methodologies.

34.3.1 Okadaic Acid and Dinophysistoxins For the DSP toxins (okadaic acid [OA] and dinophysistoxins [DTXs]), several studies have been conducted on the fragmentation of OA by using various types of ionization such as fast atom bombardment (FAB), thermospray, ion spray, and electrospray [31,32], where the latter is nowadays used

659

Analysis of Marine Biotoxins

O HO H3C

O OH

O O O–

[M–H]– m/z 563

m/z 255

CH3

CH3

H

OH

CH2 H3C O

O H

O OH CH3

O H

m/z 563

FIGURE 34.3  Proposed fragmentation pathway of okadaic acid in ESI−.

most frequently. OA and DTXs contain a carboxylic acid functionality, and therefore, both negative ESI and positive ESI can be used. For both types of ionization, fragmentation pathways have been proposed [33,34]. In positive ionization, the obtained main fragments are not specific and consist of multiple losses of water ([M + H–nH2O]+ where n = 1–4). In the negative ionization mode, the fragments obtained are more from skeletal fragmentation (Figure 34.3). Also, sensitivity is somewhat better in the negative ionization mode because of the lower number of background ions present. In negative ionization mode, OA and DTXs tend to produce the same fragments such as m/z 255, 563, and 151. OA and DTX2 have the same elemental composition and molecular weight (mw 805); therefore, the MS is not able to distinguish between these toxins. Recently, Carey et al. showed that in positive ionization, the fragmentation of the sodium adduct ([M + Na]+ m/z 827) of both OA and DTX2 resulted in different product ions, respectively, m/z 595, 443, and 151 for OA and m/z 581, 429, and 165 for DTX2 [35]. By using the different transitions of m/z 827 → 595 and m/z 827 → 581 for, respectively, OA and DTX2, a clear separation in both chromatography and mass spectrometry could be obtained. With the fragmentation pathways more or less fully elucidated, methods for detecting different types of OA and DTXs in algae and shellfish have been described. The main methods investigating OA and DTXs are focusing on the esters, which in general are named dinophysistoxin-3 (DTX3). The majority of these esters are produced in shellfish (acyl esters) and a small number in algae, the diol esters. The diol esters are, for example, produced by Prorocentrum lima and Dinophysis acuta, and can only be found in algae as it is known that certain esterases present in shellfish can hydrolyze these diol esters toward the unconjugated toxin again. Torgersen et al. studied various esters in detail using ion trap and tandem mass spectrometric techniques [36,37]. The diol esters were studied in positive ESI mode. For the OA diol esters, a common fragment could be obtained that corresponds to the loss of the diol group ([M-diol + Na]+ m/z 827). Furthermore, the authors also found hybrid esters within shellfish. These are toxins containing a diol ester and a fatty acid ester; the most abundant observed was the OA 16:0 C-8-diol. Besides the diol and fatty acid esters, also some sulfated esters of OA were isolated from algae. These sulfonic acid-containing esters are DTX4, DTX5a, DTXb, and DTX5c [38,39]. These toxins were investigated by high-resolution mass spectrometry to obtain an elemental composition of the protonated molecule as well as fragmentation information with a high mass accuracy. With mass spectrometry, structures can be proposed only; both authors (Hu et  al. and Cruz et al.) used NMR to confirm the structure. Suarez-Gomez et al. detected DTX6 as well as several unknown okadaic acid-related compounds in algae [40,41]. From a mass spectrometric point of view, an interesting study was done by Paz et al. on DTX5c as well as on 7-hydroxymethyl-2-methylene-octa-4,7-dienyl okadate [42]. In this research, interesting molecular ions were found such as [M + Na + NH4 –H]+, [M + 2NH4 –H]+, and [M + 3Na–2H]+, which were attributed to the presence of the sulfate group. In general, in routine applications, shellfish extracts are analyzed first without hydrolysis followed by analysis after hydrolysis. The difference between the concentrations found for OA, DTX1, and DTX2 is the amount of esters (DTX3) present in the sample. Legislation (EU, FDA, and CODEX) is based on the sum of the total amount of OA equivalents per kilogram of

660

Handbook of Seafood and Seafood Products Analysis

edible shellfish, which is the sum of OA, DTX1, and DTX2 after hydrolysis and taking into account their toxicity factors. Therefore, in routine applications, there is no need to distinguish between the various esters that can be present in shellfish samples.

34.3.2 Pectenotoxins In literature, over 15 different PTXs have been described [43,44]. The first PTXs were described in 1985 by Yasumoto et al. [45]. Intoxication had occurred in Japan in 1976 and 1977 after the consumption of contaminated scallops. From these scallops, besides OA and DTX1, PTX1 and PTX2 were isolated. It seems that PTX1 is only occurring in Japanese scallops, and within Europe, the most predominant PTX in algae is PTX2. In shellfish, PTX2 is rapidly metabolized by esterases to PTX2 seco acid [46]. As the PTX molecule is neutral, it can be ionized in both positive and negative ionization although the majority of the published research on PTXs is preferring positive ionization [47]. In positive ionization, the formation of [M + H]+, [M + NH4]+, and [M + Na]+ can occur under different circumstances [48]. Under acid conditions using ammonium formate, the [M + NH4]+ is the most predominant and sensitive ion observed. Fragmentation of ammonium adducts is comparable with the fragmentation of the protonated molecule. When applying relatively high temperatures, the formation of sodium adducts seems to increase. In general, sodium adducts are more difficult to fragment and also give a different fragmentation pathway compared to the protonated molecule. Therefore, most often sodium adducts are not the preferred precursor ions to select for PTXs. When performing CID of the PTX molecule, in general, multiple losses of water can be observed (n = 1–5). Also, the closed ring structure is opened and results in a specific PTX fragment of m/z 213 and a fragment that differs depending on the structure at the C-18 position of the molecule (Figure 34.4). PTX 1, -4, and -8 contain a hydroxymethyl residual group at the C-18 position that gives a specific fragment of m/z 567. PTX2, -2sa, -11, -12, -13, and -14 contain a methyl group at C-18 that gives the fragment m/z 551, and PTX3 contains an aldehyde at this position resulting in a fragment of m/z 565. The last type of residual group that can be present on the C-18 position is a carboxylic acid, which results in a fragment of m/z 581 (specific for PTX6, -7, and -9). The general fragment of m/z 213 can be used for detection of new PTX toxins by performing a precursor scan for this mass. This approach was used to detect PTXs, which can be present as esters within shellfish. Torgersen et al. showed the presence of certain esters in shellfish [49]. Gerssen et al. showed that with sophisticated in-house prepared tools for data analysis, it is also possible to search for specific PTX fragments to find certain analogs [50]. Generally, within routine monitoring programs in Europe, the research on PTXs is limited to PTX1 and PTX2. These two PTXs are mentioned only in EU legislation. This is because at the time of establishing the EU legislation, these were the only described PTXs that showed toxicity in mice when injected i.p. Currently, discussions are ongoing on increasing the regulatory limits or deregulating PTXs from EU legislation as the research shows that the oral toxicity is very low and the compound is not regulated by other international bodies such as CODEX [51].

O

CH3

+ Ring opening OH OH OH H3C O O

m/z 213

O

O O OH H3C O

O O

O 18 CH3

CH3 CH3 m/z 551

FIGURE 34.4  Proposed fragmentation of pectenotoxin-2 in ESI+.

[M+NH4 ]+ m/z 876

661

Analysis of Marine Biotoxins

34.3.3 Yessotoxins For the mass spectrometric analysis of YTXs, methods have been described using CE, nano-LC, conventional LC, and ultra-performance, high-resolution chromatography [52–54]. The first LC-MS application was actually with nano-LC and was developed by Draisci et al. [55]. The YTX molecule consists of a ladder-shaped polyether structure containing two sulfonic acid groups and was first discovered by Murate et al. [56]. Sulfonic acid groups are strong acids with a low pKa value which remains negatively charged even under acidic mobile-phase conditions. For this reason, it is preferred to produce negatively charged ions in the MS. As the compound contains two sulfonic acids under certain chromatographic conditions, the single-charged or double-charged ion will be observed [27]. Under acidic and neutral chromatographic conditions, it is mainly the single-charged ion (for YTX m/z 1,141 [M-H]−), while under alkaline chromatographic conditions, it is mainly the double-charged ion ([M-2H]2− m/z 570). MS fragments observed are mainly by the loss of a sulfonic acid group ([M-H-SO3]−, m/z 1,061) and some low in abundance backbone fragments of the polyether ladder shape [57] (Figure 34.5). In research, over 100 different YTXs have been described, most of them present in low abundance within shellfish and/or algae [58]. By applying different types of MS acquisition modes, new YTXs can be discovered. As mentioned before, the YTXs are producing fragments by the loss of a sulfonic acid (−80 Da), which is a neutral loss. By performing a neutral loss scan of 80 Da, all the corresponding m/z values of the precursor ions will be recorded and shown in the chromatogram. By using the additional criterion that the precursor ions should be in the mass range of m/z 900–m/z 1,500, the detected masses will then probably have a YTX-like structure [58]. YTXs were of interest as these toxins show i.p. toxicity in mice, and this method was the reference method for a long time for controlling compliance of shellfish. Currently, these compounds are under debate, because of the low oral toxicity of these toxins. Therefore, currently, efforts are undertaken to deregulate or increase the regulatory level within Europe [59]. Fortunately, for routine control programs, not all analogs that have been found are mentioned in the legislation, only YTX and 45OH YTX and their corresponding homologues that contain an additional methanediyl group –CH2– should be monitored. The homologues were discovered by Satake et al. in mussels from the Adriatic Sea in the late 1990s [60]. It should be mentioned that outside Europe, these toxins are not regulated. OSO3– O

SO3HO

OH

m/z 925

m/z 1061

O

OSO3– –O SO 3

[M–H]– m/z 1141

O O

O

O O

O

O

O O

O

OH

OH

O

O m/z 396

O

m/z 855

O

O

O

O O

O

O

m/z 467

OH

FIGURE 34.5  Proposed fragmentation of single-charged and double-charged YTX.

[M–2H]2– m/z 570

662

Handbook of Seafood and Seafood Products Analysis

O HO

m/z 655 + R3 + R4

R2

R1

A H

O

B O

O C

H

H

D O

R3 H

[M+H]+ m/z 826 + R3 + R4 +

R4

m/z 362 H2N I

E O OH H OH

H H O O G H O F

m/z 446 + R3 + R4

FIGURE 34.6  Proposed fragmentation of azaspiracids using ESI+, AZA1 R3 = CH3, R4 = H; AZA2 R3 = CH3, R4 = H; and AZA3 R3 and R4 are H.

34.3.4 Azaspiracids The last group of regulated marine lipophilic toxins to be discussed is the azaspiracids (AZAs) group that currently comprises over 30 analogs. The first discovery of the presence of azaspiracids (AZA1) in shellfish was after an intoxication incident in the Netherlands where people turned ill after the consumption of Irish shellfish [61]. The first MS method was developed by Ofuji et al. after a second intoxication incident with Irish shellfish and showed the presence of other azaspiracids, AZA2 and AZA3 [62]. Ion trap MS experiments were used to elucidate and propose structures for various azaspiracids (AZA1 to -6) [63]. By now, MS revealed many analogs with typical AZA MS fragmentation existing of multiple losses of water (n = 1–6) and different ring cleavages [64]. The cleavage of the A ring in the AZA molecule gives after the losses of water the most abundant fragment of 656 Da plus the mass of the residual groups at positions R3 and R4 (Figure 34.6). For AZA1 and AZA2, the residual group R3 is a methyl group while for AZA3 this is hydrogen. The R4 for AZA1–3 is a hydrogen group. After the discovery of these AZAs, many others have been discovered or suggested. These AZAs appear in shellfish after carboxylation, hydroxylation, and other metabolic processes [65,66]. In AZA research also, different MS scanning techniques are used to reveal structures. For the discovery of new AZAs, often precursor scanning of the E ring fragment m/z 362 was used. For the AZAs conversion and metabolism, studies were performed using mass spectrometry, which resulted, for example, in the discovery of the decarboxylation of AZA17 and AZA19 to AZA3 and AZA6, respectively [67]. Also recently, other types of AZA-like molecules were discovered in different strains of the Azadinium species [68,69]. These AZA-like molecules have similar fragmentation characteristics; the only difference is that these compounds are lacking a methyl group on the I ring and therefore these new azaspiracids have a common fragment of m/z 348 [m/z 362–14 Da (CH2)]. In regulation, EU and CODEX, AZA1, -2, and -3 are the only three analogs mentioned.

34.4 NONREGULATED LIPOPHILIC MARINE BIOTOXINS: CYCLIC IMINES Nonregulated lipophilic marine biotoxins are the “emerging toxins” cyclic imines (CIs) comprising spirolides (SPXs), gymnodimines (GYMs), pinnatoxins (PnTXs), and pteriatoxins (PtTXs). These toxins are fast-acting toxins and will cause mouse death within minutes after i.p. injection. As oral toxicity studies for most of the compounds are still lacking and there is not enough evidence if these toxins are a real concern for human health, they can also be classified as interfering compounds toward the MBA. Therefore, we should be cautious to designate a compound that is causing i.p. lethality in a mouse as a new toxin without having the evidence that this compound is a real concern for human health. Fortunately, nowadays decision-makers are reserved to establish legislation when there is not enough toxicology, epidemiology, and occurrence data. CIs are under the attention of researchers and decision-makers, but more data should be gathered to make appropriate decisions

663

Analysis of Marine Biotoxins

on establishing legislation for the various CIs. One of the aspects that should be covered is the occurrence data, so analytical methods such as LC-MS need to be established for these compounds groups. Due to their lipophilic character, these compounds can be easily incorporated in the existing methods of extraction, and they show comparable retention behavior as the regulated lipophilic marine biotoxins. CIs are macrocyclic compounds with an imine functionality and a spiro-linked ether group. The presence of the imine functionality makes the molecule readily ionizable in ESI+.

34.4.1 Spirolides The first CI group discussed are the SPXs, which were investigated by Hu et al. using LC-MS [70]. In this paper, the authors described the presence of SPX B-D in scallops with LC ion spray MS. From the fragmentation data, it is clear that the main fragment produced (m/z 164) in MS/MS is the ring containing the imine functionality. This fragment is very specific for most of the SPXs found in algae and shellfish (Figure 34.7). The causative organism Alexandrium ostenfeldii was identified by using LC-MS/MS as a tool to prove the presence of SPXs, which was mainly 13-desmethyl SPX C [71]. In 2005, Aasen et al. published on the discovery of 20-methyl spirolide G. These authors used the precursor scanning functionality of the MS to discover possible new SPX analogs within shellfish and algae samples. By scanning the precursor ions that produce fragment ion m/z 164, the authors discovered a compound with a precursor ion m/z 706 and with comparable MS/MS spectra as the other SPX compounds. NMR confirmed the structure of 20-methyl SPX G [72]. Also, the presence of fatty acid esters of SPX in shellfish was discovered using the same precursor scanning technique [73]. Esters with different chain length (C-12–C-22) and different saturations were discovered (up to 6). The fatty acid ester profiles of the SPXs in shellfish are comparable with the ester profiles of the okadaic acid group toxins. When performing the analysis of various SPXs in shellfish, the most abundant spirolide is 13-desmethyl SPX C and the other compounds are present in minor amounts.

34.4.2 Gymnodimines GYMs have comparable properties as the SPXs although for the GYMs not that many analogs have been discovered. There are three major compounds—GYM A, B, and C—of which GYM A was the first one isolated from oysters and a tentative elemental composition (C32H45NO4) was determined with FABMS [74]. In 2000, Miles et al. discovered new analogs, GYM B and 18-deoxy GYM B for which electron impact MS was used to accurately determine the mass and NMR was used to confirm the structure [75]. In 2003, the same research group discovered GYM C, the isomer of GYM B, in Karenia selliformis [76]. Again, electron impact MS was used for the determination of the accurate mass; the difference in structure between GYM A and GYM B/C is the position (endo/exo) of the methylene group at the C-17 position, and an additional hydroxyl group is present [M+H]+ m/z 692

H3C

O

+

O

NH

m/z 444 m/z 404 HO

O H

O HO

m/z 164

O

H3C

FIGURE 34.7  Fragmentation of 13-desmethyl SPX C after retro Diels–Alder ring opening.

664

Handbook of Seafood and Seafood Products Analysis

at C-18. In 2011, in North Carolina (the United States), Alexandrium peruvianum was found to produce a novel GYM compound. By applying NMR and ESI in combination with high-resolution MS and MS/MS, the structure of 12-methylgymnodimine A was identified [77]. As no legislation is established on GYMs, these compounds are not incorporated in routine monitoring programs. Most methodologies developed for the routine detection of lipophilic marine biotoxins can easily incorporate the CIs and therefore also the GYMs. Just as with the SPXs, it is assumed that GYMs can also be metabolized to fatty acid esters when accumulating in shellfish. de la Iglesia et al. recently discovered in shellfish originating from North Africa (Tunisia), where harmful algae blooms of K. selliformis are present continuously, various GYMs containing fatty acid esters. By using a linear ion trap MS and orbitrap MS, the fragmentation pathway of GYM A was proposed. Furthermore, by performing a precursor scan of the fragment m/z 490, various fatty acid esters were discovered with chain lengths of C14 till C22. Accurate mass measurements using the orbitrap MS confirmed the findings by showing small mass deviations of below 3 ppm between the predicted and the measured masses [78].

34.4.3 Pinnatoxins The last group of CIs discussed are the pinnatoxins (PnTXs) and their metabolites, the pteriatoxins (PtTXs). For the PnTXs, various structures have been discovered. The elucidation of PnTX A was done by Uemura et al. who extracted PnTX A from Pinna muricata [79]. The application of both NMR and high-resolution FAB MS resulted in the elucidation of the toxin with an elemental composition of C41H61NO9. Later, Chou et  al. found PnTX D and Takada et  al. PnTX B and C [80,81]. Takada et al. used ESI-MS/MS to propose fragmentation pathways. The most abundant ion for the PnTXs are the protonated molecules (i.e., PnTX B [M + H]+ m/z 741), which is followed by a characteristic fragment m/z 164 that is the same fragment obtained as for the SPXs. More recently, Selwood et al. found PnTX E, F, and G in pacific oysters (Pinna bicolor) using tandem mass spectrometry. Selwood et  al. showed the oxidation of PnTX G and further metabolism to PnTX A–C and the formation of cysteine conjugates PtTX A–C. Similarly, PnTX F is hydrolyzed and metabolized (oxidized) toward PnTX E and D [82]. For the detection of PnTX A, -E, -F, and -G, a dedicated rapid LC-MS/MS method using start-of-the-art LC column material with sub2-μm particles has been developed, which has a run-to-run time of less than 5 min. Furthermore, fatty acids of PnTXs have been discovered in shellfish; this was done by performing a precursor scan of the fragment m/z 164 [83]. Additionally, the identity of PnTXs, which have comparable fragmentation and structures as SPXs, was identified by performing an alkaline hydrolysis. During the hydrolysis, PnTXs remain stable while SPXs are degrading [84]. PnTXs are not routinely monitored within monitoring programs, although more data should be gathered to get an idea on occurrence and exposure [85].

34.5 NONREGULATED LIPOPHILIC MARINE BIOTOXINS: CIGUATOXINS Ciguatera (CTX) fish poisoning is the most frequently occurring marine biotoxin intoxication and mainly in tropical regions. It is very difficult or even impossible to set up monitoring programs to test the presence of CTX in fish. This is due to the high variability of the toxin concentration among different fish species and to a large inter-species variation. Therefore, currently, the best method to reduce the number of intoxications is probably by creating awareness of which fish species are more vulnerable to CTX and should not be consumed. Most of the work done with LC-MS on CTXs is related to the identification of different toxins in fish [86–88]. The LC-MS method development of these toxins is mainly hampered by the lack of standards, which are not commercially available. Furthermore, the complexity of the molecules makes it difficult to analyze them by LC-MS. First of all, there is a wide variety of CTXs many of which are produced by algae (i.e., Gambierdiscus spp.) and are metabolized within fish to various different metabolites.

Analysis of Marine Biotoxins

665

The structurally different types of CTX are specific for the region they originate from. Currently, we have Pacific CTX (P-CTX), Caribbean CTX (C-CTX), and Indian CTX (I-CTX). The first MS methods on ciguatera (pacific CTX) and maitotoxin were described in the early 1990s by Lewis et al. and Yasumoto et al. [89,90]. Much later, Yogi et al. described the use of more modern LC-MS/MS equipment for the discovery of a wide variety of P-CTXs including fish metabolites [87]. Yogi et  al. reported, when using water and methanol in the mobile phase, that in positive ionization mode, the most abundant precursor ion observed is [M + Na]+ while when using acetonitrile mainly protonated molecular ions [M + H]+ are observed. The latter ion produced mainly losses of water when applying CID. The sodium adduct under water/methanol conditions is by far the most abundant ion. Even when a relatively high collision energy is applied, the sodium is very stable and almost no fragmentation occurs. Therefore, for the monitoring of the presence of P-CTXs and C-CTXs in fish, the protonated molecule as precursor ion in combination with the multiple dehydrations (n = 1–3) of the molecule as product ions have been used [91]. Alternatively, the sodium adduct can be used as both precursor ion and product ion, while applying a high collision energy. Both approaches do not produce very specific fragments. And as most laboratories do not have access to (certified) standards of CTXs, researchers should be more alert on possible false positives when using these types of fragment ions. Due to the absence of standards, analytical methods are difficult to develop. Therefore, for determining the presence of CTXs in fish after an intoxication incident, a combination of (in vitro) bioassays and mass spectrometry can be used [92].

34.6 VALIDATED AND REGULATED METHODOLOGIES In order to test compliance of shellfish with various types of legislation, most often still animalbased testing or traditional analytical techniques such as LC-UV or LC-FLD are used. The disadvantage of the animal-based testing is that certain compounds can be very toxic to the test animal but less or even nontoxic to humans. For example, the MBA for lipophilic marine biotoxins is performed by injecting a certain amount of extract of intraperitoneal. Clearly, this administration route is different from oral administration. YTXs and PTXs show i.p. a relatively high toxicity, while in oral studies with rats or mice, almost no toxicity is observed [93,94]. It seems that the MBA method using i.p. injection is a worst-case scenario testing, which can cause quite a large number of false positives and a negative impact for the industry. Of course, there are also pros of this animalbased testing. The advantage of animal-based tests compared to chemical methods is that they are toxicity-driven, although the administration route is completely different from oral exposure. So comparing chemical methods with animal-based methods is impossible as chemical methods are based on the chemical properties of compounds and animal-based testing on the toxicology. The authors’ opinion is that more toxic-effect-based in vitro assays should become available because these assays are based on the same principles as the animal tests and show the direct effect of toxic compounds. Of course, after screening with these assays, the suspect samples should be confirmed using more specific techniques such as LC-MS/MS. Chemical testing with LC-UV and LC-FLD has been widely accepted and is for some of the toxin groups like ASP and PSP also the official methodology for routine monitoring [95]. Although these methods have many pros over the MBA, there are also some cons toward the LC-MS/MS methodologies. In contrast to MS, with these techniques (LC-FLD being more specific than LC-UV), the identity of the toxin cannot be completely confirmed. This is greatly improved when using tandem mass spectrometric tools, because it is possible to confirm the identity of a compound based on the measurement of at least two transitions per toxin. The relation between the intensity of the two transitions, the so-called ion ratio, is an additional confirmatory criterion that can be used. These identity confirmation criteria, such as ion ratio and retention time, are used to compare a toxin in a standard solution and the possible toxin in a shellfish sample [96,97]. If all the criteria are fulfilled and the concentration is above the regulatory level including the measurement uncertainty, the

666

Handbook of Seafood and Seafood Products Analysis

sample can be regarded as noncompliant. The major drawbacks of this approach are that for all the toxins measured, a standard is needed, and that only those toxins will be analyzed that are defined in the MRM (or SRM) method before the analysis. With a bioassay (in vivo or in vitro), a broader scope of toxins, based on their mode of action, can be analyzed although less specific. With respect to the legislation applied by different countries for certain toxins, there is some discrepancy, like for YTXs and PTXs which are regulated in the EU, but not outside the EU, while brevetoxins are included in FDA and CODEX and are not mentioned in the EU legislation [98,99]. Of course, this has an impact on global trade. Therefore, it is desirable that worldwide the same toxins and allowed maximum levels are established in legislation as shellfish are distributed all over the world.

34.6.1 Validated LC-MS/MS Methodologies Here, the different in-house or inter-laboratory validated MS methodologies will be discussed. For DA, there seems to be a tendency to not have full collaborative studies. Probably, this is due to the fact that DA is just a single toxin and most laboratories are either using LC-UV or already in-house validated LC-MS methodologies. DA could also be included in LC-MS/MS methods dedicated to lipophilic marine biotoxins [54,100]. With respect to LC-MS/MS analysis, most validation studies have been conducted for the lipophilic marine biotoxins (both in-house and inter-laboratory). The two first LC-MS/MS method validations were done by Stobo et al. and McNabb et al. [100,101]. Both these methods showed good performance characteristics and are since then applied in more advanced laboratories. The research group of McNabb et  al. already in 2005 implemented the LC-MS/MS methodology in their routine monitoring program. The major drawback of the validation studies from that time was the lack of certified standards. In 2005, only for OA and PTX2, commercial certified standards were available at the National Research Council (NRC) in Halifax, Canada. Therefore, formally only the certified biotoxins included in this method were validated. From 2005 till now, the NRC in Canada has put a lot of effort in the development of certified reference standards and nowadays over a dozen standards for lipophilic marine biotoxins are available from the NRC. Recently, CIFGA, an European producer, also started certifying marine biotoxin standards. The limited availability of the standards and the poor performance of the MBA for lipophilic marine biotoxins resulted in multiple efforts to develop and validate LC-MS/MS methodologies that can be used for official control programs. At this moment, three full collaborative studies have been held for lipophilic marine biotoxins, including OA, DTX1, -2, AZA1, -2, -3 PTX2, and YTX. The first study was organized by BfR in Berlin, Germany [102]. The method validated consisted of a fixed extraction procedure using 80% v/v MeOH. With respect to the LC-MS/MS measurements, chromatographic conditions used by the participants were free of choice. So either acidic and/or alkaline chromatographic conditions were used. The study was conducted with good results, which were obtained for different lipophilic biotoxins in extracts and in shellfish materials. The second study was organized by RIKILT, Wageningen, the Netherlands. Compared to the BfR study, there were minor changes in the extraction and the method of constructing calibration curves. In this study, matrix-matched standards were used, which was done to compensate for matrix effects in LC-MS/MS. Furthermore, a slight change in extraction (100% methanol) and fixed alkaline chromatographic conditions were used [103]. Also, this study obtained good results with exceptional low RSDs. The last collaborative trial was organized by the European Reference Laboratory, Vigo, Spain, the study of which had a fixed extraction procedure but chromatographic conditions were open (acidic or alkaline). Also, this study showed good results, and this method was proposed as the official reference method. The outcomes of these studies convinced the EU, and their member states that these MS methods are a good replacement for the MBA. Therefore, on July 1, 2011, the official method for the detection of lipophilic marine biotoxins in Europe was mass spectrometrybased [28]. After New Zealand, the EU is now using LC-MS/MS as the official monitoring method.

Analysis of Marine Biotoxins

667

34.6.2 Future Perspectives Within the LC-MS method development for marine biotoxins, it is expected that trends that are already present in other research areas such as the mycotoxins, veterinary drugs, and pesticides will also enter this field. A major difference with these other fields is the way by which methods are allowed for official control. In marine biotoxins analysis, predescribed methods should be used as reference methods where in the other fields the method performance criteria are predescribed and laboratories are allowed to use their own developed methods for official control. Of course, these methods should perform satisfactorily within proficiency testing schemes and preferably standardized, but this is not always feasible. The problem within the marine biotoxin field is that by the time a method is incorporated in legislation a better method has likely become available. For example, for the lipophilic marine biotoxins in legislation, the LC-MS method is mentioned. This method is using conventional LC while nowadays many laboratories have access to UHPLC equipment and are able to perform a more rapid analysis with comparable or better performance characteristics [104]. Currently, the chosen approach for establishing reference methods is hampering the implementation of the latest techniques in official control programs. Currently, within CODEX, discussions are ongoing on the establishment of performance criteria for screening methods. Screening can, for example, also be done using high-resolution mass spectrometry like ToF or Orbitrap MS. These techniques were already incorporated within the studies on a wide variety of marine biotoxins or in metabolite research [50,105,106]. But for screening in official monitoring, these techniques are not yet established. Preferably, a single rapid screening method should be developed for a wide variety of marine biotoxins. If the sample is compliant, the work is done. If a sample is suspected to be noncompliant, a dedicated LC-MS/ MS method only for the specific toxin group can be applied. This approach is already used in areas of veterinary drugs, doping analysis, and currently also pesticide analysis [107–109]. A beneficial aspect of this approach is that with a high-resolution mass spectrometry, data can also be investigated retrospectively. This means when international bodies such as the European Food Safety Authority want to collect data on the occurrence of a certain new emerging toxin, for instance, pinnatoxins, only data analyses have to be performed in order to see if these toxins are present. The data analysis of high-resolution mass spectrometry still has drawbacks. Each data file can be very large (>100 Mb), which can result in laborious data handling. Thankfully, vendors of MS equipment realize this drawback and are working on the development of these data analysis tools. Currently, the data analysis hampers the implementation of these techniques in routine control programs. The last developments that are foreseen are regarding new types of MS technologies. Within the marine biotoxins field, to the best of my knowledge, no ambient ionization techniques have been applied. So, for example, the use of desorption ESI or direct analysis in real time is promising [110,111]. With these techniques, it is possible to perform a direct analysis from a surface of a sample that can be shellfish meat or algae supplements or whatever. No chromatographic separation is involved with these techniques. These techniques can be of interest for rapid screening of samples. The latest development in mass spectrometry is the use of so-called ion mobility mass spectrometry [112,113]. With this technique, it is possible to separate isomeric compounds in the gas phase. Especially with natural compounds such as marine biotoxins, it is expected that many isomeric compounds can be present. Some of these isomeric compounds will not be resolved with the applied LC separation. The ion mobility MS has the power to separate these different isomeric ions based on the cross section of the molecule. Of course, more complex datasets are generated as there is an additional dimension (drift time) added. The application of these techniques in marine biotoxins research is still in its infancy, but it is expected that in the next years, the number of applications and publications will increase.

668

Handbook of Seafood and Seafood Products Analysis

REFERENCES 1. Quilliam, M. A., and Wright, J. L. C., 1989. The amnesic shellfish poisoning mystery. Analytical Chemistry, 61, 1053A–1060A. 2. Pineiro, N., Vaquero, E., Leao, J. M., Gago-Martinez, A., and Vazquez, J. A. R., 2001. Optimization of conditions for the liquid chromatographic-electrospray ionization-mass spectrometric analysis of amnesic shellfish poisoning toxins. Chromatographia, 53, S231–S235. 3. Furey, A., Lehane, M., Gillman, M., Fernandez-Puente, P., and James, K. J., 2001. Determination of domoic acid in shellfish by liquid chromatography with electrospray ionization and multiple tandem mass spectrometry. Journal of Chromatography A, 938, 167–174. 4. Ciminiello, P., Dell’Aversano, C., Fattorusso, E. et al., 2005. Hydrophilic interaction liquid chromatography/mass spectrometry for determination of domoic acid in Adriatic shellfish. Rapid Communications in Mass Spectrometry, 19, 2030–2038. 5. McNabb, P., Selwood, A. I., Holland, P. T. et  al., 2005. Multiresidue method for determination of algal toxins in shellfish: Single-laboratory validation and interlaboratory study. Journal of AOAC International, 88, 761–772. 6. Holland, P. T., McNabb, P., Selwood, A. I., and Neil, T., 2003. Amnesic shellfish poisoning toxins in shellfish: Estimation of uncertainty of measurement for a liquid chromatography/tandem mass spectrometry method. Journal of AOAC International, 86, 1095–1100. 7. de la Iglesia, P., Barber, E., Gimenez, G., Rodriguez-Velasco, M. L., Villar-Gonzalez, A., and Diogene, J., 2011. High-throughput analysis of amnesic shellfish poisoning toxins in shellfish by ultra-performance rapid resolution LC-MS/MS. Journal of AOAC International, 94, 555–564. 8. Wiese, M., D’Agostino, P. M., Mihali, T. K., Moffitt, M. C., and Neilan, B. A., 2010. Neurotoxic alkaloids: Saxitoxin and its analogs. Marine Drugs, 8, 2185–2211. 9. Alexander, J., Benford, D., Cockburn, A. et al., 2009. Marine biotoxins in shellfish-Saxitoxin group. The European Food Safety Authority Journal, 1019, 1–76. 10. Oshima, Y., 1995. Postcolumn derivatization liquid-chromatographic method for paralytic shellfish toxins. Journal of AOAC International, 78, 528–532. 11. Lawrence, J. F., Niedzwiadek, B., and Menard, C., 2005. Quantitative determination of paralytic shellfish poisoning toxins in shellfish using prechromatographic oxidation and liquid chromatography with fluorescence detection: Collaborative study. Journal of AOAC International, 88, 1714–1732. 12. van de Riet, J., Gibbs, R. S., Muggah, P. M., Rourke, W. A., MacNeil, J. D., and Quilliam, M. A., 2011. Liquid chromatography post-column oxidation (PCOX) method for the determination of paralytic shellfish toxins in mussels, clams, oysters, and scallops: Collaborative study. Journal of AOAC International, 94, 1154–1176. 13. DeGrasse, S. L., van de Riet, J., Hatfield, R., and Turner, A., 2011. Pre- versus post-column oxidation liquid chromatography fluorescence detection of paralytic shellfish toxins. Toxicon, 57, 619–624. 14. Turner, A. D., Hatfield, R. G., Rapkova, M. et  al., 2011. Comparison of AOAC 2005.06 LC official method with other methodologies for the quantitation of paralytic shellfish poisoning toxins in UK shellfish species. Analytical and Bioanalytical Chemistry, 399, 1257–1270. 15. Dell’Aversano, C., Hess, P., and Quilliam, M. A., 2005. Hydrophilic interaction liquid chromatographymass spectrometry for the analysis of paralytic shellfish poisoning (PSP) toxins. Journal of Chromatographic A, 1081, 190–201. 16. Diener, M., Erler, K., Christian, B., and Luckas, B., 2007. Application of a new zwitterionic hydrophilic interaction chromatography column for determination of paralytic shellfish poisoning toxins. Journal of Separation Science, 30, 1821–1826. 17. Jaime, E., Hummert, C., Hess, P., and Luckas, B., 2001. Determination of paralytic shellfish poisoning toxins by high-performance ion-exchange chromatography. Journal of Chromatography A, 929, 43–49. 18. Buzy, A., Thibault, P., and Laycock, M. V., 1994. Development of a capillary electrophoresis method for the characterization of enzymatic products arising from the carbamoylase digestion of paralytic shellfish poisoning toxins. Journal of Chromatography A, 688, 301–316. 19. Locke, S. J., and Thibault, P., 1994. Improvement in detection limits for the determination of paralytic shellfish poisoning toxins in shellfish tissues using capillary electrophoresis electrospray mass-spectrometry and discontinuous buffer systems. Analytical Chemistry, 66, 3436–3446. 20. Quilliam, M. A., Hess, P., and Dell’Aversano, C., 2001. Recent developments in the analysis of phycotoxins by liquid chromatography-mass spectrometry, In Mycotoxins and Phycotoxins in Perspective at the Turn of the Century, de Koe, W. J., Samson, R. A., van Egmond, H. P., Gilbert, J., and Sabino, M., Eds. Wageningen, Ponsen and Looijen, the Netherlands, pp. 383–391.

Analysis of Marine Biotoxins

669

21. Turrell, E., Stobo, L., Lacaze, J. P., Piletsky, S., and Piletska, E., 2008. Optimization of hydrophilic interaction liquid chromatography/mass spectrometry and development of solid-phase extraction for the determination of paralytic shellfish poisoning toxins. Journal of AOAC International, 91, 1372–1386. 22. Sayfritz, S. J., Aasen, J. A., and Aune, T., 2008. Determination of paralytic shellfish poisoning toxins in Norwegian shellfish by liquid chromatography with fluorescence and tandem mass spectrometry detection. Toxicon, 52, 330–340. 23. Blay, P., Hui, J. P., Chang, J., and Melanson, J. E., 2011. Screening for multiple classes of marine biotoxins by liquid chromatography-high-resolution mass spectrometry. Analytical and Bioanalytical Chemistry, 400, 577–585. 24. Lajeunesse, A., Segura, P. A., Gelinas, M. et al., 2012. Detection and confirmation of saxitoxin analogues in freshwater benthic Lyngbya wollei algae collected in the St. Lawrence River (Canada) by liquid chromatography-tandem mass spectrometry. Journal of Chromatography A, 1219, 93–103. 25. Vale, P., 2010. New saxitoxin analogues in the marine environment: Developments in toxin chemistry, detection and biotransformation during the 2000s. Phytochemistry Reviews, 9, 525–535. 26. Stobo, L. A., Lacaze, J. P., Scott, A. C., Gallacher, S., Smith, E. A., and Quilliam, M. A., 2005. Liquid chromatography with mass spectrometry-Detection of lipophilic shellfish toxins. Journal of AOAC International, 88, 1371–1382. 27. Gerssen, A., Mulder, P. P., McElhinney, M. A., and de Boer, J., 2009. Liquid chromatography-tandem mass spectrometry method for the detection of marine lipophilic toxins under alkaline conditions. Journal of Chromatography A, 1216, 1421–1430. 28. Anon, 2011. Commission regulation (EU) No 15/2011 of 10 January 2011 amending regulation (EC) No 2074/2005 as regards recognised testing methods for detecting marine biotoxins in live bivalve molluscs 15/2011, C. R. E. N. Official Journal of European Communications, 2011, 3–6. 29. McCarron, P., Kotterman, M., de Boer, J., Rehmann, N., and Hess, P., 2007. Feasibility of gamma irradiation as a stabilisation technique in the preparation of tissue reference materials for a range of shellfish toxins. Analytical and Bioanalytical Chemistry, 387, 2487–2493. 30. McCarron, P., Kilcoyne, J., Miles, C. O., and Hess, P., 2009. Formation of Azaspiracids-3, -4, -6, and -9 via decarboxylation of carboxy azaspiracid metabolites from shellfish. Journal of Agricultural and Food Chemistry, 57, 160–169. 31. Bencsath, F. A., and Dickey, R. W., 1991. Mass spectral characteristics of okadaic acid and simple derivatives. Rapid Communications in Mass Spectrometry, 5, 283–290. 32. Pleasance, S., Quilliam, M. A., and Marr, J. C., 1992. Ion spray mass spectrometry of marine toxins. IV. Determination of diarrhetic shellfish poisoning toxins in muscle tissue by liquid chromatography/mass spectrometry. Rapid Communications in Mass Spectrometry, 6, 121–127. 33. Quilliam, M. A., 1995. Analysis of diarrhetic shellfish poisoning toxins in shellfish tissue by liquidchromatography with fluorometric and mass-spectrometric detection. Journal of AOAC International, 78, 555–570. 34. Gerssen, A., Mulder, P., van Rhijn, H., and de Boer, J., 2008. Mass spectrometric analysis of the marine lipophilic biotoxins pectenotoxin-2 and okadaic acid by four different types of mass spectrometers. Journal of Mass Spectrometry, 43, 1140–1147. 35. Carey, B., Fidalgo Saez, M. J., Hamilton, B., O’Halloran, J., van Pelt, F. N., and James, K. J., 2012. Elucidation of the mass fragmentation pathways of the polyether marine toxins, dinophysistoxins, and identification of isomer discrimination processes. Rapid Communications in Mass Spectrometry, 26, 1793–1802. 36. Torgersen, T., Miles, C. O., Rundberget, T., and Wilkins, A. L., 2008. New esters of okadaic acid in seawater and blue mussels (Mytilus edulis). Journal of Agricultural and Food Chemistry, 56, 9628–9635. 37. Torgersen, T., Wilkins, A. L., Rundberget, T., and Miles, C. O., 2008. Characterization of fatty acid esters of okadaic acid and related toxins in blue mussels (Mytilus edulis) from Norway. Rapid Communications in Mass Spectrometry, 22, 1127–1136. 38. Hu, T. M., Curtis, J. M., Walter, J. A., McLachlan, J. L., and Wright, J. L. C., 1995. Two new watersoluble DSP toxin derivatives from the dinoflagellate Prorocentrum maculosum: Possible storage and excretion products. Tetrahedron Letters, 36, 9273–9276. 39. Cruz, P. G., Daranas, A. H., Fernandez, J. J., Souto, M. L., and Norte, M., 2006. DTX5c, a new OA sulphate ester derivative from cultures of Prorocentrum belizeanum. Toxicon, 47, 920–924. 40. Suarez-Gomez, B., Souto, M. L., Cruz, P. G., Fernandez, J. J., and Norte, M., 2005. New targets in diarrhetic shellfish poisoning control. Journal of Natural Products, 68, 596–599. 41. Suarez-Gomez, B., Souto, M. L., Norte, M., and Fernandez, J. J., 2001. Isolation and structural determination of DTX-6, a new okadaic acid derivative. Journal of Natural Products, 64, 1363–1364.

670

Handbook of Seafood and Seafood Products Analysis

42. Paz, B., Daranas, A. H., Cruz, P. G. et  al., 2007. Identification and characterization of DTX-5c and 7-hydroxymethyl-2-methylene-octa-4,7-dienyl okadaate from Prorocentrum belizeanum cultures by LC-MS. Toxicon, 50, 470–478. 43. Miles, C. O., Wilkins, A. L., Hawkes, A. D. et al., 2006. Isolation and identification of pectenotoxins-13 and -14 from Dinophysis acuta in New Zealand. Toxicon, 48, 152–159. 44. Miles, C. O., Wilkins, A. L., Samdal, I. A. et al., 2004. A novel pectenotoxin, PTX-12, in Dinophysis spp. and shellfish from Norway. Chemical Research in Toxicology, 17, 1423–1433. 45. Yasumoto, T., Murata, M., Oshima, Y., Sano, M., Matsumoto, G. K., and Clardy, J., 1985. Diarrhetic shellfish toxins. Tetrahedron, 41, 1019–1025. 46. Suzuki, T., Mackenzie, L., Stirling, D., and Adamson, J., 2001. Pectenotoxin-2 seco acid: A toxin converted from pectenotoxin-2 by the New Zealand Greenshell mussel, Perna canaliculus. Toxicon, 39, 507–514. 47. Goto, H., Igarashi, T., Yamamoto, M. et al., 2001. Quantitative determination of marine toxins associated with diarrhetic shellfish poisoning by liquid chromatography coupled with mass spectrometry. Journal of Chromatography A, 907, 181–189. 48. Suzuki, T., Mitsuya, T., Matsubara, H., and Yamasaki, M., 1998. Determination of pectenotoxin-2 after solid-phase extraction from seawater and from the dinoflagellate Dinophysis fortii by liquid chromatography with electrospray mass spectrometry and ultraviolet detection-Evidence of oxidation of pectenotoxin-2 to pectenotoxin-6 in scallops. Journal of Chromatography A, 815, 155–160. 49. Torgersen, T., Sandvik, M., Lundve, B., and Lindegarth, S., 2008. Profiles and levels of fatty acid esters of okadaic acid group toxins and pectenotoxins during toxin depuration. Part II: Blue mussels (Mytilus edulis) and flat oyster (Ostrea edulis). Toxicon, 52, 418–427. 50. Gerssen, A., Mulder, P. P., and de Boer, J., 2011. Screening of lipophilic marine toxins in shellfish and algae: Development of a library using liquid chromatography coupled to orbitrap mass spectrometry. Analytica Chimica Acta, 685, 176–185. 51. Alexander, J., Benford, D., Cockburn, A. et al., 2009. Marine biotoxins in shellfish-Pectenotoxin group. The European Food Safety Authority Journal, 1109, 1–47. 52. de la Iglesia, P., Gago-Martinez, A., and Yasumoto, T., 2007. Advanced studies for the application of high-performance capillary electrophoresis for the analysis of yessotoxin and 45-hydroxyyessotoxin. Journal of Chromatography A, 1156, 160–166. 53. Draisci, R., Giannetti, L., Lucentini, L., Ferretti, E., Palleschi, L., and Marchiafava, C., 1998. Direct identification of yessotoxin in shellfish by liquid chromatography coupled with mass spectrometry and tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 12, 1291–1296. 54. Fux, E., McMillan, D., Bire, R., and Hess, P., 2007. Development of an ultra-performance liquid chromatography-mass spectrometry method for the detection of lipophilic marine toxins. Journal of Chromatography A, 1157, 273–280. 55. Draisci, R., Giannetti, L., Lucentini, L., Ferretti, E., Palleschi, L., and Marchiafava, C., 1998. Direct identification of yessotoxin in shellfish by liquid chromatography coupled with mass spectrometry and tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 12, 1291–1296. 56. Murata, M., Kumagai, M., Lee, J. S., and Yasumoto, T., 1987. Isolation and structure of yessotoxin, a novel polyether compound implicated in diarrhetic shellfish poisoning. Tetrahedron Letters, 28, 5869–5872. 57. Amandi, M. F., Furey, A., Lehane, M., Ramstad, H., and James, K. J., 2002. Liquid chromatography with electrospray ion-trap mass spectrometry for the determination of yessotoxins in shellfish. Journal of Chromatography A, 976, 329–334. 58. Miles, C. O., Samdal, I. A., Aasen, J. A. B. et al., 2005. Evidence for numerous analogs of yessotoxin in Protoceratium reticulatum. Harmful Algae, 4, 1075–1091. 59. Alexander, J., Benford, D., Cockburn, A. et al., 2008. Marine biotoxins in shellfish-Yessotoxin group. The European Food Safety Authority Journal, 907, 1–62. 60. Satake, M., Tubaro, A., Lee, J. S., and Yasumoto, T., 1997. Two new analogs of yessotoxin, homoyessotoxin and 45-hydroxyhomoyessotoxin, isolated from mussels of the Adriatic Sea. Natural Toxins, 5, 107–110. 61. Satake, M., Ofuji, K., Naoki, H. et al., 1998. Azaspiracid, a new marine toxin having unique spiro ring assemblies, isolated from Irish mussels, Mytilus edulis. Journal of the American Chemical Society, 120, 9967–9968. 62. Ofuji, K., Satake, M., McMahon, T. et  al., 1999. Two analogs of azaspiracid isolated from mussels, Mytilus edulis, involved in human intoxication in Ireland. Natural Toxins, 7, 99–102.

Analysis of Marine Biotoxins

671

63. Lehane, M., Brana-Magdalena, A., Moroney, C., Furey, A., and James, K. J., 2002. Liquid chromatography with electrospray ion trap mass spectrometry for the determination of five azaspiracids in shellfish. Journal of Chromatography A, 950, 139–147. 64. Brombacher, S., Edmonds, S., and Volmer, D. A., 2002. Studies on azaspiracid biotoxins. II. Mass spectral behavior and structural elucidation of azaspiracids analogs. Rapid Communications in Mass Spectrometry, 16, 2306–2316. 65. James, K. J., Sierra, M. D., Lehane, M., Brana-Magdalena, A., and Furey, A., 2003. Detection of five new hydroxyl analogues of azaspiracids in shellfish using multiple tandem mass spectrometry. Toxicon, 41, 227–283. 66. Rehmann, N., Hess, P., and Quilliam, M. A., 2008. Discovery of new analogs of the marine biotoxin azaspiracid in blue mussels (Mytilus edulis) by ultra-performance liquid chromatography/tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 22, 549–558. 67. McCarron, P., Kilcoyne, J., Miles, C. O., and Hess, P., 2009. Formation of azaspiracids-3, -4, -6, and -9 via decarboxylation of carboxyazaspiracid metabolites from shellfish. Journal of Agricultural and Food Chemistry, 57, 160–169. 68. Gu, H. F., Luo, Z. H., Krock, B., Witt, M., and Tillmann, U., 2013. Morphology, phylogeny and azaspiracid profile of Azadinium poporum (Dinophyceae) from the China Sea. Harmful Algae, 21–22, 64–75. 69. Krock, B., Tillmann, U., Voss, D. et al., 2012. New azaspiracids in Amphidomataceae (Dinophyceae). Toxicon, 60, 830–839. 70. Hu, T. M., Curtis, J. M., Oshima, Y. et al., 1995. Spirolide-B and spirolide-D, 2 novel macrocycles isolated from the digestive glands of shellfish. Journal of the Chemical Society-Chemical Communications, 1995, 2159–2161. 71. Cembella, A. D., Lewis, N. I., and Quilliam, M. A., 2000. The marine dinoflagellate Alexandrium ostenfeldii (Dinophyceae) as the causative organism of spirolide shellfish toxins. Phycologia, 39, 67–74. 72. Aasen, J. A. B., MacKinnon, S. L., LeBlanc, P. et al., 2005. Detection and identification of spirolides in Norwegian shellfish and plankton. Chemical Research in Toxicology, 18, 509–515. 73. Aasen, J. A. B., Hardstaff, W., Aune, T., and Quilliam, M. A., 2006. Discovery of fatty acid ester metabolites of spirolide toxins in mussels from Norway using liquid chromatography/tandem mass spectrometry. Rapid Communications in Mass Spectrometry, 20, 1531–1537. 74. Seki, T., Satake, M., Mackenzie, L., Kaspar, H. F., and Yasumoto, T., 1995. Gymnodimine, a new marine toxin of unprecedented structure isolated from New-Zealand oysters and the dinoflagellate, Gymnodinium sp. Tetrahedron Letters, 36, 7093–7096. 75. Miles, C. O., Wilkins, A. L., Stirling, D. J., and MacKenzie, A. L., 2000. New analogue of gymnodimine from a Gymnodinium species. Journal of Agricultural and Food Chemistry, 48, 1373–1376. 76. Miles, C. O., Wilkins, A. L., Stirling, D. J., and MacKenzie, A. L., 2003. Gymnodimine C, an isomer of gymnodimine B, from Karenia selliformis. Journal of Agricultural and Food Chemistry, 51, 4838–4840. 77. Van Wagoner, R. M., Misner, I., Tomas, C. R., and Wright, J. L. C., 2011. Occurrence of 12-methylgymnodimine in a spirolide-producing dinoflagellate Alexandrium peruvianum and the biogenetic implications. Tetrahedron Letters, 52, 4243–4246. 78. de la Iglesia, P., McCarron, P., Diogene, J., and Quilliam, M. A., 2013. Discovery of gymnodimine fatty acid ester metabolites in shellfish using liquid chromatography/mass spectrometry. Rapid Communications in Mass Spectrometry, 27, 643–653. 79. Uemura, D., Chou, T., Haino, T. et al., 1995. Pinnatoxin A - a toxic amphoteric macrocycle from the Okinawan bivalve Pinna-muricata. Journal of the American Chemical Society, 117, 1155–1156. 80. Takada, N., Umemura, N., Suenaga, K. et  al., 2001. Pinnatoxins B and C, the most toxic components in the pinnatoxin series from the Okinawan bivalve Pinna muricata. Tetrahedron Letters, 42, 3491–3494. 81. Chou, T., Haino, T., Kuramoto, M., and Uemura, D., 1996. Isolation and structure of pinnatoxin D, a new shellfish poison from the Okinawan bivalve Pinna muricata. Tetrahedron Letters, 37, 4027–4030. 82. Selwood, A. I., Miles, C. O., Wilkins, A. L. et al., 2010. Isolation, structural determination and acute toxicity of pinnatoxins E, F and G. Journal of Agricultural and Food Chemistry, 58, 6532–6542. 83. McCarron, P., Rourke, W. A., Hardstaff, W., Pooley, B., and Quilliam, M. A., 2012. Identification of pinnatoxins and discovery of their fatty acid ester metabolites in mussels (Mytilus edulis) from eastern Canada. Journal of Agricultural and Food Chemistry, 60, 1437–1446. 84. Rundberget, T., Aasen, J. A. B., Selwood, A. I., and Miles, C. O., 2011. Pinnatoxins and spirolides in Norwegian blue mussels and seawater. Toxicon, 58, 700–711.

672

Handbook of Seafood and Seafood Products Analysis

85. Alexander, J., Benford, D., Cockburn, A. et al., 2010. Scientific opinion on marine biotoxins in shellfishCyclic imines (spirolides, gymnodimines, pinnatoxins and pteriatoxins). The European Food Safety Authority Journal, 1628, 1–39. 86. Pottier, I., Vernoux, J. P., Jones, A., and Lewis, R. J., 2002. Characterisation of multiple Caribbean ciguatoxins and congeners in individual specimens of horse-eye jack (Caranx latus) by high-performance liquid chromatography/mass spectrometry. Toxicon, 40, 929–939. 87. Yogi, K., Oshiro, N., Inafuku, Y., Hirama, M., and Yasumoto, T., 2011. Detailed LC-MS/MS analysis of ciguatoxins revealing distinct regional and species characteristics in fish and causative alga from the Pacific. Analytical Chemistry, 83, 8886–8891. 88. Hamilton, B., Hurbungs, M., Jones, A., and Lewis, R. J., 2002. Multiple ciguatoxins present in Indian Ocean reef fish. Toxicon, 40, 1347–1353. 89. Lewis, R. J., Holmes, M. J., Alewood, P. F., and Jones, A., 1994. Ionspray mass spectrometry of ciguatoxin-1, maitotoxin-2 and -3, and related marine polyether toxins. Natural Toxins, 2, 56–63. 90. Murata, M., Naoki, H., Matsunaga, S., Satake, M., and Yasumoto, T., 1994. Structure and partial stereochemical assignments for maitotoxin, the most toxic and largest natural non-biopolymer. Journal of the American Chemical Society, 116, 7098–7107. 91. Lewis, R. J., Jones, A., and Vernoux, J. P., 1999. HPLC/tandem electrospray mass spectrometry for the determination of sub-ppb levels of Pacific and Caribbean ciguatoxins in crude extracts of fish. Analytical Chemistry, 71, 247–250. 92. Abraham, A., Jester, E. L. E., Granade, H. R., Plakas, S. M., and Dickey, R. W., 2012. Caribbean ciguatoxin profile in raw and cooked fish implicated in ciguatera. Food Chemistry, 131, 192–198. 93. Tubaro, A., Dell’Ovo, V., Sosa, S., and Florio, C., 2010. Yessotoxins: A toxicological overview. Toxicon, 56, 163–172. 94. Miles, C. O., Wilkins, A. L., Munday, R. et al., 2004. Isolation of pectenotoxin-2 from Dinophysis acuta and its conversion to pectenotoxin-2 seco acid, and preliminary assessment of their acute toxicities. Toxicon, 43, 1–9. 95. Anon, 2005. Commission Regulation (EC) No 2074/2005 of 5 December 2005 laying down implementing measures for certain products under Regulation (EC) No 853/2004 of the European Parliament and of the Council and for the organisation of official controls under Regulation (EC) No 854/2004 of the European Parliament and of the Council and Regulation (EC) No 882/2004 of the European Parliament and of the Council, derogating from Regulation (EC) No 852/2004 of the European Parliament and of the Council and amending Regulations (EC) No 853/2004 and (EC) No 854/2004 (Text with EEA relevance), 2074/2005, C. R. E. N. Official Journal of European Communications, L338, 27–59. 96. Anon, 2002. Commission Decision of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results (2002/657/EC). Official Journal of the European Communities, L221, 8–36. 97. Anon, 2011. Method validation and quality control procedures for pesticide residues analysis in food and feed, SANCO/12495/2011, D. N. https://ec.europa.eu/food/plant/plant_protection_products/guidance_documents/docs/qualcontrol_en.pdf, 2011, last accessed February 10, 2013. 98. Anon, 2004. Commission Directive 2004/853/EC specific hygiene rules for food of animal origin, 2004/853/ EC, C. d. Official Journal of European Communications, L226, 22–82. 99. Anon, 2008. Standard for live and raw bivalve molluscs, 292–2008, C. S. 100. McNabb, P., Selwood, A. I., and Holland, P. T., 2005. Multiresidue method for determination of algal toxins in shellfish: Single-laboratory validation and interlaboratory study. Journal of AOAC International, 88, 761–772. 101. Stobo, L. A., Lacaze, J. P. C. L., Scott, A. C., Gallacher, S., Smith, E. A., and Quilliam, M. A., 2005. Liquid chromatography with mass spectrometry-Detection of lipophilic shellfish toxins. Journal of AOAC International, 88, 1371–1382. 102. These, A., Klemm, C., Nausch, I., and Uhlig, S., 2011. Results of a European interlaboratory method validation study for the quantitative determination of lipophilic marine biotoxins in raw and cooked shellfish based on high-performance liquid chromatography-tandem mass spectrometry. Part I: Collaborative study. Analytical and Bioanalytical Chemistry, 399, 1245–1256. 103. van den Top, H. J., Gerssen, A., McCarron, P., and van Egmond, H. P., 2011. Quantitative determination of marine lipophilic toxins in mussels, oysters and cockles using liquid chromatography-mass spectrometry: Inter-laboratory validation study. Food Additives and Contaminants Part A, 28, 1745–1757. 104. Gerssen, A., Klijnstra, M., Cubbon, S., and Gledhill, A., UPLC-MS/MS method for the routine quantification of regulated and non-regulated lipophilic marine biotoxins in shellfish. https://www.waters.com/ webassets/cms/library/docs/720004601en.pdf, 2013, last accessed February 10, 2013.

Analysis of Marine Biotoxins

673

105. Blay, P., Hui, J. P., Chang, J., and Melanson, J. E., 2011. Screening for multiple classes of marine biotoxins by liquid chromatography-high-resolution mass spectrometry. Analytical and Bioanalytical Chemistry, 400, 577–585. 106. Kittler, K., Preiss-Weigert, A., and These, A., 2010. Identification strategy using combined mass spectrometric techniques for elucidation of phase I and phase II in vitro metabolites of lipophilic marine biotoxins. Analytical Chemistry, 82, 9329–9335. 107. Peters, R. J., Oosterink, J. E., Stolker, A. A., Georgakopoulos, C., and Nielen, M. W., 2010. Generic sample preparation combined with high-resolution liquid chromatography-time-of-flight mass spectrometry for unification of urine screening in doping-control laboratories. Analytical and Bioanalytical Chemistry, 396, 2583–2598. 108. Stolker, A. A., Rutgers, P., Oosterink, E. et al., 2008. Comprehensive screening and quantification of veterinary drugs in milk using UPLC-ToF-MS. Analytical and Bioanalytical Chemistry, 391, 2309–2322. 109. Vonaparti, A., Lyris, E., Angelis, Y. S. et al., 2010. Preventive doping control screening analysis of prohibited substances in human urine using rapid-resolution liquid chromatography/high-resolution timeof-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 24, 1595–1609. 110. Martinez-Villalba, A., Vaclavik, L., Moyano, E., Galceran, M. T., and Hajslova, J., 2013. Direct analysis in real time high-resolution mass spectrometry for high-throughput analysis of antiparasitic veterinary drugs in feed and food. Rapid Communications in Mass Spectrometry, 27, 467–475. 111. Monge, M. E., Harris, G. A., Dwivedi, P., and Fernandez, F. M., 2013. Mass spectrometry: Recent advances in direct open air surface sampling/ionization. Chemical Reviews, 113, 2269–2308. 112. Lapthorn, C., Pullen, F., and Chowdhry, B. Z., 2013. Ion mobility spectrometry-mass spectrometry (IMS-MS) of small molecules: Separating and assigning structures to ions. Mass Spectrometry Reviews, 32, 43–71. 113. Armenta, S., Alcala, M., and Blanco, M., 2011. A review of recent, unconventional applications of ion mobility spectrometry (IMS). Analytica Chimica Acta, 703, 114–123.

35

Biogenic Amines in Seafood Products Claudia Ruiz-Capillas and Ana M. Herrero Institute of Food Science, Technology and Nutrition (ICTAN-CSIC)

35.1 INTRODUCTION Biogenic amines (BAs) are biologically active nitrogenous compounds with low molecular weight and are present in the vast majority of foods, including fish and seafood. In the latter, the most abundant BA is histamine, but significant levels of putrescine and cadaverine, spermidine, spermine, tyramine, phenylalanine, tryptamine, agmatine, etc., can also be found. BAs are produced mainly by the decarboxylation of free amino acids from the action of microbial amino acid decarboxylase enzymes (Table 35.1, Figure 35.1) which are particularly important in fish and seafood as these products are extremely perishable (Ruiz-Capillas and Jiménez-Colmenero, 2009; Ruiz-Capillas and Herrero, 2019).

35.1.1 Factors Influencing the Formation of BAs in Seafood The enzymatic decarboxylation reaction of certain free amino acids leading to the formation of BAs in seafood is affected by various factors such as the raw material itself, microorganisms affecting the amount and type of enzymes and technological processing and storage conditions which directly affect the enzyme, the substrate and the reaction medium (Figure  35.1) (RuizCapillas and Jiménez-Colmenero, 2004; Ruiz-Capillas and Herrero, 2019). Raw material is decisive in determining the amount and formation of BAs in the final product. Raw seafood is also a natural source of free amino acids from which BAs are produced. Fresh muscle contains low levels of some BAs such as spermidine and spermine, and in some cases putrescine and agmatine which are characteristic of certain fish species and muscle types (Rosa and Nunes, 2003; Ruiz-Capillas et  al., 2003; Ruiz-Capillas and Moral, 2004; Auerswald et  al., 2006). Free amino acids in fish also depend on the fish species and the muscle type (red or white). For example, members of the Scombridae and Scomberosocidae families contain higher concentrations of free histidine (which is decarboxylated to histamine) (Figure 35.1, Table 35.1). Differences in muscle composition and

TABLE 35.1 Amino acid Precursors and Amino Acid Decarboxylase Enzymes in the Formation of BAs in Seafood and Seafood Products

674

Amino Acids Precursors

Amino Acid Decarboxylase

BAs

Histidine Tyrosine Tryptophan Lysine Phenylalamine Arginine Ornithine

Histidine-decarboxylase Tyrosine-decarboxylase Tryptophan-decarboxylase Lysine-decarboxylase Phenylalanine-decarboxylase Arginine-decarboxylase Ornithine-decarboxylase

Histamine Tyramine Tryptamine Cadaverine Phenylethylamine Agmatine Putrescine, spermidine, spermine

DOI: 10.1201/9781003289401-40

Biogenic Amines in Seafood Products

675

FIGURE  35.1  Factors influencing the formation of BAs in seafood and seafood products. Adapted from Ruiz-Capillas and Jiménez Colmenero (2009).

other factors (i.e., pH, ionic strength, substrate concentration, inhibitors, etc.) affect chemical changes (i.e., proteolysis) during storage and, consequently, the formation of BAs (Jorgensen et al., 2000; Ruiz-Capillas and Moral, 2002; Paarup et  al., 2002; Du et  al., 2002; Ruiz-Capillas and Jiménez-Colmenero, 2004, 2009). Microorganisms are possibly the most important factor in BA formation in seafood (RuizCapillas and Herrero, 2019). Generally speaking, BA formation in raw fish products is caused mostly by Gram-negative enteric bacteria, as opposed to their Gram-positive counterpart found in fermented and packaged products. The main bacteria related with BA production are Enterobacteriaceae, Pseudomonas spp, Enterococcus, Micrococcus, Photobacterium, and lactic acid bacteria (Figure 35.1) (EFSA, 2011; Hongpattarakere et al., 2016; Barbieri et al., 2019). The major histamine-producing bacterial species are Aeromonas hydrophila, Morganella morganii, Hafnia alvei, Klebsiella pneumoniae, Enterobacter aerogenes, Raoultella planticola, Proteus vulgaris, P. mirabilis, Enterobacter cloacae, Serratia fonticola, Citrobacter freundii, Photobacterium damselae, and Phosphoreum (Roig-Sagués et al., 2009; Fernández-No et al., 2010; Ruiz-Capillas and Herrero, 2019; Arulkumar et al., 2021; Mah and Ruiz-Capillas, 2022). These microorganisms responsible for BAs may form part of the endogenous microbiota or could be introduced by contamination and grow during the processing and storage of the fish, seafood, and seafood products. Hence, other very important factors in the formation of BAs are associated with the processing and storage conditions of seafood (Figure 35.1) (Ruiz-Capillas and Herrero, 2019). Factors such as the handling of ungutted fish, fillets and seafood, and processing temperature are decisive in the formation of Bas, and the outcome clearly depends on the type of fish in question. Conservation technologies such as refrigeration, influenced by decisive factors such as time and temperature, are closely related to the production of BA. With the exception of some polyamines, BA levels generally increase progressively throughout the time

676

Handbook of Seafood and Seafood Products Analysis

the fish is stored, and such levels increase with temperature and also depend on the fish species and type of muscle (Ruiz-Capillas and Moral, 2004; Du et al., 2002; Paarup et al., 2002; Emborg et al., 2002; Baixas-Nogueras et al., 2005; Ruiz-Capillas et al., 2003, Ruiz-Capillas and Herrero, 2019). Other conservation technologies, such as protective atmospheres, have been extensively studied in relation to the production of BA in various fish species (tuna, hake cod, sardine, salmon, lobster, etc.). In general, owing to the effect different gases have on controlling microbial growth, the application of protective atmospheres reduces BA production (Figure 35.1). The effect of this technology also depends on the type of atmosphere used (controlled, modified, etc.), the type of BA, fish species, and an appropriate combination of low temperature and specific species of microorganism capable of growing in these conditions and gas concentrations (Paarup et  al., 2002; Emborg et  al., 2002; Ruiz-Capillas et  al., 2003; Ruiz-Capillas and Moral, 2002, 2004, 2005; Özogul and Özogul, 2006). Freezing, a typical conservation method for seafood products, inhibits the formation of BA since microbial growth of the main BA-producing bacteria does not occur (Mendes et al., 1999); the presence of amines in this type of product is because they were already present in the raw material. This is like BA in canned fish; it was already present before heat treatment (Ruiz-Capillas and Herrero, 2019). Very little research has been done on the effect that other processing technologies such as hydrostatic high-pressure treatment and irradiation have on the formation of BA in seafood (Paarup et al., 2002; Mendes et al., 2005; Roig-Sagués et al., 2009). Fermentation and salting are other processes closely related to the production of BA in food. Higher levels of histamine have also been detected in fish such as mackerel and anchovy that have been subjected to these processes (Lee et al., 2005; Tsai et al., 2005; Arulkumar et al., 2021; Sivamaruthi et al., 2021). The use of additives, mainly preservatives (sulfites, nitrates, nitrites, organic acids, bacteriocines, etc.), in processing some fish reduces BA formation, mainly because they inhibit microbial growth (Ruiz-Capillas and Jiménez-Colmenero, 2009; Naila et al., 2010).

35.1.2 The Importance of Controlling BAs in Seafood BAs are important in terms of the toxicity risk they pose and their role as quality and acceptability indicators in some foods such as fish and seafood (Ruiz-Capillas and Herrero, 2019). 35.1.2.1 Toxicological Importance of BAs in Seafood The consumption of food containing high levels of BA has been associated with toxic effects and poses a potential risk health. This toxic effect is mostly caused by the compounds like histamine and tyramine. Histamine is the main BA in fish and fish products and the main component in “scombrid poisoning” or “histamine poisoning” associated with the consumption of fish from the Scombridae or Scomberesocidae families such as tuna, mackerel, sardines, and other fish containing high levels of histamine and/or other BAs such as putrescine and cadaverine which are known to enhance the toxicity of histamine (Ruiz-Capillas and Jimenez-Colmnenero, 2009). However, the toxicity of BAs depends on the ability of individuals to metabolize normal dietary intakes of BAs through their detoxification system [monoamine oxidase (MAO; EC 1.4.3.4), diamine oxidase (DAO; EC 1.4.3.6), and polyamine oxidase (PAO; EC 1.5.3.11)] and other conditioning factors (amount consumed, alcohol, gastrointestinal diseases, intake of certain drugs that inhibit these enzymes, etc.). Therefore, the effect of a given amount of histamine in seafood may be greater or lesser depending on the number of enhancers present and the efficiency of the individual’s detoxification system (Lehane and Olley, 2000; Pegg, 2013; Fernandez-Reina et al., 2018). Moreover, certain BAs, essentially putrescine and cadaverine, can react with preservatives such as nitrites to form toxic or carcinogenic compounds such as nitrosamines (Al Bulushi et al., 2009; Ruiz-Capillas and Herrero, 2019).

Biogenic Amines in Seafood Products

677

The EU institutions and the FDA regulate BAs due to the risk they pose (EC, 2005; FDA, 2022; Mah et al., 2019). A legal limit is only established for histamine and varies from one country to the next, for example, 100 mg/kg (EU) and at 50 mg/kg (FDA). However, as noted above, it is not so clear that the relationship between amount of histamine and the toxicity of a serving of fish depends exclusively on this amine. Other histamine toxicity enhancers need to be considered. Given the range of factors, it is very difficult to establish BA toxicity levels (Ruiz-Capillas and Herrero, 2019). 35.1.2.2 BAs as a Quality Indicator in Seafood The control of BAs in seafood has also been proposed as a quality indicator in many foods, including fish and seafood, to detect freshness, spoilage, defective preparation, etc., based on changes that occur in the different amines during these processes and storage conditions (Figure 35.1) (Ruiz-Capillas and Herrero, 2019). For example, BA level is a very good indicator of fish freshness during storage and is associated mainly with bacterial spoilage and other quality parameters (Veciana-Nogués et  al., 1997; Ruiz-Capillas and Moral, 2001a; Ruiz-Capillas et al., 2003; Pons-Sánchez-Cascado et al., 2006; Ruiz-Capillas and Jiménez-Colmenero, 2011; Ruiz-Capillas and Herrero, 2019). Different BA indices have been proposed (Ruiz-Capillas and Herrero, 2019). The Mietz and Karmas (1977) BA index was the first and the most widely used to assess spoilage of fish and seafood (tuna, shrimp, lobster, etc.) on the basis of BA (histamine, cadaverine, putrescine, spermidine, and spermine) content. According to this quality index (QI), the limit for fish acceptability was set at 10. Since then, many other indices have been proposed based on combinations of various amines or BA on its own. The Food and Drug Administration (FDA, 2022) recommended using not only histamine to indicate degree of spoilage, but also other scientific data to judge fish freshness such as the presence of other BAs associated with fish decomposition. The advantage of using some BAs such as agmatine and cadaverine as a QI for fish and seafood is that they reflect changes taking place in fish from the beginning of storage prior to the point of onset of appreciable microbial spoilage (Ruiz-Capillas and Moral, 2001a; Paarup et al., 2002; Rosa and Nunes, 2003; Baixas-Nogueras et al., 2005). They therefore indicate product freshness (which is why they have been called freshness indicators) and can be correlated with other spoilage compounds in fish. BAs have also been used as a QI in fish and fish products treated with other preservation technologies such as protective atmospheres and high hydrostatic pressure (Ruiz-Capillas and Moral, 2001a, b; Paarup et al., 2002; Rosa and Nunes, 2003; Roig-Sagués et al., 2009). Thanks to the thermostability of BAs, these compounds have been used as indicators of poorquality raw material in canned fish (Jorgensen et al., 2000).

35.2 DETERMINATION OF BAS IN SEAFOOD Given its toxicological importance and use as a quality indicator in seafood and seafood products, several analytical methods have been developed to determine BA levels in food to ensure its safety. The literature describes methods ranging from traditional colorimetric techniques that determine individual BA levels such as that of histamine or cadaverine, to more sophisticated and widely used methods such as high-performance liquid chromatography (HPLC), HPLC–mass spectrometry, gas chromatography (CG), and gas chromatographic–mass spectrometry, thin-layer chromatography (TLC), capillary electrophoresis (CE), flow injection analysis (FIA), biosensors, kits, etc. (Table 35.2) (Önal et al., 2007; Ruiz-Capillas and Jiménez-Colmenero, 2009; Ruiz-Capillas et al., 2016; Papageorgiou et al., 2018; Ruiz-Capillas and Herrero, 2019). Due to the complex nature of the seafood matrix, most BA determination methods entail various steps such as extraction of the BA from the product, purification of the extract, derivatization, separation, and quantification of BA.

678

TABLE 35.2 Methods for the Determination of BAs in Seafood and Seafood Products Methodology

Column/Derivatization Reagent/Detectors/Kits

BAs

Ion-exchange column, reverse-phase column // dabsyl Histamine, tyramine, tryptamine, chloride, benzoyl chloride, fluorescein, 9-fluorenylmethyl cadaverine, putrescine, agmatine, chloroformate, o-Phthalaldehyde (OPA) and naphthalene- spermine, spermidine, 2,3-dicarboxaldehyde, 2,4,6-triethyl-3,5-dimethyl phenylethylamine, serotonin, pyrylium trifluoromethanesulfonate // UV–VIS, octopamine, dopamine fluorescence, mass spectrometry

CE

FITC, 5-(4,6-dichloro-s-triazin-2-ylamino) fluorescein, carboxyfluorescein succinimidyl ester and a fluorescein named SAMF amino-reactive chameleon stain Py-1 // UV, mass spectrometry, LIF Anion-exchange column // OPA // fluorescence detection

FIA

Spermine, spermidine, putrescine, histamine, cadaverine, agmatine, phenylethylamine, tyramine, tryptamine, urocanic acid, serotonin Histamine

References AOAC (1995), Taylor et al. (1978), Veciana-Nogués et al. (1995), Ruiz-Capillas and Moral (2001a), Lange et al. (2002), Ruiz-Capillas and Moral (2003), Cinquina et al. (2004), Duflos et al. (2009), Saaid et al. (2009), Latorre-Moratalla et al. (2009b), Sánchez and Ruiz-Capillas (2012), Ruiz-Capillas and Herrero (2019), Tahmouzi et al. (2011), Simat and Dalgaard (2011), Self et al. (2011), Shiono et al. (2021), Kočar et al. (2021) Sarkadi (1999), Lange et al. (2002), Steiner et al. (2009), Do Lago et al. (2022)

Hungerford et al. (1990), Ruiz-Capillas et al. (2016), Hungerford et al. (2001) Hungerford and Arefyev (1992), Draisci et al. (1998), Niculescu et al. (2000a, b), Frébort et al. (2000), Compagnone et al. (2001), Lange and Wittmann (2002), Takagi and Shikata (2004), Castilho et al. (2005), Mei et al. (2007); Carelli et al. (2007), Alonso-Lomillo et al. (2010), Kivirand and Rinken (2011), Kivirand and Rinken (2011), Bóka et al. (2012), Ruiz-Capillas et al. (2016), Vasconcelos et al. (2021) Shakila et al. (2001), Lapa-Guimarães and Pickova (2004), Tao et al. (2011)

Biosensors

Immobilization (enzymes (MAO, DAO, PAO), Histamine, putrescine, cadaverine, microorganisms, antibodies, and nucleic acids) // tyramine, cystamine, agmatine, translators,: electrochemical (voltametric or spermidine amperometric, potentiometry, electrochemical impedance spectroscopy), optical and piezo-electric

TLC

Solvent (chloroform-methanol-ammonia, methanolHistamine, putrescine, cadaverine, ammonia, acetone-ammonia, n-butanol-acetone-water, tyramine, spermidine, ammonia, etc.) // ninhydrin, dansyl reagent // UV, fluorescence agmatine, putrescine, tryptamine, densitometry spermine, tyramine, phenylethylamine Derivatizers: trifluoroacetyl, trimethylsilyl or Histamine, putrescine, cadaverine, Huang et al. (2016), Munir et al. (2017), Munir and Badri (2020) 2,4-dinitrophenyl, pentafluorobenzoyl chloride, alkylated tyramine, heptylamine, spermidine chloroformates // detector: flame ionization, electron capture, thermal conductivity, mass spectrometry ALERT® kit, Veratox histamine kit, Fast Histamine Histamine Staruszkiewicz and Rogers (2001), Hungerford and Wu (2012) ELISA kit (HistaSure™), BiooScientific MaxSignal histamine test kit, Reveal Histamine Screening tests

GC

Commercial kits

Handbook of Seafood and Seafood Products Analysis

HPLC

Biogenic Amines in Seafood Products

679

35.2.1 Extraction Process The first step in most BA determination methods is extraction of these compounds from the seafood or seafood product. This is crucial for the proper subsequent separation of the BA and for accuracy. Many factors associated with the characteristics of seafood products impact this step. These include composition (moisture, protein, fat, etc.), treatments to which it has been subjected (refrigeration, freezing, fermentation, etc.), and also the solvents used in the extraction. Many different solvents can be used to extract BAs from fish and seafood, including hydrochloric acid, trichloroacetic acid (TCA), perchloric acid (PCA) and other organic solvents such as methanol, dichloromethane, acetone, and acetonitrile (Ruiz-Capillas and Herrero, 2019). Taylor et al. (1978) and the AOAC method (1995) used methanol for histamine analysis in some seafood and fish. However, PCA and TCA are the most commonly used solvents. The main function of these solvents is to precipitate protein and release the non-protein nitrogen in the supernatant where BAs will be found together with other compounds (i.e., trimethylamine products, volatile base nitrogen, ammonia, urea, and some free amino acids) that can interfere with the determination of BA (RuizCapillas and Moral, 2001a; Ruiz-Capillas and Herrero, 2019). TCA and PCA are used at different concentrations (5%, 6%, 7.5%), depending on the composition of the fish or seafood. For example, if the matrix has a high level of water or protein, the concentration should be higher to ensure the total precipitation of matrix proteins and the total extraction of BAs (Ruiz-Capillas and Horner, 1999; Ruiz-Capillas and Jiménez-Colmenero, 2009). In some cases, the extraction process in fish and seafood includes a purification or cleanup phase of the extract and should be based on ion-exchange resins and/or a solid phase (Mohammed et al., 2016). Purification mostly depends on the determination process used for the separation of BA. Sagratini et al. (2012) also tested a sample cleanup procedure for the simultaneous determination of eight underivatized BAs in fish. However, this process is limited in terms of its application to routine analyses and could lead to amine loss. The integration of the cleanup process in HPLC equipment has marked a step forward in this sense. For example, HPLC equipment normally has a small cleanseparation ion-exchange (guard) column which is set up online prior to the separation column used to determine BA (Ruiz-Capillas and Moral, 2001a).

35.2.2 Derivatization Process Absorption is unsatisfactory and fluorescence insignificant in BAs due to low volatility and lack of chromophores. That is why BA determination requires derivatization to increase their detection sensitivity in UV–VIS or fluorescence (Table 35.2). This derivatization can be done in the analysis equipment itself (such as HPLC, FIA, etc.) or as an added step in the extraction and preparation of the extract prior to separation (Ruiz-Capillas and Herrero, 2019). There are many known derivatization reagents such as dabsyl chloride, benzoyl chloride, fluorescein, 9-fluorenylmethyl chloroformate, o-Phthalaldehyde (OPA), and naphthalene-2,3-dicarboxaldehyde. Of these, OPA and dansyl chloride are the most widely used (Table  35.2). Dansyl chloride forms stable compounds after reaction with both primary and secondary amino groups, and the products are more stable than those formed using OPA. This latter reagent reacts rapidly with primary amines in the presence of a reducing agent like N-acetylcysteine, 2-mercaptoethanol, or thiofluor, which is a stable solid substitute for 2-mercaptoethanol during the preparation of the OPA reagent. The products of this reaction, 1-alkyl-2-alkylthio-substituted isoindoles, exhibit optimal excitation at 330 nm and maximum emission at 465 nm (RuizCapillas and Moral, 2001a; Tapia-Salazar et al., 2000; Triki et al., 2012). Moreover, OPAs are faster and much simpler for purposes of sample pretreatment, which can be fully automated using an autosampler, and are more sensitive because florescence detection is used rather than spectrophotometric detection as in the case of dansyl chloride (Lapa-Guimarães and Pickova, 2004; Latorre-Moratalla et al., 2009a).

680

Handbook of Seafood and Seafood Products Analysis

BA derivatization may be performed pre-column, on-column, or post-column chromatographic separation. Automatic online post-column derivatization is the most common procedure as it offers several advantages: It entails less handling, thus reducing the likelihood of interferences or artifacts in the sample, the analysis time is shorter, and derivatization occurs at the same time, thus enhancing the reproducibility and sensitivity of the analysis. Pre-derivatization involves more sample preparation steps which can cause problems in the analysis later (Zhao et al., 2007). Dansyl chloride is normally used for pre-column derivatization with reverse-phase separation coupled with UV detection mainly because it produces stable derivatized compounds. However, the dansylation reaction is a time-consuming process (10–60 min) and requires an external heat source (40°C–70°C). OPAs are used in ion-pair reverse-phase HPLC with post- and pre-column derivatization coupled with fluorescence detection (Ruiz-Capillas and Moral, 2001a; Tapia-Salazar et al., 2000; Ruiz-Capillas and Herrero, 2019). Shiono et al. (2021) used a new derivatization reagent, 2,4,6-triethyl-3,5-dimethyl pyrylium trifluoromethanesulfonate (Py-Tag) for the simultaneous determination of four BAs in fish and fish products on the Japanese market by liquid chromatography–tandem mass spectrometry (LC–MS/ MS). Py-Tag reacts with BA quickly (15 min), and PyTag derivatives can be detected with high efficiency in the MS, reducing eliminate matrix effects and does not require cleanup using a solidphase extraction column or similar (Table 35.2). Sample cleanup and analyte preconcentration are often applied to enhance the performance of analytical methods, to improve the detectability of analytes and to avoid interferences, but they have the drawback of being time-consuming and sometimes reducing analyte recovery.

35.2.3 Separation and Quantification Process There are many methods for the separation and quantification of BAs in seafood and seafood products (Table 35.2). Some of these are described later. 35.2.3.1 High-Performance Liquid Chromatography (HPLC) HPLC is the most popular and frequently reported for the separation and quantification of BAs in fish and seafood (Ruiz-Capillas and Herrero, 2019). It is the specific analytical method in the European Commission (EC). This procedure offers high resolution, sensitivity, and versatility, and sample treatments are generally simple. Moreover, it offers the advantage of being able to analyze several BAs simultaneously (Table 35.2). Most HPLC methods are based on conventional methods to determine histamine such as the AOAC fluorometric method (1995) or Taylor et al. (1978). These traditional colorimetric methods use OPA as a derivatizer. However, these methods have been replaced by chromatographic determinations, mainly HPLC, which can determine a greater number of BAs. Different types of columns have been used for the separation of amines using HPLC determinations, such as ion-exchange column (Ruiz-Capillas and Moral, 2003, Cinquina et al., 2004; Duflos et al., 2009) or a reverse-phase column, using different derivatizers (as referred in the previous section) and different detectors such as UV–Vis, fluorescent, and mass spectrometry. Ion-exchange column with a post-column derivatization with OPA and fluorescent detection is the most commonly used method for the simulated analysis of BA in fish (Veciana-Nogués et  al., 1995; Ruiz-Capillas and Moral, 2001a; Sánchez and Ruiz-Capillas, 2012). HPLC with ­o-phthaldialdehyde (OPA) has also been used in an automated pre-column derivatization which enables the determination of aliphatic amines and amino acids with a pretreatment of the sample (Lange et al., 2002). Ion-exchange chromatography for the simultaneous determination of underivatized BA based on conductivity detector ion-exchange chromatography has also been used (Cinquina et al., 2004). Tahmouzi et al. (2011) compared two HPLC methods, with fluorescence and UV detection, using pre-column OPA and benzoyl chloride derivatization, respectively, for the swift determination of

Biogenic Amines in Seafood Products

681

histamine in skipjack tuna. Results showed that the derivatization process with benzoyl chloride was longer and more complex. Moreover, fluorescence detection was found to be more precise and sensitive. Saaid et al. (2009) used reversed-phase HPLC with ODS2 column and UV detection to determine BAs derivatized with dansyl chloride before analyzing canned and salt-cured fish. Simat and Dalgaard (2011) compared pre-column derivatization with dansyl chloride and postcolumn derivatization with o-phthalaldehyde with small-diameter column particles to enhance the HPLC determination of histamine and other BAs in seafood. These authors reported quicker analyses and less eluent consumption, and found that sensitivity, recovery, and repeatability for the modified method were similar to or better than those of classic HPLC methods. This author also pointed out that HPLC systems are more robust and less costly than ultra-highpressure liquid chromatography (UHPLC) equipment that is also used to determine BA in seafood. Latorre-Moratalla et al. (2009b) developed an UHPLC method coupled with an online OPA postcolumn derivatization to determine 12 BAs and polyamines in different food matrixes (wine, fish, cheese, and dry fermented sausage) in a single chromatographic run on a 7-min elution program. The C18 column was used at 42°C while the post-column reaction equipment was kept at room temperature. Fluorometric detection at 340 nm for excitation and 445 nm for emission was applied. Detection limits lower than 0.2 mg/L and a determination limit below 0.3 mg/L were found for all amines. Ultra-high-performance hydrophilic interaction chromatography (UHPLC-HILIC) coupled with orbitrap MS for detection were also developed to determine eight BA compounds in canned and frozen tuna. Tuna samples were extracted with matrix solid-phase dispersion using CN-silica sorbent and eluted with a mixture of aqueous ammonium formate buffer and acetonitrile. Instrumental conditions included an injection volume of 5.0 μL, a column temperature of 30°C, and a flow rate of 0.75 mL/min. The mobile phase was ammonium formate buffer and acetonitrile (Self et al., 2011). Other authors have proposed liquid chromatography–tandem mass spectrometry (LC–MS/ MS) to determine BAs in fish and fish products. Shiono et al. (2021) proposed this determination with 2,4,6-triethyl-3,5-dimethyl pyrylium trifluoromethanesulfonate (Py-Tag) as the derivatization reagent and a quantification limit of 2 mg/kg for histamine, tyramine, and phenylethylamine and 10 mg/kg for cadaverine. The advantage of this method is that it does not require cleanup using a solid-phase extraction column and the derivatization reaction time was only 15 min. Kočar et al. (2021) also proposed IC-MS/MS to determine BA in fresh and processed fish products based on ion-exchange chromatography coupled with mass spectrometry detection, but with the advantage that it did not require derivatization. 35.2.3.2 Capillary Electrophoresis (CE) CE has proved useful in the analysis of BAs in different food and matrices and is a good alternative to other chromatographic methods (Table  35.2). Different detection methods such as UV absorbance, mass spectrometry, and laser-induced fluorescence (LIF) have been used for BA determination with CE (Sarkadi, 1999; Lange et al., 2002; Steiner et al., 2009). This method also requires BA derivatization since these are non-fluorescent on their own or only excitable with UV lasers. On this basis, BAs have been labelled with different fluorescein derivatives such as FITC, 5-(4,6-dichloros-triazin-2-ylamino) fluorescein, carboxyfluorescein succinimidyl ester, and a fluorescein named SAMF amino-reactive chameleon stain Py-1. Steiner et al. (2009) developed a method to determine histamine in various fish samples via micellar electrokinetic chromatography along with LIF detection using the amino-reactive chameleon stain Py-1. The detection range was limited from 0.1 to 0.9 µmol/L, with RSDs ranging from 1.1% to 4.2% at 10 µmol/L. Lange et al. (2002) tested a CE method to analyze BAs in food and compared this method with automated HPLC determination [(pre-column derivatization with o-phthaldialdehyde (OPA)]. BAs were separated in less than 9 min, and there was a good correlation between CE and HPLC (correlation coefficient for histamine: 0.994). The main advantage of CE to measure histamine and

682

Handbook of Seafood and Seafood Products Analysis

tyramine is that it does not require laborious sample pretreatment which is time-consuming and frequently poses interference problems. CE has also been tested coupled to MS. Do Lago et al. (2022) determined 11 BAs by CE–tandem mass spectrometry (CE-MS/MS) in commercial samples of salmon and shrimp using a polyvinyl alcohol-coated (PVA) capillary which enhances separation efficiency. The correlation coefficients of the calibration curves in the range of 0.02–100 µg/mL were greater than 0.998, and the limits of detection (LODs) and limits of quantification were in the 1.1–2.7 and 3.6–8.9 ng/mL ranges, respectively. The recovery values ranged between 86% and 113% with RSD under 5.5%. 35.2.3.3 Flow Injection Analysis (FIA) FIA has also been used to determine BAs in seafood (Ruiz-Capillas et al., 2016). The first FIA method to determine histamine in fresh and canned fish, tuna, mackerel, and mahi-mahi was developed in 1990 by Hungerford et al. (1990) (Table 35.2). This system was based on the AOAC method to determine histamine in fish by means of the reaction of histamine/OPA with fluorescence detection. This methodology also requires an extraction process like the one detailed in the previous section, to remove interfering components and isolate BAs before FIA analysis proper can be performed (Ruiz-Capillas and Jiménez-Colmenero, 2009; Ruiz-Capillas et al., 2016). In some cases, an anion-exchange column is also used to eliminate sample matrix interferences. The derivatization process with OPA is usually carried out in the FIA system itself rather than prior to injection. These methods correlate closely with traditional methods (R2 = 0.99) and with a LOD of around 0.8 mg/kg histamine. FIA offers major advantages such as easy control of the chemical reaction, rapid analysis, low operating cost, environmentally friendly reagents, accuracy and precision, suitable for use by untrained personnel, etc. (Ruiz-Capillas et al., 2016; Hungerford et al., 2001). FIA has been developing over the years, and the original high-pressure FIA method could be adapted to commercially available low-pressure FIA systems. This implies the incorporation of an OPA reactor, thermostated at 40°C, with no need of a column, all adapted to a commercial Foss Tecator 5010 Analyser (Hungerford et al., 2001). The FIA method has been extensively used in combination with immobilized enzymes and electrodes to determine BA in fish and seafood as described later. 35.2.3.4 Biosensors Biosensors are another way to determine BA and have grown very popular in recent decades (Table 35.2). They stand out for their simplicity, low cost, sensitivity, and automation, offering swift, simple, and cost-effective solutions to detect BAs compared to more complex methods such as chromatography. The problem with these analyses is that they typically determine BA either individually or in low number (Ruiz-Capillas et al., 2016). The biosensor technique is based on the immobilization of biological components (enzymes, microorganisms, antibodies, and nucleic acids) that specifically recognize the analyte through a biochemical reaction and produce a signal (electrical, magnetic, mechanical, thermal or optical) which is then processed through a translator making it quantifiable and proportional to the analyte concentration (Kivirand and Rinken, 2011; Vasconcelos et al., 2021). One of the most important classes of enzymes used to determine BAs are amine oxidases such as MAO, DAO, and PAO. They catalyze the oxidation deamination of amines in aldehydes in the presence of molecular oxygen (as an electron acceptor), producing ammonia and hydrogen peroxide. Different translators, that is, electrochemical (voltametric or amperometric, potentiometry, electrochemical impedance spectroscopy), optical, and piezo-electric, have been used to determine BAs (Ruiz-Capillas and Jiménez-Colmenero, 2009). Hungerford and Arefyev (1992) proposed a flow injection procedure with DAO immobilized to sepharose using a manifold based on double injection, the immobilized enzyme, and selective fluorimetric detection of endogenous histamine substrates to determine histamine levels in fish.

Biogenic Amines in Seafood Products

683

Immobilized enzymes and electrodes or reactors are usually used in combination with FIA. Many FIA studies using sensors or biosensors have also been reported for BA in food based on commercial or home-purified enzymes (Niculescu et al., 2000a, b; Compagnone et al., 2001; Lange and Wittmann, 2002; Alonso-Lomillo et al., 2010; Kivirand and Rinken, 2011; Bóka et al., 2012; Vasconcelos et al., 2021). Frébort et al. (2000) also tested an amine oxidase-based flow biosensor to assess fish freshness. This enzyme-immobilizing system is a flow enzyme reactor with spectrophotometric detection of enzymatically produced hydrogen peroxide by a peroxidase/guaiacol. Electrochemical biosensors to determine BA content have also been assembled using DAO and PAO covalently immobilized onto polymeric membranes with amperometric detectors to determine BA in salted anchovy samples (Draisci et al., 1998). In that study, in the presence of their substrates, both enzymes produced H2O2 which was detected on a platinum electrode polarized at +650 mV versus Ag/AgCl. Carelli et  al. (2007) used an amperometric biosensor to determine total BA content in food samples (cheese and anchovy) using a commercial diamine oxidase (from Porcine kidney E.C. 1.4.3.6) entrapped by glutaraldehyde on an electrosynthesized bilayer film. This biosensor exhibited good sensitivity, stability, and complete suppression of electroactive interferences. According to this author, the values obtained using this optimized biosensor did not differ significantly from those obtained using an ion-chromatography system. Niculescu et al. (2000a) also reported an amperometric biosensor for histamine detection based on the amine oxidase enzyme. Co-immobilized horseradish peroxidase has also been used coupled to a glassy carbon electrode with ferrocene monocarboxylic acid as mediator dissolved in the carrier stream (Castilho et al., 2005). Histamine dehydrogenase (HmDH)-based electrode for amperometric detection has also been proposed to determine histamine in fish samples (Takagi and Shikata, 2004). The histamine dehydrogenase was immobilized on a glassy carbon electrode using Os2+ /3+ in the osmium-derivatized polymer as an electron transfer mediator. This electrode is highly selective for histamine and is not sensitive to other primary amines including common BAs such as putrescine, cadaverine, and tyramine. Histamine determination studies on fish have also been conducted using aryl oxalate-sulforhodamine and a chemiluminescence detector. The advantage of the chemiluminescence detection method proposed by these authors is its sensitivity, simplicity of the device, and the lack of a derivatization reaction. A chemiluminescent plant tissue-based biosensor system employing sequential injection analysis (SIA) was also used to check for putrescine and cadaverine in fish. Pea-seedling tissue was used as an active source of DAO (diamine oxidase) which is packed in a mini-PTFE column incorporated in the SIA system. In this system, amine analysis is based on enzymatic conversion in the column to produce hydrogen peroxide, which is detected through a chemiluminescence reaction involving luminol and Co(2+) (Mei et al., 2007). 35.2.3.5 Thin-Layer Chromatography (TLC) BA determination by TLC has also been tested in fish and fish products (Table 35.2). Different solvents (chloroform-methanol-ammonia, methanol-ammonia, acetone-ammonia, n-butanol-acetonewater, etc.) and reagent (ninhydrin) were evaluated for their ability to separate these BAs using this TLC (Lapa-Guimarães and Pickova, 2004)-tested new solvent systems [chloroform–diethyl ether–triethylamine (6:4:1)], followed by chloroform–triethylamine (6:1) for TLC fluorescence densitometry determination of nine BAs in fish and squid with a view to using a less harmful solvent (diethyl ether instead of benzene). They found that they were able to separate more BAs. More recently, Pauly’s reagent (sulfanilic acid dissolved in concentrated hydrochloric acid) was tested for rapid histamine analysis in fish and fish products by TLC (Tao et al., 2011). TLC is a simple, swift, sensitive, reproducible method, does not require expensive instruments or tedious pretreatment, and uses only small amounts of solvent compared to HPLC (Shakila et al., 2001; Tao et al., 2011).

684

Handbook of Seafood and Seafood Products Analysis

35.2.3.6 Gas Chromatography (GC) GC has been used to determine BA in fish and seafood but is not as commonly used as liquid chromatography or CE (Table 35.2). Before analysis, derivatization of BA is necessary to increase volatile properties and to decrease polarities of BAs. Different derivatizers have been used such as trifluoroacetyl, trimethylsilyl or 2,4-dinitrophenyl, pentafluorobenzoyl chloride, and alkylated chloroformates. These derivatizers are associated with the detector used (flame ionization, electron capture, thermal conductivity, etc.) in the GC determination (Munir et al., 2017; Munir and Badri, 2020). Munir et al. (2017) optimized and validated a CG-flame ionization detector (GC-FID) followed by confirmation using mass spectrometry (MS) for the simultaneous determination of five BAs (heptylamine, histamine, tyramine, cadaverine, and spermidine) in fresh and salted fish samples such as mackerel, sardine, whiptail, gourami, and toli shad. For this determination, the authors used BSA (N,O-bis(trimethylsilyl) acetamide) + TMCS (trimethylchlorosilane) as a derivatization agent. LODs were in the 1.20–2.90  μg/mL range. The recovery efficiency for BAs ranged between 98.41% and 116.39%. More recent works have focused on determining BAs in fish samples using isobutyl chloroformate coupled with solid-phase microextraction based on fiber derivatization coupled with CG and mass spectrometry. LODs of this method were 2.98–45.3 μg/kg with recoveries ranging from 78.9% to 110% (Huang et al., 2016). GC methods of histamine detection in fish have also been tested without derivatization. Hwang et al. (2003) developed a simple GC analysis of histamine in fish samples (shrimp and tuna) without derivatization. The histamine was initially extracted with alkaline methanol and injected into a GC column (CP-SIL 19CB) for analysis. The detection limit for histamine by this method was about 5 μg/g. Direct determination of histamine reduces analysis time as prior derivatization is unnecessary, and this also prevents errors arising from the reaction of derivative synthesis. 35.2.3.7 Commercial Kits Many commercial test kits which are fast and do not require expensive equipment are now available to determine histamine in fish and seafood (Table  35.2) (Staruszkiewicz and Rogers, 2001; Hungerford and Wu, 2012). These kits are based on an enzymatic immunoassay, mainly the ELISA. Examples include the ALERT® kit and the Veratox histamine kit. These are direct competitive enzyme-linked immunosorbent assays (ELISAs) to quantitatively analyze histamine in scombroid fish species such as tuna, bluefish, and mahi-mahi. Histamine is extracted from a sample using a quick water extraction process. Free histamine in the sample controls and competes with enzymelabelled histamine (conjugate) for antibody-binding sites. After a wash step, the substrate reacts with the bound enzyme conjugate to produce a blue color. A microwell reader is used to produce optical densities. Control optical densities are used to form a standard curve, and sample optical densities are plotted against the curve to calculate the exact concentration of histamine. The results are read using a microwell reader at 450–650 nm. This is a simple, fast, and relatively low-cost method compared to other methodologies such as HPLC, and no previous derivatization is required. Moreover, the Fast Histamine ELISA kit (HistaSure™) is available on the market at (https://www. immusmol.com/shop/histamine-elisa-kit-any-fish-sample/), and according to commercial information, this fast-track enzyme immunoassay (ELISA) is to quantitatively determine histamine levels in fresh fish, frozen fish, canned fish, and fish meals. Assays take between 25 and 30 min, and the histamine level detection range is between 0.44 and 300 ppm in any fish sample. Hungerford and Wu (2012) compared three test kits (Veratox for histamine kits (Neogen Cat No. 9505), BiooScientific MaxSignal histamine test kits (Cat No. 1032), and Reveal Histamine Screening tests (obtained from Neogen as “beta test” samples)) to quantify histamine in fish (fresh and frozen yellowfin tuna and mahi-mahi, as well as canned mackerel in broth and canned tuna in oil and in broth). These authors found that the MaxSignal kit was more accurate and also more time-consuming than the Veratox kit. Overall, the MaxSignal enzymatic assay and The Reveal for histamine determination appear to be viable alternatives to competitive ELISAs like Veratox and offer another promising histamine screening tool.

Biogenic Amines in Seafood Products

685

35.3 CONCLUSIONS BAs are compounds traditionally used to verify food safety and the quality and freshness of seafood. This verification is an essential stage in the production chain for producers, consumers, and governments, and is required by law. Different methods have been used to determine the presence of these compounds such as HPLC, TLC, GC, CE, IEC, FIA, and biosensors kit. The most widely used method is HPLC. However, chromatographic techniques have the disadvantage of requiring skilled operators, most require sample extraction and derivatization, and use large amounts of reagents. Techniques have improved, and new ones have developed to overcome these problems. Currently, kits with very suitable detection levels can be found but are limited in the sense that they can only determine BAs individually, histamine for example, and are unable to determine a significant number of amines simultaneously. Choosing a determination technique depends on the raw material in question, processing and the amount of analyte needed, the equipment available and the information required (individual amines or histamine only or a larger number of amines), analyst experience, time required for results, etc.

ACKNOWLEDGEMENTS This work was supported by the project PID2019-107542RB-C21 funded by MCIN/AEI/ 10.13039/501100011033 (ERDF a way of making Europe) and the intramural-CSIC (202370E138 and 202370E140). We would also like to thank CYTED (Reference 119RT0568; Healthy Meat Network).

REFERENCES Al Bulushi, I., Poole, S., Deeth, H.C., and Dykes, G.A. Biogenic amines in fish: Roles in intoxication, spoilage, and nitrosamine formation-A review. Crit. Rev. Food Sci. Nutr., 49, 369, 2009. Alonso-Lomillo, M.A., Domínguez-Renedo, O., Matos, P., and Arcos-Martínez, M.J. Disposable biosensors for determination of biogenic amines. Anal. Chim Acta., 665(1), 26, 2010. AOAC (Association of Official Analytical Chemists). Histamine in seafood: Fluorometric method Sec. 35.1.32, method 977.13. In Official Methods of Analysis of AOAC International; Cunniff, P.A., Ed.; AOAC International: Gaithersburg, MD; pp. 16–17; 1995. Arulkumar, A., Paramithiotis, S., and Paramasivam, S. Biogenic amines in fresh fish and fishery products and emerging control. Aquac. Fish., 8, 431, 2021. Auerswald, L., Morren, C., and Lopata, AL. Histamine levels in seventeen species of fresh and processed South African seafood. Food Chem., 98, 231, 2006. Baixas-Nogueras, S., Bover-Cid, S., Veciana-Nogués, M.T., Mariné-Font, A., and Vidal-Carou, M.C. Biogenic amines index for freshness evaluation in iced Mediterranean hake (Merluccius merluccius). J. Food Prot., 68, 2433, 2005. Barbieri, F., Montanari, C., Gardini, F., and Tabanelli, G. Biogenic amine production by lactic acid bacteria: A review. Foods, 8(1), 17, 2019. Bóka, B., Adányi, N., Virág, D., Sebela, M., and Kiss, A. Spoilage detection with biogenic amine biosensors, comparison of different enzyme electrodes. Electroanalysis, 24(1), 181, 2012. Carelli, D., Centonze, D., Palermo, C., Quinto, M., and Rotunno, T. An interference free amperometric biosensor for the detection of biogenic amines in food products. Biosens. Bioelectron., 23, 640, 2007. Castilho, T.J., Sotomayor, M.D.P.T., and Kubota, L.T. Amperometric biosensor based on horseradish peroxidase for biogenic amine determinations in biological samples. J. Pharm. Biomed. Anal., 37, 785, 2005. Cinquina, A.L., Calí, A., Longo, F., DeSantis, L., Severoni, A., and Aballe, F. Determination of biogenic amines in fish tissues by ion-exchange chromatography with conductivity detection. J. Chromatogr., 1032, 73, 2004. Compagnone, D., Isoldi, G., Moscone, D., and Palleschi, G. Amperometric detection of biogenic amines in cheese using immobilised diamine oxidase. Anal. Lett., 34, 841, 2001.

686

Handbook of Seafood and Seafood Products Analysis

Do Lago, C.L., dos Santos, V.B., Vidal, D.T.R., and Daniel, D. Analyze biogenic amines in salmon and shrimp by capillary electrophoresis-tandem MS, (Agilent technology 2022) https://hpst.cz/sites/default/files/ oldfiles/5991-6255en.pdf. Draisci, R., Volpe, G., Lucentini, L., Cecilia, A., Federico, R., and Palleschi, G. Determination of biogenic amines with an electrochemical biosensor and its application to salted anchovies. Food Chem., 62, 225, 1998. Du, W.X., Lin, C.M., Phu, A.T., Cornell, J.A., Marshall, M.R., and Wei, C.I. Development of biogenic amines in yellowfin tuna (Thunnus albacore): Effect of storage and correlation with decarboxylase-positive bacterial flora. J. Food Sci., 67, 292, 2002. Duflos, G., Inglebert, G., Himber, C., Degremont, S., Lombard, B., and Brisabois, A. Validation of standard method EN ISO 19343 for the detection and quantification of histamine in fish and fishery products using high-performance liquid chromatography. Int. J. Food Microbiol., 288, 97, 2009. EFSA. Scientific opinion on risk based control of biogenic amine formation in fermented foods. EFSA J., 9, 2393, 2011. Emborg, J., Laursen, B.G., Rathjen, T., and Dalgaard, P. Microbial spoilage and formation of biogenic amines in fresh and thawed modified atmosphere packed salmon (Salmo salar) at 2°C. J. Appl. Microbiol., 92, 790, 2002. European Commission (EC). Commission Regulation No. 2073/2005 of 15 November 2005 on microbiological criteria for foodstuffs. Off. J. Eur. Union, 50, 1, 2005. FDA (Food and Drug Administration). Fish and Fishery Products Hazards and Controls Guidance, 4th ed.; 2022. Available online: https://www.fda.gov/media/80637/download. Fernández-No, I.C., Böhme, K., Gallardo, J.M., Barros-Velázquez, J., Cañas, B., and Calo-Mata, P. Differential characterization of biogenic amine-producing bacteria involved in food poisoning using MALDI-TOF mass fingerprinting. Electrophoresis, 31, 116, 2010. Fernandez-Reina, A., Urdiales, J.L., and Sánchez-Jiménez, F. What we know and what we need to know about aromatic and cationic biogenic amines in the gastrointestinal tract. Foods, 7, 145, 2018. Frébort, I., Skoupa, L., and Pec, P. Amine oxidase-based flow biosensor for the assessment of fish freshness. Food Control, 11, 13, 2000. Hongpattarakere, T., Buntin, N., and Nuylert, A. Histamine development and bacterial diversity in microbiallychallenged tonggol (Thunnus tonggol) under temperature abuse during canning manufacture. J. Food Sci. Technol., 53(1), 245, 2016. Huang, J., Gan, N., Lv, F., Cao, Y., Ou, C., and Tang, H. Environmentally friendly solid-phase microextraction coupled with gas chromatography and mass spectrometry for the determination of biogenic amines in fish samples. J. Sep. Sci., 39, 4384, 2016. Hungerford, J.M., and Arefyev, A.A. Flow-injection assay of enzyme inhibition in fish using immobilized diamine oxidase. Anal. Chim. Acta, 261, 351, 1992. Hungerford, J.M., Hollingworth, T.A., and Wekell, M.M. Automated kinetics-enhanced flow injection method for histamine in regulatory laboratories: Rapid screening and suitability requirements. Anal. Chim. Acta, 438, 123, 2001. Hungerford, J.M., Walker, K.D., Wekell, M.M., LaRose, J.E., and Throm, H.R. Selective determination of histamine by flow injection analysis. Anal. Chem., 62, 1971, 1990. Hungerford, J.M., and Wu, W-H. Comparison study of three rapid test kits for histamine in fish: BiooScientific MaxSignal enzymatic assay, Neogen Veratox ELISA, and the Neogen Reveal Histamine Screening test. Food Control, 25(2), 448, 2012. Hwang, B.-S., Wang, J.-T., and Choong, Y.-M. A rapid gas chromatographic method for the determination of histamine in fish and fish products. Food Chem., 82(2), 329, 2003. Jorgensen, L.V., Dalgaard, P., and Huss, H.H. Multiple compound quality index for cold-smoked salmon (Salmo salar) developed by multivariate regression of biogenic amines and pH. J. Agric. Food Chem., 48, 2448, 2000. Kivirand, K., and Rinken, T. Biosensors for biogenic amines: The present state of art mini-review. Anal. Lett., 44(17), 2821, 2011. Kočar, D., Köse, S., Tufan, B., Ščavničar, A., and Pompe, M. Determination of biogenic amines in fresh fish and processed fish products using IC-MS/MS. Foods, 10(8), 1746, 2021. Lange, J., Thomas, K., and Wittmann, C. Comparison of a capillary electrophoresis method with high-performance liquid chromatography for the determination of biogenic amines in various food samples. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 779, 229, 2002.

Biogenic Amines in Seafood Products

687

Lange, J., and Wittmann, C. Enzyme sensor array for the determination of biogenic amines in food samples. Anal. Bioanal. Chem., 372, 276, 2002. Lapa-Guimarães, J., and Pickova, J. New solvent systems for thin-layer chromatographic determination of nine biogenic amines in fish and squid. J. Chromatogr. A, 1045, 223, 2004. Latorre-Moratalla, M.L., Bosch-Fusté, J., Lavizzari, T., Bover-Cid, S., Veciana-Nogués, M.T., and VidalCarou, M.C. Validation of an ultra-high pressure liquid chromatographic method for the determination of biologically active amines in food. J. Chromatogr. A, 1216(45), 7715, 2009b. Latorre-Moratalla, M.L., Bover-Cid, S., Veciana-Nogués, T., and Vidal-Carou, M.C. Thin-layer chromatography for the identification and semi-quantification of biogenic amines produced by bacteria. J. Chromatogr., 1216, 4128, 2009a. Lee, H., Kim, S.-H., Sang, C.I., Jun, H., Eun, J.B., and An, H. Histamine and other biogenic amines and bacterial isolation in retail canned anchovies. J. Food Sci., 70, C145, 2005. Lehane, L., and Olley, J. Histamine fish poisoning revisited. Int. Food. Microbiol., 58, 1, 2000. Mah, J.-H., Park, Y.K., Jin, Y.H., Lee, J.-H., and Hwang, H.-J. Bacterial production and control of biogenic amines in Asian fermented soybean foods. Foods, 8, 85, 2019. Mah, J-H., and Ruiz-Capillas C. Editorial: The Microbiological Functionality and Safety of Fermented Foods. Frontiers in Microbiology, 13, 979329, 2022. Mei, Y., Ran, L., Ying, X., Yuan, Z., and Xin, S. A sequential injection analysis/chemiluminescent plant tissuebased biosensor system for the determination of diamine. Biosensor. Bioelectron., 22, 871, 2007. Mendes, R., Alves Silva, H., Nunes, M.L., and Abecassis Empis, J.M. Effect of low-dose irradiation and refrigeration on the microflora, sensory characteristics and biogenic amines of Atlantic horse mackerel (Trachurus trachurus). Eur. Food Res. Technol., 221, 329, 2005. Mendes, R., Goncalves, A., and Nunes, M.L. Changes in free amino acids and biogenic amines during ripening of fresh and frozen sardine. J. Food Biochem., 23, 295, 1999. Mietz, J.L., and Karmas, E. Polyamine and histamine content of rockfish, salmon, lobster and shrimp as an indicator of decomposition. J. Assoc. Offic. Anal. Chem., 61, 139, 1977. Mohammed, G.I., Bashammakh, A.S., Alsibaai, A.A., Alwael, H., and El-Shahawi, M.S. A critical overview on the chemistry, clean-up and recent advances in analysis of biogenic amines in foodstuffs. Trends Anal. Chem., 78, 84, 2016. Munir, M.A., Assim, Z., and Ahmad, F. Characterisation of biogenic amines in fish collected from Sarawak using gas chromatography. B.J.R.S.T., 6(2), 21, 2017. Munir, M.A., and Badri, K.H. The importance of derivatizing reagent in chromatography applications for biogenic amine detection in food and beverages. J. Anal. Methods Chem., 2020, 5814389, 2020. Naila, A., Flint, S., Fletcher, G., Bremer, P., and Meerdink, G. Control of biogenic amines in food-existing and emerging approaches. J. Food Sci., 75(7), R139, 2010. Niculescu, M., Frébort, I., Pec, P., Galuszka, P., Mattiasson, B., and Csöregi, E. Amine oxidase based amperometric biosensors for histamine detection. Electroanalysis, 12, 369, 2000a. Niculescu, M., Nistor, C., Frébort, I., Peč, P., Mattiasson, B., and Csöregi, E. Redox hydrogel-based amperometric bienzyme electrodes for fish freshness monitoring. Anal. Chem., 72, 1591, 2000b. Önal, A. A review: Current analytical methods for the determination of biogenic amines in foods. Food Chem., 103, 1475, 2007. Özogul, F., and Özogul, Y. Biogenic amine content and biogenic amine quality indices of sardines (Sardina pilchardus) stored in modified atmosphere packaging and vacuum packaging. Food Chem., 99, 574, 2006. Paarup, T., Sanchez, J.A., Pelaez, C., and Moral, A. Sensory, chemical and bacteriological changes in vacuumpacked pressurised squid mantle (Todaropsis eblanae) stored at 4°C. Int. J. Food Microbiol., 74, 1, 2002. Papageorgiou, M., Lambropoulou, D., Morrison, C., Kłodzínska, E., Namiésnik, J., and Płotka-Wasylka, J. Literature update of analytical methods for biogenic amines determination in food and beverages. Trends Anal. Chem., 98, 128, 2018. Pegg, A.E. Toxicity of polyamines and their metabolic products. Chem. Res. Toxicol., 26, 1782, 2013. Pons-Sánchez-Cascado, S., Veciana-Nogués, M.T., Bover-Cid, S., Mariné-Font, A., and Vidal-Carou, M.C. Use of volatile and non-volatile amines to evaluate the freshness of anchovies stored in ice. J. Sci. Food Agric., 86, 699, 2006. Roig-Sagués, A.X., Ruiz-Capillas, C., Espinosa, D., and Hernández, M. The decarboxylating bacteria present in foodstuffs and the effect of emerging technologies on their formation (Chapter 8). In Biological Aspects of Biogenic Amines, Polyamines and Conjugates; Dandrifosse, G., Ed.; Transworld Research Network: Kerala, India; 2009.

688

Handbook of Seafood and Seafood Products Analysis

Rosa, R., and Nunes, M.L. Nutritional quality of red shrimp, Aristeus antennatus (Risso), pink shrimp, Parapenaeus longirostris (Lucas), and Norway lobster, Nephrops norvegicus (Linnaeus). J. Sci. Food Agric., 84, 89, 2003. Ruiz-Capillas, C., and Herrero, A. Impact of biogenic amines on food quality and safety. Foods, 8(2), 62, 2019. Ruiz-Capillas, C., Herrero, A.M., and Jiménez-Colmenero, F. Determination of biogenic amines (Chapter 34). In Flow Injection Analysis of Food Additives; Ruiz-Capillas, C., and Nollet, L.M.L., Eds.; CRC Press, Taylor and Francis Group: Boca Raton, FL; pp. 675–690; 2016. Ruiz-Capillas, C., and Horner, W.F.A. Determination of trimethylamine nitrogen and total volatile basic nitrogen in fresh fish by flow injection analysis. J. Sci. Food Agric., 79, 1982, 1999. Ruiz-Capillas, C., and Jiménez-Colmenero, F. Biogenic amines in meat and meat products. Crit. Rev. Food Sci., 44, 489, 2004. Ruiz-Capillas, C., and Jiménez-Colmenero, F. Biogenic amines in seafood products (Chapter 46). In Handbook of Seafood and Seafood Products Analysis; Nollet, L.M.L., and Toldra, F., Eds.; CRCPress, Member of Francis & Taylor Group: Boca Raton; pp. 833–850; 2009. Ruiz-Capillas, C., and Jiménez-Colmenero, F. Biogenic amines in seafood products (Chapter 27). In Safety Analysis of Food Animal Origin; Nollet, L.M.L., and Toldra, F., Eds.; CRCPress, Member of Francis & Taylor Group: Boca Raton; pp. 743–760; 2011. Ruiz-Capillas, C., and Moral, A. Production of biogenic amines and their potential use as quality control indices for hake (Merluccius merluccius L.) stored in ice. J. Food Sci., 66, 1030, 2001a. Ruiz-Capillas, C., and Moral, A. Correlation between biochemical and sensory quality indices in hake stored in ice. Food Res. Int., 34, 441, 2001b. Ruiz-Capillas, C., and Moral, A. Effect of controlled and modified atmospheres on the production of biogenic amines and free amino acids during storage of hake. Eur. Food Res. Techn. A., 214, 476, 2002. Ruiz-Capillas, C., and Moral, A. Free amino acids and biogenic amines in red and white muscle of tuna stored in controlled atmospheres. Amino Acids., 26, 125, 2004. Ruiz-Capillas, C., and Moral, A. Sensory and biochemical aspects of quality of whole bigeye tuna (Thunnus obesus) during bulk storage in controlled atmospheres. Food Chem., 89, 347, 2005. Ruiz-Capillas, C., Morales, J., and Moral, A. Preservation of bulk-stored Norway lobster at 1ºC in controlled and modified atmospheres. Eur. Food Res. Technol., 217, 466, 2003. Saaid, M., Saad, B., Hashim, N.H., Ali, A.S.M., and Saleh, M.I. Determination of biogenic amines in selected Malaysian food. Food Chem., 113, 1356, 2009. Sagratini, G., Fernández-Franzón, M., De Berardinis, F., Font, G., Vittori, S., and Mañes, J. Simultaneous determination of eight underivatised biogenic amines in fish by solid phase extraction and liquid chromatography-tandem mass spectrometry. Food Chem., 132, 537, 2012. Sánchez, J.A., and Ruiz-Capillas, C. Application of the simplex method for optimization of chromatographic analysis of biogenic amines in fish. Eur. Food Res. Technol., 234, 285, 2012. Sarkadi, L.S. Determination of biogenic amines by capillary electrophoresis. J. Chromatogr. A, 836, 305, 1999. Self, R.L., Wu, W.-H., and Marks, H.S. Simultaneous quantification of eight biogenic amine compounds in tuna by matrix solid-phase dispersion followed by HPLC-orbitrap mass spectrometry. J. Agric. Food Chem., 59(11), 5906, 2011. Shakila, R.J., Vasundhara, T.S., and Kumudavally, K.V. A comparison of the TLC-densitometry and HPLC method for the determination of biogenic amines in fish and fishery products. Food Chem., 75, 255, 2001. Shiono, K., Tsutsumi, T., Nabeshi, H., Ikeda, A., Yokoyama, J., and Akiyama, H. Simple and rapid determination of biogenic amines in fish and fish products by liquid chromatography-tandem mass spectrometry using 2,4,6-triethyl-3,5-dimethyl pyrylium trifluoromethanesulfonate as a derivatization reagent. J. Chromatogr. A, 1643, 462046, 2021. Simat, V., and Dalgaard, P. Use of small diameter column particles to enhance HPLC determination of histamine and other biogenic amines in seafood. LWT-Food Sci. Technol., 44, 399, 2011. Sivamaruthi, B.S., Kesika, P., and Chaiyasut, C. A narrative review on biogenic amines in fermented fish and meat products. J. Food Sci. Technol., 58, 1623, 2021. Staruszkiewicz, W.F., and Rogers, P.L. Performance of histamine test kits for applications to seafood. Presentation on October 26, 4th World Fish Inspection & Quality Control Congress, Vancouver, BC, 2001. Steiner, M.-S., Meier, R.J., Spangler, C., Duerkop, A., and Wolfbeis, O.S. Determination of biogenic amines by capillary electrophoresis using a chameleon type of fluorescent stain. Microchim. Acta, 167(3), 259, 2009.

Biogenic Amines in Seafood Products

689

Tahmouzi, S., Khaksar, R., and Ghasemlou, M. Development and validation of an HPLC-FLD method for rapid determination of histamine in skipjack tuna fish (Katsuwonus pelamis). Food Chem., 126, 756, 2011. Takagi, K., and Shikata, S. Flow injection determination of histamine with a histamine dehydrogenase-based electrode. Anal. Chim. Acta, 505, 189, 2004. Tao, Z., Sato, M., Han, Y., Tan, Z., Yamaguchi, T., and Nakano, T. A simple and rapid method for histamine analysis in fish and fishery products by TLC determination. Food Control, 22, 1154, 2011. Tapia-Salazar, M., Smith, T.K., and Harris, A. High performance liquid chromatographic method for detection of biogenic amines in feedstuffs, complete feeds and animal tissues. J. Agric. Food Chem., 48, 1708, 2000. Taylor, S.L., Lieber, E.R., and Leatherwood, M. A simplified method for histamine analysis of foods. J. Food Sci., 43, 247, 1978. Triki, M., Jiménez-Colmenero, F., Herrero, A.M., and Ruiz-Capillas, C. Optimisation of a chromatographic procedure for determining biogenic amine concentrations in meat and meat products employing a cationexchange column with a post-column system. Food Chem., 130, 1066, 2012. Tsai, Y.H., Lin, C.-Y., Chang, S.C., Chen, H.-C., Kung, H.-F., Wei, C.-I., and Hwang, D.-F. Occurrence of histamine and histamine-forming bacteria in salted mackerel in Taiwan. Food Microbiol., 22, 461, 2005. Vasconcelos, H., Coelho, L.C.C., Matias, A., Saraiva, C., Jorge, P.A.S., and de Almeida, J.M.M.M. Biosensors for biogenic amines: A review. Biosensors (Basel), 11(3), 82, 2021. Veciana-Nogués, M.T., Hernandez-Jover, T., Mariné Font, A., and Vidal Carou, M.C. Liquid chromatographic method for determination of biogenic amines in fish and fish products. J. AOAC Int., 78(4), 1045, 1995. Veciana-Nogués, M.T., Mariné Font, A., and Vidal Carou, M.C. Biogenic amines as hygienic quality indicators of tuna. Relationships with microbial counts, ATP-related compounds, volatile and organoleptic changes. J. Agric. Food Chem., 45, 2036, 1997. Zhao, Q.X., Xu, J., Xue, C.H., Sheng, W.J., Gao, R.C., Xue, Y., and Li, Z.J. Determination of biogenic amines in squid and white prawn by high-performance liquid chromatography with postcolumn derivatization. J. Agric. Food Chem., 55, 3083, 2007.

36

Detection of Genetically Modified Ingredients in Fish Feed Zahida Naseem SKUAST-K

Shahida Naseem University of Kashmir

Sajad Ahmad Mir Gitam School of Science, Gitam (Deemed to be University)

Danish Rizwan University of Kashmir

36.1 INTRODUCTION Feed is regarded as a vital input in aquaculture to increase fish productivity. It is a matter of major concern as it comprises up to 70% of the operational cost and decides the volume and quality of the yield (Naseem et al., 2020). Fishmeal is a key protein source in fish feeds due to its high protein content, high essential amino acids, high palatability, and bioavailability of nutrients. However, its production is declining, which consequently raises its cost. Further, the growing human population severely defies the availability of high-quality, nutrient-rich food and will increase the food requirement by 25%–70% (Hunter et al., 2017). Animal-origin foods are vital for global food security since they contribute 25% of protein and 18% of calorie intake globally (FAO STAT, 2016), of which fish shares 16% protein. Fisheries and aquaculture are the main sources of protein in the regions where cattle are rare. The contribution of aquaculture to economic growth and the world food supply is on the rise. Aquaculture now provides nearly one-third of the fish consumed by people, and that number will only rise as aquaculture develops and the global conventional fish catch from oceans and freshwater continues to drop due to overfishing and environmental destruction (FAO, 2020). Consequently, there is a growing call for commercial fish feeds for intensive fish farming. However, the scarcity and increasing cost of fishmeal demand a substitute. ­Non-conventional sources of protein, like plants as a substitute for fishmeal, have the capacity to revolutionize the aquaculture industry (Naseem et al., 2020). Artificial diets that substitute plant protein sources for fishmeal, such as soybean meal or maize gluten, have been widely adopted in aquaculture as a result of the decrease in fishmeal productivity over the last 10 years. Many of these protein sources are from GM crops (Sissener et al., 2011). Soybean meal is a common source of high-quality plant protein in prepared fish diets. Since GM soybean meal (SBM) has been produced, it is utilized as a feed component in prepared diets. Therefore, it is necessary to research the effects of modifying fish feed formulations on the final product’s safety and quality. When evaluating the safety of GM plants used as fish diet components, two significant factors are taken into account. The first is fish safety, evaluated through feeding trials to see whether nutritional performance is equivalent. The second is food safety, assessed by transgenic protein’s digestion and incorporation into the fish (Giraldo et al., 2019). 690

DOI: 10.1201/9781003289401-41

Detection of Genetically Modified Ingredients in Fish Feed

691

The term “genetically modified organisms” (GMO) refers to organisms whose genetic makeup has been improved. Reproduction or natural recombination cannot affect the genetic material in the same way. Gene technology has made the direct introduction of new traits efficient and more feasible. A coding DNA sequence is expressed for this function, for example, in the plant genome. This gene transfer may occur physically (by injection or gene gun) or biologically (plasmids and viruses). The ability to choose a trait from a variety of species and introduce it to the crop of interest allows for the expansion of genetic variation. Plants that have their genetic makeup changed in a manner that does not occur naturally through crossbreeding or natural recombination are considered genetically modified (GM) plants (WHO, 2022). Crop plants have successfully benefited from the introduction of novel traits through genetic modification. Regardless of the approach employed, integration of the novel DNA occurs through nonhomologous end-joining recombination, as opposed to homologous, or site-directed, recombination (Gelvin, 2003). Thus, the novel gene’s arbitrary insertion in the transformed plant could interfere with the endogenous gene expression, which might have unforeseen consequences like alterations in the amounts of micro- and/or macro-nutrients, antinutrient elements, or inborn plant toxins (Cellini et al., 2004). If these alterations have taken place in the GM plant, they might reduce its value as a fish feed element. Another issue is that the unique GM protein may have an impact on animals eating the plant. Since 1996, there has been a sharp rise in the proportion of GM crops in the overall harvest. Almost 130 GMOs have received commercial approval for use as food or feed. Soybean, cotton, maize, oilseed rape, and canola have GM cultivars grown at 82%, 68%, 30%, 25%, and 21% of their global areas, respectively. These plants or their byproducts are frequently used as food and feed for animals that produce food (Bawa and Anilakumar, 2013). As a result, it is becoming challenging for manufacturers of fish feed to find non-modified forms of some plant products (Kaushik and Hemre, 2008). Production of GM crops is growing annually by 4%. Certainly, this increase has insisted on the feed manufacturing industry utilizing GM plants as fish feed components. GM plant feedstuffs have favorable features such as pest/disease resistance, longer shelf life, higher nutritional value, and low anti-nutrients (Bandara, 2018). The utilization of GM crops to remove anti-nutrients and increase particular nutrients, e.g., limiting amino acids, n-3 fatty acids, etc., is promising. Advanced recombinant DNA technology has improved essential amino acid profiles in various crops. Corynobacterium glutamicum genes were incorporated in GM maize to improve the lysine profile (Lucas et al., 2007). An increased methionine profile in GM lupins was attained by incorporating the methionine gene from sunflower (Ravindran et al., 2002). The concept of an optimum protein blend made from GM feed ingredients will reduce the quantity of nitrogen excreted by fish. Moreover, GM microbes can be involved to produce enzymes with preferred properties to make them commercially feasible. These enzymes could be applied to use non-conventional feed ingredients such as sunflower and sorghum, millets, or rice bran to supply protein and energy, respectively, and to consume high-fiber diets efficiently (Eze et al., 2021). The high cost and low supply of fishmeal have created an urgent demand for non-fishmeal protein sources. Several plant protein sources, like maize gluten, canola, and soybeans, are considered good substitutes for it. However, due to their unbalanced amino acid profile and the presence of anti-nutritional factors, these plants are not considered economical to a large extent. These plants have been genetically modified to improve their nutritional profile and are used as a fishmeal substitute in fish feeds. The genetically modified crop monitoring charter at the global level is assessed with regard to labeling and traceability. The existing analytical techniques to detect, identify, and quantify the transgenic DNA in feed ingredients are discussed. These methods include multiplex PCR, digital PCR, biosensors, quantitative real-time PCR, and multiplex real-time PCR. Methods that could identify several GM events in a single reaction are particularly considered. A variety of techniques and approaches have been applied to endure on the issue of the recognition, identification, and quantification of GMOs in feed ingredients. Mostly, these methods are PCRbased, either simplex or multiplex, with certain using a real-time format. In order to give a more precise and consistent assessment of GM feed safety, further development of particular procedures for the evaluation of GM crops meant for animal consumption is necessary.

692

Handbook of Seafood and Seafood Products Analysis

36.2  GENETICALLY MODIFIED PLANTS IN FISH FEEDS 36.2.1  Roundup Ready (RR) Soybean The most typical characteristic for which GM crops have been engineered is herbicide resistance. Examples of such plants include alfalfa, cotton, soybean, canola, maize, rice, wheat, linseed, and sugar beet. Roundup Ready (RR), a glyphosate-tolerant soy variety, is the most popular soybean cultivar in the market (James, 2009), making it a prime candidate for use in animal diets. This soy expresses the CP4 EPSPS protein, which has not been linked to any known allergies or toxins and was observed to break down quickly in simulated stomach juice after being consumed in high quantities by mice. It has been determined that RR soy is chemically similar to its non-transgenic parental line, regardless of weedicide usage and the growing environment (McCann et al., 2005). Studies in longfin yellowtail and trout confirmed the efficiency of ketocarotenoids produced in GM soybeans to give fish their characteristic pinkish hue (Park et al., 2017). GM soybeans showed no negative impact on feed efficiency and growth performance of Nile tilapia (Suharman et al., 2009).

36.2.2  Bacillus thuringiensis (Bt)-Maize Plants that express several Cry-proteins fall under the class of insect-resistant GM plants. These naturally occurring proteins, frequently called Bt-toxins or Bt-proteins, are produced by the soil bacteria Bacillus thuringiensis. Various Cry-proteins attach to certain receptors in the midgut of their prey insects, where they disrupt the colon wall by creating pores and altering the penetrability of epithelial cells, ultimately killing the insect. The Cry1Ab protein showed no symptoms of acute toxicity when given to mice at a level of 4,000 mg/kg in the feed, whereas tests with the Cry1Ac protein in mice showed hyperpolarization of gut tissue, binding to mucosa, and stimulation of antibody production (Vazquez-Padron et al., 2000). Regarding compositional equivalency, it was discovered that 10 Bt-corn hybrids contained more lignin than their near-isogenic lines, which is a key plant cell structural component. Mycotoxin content in corn kernels may influence how nutritious Bt versus ordinary maize is. The former has typically been seen to be lower in Bt-maize despite significant regional and seasonal fluctuations, as insect damage might encourage fungus development (Papst et al., 2005). In certain research using different production animals fed Bt-maize, this has been cited as an explanation for the higher mass increase in the GM-fed group, even if cause-and-effect links were not demonstrated. The increased demand of a developing aquafeed manufacturing sector would be satisfied by GM maize, which would also reduce the need for imported raw materials. Atlantic salmon accepts the GM maize product MON810® well without any harmful impacts on the growth performance of fish (Hemre et al., 2007). Moreover, astaxanthin produced in GM maize will improve the pigmentation of the trout flesh (Breitenbach et al., 2016).

36.2.3 GM Cotton In fish diets, various GM cotton cultivars have been examined (Li et al., 2008). Bollgard × RR (expressing both CP4 EPSPS and Cry1Ac), RR × Bollgard II (Cry1Ac, CP4 EPSPS, and Cry2Ab2), Roundup Ready Flex (CP4 ESPS protein), and Bollgard II × RR Flex (Cry1Ac, Cry2Ab2, and CP4 EPSPS) are the four GM cotton cultivars that are employed. The latter three cotton kinds are crosses between two GM cultivars, known as “stacked events” that combine insect resistance and herbicide tolerance in the same plant. Dietary assays of cottonseed meal (CSM) from the abovementioned plants were carried out in three distinct feeding trails that continued for 2 months. Genetically related non-GM CSM control and various marketed varieties served as references. Twenty percent CSM was present in every diet. In all three experiments, gossypol levels and proximate composition of the four CSM species varied, but these variances were somewhat compensated for in the feed

Detection of Genetically Modified Ingredients in Fish Feed

693

formulation. Growth, survival, feed efficacy, and fillet composition were the variables analyzed. Bollgard II × RR were observed to produce low fish fillet protein content than the genetically identical, non-modified CSM. The fish fed Bollgard × RR was only to show a significant difference from fish fed the commercial kinds as well. Bollgard × RR and the authors come to the conclusion that all the GM CSMs are nutritiously similar to non-modified CSM.

36.2.4  RR Canola Two lines of RR canola, variation events GT73 and GT200, both carrying the CP4 EPSPS gene and a glyphosate oxidase gene from Ochrobactrum anthropi, which catalyses the breakage of glyphosate, have been studied in meals for rainbow trout. These were incorporated at 5%–20% in fish feeds with a regression design, and they were examined against the parental line and canola-free reference diet (Brown et al., 2003). The GT73 line differs from the parental line in that it retains more protein, has more body water and protein content, but less lipid, while the GT200 line is equivalent to unmodified canola. This might be because the diets did not appear to have been balanced for the little variations in nitrogen solubility and protein content between the canola lines.

36.2.5 Lupin with Enhanced Methionine The first commercially available GM crops were developed to enhance agronomic features like herbicide tolerance or insect resistance. The “second generation” GM plants refer to more complex alterations that try to alter the nutritional profile of the plants. A GM lupin cultivar used in feeds of young red seabream (Pagrus auratus) is the sole second-generation GM plant with findings for fish diets that have been published (Glencross et al., 2003). Although this lupin line was altered for the gene expression for sunflower seed albumin, which enhanced methionine levels, it has not yet been put on the market. Two commercial lupin types were utilized as a guide, one of which was quite similar to the GM variety in terms of proximate makeup. A feed with the non-GM lupin cultivar incorporated with crystalline methionine was also involved in the experiment. When compared with fish fed one of the non-GM lupin varieties (lower protein content) and ad libitum feeding (high protein), no significant differences in growth were observed. A favorable growth impact was observed with both the GM diet and the non-GM diet with added crystalline methionine as compared to the non-fortified non-GM type in a later feeding study using protein-restrictive diets and pair-feeding.

36.3  METHODS FOR DETECTION OF GM INGREDIENTS IN FISH FEED The main GM plant species are cotton, soybean, canola, and maize, and these are primarily grown in wealthy nations that control the majority of the world’s commerce in these commodities. However, the developing world is currently seeing a sharp increase in interest in cultivating GM crops. Contrarily, the trend in the European Union (EU) is unfavorable as a result of government legislation meant to safeguard the environment and human and animal health since the introduction of GM crops earlier in the 1990s (US FDA, 2008). The United States Department of Agriculture (USDA), the Environmental Protection Agency (EPA), the Animal and Plant Health Inspection Service (APHIS), and the Food and Drug Administration (FDA) are each given accountability for different parts of the GM risk assessment. According to EU Regulation 1829/2003, which mandates a labeling standard for all GM-based products, the inclusion of GM material in feed, food, and their products is regulated. The word “genetically modified” must be in the list of constituents instantly after the pertinent constituent in cases where the content of any allowed GM component exceeds 0.9% of the food or feed product. If the amount of GM material is less than this, it is deemed accidental or technologically impossible to avoid, and the produce may be traded unlabeled. The threshold for unauthorized GM constituents is set at 0.5% if the GMO source has been assayed prior and the technique suitable for detection is accessible.

694

Handbook of Seafood and Seafood Products Analysis

36.3.1 Qualitative Methods for GM Detection The majority of detection techniques either amplify transgene sequences using polymerase chain reaction (PCR) or bind to transgene gene products using immunological techniques, usually ELISA. Though particular DNA sequences could be identified by hybridization, the regulatory authorities have typically recognized PCR in its many formats (quantitative real-time PCR, qualitative PCR, and end-point quantitative PCR) (Marmiroli et al., 2003). Every PCR experiment demands that the template contain a certain minimum number of target DNA sequences and that the target DNA sequence is known. The isolation and purification of DNA from the sample are crucial steps (Cankar et al., 2006). PCR-based assays for the occurrence of GM components have been categorized as per their specificity level into those that target: (i) sequences within a specific transgene; (ii) broadly used sequences such as bla (β-lactamase), T-35S (CaMV 35S terminator), P-35S (CaMV 35S promoter), T-Nos (terminator of the nopaline synthase gene), and nptII (neomycin phosphotransferase II); (iii) sequences that are event-specific, such as the transgene integration site; and (iv) sequences which are construct-specific, for instance, the intersection between the transgene and the promoter sequence used to drive the transgene. 36.3.1.1  Multiplex PCR Methods The rapid expansion of the cultivation area of GM crops and the figure of approved transgenic events have necessitated advancements in GM detection methods. One method is to employ multiplex PCR, which allows the simultaneous recognition of various target sequences by incorporating multiple primer pairs into the PCR. Systems of this type have been created for a variety of construct-specific targets (Peano et al., 2005). Since agarose gels provide a straightforward and affordable platform, several PCR products are typically differentiated from one another based on their variance movement through them. Eight GM maize cultivars were effectively separated from one another in a nonaplex (nine construct-specific primer pairs). PCR experiment using a sample containing 0.25% of each event (Onishi et al., 2005). The sizes of the amplicons in this case ranged from 110 to 444 bp and were detected using both capillary and agarose gel electrophoresis. This test would not be appropriate for highly treated foodstuffs, in which the degradation level of DNA typically prevents the subsistence of extended sections of unharmed DNA, given the comparatively larger size of some of these PCR products. Utilizing transgene/plant genome flanking areas, it was possible to detect transgenic events NK603, GA21, MON810, and Bt11 simultaneously in maize with a LOD of 0.1% (Nadal et al., 2006). This application needed the use of a DNA sequencing device since the distinct amplicons were identified by labeling with various fluorochromes. This platform has the special benefit of being easily adaptable to a high-throughput mode. The multiplex PCR technique is the most effective method to check the presence of GMOs in the feed as it saves significant time and energy by decreasing the reaction number required to be evaluated by instantaneously amplifying multiple sequences simultaneously. 36.3.1.2  Nested PCR Nested PCR is a simple revision of conventional PCR, which actually enhances the specificity of any reaction. Its specificity is increased by decreasing the non-specific binding using two primer sets. The n-PCR generates precise results as two sets of primers sequentially amplify the same target. The nested PCR is ideal for 16s RNA analysis and microbial identification. It has high sensitivity and repeatability. In comparison to F1R1-F2R2, the n-PCR with F2R2-F3R3 could detect greater dilution ratios of 0.01% and 0.1%, respectively. In contrast to the traditional PCR advised by the EU, n-PCR had a 5- to 50-fold higher sensitivity for detecting transgenic soybean (Ran et al., 2009). The n-PCR is used in conditions where it is essential to enhance the specificity and/or sensitivity of PCR. It generally includes two sequential amplification reactions, each using a different primer pair. Oligonucleotides placed internal to the first primer pair prime the product of the first amplification reaction, which is used as the template for the second PCR (Green and Sambrook,

Detection of Genetically Modified Ingredients in Fish Feed

695

2019). This approach considerably decreases the issue of un-specific amplification, as the possibility for the internal set of primers of finding corresponding sequences within the non-specific amplification products of the external set is very low (Alasaad et al., 2016). It is especially suitable for suboptimal samples, such as those extracted from paraffin-embedded or formalin-fixed tissue. These are valued for microorganisms’ detection when present in very small amounts.

36.3.2 Quantitative Methods 36.3.2.1  Uniplex and Duplex Real-Time Quantitative PCR (qRT-PCR) Quantitative real-time PCR is the most effective method available for measuring the presence of GM material in food and agricultural items (Marmiroli and Maestri, 2007). The amplification reaction is continuously monitored and the amount of amplicon present is determined by the strength of the fluorescence signal. The chemical used to track amplification and the equipment used to track the signal both affect how specific qRT-PCR is. Two parallel reactions with an equal volume of template DNA are frequently employed to determine the amount of GM material in a sample. One of the reactions targets the GM-specific sequence, and the other targets the endogenous reference sequence. Samples are quantified either by titration against a standard curve or by comparison based on the cycle threshold of the two amplified sequences (the “ΔCt method”). A different strategy makes use of a multiplex reaction that takes advantage of the differential fluorochrome tagging of the two amplicons. This method has some technical issues, mainly because the emission spectra of the most widely used fluorochromes overlap. Fluorochromes can be replaced by interpolating dyes (such as LC Green and SYBR Green I), which have a strong attraction for double-stranded DNA and exhibit increased fluorescence when attached to DNA. By using a temperature gradient, it is possible to determine the melting curves of the two separate amplicon sequences while simultaneously monitoring the quantity of amplicons in this scenario. The two amplicons can be identified by their melting temperatures since they are sequence-specific. Sensitivity and repeatability issues, notably reducing the possibility of false negative results, are one of the main difficulties with multiplex qRT-PCR. The multiplex format’s complexity makes optimization more difficult than it is for uniplex systems. RT-PCR has been used to quantify dietary DNA in the blood, liver, spleen, head, kidney, muscle, brain, and heart of Salmo salar L. fed RR soybean and GM maize (MON810, Wiik‐Nielsen et al., 2011). 36.3.2.2  Digital PCR Digital PCR (dPCR) is utilized for absolutely quantify the target nucleic acids in a GM sample. Chip-based and droplet-based dPCR devices are used to detect GM events and for several other uses (Demeke and Dobnik, 2018). Digital PCR is highly expedient than RT-PCR as a standard curve is not required for the detection and quantification of GM samples. The dPCR assays are reported to have high reproducibility. For a high GC-rich reference sample, a collaborative dPCR investigation involving seven different laboratories revealed less than 4.5% repeatability relative standard deviation. The dPCR analyses have also been described as less sensitive to inhibitors in the DNA samples in comparison to qRT-PCR evaluations. However, the stability of droplets for dPCR is reportedly affected by ethanol (Zhao et al., 2016). 36.3.2.2.1  Droplet Digital PCR (ddPCR) The ddPCR is a new and promising technology for detecting and absolutely quantifying the target DNA sequence in a particular sample. The PCR mixture is divided into approximately 20,000 nanoliter droplets for ddPCR, some of which cover the target nucleic acid sequences. A droplet reader tallies the number of droplets that contain positive or negative amplification produced following an end-point PCR (Pinheiro et al., 2012). Compared to qPCR, ddPCR is more sensitive and accurate when quantifying low amounts of target DNA and can tolerate a significant volume of

696

Handbook of Seafood and Seafood Products Analysis

background DNA during the PCR reaction (Doi et al., 2015). Additionally, ddPCR exhibits high resistance to PCR inhibitors, and as an end-point measurement, target DNA quantification is possible. Inconsistency in amplification efficacy rarely affects ddPCR (Dingle et al., 2013). It has high sensitivity in low copy number ranges (Whale et al., 2012) and high tolerance to PCR inhibitors (Nixon et al., 2014). In comparison to qRT-PCR, ddPCR has a benefit since genetically engineered traits can be quantified absolutely without the use of reference materials (Demeke et al., 2016). Mostly, a duplex dPCR analysis comprising one reference gene and one GM event is conducted. The efficacy of dPCR will be enhanced by detecting more than one GM event in a single test. The practicality of detection and quantification of two, three, and four soybean and GM canola events simultaneously has been evaluated at 0.1%, 1%, and 5% levels by the QX200 ddPCR system. The QX200 ddPCR system consists of two fluorescence filters to effectively run multiplex ddPCR containing more than two targets (Demeke et al., 2022). For the calculation of target DNA at low concentrations, ddPCR might be a more effective method than qPCR, especially in some complicated samples (Dong et al., 2021). In some recent research, the ddPCR was used to identify GM agricultural products in aquadiets (Morisset et al., 2013; Dong et al., 2021).

36.3.3  Biosensors The main technology that places an emphasis on quick, easy, and inexpensive testing—biosensors—represents a remarkable alternative to identifying GMOs. Immunosensors and genosensors are the two types of biosensors that are used to detect proteins and DNA, respectively. These depend on electrochemical, piezoelectric, or optical transduction and are appropriate due of their ordinary instrumentation and simple miniaturization (Sousa et al., 2018). A feature encoded by a gene that has been introduced into GM raw materials and processed meals produces more protein. In order to detect its existence, a particular oligonucleotide probe (recognition layer) is applied to the surface of the sensor that captures it. In order to detect its existence, a particular oligonucleotide probe (recognition layer) is applied to the sensor surface and captures it. Molecular recognition based on hybridization of the target DNA sequence employing GMO-specific probes has enabled the improvement of several transducers to convert the hybridization event into electrical or visual signals (Arugula et al., 2014). These factors allow for the division of biosensors for GMO detection and identification into piezoelectric, electrochemical, and optical systems. 36.3.3.1  Optical Biosensors With the aid of the potent optical technique known as surface plasmon resonance (SPR), it is possible to identify a wide range of biomolecular interactions that take place when a thinly gold-plated prism comes into contact with the analyst solution passing through it. This device functions by detecting refractive index changes that take place at the interface between materials with various refraction indices. SPR biosensors can be used to check protein-protein, DNA-DNA, protein-DNA, enzyme-substrate or inhibitor, protein polysaccharide, receptor-drug, ligand-aptamer, lipid membrane-protein, and cell or virus-protein interactions. For GMO investigation, the SPR methods are based on the chemisorption of DNA probes onto the surface of gold chips and on affinity interfaces between biotinylated probes and streptavidin (SA; Placido et al., 2020). Mariotti et al. were the first to give an account of an SPR-based biosensor for GMOs screening (2002). Feriotto et al. (2002) confirmed the applicability of biospecific interaction analysis (BIA) based on SPR to identify the soybean Roundup Ready and lectin gene sequences. The BIA core is a commercialized SPR-based biosensor in which plane polarized light is reflected from a gold-plated sensor chip while occurrence of target-molecule interaction. Placido et al. (2020) were the first to report SPR biosensors designed in accordance with legal criteria for quantifying GMOs in food and feed samples. It has a lot of potential for determining the proportion of GMO, as it offers a quick reaction and is inexpensive due to the regeneration ability of the sensor surface. This approach has never been previously investigated for the quantification of GMOs in feed and food.

Detection of Genetically Modified Ingredients in Fish Feed

697

36.3.3.2  Piezoelectric Biosensors The mass-sensitive quartz crystal microbalance (QCM), a piezoelectric instrument based on an oscillating quartz crystal, has the capacity to identify changes in mass as small as a few nanograms. A mechanical resonance is created when a monolayer forms on a quartz wafer placed between two electrodes due to a resonating electric field. The frequency of both oscillations is connected to interfacial mass changes through the Sauerbrey equation: ∆f =

−2 ∆mnf 2 A ( µρ )

1

2

where Δf is the oscillation frequency, μ is the shear modulus of quartz (2.947 × 1011 g/cm/s), A is the piezoelectrically active crystal area, ρ is the density of quartz (2.648 g/cm), and Δm is the mass changes The hybridization reaction, which results in increased mass and decreased resonance frequency, is monitored in real-time by QCM (Karamollaoglu et al., 2009). Piezoelectric biosensors function on the principle of GMO detection based on increased mass as a result of the probe’s non-labeled hybridization with the target DNA sequence. 36.3.3.3  Electrochemical Biosensors The analysis and detection of GMOs by electrochemical sensors converts DNA hybridization signals into measurable electrochemical signals. These provide selectivity, sensitivity, and consistent target sequence recognition following hybridization and are based on relations between the recognition probe, solid electrode surface, and analyte DNA. Linear sweep voltammetry (LSV), square wave voltammetry (SWV), electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV), cyclic voltammetry (CV), and anodic stripping voltammetry are among the electrochemical methods used for GMO identification (ASV, Karimi-Maleh et al., 2021). This sensing technology is quick, inexpensive, and portable analysis.

REFERENCES Alasaad N, Alzubi H, Kader AA (2016) Data in support of the detection of genetically modified organisms (GMOs) in food and feed samples. Data Brief 7: 243–252. Arugula MA, Zhang Y, Simonian AL (2014) Biosensors as 21st century technology for detecting genetically modified organisms in food and feed. Anal. Chem. 86: 119−129. Bandara T (2018) Alternative feed ingredients in aquaculture: Opportunities and challenges. J. Entomol. Zool. Stud. 6: 3087–3094. Bawa AS, Anilakumar KR (2013) Genetically modified foods: Safety, risks and public concerns: A review. J. Food Sci. Technol. 50: 1035–1046. Breitenbach J, Nogueira M, Farré G, Zhu C, Capell T, Christou P et al. (2016) Engineered maize as a source of astaxanthin: Processing and application as fish feed. Transgenic Res. 25: 785–793. Brown PB, Wilson KA, Jonker Y, Nickson, TE (2003) Glyphosate tolerant canola meal is equivalent to the parental line in diets fed to rainbow trout. J. Agric. Food Chem. 51: 4268–4272. Cankar K, Stebih D, Dreo T, Zel J, Gruden K. (2006) Critical points of DNA quantification by real-time PCReffects of DNA extraction method and sample matrix on quantification of genetically modified organisms. BMC Biotechnol. 6: 1–5. Cellini F, Chesson A, Colquhoun I, Constable A, Davies HV, Engel KH et al. (2004) Unintended effects and their detection in genetically modified crops. Food Chem. Toxicol. 42(7): 1089–1125. doi:10.1016/j. fct.2004.02.003.

698

Handbook of Seafood and Seafood Products Analysis

Dingle TC, Sedlak RH, Cook L, Jerome KR (2013) Tolerance of droplet-digital PCR vs real-time quantitative PCR to inhibitory substances. Clin. Chem. 59: 1670–1672. Doi H, Uchii K, Takahara T, Matsuhashi S, Yamanaka H, Minamoto T (2015) Use of droplet digital PCR for estimation of fish abundance and biomass in environmental DNA surveys. PLoS One 10: e0122763. Dong S, Zhang D, Yu C, Zhang Z, Liu Y (2021) Using droplet digital PCR to detect plant DNA in tissues of zebrafish (Danio rerio) fed genetically modified maize. Aquac. Res. 00: 1–8. doi:10.1111/ are.15285. Demeke T, Dobnik D (2018) Critical assessment of digital PCR for the detection and quantification of genetically modified organisms. Anal. Bioanal. Chem. 410: 4039–4050. Demeke T, Holigroski M, Eng M, Xing J (2016) Absolute quantification of genetically engineered traits with droplet digital PCR: Effect of DNA treatments and spiking with non-target DNA. Food Control 68: 105–111. Demeke T, Lee SJ, Eng M (2022) Increasing the efficiency of canola and soybean GMO detection and quantification using multiplex droplet digital PCR. Biology 11: 201–210. doi:10.3390/ biology11020201. Eze BU, Akinrotimi OA, Onwujiariri CA (2021) The roles of biotechnology in fish nutrition for ­sustainable aquaculture production in Nigeria. Proceedings of the 36th Annual Conference of Fison, held at University of Port Harcourt, Choba, Rivers State Garden City 24th–29th October, 2021, pp. 897–901. FAO (2020) The State of World Fisheries and Aquaculture 2020: Sustainability in Action. FAO, Rome. Accessed on 15th of July, 2022. doi:10.4060/ca9229en. FAO STAT (2016) Food and Agriculture Data 2016. Food and Agriculture Organization of the United Nations (FAO), Rome. Accessed 20 August 2022. Feriotto G, Borgatti M, Mischiati C, Bianchi N, Gambthari R (2002) Biosensor technology and surface plasmon resonance for real-time detection of genetically modified roundup ready soybean gene sequences. J. Agric. Food Chem. 50: 955–962. Gelvin, SB. (2003) Agrobacterium-mediated plant transformation: The biology behind the “gene-jockeying” tool. Microbiol. Mol. Biol. Rev. 67: 16–37. doi:10.1128/MMBR.67.1.16-37.2003. Giraldo PA, Shinozuka H, Spangenberg GC, Cogan NO, Smith KF (2019) Safety assessment of genetically modified feed: Is there any difference from food? Front. Plant Sci. 10: 1–17. Glencross B, Curnow J, Hawkins W, Kissil GWM, Peterson D (2003) Evaluation of the feed value of a transgenic strain of the narrow-leaf lupin (Lupinus angustifolius) in the diet of the marine fish, Pagrus auratus. Aquac. Nutr. 9(3): 197–206. doi:10.1046/j.1365-2095.2003.00247.x. Green MR, Sambrook J (2019) Nested polymerase chain reaction (PCR). Cold Spring Harb. Protoc. 2019(2): pdb–rot095182. Hemre GI, Sagstad A, Bakke‐Mckellep AM, Danieli A, Acierno R, Maffia M et al. (2007) Nutritional, physiological, and histological responses in Atlantic salmon, Salmo salar L. fed diets with genetically modified maize. Aquac. Nutr. 13: 186–199. Hunter MC, Smith RG, Schipanski ME, Atwood LW, Mortensen DA (2017) Agriculture in 2050: Recalibrating targets for sustainable intensification. Bio Sci. 67: 386–391. James C (2009) Global Status of Commercialized Biotech/GM Crops. ISAAA, Ithaca, New York. Karamollaoglu İ, Oktem HA, Mutlu M (2009) QCM-based DNA biosensor for detection of genetically modified organisms (GMOs). Biochem. Eng. J. 44: 142–150. Karimi-Maleh H, Orooji Y, Karimi F, Alizadeh M, Baghayeri M, Rouhi J et al. (2021) Critical review on the use of potentiometric based biosensors for biomarkers detection. Biosens. Bioelectron. 184: 113252. Kaushik SJ, Hemre GI (2008) Plant proteins as alternative sources for fish feed and farmed fish quality. In Improving Farmed Fish Quality and Safety, edited by Ø Lie. Woodhead Publishing Limited, CRC Press, Cambridge, UK. pp. 300–319. Li MH, Hartnell GF, Robinson EH, Kronenberg JM, Healy CE, Oberle DF, Hoberg JR (2008) Evaluation of cottonseed meal derived from genetically modified cotton as feed ingredients for channel catfish, Ictalurus punctatus. Aquacult. Nutr. 14(6): 490–498. doi:10.1111/j.1365-2095.2007.00554.x. Lucas DM, Taylor ML, Hartnell GF, Nemeth MA, Glenn KC, Davis SW (2007) Broiler performance and carcass characteristics when fed diets containing lysine maize (LY038 or LY038 × MON 810), control, or conventional reference maize. Poult. Sci. 86: 2152–2161. Marmiroli N, Maestri E (2007) Polymerase chain reaction (PCR). In Food Toxicants Analysis Techniques, Strategies and Development, edited by Y Picò. Elsevier, Amsterdam. pp. 147–187. ISBN 978-0-444-52483-8. Marmiroli N, Peano C, Maestri E (2003) Advanced PCR techniques in identifying food components. In Food Authenticity and Traceability, edited by M Lees. CRC Press, Cambridge. pp. 3–33. ISBN 1-85573-526-1. Mariotti E, Minunni M, Mascini M (2002) Surface plasmon resonance biosensor for genetically modified organisms detection. Anal. Chim. Acta 453: 165–172.

Detection of Genetically Modified Ingredients in Fish Feed

699

McCann MC, Liu K, Trujillo WA, Dobert RC (2005) Glyphosate-tolerant soybeans remain compositionally equivalent to conventional soybeans (Glycine max L.) during three years of field testing. J. Agric. Food Chem. 53(13): 5331–5335. doi:10.1021/jf0504317. Morisset D, Stebih D, Milavec M, Gruden K, Zel J (2013) Quantitative analysis of food and feed samples with droplet digital PCR. PLoS One 8: e62583. Nadal A, Coll A, La Paz JL, Esteve T, Pla M (2006) A new PCR-CGE (size and color) method for simultaneous detection of genetically modified maize events. Electrophoresis 27: 3879–3888. Naseem S, Bhat SU, Gani A, Bhat FA (2020) Perspectives on utilization of macrophytes as feed ingredient for fish in future aquaculture. Rev. Aquac. 13: 282–300. Nixon G, Garson JA, Grant P, Nastouli E, Foy CA, Huggett JF (2014) Comparative study of sensitivity, linearity, and resistance to inhibition of digital and non-digital polymerase chain reaction and loop mediated isothermal amplification assays for quantification of human cytomegalovirus. Anal. Chem. 86: 4387–4394. Onishi M, Matsuoka T, Kodama T, Kashiwaba K, Futo S, Akiyama H et al. (2005) Development of a multiplex polymerase chain reaction method for simultaneous detection of eight events of genetically modified maize. J. Agric. Food 53: 9713–9721. Papst C, Utz HF, Melchinger AE, Eder J, Magg T, Klein D, Bohn M (2005) Mycotoxins produced by Fusarium spp. in isogenic Bt vs. non-Bt maize hybrids under European corn borer pressure. Agron. J. 97: 219–224. Park H, Weier S, Razvi F, Pena PA, Sims NA, Lowell J et al. (2017) Towards the development of a sustainable soya bean‐based feedstock for aquaculture. Plant Biotechnol. J. 15: 227–236. Peano C, Bordoni R, Gulli M, Mezzelani A, Samson MC, De Bellis G, Marmiroli N (2005) Multiplex polymerase chain reaction and ligation detection reaction/universal array technology for the traceability of genetically modified organisms in foods. Anal. Biochem. 346: 90–100. Pinheiro LB, Coleman VA, Hindson CM, Herrmann J, Hindson BJ, Bhat S, Emslie KR (2012) Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Anal. Chem. 84: 1003–1011. Placido A, Ferreira-da-Silva F, Leite JRSA, de-los-Santos-Álvarez N, Delerue-Matos C (2020) A convenient renewable surface plasmon resonance chip for relative quantification of genetically modified soybean in food and feed. PLoS One 15(2): e0229659. doi:10.1371/journal.pone.0229659. Ran T, Mei L, Lei W, Aihua L, Ru H, Jie S (2009) Detection of transgenic DNA in tilapias (Oreochromis niloticus, GIFT strain) fed genetically modified soybeans (Roundup Ready). Aquac. Res. 40: 1350–1357. Ravindran V, Tabe LM, Molvig L, Higgins TJV, Bryden WL (2002) Nutritional evaluation of transgenic highmethionine lupins (Lupinus angustifolius L) with broiler chickens. J. Sci. Food Agric. 82: 280–285. Sissener NH, Sanden M, Krogdahl A, Bakke AM, Johannessen LE, Hemre GI (2011) Genetically modified plants as fish feed ingredients. Can. J. Fish. Aquat. Sci. 68: 563–574. Sousa JB, Ramos-Jesus J, Fonseca RA, Delerue-Matos C, Barroso MF, Santos Jr JR (2018) Biosensors as advanced device for the transgenic plants and food and detection. In : Alina Maria Holban and Alexandru Mihai Grumezescu (Eds.) Genetically Engineered Foods, pp. 221–245. Academic Press. doi: 10.1016/ C2016-0-00358-3. Suharman I, Satoh S, Haga Y, Takeuchi T, Endo M, Hirono I, Aoki T (2009) Utilization of genetically modified soybean meal in Nile tilapia Oreochromis niloticus diets. Fish. Sci. 75: 967–973. The United States Food and Drug Administration (2008) https://www.cfsan.fda.gov/~lrd/biotechm.html. Accessed 30 July 2022. Vazquez-Padron RI, Gonzáles-Cabrera J, García-Tovar C, Neri-Bazan L, Lopéz-Revilla R, Hernández M et al. (2000) Cry1Ac protoxin from Bacillus thuringiensis sp. kurstaki HD73 binds to surface proteins in the mouse small intestine. Biochem. Biophys. Res. Commun. 271(1): 54–58. doi:10.1006/bbrc.2000.2584. Whale AS, Huggett JF, Cowen S, Speirs V, Shaw J, Ellison S et al. (2012) Comparison of microfluidic digital PCR and conventional quantitative PCR for measuring copy number variation. Nucleic Acids Res. 40: e82. WHO (2022) Food, genetically modified. https://www.who.int/topics/food_genetically_modified/en/. Accessed 16 August 2022. Wiik‐Nielsen CR, Holst‐Jensen A, Bøydler C, Berdal KG (2011) Quantification of dietary DNA in tissues of Atlantic salmon (Salmo salar L.) fed genetically modified feed ingredients. Aquac. Nutr. 17: e668–74. Zhao Y, Xia Q, Yin Y, Wang Z (2016) Comparison of droplet digital PCR and quantitative PCR assays for quantitative detection of Xanthomonas citri Subsp. citri. PLoS One, 11, e0159004.

Part 6 Chemical Safety

37 Addition of Foreign Proteins

Detection of Adulterations Véronique Verrez-Bagnis Ifremer, MASAE Microbiologie Aliment Santé Environnement

Helena Silva and Rogério Mendes Portuguese Institute for the Sea and Atmosphere

37.1 INTRODUCTION With the worldwide increase in seafood product consumption, consumer awareness of sustainability, safety, quality, and environmental and social issues has increased in parallel. Food scares such as bovine spongiform encephalopathy (BSE) or malpractices of some food producers, religious reasons, or food allergies have tremendously reinforced public awareness regarding the composition of food products [1]. Therefore, the description and/or labelling of food must be honest and accurate, particularly if the food has been processed removing the ability to distinguish one ingredient from another [2]. There are several ways in which food can be misdescribed: (i) the non-declaration of processes (e.g. previous freezing or irradiation), (ii) substitution of high-quality materials with ones of lower value, (iii) over-declaring a quantitative ingredient declaration, and (iv) extending or adulterating food with a base ingredient, such as water [2]. However, because labels do not provide sufficient guarantee about the true contents of a product, it is necessary to identify and/or authenticate the components of processed food, thus protecting both consumers and producers from illegal substitutions [3]. Woolfe and Primrose [2] wrote on the needs of methods for detecting misdescription and fraud that detecting the total substitution of one ingredient is easier than investigating partial substitution or adulteration. In many cases, it is necessary to know the possible adulterant before it can be detected, and to decide whether it is adventitious mixing or deliberate substitution, the amount of adulterant present is usually confirmed. Many different chemical and biochemical techniques have been developed for determining the authenticity of food, but some techniques work well with raw products but lose their discrimination when applied to cooked or highly processed foods [2]. Molecular authentication or molecular traceability, based on the polymerase chain reaction (PCR) amplification of deoxyribonucleic acid (DNA), which has been extensively developed, offers promising solutions for these issues [1]. In this review on seafood product adulteration by the addition of foreign proteins, the different techniques used to identify such adulterations will be summarized. In fact, there are, strangely, few studies on seafood adulteration even if a considerable number of fish products may contain muscle or other tissue from one or more fish species. Examples are cooked and sterilized fish commodities such as cakes, pies, pastries, soups, patés, and industrial products such as fishmeals. As Ascencio Gil [4] reported, there are many forms of adulteration for economic gain such as the addition of undeclared cheaper fish in fish products that are labelled using the names of higher price and quality fish species. This chapter will provide the reader, through the results of research studies, an idea of the main methods used to detect seafood adulteration by substitution or addition of unlabelled component (foreign proteins).

DOI: 10.1201/9781003289401-43

703

704

Handbook of Seafood and Seafood Products Analysis

37.2  ELECTROPHORESIS AND MASS SPECTROMETRY The identification of fish can be problematic when morphological characteristics, such as the head, skin, and fins, are removed. A well-used method for identifying raw fish is the characterization of muscle proteins using gel-based electrophoretic methods [5–11]. The methods used depend on the separation of the muscle proteins (sarcoplasmic proteins and/or myofibrillar proteins) into speciesspecific profiles, which, when compared with those of authentic species obtained under the same electrophoretic conditions, enable the species to be established unequivocally [12]. The identity of raw fish or shellfish is generally determined from the muscular water-soluble or sarcoplasmic proteins obtained by isoelectric focusing (IEF: separation of proteins according to their Isoelectric point (pI)) [5], while cooked fish is analysed by sodium dodecyl sulphate (SDS) electrophoresis of SDS protein extracts (myofibrillar, connective, and sarcoplasmic proteins) [6,8,10,11]. When such procedures are applied to detect adulteration rather than substitution of one species by another (i.e. mixed-species products), their success depends on the characteristic zones of the component species being identifiable in the profile of mixture. For most species of fish, IEF of the sarcoplasmic proteins is the preferred analytical system as the profiles generally have more species-specific components, with differences between species being much greater than SDS electrophoresis. A report of the EC project “Identification and Quantification of Species in Marine Products” [13] noted that it is possible to identify and evaluate the concentration of each species in a binary mixture (in the study: saithe and ling, respectively) by IEF if each species contained distinguishing protein bands with measurable intensity. In the same way, Podeszewski and Zarzycki [14] have previously applied the starch–gel electrophoresis on sarcoplasmic protein fractions of fish and demonstrated that this made it possible to demonstrate the presence of another fish species in minced fish meat stated to contain only one species. Under favourable circumstances, it is also possible to detect and identify foreign additions in a mixture of more than two fish species [14]. IEF was also used to detect the fraudulent replacement of cold-smoked swordfish by cheaper blue marlin and to identify the substitution of blue marlin by Mediterranean spearfish [15]. The IEF method, however, is not suitable for identifying mixtures of crustacean species, as the profiles have few zones and most of them are focused within the same narrow range of pH [12]. However, Craig et al. [12], in a study to detect adulteration of raw reformed breaded scampi (Nephrops norvegicus), demonstrated the successful application of SDS acrylamide gel electrophoresis to the identification of scampi and other crustacean species such as tropical shrimp (Penaeus indicus) and Pacific scampi (Metanephrops andamanicus) when present in reformed scampi products. In terms of gelbased electrophoretic methods, IEF is the most frequently used methodology [16–18]. Reflecting this trend, the Association of Official Analytical Methods (AOAC) validated and approved IEF as an official method for species identification (AOAC Official Method 980.16: Identification of Fish Species – Thin Layer Polyacrylamide Gel Isoelectric Focusing Method) [19]. Furthermore, as an alternative for the parallel run in IEF of suitable references and unknown samples, the U.S. Food and Drug Administration (FDA) developed a public online platform – the Regulatory Fish Encyclopedia (RFE) – with a library of tissue sarcoplasmic protein profiles of 94 commercially relevant fish species determined by IEF electrophoresis using AOAC Official Method 980.16 [20]. Similarly, a computerized databank of digitally processed IEF protein patterns for the identification of all commercial flatfish species (pleuronectiformes) regulated by Belgian law was developed and validated by the existence of larger digitally processed interspecimen similarity than between species [18]. However, IEF methods are not always able to identify closely related species, such as Thunnus [21]. In these cases, a conventional proteomic analysis of the water-soluble muscle proteins by twodimensional gel electrophoresis (2-DE) with consecutive separation according to isoelectric points and molecular weight was reported to increase differentiation between species and allow more accurate identification [22]. The use of 2-DE profiles of sarcoplasmic proteins has been reported since the late 1990s for the differentiation between fish species, such as eight gadoid species [23]

Detection of Adulterations

705

and nine flatfish species belonging to the Pleuronectidae, Scophtalmidae, and Soleidae families [24], in these cases resorting to a group of heat-resistant proteins provisionally classified as parvalbumins. Isolation of individual proteins separated on 2-DE also allowed the identification of 37 protein spots differentially expressed in two marine mussel species (Mytilus edulis and M. galloprovincialis) [25]. Because of some constraints in the use of electrophoretic methods for authentication of fish species (e.g. high complexity, lack of resolution of closely related species, degradation of proteins on processing, and need of reference fish samples), the use of mass spectrometry (MS) in the authentication process has recently shown significant progress. MS is an effective technique for protein identification, quantification, and characterization and the spectra produced may be used as speciesspecific fingerprints, thus allowing the direct comparison of samples [16]. In authentication studies, MS-based methods analyse the peptides produced by the trypsin digestion of the target protein bands, using either peptide mass fingerprinting (PMF) in which the absolute masses of the peptides are accurately measured as a fingerprint for that protein [26], or tandem MS, also known as MS/MS or MS2, in which the proteins are sequenced using the peptide sequence tag produced to identify it in a protein database [27]. The common proteomic workflow is termed the bottom-up approach, where the selected protein or proteins are transformed into peptides using enzymes, such as trypsin, and the resulting peptides are analysed by MS [28]. Another alternative is top-down proteomics in which the characterization of intact proteins is determined directly in the mass spectrometer, with no pre-enzymatic digestion and making use of the ion fragments produced by different MS fragmentation techniques [29]. Accounting for the increasing relevance of these methods, several comprehensive reviews of the application of proteomics in food science and food authentication have been published in the last years [30–32]. MS/MS analysis of proteins previously separated with 2-DE showed the existence of speciesspecific proteins between the three marine European mussel species [33]. Likewise, the generation of a reference MS/MS spectral library of 22 fish species proteins and the matching of spectra from “unknown” validation samples with library data allowed the correct identification of both fresh and processed samples [34]. Using PMF and MS/MS for the analysis of mixed samples of raw protein extracts of two related fish species (genus: Sperata) previously isolated by 2-DE, 11 protein spots were selected for identification as species-specific and the isoforms of one of the analysed p­ roteins – triosephosphate isomerase – proposed as a marker to differentiate these species [35]. 2-DE/MS discovery-based studies with top-down and bottom-up strategies identified also p­ arvalbumins, some enzymes (nucleoside diphosphate kinase A, pyruvate kinase, and beta enolase), troponin T, and myosin light chain as potential markers for the identification of species such as hake [36–38], tuna [21,39] cod, saithe, haddock, mackerel, and capelin [22]. A strong feature of proteomic techniques is that, once the identification and characterization of discriminating new peptides are completed in the discovery phase, specific strategies can be directed towards the development of fast, efficient, and easy-to-use marker detection analyses of selected proteins, to acquire relevant information about species authentication [40].

37.3  IMMUNOLOGICAL TECHNIQUES Blot hybridization, enzyme-linked immunosorbent assay (ELISA), immunodot, and immunodiffusion tests are immunological techniques, which could be used for the detection of adulteration as these techniques are specific and sensitive analytical methods. Immunoassays using antigen-specific antibodies offer a powerful tool for the detection of added proteins in a complex food protein mixture, although they have been reported to be unsuccessful at discriminating unrelated species and need the development of antibodies targeted against the specific protein of interest [41]. Some research studies have been realized on the implementation of immunological techniques for the detection of seafood product adulteration by the addition of foreign proteins.

706

Handbook of Seafood and Seafood Products Analysis

Verrez et al. [42,43] have focused on immunological methods such as immunoblots and ELISA to detect the addition of crab meat in surimi (washed fish mince)-based products. Indeed, the label of surimi-based crabsticks sometimes indicates the addition of crab flesh in these products and analytical methods should be implemented to check this assertion. The authors have shown that using anti-arginine kinase antibodies (arginine kinase is a cytoplasmic protein present in many invertebrates and absent from vertebrates), crustacean flesh could easily be detected in surimi crabsupplemented preparations with a correlation between the level of crab added to surimi and the level of immunological response. Taylor and Jones [44] have developed an immunoassay based on a non-competitive indirect ELISA using antibodies directed to albacore, bonito, skipjack, and yellowfin to detect adulteration of high-value crustacean tail meat products with lower-value white fish. In addition, polyclonal antibodies produced against the muscular protein of sardines [45], different flatfish species (sole, Greenland halibut, European plaice, and flounder) [46], and clam species [47] were used in an indirect ELISA and allowed the unequivocal identification of these species. However, the identification of red snapper [48] or the differentiation of raw and cooked grouper between other less valued fish species [49–51] was possible with monoclonal antibodies against fish muscle proteins. Other ELISA-based tests have been also reported in the literature and used to detect the mislabelling of albacore [52], cod, ling, and Alaska pollock [53]. Another study was conducted on the development of a dipstick immunoassay for the detection of trace amounts of egg proteins in food [54]. Actually, allergy against egg, for example, can be caused by relatively small amounts of egg proteins and can exhibit typical symptoms; however, lifethreatening anaphylactic reactions to egg are very rare. In this study, the authors have developed tests with antibodies against egg white and ovalbumin and they have tested different food samples. In nearly all analysed foods where egg proteins have been declared, they were detected by the dipstick method. In a study to detect the fraudulent addition of soya bean proteins to surimi products to replace fish muscle, soya bean trypsin inhibitor (STI) was chosen as the target protein and both polyclonal antibody and monoclonal antibody (A11–6) against STI were prepared [55]. Analysis of soya bean proteins in various commercial surimi products was performed semi-quantitatively by Western blot. To quantitatively detect soya bean proteins in surimi products, a sandwich ELISA (s-ELISA) was also developed. Reported results show that the rates of recovery of soya bean proteins ranged from 100.1% to 122.2%, and a coefficient of variation (CV) was less than 4.1%, whereas the limit of detection (LOD) and the limit of quantification (LOQ) were 0.68 mg SPI/mL (13.6 mg SPI/kg food) and 3.09 mg SPI/mL (61.7 mg SPI/kg food), respectively. On the contrary, seafood product could also be added to meat products and adulterate them. Indeed, surimi, which is a source of high content in myofibrillar proteins, was expected to become an additive to meat products. In the aim to distinguish the addition of Alaska pollock surimi to meat products, Dreyfuss et al. [56] have developed a test for rapid identification of pollock surimi in raw meat products. The test based on the detection by antibodies directed against proteins of Alaska pollock surimi gave positive results with all the finfish species tested (14 species). Furthermore, the test was specific for Alaska pollock surimi at 2% concentration and showed detectable sensitivity to surimi from other finfish, at concentration between 2% and 4%, and was 100% accurate in the laboratory trials. Some ELISA test kits are already commercially available for fish species identification (e.g. EZ Pangasius™: ELISA Technologies, USA; AgraQuant Fish ELISA Kit: Romer Labs, UK, Austria; and Common Bone Fish Antigen EIA ELISA Kit, versions 2 and 3: XEMA, Russia). However, the results of the evaluation of the capacity of three commercially available ELISA tests to detect a wide variety of bony and cartilaginous fish and their products were reported to have a limited performance [57]. The detection of 57 bony fish with the three ELISA kits ranged from 26% to 61%. Common European and North American species including carp, cod, and salmon species showed a higher detection rate as compared to those from the Asia-Pacific, including pangasius and

Detection of Adulterations

707

several mackerel and tuna species. Among the 17 canned bony fish products, only 65%–86% were detected, with tuna showing the lowest rate, whereas none of the cartilaginous fish (n = 9) and other vertebrates (n = 8) or shellfish (n = 5) were detected.

37.4  VISIBLE AND NEAR-INFRARED SPECTROMETRY The origin of the near-infrared (NIR) spectra of agro-food products is the absorption of the NIR light by chemical bonds of organic molecules. Spectra of food products include mainly absorption band characteristics of the main constituents of the materials (i.e. water, proteins, fat, and carbohydrates) [58]. The main limitation of the NIR technique is its indirect nature, as it measures no single target such as a specific molecule, DNA fragment, or protein. However, NIR-based methods were proposed for the detection of meat and bone meat (MBM) in compound feeds [59,60]. To prevent the transmission of BSE between animals and humans, European Authorities [61] prohibited the use of animal meat for feeding to ruminants. This measure includes fishmeal, although fish are not affected by this disease. The objective of this measure is to prevent adulteration and cross-contamination between fish and land animal meals, which is why analytical methods have been proposed for the determination of animal origin of feeding stuff. Murray et al. [60] have developed a method based on a partial least squares (PLS) discriminant analysis, using visible and NIR (VNIR) reflectance spectra. From their results, it seems that VNIR reflectance spectroscopy could routinely provide the first line of defence of the food chain against accidental contamination or fraudulent adulteration of fishmeal with meat and bone meal. In their review, van Raamsdonk et al. [58] noted that a NIR spectrometer coupled to a microscope (NIRM) could also be proposed to tackle the problem of detection of MBM in compound feed; the method can also be used to detect fishmeal ingredients. When using an NIR microscope, the subjective judgement of the microscopist is replaced by spectra that can be subjected to statistical analysis. In another possible application field, Gayo et al. [62,63] have successfully used visible or NIR (Vis/NIR) spectroscopy to detect and quantify species authenticity and adulteration in crabmeat samples. In their studies, VNIR spectroscopy has been successfully used to detect adulteration in crab meat samples adulterated with surimi-based imitation crab meat and to detect the adulteration of Atlantic blue crab (Callinectes sapidus) meat with blue swimmer crab (Portunus pelagicus) meat in 10% increments. Using a VNIR hyperspectral system coupled with selected machine learning classifications, fish fillets of six species (i.e. red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were differentiated with 100% accuracy [64]. The clear identification of high-value species (i.e. red mullet) substitution with cheaper ones (i.e. Atlantic mullet) was performed in fillets using infrared spectroscopy and chemometrics as a rapid test, and good results were also obtained with this technique for the discrimination of the replacement of plaice by cheaper flounder [65]. The capability of a handheld NIR device in distinguishing fillets and patties of Atlantic cod (Gadus morhua) from those of haddock (Melanogrammus aeglefinus) was validated in comparison with the performance of an near infrared spectroscopy (FT-NIR) benchtop spectrometer [66], thus showing that this device can be a simple, cost-effective, and reliable alternative to benchtop spectrometers in fish fillet and patty authentication. A similar pocket-sized NIR sensor, called SCiO, composed of a handheld NIR spectrophotometer was used to identify fillets from nine fish species (Merluccius merluccius, Pollachius virens, Epinephelus costae, Gadus morhua, Pleuronectes platessa, Sebastes norvegicus, Scomber scombrus, Chelidonichthys lucerna, and Synaptura cadenati) [67]. Reported results show that all the fish fillets were correctly identified with a global accuracy between 93.97% and 96.58% and were all confirmed by a validated method based on a genetic marker. However, a study using other vibrational spectroscopic techniques – mid-infrared spectroscopy (MIR) spectroscopy – to detect the substitution of Atlantic salmon mixed with rainbow trout in mini-burgers at different percentages, showed that the used PLS regression (PLS-R) predicted the presence or absence of adulteration in fish samples of an external set quickly and with high accuracy [68]. In view of the growing relevance of fast and

708

Handbook of Seafood and Seafood Products Analysis

reliable omics strategies, comprehensive reviews were recently published covering the most promising studies dealing with the use of qualitative spectroscopy (UV–Vis, NIR, MIR, Raman, nuclear magnetic resonance (NMR), and hyperspectral imaging spectroscopy (HIS)) and chemometrics for the resolution of the main authenticity issues of fish and seafood products [69,70].

37.5  MICROSCOPIC METHODS Following the measure to prohibit the use of animal proteins for feeding ruminants including fishmeals, European Commission Directive 2003/126/EC [71] indicates the analytical method to be applied for the detection and characterization of processed animal proteins (PAPs) in feeds. This analytical method is based on a microscopic technique, but the method is not applicable to fishmeals, because some typical structures detected are common to both fish and land animals, and it only gives useful results when bones are present in the sample [72]. Whereas microscopic techniques could be used to check whether fishmeals are not adulterated by the fraudulent addition of terrestrial animal meal, Van Raamsdonk et al. [58] have reviewed different proficiency studies and ring trials organized since 2003 for the detection of mammalian PAP in fishmeal. The first proficiency study, allowing the participants to apply their own protocol, revealed that microscopic detection of 1 g/kg of mammalian PAP in the presence of 50 g fishmeal/kg was realized in 44% of the cases. However, microscopic detection of 98% can be reached by providing the application of an optimal protocol and a sufficient level of expertise. Recent studies showed that training, application of a decision support system, and use of an improved microscopy protocol resulted in higher sensitivity. As Van Raamsdonk et al. [58] noted, an attractive approach to detect fishmeal adulteration by meat meal is the combination of the very low detection level of microscopy with identification by other methods (PCR and immunoassays). In another study, fishmeal adulteration was identified using a method based on microscopic image and deep learning, which included the qualitative identification and component recognition of adulterated fishmeal [73]. By comparing training loss, test accuracy, and training time on the data set, the lightweight network architecture, MobileNetv2, with inverted residual and linear bottleneck structure, showed to be more suitable for qualitative identification when using the locally constrained mask-transformed microscopic images with a resolution of 224 × 224 pixels. This approach presented high accuracy (93%) and precision (93%) and could quickly identify adulterated fishmeal and recognize components. Microscopical techniques could also be used to detect other types of seafood product adulteration than adulteration of fishmeals. Ebert and Islam [74] used histological examinations to identify, as caviar imitations, products labelled “special caviar product manufactured with sturgeon and salmon roe in the Russian way”. The caviar-like, spherical products appeared to be formed out of a homogeneous, unstructured mass and showed none of the fish roe-specific biological structures. Crab meat products are prepared in the USA from the meat derived from any of several species of edible crabs, including blue, king, queen, tanner, Dungeness, red, and stone crabs (class Crustacea; phylum Arthropoda). Considering that some manufacturers of crab cakes may add fish meat to their product, a method for microscopic detection of fish tissue in crab meat or crab cakes is proposed by the U.S. FDA. This method is based on the observation of indistinct striations in the fish meat as contrasted with the distinct striations of crab muscle fibre and allows the detection of the presence of as little as 1% fish meat in experimental batches [75].

37.6  CHROMATOGRAPHIC TECHNIQUES Though chromatographic methods have been used for the identification of fish species since the late 1980s [76,77], there are very few recent literature references on the use of these techniques to detect adulteration. However, the study of Chou et al. [78] on the differentiation of meats from fifteen animal species, including five marine species, is based on a high-performance liquid chromatography

Detection of Adulterations

709

(HPLC) method with electrochemical detection (HPLC-EC). They reported that a major advantage of European Community (EC) detection is its ability to directly detect peptides and amino acids that exhibit little or no chromogenic or fluorescent properties. In addition, under appropriate chromatographic conditions and simultaneous use of a copper nanoparticle-plated electrode, reliable detection is feasible without sample pre-treatment. In their study, Chou et al. [78] tested the applicability of the method to detect the species in mixtures only on three land animals (beef, pork, and horse meats). However, as they could identify cod, crab, salmon, scallop, and shrimp with this method, it seems possible to apply such a technique – that is fast, economical, and reliable for the identification of meats from multiple species – to detect adulteration of seafood flesh by meat components. New developments in the application of chromatography to fish species identification are mostly connected with the general tendency within the proteomic community to move to gel-free workflows, such as the use of liquid chromatography in the separation phase of the sample protein enzymatic digest, followed by MS/MS characterization of the resulting peptides. Recently, a method using this approach was proposed for the authenticity of fish species. A bottom-up LC–MS/MS assay was developed for the identification of redfish (Sebastes spp.), red snapper (Lutjanus malabaricus), European plaice (Pleuronectes platessa), sole (Solea solea), turbot (Scophthalmus maximus), and pangasius (Pangasianondon hypothalamus) using a thermolysin digest and the species-specific peptidome as differentiator [79]. In total, this method was successfully applied to an aggregate of 16 species (in addition to bonito, tuna, oyster, and blue mussel).

37.7  DNA METHODS Advances in DNA technologies have led to the rapid development of genetic methods mainly based on PCR for fish species identification, and for the detection of fish product, adulteration as protein analysis is in general not suited to fish species identification in heat-processed matrices [80]. DNA offers advantages over proteins, including stability at high temperature, presence in all tissue types, and greater variation in genetic sequence [81]. Fish and fishery product authentication can be achieved by PCR-based methods (see reviews by Leighton Jones [82], Sotelo et al. [83], Mackie [84], Lockley and Bardsley [80], and Asencio Gil [4]). Asencio Gil [4]’s work provided an extensive overview of various techniques such as PCR sequencing, species-specific PCR primers or multiplex PCR, PCR–restriction fragment length polymorphism (PCR–RFLP), PCR–single-stranded conformation polymorphism (PCR–SSCP), random amplification of polymorphic DNA (RAPD), real-time PCR, and PCR laboratory-on-a-chip. All these PCR-based techniques have high potential because of their rapidity and increased sensitivity and specificity [80]. Nevertheless, some of these techniques such as RAPD analysis may not be suitable to identify adulterations. RAPD technique is not adapted to detect the species of origin in products containing mixtures of species containing 50%–50% mixtures of species that can interbreed [85]. However, quantitative PCR (qPCR) tests such as real-time PCR have been widely used for food authentication and quantification. Recent shotgun DNA sequencing or digital drop PCR seems promising procedures though they need to be tested on a larger set of fish mixed products. In this section on DNA techniques, only those that could be used to detect adulteration are reviewed.

37.7.1 DNA Sequencing: Sanger, Metabarcoding, and Shotgun Sequencing The differences between organisms reside in their DNA sequence, and even between closely related individuals, there are sufficient differences, which allow the recognition of each individual [86]. Therefore, PCR is a technique, which when coupled to chain termination sequencing allows gene and DNA sequence information to be derived rapidly [87]. PCR sequencing is considered most suitable for the identification of seafood species, even in highly processed food products [88,89], and has been extensively used to identify various fish mainly based on mitochondrial genes such as

710

Handbook of Seafood and Seafood Products Analysis

cytochrome b or cytochrome oxidase I (COI) (e.g. see the study of Jérôme et al. [90]). Actually, COI barcoding, which uses Sanger sequencing to determine the DNA sequence of the food product, has been validated as a diagnostic marker for genetic species identification [91]. The DNA sequences of most commercial food fish species are represented in the Barcode of Life Database (BOLD) reference library and have distinguishable COI barcodes [92]. According to Staats et al. [93], the use of BOLD is advantageous as it can detect adulterant species whose presence is not known as it is a non-targeted technique. In some countries, DNA barcoding has been adopted as a regulatory standard to assign seafood mislabelling and food fraud [94]. Also, fragments of nuclear genes, such as alpha-actinin, 5S ribosomal DNA, and rhodopsin, have been sequenced for the discrimination of fish (e.g. see study of Sevilla et al. [95]). Dedicated Internet databases offer the possibility to rely on unknown sequences to reference fish species sequences (e.g. FishTrace database [96] and BOLD System V4 [97]). Even if sequencing is time-consuming, it produces large amounts of information that could be used in other PCR-based methods such as PCR–RFLP for fish species identification [98–100]. The PCR-sequencing technique seems, therefore, difficult to adapt to check seafood adulteration by substituting foreign fish in labelled one fish species product. When analysing food mixed products composed of multiple species, multiple DNA barcode templates must be sequenced in parallel, and this can be done by the next-generation sequencing (NGS) technology [101]. NGS combined with DNA barcoding is referred to as metabarcoding [102]. Metabarcoding uses universal PCR primers to amplify barcode sequences from different species. NGS technology, as in DNA barcoding, needs the use of appropriate bioinformatics pipelines for identifying the species in the sample from which DNA has been extracted. The very recent technique of shotgun DNA sequencing and mapping towards a masked reference library was used by Varunjikar et al. [103] to approximate an estimate of the per cent inclusion of each species in mixed fish tissue samples. In this method, an entire sequence is broken into fragments that are then pieced together by looking for overlap in sequences. It has some advantages as it needs much less DNA than other methods of sequencing, and it is less expensive than NGS where sometimes a large sequence must be obtained faster but still needs a good reference library, which must be available previously.

37.7.2 Species-Specific and Multiplex PCR As this method has the potential to detect qualitative admixture, it is a method adapted to adulteration analysis. Prior sequence knowledge is required to design primers, and appropriate controls should be included to preclude the possibility of false-positive or false-negative results being obtained [104]. In this idea, Colombo et al. [105] have developed a species-specific PCR to identify frozen and seasoned food labelled as pectinid scallop and suspected to be or contain vertebrate (in particular teleostean fish). Multiplex PCR can also be used in the aim to examine fishmeal for contamination with mammalian and poultry products. Bellagamba et al. [106] have looked for a method based on three speciesspecific primer pairs designed for the identification of ruminant, pig, and poultry DNA. The PCR specifically detected mammalian and poultry adulteration in fishmeals containing 0.125% beef, 0.125% sheep, 0.125% pig, 0.125% chicken, and 0.5% goat. The multiplex PCR assay for ruminant and pig adulteration in fishmeals had a detection limit of 0.25% after optimization. As different tuna species have different qualities and prices, a fraudulent replacement of valuable species by less valuable ones (e.g. Katsuwonus pelamis) may occur. Bottero et al. [107] have developed a multiplex primer extension assay (PER) to discriminate four closely related species of Thunnus (T. alalunga, T. albacores, T. obesus, and T. thynnus) and one species of Euthynnus genus (Katsuwonus pelamis) in raw and canned tuna. The technique enables the simultaneous and unambiguous identification of the five tuna species.

Detection of Adulterations

711

37.7.3 Amplified Fragment Length Polymorphism (AFLP) As Atlantic salmon (Salmo salar) is highly appreciated in the Chinese market, illegal practices can occur by adulterating or substituting rainbow trout products (Oncorhynchus mykiss) of much lower value in China for those of Atlantic salmon. Zhang and Cai [108] have developed a species-specific AFLP marker based on AFLP analysis and converted it into reliable sequence-characterized amplified regions (SCARs) for constructing a direct and fast method to detect frauds in fresh and processed products of Atlantic salmon being adulterated and substituted by rainbow trout. The SCAR marker could be amplified and visualized in 1% agarose gel in all tested rainbow trout samples but was absent in all salmon samples. Using DNA admixtures, the detection of 1% (0.5 ng) and 10% (5 ng) rainbow trout DNA in Atlantic salmon DNA for fresh and processed samples, respectively, was readily achieved. In another study, Zhang et al. [109] demonstrated that the detection sensitivity of AFLP-derived SCAR was higher than that of DNA amplicon separation by denaturing gradient gel electrophoresis (DDGE) when analysing experimental mixtures of Atlantic salmon and rainbow trout. The AFLP-derived SCAR approach was sensitive and demonstrated to be a rapid and reliable method for identifying frauds in salmon products, and it could be extended for applications of species identification in the food industry.

37.7.4 PCR-Restriction Fragment Length Polymorphism (PCR–RFLP) The PCR–RFLP technique was developed as a fingerprinting technology, which consists of performing enzymatic digestion on PCR amplicons. This technique could support species identification in fish species authentication using either electrophoresis, laboratory-on-a-chip system, or HPLC to separate the PCR product fragments. Only three examples of the use of PCR–RFLP in the detection of seafood product adulteration will be detailed in this review. The first one is the study of Hold et al. [110] who have used this technique to develop a method to differentiate between several different fish species. The method was tested in a collaborative study in which 12 European laboratories participated to ascertain whether the method was reproducible. From a total of 120 tests performed, unknown samples identified by comparison with RFLP profiles of reference species were correctly identified in 96% of cases. They have also tested the ability of this method to analyse mixed and processed fish samples. In all cases, the species contained within mixed samples were correctly identified, indicating the efficacy of the method for detecting fraudulent substitution of fish species in food products. Horstkotte and Rehbein [111] have tested the usability of fish species identification with RFLP using HPLC for sturgeon, salmon, and tuna samples. Unequivocal species identification was achieved with HPLC, despite nucleotide sequence-depending separation. Separation of DNA fragments by HPLC could be demonstrated to be a fast and reliable alternative to electrophoresis. In the aim of guaranteeing the composition and security of fishmeals, a method based on PCR and length polymorphism, followed by an RFLP, was developed by Santaclara et al. [72]. Specific primers for every species were designed and calibrated to generate a PCR product with a specific size when the DNA of each land species that can be used for the elaboration of meat meals (cow, chicken, pig, horse, sheep, and goat) was present in the sample. This methodology allows verification of the adulteration and cross-contamination of fishmeals with the six land species studied.

37.7.5 Quantitative PCR (qPCR) and Droplet Digital PCR (ddPCR) For the identification of species in mixed samples, species-specific assays paired with real-time or qPCR or ddPCR can be used or next-generation amplicon sequencing can be applied, though the latter is less appropriate for quantification [112]. The specificity and sensitivity of qPCR, combined with its high speed, robustness, reliability, and the possibility of automation, contribute to the adequacy of the method for quantifying fish species in fishery products [113]. For instance, Asencio

712

Handbook of Seafood and Seafood Products Analysis

Gil [4] reported a study of Sotelo et al. [114] who used the TaqMan assay for the identification and quantification of cod. In the same idea, Trotta et al. [115] used qPCR for the identification of fish fillets from grouper and common substitute species. They also used conventional multiplex PCR in which electrophoretic migration of different sizes of bands allowed the identification of the fish species. These two approaches, real-time PCR and multiplex PCR, made it possible to discriminate groupers from substitute fish species. Hird et al. [116] have designed a qPCR primer and probe set for the detection and quantification of haddock. The presence of this fish in concentrations of up to 7% in raw or slightly heat-treated products could be detected, while Lopez and Pardo [117] applied real-time PCR technology for the identification and quantification of albacore and yellowfin tuna. The real-time methodology described in their study was suitable to detect the fraudulent presence of yellowfin or even to identify the absence of albacore in cans labelled as white tuna. As Asencio Gil [4] reported, the accuracy of this technique could be affected by several factors, such as the DNA yield of the samples, which can be variable depending on the strength of the technique used to process the fish product, and by the fact that the sample material could be thermally processed in different ways. Owing to its cost, real-time PCR has only a remarkable interest in analysing products with an important economic value. However, the enormous utility and possible applications of real-time PCR will make it affordable for most laboratories in the near future. Fitzcharles [118] used high-resolution melting (HRM), a closed-tube post-PCR analysis, and found it advantageous over COI barcoding for routine identification of fish species. This technique is able to distinguish between samples with only a single base change in the DNA sequence, even for eggs or larvae. Fernandes et al. [119] also proposed qPCR coupled to HRM analysis as a faster, reliable, and more economical alternative to exploit DNA barcoding and developed a specific new approach for the rapid detection and discrimination of five closely related shrimp species from the Penaeidae family, even if DNA quantity and quality are low. Also, a real-time PCR assay ­targeting COI mini-barcode combined with HRM analysis was used to obtain full discrimination of five Merluccius species (M. merluccius, M. productus, M. hubbsi, M. capensis, and M. paradoxus) commonly used in several processed and non-processed foods and tested 45 commercial fish-containing foods to detect hake substitution with equal success with levels down to 0.2–20 pg of hake DNA [120]. Recently, Quintrel et al. [121] developed a multi-locus single nucleotide polymorphism (SNP) method based on the PCR–HRM analysis for the identification of several Mytilus species. ddPCR is a recent method for nucleic acid detection and quantification. The principle of this method is to perform independent PCR on a large number of small reactors in the form of droplets that contain or do not contain one copy of the target molecule template in each reactor, to achieve “single-molecule template PCR amplification” [122]. After amplification, the number of copies of the target sequence can be counted by the number of positive reactors based on the fluorescence signal. Cao et al. [123] compared the results from ddPCR and qPCR methods to detect the same DNA samples and concluded that ddPCR was 156 times more sensitive in detecting silver pomfret. The potentiality of ddPCR is also stressed by Doi et al. [124] who concluded that ddPCR is more resistant to the presence of PCR inhibitors than real-time PCR. Both methods have similar specificities, but the ddPCR technique is more complicated to carry out, more expensive, and requires twice that of the real-time PCR. The ddPCR methodology, which is now commercially available, promises to be a valuable tool for addressing important species adulteration issues.

37.7.6 Emergent Tools 37.7.6.1  DNA Microarrays (DNA Chips) DNA microarrays provide an alternative to DNA sequencing as species-specific oligonucleotide probes are arranged in replicate on specific positions on DNA chips. Nowadays, simplified colorimetric microarray techniques have been developed that help for the use of commercial DNA chips in control laboratories. Nevertheless, these commercial DNA microarrays are still only available to detect the presence of land vertebrates for meat product authentication. The main difficulty to

Detection of Adulterations

713

develop such DNA chips to distinguish fish and seafood species in food products is due to the very large number of species usually used in seafood products, or as substitutes, and thus, DNA probes cannot be limited to a single probe for one species. So, Kappel et al. [125,126] have studied as a proof of concept the recognition of 10 fish species and two crustacean species based on multiple probes per species and the comparison of the probe signal patterns with reference specimens. However, even if the DNA chips already commercialized can distinguish a fraudulent addition of terrestrial animals in seafood products, it will take a few more years before such DNA microarrays are also commercialized for marine or freshwater species. 37.7.6.2  Loop-Mediated Isothermal Amplification (LAMP) A DNA amplification technique named loop-mediated isothermal amplification (LAMP) has gained rising popularity in food authenticity detection for rapid application. LAMP is a simple, cheap, and short-time method requiring only the Bst DNA polymerase and four specially designed primers that recognize six regions on the target sequence, producing extremely high specificity and sensitivity. Moreover, DNA amplification is realized in about 1 h under a constant temperature (60°C−65°C). Over the last years, several studies have utilized LAMP assay for fish authenticity purposes, including the rapid identification of European eel (Anguilla anguilla) [127], Atlantic salmon (Salmo salar) [128], and Atlantic cod (Gadus morhua) [129]. In the same way, Xiong et al. [130] have developed a real-time LAMP assay targeting the cytb gene for quick detection of skipjack tuna (Katsuwonus pelamis) in raw and processed fish products able to detect 50 pg target DNA. LAMP assays allowing the rapid identification of one or more fish species in raw or processed food products are very promising to detect adulteration (mixing of species, substitution) of seafood products. 37.7.6.3 PCR–ELISA PCR–ELISA is a simple authentication tool based on the amplification of short specific DNA fragments with biotinylated primers and fixed after PCR on a streptavidin-coated microtitre plate. After removal of the non-solid phase-bound second DNA strand, a specific probe is hybridized and immunodetected using coupled-enzyme antibodies. In a study, Taboada et al. [53] in 2014 clearly demonstrated the applicability of this method for the identification of Atlantic cod (Gadus morhua), Alaska pollock (Gadus chalcogrammus), and ling (Molva molva) in commercial fish products and for the detection of mixed species.

37.7.7  Commercial PCR Kits for Fish Species Differentiation In the last years, advances in PCR-based methods have led to the rapid development of different commercial kits for fish species identification. There is no literature on the use of these rapid diagnostic kits for the detection of substitution or adulteration in seafood products, but without a doubt, these kits could be very useful for screening purposes in inspection programmes. The present list above of those commercial kits or commercial proposals is not exhaustive and is only valuable for at present time. • The SureFood® Fish ID (r-biopharma) is a qPCR kit for identifying different commercially popular fish species (red salmon, king salmon, humped salmon, Atlantic salmon, rainbow trout, halibut, haddock, codfish, cod, pollack, coalfish, hake, and whiting) • SGS All Species ID Food DNA Analyser Kits (Thermo Fisher Scientific) allow the labelling and amplification of DNA from fish using a PCR to prepare the sample for NGS and automated data analysis via SGS All Species ID software. • The Biofood Standard Kit (BioTools) is a multiplex PCR designed to detect the presence of plant and vertebrate material in food samples. The amplification of the cytochrome b gene, highly conserved in evolution, detects the presence of material from vertebrate species.

714

Handbook of Seafood and Seafood Products Analysis

The plant material is detected by the amplification of the rbcL gene. The sensitivity is high because there are many copies of both genes in the sample types. • Agilent – DNA Fish ID Kit uses PCR amplification of genomic DNA and RFLP analysis. The generated fragment patterns can be resolved and matched to the correct species using RFLP pattern matching software. This kit can be used for fresh, frozen, dried, salted, or minced samples.

37.8 CONCLUSION Of the wide range of analytical methods available, it is likely that DNA-based techniques will be the favourite approach for determining adulteration, because they are easy to use. However, at present time, they are generally more expensive than electrophoresis or chromatographic techniques and in some cases these latter are sufficient to clearly demonstrate an adulteration in seafood products by the addition of foreign proteins. When great precision is needed, even with a low level of DNA, ddPCR seems to be the more adequate choice even for processed foods, being more expensive than other techniques not so suitable to regular fish fraud detection. Quantifying methods are, without doubt, more adapted to analyse adulteration, and these are those to be developed.

REFERENCES 1. Teletchea, F., Maudet, C., and Hanni, C., Food and forensic molecular identification: update and challenges, Trends Biotechnol., 23, 359, 2005. 2. Woolfe, M., and Primrose, S., Food forensics: using DNA technology to combat misdescription and fraud, Trends Biotechnol., 22, 222, 2004. 3. Pascal, G., and Mahé, S., Identity, traceability, acceptability and substantial equivalence of food, Cell. Mol. Biol., 47, 1329, 2001. 4. Asensio Gil, L., PCR-based methods for fish and fishery products authentication, Trends Food Sci. Technol., 18, 558, 2007. 5. Mackie, I.M., Identifying species of fish, Analyt. Proc., 27, 89, 1990. 6. Scobbie, A.E., and Mackie, I.M., The use of sodium dodecyl sulphate polyacrylamide gel electrophoresis in fish species identification - a procedure suitable for cooked and raw fish, J. Sci. Food Agric., 44, 343, 1988. 7. Rehbein, H., Etienne, M., Jerome, M., Hattula, T., Knudsen, L.B., Jessen, F., Luten, J.B., Bouquet, W., Mackie, I.M., Ritchie, A.H., Martin, R., and Mendes, R., Influence of variation in methodology on the reliability of the isoelectric focusing method of fish species identification, Food Chem., 52, 193, 1995. 8. Rehbein, H., Kundiger, R., Yman, I.M., Ferm, M., Etienne, M., Jerome, M., Craig, A., Mackie, I., Jessen, F., Martinez, I., Mendes, R., Smelt, A., Luten, J., Pineiro, C., and Perez-Martin, R., Species identification of cooked fish by urea isoelectric focusing and sodium dodecylsulfate polyacrylamide gel electrophoresis: a collaborative study, Food Chem., 67, 333, 1999. 9. Etienne, M., Jerome, M., Fleurence, J., Rehbein, H., Kundiger, R., Mendes, R., Costa, H., and Martinez, I., Species identification of formed fishery products and high pressure-treated fish by electrophoresis: a collaborative study, Food Chem., 72, 105, 2001. 10. Piñeiro, C., Barros-Velázquez, J., Pérez-Martín, R.I., Martínez, I., Jacobsen, T., Rehbein, H., Kündiger, R., Mendes, R., Etienne, M., Jerome, M., Craig, A., Mackie, I.M., and Jessen, F., Development of a sodium dodecyl sulfate-polyacrylamide gel electrophoresis reference method for the analysis and identification of fish species in raw and heat-processed samples: a collaborative study, Electrophoresis, 20, 1425, 1999. 11. Etienne, M., Jérôme, M., Fleurence, J., Rehbein, H., Kündiger, R., Mendes, R., Costa, H., Pérez-Martín, R., and Piñeiro-González, C., Identification of fish species after cooking by SDS-PAGE and urea IEF: a collaborative study, J. Agric. Food Chem., 48, 2653, 2000. 12. Craig, A., Ritchie, A.H., and Mackie, I.M., Determining the authenticity of raw reformed breaded scampi (Nephrops norvegicus) by electrophoretic techniques, Food Chem., 52, 451, 1995. 13. European Commission, EU Project FAR UP-3-783. 1992–1995, Identification and quantitation of species in marine products, 1992–1995.

Detection of Adulterations

715

14. Podeszewski, Z., and Zarzycki, B., Identification of fish species by testing the minced-meat tissue, Food/Nahrung, 22, 377, 1978. 15. Renon, P., Bernardi, C., Malandra, R., and Biondi, P.A., Isoelectric focusing of sarcoplasmic proteins to distinguish swordfish, blue marlin and Mediterranean spearfish, Food Control, 16, 473–477, 2005. 16. Rodríguez, E.M., and Ortea, I., Food authentication of seafood species, In Proteomics in Food Science, Ed, Colgrave, M.L., Academic Press, Cambridge, MA, pp. 331–342, 2017. 17. Piñeiro, C., Barros-Velázquez, J., Pérez-Martín, R.I., and Gallardo, J.M., Specific enzyme detection following isoelectric focusing as a complimentary tool for the differentiation of related Gadoid fish species, Food Chem., 70, 241–245, 2000. 18. Bossier, P., and Cooreman, K., A databank able to be used for identifying and authenticating commercial flatfish (Pleuronectiformes) products at the species level using isoelectric focusing of native muscle proteins, Int. J. Food Sci. Technol., 35(6), 563–568, 2000. 19. AOAC, Official Methods of Analysis of the Association of Official Analytical Communities International, 16th ed., AOAC, Washington, DC, 1995. 20. U.S. Food and Drug Administration, Regulatory Fish Encyclopedia (RFE). https://www.fda.gov/food/ science-research-food/regulatory-fish-encyclopedia-rfe. Accessed on 08.08.2022. 21. Pepe, T., Ceruso, M., Carpentieri, A., Ventrone, I., Amoresano, A., and Anastasio, A., Proteomics analysis for the identification of three species of Thunnus, Vet. Res. Commun., 34, S153–S155, 2010. 22. Martinez, I., and Friis, T.J., Application of proteome analysis to seafood authentication, Proteomics, 4, 347–354, 2004. 23. Piñeiro, C., Barros-Velázquez, J., Sotelo, C.G., Pérez-Martín, R.I., and Gallardo, J.M., Two-dimensional electrophoretic study of the water-soluble protein fraction in white muscle of gadoid fish species, J. Agric. Food Chem., 46, 3991–3997, 1998. 24. Piñeiro, C., Barros-Velázquez, J., Sotelo, C.G., and Gallardo, J.M., The use of two-dimensional electrophoresis in the characterization of the water-soluble protein fraction of commercial flat fish species, Z. Lebensm. Unters. Forsch. A, 208, 342–348, 1999. 25. López, J.L., Marina, A., Vázquez, J., and Álvarez, G., A proteomic approach to the study of the marine mussels Mytilus edulis and M. galloprovincialis, Mar. Biol., 141, 217–223, 2002. 26. James, P., Quadroni, M., Carafoli, E., and Gonnet, G., Protein identification by mass profile fingerprinting, Biochem. Biophys. Res. Commun., 195(1), 58–64, 1993. 27. Mørtz, E., O’Connor, P.B., Roepstorff, P., Kelleher, N.L., Wood, T.D., McLafferty, F.W., and Mann, M., Sequence tag identification of intact proteins by matching tandem mass spectral data against sequence data bases, Proc. Natl. Acad. Sci. U.S.A., 93(16), 8264–8267, 1996. 28. Aebersold, R., and Mann, M., Mass spectrometry-based proteomics, Nature, 422, 198–207, 2003. 29. Kelleher, N.L., Top-down proteomics, Anal. Chem., 76, 197–203, 2004. 30. Piñeiro, C., Carrera, M., Cañas, B., Lekube, X., and Martinez, I., Proteomics and food analysis: principles, techniques, and applications, In Handbook of Food Analysis, Eds, Nollet, L.M., and Toldrá, F., 3rd ed., CRC Press, Boca Raton, FL, Chapter 18, pp. 369–391, 2015. 31. Ortea, I., O’Connor, G., and Maquet, A., Review on proteomics for food authentication, J. Proteom., 147, 212–225, 2016. 32. Čapla, J., Zajác, P., Čurlej, J., Belej, Ľ., Kročko, M., Bobko, M., Benešová, L., Jakabová, S., and Vlčko, T., Procedures for the identification and detection of adulteration of fish and meat products, Slovak J. Food Sci., 14, 978–994, 2020. 33. López, J.L., Marina, A., Álvarez, G., and Vázquez, J. Application of proteomics for fast identification of species-specific peptides from marine species, Proteomics, 2, 1658–1665, 2002. 34. Wulff, T., Nielsen, M.E., Deelder, A.M., Jessen, F., and Palmblad, M., Authentication of fish products by large-scale comparison of tandem mass spectra, J. Proteome Res., 12, 5253–5259, 2013. 35. Barik, S.K., Banerjee, S., Bhattacharjee, S., Das Gupta, S.K., Mohanty, S., and Mohanty, B.P., Proteomic analysis of sarcoplasmic peptides of two related fish species for food authentication, Appl. Biochem. Biotechnol., 171(4), 1011–1021, 2013. 36. Piñeiro, C., Vázquez, J., Marina, A., Barros-Velázquez, J., and Gallardo, J.M., Characterization and partial sequencing of species-specific sarcoplasmic polypeptides from commercial hake species by mass spectrometry following two-dimensional electrophoresis, Electrophoresis, 22, 1545–1552, 2001. 37. Carrera, M., Cañas, B., Piñeiro, C., Vázquez, J., and Gallardo, J.M., Identification of commercial hake and grenadier species by proteomic analysis of the parvalbumins fraction, Proteomics, 6, 5278–5287, 2006.

716

Handbook of Seafood and Seafood Products Analysis

38. Carrera, M., Cañas, B., Piñeiro, C., Vázquez, J., and Gallardo, J.M., De novo mass spectrometry sequencing and characterization of species-specific peptides from nucleoside diphosphate kinase B for the classification of commercial fish species belonging to the family Merlucciidae. J. Proteome Res., 6(8), 3070–3080, 2007. 39. Pepe, T., Ceruso, M., Carpentieri, A., Ventrone, I., Amoresano, A., Anastasio, A., and Cortesi, M.L., Differentiation of four tuna species by two-dimensional electrophoresis and mass spectrometric analysis, In Proteomic Application in Biology, Eds, Heazlewood, J.L., and Petzold, C.J., InTech, Rijeka, Croatia, 191–208, 2012. 40. Pascoal, A., Ortea, I., Gallardo, J.M., Cañas, B., Barros-Velázquez, J., and Calo-Mata, P., Species identification of the Northern shrimp (Pandalus borealis) by polymerase chain reaction-restriction fragment length polymorphism and proteomic analysis, Anal. Biochem., 421, 56–67, 2012. 41. Mathew, S., Raman, M., Parameswaran, M.K., and Rajan, D.P., Fish and Fishery Products Analysis (A Theoretical and Practical Perspective), eBook, Springer Nature, Singapore, 2019. 42. Verrez, V., Benyamin, Y., and Roustan, C., Detection of marine invertebrates in surimi-based products, In Quality Assurance in the Fish Industry, Eds, Huss, H.H., Jakobsen, M., and Liston, J., Elsevier Science Publisher B.V., Amsterdam, p. 441, 1992. 43. Verrez- Bagnis, V., and Escriche Roberto, I., The performance of ELISA and dot-blot methods for the detection of crab flesh in heated and sterilized surimi-based products, J. Sci. Food Agricul., 63, 445, 1993. 44. Taylor, W.J., and Leighton Jones, J., An immunoassay for distinguishing between crustacean tailmeat and white fish, Food Agric. Immunol., 4, 177, 1992. 45. Taylor, W.J., and Jones, J.L., An immunoassay for verifying the identity of canned sardines, Food Agric. Immunol., 4, 169–175, 1992. 46. Céspedes, A., García, T., Carrera, E., González, I., Fernández, A., Asensio, L., Hernández, P.E., and Martín, R., Indirect ELISA for the identification of sole (Solea solea), European plaice (Pleuronectes platessa), flounder (Platichthys flesus), and Greenland halibut (Reinhardtius hippoglossoides), J. Food Prot., 62, 1178–1182, 1999. 47. Fernández, A., García, T., Asensio, L., Rodríguez, M.A., González, I., Lobo, E., Hernández, P., and Martín, R., Identification of the clam species Ruditapes decussatus (grooved carpet shell), Venerupis romboides (yellow carpet shell) and Venerupis pullastra (pullet carpet shell) by ELISA, Food Agric. Immunol., 14, 65–71, 2002. 48. Huang, T., Marshall, M.R., Kao, K., Otwell, W.E., and Wei, C., Development of monoclonal antibodies for red snapper (Lutjanus campechanus) identification using enzyme-linked immunosorbent assay, J. Agric. Food Chem., 43, 2301–2307, 1995. 49. Asensio, L., González, I., Rodríguez, M.A., Hernández, P.E., García, T., and Martín, R., Development of a monoclonal antibody for grouper (Epinephelus marginatus) and wreck fish (Polyprion americanus) authentication using an indirect enzyme-linked immunosorbent assay, J. Food Sci., 68, 1900–1903, 2003. 50. Asensio, L., González, I., Rodríguez, M.A., Mayoral, B., López-Calleja, I., Hernández, P.E., García, T., and Martín, R., Development of a monoclonal antibody for grouper (Epinephelus guaza) identification using and indirect enzyme-linked immunosorbent assay, J. Food Prot., 66, 869–886, 2003. 51. Asensio, L., González, I. Pavón, M.A., García, T., and Martín, R., An indirect ELISA and a PCR technique for the detection of Grouper (Epinephelus marginatus) mislabeling, Food Addit. Contam. Part A, 25(6), 677–683, 2008. 52. Carrera, E., Terni, M., Montero, A., García, T., González, I., and Martín, R., ELISA-based detection of mislabeled albacore (Thunnus alalunga) fresh and frozen fish fillets, Food Agric. Immunol., 25(4), 569–577, 2014. 53. Taboada, L., Sánchez, A., Velasco, A., Santaclara, F.J., Pérez-Martín, R.I., and Sotelo, C.G., Identification of Atlantic cod (Gadus morhua), ling (Molva molva), and Alaska pollock (Gadus chalcogrammus) by PCR-ELISA using duplex PCR, J. Agric. Food Chem., 62(24), 5699–5706, 2014. 54. Baumgartner, S., Steiner, I., Kloiber, S., Hirmann, D., Krska, R., and Yeung, J., Towards the development of a dipstick immunoassay for the detection of trace amounts of egg proteins in food, Eur. Food Res. Technol., 214, 168, 2002. 55. Jiang, T.L., Cai, Q.F., Shen, J.D., Huang, M.J., Zhang, L.J., Liu, G.M., and Cao, M.J., Establishment of immunological methods for the detection of soybean proteins in surimi products. LWT Food Sci. Technol., 64(1), 344–349, 2015. 56. Dreyfuss, M.S., Cutrufelli, M.E., Mageau, R.P., and McNamara, A.M., Agar-gel immunodiffusion test for rapid identification of pollock surimi in raw meat products, J. Food Sci., 62, 972, 1997.

Detection of Adulterations

717

57. Ruethers, T., Taki, A.C., Khangurha, J., Roberts, J., Buddhadasa, S., Clarke, D., Hedges, C.E., Campbell, D.E., Kamath, S.D., Lopata, A., and Koeberl, M., Commercial fish ELISA kits have a limited capacity to detect different fish species and their products, J. Sci. Food Agric., 100(12), 4353– 4363, 2020. 58. van Raamsdonk, L.W.D., von Holst, C., Baeten, V., Berben, G., Boix, A., and de Jong, J., New developments in the detection and identification of processed animal proteins in feeds, Anim. Feed Sci. Technol., 133, 63, 2007. 59. Garrido-Varo, A., Pérez-Marín, M.D., Guerrero, J.E., Gómez-Cabrera, A., Haba, M.J.D.L., Bautista, J., Soldado, A., Vicente, F., Martínez, A., Roza-Delgado, B.D.L., and Termes, S., Near infrared spectroscopy for enforcement of European legislation concerning the use of animal by-products in animal feeds, Biotechnol. Agron. Soc. Environ., 9, 3, 2005. 60. Murray, I., Aucott, L.S., and Pike, I.H., Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fishmeal with meat and bone meal, J. Near Infrared Spectrosc., 9, 297, 2001. 61. European Commission, Commission decision of 27 March 2002 (2002/248/EC) amending council decision 2000/766/EC and commission decision 2001/9/EC with regard to transmissible spongiform encephalopathies and the feeding of animal proteins, Off. J. Eur. Comm., L84 28/3/2002, 71, 2002. 62. Gayo, J., and Hale, S.A., Detection and quantification of species authenticity and adulteration in crabmeat using visible and near-infrared spectroscopy, J. Agric. Food Chem., 55, 585, 2007. 63. Gayo, J., Hale, S.A., and Blanchard, S.M., Quantitative analysis and detection of adulteration in crab meat using visible and near-infrared spectroscopy, J. Agric. Food Chem., 54, 1130, 2006. 64. Qin, J., Vasefi, F., Hellberg, R.S., Akhbardeh, A., Isaacs, R.B., Yilmaz, A.G., Hwang, C., Baek, I., Schmidt, W.F., and Kim, M.S., Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques, Food Control, 114, 107234, 2020. 65. Alamprese, C., and Casiraghi, E., Application of FT-NIR and FTIR spectroscopy to fish fillet authentication, LWT Food Sci. Technol., 63(1), 720–725, 2015. 66. Grassi, S., Casiraghi, E., and Alamprese, C., Handheld NIR device: a non-targeted approach to assess authenticity of fish fillets and patties, Food Chem., 243, 382–388, 2018. 67. Sciuto, S., Esposito, G., Dell’Atti, L., Rossi, F., Riina, M.V., Merlo, G., Magnani, L., Benso, A., Bozzetta, E.M., and Acutis, P.L., A new approach against food frauds: the portable near-infrared device for fish fillets identification, Schol. J. Food Nutr., 4(1), 442–447, 2021. 68. Sousa, N., Moreira, M., Saraiva, C., and de Almeida, J., Applying fourier transform mid infrared spectroscopy to detect the adulteration of Salmo salar with Oncorhynchus mykiss, Foods, 7, 55, 2018. 69. Ghidini, S., Varrà, M.O., and Zanardi, E., Approaching authenticity issues in fish and seafood products by qualitative spectroscopy and chemometrics, Molecules, 24, 1812, 2019. 70. Hassoun, A., Mage, I., Schmidt, W.F., Temiz, H.T., Li, L., Kim, H., Nilsen, H., Biancolillo, A., AitKaddour, A., Sikorski, M., Sikorska, E., Grassi, S., and Cozzolino, D., Fraud in animal origin food products: advances in emerging spectroscopic detection methods over the past five years, Foods, 9(8), 1069, 2020. 71. European Commission, Commission Directive 2003/126/EC of 23 December 2003 on the analytical method for the determination of constituents of animal origin for the official control of feedingstuffs, Off. J. Eur. Comm., L339 24/12/2003, 0078, 2003. 72. Santaclara, F.J., Espiňeira, M., Cabado, A.G., and Vieites, J.M., Detection of land animal remains in fish meals by the polymerase chain reaction-restriction fragment length polymorphism technique, J. Agric. Food Chem., 55, 305, 2007. 73. Geng, J., Liu, J., Kong, X., Shen, B., and Niu, Z., The fishmeal adulteration identification based on microscopic image and deep learning, Comput. Electron. Agric., 198, 1–9, 2022. 74. Ebert, M., and Islam, R., Kaviar und Kaviarimitate: Verfälschungen des teuren Störrogens mit histologischer Methode nachweisbar (Caviar and caviar imitations adulterations of sturgeon roe provable by histological method), Fleischwirtschaft, 87, 124, 2007. 75. U.S. Food and Drug Administration, Macroanalytical procedures manual (MPM), chapter V-7-A Method for Microscopic Detection of Fish Tissue in Crab Meat or Crab Cakes, Electronic revised version (V2) from FDA Technical Bulletin Number 5, Macroanalytical Procedures Manual 1984. Olsen, A.R., Knight S.A., and Ziobro, G.C. (Eds.). https://www.fda.gov/food/laboratory-methods-food/mpm-v7-seafood#Crab, Accessed on 13-11-2023. 76. Ashoor, S.H., and Knox, M.J., Identification of fish species by high-performance liquid chromatography, J. Chromatogr. A, 324, 199–202, 1985.

718

Handbook of Seafood and Seafood Products Analysis

77. Knuutinen, J., and Harjula, P., Identification of fish species by reversed-phase high-performance liquid chromatography with photodiode-array detection, J. Chromatogr. B. Biomed. Sci. App., 705, 11–21, 1998. 78. Chou, C.-C., Lin, S.-P., Lee, K.-M., Hsu, C.-T., Vickroy, T.W., and Zen, J.-M., Fast differentiation of meats from fifteen animal species by liquid chromatography with electrochemical detection using copper nanoparticle plated electrodes, J. Chromatogr. B, 846, 230, 2007. 79. Lasch, P., Uhlig, P., Uhlig, C., Wilhelm, C., Bergmann, N., and Wittke, S., Development and in-house validation of an LC-MS and LC-MS/MS assay for the determination of food fraud for different fish species, J. AOAC Int., 102(5), 1330–1338, 2019. 80. Lockley, A.K., and Bardsley, R.G., DNA-based methods for food authentication, Trends Food Sci. Technol., 11, 67, 2000. 81. Mackie, I.M., Authenticity of fish, In Food Authentification, Eds, Ashurt, P.R., and Dennis, M.J., Blackie Academic and Professional, London, p. 140, 1996. 82. Leighton Jones, J., DNA probes: applications in the food industry, Trends Food Sci. Technol., 2, 28, 1991. 83. Sotelo, C.G., Pinciro, C., Gallardo, J.M., and Perez-Martin, R.I., Fish species identification in seafood products, Trends Food Sci. Technol., 4, 395, 1993. 84. Mackie, I.M., Fish speciation, Food Technol. Int. Europe, 1, 177, 1994. 85. Martinez, I., and Malmheden Yman, I., Species identification in meat products by RAPD analysis, Food Res. Int., 31, 459, 1998. 86. Primrose, S.B., The role of DNA analysis in the determination of food authenticity, In : Malcolm Burns, Lucy Foster and Michael Walker (Eds.) DNA Techniques to Verify Food Authenticity: Applications in Food Fraud, The Royal Society of Chemistry, London, UK, pp. 1–11, 2019. 87. Rapley, R., Enhancing PCR amplification and sequencing using DNA-binding proteins. Mol. Biotechnol., 2, 295–298, 1994. 88. Galimberti, A., de Mattia, F., Losa, A., Bruni, I., Federici, S., Casiraghi, M., Martellos, S., and Labra, M., DNA barcoding as a new tool for food traceability. Food Res. Int., 50, 55–63, 2013. 89. Böhme, K., Calo-Mata, P., Barros-Velázquez, J., and Ortea, I., Review of recent DNA-based methods for main food-authentication topics, J. Agric. Food Chem., 67, 3854–3864, 2019. 90. Jerome, M., Lemaire, C., Verrez-Bagnis, V., and Etienne, M., Direct sequencing method for species identification of canned sardine and sardine-type products, J. Agric. Food Chem., 51, 7326, 2003. 91. Dawnay, D.N., Ogden, R., McEwing, R., Carvalho, G.R., and Thorpe, R.S., Validation of the barcoding gene COI for use in forensic genetic species identification, Forensic Sci Int., 173(1), 1–6, 2007. 92. Ward, R.D., Hanner, R., and Hebert, P.D.N., The campaign to DNA barcode all fishes, Fish Bol. J. Fish Biol., 74(2), 329–356, 2009. 93. Staats, M., Arulandhu, A.J., Gravendeel, B., Holst-Jensen, A., Scholtens, I., Peelen, T., Prins, T.W., and Kok, E., Advances in DNA metabarcoding for food and wildlife forensic species identification, Anal Bioanal Chem., 408(17), 4615–4630, 2016. 94. Di Muri, C., Vandamme, S.G., Peace, C., Barnes, W., and Mariani, S., Biodiversity defrosted: unveiling non-compliant fish trade in ethnic food stores, Biol. Conserv., 217, 419–427, 2018. 95. Sevilla, R.G., Diez, A., Noren, M., Mouchel, O., Jerome, M., Verrez-Bagnis, V., Van Pelt, H., FavreKrey, L., Krey, G., Consortium, T.F., and Bautista, J.M., Primers and polymerase chain reaction conditions for DNA barcoding teleost fish based on the mitochondrial cytochrome b and nuclear rhodopsin genes, Mol. Ecol. Notes, 7, 730, 2007. 96. FishTrace, Genetic catalogue. https://data.jrc.ec.europa.eu/dataset/jrc-fad-fishtrace. Accessed on 13-11-2023. 97. Barcode of Life Data System (BOLD System V4), https://www.boldsystems.org/. Accessed on 13-11-2023. 98. Quinteiro, J., Sotelo, C.G., Rehbein, H., Pryde, S.E., Medina, I., Pérez-Martin, R.I., Rey-Méndez, M., and Mackie, I.M., Use of mtDNA direct polymerase chain reaction (PCR) sequencing and PCR-restriction fragment length polymorphism methodologies in species identification of canned tuna, J. Agric. Food Chem., 46, 1662, 1998. 99. Sebastio, P., Zanelli, P., and Neri, T.M., Identification of anchovy (Engraulis encrasicholus L.) and gilt sardine (Sardinella aurita) by polymerase chain reaction, sequence of their mitochondrial cytochrome b gene, and restriction analysis of polymerase chain reaction products in semipreserves, J. Agric. Food Chem., 49, 1194, 2001. 100. Ram, J.L., Ram, M.L., and Baidoun, F.F., Authentication of canned tuna and bonito by sequence and restriction site analysis of polymerase chain reaction products of mitochondrial DNA, J. Agric. Food Chem., 44, 2460, 1996.

Detection of Adulterations

719

101. Shokralla, S., Spall, J.L., Gibson, J.F., and Hajibabaei, M., Next-generation sequencing technologies for environmental DNA research, Mol Ecol., 21(8), 1794–805, 2012. 102. Ji, Y., Ashton, L., Pedley, S.M., Edwards, D.P., Tang, Y., Nakamura, A., Kitching, R., Dolman, P.M., Woodcock, P., Edwards, F.A., Larsen, T.H., Hsu, W.W., Benedick, S., Hamer, K.C., Wilcove, D.S., Bruce, C., Wang, X., Levi, T., Lott, M., Emerson, B.C., and Yu, D.W., Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding, Ecol Lett., 16(10), 1245–57, 2013. 103. Varunjikar, M.S., Moreno-Ibarguen, C., Andrade-Martinez, J.S., Tung, H.S., Belghit, I., Palmblad, M., Olsvik, P., Reyes, A., Rasinger, J., and Lie, K.K., Comparing novel shotgun DNA sequencing and stateof-theart proteomics approaches for authentication of fish species in mixed samples, Food Control, 131, 108417, 2022. 104. Edwards, M.C., and Gibbs, R.A., Multiplex PCR: advantages, development, and applications, PCR Methods Appl., 3, S65, 1994. 105. Colombo, F., Trezzi, I., Bernardi, C., Cantoni, C., and Renon, P., A case of identification of pectinid scallop (Pecten jacobaeus, Pecten maximus) in a frozen and seasoned food product with PCR technique, Food Control, 15, 527, 2004. 106. Bellagamba, F., Valfre, F., Panseri, S., and Moretti, V.M., Polymerase chain reaction-based analysis to detect terrestrial animal protein in fish meal, J. Food Prot., 66, 682, 2003. 107. Bottero, M.T., Dalmasso, A., Cappelletti, M., Secchi, C., and Civera, T., Differentiation of five tuna species by a multiplex primer-extension assay, J. Biotechnol., 129, 575, 2007. 108. Zhang, J., and Cai, Z., Differentiation of the rainbow trout (Oncorhynchus mykiss) from Atlantic salmon (Salmon salar) by the AFLP-derived SCAR, Eur. Food Res. Technol., 223, 413, 2006. 109. Zhang, J., Wang, H., and Cai, Z., The application of DGGE and AFLP-derived SCAR for discrimination between Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss), Food Control, 18, 672, 2007. 110. Hold, G.L., Russell, V.J., Pryde, S.E., Rehbein, H., Quinteiro, J., Vidal, R., Rey-Mendez, M., Sotelo, C.G., Perez-Martin, R.I., Santos, A.T., and Rosa, C., Development of a DNA-based method aimed at identifying the fish species present in food products, J. Agric. Food Chem., 49, 1175, 2001. 111. Horstkotte, B., and Rehbein, H., Fish species identification by means of restriction fragment length polymorphism and high-performance liquid chromatography, J. Food Sci., 68, 2658, 2003. 112. Lundberg, D.S., Yourstone, S., Mieczkowski, P., Jones, C.D., and Dangl, J.L., Practical innovations for high-throughput amplicon sequencing, Nat. Methods, 10(10), 999–1002, 2013. 113. Heid, C.A., Stevens, J., Livak, K.J., and Williams, P.M., Real time quantitative PCR, Genome Res., 6, 986, 1996. 114. Sotelo, C.G., Chapela, M.J., Rey, M., and Pérez-Martin, R.I., Development of an identification and quantitation system for cod (Gadus morhua) using Taqman assay, In First Joint Trans-Atlantic Fisheries Technology Conference, Reykjavik, Iceland, 195–198, 2003. 115. Trotta, M., Schonhuth, S., Pepe, T., Cortesi, M.L., Puyet, A., and Bautista, J.M., Multiplex PCR method for use in real-time PCR for identification of fish fillets from grouper (Epinephelus and Mycteroperca species) and common substitute species, J. Agric. Food Chem., 53, 2039, 2005. 116. Hird, H.J., Hold, G.L., Chisholm, J., Reece, P., Russell, V.J., Brown, J., Goodier, R., and MacArthur, R., Development of a method for the quantification of haddock (Melanogrammus aeglefinus) in commercial products using real-time PCR, Eur. Food Res. Technol., 220, 633, 2005. 117. Lopez, I., and Pardo, M.A., Application of relative quantification TaqMan real-time polymerase chain reaction technology for the identification and quantification of Thunnus alalunga and Thunnus albacares, J. Agric. Food Chem., 53, 4554, 2005. 118. Fitzcharles, E.M., Rapid discrimination between four Antarctic fish species, genus Macrourus, using HRM analysis, Fish. Res., 127–128, 166–170, 2012. 119. Fernandes, T.J.R., Silva, C.R., Costa, J., Oliveira, M.B.P.P., and Mafra, I., High resolution melting analysis of a COI mini-barcode as a new approach for Penaeidae shrimp species discrimination, Food Control, 82, 8–17, 2017. 120. Telmo, J.R., Fernandes, J.C., Beatriz, P.P., and Oliveira, I.M., COI barcode-HRM as a novel approach for the discrimination of hake species, Fish. Res., 197, 50–59, 2018. 121. Quintrel, M., Jilberto, F., Sepúlveda, M., Marín, M.E., Véliz, D., Araneda, C., and Larraín, M.A., Development and validation of a multi-locus pcr-hrm method for species identification in mytilus genus with food authenticity purposes, Foods, 10(8), 1684, 2021. 122. Deconinck, D., Hostens, K., Taverniers, I., Volckaert, F.A.M., Robbens, J., and Derycke, S., Identification and semi-quantification of Atlantic salmon in processed and mixed seafood products using Droplet Digital PCR (ddPCR), Food Chem. Toxicol., 154, 112329, 2021.

720

Handbook of Seafood and Seafood Products Analysis

123. Cao, W., Li, Y., Chen, X., Chang, Y., Li, L., Shi, L., Bai, W., and Ye, L., Species identification and quantification of silver pomfret using the droplet digital PCR assay, Food Chem., 302, 125331, 2020. 124. Doi, H., Takahara, T., Minamoto, T., Matsuhashi, S., Uchii, K., and Yamanaka, H., Droplet digital polymerase chain reaction (PCR) outperforms real-time PCR in the detection of environmental DNA from an invasive fish species, Environ. Sci. Technol., 49, 5601–5608, 2015. 125. Kappel, K., Eschbach, E., Fischer, M., and Fritsche, J., Design of a user-friendly and rapid DNA microarray assay for the authentication of ten important food fish species, Food Chem., 311, 125884, 2020. 126. Kappel, K., Fafińska, J., Fischer, M., and Fritsche, J., A DNA microarray for the authentication of giant tiger prawn (Penaeus monodon) and whiteleg shrimp (Penaeus (Litopenaeus) vannamei): a proof-ofprinciple, Anal. Bioanal. Chem., 413(19), 4837–4846, 2021. 127. Spielmann, G., Ziegler, S., Haszprunar, G., Busch, U., Huber, I., and Pavlovic, M., Using loop-mediated isothermal amplification for fast species delimitation in eels (genus Anguilla), with special reference to the European eel (Anguilla anguilla), Food Control, 101, 156–162, 2019. 128. Xiong, X., Huang, M., Xu, W., Li, Y., Cao, M., and Xiong, X., Using real time fluorescence loop-mediated isothermal amplification for rapid species authentication of Atlantic salmon (Salmo salar), J. Food Compos. Anal., 95, 103659, 2021. 129. Saull, J., Duggan, C., Hobbs, G., and Edwards, T., The detection of Atlantic cod (Gadus morhua) using loop mediated isothermal amplification in conjunction with a simplified DNA extraction process, Food Control, 59, 306–313, 2016. 130. Xiong, X., Xu, W., Guo, L., An, J., Huang, L., Qian, H., Cui, X., Li, Y., Cao, M., Xiong, X., Ying, X., and Wang, L., Development of loop-mediated isothermal amplification (LAMP) assay for rapid screening of skipjack tuna (Katsuwonus pelamis) in processed fish products, J. Food Compos. Anal., 104038, 2021.

38 Identification of Seafood Species Detection of Adulteration Negi Aditi, A. Kaarunya, and R. Meenatchi Formerly Indian Institute of Food Processing Technology

38.1 INTRODUCTION People have recently been increasingly worried about food safety and authenticity. Consumers want to know what they are purchasing, where it comes from, and when and how it was made. As a result, food authenticity concerns are increasingly highlighted, and the public is more aware of the severity of food fraud. Olive oil, honey, shellfish, dairy goods, meats, and spices are prone to deception. Dairy ingredients top the Decernis Food Fraud Database, trailed by seafood and meat or poultry. Fraudulent activities in animal-based products can come in many forms, such as differences in the production method, addition of non-declared substances, species substitution, mislabelling of the provenance (botanical or geographic origin), and farming or breeding technique, deceptive treatments, and nondeclaration of processes, such as previous microwave heating, freezing, and irradiation. India is one of the biggest fish producers in the world. It makes up 6% of the world’s total fish production. Indian rupee (INR) 1,224.3 billion was invested in the Indian fish market in 2019. This industry is expected to grow to INR 2,180 billion by 2025. It is a very organized and segmented business today. The seafood and fisheries industry currently accounts for 1% of the Indian gross domestic product (GDP). It also generates over 5% of the agricultural GDP. India is a significant fish producer and exporter on the global market. Fish and fish products make for about 1 million tonnes of India’s agricultural exports, worth over $5 million. Seafood accounts for 10% of India’s overall exports and approximately 20% of all agricultural exports. About 50 varieties of fish and shellfish are shipped to 75 countries worldwide. In 2016, India produced 10.7 million tonnes of fish and shrimp. Most seafood consumed by humans is alive, raw, chilled (45%), or frozen (31%) (FAO, 2018). Twelve per cent of seafood is processed or preserved by curing (either salted in brine or fermented or dried) and smoking. Eastern and Southeast Asia prefer live and fresh fish, whereas Europe and North America prefer frozen, processed, or preserved seafood (FAO, 2019). Seafood provides superior-quality protein, accounting for 17% of all the animal protein individuals eat (Domingo, 2016). Additionally, seafood is rich in vitamins and minerals such as vitamin E, retinol, vitamin D, and omega-3 fatty acids, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) (Jacobs et  al., 2015). Numerous health advantages of DHA and EPA have been demonstrated in research studies, such as a lower risk of heart disease, better development of the eyes and brain, alleviation of some inflammatory conditions, and a lower risk of some mental disorders (Hellberg et al., 2012). The practice of deceiving customers about their seafood for economic benefit is known as seafood fraud. According to Young’s Seafood Limited, the seafood supply chain is prone to nine forms of fraud. They are species adulteration, illegal, unreported, and unregulated (IUU) substitution, species substitution, modern-day slavery, chain of custody abuse, undeclared product extension, animal welfare, catch method fraud, and fishery substitution (Figure 38.1) (Young’s Seafood 2016). The following section focuses on resolving the most important issues in fish and seafood authentication on a particular scenario basis, including species substitution, falsification of geographical origin, misrepresentation of production method or farming system, and substitution of fresh for DOI: 10.1201/9781003289401-44

721

722

Handbook of Seafood and Seafood Products Analysis

FIGURE 38.1  Nine signs of seafood fraud.

FIGURE 38.2  An overview of analytical methods used for seafood adulteration.

frozen/thawed products, while highlighting the trends in using various techniques and the discrimination performed in each case. Figure 38.2 provides an overview of different analytical methods commonly used in the authentication of seafood.

38.2  SPECTROSCOPIC TECHNIQUES Spectroscopy deals with the investigation and measurement of electromagnetic waves interacting with materials, which can be dispersed, absorbed, or transmitted based on radiation’s frequency and the chemical/physical attributes of the material. A shift in the energy levels of atoms and nuclei,

Detection of Adulteration

723

molecules, and crystals, which make up matter, is caused by radiation when it is absorbed, thereby triggering a vibrational, rotational, and electronic transition, depending on the energy of the incident radiation. These interactions produce a spectrum encompassing numerous properties of the researched matter, which may be used in several applications when analysed using chemometrics. The physical state and chemical composition of the sample, precision, sensitivity, time of analysis, cost and availability of the instruments, the scale of operation, and the overall accuracy of the procedure should all be addressed. The most commonly used spectroscopic techniques for food authentication or adulteration are Raman, ultraviolet (UV)–visible (VIS), infrared (IR), hyperspectral imaging (HSI), and nuclear magnetic resonance (NMR).

38.2.1 UV-Vis Absorption/Fluorescence Spectroscopy The electronic excitation of molecules containing certain chromophore groups occurs due to photon absorption at two wavelength spectral regions in UV–Vis spectroscopy. During the absorption mode, light absorbed by the sample is recorded, while light emitted after absorption is recorded as fluorescence. A study was conducted in China with 60 fish samples for species substitution. Fish tissue samples were boiled in trifluoroacetic acid (TFA) and observed in the spectral region between 200 and 400 nm having maximum absorbance at 249 nm. The study showed that due to the different protein content of fishes, they retained different absorption maximum at a characteristic wavelength; on further differentiating, the species with principle component analysis (PCA) observed areas for some fish species cluster in different areas, displaying 95% of confidence region (Chai et al., 2021). The intense (Soret) peak was absorbed at 418 nm, and the β (546 nm)- and α (548 nm)bands were associated with haem and meat myoglobin absorption. However, the bright red colour of oxymyoglobin was related to the peaks at 418 and 546 nm. The decreased intensity (concentration) of red pigments in the three bands indicated the lowest spectrum, which distinguishes pure turkey meat from minced beef (Alamprese et al., 2013).

38.2.2 IR Spectroscopy The electromagnetic spectrum’s IR portion ranges between 0.76 and 350 µm, which lies between the VIS and microwave regions. It generates heat and accounts for 66% of all solar radiation. Temperatures above absolute zero release IR energy, including the sun, electric heaters, and gasfired heaters. They are emitted by almost all types of light and heat sources. Three distinct spectra of IR light exist (Xu et al., 2015).

1. Near-IR (NIR) wavelength ranges from 0.75 to 1.4 µm. 2. Mid-IR (MIR) wavelength between 1.4 and 3 µm. 3. Far-IR radiation (FIR) wavelength between 3 and 1,000 µm.

The temperature rise (heating) produced by IR radiation relies on several parameters: (i) the wavelength and intensity of the radiation, (ii) the source’s emission spectrum, (iii) the source’s temperature, (iv) the direction in which the emission falls on the surface, (v) the medium’s absorbance and scattering – the principle of absorption, reflection, and transmission results in the functioning of IR spectroscopy. Transmission, reflection, transfection, and contact (interactance) are four typical means of measuring the NIR spectra of substances. The model used relies on numerous parameters, including the sample type. When foods are exposed to NIR, the different functional groups in food items absorb the light at frequencies that correspond to the vibrations of those functional groups. In contrast, the remaining frequencies are either reflected or transmitted. The bonds such as C–H, O–H, N–H, and other functional groups have fundamental molecular vibrations that are detectable in the NIR range; hence, the concentration of dietary components in food such as fat, protein, carbohydrate, and water can

724

Handbook of Seafood and Seafood Products Analysis

be detected using IR spectroscopy. The biological components of food were identified by measuring the light that was reflected, transmitted, or absorbed and then normalized using chemometrics (Chakrabarti et al., 2020). Near- and mid-IR spectra are used in IR spectroscopy; however, VIS wavelength ranges are frequently incorporated in muscle food applications owing to pigments in the raw material. The NIR emphasizes vibration and combination overtones of fundamental N–H, C–H, and O–H bonds. In contrast, the MIR focuses on different molecules, including general stretching, bending, and wagging movements of functional groups such as C=O, C–C, N–H, C–H, and O–H (Workman, 2000). A portable NIR spectrometer can spot the difference between low and high whole fish and fillet species. Before PCA and soft independent modelling of class analogy (SIMCA) analysis, NIR spectra (906–1,648 nm) from the skin (whole fish) and meat (fish fillets) were pre-processed. PCA results only successfully differentiated entire mullet samples, although discrimination performance for mullet fillets improved significantly after SIMCA analysis (Ghidini et al., 2019). The O–H initial overtone and water absorption were identified in the NIR spectra of adulterated (minced beef) and pure turkey meat at 1,450 and 1,920 nm, respectively; at 980 nm, the second overtone was absorbed (Alamprese et  al., 2013). The lipid’s spectral region corresponded to absorbance peak at 1,041, 1,740–1,800, 1,200, and 2,440 nm owing to the C–H combination band of stretching and bending, C–H stretch first overtone, and C–H combination tone of liquids, respectively (Sinelli et al., 2010). The MIR spectra provided comprehensive information about the association of carbohydrates, protein, fat, and water through the major, minor, and smaller peaks. The prominent peak of the meat samples (pure and adulterated) was absorbed at 2,985 nm due to the asymmetric and symmetric N–H stretching of amide I (Alamprese et al., 2013). However, the fingerprint region of MIR spectra lies around 5,555–11,111 nm due to protein (C=O stretching of amide I) and water (O–H stretch). The combination effect of N–H with C–N stretching of amide II was absorbed at around 6,451 nm. In contrast, the smaller peaks absorbed between the region: 6,877–7,132 nm corresponding to the C–H stretch of CH3 and CH2 of the fat molecules and 8,333–10,526 nm corresponding to the C–O and C–C stretching of the carbohydrates (Sinelli et al., 2010).

38.2.3 Fourier Transform Infrared Spectroscopy (FT-IR) FT-IR spectroscopy is a type of vibrational spectroscopy that is extensively used and is one of the oldest ways of analysing diverse molecules. FT-IR, a type of IR spectroscopy, uses the interference between the two IR beams to obtain a signal known as an interferogram, which is proportional to the change in transit time between the two beams. Typically, a Michelson interferometer is used to accomplish this, and the signal produced by the interferometer is split into its constituent frequencies using FT algorithms. When accompanied by an interferometer, it is a mathematical procedure that enables the multiplexing of wavelengths in a single measurement, thus speeding up the analysis. This transformation is widely employed in the MIR region, while dispersive spectroscopy is commonly used in the NIR spectral region (Candoğan et al.,2021). As a result, functional groups in the analyte absorb IR radiation in FT-IR, causing atoms bound by covalent bonds to vibrate. The IR spectrum shows increasing peaks when vibrational excitations cause a shift in the bond dipole. Molecular structures that include two or more atoms exhibit vibrational bond modes, resulting in the bond length shift. These modes include symmetrical or asymmetrical and bending (twisting, deformation, wagging, and rocking) (Rohman, 2019). It is a non-destructive technique, making it excellent for analysing biological materials such as cells, tissues, and fluids, thus enabling rapid extraction of bio-molecular information without labels. Molecular fingerprints are generated when IR light travels through a sample and is absorbed by each vibrational mode at a frequency corresponding to that mode’s absorption peak (Trevisan et al., 2012). FT-IR spectroscopy uses three sampling techniques: transmission, transflection, and attenuated total reflectance (ATR). For both liquid and solid samples, a particular gas cell can also study gases in transmission mode (Candoğan et al.,2021). The FT-IR/NIR spectroscopy was successful and

Detection of Adulteration

725

served as an easy tool for identifying substituted cheaper varieties of fish species (Atlantic mullet and flounder) with valuable species (red mullet and plaice) and discrimination of fresh and frozenthawed fillets. The FT-NIR spectra consisted of several peaks with the domination of water (first and the second overtone OH) and C–H aliphatic group (first and second overtone of stretching), thus reflecting the different compositions of several species. Additionally, amines played an important role (amines I and II). The data were further processed using chemometrics for species identification and discrimination (Alamprese and Casiraghi, 2015).

38.2.4  Raman Spectroscopy Raman spectroscopy is gaining attraction in almost all disciplines of natural sciences due to its ease of use, minimal sample preparation requirements, and high sensitivity to the overall structure of biological molecules. It is based on the Raman scattering phenomenon, which asserts that when the incident light is targeted at a sample, it scatters only a small part of the light. Instead, the dispersed light conveys the vibrational band energies of molecules. Raman spectra are plots of the intensities of the scattered component of incident light against frequency shifts between the scattered and incident light. FT–Raman spectroscopy collects data from various substances, including crystals and biological tissues, without using fluorescence and stable wavelength measurements. When it comes to agricultural and food items, Raman spectroscopy is most typically used for dispersive, Fourier, surface-enhanced (SERS) Raman spectroscopy, or spatially offset Raman spectroscopy (SORS). A typical Raman spectrometer has four components: a laser source, a sample system, a detecting method, and a computer that collects and stores data (Yang and Ying, 2011). Raman spectrometers are classified by their excitation source. Dispersive Raman systems utilize VIS lasers, UV–Raman spectrometers use UV lasers, and FT–Raman systems employ a NIR laser, neodymium-doped yttrium aluminium garnet (NdYAG), which has the longest wavelength of any Raman lasers. Longer-wavelength excitation lowers photodecomposition and fluorescence effects but weakens Raman signals. Raman analysis is based on interpreting vibrational modes in spectra to determine a sample’s chemical functional group. The location and intensity of a Raman band may describe a chemical group. Raman’s chemical specificity is that special chemical/molecular bonds may be identified based on inelastic light scattering frequency shifts. First, in FT-Raman, compositional food analysis assigns frequency shifts to molecular vibrational modes. Active ring configurations and nonpolar groups yield Raman peaks. Polar groups generate weak Raman signals; hence, many polar water molecules have vibrational modes (Kizil and Irudayaraj, 2018). Raman spectroscopy served as a convenient and high-sensitivity tool to detect rainbow trout adulteration in Atlantic salmon around the Raman shift (spectral range) 250–2,500 cm−1. The eight characteristic Raman spectra of the adulterated Atlantic salmon fat were very similar, with distinct absorption peaks being observed at 1,748 (peak strength – weak; due to the C=O ester stretching), 1,659 (peak strength – strong, due to Z-alkene, C=C in fatty acid chain), 1,441 (peak strength – robust, due to C–H bend and stretch), 1,303 (peak strength – medium, due to CH2 twist), 1,268 (peak strength – medium, due to stretch modes of =CH of unsaturated fatty acids), 1,079 and 872 (peak strength – medium, due to C–C stretch), and 974 cm−1 (peak strength – strong, due to the bend vibration of trans (=CH)). The Raman peak strength increased with increasing levels of rainbow trout flesh, allowing discrimination between the Raman spectra of the various Atlantic salmon meat samples (Chen et al., 2019). Raman spectroscopy coupled with chemometrics has been utilized to investigate fish quality (fresh and frozen-thawed). The Raman spectra were collected between 200 and 2,000 cm−1 with distinctive bands due to stretching of CCC, CH2 scissoring, C=C stretching, and C=O stretching of esters (Sánchez-Alonso et al., 2012). Further reductions in the intensity of normalized Raman spectra were observed for fresh, frozen-thawed (one cycle), and PCA-processed frozen-thawed (two cycles) and the data to reduce the multidimensional data and differentiate fresh and frozen-thawed samples. The three classes of fish formed three clusters in the PCA score plot, and the differentiation was clearly evident. Furthermore, species discrimination was identified by feeding the entire spectra to PCA.

726

Handbook of Seafood and Seafood Products Analysis

The different Raman intensities were attributed to different fatty acids such as stearic, palmitic, and oleic acid, and the Principal component analysis (PCA) score plot enabled clear discrimination between three fish species with others based on similar fatty acid contents (Velioğlu et al.,2015).

38.2.5 NMR Spectroscopy NMR spectroscopy is a spectroscopic technology that investigates the behaviour of compounds in the presence of a powerful magnetic field. It is a flexible tool for food analysis. Its unintended uses have grown in popularity for two reasons: First, NMR spectral profiles can completely map out a substance’s composition, allowing for identifying all main and minor dietary components (Ghidini et al., 2019). The area of NMR region bands is directly related to the number of nuclei contributing to the signal, making the approach quantifiable. Apart from that, NMR spectroscopy is one of the few methods that can offer information on a molecule’s regio/stereochemical (Hatzakis, 2019). NMR spectroscopy is based on the magnetic properties of odd- or even-mass atomic nuclei and their interaction with an externally applied magnetic field. When subjected to magnetic flux, atomic nuclei align. Radio waves are absorbed by this process and recognized by a radiofrequency (RF) receiver. The following types of nuclei have been employed: carbon (13C), proton (1H), nitrogen (14N and 15N), and fluorine (19F). 1H-NMR provides the benefits of a quicker analysis time, greater repeatability, and a broad linear range. Carbon (13C) NMR is highly relevant in organic chemical analysis (Nimbkar et al., 2021). Different NMR methods have been optimized based on the physical condition of the substance and the desired application. Liquid-state NMR, solid-state NMR, low-field NMR, high-resolution NMR, and NMR imaging are the most utilized. Each needs special equipment, sample preparation, data collecting, and processing methods. The most prominent use of NMR stable isotope analysis is in determining the provenance of seafood, where the rationale of discrimination is based on the ratios of elements such as 2H/1H and 13C/12C, which may alter based on the water, soil, type of feed, or geographical origin. Two advanced techniques are used for the determination of isotope ratios: site-specific natural isotope fractionation from NMR (SNIF-NMR) and isotope ratio mass spectrometry (IRMS) (Abbas et al., 2018). 1H-NMR fingerprinting classifies wild and farmed salmon fish based on higher oleic and linoleic acid in farmed variety. The 1H NMR spectra of salmon fat included 11 major intensities with 1H chemical shifts ranging from 0.681 to 7.548 ppm. The salmon production system (farmed or wild) was well predicted, classified, and obtained using SIMCA using the NMR spectra. The clear differentiation between wild and farmed salmon was observed by plotting the data in the Coomans plot, where the sample coordinates reflect class distances. Farmed fish had stronger NMR discrimination power than wild salmon, with substantial chemical shifts in five regions: 2.754, 2.769, and 2.057 ppm due to protons of linoleic acyl groups and 0.874 and 0.907 ppm due to methyl protons of oleic and linoleic acyl groups, respectively (Capuano et al., 2012).

38.3  IMAGING SPECTROSCOPY 38.3.1  Hyperspectral Imaging (HSI) Another relatively new technology, HSI, can meet the requirements for remote and real-time monitoring strategies. It is quick, non-intrusive, and concurrently provides spectral and spatial characteristics, which are all important benefits. Numerous papers discuss the advantages and disadvantages of HSI-based techniques for food authenticity and adulteration assessments. Currently, inexpensive, quick, and straightforward multispectral imaging technologies are being developed to detect specific adulteration (Feng et al., 2018). In the early 1970s, HSI was invented for remote sensing applications (Xu and Sun, 2018). The development of the first charge-coupled device (CCD) detector was critical in propelling this technology ahead in food science (Xu et al., 2017). By acquiring both spectral and spatial information from an item, HSI surpasses conventional imaging methods.

Detection of Adulteration

727

In addition, hyperspectral analysis is intrinsically associated with multivariate data analysis (chemometrics) due to its three-dimensional (3D) data structure. An HSI’s dual imaging and spectroscopic capabilities allow it to gather physical (size, shape, and colour) chemical and molecular (protein, water, fat, and other hydrogen-bond components) information about the item being examined. A hyperspectral picture is a collection of photographs taken at multiple wavelengths of the same object. So, in a hyperspectral image, every pixel carries the spectrum for that particular location. Typically, the resultant spectrum functions as a fingerprint that may be used to identify the pixel’s composition. These 3D images, known as hypercubes, include two spatial dimensions (X and Y representing rows and columns, respectively) and one spectral dimension (λ-wavelengths). The fundamental idea of HSI is premised on the reality that, owing to differences in chemical composition and underlying physical structure, all sample materials constantly scatter, reflect, release, and absorb energy in different patterns at specified wavebands. Because food has hundreds of chemical bonds, exposing a sample to electromagnetic radiation results in a complicated spectrum with numerous overlapping peaks. Typical components include an illumination unit, a spectrum analyzer with a C-mount lens, CCD or complementary metal oxide semiconductor (CMOS) cameras, and a processor with image-capturing software (Xu and Sun, 2018). VIS-NIR HSI was used to differentiate frozen-thawed, cold-stored, and fresh samples of shelled shrimp by collecting data between 328 and 1115 nm. Grey-level co-occurrence matrix (GLCM) was utilized for textural extraction, whereas successive projection algorithm (SPA) and uninformative variable elimination (UV) based on partial least squares (PLS) regression coefficients were employed to choose feature wavelengths. RF and SIMCA have been selected as the classifiers. The study stressed the significance of data fusion in terms of merging spectral and textural data to gain more excellent categorization rates between various groups (Qu et al., 2015).

38.3.2  Raman Chemical Imaging Due to water absorption of IR rays, long IR wavelengths restricting resolution, and non-backgroundfree detection, IR imaging for food authentication is hindered. However, Raman spectroscopy may be used in ambient and aquatic environments since water has a very low Raman scattering rate (Yaseen et al., 2017; Wang and Sun, 2018). Most Raman peaks, however, have a restricted bandwidth. Therefore, a wealth of important information about molecule structures may be gathered using molecular-specific fingerprint Raman spectra. Hence, Raman chemical imaging is ideal for analysing the distribution of compounds using Raman peaks. Raman spectroscopy relies on the scattering effect to understand molecular vibrations; three types of phenomenon occur when a molecule is irradiated with monochromatic light – anti-stokes, Stokes Raman scattering, and Raleigh scattering. The food matrix is heterogeneous; therefore, the spatial pattern of the element within the test sample cannot be derived from the average spectrum, which does not necessarily represent the entire sample. The Raman chemical imaging system combines digital photography with Raman spectroscopy in a single instrument to study chemical composition and distribution. Typical Raman chemical imaging equipment includes a light source, beam splitter, motorized stage, spectrograph, CCD detector, and computer system. This device makes use of excitation from a 785-nm line laser. The beam splitter normalizes laser incidence on a sample surface. A two-axis motorized positioning stage beneath the Raman probe moves the samples in two directions (X and Y). The Raman spectrometer disperses the excited Raman signals into several wavelengths before they hit the CCD detector. It provides a spatial–spectral image from scattered light. Raman chemical imaging data are provided as a 3D cube called a Raman hypercube with one wavelength and two spatial dimensions (x, y). Each pixel’s spectral fingerprint may be utilized to identify its chemical component. The four other advancements in Raman-based imaging include SRS, tip-enhanced Raman scattering (TERS), coherent anti-SRS, and surface-enhanced Raman scattering (SRES) (Wang and Sun, 2018).

728

Handbook of Seafood and Seafood Products Analysis

38.4  STABLE ISOTOPE ANALYSIS Stable isotopes vary only in the amount of neutrons in the atom’s nucleus, yielding heavier elements and a more diverse environmental distribution. Water and feed absorb these atoms into animal tissues, and their ratio varies by environment. Light (bio-elements) and heavy isotopes are distinguished by atomic mass. The most studied light isotope ratios are 2H/1H, 13C/12C, 15N/14N, and 18O/16O; 34S/32S is less frequent. In heavy isotopes, 87Sr/86Sr is the most frequent food authentication ratio, followed by 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, and 143Nd/144Nd (Danezis et al., 2016; Camin et al., 2017). Stable isotope ratios (SIRs) alter with climate, soil pedology, geographical origin, and geology of food component origin. There is a correlation between O and H isotope information for water from the source region and organic matter in food. Climatic and agricultural activities influence N and C isotopes, whereas geography, volcanism, proximity to the coast, and specific anthropogenic effects influence S isotopes. SIR studies of nitrogen (N) and carbon (C) were used in research to pinpoint the origin of all commercial fish species (Carrera and Gallardo, 2017). A favourable correlation between the H/O ratios of Italian rainbow trout fillets and the O ratio of tank water was examined in a study (Camin et al., 2018). Various legal and non-legal operations have relied on isotope ratio studies to determine the geographic provenance of samples. To assert a food’s geographical origin, the stable isotopic values of the test specimens must be compared to preceding isotope levels using a reputable database or databank with the targeted organisms if the items have been treated in any manner, such as freezing or ice storage. When determining isotopic ratios, a variety of other techniques are employed. These include IRMS, multi-collector (MC) inductively coupled plasma mass spectrometry (ICP-MS), and thermal ionization MS (TIMS). Isotope ratios of light and heavy isotopes are determined using IRMS and MC-ICP-MS, respectively, whereas TIMS and MC-ICP-MS are employed for heavier isotopes. An NMR fitted with a deuterium probe may also be used to analyse the 2H/1H ratio in tiny molecules such as ethanol (Danezis et al., 2016).

38.5  CHROMATOGRAPHIC TECHNIQUES As for the authenticity of food of animal origin by the detection of individual components, its achievement through the identification of ingredient replacement requires methods such as chromatographic techniques, thereby making it potential to identify the food composition and characterization. Food authenticity relies heavily on chromatographic procedures, which have to overcome a myriad of challenges. Proteins, carbohydrates, amino acids, fatty acids, nucleic acids, and other molecular components (additives such as preservatives, colorants, flavour enhancers, and other extraneous compounds) make up the vast majority of food substrates. Small organic molecules (1,000 Da) to macromolecules, which may have a broad range of polarities, are all examples of these substances (amino acids). Hence, due to the compositional complexity of foods, chromatographic techniques authenticate based on the identification of unique marker compounds or through minor analytical differences in patterns (Danezis et al., 2016). High-resolution chromatographic technologies (liquid chromatography (LC) and gas chromatography (GC)) connected to MS are efficient for food authenticity. The most common instruments are LC-MS/MS, GC-MS/MS, and LC-time of flight (TOF)-MS (LC-TOF-MS). Typically, LC separation focuses on three key chemical component characteristics: molecular size, polarity, and electrical charge. It is generally employed to identify carbohydrates, pigments, amino acids, proteins, chiral compounds, vitamins, phenolic compounds, triglycerides, etc. In contrast, GC is better suited to investigate inherently semi-volatile volatile compounds (Reid et al., 2006; Danezis et al., 2016). Direct MS analysis methodologies, such as matrix-assisted laser desorption/ionizationtime of flight (MALDI-TOF), real-time techniques (e.g., Direct Analysis in Real-Time (DART)), and HR-MS, have been successfully applied to several authentication difficulties. For example, DART HR-MS (High-resolution mass spectrometry) was created to distinguish between wild and farmed Canadian, Chilean, and Norwegian salmon. PCA applied to the 30 most abundant fatty acid

Detection of Adulteration

729

signals from DART HR-MS analysis of fillet lipid fractions permitted quick categorization of wild and farmed fish. In contrast, discriminant analysis (DA) produced a 100% accurate classification rate (Fiorino et al., 2018). In a study, researchers used hydrophilic interaction chromatography MS coupled with PCA to examine the difference between king salmon, rainbow trout, and Atlantic salmon and their genetic origin (Yu et al., 2020). Another recent work on the feasibility of ultrahigh-performance LC (UHPLC)-triple TOF-MS/MS was used to quantify lipid content in shrimp muscle tissue. Around 600 lipid compounds from 14 groups were discovered and measured, and chemometrics such as PCA permitted species differentiation (Sun et al., 2020).

38.6  DNA-BASED TECHNIQUES 38.6.1 PCR-Based Techniques Various deoxyribonucleic acid (DNA) and protein-based analytical approaches are available to identify adulteration in seafood species. However, protein-based strategies are often employed to analyse fresh or minimally processed seafood and are not well-suited to analysing highly processed items. DNA-based technologies such as improved resilience to heat degradation can overcome the protein-based method’s weakness. In addition, the presence of DNA in almost all cells will enhance specificity and sensitivity (Hellberg and Morrissey, 2011). DNA-based species-specific authentication mainly relies on the mutations that are developed and change through time inside the genomes of species (Naaum and Hanner, 2015). The first step in DNA-based technique includes extraction of DNA, followed by the amplification by polymerase chain reaction (PCR), and species identification using different methods is common. For example, PCR-restriction fragment length polymorphism (RFLP) is used for authentication, species-specific PCR, real-time PCR, high-resolution melting (HRM), Sanger sequencing, microarrays, random-amplified polymorphic DNA (RAPD), and amplified fragment length polymorphism (AFLP) are the mainly deployed for the seafood species authentication. However, isothermal amplification (LAMP), PCR–enzyme-linked immunosorbent assay (ELISA), lateral flow immunoassay, and next-generation sequencing (NGS) are the advanced DNA-based technologies being developed and deployed for seafood authentication. RFLP, Sanger sequencing, and NGS use universal primers to allow the identification of a wide variety of species (Silva and Hellberg, 2021). RAPD, PCR–single-strand conformation polymorphism (SSCP), and AFLP are different approaches for identifying seafood species used in previous investigations. However, these methods are rarely in use now because of poor repeatability and reproducibility during seafood species authentication on a large scale. Examples of the molecular techniques used for seafood authentication are presented in Table 38.1. Real-time PCR has shown to be a valuable method for authenticating species in various seafood items. This approach contains species-specific PCR with fluorescent-based technologies to allow species identification in real time when amplification occurs with either SYBR Green (basic fluorescent dye) or TaqMan probes (species-specific probes). Real-time PCR technologies focus on mitochondrial genes, such as 16S ribosomal ribonucleic acid (RNA) (16S rRNA), cytochrome c oxidase subunit 1 (COI), and cytochrome b (cyt b) oxidase subunit 1 (COI). In addition, RT-PCR targets DNA fragments 400 bp) of the genetic targets used in species identification research and “mini-barcode” used to describe shorter versions (400 bp in length. For example, the most common genetic marker used for DNA barcoding of seafood species is a ~650 bp region of COI. This is commonly called the standard, full-length, or full DNA barcode. However, there remain challenges associated with the

DNA Barcoding Methodologies for Identifying Fish Species

751

use of these methods in cooked seafood (Shokralla et al., 2015). Exposing seafood to high temperatures, pressures, and other forms of processing can lead to DNA degradation, thus complicating the ability to identify the species. For example, a prior study by Pollack et al. (2018) reported that the canning of fish resulted in marked decreases in sequencing success and sequence quality as compared to other methods of cooking, such as baking, broiling, smoking, and frying. In instances where DNA is degraded due to processing, shorter segments of DNA (