Advanced Spectroscopic Techniques for Food Quality (Food Chemistry, Function and Analysis) [1 ed.] 9781839164040, 9781839165849, 1839164042

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Advanced Spectroscopic Techniques for Food Quality

Food Chemistry, Function and Analysis Series editors:

Gary Williamson, Monash University, Australia Alejandro G. Marangoni, University of Guelph, Canada Graham A. Bonwick, AgriFoodX Limited, UK Catherine S. Birch, AgriFoodX Limited, UK

Titles in the series:

1: Food Biosensors 2: Sensing Techniques for Food Safety and Quality Control 3: Edible Oil Structuring: Concepts, Methods and Applications 4: Food Irradiation Technologies: Concepts, Applications and Outcomes 5: Non-­extractable Polyphenols and Carotenoids: Importance in Human Nutrition and Health 6: Cereal Grain-­based Functional Foods: Carbohydrate and Phytochemical Components 7: Steviol Glycosides: Cultivation, Processing, Analysis and Applications in Food 8: Legumes: Nutritional Quality, Processing and Potential Health Benefits 9: Tomato Chemistry, Industrial Processing and Product Development 10: Food Contact Materials Analysis: Mass Spectrometry Techniques 11: Vitamin E: Chemistry and Nutritional Benefits 12: Anthocyanins from Natural Sources: Exploiting Targeted Delivery for Improved Health 13: Carotenoid Esters in Foods: Physical, Chemical and Biological Properties 14: Eggs as Functional Foods and Nutraceuticals for Human Health 15: Rapid Antibody-­based Technologies in Food Analysis 16: DNA Techniques to Verify Food Authenticity: Applications in Food Fraud 17: Advanced Gas Chromatography in Food Analysis 18: Handbook of Food Structure Development 19: Mitigating Contamination from Food Processing 20: Biogenic Amines in Food: Analysis, Occurrence and Toxicity 21: Nutrition and Cancer Prevention: From Molecular Mechanisms to Dietary Recommendations 22: Health Claims and Food Labelling 23: Nutraceuticals and Human Health: The Food-­to-­supplement Paradigm 24: Nutritional Signalling Pathway Activities in Obesity and Diabetes 25: The Chemistry and Bioactive Components of Turmeric 26: Foodomics 27: Food Proteins and Peptides: Emerging Biofunctions, Food and Biomaterial Applications 28: Handbook of Antioxidant Methodology: Approaches to Activity Determination

29: Fats and Associated Compounds: Consumption and Human Health 30: Oral Processing and Consumer Perception: Biophysics, Food Microstructures and Health 31: Development of Trans-­free Lipid Systems and their Use in Food Products 32: Advanced Spectroscopic Techniques for Food Quality

How to obtain future titles on publication:

A standing order plan is available for this series. A standing order will bring delivery of each new volume immediately on publication.

For further information please contact:

Book Sales Department, Royal Society of Chemistry, Thomas Graham House, Science Park, Milton Road, Cambridge CB4 0WF, UK Telephone: +44 (0)1223 420066, Fax: +44 (0)1223 420247 Email: [email protected] Visit our website at www.rsc.org/books

     

Advanced Spectroscopic Techniques for Food Quality Edited by

Ashutosh Kumar Shukla

Ewing Christian College, India Email: [email protected]

Food Chemistry, Function and Analysis No. 32 Print ISBN: 978-­1-­83916-­404-­0 PDF ISBN: 978-­1-­83916-­584-­9 EPUB ISBN: 978-­1-­83916-­585-­6 Print ISSN: 2398-­0656 Electronic ISSN: 2398-­0664 A catalogue record for this book is available from the British Library © The Royal Society of Chemistry 2022 All rights reserved Apart from fair dealing for the purposes of research for non-­commercial purposes or for private study, criticism or review, as permitted under the Copyright, Designs and Patents Act 1988 and the Copyright and Related Rights Regulations 2003, this publication may not be reproduced, stored or transmitted, in any form or by any means, without the prior permission in writing of The Royal Society of Chemistry or the copyright owner, or in the case of reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency in the UK, or in accordance with the terms of the licences issued by the appropriate Reproduction Rights Organization outside the UK. Enquiries concerning reproduction outside the terms stated here should be sent to The Royal Society of Chemistry at the address printed on this page. Whilst this material has been produced with all due care, The Royal Society of Chemistry cannot be held responsible or liable for its accuracy and completeness, nor for any consequences arising from any errors or the use of the information contained in this publication. The publication of advertisements does not constitute any endorsement by The Royal Society of Chemistry or Authors of any products advertised. The views and opinions advanced by contributors do not necessarily reflect those of The Royal Society of Chemistry which shall not be liable for any resulting loss or damage arising as a result of reliance upon this material. The Royal Society of Chemistry is a charity, registered in England and Wales, Number 207890, and a company incorporated in England by Royal Charter (Registered No. RC000524), registered office: Burlington House, Piccadilly, London W1J 0BA, UK, Telephone: +44 (0) 20 7437 8656. Visit our website at www.rsc.org/books Printed in the United Kingdom by CPI Group (UK) Ltd, Croydon CR0 4YY, UK

Preface This collection of chapters describe the applications of different spectroscopic techniques in food quality analysis. There are 10 chapters contributed by experts from different laboratories and countries. The first chapter, Spectroscopic techniques for the analysis of food quality, chemistry, and function, by Monalisa Mishra, highlights the importance of food quality analysis and introduces the reader to different spectroscopic techniques used for the characterization of food items. The second chapter, Spectroscopic techniques for quality assessment of tea and coffee, by Anna Dankowska, Katarzyna Włodarska, Abhishek Mandal and Ewa Sikorska, discusses the applications of different non-­destructive spectroscopic techniques coupled with multivariate analysis to the quality analysis of tea and coffee, including green tea. Chapter 3, Fruit/juice quality assessment using spectroscopic data analysis, by M. Moncada-­Basualto, J. Pozo-­Martínez and C. Olea-­Azar, considers the importance of various techniques based on different regions of the electromagnetic spectrum and talks about quality analysis applications for fruits and juices. Chapter 4, Advanced analytical methods for the detection of irradiated foods, by Grzegorz Piotr Guzik and Wacław Stachowicz, presents the applications of thermoluminescence and electron spin resonance spectroscopy for the quality analysis of irradiated foodstuffs. Chapter 5, Review of laser-­induced breakdown spectroscopy (LIBS) in food analysis, by J. O. Cáceres, reports recent trends in food quality analysis using LIBS. Daniel Cozzolino discusses novel applications and developments utilizing vibrational spectroscopy for estimating compositional parameters of interest in the wine industry in Chapter 6, entitled Visible and near-­infrared spectroscopy for quality analysis of wine. Abdul Rohman and his co-­authors describe different methods for the quality analysis/authentication of olive oil, virgin coconut oil and cod liver oil

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in Chapters 7, 8 and 9, respectively. In Chapter 10, On-­site food authenticity testing: advances in miniaturization of spectrometers and machine learning, C. N. Ravishankar and his co-­authors present several chemometric data analysis tools, software and machine learning algorithms for food authenticity testing using miniature vibrational spectroscopy devices. Fundamentals of the techniques and instrumentation have been included wherever it was felt necessary so as to bring a sense of completeness in individual chapters and hence to suit the needs of novice researchers. I sincerely thank Nicki Dennis, Book Commissioning Editor, Food Science and Chemistry, at The Royal Society of Chemistry for giving me the chance to compile this volume for interested readers. I also wish to thank Liv Towers, Editorial Assistant, Books, for her support during the different stages. Finally, my grateful thanks go to the various expert authors for contributing to this volume and helping me to keep to the schedule. Ashutosh Kumar Shukla Prayagraj, India

Contents Chapter 1 S  pectroscopic Techniques for the Analysis of Food Quality, Chemistry, and Function  Monalisa Mishra

1.1 Introduction  1.2 Structure and Chemistry in Determining Food Quality  1.3 Spectroscopic Methods for Determining Food Quality  1.3.1 UV–Visible Spectroscopy  1.3.2 Fluorescence Spectroscopy  1.3.3 Infrared Spectroscopy  1.3.4 Mid-­infrared Spectroscopy  1.3.5 Near-­infrared Spectroscopy  1.3.6 Far-­infrared Spectroscopy  1.3.7 Raman Spectroscopy  1.3.8 NMR Spectroscopy  1.3.9 Atomic Emission Spectroscopy  1.4 Conclusion  Acknowledgements  References 

Chapter 2 S  pectroscopic Techniques for Quality Assessment of Tea and Coffee  Anna Dankowska, Katarzyna Włodarska, Abhishek Mandal and Ewa Sikorska

2.1 Introduction  2.2 Evaluation of the Quality of Tea and Coffee Using Spectroscopic Techniques 

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1 1 2 4 4 5 7 7 8 10 10 11 11 12 12 12 23

23 25

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2.2.1 Quality Characteristics of Tea and Coffee  2.2.2 General Scheme of Using Spectroscopic Methods for Quality Assessment of Tea and Coffee  2.2.3 Acquisition and Characteristics of Spectra of Tea and Coffee  2.2.4 Multivariate Data Analysis  2.2.5 Applications  2.3 Spectroscopic Techniques for Assessment of the Quality of Tea  2.3.1 Authentication of Tea  2.3.2 Tea Composition  2.3.3 Sensory Properties of Tea  2.3.4 Tea Processing  2.4 Spectroscopic Techniques for Coffee Quality Assessment  2.4.1 Authentication of Coffee  2.4.2 Coffee Composition  2.4.3 Sensory Properties of Coffee  2.4.4 Coffee Processing  2.5 Conclusion  List of Abbreviations  References 

Chapter 3 F  ruit/Juice Quality Assessment Using Spectroscopic Data Analysis  M. Moncada-­Basualto, J. Pozo-­Martínez and C. Olea-­Azar

3.1 Introduction  3.2 Spectroscopic Methods for Food Analysis  3.2.1 Fruit Analysis  3.2.2 Juice Analysis  List of Abbreviations  Acknowledgements  References 

25 26 27 32 34 34 34 41 44 45 45 46 51 54 56 57 58 61 68 68 69 70 73 77 77 77

Chapter 4 A  dvanced Analytical Methods for the Detection of Irradiated Foods  Grzegorz Piotr Guzik and Wacław Stachowicz

80



80 81 83 83 86

4.1 Foreword  4.2 Introduction  4.3 Detection Methods in Current Use  4.3.1 Thermoluminescence (TL) Method  4.3.2 Photostimulated Luminescence (PSL) Method 

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4.3.3 Electron Paramagnetic Resonance (EPR/ESR)  4.4 Concluding Remarks  References 

Chapter 5 R  eview of Laser-­induced Breakdown Spectroscopy (LIBS) in Food Analysis  J. O. Cáceres

5.1 Introduction  5.2 Brief Introduction to the Principles of Laser-­induced Breakdown Spectroscopy  5.3 Application of LIBS to Foods  5.3.1 Heavy Metal Detection and Quantification  5.3.2 Food Contamination  5.3.3 Food Adulteration  5.3.4 Other Food–LIBS Correlations  5.4 Conclusion  References 

Chapter 6 V  isible and Near-­infrared Spectroscopy for Quality Analysis of Wine  Daniel Cozzolino

6.1 Introduction  6.2 Applications  6.2.1 Analysis of Grape Juice and Must  6.2.2 Wine Compositional Analysis  6.2.3 Monitoring Wine Fermentation  6.3 Concluding Remarks  References 

Chapter 7 A  pplication of FTIR Spectroscopy and Chromatography in Combination With Chemometrics for the Quality Control of Olive Oil  Gunawan Indrayanto and Abdul Rohman

7.1 Introduction  7.2 Olive Oil  7.3 Official Methods for the Quality Control of Olive Oils  7.3.1 United States Pharmacopeia (USP)  7.3.2 British Pharmacopoeia 2020  7.3.3 Japanese Pharmacopoeia, 17th Edition  7.3.4 International Olive Council (IOC) Standards, Methods, and Guide  7.4 Chemometrics 

87 90 90 94 94 95 99 99 101 102 103 104 105 111 111 114 114 116 124 126 129

133 133 134 136 136 139 140 141 145

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7.5 Chromatographic Method for the Analysis of Olive Oil  7.6 FTIR Spectroscopic Methods for Quality Control of Olive Oil  7.7 Validation Methods  Acknowledgements  References 

Chapter 8 A  pplication of Molecular Spectroscopy and Chromatography in Combination with Chemometrics for the Authentication of Virgin Coconut Oil  Anjar Windarsih, Lily Arsanti Lestari, Yuny Erwanto, Nurrulhidayah Ahmad Fadzillah and Abdul Rohman

8.1 Introduction to Virgin Coconut Oil  8.2 Chemometrics  8.3 Authentication of Virgin Coconut Oil  8.3.1 Authentication Analysis of VCO Using FTIR Spectroscopy  8.3.2 Authentication Analysis of VCO Using NMR Spectroscopy  8.3.3 Authentication Analysis of VCO Using Chromatography-­based Techniques  Acknowledgements  References 

Chapter 9 A  pplication of Molecular Spectroscopy and Chromatography in Combination with Chemometrics for the Authentication of Cod Liver Oil  Agustina A. M. B. Hastuti and Abdul Rohman

9.1 Introduction  9.2 Cod Liver Oil  9.3 Authentication of Cod Liver Oil Using Molecular Spectroscopy  9.3.1 Infrared and Raman Spectroscopy  9.3.2 NMR Spectroscopy  9.4 Authentication of CLO Using Chromatography  9.5 Conclusion  References 

147 163 170 172 172

181

182 183 184 184 186 189 192 192

197 197 198 200 200 203 204 206 206

Chapter 10 O  n-­site Food Authenticity Testing: Advances in Miniaturization of Spectrometers and Machine Learning  211 Niladri Sekhar Chatterjee, R. G. Kumar Lekshmi, Devananda Uchoi, Kaushik Banerjee, Pankaj Kishore, V. Minimol, Satyen Panda, Suseela Mathew and C. N. Ravishankar

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10.1 Introduction  10.2 Principle of Food Authenticity Testing Using Spectrometers  10.2.1 FTIR Spectroscopy in Food Authenticity Testing  10.2.2 FTNIR Spectroscopy in Food Authenticity Testing  10.2.3 Raman Spectroscopy in Food Authenticity Testing  10.2.4 Hyperspectral Imaging in Food Authenticity Testing  10.2.5 Working Principle of Miniaturized Spectrometers  10.2.6 Workflow for Food Authenticity Testing Using Miniaturized Spectrometers  10.3 Chemometric Data Analysis Tools and Algorithms  10.4 Commercial Miniature Spectrometers for Food Testing  10.5 Commercial Ventures with Miniature Spectrometers and Food  10.6 Food Commodities Vulnerable to Food Fraud and Authenticity Testing Needs  10.7 Case Studies  10.7.1 Application of Miniature Spectrometers in Authenticity Testing of Honey  10.7.2 Application of Miniature Spectrometers in Authenticity Testing of Wines and Beverages  10.7.3 Application of Miniature Spectrometers in Authenticity Testing of Milk and Milk Products  10.7.4 Application of Miniature Spectrometers in Authenticity Testing of Meat and Meat Products  10.7.5 Application of Miniature Spectrometers in Authenticity Testing of Miscellaneous Food Products  10.8 Conclusion  References 

Subject Index 

211 212 213 214 215 215 216 217 218 221 224 224 225 225 228 229 230 230 232 232 242

Chapter 1

Spectroscopic Techniques for the Analysis of Food Quality, Chemistry, and Function Monalisa Mishra* Neural Developmental Biology Laboratory, Department of Life Science, NIT Rourkela, Rourkela, Odisha, India *E-­mail: [email protected]

1.1  Introduction The term ‘food’ is a compound word that is used to describe a material that provides nutritional support to alleviate the hunger of every organism.1 Materials covered by this broad term can be obtained from diverse resources such as plants, animals, and fungi. The food is ingested by different means to meet the physiological needs of a particular animal2 and, according to these needs, the types of food vary from animal to animal. Based on their food habits, animals can be classified as herbivorous, carnivorous, and omnivorous.3 With the continuing advancement of science and technology, the quality of food consumed by humans has improved over time, and consequently surpluses and deficiencies in the amounts or types of food have occurred within the body, causing several metabolic disorders including diabetes. This suggests that food has a determinant role in regulating the physiological and metabolic activity of the organism.

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Figure 1.1  A  mechanistic representation of how the structure of food stimulates the sensory organs.

The primary composition of foods includes carbohydrates, proteins, lipids, vitamins, and minerals in different combinations. Phenols, flavones, and colourants are often found as secondary components of food.4 These secondary components help to enhance the aroma and taste of a food by stimulating the sensory organs.5,6 Similarly, the presence of bacteria can alter the metabolic, physiological, and behavioural conditions of humans. Recently, an oroneural connection was established in humans.7–9 Different types of food are also known to improve the psychological condition.10 As a consequence, the determination of food composition, authentication, assessment, prediction, and microbial composition are essential for determining the quality of foods11 (Figure 1.1).

1.2  S  tructure and Chemistry in Determining   Food Quality The structure of a food gives an impression of its quality at a first look. The nanostructures present in a food contribute to its colour and shape, which stimulate the sensory organs (Figure 1.2). For example, the actin–myosin complex in meat, starch granules of plant foods, and micelles of milk are nanostructures present within the food. The amorphous and helical region of starch determines the starch quality. Each food has a shelf life, which changes as a function of time. During processing of a food, consideration of the shelf life is important since it has a role in obesity and diabetes. Hence great importance is attached to understanding the structure of foods.

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Figure 1.2  Factors  that affect the quality of food.

Self-­assembly of β-­lactoglobulin and α-­lactalbumin occurs in whey protein. A correlation between the particle size of a food and its effect on the tongue and palate has been described.12 In addition to nanostructure, other parameters are also used to detect the quality of food.13 One such parameter is flavonoids, which are low molecular weight polyphenolic substances having a heterocyclic ring (ring C). Flavonoids are resistant to heat, oxygen, humidity, and light. A hydroxyl group on C3 of ring C (flavanol) contributes to the photostability.14 The distribution of flavonoids is species specific for plants. Tea, red wine, fruits, and vegetables are enriched with flavonoids. Although quercetin is the main flavonoid, myricetin, kaempferol, apigenin, and luteolin are also found in foods. Humans need a minimum of 1 g per day of flavonoids for the body.15 The amount of flavanol can be determined from plasma and urine analyses, hence it is often used as a biomarker. External factors such as the season, ripeness, and food preparation and processing can change the amount of flavanol present. A minimum of 10 nM of flavanol is needed in our daily intake of food.16 The amount of quercetin depends on the types of bacteria present in the gut. Flavonoids are conjugated in the liver and are eliminated from the kidney. Flavonoids can be determined using many spectroscopic techniques.17 The matrix of the food is analysed in several industries such as the milk, meat, coffee, and wine trades.18 Although the quality of food can be roughly determined using the sensory organs, many sophisticated types of equipment are also used for this purpose.19

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1.3  S  pectroscopic Methods for Determining   Food Quality Among numerous techniques, spectroscopy is indispensable for determining the quality of foods.20–24 It is based on the combined action of absorption, transmission, and emission of electromagnetic light radiation with additional materials based on the wavelength of radiation.25 It is an interaction between light and the molecules following the effective collision theory. Light is electromagnetic radiation and the spectra vary according to the wavelength, frequency, and energy.26 It is used as an analytical tool in several branches of science. It can detect the composition of foods, microbes, pest pathogens, and adulteration that occur within the food27–29 (Figure 1.2). Spectroscopic techniques such as UV–visible, fluorescence, infrared, mid-­ infrared, near-­infrared, Raman, and nuclear magnetic resonance spectroscopy are used to determine food quality.

1.3.1  UV–Visible Spectroscopy UV–visible spectroscopy is used in several food industries. It is based on the principle of the Beer–Lambert law30 (Figure 1.3): A = −log(I/I0) = εcl where I0 is the intensity of the incident light, I is the intensity of the transmitted light, c is the concentration of the sample, l is the pathlength through which the light travels in the sample, and ε is the molar extinction coefficient at a particular wavelength.

Figure 1.3  Working  principle of UV–visible spectroscopy.

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UV–visible spectroscopy uses electromagnetic radiation in the range 100–750 nm. The UV range covers 100–380 nm and the visible range covers 380–750 nm.31 It detects two different aspects: (1) colour and (2) fat oxidation. In oils, the greater the carotenoid (a pigment or chromophore such as chlorophyll, which most plants contain) content, the better is the antioxidant activity.32–35 The presence of chlorophyll makes olive oil bitter.36,37 To determine the fat oxidation, the p-­anisidine value (AV) is measured.38 This is the quantity of aldehyde produced during fat oxidation as a function of temperature, oxygen, and light.39 In oils, an AV of >8 is not permissible.

1.3.1.1 In the Oil Industry UV–visible spectroscopy is widely used to determine the quality of oil by measuring anisidine, which is generated during the oxidation of food. The AV also determines the total oxidation value (Totox), which indicates the fat deterioration.40 The Totox value is represented by two times the peroxide value (PV) plus AV (2PV + AV). It is measured at 350 nm. The deterioration of fat is measured by the PV. When lipids are exposed to heat, daylight, and oxygen, they undergo decomposition and form peroxides and hydroperoxides. The PV value can be measured easily using a UV–visible spectrometer at an absorbance maximum of 240 nm.41 The quality of oil is also determined by the presence of chlorophyll and carotenoids. The greater the amount of carotenoids, the better antioxidant properties the oil has. Carotenoids can be determined at 440 nm42 and chlorophyll at 666 nm. The absorbance peak also varies with the solvent that is used in the analysis. The green colouration of olive oil is due to the presence41 of chlorophyll and pheophytin.43,44 Since pheophytin-­α has oxidative properties, the lower its content the better is the oil quality.44

1.3.2  Fluorescence Spectroscopy The application of fluorescence spectroscopy is a rapid means to analyse a sample in a non-­destructive way. It detects the fluorescence based on a naturally present fluorophore within the sample.45 Many microbes and their colonies possess fluorescence, hence it is easy to detect the presence of any microbes based on their fluorescence spectra.46,47 Many foods possess fluorophores. There is also a chance that food may become contaminated due to the presence of microbes. Since foods contain fluorophores, many industries use the fluorescence technique to determine food quality. The method is based on the emission of light after absorption of ultraviolet or visible light by a fluorophore.48,49 The fluorophores present in foods include rhodamine B, quinine, Acridine Orange, fluorescein, and pyridine. This technique is often used in combination with other techniques such as high-­performance liquid chromatography (HPLC). It is a very sensitive method that can determine food quality with high specificity in several industries.

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1.3.2.1 In the Dairy and Honey Industries Milk possesses many proteins, amino acids, and vitamins that have fluorescence properties. Among the amino acids, tryptophan, tyrosine, and phenylalanine are commonly found, and among the vitamins, vitamins A and B2 are found in milk. The numbers of vitamins, proteins, and amino acids within milk vary with how it has been processed. The fluorescence spectra of vitamin A present in cheese was found to vary among eight different types of cheese.50 Yogurt possesses three different types of fluorophore, tryptophan, riboflavin, and lumichrome, and the presence of these three fluorophores allows the determination of the quality of yogurt using fluorescence spectroscopy.51 Honey is a product made by bees from the nectar of flowers,52 and its properties change during packing and transportation. Honey possesses phenolic compounds that are derivatives of phenolic acid present within the flower. The amount of phenolic compounds can be determined by fluorescence spectroscopy.53

1.3.2.2 Meat and Seafood Meat and seafood lose their quality owing to oxidation, autolysis of enzymes, and growth of microbes.54,55 All these phenomena can be detected by monitoring the fluorophores present within the sample. Meat possesses fluorophores such as tryptophan, nicotinamide adenine dinucleotide (NADH), porphyrins, riboflavin, and vitamin A.56,57 Collagen is present in the adipose or connective tissue58 and provides texture or tenderness to the tissue. The greater the amount of collagen, the better is the quality of the meat. Beef tenderness is detected using fluorescence properties.59 Fat/lean meat is also analysed by monitoring the fluorescence of tryptophan.60–62 In spoiled meat, oxidation of lipids and proteins occurs. Thiobarbituric acid reactive substance (TBARS) is considered a parameter of lipid peroxidation.63 The amount of TBARS can be measured from the fluorescence spectra. The extent of oxidation varies within the meat product with different storage and processing conditions. Seafood and fish possess the naturally present fluorophore vitamin A, amino acids, NADH, riboflavin, and oxidation products.64 Oxidation causes spoiling of food. The quality of fish such as mackerel, salmon, and cod can be determined using fluorescence spectroscopy.65 The fluorescence spectra vary with the storage time in cold conditions.65

1.3.2.3 Detection of Bacteria Microbes can degrade the quality of food and in extreme cases it can make it toxic. Hence it is essential to keep foods free from microbes. Fluorescence spectroscopy is widely used in poultry to distinguish different bacteria. Lactic acid bacteria are often detected in sausages and can be identified using fluorescence spectroscopy.66 The toxins of these bacteria are identified using fluorescence spectroscopy.67–69 Salicylates can also be determined by

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using this technique. This method can also detect structural changes in proteins and carbohydrates. By altering the excitation and emission wavelengths, the food quality of several compounds can be determined. Most mycotoxins have blue fluorescence;70 the exception is aflatoxin G1 and G2, which have ­yellow–green fluorescence.71 Accordingly, using fluorescence spectroscopy, the toxin can be detected. Ochratoxin A, a fluorescent compound, may be present in roasted coffee, corn, and sorghum,72 and fluorescence spectroscopy can help to determine the food value of these materials. Cereals such as rice and maize have also been investigated using fluorescence techniques.73 In a corn sample, fluorescein was labeled to detect the fumonsin B1.74 For a rapid test for the toxin deoxynivalenol in wheat, a fluorescence polarization immunoassay was developed.75 Fluorescence spectroscopic properties can distinguish between rice and maize flour.76 For wheat kernels, red and white emission spectra were recorded.77 The morphological variation between two different varieties of wheat can be distinguished by using the fluorescence technique.78,79 The refinement and milling of wheat flour can be monitored by measuring the emission spectra of ferulic acid and riboflavin.80,81 The quality of olive oil was determined by using fluorescence spectroscopy.82 Oils such as sunflower, cotton, soybean, and corn oils have a fluorescence band at 439–450 nm.83 Only olive oil has a band at 440 and 455 nm, a medium band at 681 nm, and a strong band at 525 nm.84 The 681 nm band represents chlorophyll85 and the 525 nm band represents vitamin E.84 Fluorescence spectroscopy can be used to determine the anisidine and iodine values of oligomers and monomers.84

1.3.3  Infrared Spectroscopy Infrared (IR) radiation was discovered in 1800 by William Herschel.86 Its range varies between 78 nm and 1 mm. This technique uses the vibration of atoms and molecules. Different vibrations were observed in the IR region. IR spectra provide evidence of molecular structure from the frequency of the normal mode of vibration. In the case of the normal modes, the sample executes harmonic oscillations. There are six normal modes. The vibrations of functional groups such as OH, NH2, CH3, and C=O are responsible for bands near the IR spectrum.87 Any compound containing a C=O group shows strong bands at 1899 and 1650 cm−1. The NH2 group gives an IR band between 3400 and 3300 cm−1. A compound with a C6H5 group gives peaks at 1600 and 1500 cm−1. Thus the IR spectrum is considered as the fingerprint of the molecule. Based on the range, it is divided into three forms: (1) near-­infrared (780 nm–5 µm), (2) mid-­infrared (5–30 µm), and (3) far-­infrared (30–1000 µm).88

1.3.4  Mid-­infrared Spectroscopy Mid-­infrared (MIR) spectroscopy can detect functional groups and carbon, nitrogen, and lignin.89 It is used to study soil and food. MIR spectroscopy is used with attenuated total reflectance (ATR). It is also used in combination

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with the Fourier transform process. It uses the diffuse reflectance infrared Fourier transform (DRIFT) process. Soil properties, organic matter, and the presence of fungus in the food can be detected using this technique. This technique helps to establish the structure–function relationships between foods, hence it is valuable in the food industry. Fourier transform infrared (FTIR) spectroscopy is widely used. The MIR region stretches from 4000 to 400 cm−1. The X–H range is 4000–2500 cm−1, triple bond 2500–2000 cm−1, and double bond 2000–1500 cm−1. The X–H stretch is due to the presence of O–H, C–H, and N–H stretching. The vibrations of C≡C, C≡N, C=C, C=O, and C=N can be determined using this technique. MIR spectroscopy is used to investigate the chemistry of fats and oils. The presence of a 996 cm−1 band corresponds to a trans double bond.90 Excess cis–trans conversion occurs during hydrogenation, the conversion of oil to fat, to enhance the oxidative stability of polyunsaturated oils.91 Spoiling of oils, fats, and lipids occurs due to lipid autoxidation. The peroxide value determines the oxidative status and firmness of refined and olive oil. This technique detects a peak at 3444 cm−1, which corresponds to hydroperoxide (–OOH). Corn, peanut, sunflower, cottonseed, and soybean oils are also analysed using this technique.

1.3.4.1 Soil Study MIR spectroscopy detects the composition, properties, and organic matter present within a soil. The DRIFT technique can detect the composition of soil and humus chemically. ATR also detects soil organic matter.92 Using different MIR wavelengths, feldspars, quartz, silicates, clay, and minerals such as carbonates and calcites can be detected in soil.93 MIR spectroscopy can also detect the organic matter present within the soil, such as lignins, cellulose, carbohydrates, fats, and proteins.94

1.3.4.2 Analysis of Humus Natural compounds originating from plants and animals are present in soil, rivers, and coal. The composition of humus can be determined by using the DRIFT method.95

1.3.4.3 Quality Control MIR spectroscopy is employed to detect fungal disease and mycotoxins in cereals during processing and storage.96,97

1.3.5  Near-­infrared Spectroscopy Near-­infrared (NIR) spectroscopy is a primary technique for the rapid detection of moisture, carbohydrates, proteins, and fat within a food. However, the composition of an unknown sample needs to be calibrated with

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a known sample, and recalibration is essential for each sample. Hence the method lacks sensitivity for a sample having only a minor amount of the component of interest. The vibration of C–H, O–H, N–H, S–H, and C=O bonds occurs with absorption in the NIR region.98 Wheat and wheat products are easily analysed using this technique.99 In Australia, the optimization of fertilizer is achieved by determining the total nitrogen and carbohydrate in plant tissue.100 Spectroscopy coupled with chromatography can be used to determine the quality of wheat.101 Seventy-­five relative reflectance intensities were extracted from the scanned images of bulk wheat samples and used for the differentiation of wheat classes using a statistical classifier and an artificial neural network (ANN) classifier.102,103 It was found that kernels damaged by insects contained less starch than healthy kernels. NIR spectroscopy has also been used to check the adulteration of milk powder, orange juice, sugar, vegetables, and coffee, and also to measure the amount of dry matter as a parameter to determine the maturity of fruit and vegetables.104 The amount of dry matter indicates the right time to harvest the food product so that it can be transported and stored safely for a longer time.105 Other applications of NIR spectroscopy include maturity determination, processing, pest detection, toxin measurement, drought management, fertilizer application, and post-­harvest quality control:    1. Processing: NIR spectroscopy is used to determine the dry matter and water content during the processing of food samples. It can be used to check the freshness of mushrooms, cereals, fruits, and vegetables.106 2. Pest, disease, and toxin detection: NIR spectroscopy can detect the amount of mycotoxin in cereals and grains.107 3. Drought management: The correct water level is essential for the proper growth of plants. The amount of water present can be determined so that appropriate irrigation can be applied to maintain the water supply.108 4. Application of fertilizer: The growth of desired plant leaves can be monitored so that the required nutrients can be added to improve the growth of the plant.109 5. Post-­harvest control: After harvesting and during transportation and storage, damage may occur, which can be assessed by using NIR spectroscopy. The structure and ripeness of apples can be measured and the effect of the ethylene storage atmosphere can be determined.110    Several modern types of advanced optical equipment based on the NIR principle have been developed for fine-tuned analysis. An example is the Felix Instruments leaf spectrometer, used with a quality meter.111 The CI-­710 Miniature Leaf Spectrometer was used to detect the leaf quality and the effects of pests, drought, and pathogens.112 The quality meter can detect the acidity, colour, and total soluble solids of fruit. The instrument is marketed as the F-­750 product quality meter for mango113 and the F-­751 quality meter for avocado testing.114

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1.3.6  Far-­infrared Spectroscopy The far-­infrared (FIR) region lies between 400 and 10 cm−1. The region below 200 cm−1 is difficult to interpret. Hydrogen atoms, organometallic compounds, and inorganic compounds absorb in the FIR region, and hydrogen bond stretching can also be detected in this region.

1.3.7  Raman Spectroscopy When photons interact with molecules in matter the Raman effect or Raman scattering is produced. During this process, the photon loses vibrational energy in the Stokes process and gains vibrational energy in the anti-­Stokes process. Such communication is possible for the interactions of atoms, which moderate the polarizability of the molecule. Such communication is possible for the cross-­talk of incident photons with atoms. Intense Raman bands are detected from non-­polar groups, predominantly from aromatic rings, and the vibrations produce an inflection of polarizability. The Raman spectrum is obtained as wavenumbers (cm−1) and the variation between excited and emitted energies is detected as vibrational spectra. This has a weak effect since the possibility of energy exchange is very small. For non-­ polar groups, particularly aromatic rings, the vibrations produce considerable modulation of the polarizability. Raman spectroscopy is a vibrational spectroscopic technique that uses the Raman effect, and is used to identify molecules via the measurement of the vibration of atoms. Each molecule has its own identification features associated with the groups present within it, and the positions, widths, and intensities of bands can be measured. Raman spectroscopy has several advantages, e.g. (1) it uses the vibrations of atoms, (2) samples can be analysed in the normal environment with no interference from the solvent, (3) it shows a relative enhancement of intensity, and (4) it can analyse samples at the picosecond level.115 Raman spectroscopy is a complementary technique to IR spectroscopy and can detect carbohydrates, proteins, and fats present within a food.116 Interaction of water with food proteins causes a decreased intensity of H–H stretching at 3250 cm−1 and the C–H band at 2938–2942 cm−1,117 due to the interaction between water and food proteins. The –CO–NH– amide or peptide bond can be determined by many sensitive methods.118 Amide III bands have been used to detect the secondary structure of proteins.119 The storage process can modify the structure of carbohydrates, especially the presence of water, which can be detected by using Raman spectroscopy. The –CO–NH amide or peptide bond is used for secondary structure determination. Raman spectroscopy can characterize and quantify the lipid content of foods.120 It detects the degree of unsaturation of cis and trans isomers and alterations of foods, such as isomerization and autoxidation. The quality of olive oil can be determined by low-­resolution Raman spectroscopy. The peroxide value can be determined directly using this technique. Soybean, corn, and olive residue quality can also be measured by Raman spectroscopy.121

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More recently, Raman spectroscopy has been combined with microscopy to detect the chemical composition of heterogeneous foods.122 This combined technique uses quality and quantitative methods to detect food values. Several organic compounds can be identified from their unique absorption pattern. Microscopic samples can be analysed without changing their properties. Confocal Raman microscopy can determine the chemical composition of wheat.123,124 This technique can also determine the protein content during milling. Alterations of the secondary structure of proteins, conformational changes of puroindolines, and lipid-­binding proteins can also be detected using Raman spectroscopy.125

1.3.8  NMR Spectroscopy NMR spectroscopy uses the cross-­talk between the magnetic properties of atoms and compounds and the applied magnetic field. It is used for measurements for soil testing, plant tissues, and food products.126 It can detect the genotype of grapes used to make wine. It also analyses the soil in which the vines are grown. It can monitor the ripening, drying, and adulteration of food materials. NMR spectroscopy provides information on mixtures of metabolites127,128.1H NMR spectroscopy provides information on the metabolic composition of a sample without further knowledge of its composition.129,130 For liquid samples, preparation steps such as derivatization and centrifugation are not needed. NMR metal profiling has been carried out on grape samples.131 1H NMR spectroscopy is combined with multivariate analysis to determine the biotic and abiotic stress in plants132 and variations associated with genes.133,134 Metabolites in fruit juice, olive oil, wine, tomatoes, and beer were detected using this technique.135–138 Flavonoids are often detected using the NMR technique.17 Soluble sugars, organic acids, and amino acids can be distinguished by their 1H NMR spectra. 1H NMR spectroscopy cannot determine phenolic compounds in a precise manner. Phenols, flavanols, and anthocyanins can be determined in a more precise manner by using 2D NMR spectroscopy.139 1H NMR spectroscopy has also been used to determine the classical p-­anisidine value (AV) of oil, and 79 edible oils were checked for their AV values.140 Portable NMR instrumentation is available for food analysis.141,142 The metabolomics of foods can be determined from NMR spectra. Since the spectra contain a considerable amount of information, they are analysed by using Student's t-­test and analysis of variance. Overlapping of the spectra sometimes prevents the identification of compounds.

1.3.9  Atomic Emission Spectroscopy Atomic emission spectroscopy (AES) is used to determine the chemicals and also elements present within foods. Light from a plasma or flame excites the atoms present within the sample, generating photons or light of a particular wavelength. AES can detect trace elements. Most foods include many major

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and minor elements, and also some trace elements such as arsenic, nickel, silicon, boron, and cobalt, which must be within permitted levels. Cadmium, iron, and sodium are mandatory elements and can be detected by AES. The medicinal properties of plants and the composition of wine have also been investigated using AES.

1.4  Conclusion Spectroscopic techniques are indispensable methods for determining the quality and composition of foods, both quantitatively and qualitatively. They provide structure–function relationships between proteins, and can be used alone or in combination with other analytical techniques. For some spectroscopic techniques, sample preparation is not needed, which saves time. Spectroscopic techniques can measure the AV value, PV value, and colour to determine the quality of the food. They can rapidly differentiate the variations between different types of wheat and oils and bacterial infection. The lipid, carbohydrate, fat, and water content of foods can be determined easily using spectroscopic techniques.

Acknowledgements The author's laboratory is supported by financial assistance from the Department of Biotechnology, Ministry of Science and Technology [SERB/ EMR/2017/003054, BT/PR21857/NNT/28/1238/2017, and Odisha DBT 3325/ ST (BIO)-­02/2017].

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95. S. Thai, L. Pavlů, V. Tejnecký, P. Vokurková, S. Nozari and L. Borůvka, Comparison of soil organic matter composition under different land uses by DRIFT spectroscopy, Plant, Soil Environ., 2021, 67(5), 255–263. 96. W.-­H. Su, H.-­J. He and D.-­W. Sun, Non-­destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review, Crit. Rev. Food Sci. Nutr., 2017, 57(5), 1039–1051. 97. M. Hossain and T. Goto, Near-­and mid-­infrared spectroscopy as efficient tools for detection of fungal and mycotoxin contamination in agricultural commodities, World Mycotoxin J., 2014, 7(4), 507–515. 98. G. Socrates, Infrared and Raman Characteristic Group Frequencies: Tables and Charts. John Wiley & Sons: 2004. 99. B. G. Osborne, Near-­infrared spectroscopy in food analysis, in Encyclopedia of Analytical Chemistry: Applications, Theory and Instrumentation, 2006. 100. J. E. Erickson, K. R. Woodard and L. E. Sollenberger, Optimizing sweet sorghum production for biofuel in the southeastern USA through nitrogen fertilization and top removal, BioEnergy Res., 2012, 5(1), 86–94. 101. C. Decock, K. Denef, S. Bode, J. Six and P. Boeckx, Critical assessment of the applicability of gas chromatography-­combustion-­isotope ratio mass spectrometry to determine amino sugar dynamics in soil, Rapid Commun. Mass Spectrom., 2009, 23(8), 1201–1211. 102. S. Mahesh, A. Manickavasagan, D. Jayas, J. Paliwal and N. White, Feasibility of near-­infrared hyperspectral imaging to differentiate Canadian wheat classes, Biosyst. Eng., 2008, 101(1), 50–57. 103. C. Murru, C. Chimeno-­Trinchet, M. E. Díaz-­García, R. Badía-­Laíño and A. Fernández-­González, Artificial Neural Network and Attenuated Total Reflectance-­Fourier Transform Infrared Spectroscopy to identify the chemical variables related to ripeness and variety classification of grapes for Protected. Designation of Origin wine production, Comput. Electron. Agric., 2019, 164, 104922. 104. B. M. Nicolai, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron and J. Lammertyn, Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review, Postharvest Biol. Technol., 2007, 46(2), 99–118. 105. N. Magan and D. Aldred, Post-­harvest control strategies: Minimizing mycotoxins in the food chain, Int. J. Food Microbiol., 2007, 119(1–2), 131–139. 106. M. Moss, Fungi, quality and safety issues in fresh fruits and vegetables, J. Appl. Microbiol., 2008, 104(5), 1239–1243. 107. P. Rafai, A. Bata, L. Jakab and A. Vanyi, Evaluation of mycotoxin-­ contaminated cereals for their use in animal feeds in Hungary, Food Addit. Contam., 2000, 17(9), 799–808. 108. A. Sušnik, A. Valher, G. Gregorič and M. Trošt, Tools for agricultural drought detection in the frame of Drought Management Centre for Southeastern Europe–DMCSEE, Acta Agric. Slov., 2012, 99, 235–253.

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109. P. Thorburn, A. Webster, I. Biggs, J. Biggs, S. Staunton and S. Park, Systems to balance production and environmental goals of nitrogen fertiliser management, Proc. Int. Soc. Sugar Cane Technol., 2007, 302–309. 110. G. Giovanelli, N. Sinelli, R. Beghi, R. Guidetti and E. Casiraghi, NIR spectroscopy for the optimization of postharvest apple management, Postharvest Biol. Technol., 2014, 87, 13–20. 111. M. A. de Freitas, A. I. Silva Alves, J. C. Andrade, M. C. Leite-­Andrade, A. T. Lucas dos Santos, T. Felix de Oliveira, F. A. G. dos Santos, M. D. Silva Buonafina, H. D. Melo Coutinho and I. R. Alencar de Menezes, Evaluation of the antifungal activity of the Licania rigida leaf ethanolic extract against biofilms formed by Candida sp. isolates in acrylic resin discs, Antibiotics, 2019, 8(4), 250. 112. I. Alsiņa, M. Dūma, L. Dubova, A. Šenberga and S. Daģis, Comparison of different chlorophylls determination methods for leafy vegetables, Agron. Res., 2016, 14(2), 309–316. 113. N. Anderson, K. Walsh, P. Subedi and C. Hayes, Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content, Postharvest Biol. Technol., 2020, 168, 111202. 114. K. B. Walsh, J. Blasco, M. Zude-­Sasse and X. Sun, Visible-­NIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: The science behind three decades of commercial use, Postharvest Biol. Technol., 2020, 168, 111246. 115. P. Hou, J. Ager, J. Mougin and A. Galerie, Limitations and advantages of Raman spectroscopy for the determination of oxidation stresses, Oxid. Met., 2011, 75(5), 229–245. 116. L. G. Thygesen, M. M. Løkke, E. Micklander and S. B. Engelsen, Vibrational microspectroscopy of food. Raman vs. FT-­IR, Trends Food Sci. Technol., 2003, 14(1–2), 50–57. 117. V. K. Singh, V. Kumar, R. Prakash, J. Sharma, A. Kumar and P. Singh, Recent Developments and Applications of Novel Analytical Techniques for the Analysis of Plant Materials, in Technological Advancements in Plant Sciences, ed. R. P. Narayan, D. K. Tripathi and R. K. Gaur, Nova Science Publishers, New York, 2016, ch. 3. 118. E. Li-­Chan, S. Nakai and M. Hirotsuka, Raman spectroscopy as a probe of protein structure in food systems, in Protein Structure-­function Relationships in Foods, Springer, 1994, pp. 163–197. 119. J. T. Pelton and L. R. McLean, Spectroscopic methods for analysis of protein secondary structure, Anal. Chem., 2000, 277(2), 167–176. 120. A. Nawrocka and J. Lamorska, Determination of food quality by using spectroscopic methods, in Advances in Agrophysical Research, IntechOpen, 2013. 121. F. Tsopelas, D. Konstantopoulos and A. T. Kakoulidou, Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration, Anal. Chim. Acta, 2018, 1015, 8–19.

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122. C. Cai, J. Huang, L. Zhao, Q. Liu, C. Zhang and C. Wei, Heterogeneous structure and spatial distribution in endosperm of high-­amylose rice starch granules with different morphologies, J. Agric. Food Chem., 2014, 62(41), 10143–10152. 123. O. Piot, J.-­C. Autran and M. Manfait, Spatial distribution of protein and phenolic constituents in wheat grain as probed by confocal Raman microspectroscopy, J. Cereal Sci., 2000, 32(1), 57–71. 124. A.-­S. Jääskeläinen, U. Holopainen-­Mantila, T. Tamminen and T. Vuorinen, Endosperm and aleurone cell structure in barley and wheat as studied by optical and Raman microscopy, J. Cereal Sci., 2013, 57(3), 543–550. 125. K.-­M. Turnbull and S. Rahman, Endosperm texture in wheat, J. Cereal Sci., 2002, 36(3), 327–337. 126. A. Trimigno, F. C. Marincola, N. Dellarosa, G. Picone and L. Laghi, Definition of food quality by NMR-­based foodomics, Curr. Opin. Food Sci., 2015, 4, 99–104. 127. H. K. Kim, Y. H. Choi and R. Verpoorte, NMR-­based metabolomic analysis of plants, Nat. Protoc., 2010, 5(3), 536–549. 128. I. J. Colquhoun, Use of NMR for metabolic profiling in plant systems, J. Pestic. Sci., 2007, 32(3), 200–212. 129. D. S. Wishart, Quantitative metabolomics using NMR, TrAC, Trends Anal. Chem., 2008, 27(3), 228–237. 130. G. E. Pereira, J.-­P. Gaudillere, C. van Leeuwen, G. Hilbert, M. Maucourt, C. Deborde, A. Moing and D. Rolin, 1H NMR metabolite fingerprints of grape berry: Comparison of vintage and soil effects in Bordeaux grapevine growing areas, Anal. Chim. Acta, 2006, 563(1–2), 346–352. 131. G. Mulas, M. G. Galaffu, L. Pretti, G. Nieddu, L. Mercenaro, R. Tonelli and R. Anedda, NMR analysis of seven selections of vermentino grape berry: Metabolites composition and development, J. Agric. Food Chem., 2011, 59(3), 793–802. 132. N. J. Bailey, M. Oven, E. Holmes, J. K. Nicholson and M. H. Zenk, Metabolomic analysis of the consequences of cadmium exposure in Silene cucubalus cell cultures via 1H NMR spectroscopy and chemometrics, Phytochemistry, 2003, 62(6), 851–858. 133. M. Defernez, Y. M. Gunning, A. J. Parr, L. V. Shepherd, H. V. Davies and I. J. Colquhoun, NMR and HPLC-­UV profiling of potatoes with genetic modifications to metabolic pathways, J. Agric. Food Chem., 2004, 52(20), 6075–6085. 134. G. Le Gall, I. J. Colquhoun, A. L. Davis, G. J. Collins and M. E. Verhoeyen, Metabolite profiling of tomato (Lycopersicon esculentum) using 1H NMR spectroscopy as a tool to detect potential unintended effects following a genetic modification, J. Agric. Food Chem., 2003, 51(9), 2447–2456. 135. G. Le Gall, M. Puaud and I. J. Colquhoun, Discrimination between orange juice and pulp wash by 1H nuclear magnetic resonance spectroscopy: Identification of marker compounds, J. Agric. Food Chem., 2001, 49(2), 580–588.

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136. P. S. Belton, I. J. Colquhoun, E. K. Kemsley, I. Delgadillo, P. Roma, M. J. Dennis, M. Sharman, E. Holmes, J. K. Nicholson and M. Spraul, Application of chemometrics to the 1H NMR spectra of apple juices: Discrimination between apple varieties, Food Chem., 1998, 61(1–2), 207–213. 137. L. Mannina, M. Patumi, N. Proietti, D. Bassi and A. L. Segre, Geographical characterization of Italian extra virgin olive oils using high-­field 1H NMR spectroscopy, J. Agric. Food Chem., 2001, 49(6), 2687–2696. 138. G. Le Gall, I. J. Colquhoun and M. Defernez, Metabolite profiling using 1H NMR spectroscopy for quality assessment of green tea, Camellia sinensis (L.), J. Agric. Food Chem., 2004, 52(4), 692–700. 139. E. Peynaud, Connaissance et travail du vin, John Wiley & Sons, 1984. 140. C. Skiera, P. Steliopoulos, T. Kuballa, U. Holzgrabe and B. Diehl, 1 H NMR approach as an alternative to the classical p-­anisidine value method, Eur. Food Res. Technol., 2012, 235(6), 1101–1105. 141. D. Capitani, A. P. Sobolev, V. Di Tullio, L. Mannina and N. Proietti, Portable NMR in food analysis, Chem. Biol. Technol. Agric., 2017, 4(1), 1–14. 142. E. Kirtil, S. Cikrikci, M. J. McCarthy and M. H. Oztop, Recent advances in time domain NMR & MRI sensors and their food applications, Curr. Opin. Food Sci., 2017, 17, 9–15.

Chapter 2

Spectroscopic Techniques for Quality Assessment of Tea and Coffee Anna Dankowskaa, Katarzyna Włodarskaa, Abhishek Mandalb and Ewa Sikorska*a a

Institute of Quality Science, Poznań University of Economics and Business, Poznań, Poland; bDivision of Agricultural Chemicals, ICAR – Indian Agricultural Research Institute, New Delhi, India *E-­mail: [email protected]

2.1  Introduction Tea and coffee are among the most commonly consumed non-­alcoholic beverages worldwide. Therefore, these commodities are very important for the economies of both producing and exporting countries. Tea has a long and illustrious history as a culturally significant and widely consumed beverage. Its health benefits, refreshing taste, and complex flavor contribute to its global popularity. Tea consumption and production have risen dramatically in recent years owing to mounting evidence of its biological activities and health advantages. Currently, the tea plant is widely cultivated in many regions of the world, particularly in Asian and African countries, such as China, India, Kenya, Sri Lanka, and Vietnam, and tea production contributes significantly to the economy of these countries.1 Based on the different manufacturing processes and special characteristics, tea can be categorized into various types with distinctive properties, based both on   Food Chemistry, Function and Analysis No. 32 Advanced Spectroscopic Techniques for Food Quality Edited by Ashutosh Kumar Shukla © The Royal Society of Chemistry 2022 Published by the Royal Society of Chemistry, www.rsc.org

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chemical composition and on sensory characteristics. Most of the tea products can be classified into black tea and green tea, which account for almost 80 and 20% of the world's tea production, respectively.2 Other categories of tea such as oolong, yellow, white, and dark have less economic value. Coffee is also one of the most frequently consumed beverages in the world, and coffee beans are among the top trade products. Coffee plants are grown in over 70 countries,3 with Brazil being the largest producer and exporter of coffee beans globally.4 There are several dozen coffee species in the world and two of these are economically and commercially important: Arabica and Robusta coffee. Arabica coffee dominates the world coffee market with a market share of above 70%5 and it is preferred over other coffee species because of its excellent sensory properties. The commercial value of tea and coffee makes these products the subject of numerous fraudulent activities such as adulteration and food fraud. Therefore, quality assessment has become an increasingly important aspect for both consumers and manufacturers of tea and coffee.6,7 The discrimination of products according to different parameters such as species, place of origin, and maturity is mainly based on sensory assessment by professional experts in accordance with national standardized methods. Traditionally, experienced sensory assessors, systematically trained, are needed for accurate assessment, which makes the procedures laborious, time consuming, destructive, and not suitable for online monitoring of product quality and safety. Furthermore, sensory evaluations are vulnerable to both physical and emotional conditions of the assessors, leading to results with low repeatability.8,9 Therefore, there is an increasing need for more scientific, reliable, accurate, and rapid methods to classify products that overcome the limitations of sensory assessment. The chemical composition of tea and coffee is complex and dictates the cup profile, meaning the flavor and aroma, consequently determining their price during trading or export. Identification of coffee and tea species and various quality parameters specific to them is crucial for the trading and consumption of these products. Geographical factors including climatic conditions, soil, altitude of growth, sun exposure time, and rainfall also determine the special aroma of tea and coffee. Therefore, the effective identification of geographical origin is crucial for quality control of the product and assurance of consumers' interests.2,10 Water-­soluble compounds, including caffeine, trigonelline, nicotinic acid, and chlorogenic acids, and also fat-­soluble compounds such as kahweol and cafestol, can be used as markers or discriminators for coffee and tea species. Metal concentration, sterolic profile, fatty acid profile, tocopherols, and triglycerides have all been suggested as potential chemical identifiers for specific coffee species.11 The concentration of those components is conventionally determined by analytical methods including high-­performance liquid chromatography (HPLC), high-­performance liquid chromatography coupled with mass spectrometry (HPLC-­MS), gas chromatography coupled with mass spectrometry (GC-­MS), capillary electrophoresis, etc. Although these methods provide high

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levels of accuracy and precision, they are all laboratory wet-­chemistry analyses centric, are time consuming, expensive, and labor intensive, and are not suitable for online applications. Conventional analysis requires advanced equipment and chemical knowledge of certified laboratories, and therefore is of limited accessibility. Recently, spectroscopic techniques have been demonstrated to be fast, simple, non-­destructive, and chemical-­free analytical tools that can replace conventional chemical analysis.12,13 Online analysis of samples can be performed in their natural state without being destroyed, and several components and attributes can be analyzed simultaneously using these techniques.2 Therefore, they can be used in the routine quality control of a large number of samples, as an alternative to conventional analytical methods. Numerous studies have been reported for the identification of tea and coffee varieties, geographical origin, maturity, and moisture content, and for both qualitative and quantitative determinations of constituents using absorption spectroscopy and fluorescence techniques coupled with chemometrics. These studies achieved good performance, indicating the feasibility of using spectroscopy in the tea and coffee industries to identify many different types of possible adulteration.3,10,14 This chapter presents an overview of the latest advances in the application of absorption spectroscopy in the ultraviolet (UV), visible (VIS), near-­infrared (NIR), and mid-­infrared (MIR) regions and also fluorescence spectroscopy coupled with chemometrics in the evaluation of the quality parameters of tea and coffee, with emphasis on the latest research, mostly published in the last decade.

2.2  E  valuation of the Quality of Tea and Coffee Using Spectroscopic Techniques 2.2.1  Quality Characteristics of Tea and Coffee The cup quality of tea and coffee is related to the chemical composition of tea leaves and coffee beans. Many beneficial bioactive compounds have been isolated and identified from dark tea, including alkaloids, free amino acids, peptides, polyphenols, pigments, polysaccharides, and volatile compounds.1 Fresh leaves of tea contain on average polyphenolic compounds (36%), carbohydrates (pectins, glucose, fructose, cellulose) (25%), proteins (15%), lignin (6.5%), minerals and trace elements (magnesium, chromium, iron, copper, zinc, sodium, cobalt, potassium, etc.) (5%), amino acids [such as theanine (5-­N-­ethylglutamine), glutamic acid, tryptophan, aspartic acid] (4%), lipids (2%), organic acids (1.5%), and chlorophyll (0.5%), and also carotenoids and ethereal substances below 0.1% and vitamins (B, C, E).15 The tea fermentation process allows the leaves to undergo enzymatic oxidation, in which polyphenol oxidase causes polymerization of flavan-­3-­ ols to catechin oligomers, namely bisflavanols, theaflavins, thearubigins, and others. As a result, only ∼15% of catechins from green tea remain

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unchanged, the remainder being transformed to theaflavins and thearubigins. Tea infusion is also a valuable source of essential macroelements (including Ca, K, Mg, and Na), microelements (including Cu, Fe, Mn, and Zn) and trace elements (such as Co, Cr, Ni, and Se), in addition to non-­ essential and toxic elements (e.g. Al, As, Ba, Cd, Hg, Pb, Sb, and Sr).16 Many health-­promoting properties have been associated with compounds present in tea, such as antioxidant capacity, antitumorigenic activity, cardiovascular disease prevention, and immunity-­boosting activity.17,18 Therefore, tea consumption can be directly related to a reduced risk of developing life-­threatening diseases. Green coffee beans of both species contain a wide range of chemical compounds that determine the quality of the final product. Green coffee beans contain mainly carbohydrates, with polysaccharides as the predominant type, accounting for about 60% dry weight of the bean, followed by the presence of lipids (10–16%), proteins (10%), and in smaller amounts phenolic compounds dominated by chlorogenic acids, minerals, caffeine, trigonelline, free amino acids, and others.19 Green coffee is known as one of the main food sources of chlorogenic acids.11 During the roasting process, the chemical composition of the green beans is changed as a result of degradation and transformation of the compounds present in the beans, and several hundred substances associated with the coffee flavor are formed.20 Decreases in carbohydrates, proteins, chlorogenic acids, and free amino acids are observed in coffee beans after roasting, whereas lipids, minerals, aliphatic acids, caffeine, and trigonelline remain at similar levels to the original values.19 During coffee roasting, melanoidins are formed as a result of thermochemical transformation of polysaccharides, proteins, and phenolic compounds.21 Melanoidins are the final products of the Maillard reaction and are estimated to account for up to 25% dry weight of roasted coffee beans.19,22 Caffeine, the most well-­known substance present in coffee, stimulates the central nervous system.4

2.2.2  G  eneral Scheme of Using Spectroscopic Methods for Quality Assessment of Tea and Coffee The general idea of using spectroscopic methods coupled with chemometrics for the evaluation of the quality of tea and coffee is presented in Figure 2.1. This approach usually requires the development of multivariate models, which describe the relationship between the spectral and chemical characteristics of samples. Calibration models are developed using multivariate regression methods and allow the quantification of components or evaluation of quality parameters. Classification models are used for the identification of samples belonging to the respective categories. Advanced chemometric methods are used for the development of optimal models. The developed models permit the determination of target parameters or the identification of sample membership based on spectral measurements.

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Figure 2.1  General  idea of the application of spectroscopic techniques for the quality assessment of tea and coffee.

The process of building multivariate models involves several steps, such as collection of sample sets, determination of target parameters using conventional reference methods, acquisition of spectra, and the development, optimization, and validation of multivariate models using chemometric methods. Validated models could be used for quality assessment, replacing laboratory wet-­chemistry reference methods.

2.2.3  A  cquisition and Characteristics of Spectra of   Tea and Coffee The most commonly used spectroscopic technique for the evaluation of food quality is NIR spectroscopy (NIRS). This method is also most frequently applied in tea and coffee studies.13,23 Other optical spectroscopic techniques often used for this purpose are MIR spectroscopy, absorption spectroscopy in the UV and VIS regions, and fluorescence spectroscopy. The choice of spectroscopic technique is determined by the sample parameters and the objectives of the research. Various acquisition modes have been employed for measurements, including transmittance, diffuse reflectance (DR), and attenuated total reflectance (ATR), and the optimal mode is usually determined by the physical state and features of the sample.

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2.2.3.1 NIR Spectra of Tea and Coffee The NIR spectra of tea leaves and coffee beans are usually measured using the DR technique, which permits direct measurements of solid samples. However, the spectral characteristics are dependent on the degree of granulation of the sample. Therefore, most measurements are made with milled, powdered coffee beans and tea leaves.24 As an alternative to conventional spectroscopic measurements, near-­infrared hyperspectral imaging (NIRHSI) can be applied to develop non-­destructive methods, additionally providing information about the spatial distribution of the studied analytes.25 The application of NIRS for tea infusion measurements has been reported.26 The NIR spectra of tea and coffee have been recorded using both laboratory benchtop instruments and portable sensors. For example, portable micro near-­infrared (microNIR) spectrometers have been used for the identification and quantification of adulterations of Arabica coffee27 and for the direct determination of the cup profile in coffee blends.28 In tea studies, a portable spectrometer was used for the determination of several components in instant green tea29 and smartphone-­based microNIRS was used for the qualitative and quantitative analysis of adulterants in green tea.30 The use of portable, handheld NIR devices can shorten the time of analysis and simplify the sample preparation steps, in addition to guaranteeing the efficiency of real-­time data acquisition at the location where the samples occur.27 It is particularly useful for industrial quality control owing to its fast, simple, robust, and less expensive nature compared with benchtop equipment. The NIR spectra of tea and coffee are shown in Figure 2.2. Despite their different compositions, tea and coffee contain the same functional groups that contribute to the absorbance in the NIR region. The NIR spectra of tea9 and coffee23 in the range 800–2500 nm contain broad overlapping bands, mainly corresponding to overtones and combinations of vibrational modes involving C–H, O–H, N–H and C=C bonds, which are related to the fundamental vibrations observed in the MIR region. The difference in the composition of tea and coffee is reflected in their uniquely differing spectral patterns. The absorption bands with maxima at 8360 cm−1 (1196 nm) and 5780 cm−1 (1730 nm) correspond to the second and first overtones of C–H stretching vibrations of CH2 and CH3 groups, respectively.31–33 The bands pertaining to the combination vibrations of the bending and symmetric stretching of the C–H group occur at 4329 and 4255 cm−1 (2310 and 2350 nm), respectively.32 The band with a maximum at 6780 cm−1 (1475 nm) is ascribed to the first overtone of O–H stretching vibrations.31 The band at 5180 cm−1 (1930 nm) corresponds to the combination of O–H stretching and deformation.33 In the range 6800–6600 cm−1 (1470–1515 nm) occur the bands originating from first overtone of N–H stretching vibrations.33,34 The band at 4694 cm−1 (2130 nm) is caused by the combination vibration of N–H bending and C=O stretching.32 At around 4600 cm−1 (2174 nm) are present bands related to NH2 vibrations in amide I groups and NH bending in amide III groups.33

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Figure 2.2  NIR  spectra of green tea and Arabica coffee measured on powdered samples using diffuse transmittance techniques.

2.2.3.2 MIR Spectra of Tea and Coffee The MIR spectra of tea and coffee are most frequently measured in the solid state using ATR techniques. Other techniques such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and transmittance spectroscopy using KBr discs have been applied to the discrimination of defective and non-­defective coffee beans.35,36 For example, the transmittance technique was used for the recording of the FT-­MIR spectra of fermented and unfermented tea samples. Using this technique, sample preparation involved finely ground samples and the formation of thin KBr disks.37 The MIR spectra of green tea and Arabica coffee are shown in Figure 2.3. These spectra consist of well-­resolved bands that correspond to the fundamental vibrations of specific functional groups of the main constituents. The broad band at 3308 cm−1 corresponds to OH stretching vibrations.38 The bands at 2923 and 2853 cm−1 correspond to the asymmetric and symmetric stretching vibrations of aliphatic C–H bonds, respectively. The band at 1743 cm−1 is ascribed to the axial deformation of the C=O bond of fatty acid esters.39 The band at 1652 cm−1 originates from stretching of the C=C bond of aromatic compounds. The band at around 1650 cm−1 was ascribed to the vibration of the amide I group (N–H bending), the band at around 1525 cm−1 was assigned to the amide II group, and the band at 1239 cm−1 was related to the amide III group in tea samples.40 In addition, the presence of the band at 1458 cm−1 was ascribed to the symmetric mode of the methylene group.40 The band at 1150 cm−1 was assigned to the antisymmetric stretching

Chapter 2

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Figure 2.3  MIR  spectra of green tea and Arabica coffee measured on powdered samples using the ATR technique.

of the C–O–C bridge.40 The bands at 1374 and 1026 cm−1 correspond to the stretching and deformation vibrations, respectively, of the C–O bond.

2.2.3.3 UV–VIS Spectra of Tea and Coffee The absorption spectra in the UV–VIS region have been reported for both solid and liquid samples using tea and coffee extracts or infusions. In some studies, spectra in the VIS region were measured with NIR spectra. The UV–VIS absorption spectra of tea and coffee are presented in Figure 2.4. Tea and coffee exhibit intense absorption in the UV range, attributed to compounds with species such as carbonyl groups, nitro groups, double and triple bonds, conjugated double bonds, etc. In tea spectra in the UV–VIS region, three absorption bands have been observed in the ranges 190–250, 250–300, and 300–400 nm. These absorption bands were related to phenolic compounds present in the tea infusions, and caffeine, which shows maximum absorption at around 275 nm.41 Polyphenolic compounds, particularly catechins, flavonoids, and phenolic acids, are responsible for the absorption of green tea in the UV region. The four major catechins present in green tea are (−)-­epigallocatechin-­3-­gallate, (−)-­e pigallocatechin, (−)-­e picatechin-­3 -­g allate, and (−)-­e picatechin. (−)-­Epigallocatechin-­3-­gallate and (−)-­epicatechin-­3-­gallate exhibit absorption bands in methanol between 246 and 325 nm with maxima at wavelengths of 276 and 279.2 nm, respectively. The absorption of kaempferol,

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31

Figure 2.4  UV–VIS  absorption spectra of black tea and Arabica coffee infusions. myricetin, and quercetin is observed with maxima at about 367 nm. Phenolic acids, including gallic, chlorogenic, and caffeic acids, showed absorbance maxima at 273 and 330 nm.42 For powdered green tea leaves in the VIS region, two absorption bands have been observed, located in the 460–470 and 665–675 nm spectral ranges, corresponding to the lipid-­soluble pigments lutein, β-­carotene, chlorophyll b, chlorophyll a, and pheophytin a, related to the green color of tea.31 On the other hand, the attractive dark yellow, orange, and red colors of tea infusions are usually related to polymers, i.e., theaflavins, thearubigins, and theabrownins.43,44 Dark teas are particularly rich in these polymeric pigments, which are generated by the oxidation of tea catechins during pile fermentation. Theabrownin is the most abundant and bioactive pigment in dark teas.1 In the absorption spectrum of a coffee brew in the range 200–700 nm, two absorption bands have been reported with maxima at 280 and 325 nm.19 The absorption band with a maximum at 280 nm was related to the presence of the aromatic rings of proteins, caffeine, chlorogenic acids, and caffeic acid,45 and that with a maximum at 325 nm was ascribed to chlorogenic acids and caffeic acid. The brown color of a coffee brew is related to the content of melanoidins, which has been evaluated by measuring the absorption at 405 nm. Melanoidins are high molecular weight nitrogenous brown-­colored compounds, the final products of the Maillard reaction, which encompasses a network of various reactions between reducing sugars and compounds with a free amino group, forming a variety of products.19

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2.2.3.4 Fluorescence Spectra of Tea and Coffee Fluorescence spectra have been reported mainly for liquid samples using tea and coffee extracts or infusions, although some studies involved fluorescence measurements of solid samples of tea and coffee. Synchronous fluorescence spectroscopy was used for direct measurements of solid coffee samples46 and tea infusions.47 Laser-­induced fluorescence was used for direct measurements of tea leaves for the purpose of classification and quality evaluation of Chinese oolong teas and jasmine teas.48 Fluorescence spectra provide enhanced selectivity and sensitivity compared with absorption spectra, but are limited to fluorescent components.49 Various approaches have been used for fluorescence measurements of tea and coffee, such as conventional emission spectra and, more suitable for the characterization of multi-­fluorophoric systems, synchronous fluorescence spectra and excitation–emission matrices (EEM).45,50 Measurements of tea have been performed directly to characterize autofluorescence and/or after a derivatization reaction. For example, amino acids in green tea samples were first derivatized with formaldehyde and acetylacetone solution to form highly fluorescent products.51 The fluorescence patterns of teas have features characteristic of chlorophyll pigments with peaks at around 690 and 735 nm. The fluorescence in the blue–green spectral region is due to a large number of components, such as tea polyphenols, flavonoids, and wax protecting against dehydration and UV exposure of the living plants.48 For a green tea extract, the two fluorophores have been characterized based on parallel factor analysis (PARAFAC) of EEM. That with an emission maximum at around 420 nm was ascribed to the catechins, and the other with an emission maximum between 500 and 550 nm was attributed to carotenoids.52 Various coffee components have been reported to exhibit fluorescence, such as chlorogenic acids, caffeic acid, ferulic acid, dimethoxycinnamic acid, alkaloids, theobromine, and theophylline.46 In the EEM of extracts of green coffee powder, emission was ascribed to caffeic acid, usually esterified to quinic acid, and the chlorogenic acids, quercetin, and tocopherol, a major component of coffee oil's unsaponifiable fraction.53 In another study, the signals in the synchronous fluorescence spectra of roasted Arabica and Robusta coffees were related to the tocochromanols, polyphenols, fatty acids, and chlorophylls.45

2.2.4  Multivariate Data Analysis The extraction of information of interest from spectral data is an essential task. Numerous methods have been applied to the analysis of spectral data of tea and coffee, including chemometric, machine learning (ML) and deep learning (DL) methods.54 The first step often comprises exploratory data analysis using principal component analysis (PCA). The objective of this analysis is to find any

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patterns and trends in the sample set, and to identify samples with similar or different spectral properties. In this step, some outlying samples may also be detected. PCA is also used for the reduction of data dimensionality, and another method used for this purpose is multidimensional scaling (MDS).55 Non-­linear dimension reduction methods such as kernel principal component analysis (KPCA), sparse principal component analysis (SPCA), and local tangent space alignment (LTSA) have been also used to reduce the dimensionality of spectral data.55 For second-­order data, a method such as EEM fluorescence spectroscopy combined with PARAFAC as a three-­way decomposition method was applied to extract analytical information from fluorescence patterns.52 Calibration models are built using regression methods, such as multiple linear regression (MLR), principal component regression (PCR), and partial least-­squares regression (PLSR). PLSR is most widely used for modeling the relationship between chemical and spectral data. It is a linear method based on the reduction of the number of variables, helping to overcome the problems of poor selectivity and collinearity of optical spectra. The N-­way partial least-­squares regression (N-­PLSR) method is used for second-­order data. The non-­linear regression methods used for the development of calibration models in tea and coffee studies include support vector machine (SVM), least-­ squares support vector machine (LSSVM), relevance vector machine (RVM), artificial neural networks (ANNs), back-­propagation neural network (BPNN), random forest (RF), and extreme learning machine (ELM).9,23,55–57 Classification models have been developed using methods such as partial least-­squares discriminant analysis (PLS-­DA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), k-­nearest neighbors (KNN), soft independent modeling of class analogy (SIMCA), support vector machine (SVM), library support vector machine (Lib-­SVM), artificial neural networks (ANNs), back-­propagation neural networks (BPNNs), extreme learning machine (ELM), decision tree (DT), multilayer perceptron (MLP), elastic net, and Gaussian process regression (GPR).3,9,23,39,41,58–60 Multivariate models are optimized using preprocessing methods and variable selection. An important step in multivariate analysis is the selection from measured spectra of variables that provide useful analytical information and elimination of the variables that mostly contain noise. Various methods of variable selection have been used to select informative variables in tea and coffee spectra, such as regression coefficient-­based selection, uninformative variable elimination (UVE), variable importance in projection (VIP), selectivity ratio (SR), genetic algorithms (GAs), interval partial least squares (iPLS), successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), shuffled frog leaping algorithm (SFLA), bootstrapping soft shrinkage (BOSS), variable iterative space shrinkage approach (VISSA), model adaptive space shrinkage (MASS) algorithms, flower pollination algorithm (FPA), ordered predictors selection (OPS), swarm intelligence algorithms with partial least squares (PLS) such as simulated annealing PLS

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(SA-­PLS), ant colony optimization interval PLS (ACO-­iPLS), genetic algorithm PLS (GA-­PLS), and synergy interval PLS (SiPLS).56,61–64 The quality of tea and coffee is determined by several factors, and multi-­ block data analysis methods are useful for studying multiple information describing the same samples. The data from the spectra measured in different spectral regions may be combined in different ways to enhance the performance of the multivariate models, using different strategies: low-­, mid-­, and high-­level fusion and a multi-­block data analysis using the common dimension (ComDim) approach.65

2.2.5  Applications Spectroscopic techniques are widely used for the evaluation of the quality, safety, and authenticity of tea and coffee. In many cases, they can replace or complement conventional techniques in routine high-­speed quality control.4,9,66–68 Spectroscopic techniques have been successfully applied with different purposes, from the authentication of geographical origin, varietal differentiation, and detection of various types of adulteration and contamination, to the prediction of chemical composition, evolution of sensory properties of tea and coffee, and control of the production process. The important feature of spectroscopic techniques is their feasibility for the simultaneous determination of many components based on measured spectra. This feature has been widely exploited in tea and coffee analysis. Examples chosen to illustrate the wide range of applications of optical spectroscopy in tea and coffee analysis are presented in the following sections.

2.3  S  pectroscopic Techniques for Assessment of the Quality of Tea Spectroscopic techniques coupled with multivariate analysis are applied for the evaluation of various aspects of tea quality, and also its safety and authenticity. Examples of these applications discussed in the present section are shown in Figure 2.5.

2.3.1  Authentication of Tea One of the important fields of application of spectroscopy in tea analysis is its authentication. Spectroscopic methods are widely used for tea classification according to various criteria.66

2.3.1.1 Tea Category Tea is usually classified into six categories, namely black, green, oolong, yellow, white, and dark tea, based on different manufacturing processes, primarily the degree of fermentation, and sensorial characteristics.1,47,69 In general,

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Figure 2.5  Application  of spectroscopic techniques and multivariate analysis for quality assessment of tea.

green and oolong tea are more preferred in Oriental countries, whereas black tea is more preferred in Western countries.70 Black tea is a fully fermented product and the fermentation of tea leaves induces enzymatic oxidation of tea catechins and their condensation to theaflavins and further polymerization to thearubigins. Both theaflavins and thearubigins are responsible for the typical color and strong, astringent flavor of black tea.47,69,71 Green tea, unlike black tea, is not a fermented product obtained by inactivating the polyphenol oxidase enzyme with high-­ temperature treatment to prevent the oxidation of catechins in tea leaves.47 Therefore, it contains a large number of polyphenolic compounds, which determine the intense bitterness and astringency of tea infusions. The quality of green tea is determined mostly by the steaming process of the fresh tea leaves and also the characteristics of post-­harvest green tea leaves that vary considerably depending on the growing conditions and the picking season.72 The characteristic color of green tea is related to lipid-­soluble pigments, mainly chlorophyll a and b, lutein, and β-­carotene.31 Oolong tea is similar to black tea, prepared by partial fermentation to achieve the intended sensory profiles. It is characterized by a unique color and aroma (both fruity and floral), resulting from light withering, light rolling, and partial fermentation.47,73 Its production process is much more complicated than that of black tea products and each stage affects the taste of the final tea brew. The color of oolong tea is intermediate between those of green and black teas.74 Rare and precious, yellow tea has gained increasing popularity in recent years because of its pleasant mellow taste.75 It has the typical yellow color of tea leaves and infusions due to its unique manufacturing processes. The processing of yellow tea involves the partial fermentation of the collected leaves, including little-­leaf yellow tea, bud yellow tea, and large-­leaf yellow tea.69,76,77

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White tea is a partially fermented, minimally processed product with an attractive flavor and health benefits.31,69 White tea is mainly produced in China and the market demand for this product in increasing. The quality of white tea is mainly graded by maturity of the fresh leaves. Lower maturity of the fresh leaves guarantees a higher product grade and a more preferable sensory profile.8 White tea retains higher levels of polyphenolic compounds and the lowest level of caffeine compared with any other tea.78 Among the categories of tea mentioned, dark tea is a uniquely fermented tea, produced by solid-­state fermentation, requiring the participation of microorganisms.1,79 Dark tea has special sensory characteristics: orange–red tea infusion, unique stale, aged, and fungal aroma, and mellow, sweet, and smooth flavor with lower intensities of bitterness and astringency.79 Dark tea products are diverse in their volatile profiles mainly because of the various raw materials and also the dominant microorganisms used in the post-­ fermentation process.1,80 In one study, VIS and NIR-­HIS were used for the classification of five Chinese tea categories with different degrees of fermentation.59 A total of 206 samples of green, yellow, white, black, and oolong teas were analyzed. LDA, Lib-­SVM, and ELM classification models were developed based on full spectra, spectral features, textural features, and data fusion. A correct classification rate of 98.39% was obtained using the Lib-­SVM model developed for the fusion of experimental data. In another study, discrimination of four commercial blends of green tea in bagged (inside its sachet) and non-­bagged conditions was demonstrated using NIR spectroscopy.81 Four classes of teas of different flavor were studied: traditional, mint, lemon, and mixed. Two spectrometers were utilized for the acquisition of spectra: a benchtop and a handheld model. Bayesian optimization was applied to choose optimal parameters for the models. Satisfactory classification models were constructed for non-­bagged tea for both the benchtop and handheld instruments, with accuracies of 90 and 93%, respectively. The performance of the benchtop instrument classification models was superior to that with the handheld device, with accuracies of 93 and 82%, respectively. In comparison with PLS-­DA models, SVM-­based classification models performed better for handheld and tea-­inside-­teabag measurements. In a further study, FT-­MIR spectroscopy was utilized to discriminate the type of unfermented and fermented tuocha tea and its age.37 Transmittance FT-­MIR spectra of 80 fermented and 98 unfermented tea samples from Yunnan province in China were measured in the form of KBr disks. The PLS-­DA model used to discriminate unfermented and fermented teas was characterized by a sensitivity and specificity of 93 and 96%, respectively. The PLS1 model allowed modeling of the age of unfermented tea samples [root mean square error of prediction (RMSEP) = 1.47 months], whereas a back-­ propagation artificial neural network (BP-­ANN) model provided satisfactory results for calibrating the age of fermented tea (RMSEP = 1.67 months).

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In another study of interest, total luminescence spectroscopy was utilized to discriminate between seven different types of tea: four bottled liquid Japanese teas (oolong, green, houji, and black teas) and three leaf teas (Kenya, Assam, and Ceylon teas).82 Results of PCA revealed the ability of fluorescence techniques to differentiate between green, black, and oolong teas. UV–VIS, synchronous fluorescence (SF), and NIRS spectroscopic techniques may provide complementary information about tea samples.41 These techniques were used for the classification of black, green, white, yellow, dark, and oolong teas with respect to the production process. Four classification methods, LDA, QDA, RDA, and SVM, were used. For individual spectra and data fusion models, the lowest error rates in the calibration and validation data sets were obtained using the QDA and SVM methods, and did not exceed 3.3 and 0.0%, respectively. The lowest classification error rates for the individual UV–VIS, SF, and NIR spectroscopic methods were obtained with the use of RDA (12.8%), SVM (6.7%), and QDA (2.7%), respectively. Very low classification errors in the validation data sets, below 3%, were obtained for all types of spectra combined with the SVM method.

2.3.1.2 Tea Grade In the 1980s, a method for distinguishing commercial black tea samples using NIR reflectance spectroscopy was proposed.83 Two sets of black teas with different sensory properties were discriminated with a 91% success rate, based on four wavelengths in the NIR region (1660, 1720, 2050, and 2300 nm) and the Mahalanobis distance. A method based on NIRS was established for the classification of special-­ grade flat green tea, which is a premium product.33 PLSR and synergy interval partial least squares (SiPLS) were used to predict the sensory scores of green tea. The optimal model obtained using SiPLS permitted the discrimination of special-­grade flat green tea with a prediction accuracy of 97 and 93% in cross-­validation and external validation, respectively. PCA revealed a potential correlation between specific NIR spectral regions and the presence of polyphenols and alkaloids in tea samples. A rapid method based on NIRS was developed to assess black tea tenderness and rankings.58 A total of 700 standard samples of tea of seven quality classes were analyzed. The multivariable selection algorithm improved genetic algorithm–particle swarm optimization (IGA-­PSO) was applied and compared with the individual methods (IGA and PSO). The classification models were developed using DT, PLS-­DA, and SVM based on different kernel functions. The IGA-­PSO-­SVM model with a radial basis function provided the best prediction results with a correct discriminant rate of 95.28%. In another study, NIRS combined with multivariate calibration and feature variable selection methods was proposed for the classification of Keemun black tea.56 A total of 700 samples of tea pertaining to seven quality grades were investigated. Four variable selection methods were used and compared: GA, SPA, CARS, and SFLA. Models for the identification of Keemun black tea

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rank quality were built using LSSVM, BPNN, and RF methods combined with the optimized variables. The CARS–LSSVM model with the best predictive performance was characterized by an RMSEP of 0.0413, a prediction set correlation coefficient (Rp) of 0.9884, and a correct validational discriminant rate of 99.01%. Appearance and flavor are crucial indicators of the quality of Keemun black tea and the basis of its classification into seven grades. A method to evaluate the flavor and appearance quality of Keemun black tea using NIRS and computer vision systems (CVS) was developed.84 A total of 80 tea samples for each of the seven grades were analyzed. PCA, local linear embedding, isometric feature mapping, and convolutional neural network (CNN) methods were used for feature extraction. A feature-­level fusion strategy was applied to combine the softmax function and ANN to classify samples based on NIR and CVS features. For an individual NIR spectrum, CNN achieved the highest classification accuracy with the softmax classification model. The fusion of NIR and CVS features proved to be the optimal combination; the accuracy of calibration, validation, and testing sets increased from 99.29, 96.67, and 98.57%, respectively (when the optimal features from a single sensor were used) to 100.00, 99.29, and 100.00%, respectively (when features from multiple-­sensors were used). An interesting study was conducted to identify Tieguanyin tea grades rapidly and non-­destructively, for which fluorescence hyperspectral imaging (FHSI) was applied.62 A total of 309 tea samples of three different grades were collected. The characteristic wavelengths were respectively selected by BOSS, VISSA, and MASS algorithms. SVM was used to develop the classification models. The VISSA–SVM model exhibited the best classification performance. The artificial bee colony (ABC) algorithm was applied to optimize the parameters of the SVM model. The accuracy of the test set of the VISSA–ABC– SVM model reached 97.44%.

2.3.1.3 Tea Geographical Origin Rapid discrimination of roast green tea according to its geographical origin is crucial to quality control. In one such study, Fourier transform near-­ infrared (FT-­NIR) spectroscopy was used to discriminate Chinese green tea from four provinces according to their geographical origins.85 Four classification methods, LDA, KNN, ANN, and SVM, were used to develop the discrimination models based on PCA results. The optimal SVM model allowed a discrimination rate of 100% in the training and prediction set, and thus exhibited the best performance among the four models studied. An efficient procedure was proposed for validating the authenticity and origin of tea samples from four different areas using PLS and Euclidean distance methods for the analysis of NIR spectra.86 The classification models were constructed and applied in a two-­step procedure. After the first identification step using the PLS method, followed by the application of the Euclidean distance method, accuracy rates for the classification of tea samples were 98.43% in the calibration set and 96.84% in the test set.

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Another study reported that the geographical origin of oolong Anxi Tieguanyin tea samples was effectively discriminated by NIRS.87 A PLS-­DA classification model was developed using 450 authentic samples collected in Anxi county, the original producing area of Tieguanyin tea, and a further 120 Tieguanyin samples with similar appearance, but collected from unprotected producing areas in China. The best PLS-­DA model was characterized with a sensitivity and specificity of 0.931 and 1.000, respectively. A novel ensemble strategy (ES) was proposed to tackle large class-­number classification (LCNC) problems, increased data complexity, and class overlapping, for discriminating the geographical origins of 25 green tea samples using NIR spectroscopy and PLS-­DA.88 Another study attempted to use NIRS for the efficient identification of the origins of Shandong green tea.89 Three different regression methods were compared. The PLS model exhibited better performance than BP-­ANN and SVM methods. For the PLS model, the accuracies of identification were up to 100% for both training and testing sets. Darjeeling black tea is a well-­known variety that is included in the list of protected designations of origin (PDO) and protected geographical indications (PGI). In one study, NIRS was not only used to distinguish PGI Darjeeling tea from other kinds of black teas, but also to detect adulterated Darjeeling tea samples.90 Two classification methods, PLS-­DA and SIMCA, provided satisfactory results in discriminating PGI samples from the other teas and from the adulterated Darjeeling teas. In an interesting study, a fluorescence technique was applied to discriminate between teas from 11 different Sri Lankan plantations.91 The EEM of solvent extracts of tea samples were analyzed using PCA and LDA, which allowed a 100% correct classification of all teas studied. EEM fluorescence spectroscopy has also been proposed for the characterization of green tea extracts.52 Two main fluorophores in green teas, catechins and carotenoids, have been found and characterized using PARAFAC. Moreover, the PARAFAC results permitted differentiation between Chinese and Japanese tea infusions. The quality of green tea samples obtained from the South and East Asian regions was evaluated by techniques such as UV–VIS and FT-­MIR spectroscopy and HPLC.42 A total of 38 samples were analyzed. Unsupervised recognition techniques comprising hierarchical cluster analysis (HCA) and PCA based on UV data allowed the successful distribution of the studied samples into informative clusters. The VIS data allowed the differentiation of samples only according to their powdered condition. PCA of FT-­MIR and HPLC data could hardly discriminate any of the samples. The classification models built using SIMCA and PLS-­DA revealed a good class separation between the South and East Asian teas. A method based on UV–VIS spectroscopy was proposed for differentiating tea infusions prepared in boiling water, simulating a home-­made cup of tea as ingested by consumers.92 Five classes of tea were studied: Argentinean green tea, Brazilian green tea, Argentinean black tea, Brazilian black tea, and Sri Lankan black tea. The classification models were developed for classification of the teas according to their variety and geographic origin, using KNN,

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classification and regression trees (CART), SIMCA, PLS-­DA, PCA–LDA, and SPA–LDA methods. The best classification results were obtained using the PCA–LDA and SPA–LDA methods.

2.3.1.4 Adulteration and Contamination Smartphone-­based microNIRS was used to analyze qualitatively and quantitatively sugar and glutinous rice flour adulterants in green tea.30 Green tea adulterated with these compounds has a higher sensitivity to water, compromising the tea's safety. A total of 475 samples of pure and adulterated tea were prepared and scanned using a microNIR spectrometer. A multi-­layer algorithm model was used for the qualitative and quantitative data analysis. The discrimination rate of the optimal SVM model was 97.47% for the prediction set. Variable selection methods were used to improve the performances of PLSR and SVM regression models. Non-­linear SVR models based on wavelengths selected by an iteratively retaining informative variables algorithm allowed the satisfactory identification of sugar and glutinous rice flour adulteration. FT-­MIR spectroscopy was used to evaluate tartrazine adulteration in tea powder.93 PLSR and its variants with various variable selection methods, including backward interval partial least squares (BiPLS), GA-­PLS, a simple real-­coded genetic algorithm (RCGA) variant of GA-­PLS, and competitive adaptive reweighted sampling partial least squares (CARS-­PLS), were used to construct the calibration models. The RCGA-­PLS model allowed the determination of tartrazine in the concentration range 0–30 mg g−1 with a correlation coefficient of 0.987. A method based on a second-­order multivariate calibration and EEM data was developed to quantify the mycotoxin ochratoxin A in tea and coffee samples.94 To optimize the experimental conditions, the effects of pH and various organized media on the fluorescence signal of ochratoxin A were studied. Both PARAFAC and the multivariate curve resolution-­alternating least squares (MCR-­ALS) algorithm provided satisfactory results for the tea samples with limits of detection in the range 0.2–0.3 ng mL−1 and relative prediction errors between 2 and 4%, for both fortified and real samples. The usefulness of this method for testing for mycotoxins in roasted coffee beans has also been demonstrated. Determination of lead chrome green in green tea was performed by FT-­ MIR transmission spectroscopy.95 The qualitative analysis of lead chrome green in tea was conducted using PLS-­DA with a 100% correct rate of classification. A combination of iPLS regression and SPA allowed the selection of important variables for the quantitative analysis of an adulterant in green tea. The best model was obtained using LSSVM, with a high coefficient of determination (Rp2 = 0.864). Sibutramine may be illicitly included in dietetic herbal slimming foods, teas, and dietary supplements marketed as “100% natural” to enhance weight loss. Detection of sibutramine in green tea, green coffee, and mixed

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herbal tea was performed using ATR–FT-­MIR spectroscopy combined with chemometrics.96 Unadulterated and adulterated samples were discriminated with respect to their sibutramine contents with perfect accuracy using HCA and PCA methods.

2.3.2  Tea Composition The chemical components of tea leaves include mainly polyphenols and alkaloids, and in lower amounts volatile oils, polysaccharides, amino acids, lipids, vitamins, and inorganic elements.78 In the 1980s, a study of 134 black teas with wide-­ranging type, origin, and quality demonstrated that the moisture content, theaflavin content, and tea tasters' assessment of overall quality can be successfully estimated by NIR reflectance spectroscopy of unground tea leaves.97

2.3.2.1 Polyphenols Polyphenols are the main components of fresh tea leaves and the polyphenolic content decreases with increasing leaf maturity.8 Therefore, the steps of harvesting and plucking of tea shoots have a direct influence on the final cup quality.14 Polyphenols present in tea include flavan-­3-­ols, flavandiols, and phenolic acids and constitute up to 30% dry weight.98 The flavan-­3-­ols (also known as catechins) are the major phenolics in tea leaves, constituting 70–80% of the total content. The major catechins include (+)-­catechin, (−)-­catechin gallate, (−)-­e picatechin, (−)-­e picatechin gallate, (−)-­e pigallocatechin, (−)-­gallocatechin, (−)-­gallocatechin gallate, and (−)-­epigallocatechin gallate, with the last component playing the most important role in the total antioxidant capacity of tea.99,100 Among tea varieties, green tea contains a high level of polyphenols as a result of the low level of fermentation. Tea polyphenols account for 30–42% of the dry weight of green tea leaves.99 Green tea infusion is usually prepared by brewing tea leaves in hot water at 80 °C for 3–5 min; higher temperatures and a prolonged time of extraction cause a decrease in the epicatechins content.71 Spectroscopic techniques, together with chemometrics, have been used for the determination of the total polyphenols content and total antioxidant activity, and also for the simultaneous measurement of individual polyphenols. NIRS was applied to the determination of the total anthocyanin content in flowering tea.101 Flowering tea, or blooming tea, consists of dried flowers from one or more plant species, and has become a popular beverage consumed across the world. Flowering tea is a rich source of anthocyanins, which are an important quality index in addition to being the main source of the antioxidant activity of this beverage. Calibration models for total anthocyanins content were constructed using ACO-­iPLS and GA-­iPLS. ACO-­iPLS allowed the selection of characteristic wavelength regions that corresponded to the absorption bands of anthocyanins in the UV–VIS range. The performance of the optimal ACO-­iPLS model developed for total anthocyanins

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content [R = 0.9856, root mean square error of cross-­validation (RMSECV) = 0.1198 mg g−1] was better than those of the full-­spectrum PLS, iPLS, and GA-­iPLS models. The usability of field-­based hyperspectral data to estimate tea polyphenols has been demonstrated.102 For this purpose, leaf reflectance spectra were measured. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentrations by minimizing the effects of other factors such as age of the bushes and management practices. The PLSR model allowed the prediction of polyphenols in fresh tea leaves with R2 = 0.81 and root mean square error (RMSE) = 1.39 mg g−1. The polyphenols content of tea was determined by MIR spectroscopy coupled with iPLS and random frog techniques in 14 cultivars of tea trees.103 The models were constructed for large, middle-­leaf cultivars and for all the cultivars. iPLS was used for variable selection. The optimal regression model with a validation correlation coefficient of 0.9059 was obtained using the BiPLS method. Using the optimal subinterval selected by BiPLS, a further selection of variables was carried out using a random frog method. The usability of fluorescence spectroscopy to determine the polyphenols content and antioxidant activity of tea has also been demonstrated.47 Synchronous fluorescence spectroscopy was utilized to monitor the antioxidant activity and total phenolic content of infusions of different kinds of teas, including black tea, oolong tea, green tea, and green tea powder (matcha). PLSR permitted the evaluation of the total phenolic content and antioxidant activity of teas with R2 values of 0.932 and 0.918, respectively, for validation in fermented teas (black tea–oolong), and with R2 values of 0.961 and 0.860, respectively, for validation in green tea and green tea powder.

2.3.2.2 Caffeine The most important alkaloid present in tea is caffeine, which constitutes 1–5% of the dry matter of tea shoots.13,47 The high content of caffeine in black tea contributes to its creamy properties; black tea with a low caffeine level is considered to be of inferior quality. NIR spectroscopy was applied to the determination of the caffeine content in instant green tea and granules.104 A calibration model was developed using pure caffeine standards with various concentrations. Using PLS and spectra preprocessing, the caffeine content in tea samples was accurately predicted up to R2 values >0.98. The developed procedure was further validated by recovery studies in comparison with a UV spectroscopic method of caffeine determination. NIR spectroscopy was used for the determination of the caffeine content in infusions of 25 commercially available tea samples. A PLSR model was developed by using the NIR spectra of caffeine standards prepared in tea samples. For the PLSR model, the root mean square error of calibration (RMSEC) was calculated as 0.03 ppm and the limit of quantification (LOQ) as 0.3 ppm.26

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2.3.2.3 Theanine Theanine is the unique non-­proteinogenic amino acid found solely in tea, formed during fermentation.63 The determination of the theanine content in tea is of great importance owing to its effect on the organoleptic properties and its beneficial healthcare effects. Theanine elicits a caramel-­like aroma and flavor that improve the taste by alleviating the bitterness and astringency in tea infusions. Beneficial properties of this compound have been reported, such as anti-­obesity, anti-­diabetic, anti-­stress, and hypolipidemic attributes. NIR spectroscopy was used for the determination of the theanine content of oolong tea.63 The spectra of 162 tea samples were analyzed using PLSR with different wavelength-­selection methods, including regression coefficient-­based selection, UVE, VIP, SR, and FPA. The analysis showed that the PLSR with FPA method achieved better predictive results than PLSR with the full spectrum. The developed simplified model based on wavelengths selected using FPA was characterized by an R2 value of 0.8794 for prediction. The optimized model using FPA had a better performance than other wavelength-­selection methods used in the study. The combination of FPA and a non-­linear regression model of Gaussian process regression (GPR) further improved the predictive performance of the calibration model.

2.3.2.4 Lipid-­soluble Pigments One of the most important factors in the commercial value and consumer acceptance of tea is its color. The color of green tea is primarily determined by the presence of lipid-­soluble pigments such as chlorophyll a, chlorophyll b and their derivatives pheophytin a and pheophytin b formed during the tea-­ making process, and also lutein and β-­carotene. Six main types of lipid-­soluble pigments in green tea leaves and color parameters were determined using VIS and VIS–NIR spectroscopy.31 A total of 135 powdered tea leaf samples with five kinds and three grades were used in the study. The MLR models based on the variables selected by the SPA method were characterized by high Rp2 values for lutein (0.975), chlorophyll b (0.973), chlorophyll a (0.993), pheophytin b (0.919), pheophytin a (0.962), and β-­carotene (0.965). Chlorophyll and pheophytin were determined in five different branded green teas using FT-­MIR spectroscopy.40 The best performance was obtained for non-­linear models developed using the LSSVM method based on characteristic wavenumbers selected by BiPLS and SPA. The optimal models were characterized by high Rp2 values for all pigments studied: chlorophyll b (0.87), chlorophyll a (0.80), pheophytin b (0.85), and pheophytin a (0.89). FT-­MIR spectroscopy was used to determine the β-­carotene and lutein concentrations in green tea.57 PLSR, LSSVM, and ELM methods were used for data analysis. Effective wavenumbers for β-­carotene and lutein were selected based on the weighted regression coefficients of the PLS regression models.

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Simplified calibration models were developed using the extracted effective wavenumbers. With correlation coefficients of prediction (Rp) of 0.946 for β-­carotene and 0.937 for lutein, the ELM models performed best.

2.3.2.5 Simultaneous Determination of Several Components VIS–NIRS was used to determine the contents of caffeine and nine individual catechins in the leaves of green tea.2 A total of 665 samples were studied. The modified partial least-­squares regression (MPLSR) models for caffeine, (−)-­epigallocatechin, (+)-­catechin, (−)-­epigallocatechin 3-­gallate, (−)-­epicatechin, (−)-­epicatechin gallate, and total catechins had high predictive ability, characterized by R2 values higher than 0.90 in the external validation set values. The calibration models of (−)-­gallocatechin and (−)-­epigallocatechin 3-­(3″-­O-­methyl)gallate had R2 values below 0.8. NIRS was applied to the simultaneous determination of bioactive constituents and the antioxidant capability of green tea.64 Swarm intelligence algorithms with PLS such as SA-­PLS, ACO-­PLS, GA-­PLS, and Si-­PLS, were used to select informative variables from the spectra. The SA-­PLS and SiPLS models exhibited the best performance and high Rp2 values for the determination of epigallocatechin gallate (0.97), epigallocatechin (0.97), epicatechin gallate (0.96), epicatechin (0.91), catechin (0.98), caffeine (0.96), theanine (0.93), and antioxidant capability (0.80). A portable NIR spectrometer combined with variable selection methods was effectively used for the rapid determination of caffeine, total catechins, and four individual catechins in instant green tea.29 The performances of portable and benchtop NIR spectrometers were compared. The PLSR results indicated no significant differences between the two instruments for the determination of caffeine, total catechins, (−)-­epigallocatechin, (−)-­epigallocatechin-­3-­gallate, (−)-­epicatechin, and (−)-­epicatechin gallate. NIRS was utilized for the determination of total polyphenols, free amino acids, and the polyphenols-­to-­amino acids ratio in matcha tea.105 SPA, GA, and simulated annealing (SA) methods were used for variable selection based on the SiPLS results. The SiPLS–SPA and SiPLS–SA models had a higher predictive performance with high values of the correlation coefficient (Rp) in the prediction set for total polyphenols (Rp >0.97), free amino acids (Rp >0.98), and polyphenols-­to-­amino acids ratio (Rp >0.98). NIRS was applied to the prediction of amino acids, caffeine, theaflavins, and water extract in black tea.106 Four variants of PLSR, PLS, SiPLS, GA-­PLS, and BiPLS, were used for developing calibration models. GA-­PLS proved to be the best model for the quantification of amino acids and water extract, whereas BiPLS exhibited the best performance for the determination of caffeine and theaflavins.

2.3.3  Sensory Properties of Tea FT-­NIR spectroscopy combined with back-­propagation adaptive boosting (BP-­AdaBoost) and SiPLS were applied for the prediction of taste attributes and taste-­related components in black tea.107 A total of 160 tea samples from

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different countries were analyzed. BP-­AdaBoost models provided better prediction results than SiPLS models for taste scores, total polyphenols, water extracts, free amino acids, caffeine, individual theaflavins, and total catechins. The VIS–NIR transmittance spectroscopy technique was used to predict the color of black tea infusions evaluated by a sensory panel.44 The non-­ linear BP-­ANN model developed based on the optimal variables selected by GA (GA–BP-­ANN) was characterized by a correlation coefficient of 0.8935 for the prediction set. NIR spectroscopy combined with non-­linear methods was used for the effective evaluation of the sensory quality of green tea.55 The dimension of the spectral data was reduced using non-­linear methods such as KPCA, SPCA, and LTSA, and compared with linear methods such as PCA and MDS. A non-­linear RVM method was used to build calibration models for the prediction of the sensory scores evaluated by expert panels from NIR spectra, and compared with BP–ANN and a linear PLS model. The performance of the LTSA–RVM model (Rp = 0.963) was superior to that of the other methods. The comprehensive study of the chemical composition and sensory properties of green tea led to the development of the NIRS roadmap coupled with chemometrics, which was proposed as a support for quality control within the entire green tea sensory evaluation chain.108 Laser-­induced fluorescence was used for the classification and quality evaluation of Chinese oolong teas and jasmine teas.48 Singular value decomposition (SVD) was used for the analysis of spectra of the tea samples. The agreement between the grades evaluated by tea experts and the LDA classification model demonstrated the potential of fluorescence spectroscopy for practical tea grade assessment.

2.3.4  Tea Processing Fermentation is the most crucial step determining the quality of black tea. The combination of FT-­NIRS and a computer vision system (CVS) was used for evaluating the degree of black tea fermentation.109 A total of 110 samples of black tea at different degrees of fermentation were studied. Two mid-­level strategies were applied to analyze the fusion signals of FT-­NIRS and CVS. Supervised pattern recognition methods, KNN, LDA, and SVM, were used for developing classification models. The combined FT-­NIRS and CVS data yielded better results for evaluating black tea fermentation than the individual methods. The mid-­level fusion strategy based on PCA feature extraction combined with SVM was the most effective approach, providing 100% classification accuracies for the calibration and prediction sets.

2.4  S  pectroscopic Techniques for Coffee Quality Assessment Spectroscopic techniques coupled with multivariate analysis are widely used for the evaluation of coffee quality. The quality aspects covered in this section are illustrated in Figure 2.6.

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Figure 2.6  Application  of spectroscopic techniques and multivariate analysis for quality assessment of coffee.

2.4.1  Authentication of Coffee The quality of coffee beans varies greatly and is significantly related to the variety of the coffee plant and also the growing conditions such as the soil composition, climate, and the year of harvest. The processing conditions such as roasting, storage, and trade may also modify the composition of coffee beans. Since the market value of coffee is relatively high, the adulteration of roasted coffee is both frequent and diverse. Adulteration involves tampering with the quality of the beans with respect to various aspects, including species, geographical origin, and mixing of defective beans, and also the addition of other cheaper substances such as coffee husks, coffee stems, maize, barley, chicory, soybeans, rye, corn, wheat middlings, malt, starch, maltodextrins, glucose syrups, and caramelized sugar, among others.6,110,111

2.4.1.1 Coffee Geographical Origin The properties of coffee are greatly affected by the chemical composition of the raw coffee beans, which is, in turn, highly related to their geographical growing regions. A few studies have reported the use of NIRS for the determination of the geographic origin of coffee. In one such study, evaluation of the geographic and genotypic origin of green Arabica coffee was performed using NIRS coupled with PLS-­DA.112 Ninety samples of four Arabica coffee genotypes, cultivated in four different locations in Brazil, were studied. The best classification model permitted the correct (94.4%) identification of validation samples according to both their geographic and genotypic origins. Green coffee beans from two continents (South America and Asia) and nine countries were classified based on their geographical origin using NIRS.113 The relevant aspect of this study was the inter-­laboratory comparison of

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results, which is an important point when assessing an analytical method for its reliability and accessibility. FT-­NIR spectra of 191 coffee samples were recorded in two different laboratories. Laboratory-­independent PLS-­DA and iPLS-­DA models were developed using a hierarchical approach, i.e. considering first the continent and then the country of origin as discrimination criteria. The best classification model allowed the correct identification and prediction of more than 98% of the samples according to their continent of origin, whereas 100% of samples were correctly identified using the country-­ based classification model. The inter-­laboratory reliability of the studied method was also confirmed. The cross-­laboratory model validation demonstrated the potential transferability of a developed method among different production sites or industries. The performances of NIRS and proton–transfer reaction mass spectrometry (PTR-­MS) were compared for the authentication of the geographical provenance of Brazilian specialty coffee and discrimination between samples originating from organic and conventional farming systems.114 Organic (n = 19) and conventional (n = 26) roasted beans of coffee from five distinct producing regions were analyzed. Differentiating the geographic location of coffee was rather complex. The PLS-­DA models based on PTR-­MS data had slightly better classification rates (69%) than those based on NIR spectra (61%). In contrast, the PLS-­DA models allowed better classification of samples depending on the farming system (>80%) independently of the data acquisition technique. The combination of FT-­MIR spectroscopy and radial-­basis function networks (RBFs) allowed the classification of samples of Arabica coffee, both geographically (100% correct classification) and genotypically (94.44% correct classification).39 In another study, infrared spectroscopy and SVM were used for the geographical classification of different genotypes of Arabica coffee.115 The spectra of green Arabica coffee of 20 genotypes, collected at four locations in Brazil, were measured in the NIR and FT-­MIR regions. SVM optimized by a GA was applied for data analysis. With the application of the NIRS–SVM approach, all test samples were correctly classified with a sensitivity and specificity of 100%. With the use of the FT-­MIR–SVM method, the results were slightly poorer, but still satisfactory.115 Fluorescence spectroscopy was applied in order to develop a geographical discrimination model of coffees produced in four major production areas in Minas Gerais, Brazil.53 An aqueous extract of green coffee powder was used to obtain fluorescence spectra. PARAFAC, N-­PLS-­DA, and unfolded partial least-­ squares discriminant analysis (U-­PLS-­DA) were used in order to discriminate between samples. The U-­PLS-­DA classification models generally performed well in the identification of coffees produced in different regions. In another study, the use of direct, solid sample analysis with synchronous fluorescence spectroscopy coupled with chemometric methods was proposed for the classification of the origin of coffee samples, and the performances of linear and non-­linear methods were compared. The best classification results were obtained from the raw data resulting from the fusion at a low level of the

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synchronous fluorescence spectra recorded at offsets of 10 and 40 nm with the Pareto optimization criterion.46 The abilities of different spectroscopic techniques for the determination of the geographical origin of roasted Colombian coffee were compared.116 Spectroscopic techniques such as nuclear magnetic resonance (NMR), NIRS, and MIR spectroscopy were used. A total of 97 samples of roasted coffee beans were collected from 14 countries worldwide, including Arabica from Colombia (34 samples) and Latin American countries and Robusta coffee from Asia and Africa. The best models were obtained with oPLS-­DA for ATR–MIR, oPLS-­DA for NMR, and PLS-­DA for NIRS. All spectroscopic techniques permitted the successful discrimination of samples by species. For origin determination, ATR–MIR and 1H NMR spectroscopy showed comparable performances for discrimination between Colombian coffee samples, whereas NIRS exhibited a relatively lower efficiency.

2.4.1.2 Species Authenticity Among the numerous species of the genus Coffea, only two are economically and commercially important: Coffea arabica L. (Arabica coffee) and Coffea canephora Pierre ex A. Froehner (Robusta coffee). They are characterized by different physicochemical and sensory properties. Arabica coffee has an aromatic composition more appreciated by consumers, hence it has greater commercial value than the Robusta variety.11 The identification of coffee bean varieties is crucial for the coffee trade and consumption. The green beans of Arabica and Robusta have different colors, shapes, and sizes, but after roasting and grinding these species cannot be distinguished visually, hence it is necessary to use chemical parameters for their discrimination. Several components can be used as indicators or discriminators of Arabica and Robusta species, including water-­soluble compounds (caffeine, trigonelline, nicotinic acid, and chlorogenic acids) and fat-­soluble compounds (kahweol and cafestol). Additionally, it has been reported that the metal content, sterolic profile, fatty acid profile, tocopherols, and triglycerides could also be used as suitable chemical descriptors of a certain coffee variety.11 In a recent study, NIR-­HIS was used for the discrimination of green beans of Arabica and Robusta coffee species.25 A total of 33 samples were collected – 18 samples of Robusta and 15 samples of Arabica. Sparse PCA coupled with KNN (SPCA–KNN) and sparse PLS-­DA (sPLS-­DA) were compared with the corresponding classical methods (PCA–KNN and PLS-­DA) to classify Arabica and Robusta coffee species. The sparse methods made it possible to obtain similar results to those given by the classical methods, with the advantage of obtaining more interpretable models. For the prediction of the test set samples, the study reported an improvement of efficiency from PCA–KNN (91.3%) to sPCA–KNN (100%), whereas the efficiency was the same (100%) for both PLS-­DA and sPLS-­DA.

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ATR–FT-­MIR spectroscopy and paper spray mass spectrometry (PS-­MS) were applied to the quantification and characterization of Robusta and Arabica coffee blends.61 A total of 120 ground coffee blends (0.0–33.0%) at different degrees of roasting were formulated. PLSR models were built individually for each type of spectra and for fused data. Better prediction results were obtained with low-­ and medium-­level data fusion. Data fusion models were improved by variable selection, using GAs and OPS. The lowest prediction error, RMSEP = 1.8%, and residual prediction deviation, RPD = 4.2, were provided by the OPS-­based low-­level data fusion model. In another study, NIRS and total reflection X-­ray fluorescence (TXRF) spectroscopy were applied to determine the proportions of Robusta and Arabica in coffee blends.117 Robusta (n = 10) and Arabica (n = 30) coffee samples were obtained from various coffee producers, varying in crop year and grain quality. Moreover, 80 coffee blends were prepared using pure coffees. PLSR models were built using data fusion at low and medium levels. Three methods of variable selection, selection of predictive variables based on their importance indices (SVPII), GA, and OPS, were tested to optimize the PLSR models. SVPII selected the NIR regions best correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, and Sr. Data fusion between atomic and molecular spectra allowed better differentiation between coffee varieties.117 In a further study, aqueous methanolic extracts of 38 green bean coffee samples, which varied in their variety and processing, were characterized using NMR, MIR, and circular dichroism (CD) spectroscopy along with liquid chromatography-­mass spectrometry (LC-­MS).118 Based on PCA analysis of the MIR spectra and LC-­MS data, the distinction between Arabica and Robusta green coffee beans was successfully achieved. Neither CD nor NMR spectroscopy allowed the discrimination of coffee samples of different varieties. Another study used negative-­ion mode electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry [ESI(−)-­FT-­ICRMS] and ATR–FT-­MIR spectroscopy to quantify Robusta coffee in Arabica coffee blends.38 The univariate calibration model obtained for the ESI(−)-­FT-­ICRMS data had limits of detection (LOD) and quantification (LOQ) of 0.2 and 0.3 wt%, respectively. The multivariate PLSR model developed using ATR-­FT-­ MIR spectra had LOD and LOQ values of 1.3 and 4.3 wt%, respectively, and an RMSEP of 9.2 wt%, with coefficients of determination for prediction of 0.9295. The potential of data fusion of synchronous fluorescence and UV–VIS spectroscopy for the quantification of concentrations of roasted Arabica and Robusta in coffee blends was studied.45 Water extracts of 33 coffee samples (25 Arabica and eight Robusta samples) from different countries were used. PCA–MLR models were used to calculate the levels of undeclared addition. The lowest reported RMSECV was 7.9%. LDA analysis was applied to fluorescence and UV data of coffee samples. The best performance of PCA–LDA analysis was observed for the data fusion of UV and synchronous fluorescence spectra measured at an offset of 60 nm. LDA results showed that low-­level

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data fusion of both UV absorption and fluorescence spectroscopy can lead to over 96% correct classification in the test set. Fluorescence spectroscopy was used for the discrimination of several Indonesian specialty coffees.119 EEMs of water extracts of three types of Indonesian Robusta coffee, Peaberry, PagarAlam, and Civet, were analyzed by PCA and SIMCA. PCA analysis revealed that it is possible to discriminate between types of coffee based on information from EEM spectral data. Using the SIMCA method, a discrimination model of Indonesian specialty coffee was successfully developed, which subsequently resulted in a high performance of discrimination with 100% sensitivity and specificity for each type of coffee analyzed.

2.4.1.3 Discrimination Between Defective and Non-­defective Samples The most important defects of coffee beans are black, sour, or immature beans. Bean fermentation is associated with blackening and souring defects, whereas immature beans are produced from immature fruits. Black beans are related to a heavy flavor of the coffee brew, sour beans contribute to a sour and oniony flavor, and immature beans contribute to the astringency of the brew.120 Furthermore, the primary concern about defective beans is the possible presence of ochratoxin A.6 DRIFTS was applied to discrimination between defective and non-­defective coffees.35 LDA was used for the classification of the samples into four groups: non-­defective, black, dark sour, and light sour, with immature beans. The recognition and prediction abilities of the LDA models obtained ranged from 95 to 100%. The results indicated that DRIFTS showed good potential for discrimination between defective and non-­defective coffee after roasting and grinding. In another study, FT-­MIR spectroscopy was applied to the discrimination of defective and non-­defective coffee beans.36 The reflectance spectra [ATR and diffuse reflectance (DR)] and the transmittance spectra in KBr discs were measured. Good separation between defective and non-­defective coffee beans was achieved based on both PCA and cluster analysis of the reflectance spectra and of the first derivatives of the transmittance spectra. Subsequently, the same group aimed to evaluate the potential of FT-­MIR spectroscopy and NIRS for the discrimination of roasted, defective, and non-­ defective coffees.60 Coffee beans were manually sorted into five lots or sample classes: non-­defective, immature, black, and sour (light and dark colored). ATR–FT-­MIR and NIR spectra were measured. The classification models based on elastic net made it possible to obtain a high percentage of correct classification, and the discriminant variables extracted provided a good interpretation of the models. The discrimination of defective and non-­defective beans was related to the content of the main chemical descriptors of coffee, such as carbohydrates, amino acids, lipids, caffeine, and chlorogenic acids.60 In a later study, the performances of FT-­MIR spectroscopy and NIRS for the quantification of defective beans in roasted Arabica coffees were compared.121 Quantitative models were developed by PLSR. The correlation coefficients

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for the FT-­MIR and NIR models developed to quantify the amount of defective roasted coffees mixed with non-­defective coffees were 0.891 and 0.953, respectively. A comparison between the two techniques indicated that NIRS provided more robust models of quantification.

2.4.1.4 Adulteration and Contamination In a study on the adulteration of coffee, NIRS was used for the identification and quantification of barley addition to roasted and ground coffee samples.111 Nine types of commercially available coffee samples, including pure Arabica, Robusta, and their mixtures, and four types of barley samples, with different roasting degrees, were used to prepare experimental blends. A GA was applied for the selection of informative variables from their spectra. PLSR was applied to build the models for predicting the percentage addition of barley to the coffee samples. Low prediction errors achieved on a test set showed that NIRS can be applied to quantify adulterants in coffee powder. In another study, a portable microNIR spectrometer was used for the identification and quantification of adulterations of Arabica coffee.27 A total of 125 blends were prepared, containing different adulterants (corn and peels/ sticks and Robusta coffee). The developed PLSR model permitted the prediction of adulterations with minimum LOQs of 5–8 wt%. A study focusing on the prevention of fraudulent labeling of coffee reported the use of UV–VIS spectroscopy for the simple and rapid analysis of aqueous extracts of ground roasted coffees.122 The method was used for the identification of adulterants, such as husks and sticks, in ground roasted coffees. The UV–VIS spectra of extracts of the ground roasted coffees were measured and analyzed using SPA for variable selection in combination with LDA (SPA–LDA). This method made it possible to obtain a 100% classification in both training and test sets. A method based on fluorescence measurements was developed to quantify the mycotoxin ochratoxin A in coffee and tea samples.94 Fluorescence spectroscopy appears to be a suitable strategy for the determination of ochratoxin A, which is inherently fluorescent. The EEM were recorded for extracts of ground roasted coffee and green coffee samples and were analyzed using PARAFAC and MCR-­ALS. The MCR-­ALS algorithm provided an adequate fit to the data for both samples, whereas PARAFAC was satisfactory only for the tea samples. The LODs were in the range 0.2–0.3 ng mL−1 and prediction errors were between 2 and 4%. The predicted concentrations of ochratoxin A were in agreement with the values obtained by a reference method, based on HPLC with fluorescence detection.

2.4.2  Coffee Composition 2.4.2.1 Moisture Water is one of the important components of green coffee. A water content above 12.5% has a negative impact on the quality of green coffee, as it causes, among others, microbial growth, mycotoxin formation, an unstable

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production process and inferior sensory quality of roasted coffee.23 NIRS and color measurements have been used to study different drying methods, establishing the degree of degradation in the drying process, and to establish the specificity and accuracy of the standard determination methods for water content.123 The drying process was monitored on the basis of the absorption at 1940 nm, owing to the specificity of this band for water molecules. The effectiveness of water evaporation during coffee drying was evaluated by comparing the weight loss results with the residual NIRS absorption left over after drying. It was concluded that neither the ISO 1446 nor the ISO 6673 method allows complete drying of green coffee.

2.4.2.2 Acidity and pH The pH value is an important parameter of coffee quality and may indicate undesirable changes in the coffee beans, such as pre-­ or post-­harvest fermentation. Coffee contains many different organic acids, the most abundant of which are acetic, citric, chlorogenic, malic, and quinic acids. The acid content depends on, among others, the ripeness of the beans and the conditions of cultivation and processing. In a relevant study, the pH and acidity of green coffee were determined using NIRS.124 A total of 250 green Arabica coffee samples were analyzed. The PLSR models for pH and acidity had correlation coefficients of 0.78 and 0.92, respectively, for test set validation.

2.4.2.3 Caffeine The caffeine content is an important indicator of coffee quality, hence the development of rapid and non-­destructive methods for its determination has been the subject of numerous studies. NIRS was utilized to determine the caffeine content in a study in which 72 ground coffee samples were analyzed.125 The application of second-­derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection resulted in a significantly improved PLSR model, with an RMSEP of 0.378 mg g−1 and a mean relative error of 1.976% for the test set. A method was also developed for the quantification of caffeine in extracts of unroasted coffee beans using UV–VIS spectroscopy.126 In another study, UV–VIS spectroscopy was used for the determination of caffeine levels in coffee powder after extraction.127 The absorbance was measured at 276 nm. This study demonstrated that the amount of soluble caffeine in boiling water was determined by the surface area of the coffee powder. The caffeine content was higher for finely ground samples and lower for coarser samples. The development of methods for the determination of the caffeine content in aqueous solutions of green coffee beans based on ATR–FT-­MIR and fluorescence spectroscopy was reported.128 Caffeine was also directly determined in dimethylformamide solution using NIRS with a univariate calibration technique.

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2.4.2.4 Polyphenols Chlorogenic acid in roasted coffee beans was determined using NIRS.129 The optimal PLSR model was characterized by RCV2 = 0.76 and RMSECV = 1.16% and was suitable for the rapid and non-­destructive determination of chlorogenic acid concentrations in roasted coffee beans. NIRS was applied to the quantification of the total phenolic content and evaluation of sensory scores for green beans of Arabica coffee.130 The PLSR models demonstrated a high predictive ability. The highest correlation coefficient for total phenolic content was 0.89 for validation of the test set. All sensory attributes studied, including acidity, body, balance, flavor, aftertaste, fragrance, overall cup preference, and total specialty quality, were predicted with satisfactory results. The best model was built for the body attribute, which presented a correlation coefficient of 0.85 for the test set validation. FT-­NIRS was used for the assessment of bioactive components of spent coffee grounds.131,132 Spent coffee grounds are produced in significant volumes annually worldwide, and are a rich source of bioactive compounds. Recovery of these compounds is of high commercial interest, since they can be further used in food, cosmetic, and pharmaceutical products. FT-­NIRS was used to evaluate the antioxidant capacity and total phenolic and total flavonoid contents of spent coffee ground samples (n = 101).131 All PLSR models were characterized by high R2 values for an independent test set for the antioxidant capacity of spent coffee grounds (0.93), antioxidant capacity of ethanolic extracts (0.96), total flavonoid content (0.95), and total phenolic content (0.95). The results confirmed that NIRS is a promising technique for the routine assessment of the antioxidant properties of coffee by-­products. In another study, NIRS was proposed for the determination of the content of three main phenolics, caffeic acid, (+)-­catechin, and chlorogenic acid, and three methylxanthines, caffeine, theobromine, and theophylline, in spent coffee grounds obtained from different coffee brands and diverse coffee machines.132 PLSR models optimized by variable selection showed good performance and high determination coefficients for the test set (Rp2) of the studied compounds: caffeine (0.95), caffeic acid (0.92), (+)-­catechin (0.88), chlorogenic acid (0.71), and theophylline (0.84). A rapid method for screening the antioxidant content in coffee extracts was developed using fluorescence.133 The antioxidant properties of extracts of 15 types of Arabica bean coffee were predicted from their EEM using PLSR and N-­PLSR. The oxygen radical absorbance capacity (ORAC) parameters of coffee extracts were predicted from EEM within an error of prediction equal to 6.29%. The total phenolic content was evaluated using the PLSR model with a prediction error equal to 7.02%.

2.4.2.5 Diterpenes (Cafestol and Kahweol) NIRS was used in a study to determine the amount of diterpenes (cafestol and kahweol) in green coffee.134 A total of 126 Ethiopian accessions coffee collection and 44 modern cultivars were used in the study. The calibration models

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for cafestol and kahweol were developed using modified PLSR (MPLSR). The coefficients of prediction for the determination of cafestol (R2 = 0.89) and kahweol (R2 = 0.88) confirmed the validity of NIRS for the determination of the diterpene content in green coffee.

2.4.2.6 Simultaneous Determination of Several Components A study used the diffuse reflectance NIR spectra of over 50 Arabica roasted coffee samples with the aim of predicting acidity, bitterness, flavor, purity, and overall quality of coffee beverage.135 All samples were submitted to a sensory evaluation by experts. In order to take only significant spectral regions into account, the OPS algorithm was applied. The regions of the spectra selected using this algorithm were related to the absorption of pure caffeine, trigonelline, 5-­caffeoylquinic acid, cellulose, coffee lipids, sucrose, and casein. The relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain was found using the PLSR method. Another study reported the prediction of several properties, such as moisture content, soluble solids, and total and reducing sugars, in green coffee samples using NIRS with PLSR.136 For this purpose, 250 samples of Brazilian green coffee were analyzed. PLSR was used to develop calibration and validation models. The highest determination coefficients obtained for different chemical properties of coffee in the validation set were moisture (0.810), soluble solids (0.516), total sugars (0.694), and reducing sugars (0.781). NIR-­HIS was applied to the control of extractable contents of caffeine, chlorogenic acid, total phenolics, and melanoidins in coffee beans.24 In the test set, MPLSR calibration models had acceptable standard errors of prediction for caffeine (12.01%), chlorogenic acid (15.61%), and total phenolics (17.61%). NIRS with PLSR was proposed for the offline and online quantification of caffeine, trigonelline, and 5-­caffeoylquinic acid in coffee beans and coffee beverages.137 In another study, to determine caffeine and trigonelline ATR–FT-­MIR spectroscopy was applied to 20 samples of green coffee beans collected from the principal coffee-­growing areas of Ethiopia.138 The caffeine and trigonelline contents in the aqueous extract of green coffee beans were found to be in the range 0.84–1.15 and 0.83–1.13% w/w, respectively. The LODs for caffeine and trigonelline were 140 and 100 mg L−1 and the LOQs were 470 and 330 mg L−1, respectively. The precision was reported to be 3.0% for caffeine and 4.3% for trigonelline. The accuracy of the developed analytical method was also reported to be satisfactory. Fluorescence spectroscopy was applied to the determination of caffeine, theobromine, and trigonelline in green coffee bean extracts.139 The method revealed comparable recoveries and reproducibilities with respect to the results obtained using chromatographic methods.

2.4.3  Sensory Properties of Coffee The chemistry of coffee flavor is extremely complex and is still not completely understood.20 The most important sensory attributes related to the quality of the coffee brew include sweetness, acidity, and bitterness.140,141 Sweetness is

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associated with simple carbohydrates, represented mainly by sucrose, glucose, and fructose. Quinic, citric, and malic acids contribute to the acidity of the coffee brew, whereas chlorogenic acids contribute to bitterness. Caffeine is also associated with bitterness, whereas lipids contribute to aroma formation.140 A method based on NIRS was reported for the determination of the sensory attributes of Brazilian coffee blends elicited in professional coffee cupping.142 A total of 217 commercial coffee samples classified as different beverage types and with different degrees of roasting were subjected to the “cupping test” by official cuppers. PLSR models developed for the powder fragrance, drink aroma, acidity, bitterness, flavor, body, astringency, residual flavor, and overall quality showed good predictive capacity. The findings showed that direct NIRS analysis of roasted and ground samples without beverage preparation allows the efficient prediction of all sensorial attributes of coffee and could be a promising tool for the coffee industry. In a subsequent study, coffee cup profiles were evaluated using a handheld NIR spectrometer for direct measurements of roasted and ground coffee blends.28 The sensitivity and specificity of the PLS-­DA model were 91–100, 84–100, and 73–95% in the training, prediction, and internal cross-­validation sets, respectively. The flavors of specialty coffee were predicted by methods based on NIRS coupled with machine-­ and deep-­learning methods.54 For the prediction of seven coffee flavor categories, models with comparable performance were obtained using the machine-­learning method (i.e. SVM) and the deep convolutional neural network (DCNN) method. The visualization method – a focusing plot – revealed the correlation among the highly weighted spectral region of the DCNN model, the predicted flavor categories, and the corresponding chemical components. The effective models provided moderate prediction for seven flavor categories based on 266 samples. Another study used the common dimension (ComDim) multi-­block method to evaluate 1H NMR metabolite fingerprints, NIR spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles, and also to investigate relationships between these multiple data blocks.65 ATR–FT-­MIR spectroscopy coupled with PLS-­DA was successfully applied to discriminate between espresso coffees with different sensory characteristics, evaluated by a panel of coffee tasters.143 Sensory evaluation using the protocol of the Specialty Coffee Association (SCA) and FT-­MIR spectroscopy were used for the characterization of specialty coffees.144 Samples from three post-­harvest methods (dry, semi-­dry, and wet) were analyzed. LDA models based on FT-­MIR spectroscopy permitted the discrimination of samples according to the post-­harvest treatments for green beans, medium roast coffee, and dark roast coffee. Colombian defective and non-­defective coffee samples were compared using sensory qualities and chemical indicators of the MIR spectra.145 The sensory attributes differentiated samples of high-­quality medium roast coffee from the other samples. Using the chemical descriptors obtained from

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the MIR spectra, it was possible to differentiate between high-­quality, commercial, and instant coffee. Spectroscopic and chromatographic methods were used for the discrimination of specialty and traditional coffees, previously classified by the SCA cupping method.146 Factor analysis showed that the spectra of aqueous and dichloromethane extracts provided the most divergent information about metabolites. Spectra of aqueous extracts allowed better discrimination for specialty and traditional coffees. Among the techniques used, chromatography and UV–VIS spectrophotometry allowed the detection of metabolites that discriminated between the coffees. The MIR spectra showed a lower performance in terms of the discriminatory efficiency between coffees. The main metabolites responsible for discrimination were chlorogenic acids, trigonelline, organic acids, lipids, and fatty acids.

2.4.4  Coffee Processing The process of roasting of coffee beans is responsible for the final product quality and compounds that participate in Maillard reactions affect the flavor, aroma, and color characteristics.140 However, roasting also leads to the formation of undesirable, toxic compounds, such as acrylamide, furan, and polyaromatic hydrocarbons including benzo[a]pyrene.147 Discrimination of commercial Brazilian coffees with different caffeine extraction and roasting degrees was performed in a study using FT-­MIR diffuse reflectance spectroscopy.148 The ordered-­predictors selection method was used for variable selection. Using PLS-­DA, it was possible to distinguish between decaffeinated and regular medium and dark roasted coffees. It was possible to classify correctly 100% of samples in the external and prediction sample sets, based on their degree of roasting. NIRS was used for real-­time monitoring of coffee roasting processes. NIRS was used for monitoring the acidity during the roasting process, which is one of the main characteristics of the organoleptic profile of coffee.149 Arabica and Robusta coffee varieties of different origins were roasted under different process conditions. Acidity profiles were estimated from the NIR spectra and were in agreement with the values obtained using reference analytical methods. Another study evaluated a real-­time and in situ analytical method based on NIRS proposed to predict two of the most relevant coffee parameters during the roasting process, sucrose and color.149 Different coffee varieties (Arabica and Robusta), of different origins (Brazil, East Timor, India, and Uganda) and roasting process procedures (slow and fast) were taken into account. The PLSR results demonstrated the applicability of this methodology, with determination coefficients of >0.85 for all modeled parameters. The estimated relationship between sucrose and color development during the roasting process may be used for the design of real-­time coffee products with similar visual appearances and distinct organoleptic profiles.

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NIRS and multivariate statistical process control (MSPC) based on PCA were proposed for real-­time monitoring of the coffee roasting process.150 The main objective was the development of an MSPC methodology for the early detection of disturbances to the roasting process. The developed approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real time. Another study reported the use of two NIR instruments for real-­time comparison and also the characterization of green and roasted beans and ground coffee samples.151 A benchtop spectrophotometer suitable for in-­line analyses and a portable spectrophotometer for at-­line analyses were tested. PLSR was used to develop models to predict moisture content, tap density, and powder granulometry of ground coffee and to estimate moisture on roasted beans. No significant differences were observed for the majority of the dataset analyzed when model results from the two devices were considered. NIRS and PLSR were used to monitor coffee processing. Roasted coffee samples analyzed in the study were sampled during the period of 5 months of coffee processing, differentiated by type of preparation and degree of roasting.152 The parameters of merit indicated that the prediction models developed for granulometry, color, moisture content, and infusion time can be safely used in the coffee industry as an alternative to reference methods. The development of NIRS-­based non-­invasive real-­time methodology for the quantification of antioxidant properties, total antioxidant capacity, and total phenolic content of coffee during the roasting process was reported.153 The procedure involved the monitoring of the roasting process by pointing a diffuse reflectance NIR probe directly at the roasting chamber through a glass window. The figures of merit of the chemometric models to estimate total antioxidant capacity and total phenolic content showed determination coefficients (Rp2) of >0.90. Hence NIRS was found to be a satisfactory real-­time tool for monitoring the content of antioxidant compounds in coffee during the roasting process, thus complementing other established procedures.

2.5  Conclusion Spectroscopic techniques are simple and rapid, allowing direct and non-­ destructive measurements and also the simultaneous determination of multiple components or properties in a matter of minutes in an environmentally friendly manner. This overview of the applications of optical spectroscopy in tea and coffee analysis demonstrates the potential of these techniques as important tools for quality assessment and authenticity testing of tea and coffee. These methods, particularly NIRS, have been successfully applied both in practice and in routine quality control of tea and coffee. However, more extensive use of these methods in practice and regulatory recommendations require further studies. As fingerprinting methods have the potential to be particularly useful for the authentication of tea and coffee, one of the most appealing applications of spectroscopy is the prediction of sensory properties and sensory grades of

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tea and coffee, with fast and simple objective methods replacing costly and time-­consuming expert panel evaluation. Optical spectroscopy is one such method of choice for process control, permitting the simultaneous, real-­time monitoring of several critical process parameters for authenticity testing and food fraud prevention in both tea and coffee. The successful implementation of optical spectroscopy for the quality control and authentication of tea and coffee requires calibration and classification models based on large, representative data sets. The development of representative databases that encompass the natural variations in the chemical composition and spectral signatures of samples is necessary for the successful implementation of spectroscopy for the quality control of these beverages. As data analytical technologies advance, algorithms will be able to model non-­linear relationships and use data from multiple sources simultaneously, offering complementary information about samples. On the other hand, the development of low-­cost, handheld sensors that can be used directly for quality control at the sample site, at every level of the supply chain, fosters the application of optical spectroscopy. Tea and coffee quality management can benefit from the advancement of spectroscopic and chemometric methods by providing consumers with safe and high-­quality products.

List of Abbreviations ABC Artificial bee colony ACO-­PLS Ant colony optimization partial least squares ANN Artificial neural network ATR Attenuated total reflectance BiPLS Backward interval partial least squares BOSS Bootstrapping soft shrinkage BP-­AdaBoost Back-­propagation adaptive boosting BP-­ANN Back-­propagation artificial neural network BPNN Back-­propagation neural network CARS Competitive adaptive reweighted sampling CARS-­PLS Competitive adaptive reweighted sampling partial least squares CART Classification and regression trees CD Circular dichroism CNN Convolutional neural network ComDim Common dimension CVS Computer vision system DCNN Deep convolutional neural network DL Deep learning DR Diffuse reflectance DRIFTS Diffuse reflectance infrared Fourier transform spectroscopy DT Decision tree

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EEM Excitation–emission matrices ELM Extreme learning machine ES Ensemble strategy ESI(−)-­FT-­ICRMS Negative-­ion mode electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry FHSI Fluorescence hyperspectral imaging FPA Flower pollination algorithm FT-­MIR Fourier transform mid-­infrared FT-­NIR Fourier transform near-­infrared GA Genetic algorithm GA-­iPLS Genetic algorithm interval partial least squares GA-­PLS Genetic algorithm partial least squares GPR Gaussian process regression HCA Hierarchical cluster analysis HPLC High-­performance liquid chromatography IGA-­PSO Improved genetic algorithm–particle swarm optimization iPLS Interval partial least squares iPLS-­DA Interval partial least-­squares discriminant analysis KNN k-­nearest neighbors KPCA Kernel principal component analysis LC-­MS Liquid chromatography-­mass spectrometry LCNC Large class-­number classification LDA Linear discriminant analysis Lib-­SVM Library support vector machine LOD Limit of detection LOQ Limit of quantification LSSVM Least-­squares support vector machine LTSA Local tangent space alignment MASS Model adaptive space shrinkage MCR-­ALS Multivariate curve resolution-­alternating least squares MDS Multidimensional scaling MIR Mid-­infrared ML Machine learning MLP Multilayer perceptron MLR Multiple linear regression MPLSR Modified partial least-­squares regression MSPC Multivariate statistical process control NIR Near-­infrared NIR-­HSI Near-­infrared hyperspectral imaging NIRS Near-­infrared spectroscopy NMR Nuclear magnetic resonance N-­PLSR N-­way partial least-­squares regression N-­PLS-­DA N-­way partial least-­squares discriminant analysis oPLS-DA Orthogonal partial least squares discriminant analysis OPS Ordered predictors selection

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ORAC Oxygen radical absorbance capacity PARAFAC Parallel factor analysis PCA Principal component analysis PCR Principal component regression PDO Protected designations of origin PGI Protected geographical indications PLS Partial least squares PLS-­DA Partial least-­squares discriminant analysis PLSR Partial least-­squares regression PS-­MS Paper spray mass spectrometry PTR-­MS Proton-­transfer reaction mass spectrometry QDA Quadratic discriminant analysis RBF Radial-­basis function network RCGA Real-­coded genetic algorithm RDA Regularized discriminant analysis RF Random forest RMSE Root mean square error RMSEC Root mean square error of calibration RMSECV Root mean square error of cross-­validation RMSEP Root mean square error of prediction RPD Residual prediction deviation RVM Relevance vector machine SA Simulated annealing SA-­PLS Simulated annealing partial least squares SCA Specialty Coffee Association SCARS Stability competitive adaptive reweighted sampling SF Synchronous fluorescence SFLA Shuffled frog leaping algorithm SIMCA Soft independent modeling of class analogy SiPLS Synergy interval partial least squares SPA Successive projection algorithm SPCA Sparse principal component analysis SPLS Sparse partial least squares SR Selectivity ratio SVD Singular value decomposition SVM Support vector machine SVPII Selection of predictive variables based on their importance indices TXRF Total reflection X-­ray fluorescence U-­PLS-­DA Unfolded partial least-­squares discriminant analysis UV Ultraviolet UVE Uninformative variable elimination VIP Variable importance in projection VIS Visible VISSA Variable iterative space shrinkage approach

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118. S. Deshpande, R. M. El-­Abassy, R. Jaiswal, P. Eravuchira, B. von der Kammer, A. Materny and N. Kuhnert, Anal. Methods, 2014, 6, 3268–3276. 119. D. Suhandy and M. Yulia, IOP Conf. Ser.: Mater. Sci. Eng., 2018, 334, 012059. 120. J. Mendonça, A. Franca and L. Oliveira, J. Food Eng., 2009, 92, 474–479. 121. A. P. Craig, A. S. Franca, L. S. Oliveira, J. Irudayaraj and K. Ileleji, Talanta, 2015, 134, 379–386. 122. U. T. d. C. P. Souto, M. F. Barbosa, H. V. Dantas, A. S. de Pontes, W. d. S. Lyra, P. H. G. D. Diniz, M. C. U. de Araújo and E. C. da Silva, LWT -­Food Sci. Technol., 2015, 63, 1037–1041. 123. C. T. Reh, A. Gerber, J. Prodolliet and G. Vuataz, Food Chem., 2006, 96, 423–430. 124. C. d. S. Araújo, L. L. Macedo, W. C. Vimercati, A. Ferreira, L. C. Prezotti and S. H. Saraiva, J. Sci. Food Agric., 2020, 100, 2488–2493. 125. X. Zhang, W. Li, B. Yin, W. Chen, D. P. Kelly, X. Wang, K. Zheng and Y. Du, Spectrochim. Acta, Part A, 2013, 114, 350–356. 126. A. Belay, K. Ture, M. Redi and A. Asfaw, Food Chem., 2008, 108, 310–315. 127. R. A. Fadri, K. Sayuti, N. Nazir and I. Suliansyah, IOP Conf. Ser.: Earth Environ. Sci., 2020, 515, 012071. 128. B. Weldegebreal, M. Redi-­Abshiro and B. S. Chandravanshi, Chem. Cent. J., 2017, 11, 126. 129. J. Shan, T. Suzuki, D. Suhandy, Y. Ogawa and N. Kondo, Eng. Agric. Environ. Food., 2014, 7, 139–142. 130. C. da Silva Araújo, L. L. Macedo, W. C. Vimercati and S. H. Saraiva, Food Anal. Methods, 2021, 14(9), 1943–1952. 131. R. N. M. J. Páscoa, L. M. Magalhães and J. A. Lopes, Food Res. Int., 2013, 51, 579–586. 132. L. M. Magalhães, S. Machado, M. A. Segundo, J. A. Lopes and R. N. M. J. Páscoa, Talanta, 2016, 147, 460–467. 133. J. Orzel and M. Daszykowski, Chemom. Intell. Lab. Syst., 2014, 137, 74–81. 134. M. B. S. Scholz, N. F. Pagiatto, C. S. G. Kitzberger, L. F. P. Pereira, F. Davrieux, P. Charmetant and T. Leroy, Food Res. Int., 2014, 61, 176–182. 135. J. S. Ribeiro, M. M. C. Ferreira and T. J. G. Salva, Talanta, 2011, 83, 1352–1358. 136. L. Levate Macedo, C. da Silva Araújo, W. Costa Vimercati, P. R. Gherardi Hein, C. J. Pimenta and S. Henriques Saraiva, J. Sci. Food Agric., 2021, 101, 3500–3507. 137. J. S. Ribeiro, T. d. J. G. Salva and M. B. Silvarolla, Food Control, 2021, 125, 107967. 138. H. Yisak, M. Redi-­Abshiro and B. S. Chandravanshi, Vib. Spectrosc., 2018, 97, 33–38. 139. H. Yisak, M. Redi-­Abshiro and B. S. Chandravanshi, Chem. Cent. J., 2018, 12, 59. 140. M. d. S. G. Barbosa, M. B. d. S. Scholz, C. S. G. Kitzberger and M. d. T. Benassi, Food Chem., 2019, 292, 275–280.

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141. I. Esteban-­Díez, J. M. González-­Sáiz and C. Pizarro, Anal. Chim. Acta, 2004, 525, 171–182. 142. M. R. Baqueta, A. Coqueiro and P. Valderrama, J. Food Sci., 2019, 84, 1247–1255. 143. V. Belchior, B. G. Botelho, L. S. Oliveira and A. S. Franca, Food Chem., 2019, 273, 178–185. 144. Y. F. Barrios-­Rodríguez, C. A. Rojas Reyes, J. S. Triana Campos, J. Girón-­ Hernández and J. Rodríguez-­Gamir, LWT -­Food Sci. Technol., 2021, 145, 111304. 145. Y. F. Barrios Rodriguez, K. T. Salas Calderon and J. Giron Hernandez, Coffee Sci., 2020, 15, e151659. 146. M. B. Abreu, G. G. Marcheafave, R. E. Bruns, I. S. Scarminio and M. L. Zeraik, Food Anal. Methods, 2020, 13, 2204–2212. 147. T. Lee, H. Park, P. Puligundla, G.-­H. Koh, J. Yoon and C. Mok, Food Chem., 2020, 328, 127117. 148. J. S. Ribeiro, T. J. Salva and M. M. C. Ferreira, J. Food Qual., 2010, 33, 212–227. 149. J. R. Santos, M. Lopo, A. O. S. S. Rangel and J. A. Lopes, Food Control, 2016, 60, 408–415. 150. T. A. Catelani, J. R. Santos, R. N. M. J. Páscoa, L. Pezza, H. R. Pezza and J. A. Lopes, Talanta, 2018, 179, 292–299. 151. A. Tugnolo, R. Beghi, V. Giovenzana and R. Guidetti, J. Near Infrared Spectrosc., 2019, 27, 93–104. 152. M. R. Baqueta, A. Coqueiro, P. H. Março and P. Valderrama, Food Anal. Methods, 2020, 13, 50–60. 153. T. A. Catelani, R. N. M. J. Páscoa, J. R. Santos, L. Pezza, H. R. Pezza, J. L. F. C. Lima and J. A. Lopes, Food Bioprocess Technol., 2017, 10, 630–638.

Chapter 3

Fruit/Juice Quality Assessment Using Spectroscopic Data Analysis M. Moncada-­Basualtoa,b, J. Pozo-­Martíneza,b and C. Olea-­Azar*a a

Facultad de Ciencias Química y Farmaceuticas, Universidad de Chile, Santiago, Chile; bInstituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, Chile *E-­mail: [email protected]

3.1  Introduction Since the last part of the 20th century and the beginning of the 21st century, there has been increasing interest in the consumption of healthy and good-­ quality foods, with new trends in healthy lifestyles, and with growing concern about high rates of obesity. An unhealthy diet is one of the four main drivers of non-­communicable diseases (NCDs) in all regions covered by the World Health Organization (WHO), with the highest burden in Europe, according to a WHO report.1 Overnutrition has increased markedly in recent decades, and one-­fifth of the world's adults are expected to be obese by 2025. The availability and accessibility of healthy foods will be a challenge that will be influenced by income, individual preferences and beliefs, cultural traditions and geographical and environmental aspects, including climate change.1,2   Food Chemistry, Function and Analysis No. 32 Advanced Spectroscopic Techniques for Food Quality Edited by Ashutosh Kumar Shukla © The Royal Society of Chemistry 2022 Published by the Royal Society of Chemistry, www.rsc.org

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In this context, the quality of food has been of great interest and even a marketing driver when advertising some foods and non-­alcoholic beverages. Terms such as functional foods, healthy lifestyle, Mediterranean diet and healthy foods are now common. However, what is a healthy food? How is the quality of a food or non-­alcoholic drink determined? These are common questions. The term “functional food” first appeared in Japan in 1984. The Japanese government defined a new product category, food for specific health purposes (FOSHU), as “food that contains an ingredient with health functions, and officially approved to claim its physiological effects on the human body”.3 European countries adopted the concept of functional foods more than 10 years later, when the European Parliament and Council introduced a regulation on nutrition and health claims [Reg. (EU) n. 1924/2006], but also in this case no formal definition was mentioned. Research interest in functional foods has increased sharply in the 21st century, and this growing attention globally has greatly influenced their market, which was estimated to be US$162 billion in 2018 and projected to reach US$280 billion by 2025, with an annual growth rate of around 8%. A more current definition defines a functional food as “natural or processed foods that contain biologically active compounds; that, in defined, effective and non-­toxic amounts, provide a clinically proven and documented health benefit using specific biomarkers for the prevention, management or treatment of chronic diseases or their symptoms”.3 Therefore, the generation and regulation of food systems that allow these characteristics to be determined are of great importance for the food industry. Hence methods for the analysis of the quality or composition of foods are of great importance and in this chapter efficient strategies for the analysis of fruits and juices by spectroscopic methods are analyzed.

3.2  Spectroscopic Methods for Food Analysis The food industry involves all activities connected with the production, storage, processing and distribution of different food products, complying with strict quality parameters at each stage. Controlling the safety and quality of food is an essential compulsory activity, where the control of a series of established parameters is carried out in order to guarantee that the products comply with safety and quality standards so as to be suitable for human consumption.4 Foods are classified into different groups based on their nutritional similarity and composition; fruits are classified in group 5 of foods that have high contents of water and sugars, and are an important source of vitamins, minerals and antioxidants, which play a fundamental role in health and some of which can be considered as functional foods.5,6 In addition to fresh fruit, the fruit industry provides the raw material for obtaining various derivatives, such as juices, nectars, jams, dehydrated products and canned and frozen fruits.7 The juice industry has seen a strong growth in

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consumption worldwide, with a predicted increase of 5% per year, due to the increase in healthy lifestyles and the possible benefits they could have on health. Therefore, it is essential to maintain the quality assurance of these products, since they are subject to alterations and degradation during transport and storage, affecting their organoleptic properties and nutritional value,8,9 due to vitamin degradation, color deterioration and loss of aroma and flavor. Various techniques have been used to determine food quality parameters and indicators, such as gas chromatography, liquid chromatography, mass spectrometry and atomic spectroscopy.10 However, the tests are destructive and/or are subject to high acquisition costs and long analysis times, which are obstacles for obtaining fast, precise and reproducible results. The ongoing technological advances in the development of food regulation activities have made it possible to implement new techniques that allow the rapid evaluation of the quality assurance of fruits and juices in situ, without altering or destroying the sample. Among these non-­invasive techniques are vibrational spectroscopy [infrared (IR) and Raman], ultraviolet–visible (UV–VIS), nuclear magnetic resonance (NMR) and fluorescence (FL) spectroscopy.11 These techniques are mainly based on the study of the physical properties of analytes through the interaction of matter with electromagnetic radiation, causing the emission or absorption of energy. Depending on the region of the electromagnetic spectrum, certain information can be obtained from the sample, showing different ranges that are necessary to evaluate the quality of food.12 These regions are as follows:    ●● Ultraviolet region (10–350 nm), which allows the characterization of proteins, peptides, vitamins and amino acids. ●● Visible region (350–800 nm), which allows the identification of artificial and natural colorants; this is the main region for the study of colorants in juices. ●● Near-­infrared (NIR) region (800–2500 nm or 12 500–4000 cm−1), which allows the analysis of food composition. ●● Mid-­infrared region (2500–25 000 nm or 4000–400 cm−1), which allows the identification of the structure and conformation of proteins, polysaccharides and lipids.    All these spectroscopic techniques provide characteristic “fingerprint” spectra of the components present in fruits and juices. Each spectrum contains data corresponding to the analytes present in the sample and their interactions, allowing detailed information on the product to be obtained.11

3.2.1  Fruit Analysis Food matrices are complex, so the easy detection of analytes in the spectrum is difficult, requiring the use of chemometric methods for their identification. The main objectives of chemometric methods are the identification of the patterns in the spectra, the classification of the samples and the

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relationships between the spectra and the properties evaluated. Based on the above, the robustness of the non-­invasive NIR method was studied5 in conjunction with the artificial neural network (ANN) statistical model to evaluate the percentage of dry matter in mango, since this parameter is fundamental for determining the quality in terms of maturity of the fruit. A data set obtained from the spectra of 4675 oven-­dried samples was used to optimize the preprocessing regimen and identify factors that affect the robustness of the model. The spectra recorded for the fruits were characterized by a high-­intensity absorption peak at 680 nm, associated with chlorophyll, and smaller bands at 840 and 960 nm, associated with OH absorbance characteristics, as expected for the fruit. The spectra of the ripening fruit showed a decrease in the chlorophyll peak, but were similar to those of the green fruit at wavelengths greater than 760 nm. The lower apparent absorption values in the 700–900 nm region for ripening fruits point to a lower dispersion coefficient than for green fruits. A change in scattering properties could occur with a change in the amount of intercellular water or the spacing of cells, and will affect the apparent absorption spectrum of a sample and therefore the prediction of dry matter. The results obtained indicated that the ANN model has a prediction of 89%, which was comparable to that of the commonly used partial least-­squares regression (PLSR) model of 88%. Therefore, the application of this ANN model to evaluate the content of dry matter in mango was recommended owing to its ease of use in evaluating a complex matrix. Therefore, the analysis of the IR spectra provided information about the maturity of the food product in a simple and useful way for the evaluation of quality in terms of maturation. Likewise, it has been shown that discarded products of this fruit can be used to prevent the oxidation of meat products, so the better quality of these products could be significant when they are used in other areas of the food industry.13 NMR spectroscopy has also proved useful for the evaluation of fruit quality. The decomposition of blueberries during storage at room temperature, being one of the main causes of loss of quality and food safety, was evaluated.14 Low-­field nuclear magnetic resonance (LF-­NMR) imaging, magnetic resonance imaging (MRI) and the statistical model of a back-­propagation neural network (BPNN) were used to identify the decomposition of blueberries. The LF-­NMR analysis indicated that the displacement of the signals implied the generation of other components in the blueberries due to decomposition in relation to the mature non-­decomposed fruit. This was corroborated by the MRI images, where the dark region of an image accounted for the decomposition due to water loss associated with tissue destruction and integration and water flow changes. The latter is due to the degradation of macromolecules to smaller molecules, involving an increase in permeability (Figure 3.1). The use of BPNN gave a method precision of 98.8% for fruits with moderate and severe rot. It was concluded that the implementation of LF-­NMR, MRI and BPNN could be a useful tool to determine rapidly and accurately the decomposition of fruits during storage.

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Figure 3.1  NMR  images of blueberries in different sections (longitudinal and hor-

izontal) and at different ripening times. Reproduced from ref. 14 with permission from Elsevier, Copyright 2019.

Despite the application of spectroscopic techniques to a wide range of food products, in the area of freshly cut and ready-­to-­eat products such as fruits, they are limited, making it impossible to control the quality of the processed fruit quickly and accurately. In this context, the prediction of the quality of ready-­to-­eat pineapple was evaluated using different molecular spectroscopic techniques [IR, FL and visible (VIS)] and data analysis tools and predictive models such as The Unscrambler and SorfML.15 It was found that the combination of the different sensors with each of the software tools gave selectivity and precision values higher than 85%, and it was concluded that there is a similar trend among the statistical analysis tools for the control of pineapple quality. It was also established that for the implementation of different data analysis platforms it is necessary to understand the application and limitations in order to evaluate the results critically, with the two statistical programs implemented being an easy-­to-­use interface. To comply with the comprehensive control of food quality throughout the production chain, it is essential to evaluate some critical stages, such as raw materials, processing and finished product. Based on this, the implementation of mid-­infrared spectroscopy (MIRS) to evaluate the quality of tomato products at different steps along the production chain was proposed.16 To evaluate the spectroscopic data, the multi-­year combination model together with PLSR was used, allowing the addition of data from previous years' analyses. The results demonstrated that the MIRS technique predicted the soluble solids content (SSC), acidity and dry matter content with high precision

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throughout the processing chain, indicating the robustness of the calibration models (directly related to the variability of the sample). It was concluded that the MIRS technique in conjunction with chemometrics can be used as a powerful tool to control the quality of both the raw material and the processed product. During transport and storage of fruits, chemical reactions between the many nutrients lead to food spoilage, and portable spectroscopic equipment is an optimal tool for evaluating quality. Studies carried out on grape, melon, tomato and watermelon17 determined that the high-­resolution portable NMR technique allows detailed inspection of the food despite the heterogeneous distribution of the tissues, identifying the important metabolic agents as biomarkers for the quality control of the fruits, without the need for pretreatment or destruction of the sample. All the recorded spectra were one-­ and two-­dimensional protonic obtained by applying the IDEALL-­II sequences. The detection of fruits with internal disorders, damage at the seed level and black pulps during the harvesting and storage processes are important, since they affect the quality of the fruit and can lead to economic losses. Portable VIS–NIR spectroscopy was evaluated for the prediction and detection of internal physiological disorders in mango (Figure 3.2, top).18 Logistic models, linear discriminant analysis (LDA), vectors of support, functional data and active forest modeling approaches were used for the analysis of the spectroscopic data. The spectral data analyzed to build the models were collected after harvesting and after storage and revealed that the reflectance spectra curves showed two characteristic peaks at 550 and 800 nm, and the healthy fruit maintained a higher intensity of reflectance than the gelatin seed and black pulp fruit (Figure 3.2, bottom). It was found that a prediction of internal physiological disorders can be made at harvest and after storage, the best classification models being the logistic model and LDA. The harvest prediction had a precision of 65% for the logistic model and 63% for LDA, and after storage the precision was 71 and 76%, respectively. It was concluded that the implementation of this fast, low-­cost and non-­destructive technique will allow protocols to be established to monitor and reduce post-­harvest losses, guaranteeing a high-­quality fruit for consumption.

3.2.2  Juice Analysis The market for juices as products derived from fruits has increased considerably in recent years, so maintaining quality is of the utmost importance. The parameters that need to be controlled include color, composition, amount of water and adulterations, among others. In this context, the use of the total spectrum and synchronous fluorescence technique, in conjunction with the parallel factor analysis (PARAFAC) model, was proposed11 for the identification of different beverages from red fruits, which are prone to fraud due to incorrect labeling of the composition, dilution with water and the use of low-­ quality ingredients.

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Figure 3.2  Top:  visible symptoms of internal physiological disorders in Keitt man-

goes after 30 days at 12 °C. Bottom: VIS–NIR mean spectral curves, without preprocessing, collected after 30 days at 12 °C for Keitt mangoes, healthy or with symptoms of physiological alterations of the gelatin seed or black pulp. Reproduced from ref. 18 with permission from Elsevier, Copyright 2020.

The total fluorescence spectra of all the studied beverages were obtained by recording the emission spectrum for a series of excitation wavelengths, thus showing the fluorescence intensity as a function of the excitation and emission wavelengths. Figure 3.3 shows the spectra of representative samples of each of the four drinks studied (chokeberry, blackcurrant, strawberry and raspberry). Similar characteristics are present in all the recorded spectra. Specifically, three emission bands are observed, with their respective excitation/emission maxima at (i) 276–280/314–338 nm, (ii) 310–345/390–455 nm and (iii) 380–465/585–645 nm. Differences in the exact positions of the maxima and the relative intensities of the particular bands are observed for

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Figure 3.3  Excitation–emission  matrices of juices from different berries: (A) choke-

berry, (B) blackcurrant, (C) strawberry and (D) raspberry. Reproduced from ref. 11 with permission from IOP Publishing, Copyright 2013.

particular juices. Additionally, a fourth emission band is present with excitation/emission maxima at 386–420/499–560 nm in some of the strawberry beverages only. The results obtained indicated that the use of the two types of fluorescence spectra provides the general characteristics of the juices; the use of PLSR gave a prediction error of 4.86%, which when using the PARAFAC model increased to 15.27%. It was concluded that the optimal parameters (excitation and emission wavelengths) must be carefully selected in order to achieve the best discrimination and obtain optimum performance of the model. Finally, the fluorescence technique showed good potential as a tool for the rapid evaluation of the authenticity of red fruit drinks. Fruit juices are produced by two routes: direct or not from concentrate (NFC), and reconstituted from concentrate (FC) by adding water. Juices from the direct route preserve the properties of the fresh fruit better and have more attractive sensory properties for consumers,19,20 but can be subject to fraud in the manufacturing process, therefore reducing the quality of the juice.

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The main techniques for determining juice quality are very expensive, time consuming and destructive, so the use of rapid techniques without destroying the sample is the current goal. The development of a new multivariate analysis technique of synchronous fluorescence spectroscopy to determine the quality of apple juice was evaluated, in which the total synchronous fluorescence spectra of NFC and FC juices were recorded, and principal component analysis (PCA) and partial least-­squares discriminant analysis (PLS-­DA) were used to develop classification models.21 It was found that the best classification model was achieved with synchronous total spectra with a validation error of 0.05% and a prediction error of 0.08%. Likewise, PCA analysis found differences between NFC and FC juices. In conclusion, the development of this technique may be important for possible applications in determining the authenticity of juices. The authentication of pomegranate juice was studied by means of Fourier transform infrared (FTIR) spectroscopy and chemometrics with the use of concentrated pomegranate juice.22 PCA was applied to differentiate and classify adulterated samples, in addition to partial-­least squares (PLS) analysis. The results showed that the technique could explain 99% of the variability with a correlation coefficient of 0.9751, indicating a good prediction. It was thus established that the FTIR technique is a good alternative for the rapid classification and detection of adulteration of juices. Many routine analyses have been developed to determine the quality of fruit juices with control parameters such as SSC and titratable acidity (TA), which are related to the sensory attributes and stability of the juice. For the determination of these parameters, the use of separate analytical methods is required, which is time consuming and expensive and requires sample preparation. Therefore, the application of spectroscopic techniques in conjunction with chemometrics, allowing several parameters to be determined simultaneously, rapidly and easily, can be a valuable alternative to traditional techniques. Thus, the viability and performance of different spectroscopic techniques (UV, VIS and NIR) to determine quality parameters such as the SSC and TA of apple juice were studied, and PLSR, the root mean square error of cross-­validation (RMSECV) and root mean square error of prediction (RMSEP) were used for the treatment of the spectroscopic data.23 It was found that the spectral data had good validation with a correlation coefficient of 0.90 for SSC and 0.94 for TA and with a prediction of 0.95 and 0.96, respectively. In addition, it was determined that the prediction accuracy of the SSC/TA model having a correlation coefficient of 0.72 decreased as a function of the separate predictions of SSC and TA. Finally, the prediction of the model to determine the pH was 0.82. Hence it was concluded that the selection of different variables improved the predictive capacity of the models, allowing the SSC to be determined with good precision and the TA and pH with moderate precision, demonstrating the usefulness of spectroscopy to test the quality of apple juice rapidly and accurately.

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List of Abbreviations ANN Artificial neural network BPNN Back-­propagation neural network FC From concentrate FL Fluorescence FTIR Fourier transform infrared IR Infrared LDA Linear discriminant analysis LF-­NMR Low-­field nuclear magnetic resonance MRI Magnetic resonance imaging NCD Non-­communicable disease NFC Not from concentrate NIR Near-­infrared NMR Nuclear magnetic resonance PARAFAC Parallel factor analysis PCA Principal component analysis PLS Partial least-­squares PLS-­DA Partial least-­squares discriminant analysis PLSR Partial least-­squares regression RMSECV Root mean square error of cross-­validation RMSEP Root mean square error of prediction SSC Soluble solids content TA Titratable acidity UV–VIS Ultraviolet–visible VIS Visible WHO World Health Organization

Acknowledgements This chapter was partially supported by the FONDECYT 1190340 and FONDECYT POSTDOCTORADO 3190449.

References 1. M. Santos, et al., Nutrient profile models a useful tool to facilitate healthier food choices: A comprehensive review, Trends Food Sci. Technol., 2021, 110, 120–131. 2. P. M. Ferrão, et al., Inhibition of TGF-­β pathway reverts extracellular matrix remodeling in T. cruzi-­infected cardiac spheroids, Exp. Cell Res., 2018, 362, 260–267. 3. M. Alongi and M. Anese, Re-­thinking functional food development through a holistic approach, J. Funct. Foods., 2021, 81, 104466. 4. Quality Control in the Food Industry, in Applied Reliability and Quality: Fundamentals, Methods and Procedures, ed. B. S. Dhillon, Springer London, 2007, pp. 175–187. DOI: 10.1007/978-­1-­84628-­498-­4_11.

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5. N. T. Anderson, K. B. Walsh, P. P. Subedi and C. H. Hayes, Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content, Postharvest Biol. Technol., 2020, 168, 111202. 6. N. K. Mahanti and S. K. Chakraborty, Application of chemometrics to identify artificial ripening in sapota (Manilkara Zapota) using visible near infrared absorbance spectra, Comput. Electron. Agric., 2020, 175, 105539. 7. R. A. Salvino, M. F. Colella and G. De Luca, NMR-­based metabolomics analysis of Calabrian citrus fruit juices and its application to industrial process quality control, Food Control., 2021, 121, 107619. 8. C. R. de Oliveira, R. L. Carneiro and A. G. Ferreira, Tracking the degradation of fresh orange juice and discrimination of orange varieties: An example of NMR in coordination with chemometrics analyses, Food Chem., 2014, 164, 446–453. 9. K. B. Petrotos and H. N. Lazarides, Osmotic concentration of liquid foods, J. Food Eng., 2001, 49, 201–206. 10. D. I. Ellis, H. Muhamadali, S. A. Haughey, C. T. Elliott and R. Goodacre, Point-­and-­shoot: rapid quantitative detection methods for on-­site food fraud analysis – moving out of the laboratory and into the food supply chain, Anal. Methods, 2015, 7, 9401–9414. 11. E. Sikorska, K. Włodarska and I. Khmelinskii, Application of multidimensional and conventional fluorescence techniques for classification of beverages originating from various berry fruit, Methods Appl. Fluoresc., 2020, 8, 15006. 12. É. Dufour, Chapter 1 -­ Principles of Infrared Spectroscopy, in Infrared Spectroscopy for Food Quality Analysis and Control, ed. D.-­W. Sun, Academic Press, 2009, pp. 1–27. https://doi.org/10.1016/B978-­0-­12-­374136-­3. 00001-­8. 13. M. Moncada-­Basualto and C. Olea-­Azar, Spectrophotometric Methods and Electronic Spin Resonance for Evaluation of Antioxidant Capacity of Food, in Spectroscopic Techniques & Artificial Intelligence for Food and Beverage Analysis, ed. A. K. Shukla, Springer Singapore, 2020, pp. 53–75. DOI: 10.1007/978-­981-­15-­6495-­6_3. 14. S. Qiao, Y. Tian, P. Song, K. He and S. Song, Analysis and detection of decayed blueberry by low field nuclear magnetic resonance and imaging, Postharvest Biol. Technol., 2019, 156, 110951. 15. E. Manthou, et al., 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, Comput. Electron. Agric., 2020, 175, 105529. 16. S. Bureau, A. Arbex de Castro Vilas Boas, R. Giovinazzo, B. Jaillais and D. Page, Toward the implementation of mid-­infrared spectroscopy along the processing chain to improve quality of the tomato based products, LWT., 2020, 130, 109518.

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17. M. Wu, H. Cai, X. Cui, Z. Wei and H. Ke, Fast inspection of fruits using nuclear magnetic resonance spectroscopy, J. Chinese Chem. Soc., 2020, 67, 1794–1799. 18. R. Mogollón, et al., Non-­destructive prediction and detection of internal physiological disorders in ‘Keitt’ mango using a hand-­held Vis-­NIR spectrometer, Postharvest Biol. Technol., 2020, 167, 111251. 19. P. Y. Lee, K. Lusk, M. Mirosa and I. Oey, Effect of information on Chinese consumers' acceptance of thermal and non-­thermal treated apple juices: A study of young Chinese immigrants in New Zealand, Food Qual. Prefer., 2016, 48, 118–129. 20. J. Markowski, A. Baron, J.-­M. Le Quéré and W. Płocharski, Composition of clear and cloudy juices from French and Polish apples in relation to processing technology, LWT -­Food Sci. Technol., 2015, 62, 813–820. 21. K. Włodarska, I. Khmelinskii and E. Sikorska, Authentication of apple juice categories based on multivariate analysis of the synchronous fluorescence spectra, Food Control., 2018, 86, 42–49. 22. H. Vardin, A. Tay, B. Ozen and L. Mauer, Authentication of pomegranate juice concentrate using FTIR spectroscopy and chemometrics, Food Chem., 2008, 108, 742–748. 23. K. Włodarska, et al., Rapid screening of apple juice quality using ultraviolet, visible, and near infrared spectroscopy and chemometrics: A comparative study, J. Microchem., 2021, 164, 106051.

Chapter 4

Advanced Analytical Methods for the Detection of Irradiated Foods Grzegorz Piotr Guzik*a and Wacław Stachowicz ( 0000-0001-9946-3133)a a

Institute of Nuclear Chemistry and Technology, Dorodna 16, 03-­195 Warsaw, Poland *E-­mail: [email protected], [email protected]

4.1  Foreword The alternative use of gamma rays for the pasteurisation of food was considered soon after the discovery of polonium and radium by Pierre and Marie Curie in 1898. The basis of this approach lies in the killing with the use of gamma rays of pathogenic bacteria responsible for the spread of dangerous food-­borne diseases. The Nobel Prize winner Marie Curie was actively engaged in work on this problem. In 1929 she published a paper on the probable character of deactivation curves of bacteria under the action of X-­rays.1 However, 25 years then passed until her theoretical considerations were positively confronted with experimental results. The reason was insufficient intensity of the gamma rays emitted from polonium and radium to obtain measurable results. Not until the 1950s did high-­efficiency gamma radiation sources become available equipped with 60Co and 137Cs radioisotopes

  Food Chemistry, Function and Analysis No. 32 Advanced Spectroscopic Techniques for Food Quality Edited by Ashutosh Kumar Shukla © The Royal Society of Chemistry 2022 Published by the Royal Society of Chemistry, www.rsc.org

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produced in nuclear reactors. This was the beginning of the development of experimental and technological studies on irradiated foods.2 The important question that immediately arises with an irradiated food is whether it is safe for potential consumers3 and whether it retains the nutritional quality and wholesomeness of the unirradiated product. With the aim of answering these questions, a number of toxicological, microbiological, biochemical, medical and chemical studies on irradiated foods have been undertaken in many countries.13,14,18,22,26,35 Twelve years of extensive studies resulted in more than 2000 valuable publications on essential problems of food irradiation. Examples are cited in the literature.27–29 The assembly of independent experts set up by the FAO–IAEA–WHO board, taking advantage of critical analysis of the above-­mentioned scientific studies on irradiated foods, issued the statement that no objections were found concerning the quality of irradiated foods and gave their opinion that irradiated food is safe for consumers and can be recommended for general consumption.9 Consequently, the decisive body, the FAO–IAEA–WHO Experts Committee, published in 1980 the opinion that the treatment of food with ionising radiation is considered a suitable and safe method of food preservation, as an alternative to thermal pasteurisation and deep freezing. To put the finishing touch to the story, the FAO-WHO Codex Commission elaborated and issued a first edition of the Codex in 1983, and new revised version in 2003, the fifteenth volume of the FAO–WHO Codex Alimentarius entitled General Standard for Irradiated Foods.9 This document, supported by the authority of 122 FAO–WHO member states, opened the gate for the development of the industrial application of ionising radiation for microbial decontamination and/or for the preservation of food and food commodities. Currently, a number of authorised irradiation facilities exist in countries specialising in food irradiation, equipped with powerful 60Co sources or high-­energy accelerators. In various countries, different regulations concerning food irradiation and permissions for the production and distribution of defined assortments of foodstuffs are in force. In the European Union, food irradiation is regulated with Directives 1999/2/ EC and 1999/3/EC of the European Parliament. Dried aromatic herbs, spices and vegetable seasonings only are authorised for irradiation treatment.11,12

4.2  Introduction The introduction of irradiated foods in the food market and trade was at first treated cautiously or negatively by consumers, and acceptance was exceptional. The labelling of irradiated food required by the Codex was not executed since no method was known to make possible the differentiation between irradiated and unirradiated foodstuffs. The international bodies engaged and/or interested in the development of this new food technology concluded that propagation of the knowledge on food irradiation on an international scale is desirable. With the intention of propagating objective knowledge on the safety of irradiated foods for consumers and on the advantages of radiation pasteurisation, which is a clean and effective method of

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microbial decontamination of food outside and inside at ambient temperature, a worldwide conference on Acceptance, Control of and Trade in Irradiated Food was organised jointly by the FAO, WHO, IAEA and the International Trade Centre UNCTAD/GATT and held in Geneva on 12–16 December 1988. The invited participants were official representatives of governments and experts from almost 100 countries.10 Following numerous open, free discussions and the exchange of very different opinions over three days, the country working groups came to a consensus on the basic statements concerning the essential aspects of food irradiation. The agenda of the report compiling the opinions of the country working groups became the basis for writing the International Document on Food Irradiation, highlighting the essential issues relating to the acceptance of irradiated foods by consumers, governmental and intergovernmental activities in the field, the control of the irradiation process and trade. The essential part of this document is the list of recommendations. The document was signed by authorised country officials but under the terms that the validity of the document depends on the urgent execution of recommendations. One of important recommendations concerns the development of methods for the detection of irradiated food in order “to create the conditions for an independent and reliable control system of radiation-­treated food, to facilitate the world food trade and to strengthen consumer confidence in the information of the product delivered”. It should be noted that the identification of irradiated food is in general not an easy task. The texture and taste of an irradiated food do not differ from those of the unirradiated food. The irradiation process of any matter, including food, is governed by the Compton effect, which consists in the transmission by the incident gamma photon of part of its energy to an electron belonging to a molecule of food with the simultaneous formation of a new photon with lower energy. The cascades of energised free electrons attack bacteria and viruses, losing their excess energy, and become capable of neutralising ionised molecules, which revert to their original states. The ionisation stage of the irradiation process is very fast and is estimated to last ca. 10−8 s. Only a very small proportion of molecular ions undergo further transformations, giving rise to radicals and new molecules. It has been estimated that for each 106 bonds in a food, only six become broken, giving rise to new species. Such conditions with irradiated foods represent a serious difficulty in developing a reliable detection method for irradiated foods. Nevertheless, extensive studies have been undertaken in many countries on the development of new analytical methods that could be suitable for the identification of irradiated foods. It should be noted that numerous more or less sophisticated analytical methods dedicated to the control of food quality were found to be useless for this purpose. Among 20 of the most promising detection methods based on physical, chemical and biological backgrounds submitted to the FAO–WHO Scientific Committee, only 10 passed the testing satisfactorily and became positively evaluated, sensitive and reliable enough to be proposed for practical applications. The European Committee for Standardization (CEN) in Brussels elaborated and issued approved methods for the detection of irradiated food in the form of European Standards

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recommendations for application in food control and industrial laboratories. CEN standards are available in English and in other common languages. The list of European Standards for the Detection of Irradiated Food is as follows:    ●● EN 1788:2002: Thermoluminescence detection of irradiated food from which silicate minerals can be isolated:4 Detection of herbs, spices, vegetable seasonings and extracts and shrimps. ●● EN 13751:2009: Detection of irradiated food using photostimulated luminescence:5 Detection of powdered herbs, spices and seasonings. ●● EN 1786:2000: Detection of irradiated food containing bone:6 ESR spectroscopy of bones, shells and crustaceans. ●● EN 1787:2001: Detection of irradiated food containing cellulose:7 ESR spectroscopy of food of vegetable origin. ●● EN 13708:2003: Detection of irradiated food containing crystalline sugar:8 Detection of irradiated dried fruits by ESR spectroscopy. ●● EN 1784:1996: Detection of irradiated food containing fat: Gas chromatographic analysis of C8–C12 hydrocarbons separated from meat and poultry.36 ●● EN 1785:1996: Detection of irradiated food containing fat: Gas chromatographic/mass spectrometric analysis of 2-­alkylcyclobutanones separated from meat, poultry and avocado.37 ●● EN 13784:2001: DNA Comet Assay for the detection of irradiated foodstuffs – screening method:38 Detection of fresh meat from cows, pigs and fish. ●● EN 13783:2001: Detection of irradiated food using direct epifluorescent filter technique/aerobic plate count (DEFT/APC) – screening method:39 Detection of irradiated food by epifluorescent filtration with counting of oxygenated microorganisms.    Actually, the irradiated foods which are found today in commercial trade consist of the assortments that were naturally selected by taking into consideration technical and financial aspects such as the scale of a long-­lasting demand for a given product in the market and the cost of the product compared with the cost of its irradiation treatment. These are the factors that are decisive for further continuation of an irradiation procedure. Consequently, the need for the detection of irradiation in less popular food groups becomes limited by the cost of those methods which meet the present requirements. Among the 10 European Standards for the detection of irradiation offered by CEN, mainly five are now in continuing use and attract mutual interest from both the analysts and producers or distributors of food today.

4.3  Detection Methods in Current Use 4.3.1  Thermoluminescence (TL) Method The thermoluminescence (TL) method for the detection of irradiated food is currently the leading analytical tool for the identification of radiation treatment of food.16,17,19,20 The method is based on the phenomenon of trapping during food irradiation of the incident radiation energy

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in crystal bends of minerals present in irradiated food. Silicate minerals such as feldspar and quartz occur in all kinds of vegetable food as permanent impurities. Minerals are a basic component of soil particles that become durably affixed to the surface of leaves and stems of plants during their growth and development, and mineral contamination of fresh plants remains unchanged in all vegetable foods obtained from these plants after processing and is present in all kinds of vegetable spices, for example. It has also been demonstrated that minerals with their trapping sites for radiation energy survive in processed food. The radiation energy trapped during irradiation remains stabilised in food minerals for years. However, the heating of irradiated food to temperatures exceeding 250 °C results in complete release of the radiation energy from crystalline traps in the form of specific luminescence called thermoluminescence, which can be employed in practical scientific applications. Analytical procedures using TL consist in programmed heating of an irradiated food sample with a measuring device – the thermoluminescence reader – involving the gradual release of radiation-­originated luminescence from trapping sites followed by recording of the luminescence intensity at successive temperature points, giving a TL glow curve as shown in Figure 4.1. A maximum in the glow curve is observed at temperatures in the range 150–250 °C, providing evidence of sample irradiation (Figure 4.1). In order to verify

Figure 4.1  Thermoluminescence  glow curve of red gold paprika (red line) irradiated with 4 kGy of 60Co gamma radiation. The area below the glow curve within the temperature range 150–250 °C represents the intensity of TL at the maximum point. The blue line denotes the glow curve of unirradiated paprika. The weak maximum near 350 °C is derived from the bleaching of radioactive uranium and thorium components of rocks.

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the sensitivity, reproducibility and reliability of the method, numerous test studies have been performed in many laboratories for the control of food quality around the world.32,33 It has been demonstrated that the TL method is the optimum analytical tool for the detection of radiation treatment of many kinds of common foods such as dried seasonings, herbs, fruits, fresh and dried vegetables, spices and spiced food, and also shrimps and oysters with sand deposits collected in their intestines.20,22,24 Vegetable spices which are exclusively controlled with the TL method have for many years remained the main food product undergoing radiation processing. Hence TL is recognised today as the most universal detection method for irradiated food. Many laboratories32,33 around the world authorised for the control of food irradiation have adopted the TL detection method following the analytical procedure in European Standard EN 1788:2002.4 The essential stage of the TL analytical procedure is the separation of the mineral fraction from the remaining vegetable matter. The separation technique adopted is based on the difference between the density of the mineral and that of the dominant vegetable remainder. The isolation of the mineral is achieved by immersing the crushed food sample in an aqueous solution of sodium polytungstate of density ca. 2 g cm−3. Vigorous stirring of the suspension obtained followed by decantation during several minutes results in the deposition of mineral “sand” in the bottom of the vessel while organic remains float on the surface of the polytungstate solution. The mineral deposit is carefully collected, dried and subjected to TL measurements using the measuring parameters recommended in the cited European Standard. In analytical practice, the results for the investigated food sample are expressed as the ratio of the TL glow recorded for the sample (glow 1) to the TL glow of the same sample irradiated with 1 kGy of normalising gamma radiation (glow 2), both registered with the photomultiplier of the TL reader and expressed in counts per second. If the glow 1-­to-­glow 2 ratio is >0.1 it means that the sample was irradiated, whereas a ratio of 900) and cultivars (n > 25) and different seasons were utilized to test the accuracy of these techniques to measure total YAN, FAN, and ammonia in grape must and juice samples.48 The results indicated that both FTMIR and FTNIR spectroscopy gave satisfactory results, whereas the MIR instrument equipped with an ATR cell can only be used for screening purposes. Considering the accuracy, robustness, high throughput, and cost-­effective nature of these techniques, the models produced by both FTMIR and FTNIR spectroscopy can provide winemakers with the opportunity to make appropriate and more precise nutrient supplementation decisions, allowing the targeting of specific wine styles and quality profiles.48

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It is well known that nitrogen is a limiting factor for the development of wine alcoholic fermentation, where the addition of nutrients and different nitrogen sources is a common practice for many winemakers.49 The changes produced in the ferments by the addition of different sources and levels of nitrogen during alcoholic fermentation were monitored using ATR-­FTMIR spectroscopy. The results showed the feasibility of this technique to observe differences in the growth yeast capacity depending on the type of nutrients added.49

6.2.2  Wine Compositional Analysis As stated in the previous sections, the utilization of either NIR or MIR spectroscopy to measure compositional parameters in wine (e.g. alcohol, sugars) is not new.6–11 However, both advances in and easy access of instrumentation have allowed the development of a wide variety of applications, including the measurement of both phenolic and volatile compounds and sensory properties, among other novel properties and quality parameters.6–11 This section discusses novel application of vibrational spectroscopy for the analysis of wine. Volatile chemical compounds derived from a wide range of biochemical and chemical pathways are responsible for the aroma of wines.50 These volatiles are produced throughout the wine-­making process from grape berry metabolism, crushing of the berries, and fermentation processes (e.g. yeast and malolactic bacteria) during the ageing and storage of wine,50 and during production many of the compounds found in wine are expected to be present at varying concentrations. Not surprisingly, the large number of chemical classes of compounds found in wine that are present at different concentrations (ng L−1 to mg L−1) exhibit varying potencies and exhibit a broad range of volatilities.50 Louw et al. combined chemometrics with FTMIR spectroscopy to identify compositional trends and to differentiate between the volatile composition profiles of South African young wine cultivars.51 The proposed method allowed the rapid classification of wine samples according to cultivars and wine geographical origin. They also reported significant differences in the composition of the cultivar wines, while also noting marked similarities in the composition of Pinotage wines and white wines regarding compounds such as 2-­phenylethanol, butyric acid, ethyl acetate, isoamyl acetate, isoamyl alcohol, and isobutyric acid.51 Salinas and co-­workers reported the ability of NIR spectroscopy to determine levels of oak volatile compounds and ethylphenols in aged red wines.52 Wine samples (n > 500) aged using both different storage times and oak barrel types were analysed and classified with calibration models developed using PLS regression based on both reference data obtained by gas chromatography (GC) and NIR spectra. They reported the usefulness of the technique as R2 values of >0.86 were achieved for wines aged both in American and in French oak barrels, and for Reserva and Gran Reserva wines, respectively.

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Lorenzo et al. investigated the potential of NIR spectroscopy for the determination of the fermentative volatile components of multiple (n = 240) aged red wine samples from four different geographic zones.53 The results illustrated that calibration models combining PLS regression of fermentative volatile compounds with reference data obtained from the use of GC coupled with mass spectrometry (MS) and NIR spectroscopy were excellent.53 It was concluded that NIR spectroscopy could be utilized as a rapid and simple tool to determine fermentative volatile compounds in the wine samples analysed. Smyth et al. combined GC-­MS and NIR spectroscopy and demonstrated the ability of vibrational spectroscopy to measure volatile compounds in commercial Riesling wines.54 PLS regression was used to establish a correlation between the NIR and GC data, and full data validation of the model was conducted, yielding R2 values of 0.74 (SECV: 313.6 µg L−1) for esters, 0.90 (SECV: 20.9 µg L−1) for monoterpenes, and 0.80 (SECV: 1658 µg L−1) for short-­chain fatty acids. A study by Versari et al. further demonstrated the capability of FTMIR spectroscopy to predict the composition of wine samples.55 The utilization of PLS regression combined with FTMIR spectroscopy allowed the total antioxidant capacity of red wines to be predicted. The model demonstrated a good correlation (r = 0.85), and it was also noted that the PLS model provided a predictive error that was consistent with the uncertainty derived from the reference method. The ability of a gustatory sensor engineered from an electronic tongue based on FTMIR spectroscopy to reproduce the findings of a tasting panel response to the “tannin amount mouthfeel” was assessed.56 PLS regression was used to model the sensory responses in a large set of different red wine samples (n > 30). To establish the optimum correlation of the panel's subjective sensorial response, six different variable selection techniques were investigated. It was determined that the iterative predictor weighting technique produced the best results, with a root mean square error of cross-­validation (RMSECV) of 0.13.56 Rudnitskaya et al. evaluated a combination of FTMIR spectroscopy and high-­performance liquid chromatography (HPLC) to determine the bitterness of a set of single-­cultivar Pinotage wine samples.57 A trained sensory panel was utilized to assess the bitterness of the wine samples, some of which had medium to high bitterness levels. PLS regression was used to develop calibration models for the classification of the wine samples. The authors reported that both the chemical and spectral analysis data could distinguish between the bitter and control wines with a correct classification rate of 91 and 94%, respectively. In addition, FTMIR spectroscopy allowed users to predict the intensity of the bitterness of a given wine sample.57 Fu et al.58 reported that the oenological parameters of white wines packaged in “bag-­in-­a-­box” containers were influenced by exposure to elevated temperatures and oxygen. Several parameters, including colour, free and total sulfur levels, total aldehyde, and total phenol content were measured and correlated with both ATR-­MIR spectral data and defined oxygen transmission rates using PLS regression. The results indicated that the method

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was useful in determining the colour and sulfur content of the wines and could be used to determine the appropriate storage time/shelf life of bag-­in-­ a-­box wine samples. It is well known that during wine ageing in wooden barrels the sensory properties of the wine might be affected by the extraction of wood phenols and compounds containing aromatic groups such as lignins, volatile phenols, and hydrolysable tannins.59 Among the hydrolysable tannins, ellagitannins have been found not only to modify the sensory character of wine but also to generate new products with compounds already present in the wine samples analysed where their quantification is of high importance.59 However, the analysis of ellagitannins requires the use of sophisticated equipment and many preparative steps, making their quantification in wines laborious and not cost-­effective. The feasibility of FTMIR spectroscopy coupled with chemometrics for the quantification of ellagitannin concentration in wines was examined,59 utilizing PLS regression to develop calibration models based on the fingerprint region of the MIR spectrum between 1821 and 950 cm−1. The reported correlation coefficient was R2 = 0.93 with root mean square error of calibration (RMSEC) = 1.17 and RMSEP = 1.57. These results indicated that FTMIR spectroscopy could be used for the rapid, non-­destructive, and economical estimation of a wine's total ellagitannin content. The development of a methodology based on multiparametric methods that included FTMIR spectroscopy and a voltammetric e-­tongue to evaluate simultaneously 14 parameters related to the phenolic content of red wines was reported.60 Different types of Spanish red wine samples made from different grape varieties from different regions and with different ageing were analysed using both analytical systems. Input variables used for MVA were extracted from the FTMIR spectra and voltammograms using the kernel method for data analysis. The utilization of PCA analysis allowed the wine samples to be classified according to their phenolic content, where the first three principal components explained 99.8% of the total variance between the samples for FTMIR analysis and 85.8% for the e-­tongue analysis. PLS calibration models were used to establish regression models with phenolic content parameters measured by UV–VIS spectroscopy [total phenolic index (TPI), Folin–Ciocalteu, CIELab, and Glories methods] with high correlation coefficients (R2 > 0.85) and low root mean square errors (RMSEs) ( 100) were analysed in transmission mode using an FTNIR instrument (range 5435–6357 cm−1). Boxplots and PCA were used to identify clusters of samples and to detect outliers. PLS regression models were developed and coefficients of determination (R2) varying from 0.94 to 0.97 were reported. It was concluded that NIR spectroscopy combined with MVA can be considered as a rapid tool to determine volatile compounds in Vinho Verde wine samples. Samples of Cabernet Sauvignon wines grown and produced in Mexico (five wine regions, 19 wineries, and vintages between 2004 and 2012) were characterized by total phenolic compounds (TPC), anthocyanins (AC), tannins (TC), flavonoids (FC), and antioxidant capacity {ABTS [2,2-­azinobis(3-­ethyl benzothiazoline-­6-­sulfonate)] and DPPH (1,1-­diphenyl-­2-­picryl hydrazine) assays}.69 Both PLS and PCR calibration models were used to develop calibrations for the above parameters based on FTMIR spectroscopy. The spectral region selected for each model was between 1550 and 824 cm−1. The PLS1

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model showed the best predictive ability where the coefficient of determination in validation (R2 = 0.93–0.95) and residual predictive deviation (RPD = 4.28–6.95) were obtained for TPC, FC, ABTS, and DPPH. Good calibration models for TC and AC (R2 = 0.91–0.92 and RPD = 3.75–3.84) were obtained. Polyphenolic compounds are considered to have a major impact on the quality of red wines, where sensory perception, such as astringency and bitterness, are mainly related to condensed tannins, whereas colour intensity and evolution are due to the anthocyanin content.70 Both FTMIR and UV–VIS spectroscopy combined with MVA were considered as potential methods to predict the polyphenolic content of wines, but they have not yet been compared in terms of the efficiency of each wavelength region. For this purpose, a large selection of wine samples covering different vintages, varieties, and regions were investigated.70 Tannin concentration was measured using precipitation with protein and polysaccharide and by the Bate–Smith assay. The free anthocyanin concentration was determined by bisulfite bleaching and the monomers-­to-­polymers ratio was determined using the Adams–Harbertson method. The molecular anthocyanin concentration was also obtained by HPLC and UV–VIS spectroscopy. The spectra of the wines were obtained using UV–VIS and FTMIR instruments. The PLS model yielded coefficients of determination for cross-­validation of >0.7 for most of the parameters evaluated. The two spectroscopic methods provided very similar results; FTMIR spectroscopy showed greater robustness for the prediction of tannin concentration whereas UV–VIS spectroscopy appeared to be more relevant to determine anthocyanin concentration and evolution. Models developed by the combination of the two spectral ranges gave slightly better results. When a selection of different wavelengths in the VIS range were combined with the FTMIR spectrum, the results showed that the prediction of anthocyanin parameters improved considerably. The importance of the VIS region in combination with MIR to predict these types of compounds in wine was highlighted.70 It is well known that alcohol, total sugar, total acid, and total phenol contents are the main indicators of wine quality.71 The simultaneous measurement of these parameters was evaluated using NIR spectroscopy with different wavelength optimization techniques.71 Equidistant combination partial least squares (EC-­PLS) was used for large-­scale wavelength screening in addition to wavelength step-­by-­step phase-­out PLS (WSP-­PLS) as an exhaustive method for secondary optimization.71 The root mean square error, correlation coefficient for prediction, and ratio of performance to deviation (RPD) were 0.41 v/v, 0.947, and 3.2 for alcohol, 1.48 g L−1, 0.992, and 6.8 for total sugar, 0.68 g L−1, 0.981, and 5.1 for total acid, and 0.181 g L−1, 0.948, and 2.9 for total phenol. These results demonstrated the high correlation, low errors, and good overall prediction performance. It was also highlighted that the proposed wavelengths provided a valuable reference for designing small, dedicated instruments. Marsala is a fortified dessert wine produced exclusively in the province of Trapani (Sicily, Italy). Twenty-­nine categories of Marsala are available on the

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market, differentiated according to the grape variety, production technology, and ageing.72 ATR-­FTMIR spectroscopy combined with MVA was evaluated for monitoring the processing of this wine and PCA was used to identify wine samples with different sugar contents and to distinguish tanned samples (Fine, Superiore, and Superior Reserve) from the most valuable Virgin samples.72 LDA was applied to the spectral data with a coefficient of variation of >20% to discriminate among Marsala wines with different ageing times. The results indicated complete discrimination of 100%, where the confusion matrix in cross-­validation was equal to 87.76%, indicating a high percentage of correct classification also in prediction. It was concluded that the proposed method is promising as it is simple and rapid, and no sample pretreatment steps are required. Moreover, it is environmentally friendly since no organic solvents are used. It could be of great interest for verifying the conformity of Marsala wines with the declared branding. The implementation of NIR spectroscopy as an analytical method for the quantification of different wine parameters is limited owing to the aqueous nature of wine.73 Water molecules contribute to a poor signal-­to-­noise ratio and suppress the vibrational frequencies of important groups, preventing the quantification of most compounds present. An alternative approach using lyophilized samples as a preprocessing method during NIR analysis was proposed.73 Parameters such as alcohol content, volumetric mass, total dry extract, total sugars, total acidity, volatile acidity, pH, free sulfur dioxide, and total sulfur dioxide were measured in a diverse range of wines, including red, white, and rosé. The PLS regression models developed accurately quantified total sugars, pH, volumetric mass, and total dry extract with a range error ratio above 10. The quantification of the remaining parameters yielded unsatisfactory results. It was concluded that this methodology can be an alternative for the quantification of major wine quality descriptors by avoiding the interference of water bands. Commercial red wine grape varieties grown in different Bulgarian regions in two consecutive years were analysed using both chemical and spectroscopic methods.74 Antioxidants, including trans-­resveratrol, quercetin, and total phenols, and antioxidant potential in grapes and wines were studied by HPLC and UV spectroscopy, and used to develop calibration models based on NIR spectroscopy.74 The models based on NIR spectroscopy showed very good accuracy in these measurements. It could be a promising technique for the quantification of antioxidant parameters. Preprocessing of spectra is very important in order to compensate for any issues that arise during collection of the spectra (e.g. scattering, pathlength), and several preprocessing techniques applied to the MIR spectra of wine samples were tested during the development of calibration models to predict sensory properties in both Cabernet Sauvignon and Chardonnay wine samples.75 Derivatives calculated using Savitsky–Golay (SG) smoothing points, polynomial order, and extended multiplicative signal correction (EMSC) polynomial methods were investigated. PLS regression was used to relate the wine sensory data with the MIR spectra, and the R2 values were

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further analysed with multivariate analysis of variance (MANOVA). SG transformations were significant factors from the MANOVA that influenced R2, whereas EMSC had no influence. It was concluded that the PLS calibration-­ predicted wine sensory characteristics gave poor to moderate R2 values. The consistent prediction of wine sensory attributes within a variety and across vintages is challenging, regardless of whether grape or wine spectra are used as predictors.75 A recent review discussed a wide range of spectroscopic techniques applied to wine and wine vinegar characterization, authentication, and quality control, and the discussion focused on the already known concept of wine “fingerprints”.76 It was suggested that the synergistic relationship with chemometric approaches provides a rapid analytical methodology that will be able to solve the different challenges facing the implementation of analytical methods based on the utilization of vibrational spectroscopy. In this context, a critical change in fingerprint terminology by utilization of the term “spectralprint” to assess wine samples was proposed.76 The application of eigen-­directed network analysis to FTMIR spectroscopic data sets of wine samples was discussed by Kumar et al.77 They suggested that a network can generally be viewed as a collection of nodes connected to each other through links, often also called edges. Networking on FTMIR data sets of these samples in the eigenspace layout was found to impart the required aesthetic values and also the chemical significance to the positioning of the nodes. The proposed methodology was able to capture the compositional differences among the analysed wine samples and to classify them in two groups. Eigen-­directed network analysis also allowed a swift assessment regarding inter-­ and intra-­group homogeneity. The proposed methodology was found to outperform network analysis in force-­directed layout and PCA, indicating that eigen-­directed network analysis can provide a simplified illustration of highly correlated spectral data sets, enabling rapid and intuitive interpretation. Previous studies have evaluated and reported the potential ability of both MIR and NIR spectroscopy to predict a wide range of sensory properties, and a recent review by Chapman et al. summarized some of these applications to foods and beverages.78 Recently, the quality of wine and the characterization of a wide range of properties associated with the presence and intensity of different flavours and faults were reported.79 Different wines were assessed using tasting panels that evaluated the sensory properties of wine. Wine samples were also analysed by combining VIS and NIR spectroscopy. Calibration models were developed for the different sensory properties measured as positive or negative depending of the style and variety of wine being analysed (e.g. red versus white wines). The results highlighted the ability of VIS and NIR spectroscopy to predict some of the most important sensory properties in the set of wines analysed, including flavour intensity, astringency, colour intensity, length, persistency, pleasantness, and balance. Most of the correlation coefficients obtained were equal to or above 0.9. It was also indicated that both VIS and NIR spectroscopy were able to predict wine defects

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such as oxidation, unclean, ethyl acetate and acetic acid. It was highlighted that the main use of these techniques might be connected with the screening of wines (e.g. contrasting or confirming positive and negative properties) before formal sensory analysis using expert tasting panels. In addition, these techniques can be used to resolve discrepancies between tasters or as a rapid method to detect wine faults in a routine basis in the laboratory before tasting.79

6.2.3  Monitoring Wine Fermentation Modern and portable instrumentation, fibre optics, and wireless technologies have allowed the incorporation of vibrational spectroscopy into the process and/or production plant, beyond the analytical laboratory.80–86 It is in this context that vibrational spectroscopy has played an important role in the so-­called process analytical technologies (PAT) approach.87–90 This approach has been used to collect chemical information during the processing of pharmaceuticals and foods (e.g. spatial and temporal information), not only to monitor the composition of the product but also to provide information about the process itself (e.g. yield, faults, quality assurance). The implementation of vibrational spectroscopy in processing has been possible due to the availability of a wide range and types of sensors that can provide fast, reliable, and robust analytical data. The integration of vibrational spectroscopy both with other sensing techniques and with MVA has indicated that PAT accelerated and fostered the so-­called multidisciplinary approach between industry and research. The design of state-­of-­the-­art sensors having high specificity and resolution has improved the amount of data collected and therefore the information obtained to manage the food manufacturing process better.87–90 The conversion of grape juice into a high-­value product such as wine involves the complex microbial processes of fermentation.80–86 Therefore, to ensure product quality, it is very important to monitor accurately and rapidly and in turn control substrate conversion (e.g. sugars to ethanol, malic acid to lactic acid). It is not new that optimal process monitoring and control are essential to ensure the wine industry's compliance with safety standards and its commercial viability and sustainability. The incorporation of monitoring systems in the process requires rapid, low-­cost, and non-­invasive tools to determine chemical and other properties of the raw materials (e.g. grape berries and juice), process streams (ferments/must), and the final product (wine).80–86 Several examples where the ability of these techniques for monitoring wine processing has been demonstrated can be found in the literature. Research has also been conducted to assess the potential of VIS–NIR spectroscopy both to predict in real time the concentration and then monitor the extraction of phenolic compounds during red wine fermentation.80–86 The utilization of VIS–NIR spectroscopy to predict the concentrations of major anthocyanins (e.g. malvidin-­3-­glucoside), pigmented polymers, and tannins during the fermentation of Cabernet Sauvignon and Shiraz wine samples was demonstrated.80–86

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Fernández-­Novales et al. evaluated a miniature fibre-­optic NIR instrument to predict the sugar content and density of white wine samples during fermentation.91 They used PLS regression and multiple linear regression (MLR) to establish a robust prediction model to measure the reducing sugar content with high accuracy, which yielded an R2 value of 0.98, an SECV of 13.62 g L−1, and an RMSECV of 13.58 g L−1. The study also demonstrated that four spectral bands within the fingerprint region (909, 951, 961, and 975 nm) were indicative of sugars in grape, must, and wine samples. It was concluded that this protocol could be used to develop a simple, low-­cost, and effective monitoring instrument. Di Egidio et al. directly compared the capabilities of NIR and MIR methods to monitor glucose, fructose, ethanol, glycerol, total phenolic content, total anthocyanins, and total flavonoids in red wine samples during fermentation.92 The spectral data were analysed using a combination of PCA and LDA classification methods. They reported a high percentage of correct classification for both techniques, i.e. 87 and 100% for NIR and MIR spectroscopy, respectively. This study involved the monitoring of microfermentation trials during the 2008 vintage harvest in the Valtellina (northern Italy) viticultural area. Wynne et al. combined FTMIR spectroscopy with two-­ dimensional correlation techniques to monitor the compositional changes during the fermentation of commercial red wine.93 They highlighted the potential of the technique to monitor wine fermentation online. The utilization of UV–VIS spectroscopy in combination with MVA was evaluated by Longo et al. to differentiate grape juice press fractions (Vitis vinifera cv. Pinot Noir) during sparkling wine production.94 They evaluated two measurement modes: reflectance (inline fibre-­optic probe) and transmission (benchtop instrument). A combination of different wavelengths in the UV–VIS range were investigated and their ability to measure total phenolic concentrations in grape juice press fractions was evaluated. The PLS calibration models for total phenolics were developed using samples from the press fraction (230–700 nm), where MLR models were developed using a small number of important wavelengths (230, 240, 280, 290, and 520 nm). The performances of the calibrations were similar, but the best-­performing calibration utilized the reflectance spectra at 240 and 290 nm [coefficient of determination of validation (R2Val) = 0.95; SECV = 0.023 g L−1; CV = 4.2%]. It was concluded that reflectance spectroscopy can be utilized for the inline prediction of total phenolics in grape juice with acceptable accuracy.94 Bacterial spoilage can occur during wine alcoholic fermentation, resulting in economic losses and the production of low-­quality wines.95 If spoilage is suspected, samples are usually delivered to an oenological laboratory for the offline analysis of specific quality control parameters. The utilization of ATR-­MIR spectroscopy as a rapid analytical tool to monitor the fermentation process in real time was evaluated.95 A portable ATR-­MIR instrument was evaluated to detect white wine spoilage during alcoholic fermentation due to the action of lactic bacteria. In this study, small-­scale alcoholic fermentations were conducted under normal operational conditions (NOC) and in simulating a bacterial spoilage with the addition of lactic bacteria [malolactic

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fermentation (MLF)] to evaluate the capability of the spectroscopy to detect deviations from NOC. Control charts were developed based on Q residuals and Hotelling's T2 statistics. It was also reported that the MLF samples were detected before the end of alcoholic fermentation using the Q residual charts after 80 h and Hotelling's T2 chart was able to differentiate the samples after 100 h.95 The same group also monitored small-­scale must fermentations (microvinifications) of NOC and three fermentations that were intentionally deviated from NOC–YAN.96 They evaluated different multivariate analysis strategies (global and local models) to describe the evolution of the NOC fermentation and for early detection of the abnormal fermentations. Global models based on PCA and PLS discriminant analysis (PLS-­DA) were utilized to describe the progress of fermentations and to classify NOC and YAN fermentations correctly.96 Abnormal deviations were successfully detected by developing one model for each sampling time. YAN experiments could be identified after the beginning of the fermentations using Hotelling's T2 and residual F statistics. It was concluded that ATR-­FTIR spectroscopy combined with MVA showed great potential as a fast and simple at-­line analysis tool to monitor wine fermentation and for the early detection of fermentation problems.96 A recent study by the same group aimed to provide a step-­b y-­step guide highlighting the critical points to be faced when monitoring a wine fermentation following the PAT approach.97 The authors evaluated the use of ATR-­MIR spectroscopy combined with different chemometric techniques. The different steps and changes that occurred during the vinification process were evaluated in detail, including the sampling protocols, the most appropriate chemometric method, and data pretreatment techniques.97 It was highlighted how sampling is a key step during the implementation of the PAT approaches using vibrational spectroscopy during wine making. According to the authors, the modelling techniques utilized, such as PCA and PLS regression, proved to be effective for monitoring wine production while at the same time being affordable even for non-­expert users of chemometrics. This study highlighted how to collect and predict a wide range of chemical parameters during wine fermentation, in order to visualize the time course of the fermentation, and to measure and predict chemical compositional parameters during the alcoholic fermentation. Additionally, the authors showed that using the PAT approach, it was possible to monitor and detect deviations from the “normal” wine fermentation conditions.97

6.3  Concluding Remarks As illustrated in this chapter, applications of vibrational spectroscopy (e.g. VIS, NIR, and MIR) combined with MVA methods in the oenological field (Figure 6.2) have attracted increasing interest from numerous research groups worldwide. Nevertheless, the practical implementation of these analytical techniques is still in its infancy in the wine industry, with the analysis

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Figure 6.2  Schematic  representation of the combination of vibrational spectroscopy and multivariate data analysis to monitor wine fermentation. Blue dots illustrate the trend in a normal fermentation (from juice to wine) and the red dots indicate a deviation from the normal trend (e.g. stuck fermentation).

of grapes and grape products (e.g. juice, must, wine) generally confined to the measurement of single compositional parameters such as ethanol, pH, total acidity, and TSS using conventional and older methods. Most of the research literature available in the field has reported only feasibility studies. Most of these studies utilized limited or small data sets (e.g. just a few samples) and in most cases cross-­validation was the chosen validation method rather than using an independent or external validation test. Some of these studies also exhibited a lack of critical analysis and evaluation of the models developed (e.g. calibration or classification), with limited or no interpretation of loadings and/or regression coefficients commonly being observed. In many cases, the lack of both an understanding of the reference method and also knowledge of the standard error of the laboratory method utilized to develop the calibration models resulted in a poor or limited interpretation of the results obtained. The combination and utilization of sensing techniques (e.g. vibrational spectroscopy) with MVA have increased the amount of data and concomitantly permitted the generation of easily accessible and reliable information available for the chemical analysis of grapes, processing (e.g. the fermentation process), and wine. Moreover, the combination of these techniques with the internet of things (IoT), cloud computing technologies, and a wide range of sensing techniques has pushed the traditional wine practices towards the so-­called smart/digital systems. One of the interesting aspects of the integration of these technologies in the modern wine industry is that it requires sources information and

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knowledge from many fields (e.g. winemakers, growers, spectroscopy, analytical chemistry, data analysis), fostering a truly multidisciplinary approach. The future development of these applications will provide the wine industry with clean, fast, and non-­destructive tools to monitor composition, to determine changes in chemical and physical properties, to detect unwanted issues during processing (e.g. stuck fermentation, contamination) in both raw ingredients (e.g. grapes) and juice (e.g. must), and to guarantee the quality of the wine (e.g. authenticity, fraud, quality). There is no doubt that the incorporation of digital and technological innovations is driving and producing an increase in the information generated along the supply and value chains in the wine industry. Nevertheless, knowledge and understanding of both grape/wine properties and complex relationships (e.g. composition, sensory properties, processing) in the context of a wide range of varieties, styles, and regions is still lacking. Despite these issues, the potential demonstrated by the utilization of clean and rapid analytical techniques and data analysis is providing a new breath of fresh air and novel developments in the wine industry and for research and development (R&D) organizations. Overall, challenges together with the complexity of issues related to food security (e.g. wine authenticity, regionality, provenance), beyond the simple measurement of a chemical property (e.g. ethanol content, tannin profiles), have limited the understanding and ability to target rapidly all the subtle changes that influence wine production systems and the supply and value chain. However, the incorporation of spectroscopic techniques with the use of data analysis is equipping the grape and wine industry with a new set of toolboxes to respond rapidly to these challenges. However, the development new applications or methods using these technologies is not a trivial task, where aspects of calibration (e.g. algorithm selection, validation), sample selection, spectral preprocessing, and the fundamental understanding of wet chemical and reference methods are often disregarded or not considered. Failure to understand the repeatability and robustness of wet chemical and biochemical methods can drastically impact the interpretation and the ability to assess the real reliability of an application based on vibrational spectroscopy. These issues emphasize the importance of appropriate training for the developers of vibrational spectroscopy applications (e.g. calibration or classification models), because despite the simplicity of routine analysis, the interpretation and development of calibration models, particularly the MVA component, are far more complex. The application of these methods during wine processing (e.g. fermentation monitoring, PAT) has been shown to be environmentally friendly and non-­invasive/non-­destructive, with the bonus of reducing the time and cost of analysis. For example, this was highlighted by the ability of these techniques to measure a wide range of chemical parameters (e.g. organic acids, volatile and phenolic compounds, anthocyanins, and sugars) during wine fermentation, which might have a significant impact on the sustainability of the wine industry. This has resulted in the development and production of

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commercial IR instruments and online sensors. In addition, the incorporation of vibrational spectroscopy has the potential to assess, evaluate, or monitor other key features such as grape and wine authenticity and provenance and to detect faults and frauds. Despite the clear advantages of the utilization of vibrational spectroscopy, the adoption of the technology by the wine industry has been hindered by several critical aspects, including a limited understanding of the technology and the availability of customised cost-­effective and versatile instrumentation. As the technology associated with IR spectroscopic instruments and chemometrics continues to evolve and adapt, it is the author's opinion that the resulting monitoring systems will further assist the wine industry in its efforts objectively to define and measure grape and wine composition aspects in a sustainable and cost-­effective manner, ultimately assuring both producers and consumers of the overall quality of the final product.

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Chapter 7

Application of FTIR Spectroscopy and Chromatography in Combination With Chemometrics for the Quality Control of Olive Oil Gunawan Indrayantoa and Abdul Rohman*b,c a

Faculty of Pharmacy, Universitas Surabaya, Surabaya, East Java, Indonesia; Center of Excellence, Institute for Halal Industry and System (IHIS), Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; cDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia *E-­mail: [email protected], [email protected] b

7.1  Introduction Edible oils are essential for human life where they are used as frying or cooking oils. In addition, edible oils are ingredients used in food products, pharmaceutical preparations, and cosmetic commodities. Edible oils are also sources of nutrition needed for the human diet, and are necessary for the appropriate development of human tissues.1 Owing to export constraints   Food Chemistry, Function and Analysis No. 32 Advanced Spectroscopic Techniques for Food Quality Edited by Ashutosh Kumar Shukla © The Royal Society of Chemistry 2022 Published by the Royal Society of Chemistry, www.rsc.org

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and the high demand for edible oils, adulteration practices motivated to gain economic benefits have occurred extensively, especially with edible oils that command a high price in the fats and oils industry, such as olive oils (OOs).2 Hence quality control including assuring the authenticity of expensive edible oils is essential for guaranteeing consumer protection. Adulteration has been a continuing major concern not only for consumers but also for producers and regulators owing to the risks to human health, reduction of food quality, and loss of nutritional value.3 OO is oil obtained from fruits of the olive tree (Olea europaea L.) and is widely distributed in Mediterranean countries.4 In the Mediterranean basin, OO is the main dietary oil used for cooking and salad preparation. OO has gained popularity in the fats and oils industry owing to its pleasant organoleptic characteristics and its associated beneficial effects to human health, as reported in some clinical studies, especially for the prevention of some degenerative diseases such as cardiovascular disease, hypertension, and obesity.5 Some polyphenols contained in OO contribute to the cardioprotective effects.6 In addition, the minor components present in OO also correlate with taste and nutritional value, physicochemical characteristics of the product, and important markers for OO quality, purity, and authenticity.7 The authenticity of edible fats and oils is typically investigated by identifying some differences between authentic and adulterated oils in terms of the contents of chemicals contained in the evaluated oils. Chemically, edible fats and oils are composed of 98% of triacylglycerols (TAGs) with different substitution patterns, lengths, and degrees of unsaturation and 2% of minor components such as sterols, phospholipids, carotenes, and lipid-­soluble vitamins. The differences in TAG composition, fatty acids, and minor components can be used as indicators of adulteration practice.7 For example, fatty acid components of edible fats and oils are fingerprints in nature and can be saturated, monounsaturated, and polyunsaturated fatty acids. The differences in fatty acid composition allow the identification of possible adulteration of authentic oils.8

7.2  Olive Oil The United States Pharmacopeia (USP) defines OO as the refined fixed oil obtained from the ripe fruit of Olea europaea L. (Oleaceae). There are some minor components present in OO such as tocopherols, β-­carotene, squalene, lutein, and lipophilic and hydrophilic phenols reported to have biological activities, mainly antioxidant activity.9 OO is a fatty juice and may be consumed after proper processing of olives. This oil has gained in popularity in recent years owing to its pleasant taste. It is consumed not only by people in Mediterranean countries but also worldwide because of its unique flavor.10 OO also contains high amounts of monounsaturated fatty acids, especially oleic acid, which is beneficial to health, especially in reducing mortality caused by cardiovascular disease.11

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In the fats and oils industry, OO can be graded into six categories, namely (1) extra virgin olive oil (EVOO), which is considered as the healthiest and most sought after grade, having acidity up to 0.8 g per 100 g, calculated as oleic acid; EVOO does not undergo any refining process; (2) virgin olive oil with acidity of about 2.0%; (3) refined olive oil (RFO) with acidity of 0.3%; RFO is OO obtained from virgin OOs using refining methods without alterations to the in glyceridic structure; (4) regular OO, which is a mixture of refined OO and virgin OO with a free acidity of 0.1%; (5) refined residue oil; and (6) olive residue oil, a blend of refined residue oil and virgin OO. EVOO represents the highest quality of OO and accounts for only 10% of the total oil produced. Its taste, aroma, and mouthfeel are used by experts to judge the quality of EVOO.12 OO, especially EVOO, has been related to some health benefits due to the high content of monounsaturated fatty acids, mainly oleic acids, and various minor components including non-­polar compounds such as α-­tocopherol and sterols and polar phenols coming from the derivatives of oleuropein and ligstroside.13 The main components of OO are the fatty acids, of which monounsaturated fatty acids (MUFAs) of oleic acid (C18:1) represent 55–83%, polyunsaturated fatty acids (PUFAs) such as linoleic acid (C18:2) and linoleic acid (C18:3) 4–20%, and saturated fatty acids (SFAs) such as palmitic acid (C16:0) and stearic acid (C18:0) 8–14% of the total fatty acids. The minor components of OO constitute 1–2% of the total content of OO. The minor components can be further classified into (1) the unsaponifiable fraction, which can be extracted with organic solvents after the saponification of OO containing sterols, tocopherol or vitamin E, squalene and other triterpenes, and pigments, and (2) the soluble fraction, which includes phenolic compounds.14 The positive effects of OO on human health specifically are due to its capability for maintaining the levels of normal blood cholesterol, maybe due to oleic acid and other MUFAs, protecting cells from oxidation by reactive oxygen species (ROS) or reactive nitrogen species (NOS) due to antioxidant components such as vitamin E, carotenoids, polyphenolic compounds, secoiridoids (oleuroepein, oleuroepein aglycone, dimethyloleuroepein, ligstroside), hydroxytyrosol, tocopherol, phenolic acids (caffeic acid, vanillic acid, syringic acid), and lignans. The United States Food and Drug Administration (FDA) allows the claim relating to the capability of daily consumption of 23 g of OO to reduce the risk of coronary heart disease.6 Some health benefit effects of dietary OO on human health have been reported. An OO-­rich diet is reported to prevent cardiovascular diseases, reduce plasma triacylglycerol, increase high-­density lipoprotein (HDL) cholesterol levels, improve the postprandial lipoprotein metabolism, and reduce blood pressure and the risk of hypertension, as demonstrated through epidemiological studies.15 MUFAs are considered to contribute to these activities.11 OO and specific components extracted from Olea europaea are also reported to have anti-­cancer activities, based on studies of either in vitro or in vivo cancer models. Compounds such as oleuropein and verbascoside were found to have independent cytotoxic effects in animal models of cancer, as reviewed

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by Antoniou and Hull. The polyphenol compounds present in EVOO also have anti-­proliferative activities in three liver cancer cell lines (HepG2, Huh7, and Hep3B). This effect was associated with induction of autophagy and could be potentiated by tumor necrosis factor α (TNFα) by lowering the concentration of oleocanthal needed for cytotoxicity.17 Reboredo-­Rodríguez et al. reviewed the minor components, especially phenolic compounds, isolated from OO for the management of cardiovascular diseases and cancer.18 Owing to its beneficial activities and its pleasant taste, OO commands a high price in the fats and oils industry.19 As a consequence, several regulations, standards, and analytical techniques have been introduced for monitoring the quality of OO, purity assessment through the analysis of specific components such as sterols, triacylglycerol, stigmastadienes, wax composition, and triterpenic dialcohols, and also for detecting the possibility of adulteration practice. Some reviews have summarized these tasks, including the use of liquid chromatography and other chromatographic techniques such as liquid chromatography-­mass spectrometry (LC-­MS), gas chromatography-­mass spectrometry (GC-­MS), and thin-­layer chromatography (TLC),20,21 differential scanning calorimetry, and spectroscopic methods combined with multivariate data analysis (chemometrics), especially FTIR spectroscopy.2,10,22 Recently, metabolomics approaches have been used for authentication studies of OO.13

7.3  O  fficial Methods for the Quality Control of   Olive Oils 7.3.1  United States Pharmacopeia (USP) 7.3.1.1 Identification9 7.3.1.1.1  Determination of Fatty Acid Composition Using Gas Chromatography23.  GC conditions: Detector: flame ionization detector (FID) (250 °C). Splitless injection system, with a 30 m × 0.53 mm i.d. fused-­silica capillary column bonded with a 1.0 µm layer of phase G16. The chromatograph is programmed to maintain the column temperature at 70 °C for about 2 min after injection, then to increase the temperature at the rate of 5 °C min−1 to 240 °C, and finally to maintain this temperature for 5 min. The injection port temperature is maintained at about 220 °C. The carrier gas is helium with a linear velocity of about 50 cm s−1. Standard: Nu-­Chek mixture 17A. Acceptance criteria are given in Table 7.1. 7.3.1.1.2  Determination of Triglyceride Profile by TLC24.  TLC conditions: Plate: high-­performance thin-­layer chromatography (HPTLC) [20 cm × 10 cm, silica gel 60 RP-­18 (or RP-­18 F254), 0.15–0.2 mm layer, 4–8 µm particle size]. Mobile phase: methylene chloride–glacial acetic acid–acetone (20 : 40 : 50). Spray reagent: 25 mg mL−1 phosphomolybdic acid in 96% alcohol. Standard: dissolve 25 µL of USP Olive Oil Reference Standard in 3 mL of methylene chloride. Predevelop the plate with methylene chloride to the upper edge and dry the plate at 120 °C for 10 min. Develop over a path of 7 cm using mobile

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Table 7.1  Acceptance  criteria of fatty acid compositions in olive oil. Carbon chain length

No. of double bonds

%

8. Methods can be found in the European Pharmacopoeia.28 Composition of Fatty Acids in Oil. See Section 7.3.2.1.1, Method B. Sterols. The detailed GC-­FID method is described in the European Pharmacopoeia.28 Column: fused-­silica stationary phase (20–30 m × 0.25–0.32 mm i.d., 0.25 µm phenyl(5)methyl(95)polysiloxane or cyanopropyl(7)phenyl(7)

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methyl(86)polysiloxane, 260 °C. Injection: 280 °C. Detector: FID, 290 °C. Carrier gas: H2–He (30–50 cm s−1). Splitting ratio: 1 : 50 (H2) or 1 : 100 (He). Acceptance criteria: Composition of the sterol fraction of the oil: cholesterol, maximum 0.5%; campesterol, maximum 4.0%; Δ7-­stigmasterol, maximum 0.5%; sum of contents of Δ5,23-­stigmastadienol, clerosterol, β-­sitosterol, sitostanol, Δ5-­avenasterol, and Δ5,24-­stigmastadienol, minimum 93.0%. The content of stigmasterol is not greater than that of campesterol. Sesame Oil. In a ground-­glass-­stoppered cylinder, shake 10 mL of oil for about 1 min with a mixture of 0.5 mL of a 0.35% v/v solution of furfural R in acetic anhydride R and 4.5 mL of acetic anhydride R. Filter through a filter-­ paper impregnated with acetic anhydride R. To the filtrate add 0.2 mL of sulfuric acid R. No bluish green color develops.

7.3.2.2 Refined Olive Oil25 Definition: Refined OO is fatty oil obtained by refining of crude OO, obtained by cold expression or other suitable mechanical means from the ripe drupes of Olea europaea L. A suitable antioxidant may be added. Appearance, solubility, relative density: see Section 7.3.2.1. 7.3.2.2.1  Identification.  First identification, methods A and C; second identification, methods A and B:    A. Acid value (NMT 0.3 determined on 10 g). B. Identification of fatty oils by TLC (see Section 7.3.1.1.2). C. Composition of fatty acids (see Section 7.3.1.1.2). 7.3.2.2.2  Specific Test General Test. Acid value, NMT 0.3; peroxide value, NMT 10.0; unsaponifiable matter, NMT 1.5%; ultraviolet absorbance, maximum 1.20, determined at the absorption maximum of 270 nm; to 1.00 g of sample add cyclohexane R and dilute to 100.0 mL with the same solvent. Methods can be found in the European Pharmacopoeia.28 Composition of Fatty Acids. See Section 7.3.2.1.2.2. Sterols. See Section 7.3.2.1.2.3. Sesame Oil. See Section 7.3.2.1.2.4.

7.3.3  Japanese Pharmacopoeia, 17th Edition OO is the fixed oil obtained by expression from the ripe fruit of Olea europaea L. (Oleaceae). OO is a light-­yellow oil. It has a faint odor, which is not rancid, and has a bland taste. It is miscible with diethyl ether and

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petroleum ether. It is slightly soluble in ethanol (95%). All or part of it congeals between 0 and 6 °C. The congealing temperature of fatty acids is 17–26 °C.29

7.3.3.1 General Test29 Specific gravity: d25 25 = 0.908–0.914. Acid value: NMT 1.0. Saponification value: 186–194. Iodine value: 79–88. Methods can be found in the Japanese Pharmacopoeia.29

7.3.3.2 Purity29 Drying oil: Mix 2 mL of OO with 10 mL of dilute nitric acid (1 : 4), add 1 g of powdered sodium nitrite little by little with thorough shaking, and allow the mixture to stand in a cold place for 4–10 h. The mixture congeals to a white solid. Peanut oil: Dissolve OO (1.0 g) in sulfuric acid–hexane–methanol (60 mL), boil the solution in a flask for 2.5 h in a water bath with a reflux condenser, cool, transfer to a separator, and add 100 mL of water. Wash the flask with petroleum ether (50 mL), add the washings to the separator, shake, allow to stand, and separate the petroleum ether layer. Extract the water layer with petroleum ether (50 mL) and combine the petroleum ether layers. Wash the petroleum ether solution repeatedly with water (20–30 mL) until the washings show no further acid reaction to Methyl Orange. Add NaSO4·0.5H2O (5 g), shake, filter, wash the NaSO4·0 H2O with petroleum ether (2 × 10 mL), filter the washings using the former separator, combine the filtrates, and distil the petroleum ether on a water bath, passing nitrogen. Dissolve the residue in acetone to make exactly 20 mL and use this solution as the sample solution. Separately, dissolve methyl behenate (0.067 g) in acetone (50.0 mL), then pipet 2 mL, add acetone to 20.0 mL, and use this solution as the standard. Perform the test with exactly 2 mL each of the sample solution and standard solution for analysis using GC. Measure the peak heights of the test solution, standard solution, and methyl behenate solutions. The peak of the test solution is not higher than that of the sample solution. GC conditions: Detector: FID. Column: glass (2 m × 3 mm i.d.), packed with silanized siliceous earth for GC (150–180 mm), coated with 20 mol L−1 poly(ethylene glycol) in a ratio of 5%. Column temperature: 220 °C. Carrier gas: nitrogen. Flow rate: adjust so that the retention time of methyl behenate is about 18 min. Detection sensitivity: adjust so that the peak height of methyl behenate obtained from 2 mL of the standard solution is 5–10 mm.

7.3.4  I nternational Olive Council (IOC) Standards,   Methods, and Guide Methods and guidelines described in ref. 30 are summarized here. Interested readers can refer to the IOC website30 for detailed methods and guidelines.

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7.3.4.1 Trade Standards 1. Trade Standards on Olive Oils and Olive Pomace Oils: Decision No. DEC-­ III.4/111-­VI/2020, Standard COI/T.15/NC No. 3/Rev.15/2019. This standard describes various definitions of olive oils and olive pomace oils, purity and acceptance criteria, quality criteria, additives, contaminants, etc.    Virgin OOs are oils that are obtained from the fruit of the olive tree (Olea europaea L.) solely by mechanical or other physical means under conditions, particularly thermal conditions, that do not lead to alterations in the oil, and which have not undergone any treatment other than washing, decantation, centrifugation, and filtration. Virgin OOs are classified and designated as follows: virgin OOs fit for consumption as they are (extra virgin OOs, virgin OOs, ordinary virgin OOs), and virgin OOs that must undergo processing prior to consumption (lampante virgin OOs, refined OOs, OOs composed of refined OOs and virgin OOs). Olive pomace oil is the oil obtained by treating olive pomace with solvents or other physical treatments, to the exclusion of oils obtained by re-­esterification processes and of any mixture with oils of other kinds. It is marketed in accordance with the following designations and definitions: crude olive pomace oil, refined olive pomace oils, and olive pomace oil composed of refined olive pomace oil and virgin OOs. Purity and acceptance criteria of fatty acid composition, trans-­fatty acid contents, sterol and triterpene dialcohol composition, wax content, maximum difference between the actual and theoretical equivalent carbon number 42 (ECN 42) triacylglycerol content, stigmastadiene content, content of 2-­glyceryl monopalmitate, and unsaponifiable matter are described.30    2. Trade Standards on Table Olive Oils: Resolution RES-­2/91-­IV/04, Standard COI/OT/NC No. 1/200    This standard applies to the fruit of the cultivated olive tree (Olea europaea L.) which has been suitably treated or processed and which is offered for trade and for final consumption as table olives. Various descriptions, types, definitions of table OOs, composition and quality factors, food additive/processing, and contaminants are described.30

7.3.4.2 Chemical Testing Methods The IOC describes 13 chemical methods of testing for the quality control of OOs; the 14th method describes the evaluation of the precision of the 13 methods.    1. COI/T.20/Doc. No. 8 – Determination of tetrachloroethylene in olive oil by GC. Method COI/T.20/Doc. No. 8/Corr. 1/1990.

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2. COI/T.20/Doc. No.11 – Method for determination of stigmadienes in vegetable oils. Method COI/T.20/Doc. No. 11/Rev. 3 – 2017. These methods are applicable for virgin OOs and crude olive pomace oil (by GC). 3. COI/T.20/Doc. No. 16 – Determination of sterenes in refined vegetable oil. Method COI/T.20/Doc. No. 16/Rev. 2 – 2017. These methods are applicable for the determination of sterenes (campestadienes and stigmastadienes) and hydrocarbons originating from sterols during refining or desterolizing treatments applied to vegetable oils (by column chromatography and GC). 4. COI/T.20/Doc. No. 19 – Spectrophotometric investigation in the ultraviolet. Method COI/T.20/Doc. No. 19/Rev. 5 – 2019. The method describes the procedure for performing a spectrophotometric examination of OO in the ultraviolet region. 5. COI/T.20/Doc. No. 20 – Determination of the difference between the actual and theoretical content of tetraglycerols with ECN 42. Method COI/T.20/Doc. No. 20/Rev. 4 – 2017. This method is applicable for the determination of the absolute difference between the experimental values of triacylglycerols (TAGs) with ECN 42 (ECN42HPLC) obtained by determination in the oil by high-­performance liquid chromatography (HPLC) and the theoretical value of TAGs with an ECN of 42 (ECN 42theoretical) calculated from the fatty acid composition. A computer program (Excel) is also provided. 6. COI/T.20/Doc. No. 23 – Determination of the percentage of 2-­glyceryl monopalmitate. Method COI/T.20/Doc. No. 23/Rev.1 – 2017. This method describes the analytical procedure for the determination of the percentage of palmitic acid at the 2-­position of the triacylglycerols by means of 2-­glyceryl monopalmitate evaluation (by column chromatography and GC). 7. COI/T.20/Doc. No. 25 – Evaluation of the coherence of TAG composition and fatty acid composition. Method COI/T.20/Doc. No. 25/Rev. 2 – 2018. The method is used to check the coherence of the triacylglycerol composition of OO with its fatty acid composition. The theoretical triacylglycerol composition calculated from the fatty acid composition is similar in OO to the experimental triacylglycerol composition. The method only indicates if the OO triacylglycerol composition is coherent or non-­coherent. Methods used are HPLC and GC. A computer program (Excel) is also provided. 8. COI/T.20/Doc. No. 26 – Determination of the sterol composition and content and alcoholic composition by capillary GC. Method COI/T.20/ Doc. No. 26/Rev. 5 – 2020. The method describes a procedure for determining the individual and total alcoholic compound content of OOs and olive pomace oils and also of blends of these two oils. The alcoholic compounds in olive and olive pomace oils comprise aliphatic alcohols, sterols, and triterpenic dialcohols. 9. COI/T.20/Doc. No. 28 – Determination of the content of waxes and fatty acid methyl esters by capillary GC. Method COI/T.20/Doc. No. 28/Rev.

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2 – 2017. This method is for the determination of the content of waxes and fatty acid methyl and ethyl esters in OOs. The method is recommended as a tool for distinguishing between OO and olive pomace oil and as a quality parameter for extra virgin OOs enabling the detection of fraudulent mixtures of extra virgin OOs with lower quality oils whether they are virgin, ordinary, lampante, or some deodorized oils. 10. COI/T.20/Doc. No. 29 – Determination of biophenols in olive oil by HPLC. Method COI/T.20/Doc. No. 29/Rev. 1 – 2017. 11. COI/T.20/Doc. No. 33 – Determination of fatty acid methyl esters by GC. Method COI/T.20/Doc. No. 33/Rev. 1 – 2017. This method permits the determination of fatty acid methyl esters from C12 to C24, including saturated, cis-­ and trans-­monounsaturated, and cis-­ and trans-­ polyunsaturated fatty acid methyl esters. 12. COI/T.20/Doc. No. 34 – Determination of free fatty acids by cold method. Method COI/T.20/Doc. No. 34/Rev. 1 – 2017. This method describes the determination of free fatty acids in OOs and olive pomace oils. The content of free fatty acids is expressed as acidity, calculated as the percentage of oleic acid (by titration). 13. COI/T.20/Doc. No. 35 – Determination of peroxide value. Method COI/T.20/Doc. No. 35/Rev. 1 – 2017 (by titration). 14. COI/T.20/Doc. No. 42-­2 – Precision values of the methods of analysis adopted by the IOC. Precision values of methods COI/T.20/Doc. No. 42-­2/Rev. 3 – 2019.

7.3.4.3 Methods of Analysis for Provisional Approval 1. COI/T.20/Doc. No. 31 – Determination of the content of waxes and fatty acid methyl esters by capillary GC using 3 g of silica. Method COI/T.20/ Doc. No. 31 – 2012. Precision values of the methods for the collaborative studies of six samples of OO are described. 2. COI/T.20/Doc. No. 32 – Determination of the composition of triacylglycerols and composition and content of diacylglycerols by capillary GC in vegetable oils. Method COI/T.20/Doc. No. 32 – 2013. Precision values of the methods for the collaborative studies of five samples of OO are described.

7.3.4.4 Other Guidelines and Methods The following guidelines and methods are also described by the IOC:30    1. Method of Sensory Analysis of Virgin Olive Oils (eight methods). 2. Sensory Analysis of Table Olive Oil (two methods). 3. Best Practice Guide (one method). 4. Consumer Guideline on the Best Storage Conditions for Olive Oil and Olive Pomace Oil (one method). 5. Quality Management Guide for Olive Oil and Olive Pomace Industries (four guides). 6. Quality Management Guide for Table Olive Industry (one method).

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7. Guide for the Determination of the Characteristics of Olive Oil (one method). 8. Best Practice Guideline for the Storage of Olive Oil and Olive Pomace Oil (one method).    Among the analytical methods used for quality control of OOs, chromatographic and spectroscopic methods have been widely reported in the literature. The information provided by responses obtained from chromatograms and spectra is complex and huge, hence multivariate data analysis or chemometrics is used, as outlined in the following section.

7.4  Chemometrics The large responses and great number of variables that can occur during analysis make data treatment difficult, and advanced statistical tools such as chemometrics can be applied.31 The International Chemometrics Society (ICS) defines chemometrics as “the science of relating chemical measurements made on a chemical system to the property of interest (such as concentration) through the application of mathematical or statistical methods”.32 Some commonly used statistical and chemometric software tools are listed in Table 7.3. Chemometric techniques have evolved as leading tools commonly used for analytical chemists to achieve faster data treatment. In analytical fields, numerous chemometric methods have been employed, namely data or response (spectra or chromatogram) pretreatment, chemometrics for classification, and multivariate calibration.33 The preprocessing data used included baseline corrections, smoothing, standard normal variate, mean centering, spectra normalizations, Savitzy–Golay-­based derivatization, signal correction and compression, and multiplicative correction. Table 7.3  Some  software typically used in chemometrics analysis. Software

Company

URL

Design-­Expert Fusion Pro JMP Matlab MINITAB Modde Nemrod-­W Origin R SPSS

Stat-­Ease Inc. S-­Matrix Corp. SAS Institute Inc. The Mathworks Inc. Minitab Inc. Umetrics LPRAI, Marseille, France Microcal Software The R Foundation IBM

Statgraphics STATISTICA Unscrambler

Statpoint Technologies StatSoft CAMO AS

Virtual Column

ACROSS and the University of Tasmania

http://www.statease.com/ http://www.smatrix.com/ http://www.sas.com/ http://www.mathworks.com.au http://www.minitab.com http://www.umetrics.com/modde https://nemrodw.software.informer.com/ http://www.originlab.com/ https://www.r-­project.org/ https://www.ibm.com/analytics/ spss-­statistics-­software http://www.statgraphics.com/ https://www.statistica.com/en/ https://download.cnet.com/ The-Unscrambler/3000-2064_ 4-10467646.html http://www.virtualcolumn.com

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Chemometric classification is typically performed using three approaches, namely (1) exploratory data analysis, (2) unsupervised pattern recognition, and (3) supervised pattern recognition techniques. Exploratory data analysis and unsupervised pattern recognition are commonly used for simplification by reducing the amount of original data and for gaining a better knowledge of chemical data sets. Therefore, the main challenge of these approaches is to remove the redundancy and noise while retaining the meaningful information contained in original data.34 Exploration data analysis involves variable (data) reduction techniques defining a number of latent variables by making linear combinations of the original variables, which include principal component analysis (PCA), projection pursuit (PP), and factor analysis (FA). Among these, PCA is the most widely applied method used in the reduction of data dimensionality.35 Unsupervised pattern recognition differs from exploratory data analysis because the aim of the former is to detect similarities, whereas exploratory data analysis has no particular prejudice as to whether or how many groups will be found. Unsupervised pattern recognition techniques include cluster analysis (CA) and similarity analysis (SA). CA, comprising fuzzy clustering (FC) and hierarchical clustering analysis (HCA), can be used for preliminary evaluation of the information contents in the data matrices. The objects (samples) are classified based on similarities of the variables used.36 Supervised pattern recognition (SPR) tries to assign the class membership of the objects (samples) to a certain group known as training sets in order to classify new unknown samples (test samples) in one of the known classes on the basis of the variables used. SPR can be differentiated into class modeling methods and discrimination methods. The most often used class modeling method is known as SIMCA (soft independent modeling of class analogy). SIMCA consider the objects (samples) that fit the class model for a category as part of the class model, and classifies as non-­members those that do not fit. Discrimination models include linear discriminant analysis (LDA), partial least-­squares discriminant analysis (PLS-­DA), artificial neural networks (ANNs), and k-­nearest neighbors (KNN). All these discrimination models are used to build models using certain variables (e.g. FTIR spectra) based on all the categories involved in the discrimination, whereas disjoint class modeling methods create a separate model for each category. The most commonly reported discrimination methods are discriminant analysis using either linear or partial least-­squares algorithms. LDA is based on linear discriminant functions (LDFs) in which variance ratio of between-­class membership of objects is minimized, while the variance ratio of within-­class objects is maximized. PLS-­DA is intended to find the variables and directions in the multivariate space capable of discriminating the established classes in the calibration set.37 ANNs attempt to find the most appropriate grouping of training, learning, and transfer function for classifying the data sets (variables) with growing numbers of features and classified sets. In a simple form, ANNs try to imitate the operation of neurons in the brain.38 KNN is one of the most popular classification techniques based on distance algorithms. KNN is based on measuring the distances between the training samples and test samples to determine the final classification output.39

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The chemometrics of multivariate calibration is usually used to predict the levels of analyte(s) of interest in unknown samples.40 Calibration is the mathematical relationship between the predictor (independent variables) and response variables (dependent variables). The chemometrics of multivariate calibration uses several variables, such as employing the absorbance values at several wavelength or wavenumber regions. Multivariate calibration is usually used to develop calibration and validation models capable of correlating the actual values for analytes as determined with reference method with predicted values using several variables assessed.41 Various multivariate calibrations have been applied to the quantitative analysis of non-­halal components, including stepwise multiple linear regression (SMLR), principle component regression (PCR), and partial least-­squares regression (PLSR).42 These calibrations are considered as inverse calibrations in which concentrations (on the y-­axis) are modeled using absorbances at several wavelengths/wavenumbers (x-­axis).43 The accuracy of calibration and validation models using multivariate calibration was evaluated using the coefficient of determination (R2) for the relationship between two variables, and the precision of models was assessed using the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). RMSEC and RMSEP were obtained using the following equations: m

RMSEC 

 Yˆ  Y  i 1

i

M 1

n

RMSEP 

 Yˆ  Y  i 1

2

i

i

2

i

N

where Yi and Ŷi represent actual and predicted values of the analytes and M and N are the number of data in the calibration and validation set, respectively.44

7.5  C  hromatographic Method for the Analysis of Olive Oil OO is one of the most important edible fats worldwide owing to its nutritional value. Because of the high demand for and high cost of OO, fraudsters often mix it with low-­cost and poor-­quality edible oils to gain economic profits. Casadei et al.45 reported that the most frequent frauds with OOs involve either mixing or dilution with lower quality products or giving false declarations. Therefore, the quality control (QC) of OOs is crucial in the OO industry. In a QC laboratory, the methods of analysis should have following attributes: be sensitive, accurate, precise, and robust, but also as efficient as possible, and must be as simple and fast as possible with low operating costs. Pharmacopeias and the IOC have described official methods of analysis of OOs and their related products and their acceptance criteria; most of the official

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methods described involve chromatographic techniques (see Sections 7.3.1, 7.3.2, and 7.3.3). Some recent (2019–2021) review articles describing various new methods of analysis and/or characterization of OOs have been published. Meenu et al.10 reviewed the application of spectroscopic [FTIR, near-­infrared (NIR), Raman, NMR, MS, UV, and fluorescence], thermal, and chromatographic methods for detecting the adulteration of EVOO. Chemometric methods were applied to evaluate the data in this review, and validation of the described methods was discussed in detail. Mota et al.46 summarized the application of GC-­MS combined with multivariate analysis for the authentication and quality assessment of olive, cold-­pressed rapeseed, sesame, and peanut oils, and some other seed oils. Methods for the determination of various compounds such as phytosterols, vitamin E, S-­(E)-­elenolide, and policosanol were also discussed. Beneito-­Cambra et al.47 described the application of direct MS for the analysis of OO and other vegetable oils. The techniques discussed in this review included (1) direct infusion MS using atmospheric pressure ionization sources, (2) ambient desorption ionization MS, (3) matrix-­assisted laser desorption/ionization (MALDI) and other related methods, and (4) direct MS methods for volatile analysis of vegetable oils with ionization under vacuum conditions. Kalogiouri et al.48 reviewed the application of LC-­high resolution (HR) MS combined with chemometrics for the authentication of OOs. The chemometric methods discussed included unsupervised methods [PCA, HCA, and self-­organizing map (SOM)] and supervised methods (LDA, PLS-­DA, orthogonal partial least-­squares discriminant analysis (OPLS-­DA), random forests (RF), and counter-­propagation artificial neural networks (CP-­ANNs)]. Detailed pretreatment methods were also described. Pasias et al.49 proposed a useful design tree method for the classification of EVOOs. Prior to performing classification using chemometric methods, the acidity, peroxide, K232 (UV coefficient), K270 values and the maturity levels of the samples should be evaluated. In addition, these parameters should meet the acceptance criteria. Conte et al.20 published an interesting review that critiqued existing regulatory methods and standards, highlighted weaknesses, and proposed possible solutions to safeguard the consumer and protect the OO market. According to the authors, it is urgent to identify and/or improve analytical solutions that can detect both common and emerging frauds and to provide all the information required by the international market. Cuadros-­Rodríguez et al.50 published a review on methods of standardization of chromatographic signals to obtain instrument-­agnostic fingerprints in GC. As an example, the standardization of two important fractions of OOs (volatile organic compounds and triacylglycerols) was discussed. The same methods of standardization were applied to reversed-­phase LC.51 As an example, the characterization of bisphenols in virgin OO using HPLC-­UV spectroscopy was discussed, and the standard retention scores (SRSs) can be determined. Unfortunately, in most of these reviews, the cited methods could not be directly applied in the QC laboratory owing to insufficient validation data, as required by Pharmacopeias (see the validation section, Section 7.7). Table 7.4 summarizes recent publications (2019–2021) that reported the various chromatographic methods of analysis of OOs, compounds in OOs, and related

Method

Sample

HPTLC

Nine different Egyptian olive varieties

HPTLC

Aim

Sample preparation

Chromatographic conditions

Results

Validation

Ref.

HPTLC plates (RP-­18 F254); Completed PCA–HCA 52 Distinguish- Dried leaf powder validation extracted by perresults post-­ mobile phase, MeOH–ACN ing the parameters colation until derivatization (7 : 3 v/v); derivatization different for quantitaexhaustion; filtrate were better using NP–PEG; UV detecolive varitive assay of evaporated using than results tion at 365 nm; image eties using oleuropeins rotary evaporator prior to deriprocessing using ImageJ, (1) comwere reported. vatization. 151 h; quantitative densibination Validation Densitometric tometric determination of of HPTLC methods assay of oleuoleuropeins at 240 nm image (PCA–HCA) ropein was analysis according better than with PCA– to Want et the ImageJ HCA and al.71 were not method (2) oleuropein reported content 75 HPTLC plates (silica gel 60 The two methods Completed Quantifica- Dried leaf powders CommerF254); mobile phase, method validation extracted by percolacan be applied tion of cial olive parameters for tion (3× MeOH, 24 h); for the quantioleuro(1) EtOAc–MeOH–H2O leaves quantitative combined extracts tative assay of pein in (8 : 1 : 0.5 v/v/v), method (2) from assay of oleuwere evaporated using oleuropeins. two comEtOH–H2O (5.5 : 4.5 v/v); Saudi Araropeins were a rotary evaporator Method 2 was mercial scanned at 200 nm bia and reported for (38 °C) recommended samples of Egypt and both methods owing to its leaves and extract of green solvent capsules olives in mixtures capsules

Application of FTIR Spectroscopy and Chromatography

Table 7.4  Chromatographic  methods of analysis of olive oil and olive leaves.a

(continued) 149

150

Table 7.4  (continued) Method

Sample

Aim

Sample preparation

Chromatographic conditions

Results

Validation

Ref.

GC-­MS

!5 varieties of EVOOs from the three largest exporting countries of OOs (Spain, Italy, Greece)

Classification of EVOOs based on FF and EN using MVA

56 Repeatabilities Aroma was extracted Silica capillary column: DB-­5 The similariof retention using SPME fiber and DB-­Wax (30 m × 0.25 ties of all 15 times and (50/30 µm) of mm i.d.). Initial temperaEVOOs were peak areas DVB–CAR–PDMS ture 40 °C (held 3 min), >0.80 based were reported. (conditioned at increased to 200 °C (at 5 °C on IAC and Validation 250 °C, 30 min); IS, min−1), then at 10 °C min−1 CC. FF showed method of FF 4-­methyl-­2-­pentanol. excellent disto 230 °C (held 3 min), then using simi10 mL of EVOO samcrimination finally to 250 °C (held 3 larity analysis ple was transferred between mixed min); injection port, 250 °C; was reported. into a 40 mL vial; then oils. LDA disionizing source, 230 °C. Validation 1 mL of IS (200.5 µg criminated difEI-­MS: 70 eV (35–350 amu). method of µL−1), equilibrium at ferent mixed Compounds were identified PCA–LDA and oils; EVOOs using NIST 14.0 mass spec60 °C (20 min); SPME confirmation and mixed oils tra libraries fiber enclosed in a of compounds could be differstainless-­steel needle (EI-­MS) entiated using was passed through according to PLS-­DA a hole to expose the ref. 68 were fiber at a distance of not reported 1 cm above the liquid surface for 40 min. The vials were swirled during the SPME exposure (100 rpm)

Chapter 7

GC-­FID-­MS

Establishing GC: samples were dis57 PLS-­DA gave the Validation 65 single-­ GC : Rtx-­65 TG capillary colsolved in CHCl3 (0.2% method of method best results variety umn, 30 m × 0.25 mm i.d., PCA accordfor using both (Arbew/w) film thickness 0.1 µm. ing to Want authentimethods. quina) Temperature increased et al.71 was cation of Sensitivity and EVOO from 300 to 370 °C at 10 °C the geospecificity were samples min−1. Injection, 320 °C; He not reported; graphical 100% in almost from three PLS-­DA/ flow rate, 1.5 mL min−1. origin of HPLC: 100 mg was mixed RP-­HPLC : Develosil C30-­UG-­5 all classes; the geographiSIMCA: SensiArbequina error was 0% cal origins tivity and specwith 0.1 g of hexane, column, 250 × 4.6 mm i.d, monovarifor all classes, in Spain ificity were diluted 1 : 1 using hexfilm thickness 5 µm. Mobile etal EVOO with an inconreported ane before injection phase, ACN–IPA (40 : 60 v/v); clusive ratio of flow rate, 1.2 mL min−1 just 4% GC equipped with capillary The sample was disTAG components Peak repeatabil- 76 EVOO Separation flow technology Deans ity (retention solved in hexane (0.5% in EVOO can samples and idenswitch. Column: 1D, SLB-­ time and area), v/v) and 1 µL of this be detected (Victoria, tification 5ms (15 m × 0.25 mm i.d., LOD and consolution was injected using MS with Australia) of TAGs film thickness 0.25 µm); 2D, firmation of into the GC column for a 2D column using Rtx-­65 (9 m × 0.25 mm i.d., the identity of analysis. IS: glyceryl by comparison heart-­cut film thickness 0.1 µm) and TAGs by MS triheptadecanoate with TAG stanmultidirestrictor reactivated fused-­ were reported; dards. Repeatmensional silica (DFS, 1.72 m × 0.18 mm intermediate ability: RSD of GC-­MS i.d.); after 2D separation, EI-­ precision and retention time MS (70 eV, m/z 50–700) accuracy were was 0.98 either in calibration or validation models. The RMSEC, RMSECV, and RMSEP values obtained were 0.2714, 0.4567, and 10.2016, respectively.64 High R2 values and low values of RMSEC and RMSECV indicated that the developed method is accurate and precise enough for the prediction of squalene in OOs.65

7.7  Validation Methods To achieve reliable results of any analysis in a QC laboratory, all methods used should be validated and monitored during routine application. The objective of the validation method is to demonstrate that the performance of a method is suitable for its intended purposes. General validation methods and their acceptance criteria for pharmaceutical applications have been described in all current Pharmacopeias (e.g. USP 44–NF 39, 2021; British Pharmacopoeia 2020, Indonesian Pharmacopoeia VI, 2020; European Pharmacopoeia 10, 2020, etc.). Prior to the application of the official pharmacopeial methods for the QC of OO samples, the method should first be verified, and the system suitability must be evaluated; the acceptance criteria can be referred to the Pharmacopeia used. Detailed validation of chromatographic methods of analysis for the application of drugs from herbs and/or botanicals have been reviewed and discussed by Indrayanto.66

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The most important validation parameter of chromatographic methods for analyzing samples derived from botanicals (including oils) is the selectivity or specificity of the method. For this purpose, the identity and purity of peaks of the target compounds in samples must be evaluated by using a diode-­array detector (DAD) or MS detector. If a non-­specific detector [e.g. FID, fixed-­wavelength UV detector, and evaporative light-­scattering detector (ELSD)] is used, the identity and purity of the target peaks can be confirmed by repeating the measurements using different chromatographic conditions as recommended by the Eurachem Guide;67 according to our experience, at least three different chromatographic conditions should be applied. Determining the identity of the target peaks by MS cannot be performed by comparison with standards and/or an MS library alone. Confirmation of the identity of the target peak in samples using an MS or MS/MS detector can be performed by determining its identification point (IP) score according to the guidance of European Commission Decision 2002/657/EC;68 the IP of the target peaks should be not less than 4, and the ratio of two specific daughter ions should meet the criteria of the guidance. A detailed discussion regarding methods of peak identification by MS and MS/MS was presented by Kruve et al.69 As described in Table 7.3, combinations of chromatographic methods and chemometrics have often been applied for the QC of OOs and/or related samples. Chemometric validation methods comprises validation of a model and validation of the method. Model validation involves the use of an internal validation set or cross-­validation to assess the appropriate parameters of a model via identification or quantification errors and uncertainty. Parameters often include the range of variables, the type of preprocessing, the model rank, the choice of the algorithm, and others. For detailed discussion, see USP 44–NF 39, General Chapter : Chemometrics,65 and/or the European Pharmacopoeia 10, Section 5.21: Chemometric Methods Applied to Analytical Data.70 It is recommended to evaluate the coefficient of variation (CV) of the variables used and validate the PCA model using QC samples according to a Nature Protocol71 prior to performing other algorithms (HCA, PLS-­DA, SIMCA, etc.); the CV should be not more than 30%, and a tight clustering of the QC samples must be observed by the score plot. Variables that have CV >30% should be removed. Validation methods for pharmaceuticals and related products must be based on a fully independent external validation set, and it must meet the validation requirements as described in the current Pharmacopeia. According to USP 44–NF 39, General Chapter ,72 validation parameters that must be evaluated are specificity, linearity, accuracy, repeatability, intermediate precision, range, and robustness. The acceptance criteria can be referred to each of the monographs (olive oil) in the Pharmacopeias. Reproducibility data for some methods of the IOC for the QC of OOs have also been described (see Sections 7.3.1–7.3.4). Method evaluations of the accuracy and precision using comparison of a proposed method with a reference or validated method should be performed according to USP 44–NF 39, General Chapter :

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Analytical Data Interpretation and Treatment; it could not be accomplished by using only a t-­test and/or analysis of variance (ANOVA). Researchers have often compared some methods used for the detection of adulterants or characterization of OO samples from different locations or production methods, in order to find the most efficient method; in this case, the methods described in USP 44–NF 39, General Chapter : Evaluation of Screening Technology for Assessing Medicine Quality74 can be applied. All validated methods that have been applied in a QC laboratory should always be monitored during their routine application, for evaluating whether the methods used are still valid; if the results are not in the prescribed specification range, revalidation should be performed. For validation of FTIR spectroscopy using multivariate calibration, the validation method commonly used is cross-­validation using the leave-­one-­out technique. In cross-­validation, one of the calibration samples is left out from the multivariate calibration models used, and the remaining calibration samples are exploited for developing a new calibration model. The removed sample is then calculated using the newly developed PLS model. This procedure was repeated by leaving out calibration samples one by one. The statistical parameters used to evaluate the performance of cross-­validation are R2 (for accuracy of the model) and also RMSECV and predicted residual error sum of squares (PRESS) (for precision of the model).38

Acknowledgements The authors wish to acknowledge Ms Nurliya Irfiani (PT Merck Sharp Dohme Pharma Tbk, Pandaan, Pasuruan, Indonesia) for providing references for the European Pharmacopoeia 10 and Mrs Wahyu Dewi Tamayanti (Institute of Life, National Yang Ming Chiao Tung University, Taipei, Taiwan) for supplying references for the British Pharmacopoeia, 2020.

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Through Combined Liquid Chromatography and NMR Approaches, Food Anal. Methods, 2019, 12(8), 1759–1770. 84. F. Stilo, C. Cordero, B. Sgorbini, C. Bicchi and E. Liberto, Highly informative fingerprinting of extra-­virgin olive oil volatiles: The role of high concentration-­capacity sampling in combination with comprehensive two-­dimensional gas chromatography, Separations, 2019, 6(3), 34. 85. A. Maléchaux, S. Laroussi-­Mezghani, Y. Le Dréau, J. Artaud and N. Dupuy, Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties, Food Chem., 2020, 309, 125588. 86. M. H. Abdelrahman, R. O. Hussain, D. S. Shaheed, M. Abukhader and S. A. Khan, Gas chromatography-­mass spectrometry analysis and in vitro biological studies on fixed oil isolated from the waste pits of two varieties of Olea europaea L. OCL -­Oilseeds fats, Crop Lipids, 2019, 26(4), 28. 87. N. Jurado-­Campos, M. García-­Nicolás, M. Pastor-­Belda, T. Bußmann, N. Arroyo-­Manzanares and B. Jiménez, et al., Exploration of the potential of different analytical techniques to authenticate organic vs. conventional olives and olive oils from two varieties using untargeted fingerprinting approaches, Food Control, 2021, 124, 107828. 88. A. M. Jiménez-­Carvelo, M. T. Osorio, A. Koidis, A. González-­Casado and L. Cuadros-­Rodríguez, Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-­ATR and Raman spectroscopy, LWT-­-­Food Sci. Technol., 2017, 86, 174–184. 89. A. Hirri, M. Bassbasi, S. Platikanov, R. Tauler and A. Oussama, FTIR Spectroscopy and PLS-­DA Classification and Prediction of Four Commercial Grade Virgin Olive Oils from Morocco, Food Anal. Methods, 2016, 9(4), 974–981. 90. I. Sota-­Uba, M. Bamidele, J. Moulton, K. Booksh and B. K. Lavine, Authentication of edible oils using Fourier transform infrared spectroscopy and pattern recognition methods, Chemom. Intell. Lab. Syst., 2021, 210(Jan), 104251. 91. F. T. Borghi, P. C. Santos, F. D. Santos, M. H. C. Nascimento, T. Corrêa and M. Cesconetto, et al., Quantification and classification of vegetable oils in extra virgin olive oil samples using a portable near-­infrared spectrometer associated with chemometrics, Microchem. J., 2020, 159(Sep), 105544. 92. P. F. Filoda, L. F. Fetter, F. Fornasier, R. de C. de S. Schneider, G. A. Helfer and B. Tischer, et al., Fast Methodology for Identification of Olive Oil Adulterated with a Mix of Different Vegetable Oils, Food Anal. Methods, 2019, 12(1), 293–304. 93. Irnawati, S. Riyanto, S. Martono and A. Rohman, Analysis of palm oil as oil adulterant in olive and pumpkin seed oils in ternary mixture systems using FTIR spectroscopy and chemometrics, Int. J. Appl. Pharm., 2019, 11(5), 210–215. 94. A. Rohman, Y. bin Che Man, A. Ismail and P. Hashim, FTIR Spectroscopy Coupled with Chemometrics of Multivariate Calibration and

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Discriminant Analysis for Authentication of Extra Virgin Olive Oil, International Journal of Food Properties, 2017, 20, S1173–S1181. 95. A. T. Nurwahidah, Rumiyati, S. Riyanto, A. F. Nurrulhidayah, K. Betania and A. Rohman, Fourier Transform Infrared Spectroscopy (FTIR) coupled with multivariate calibration and discriminant analysis for authentication of extra virgin olive oil from rambutan seed fat, Food Res., 2019, 3(6), 727–733.

Chapter 8

Application of Molecular Spectroscopy and Chromatography in Combination with Chemometrics for the Authentication of Virgin Coconut Oil Anjar Windarsiha,b, Lily Arsanti Lestaria,c, Yuny Erwantoa,d, Nurrulhidayah Ahmad Fadzillahe and Abdul Rohman*a,f a

Center of Excellence, Institute for Halal Industry and Systems, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; bResearch Division for Natural Product Technology (BPTBA), National Research and Innovation Agency (BRIN), Yogyakarta 55861, Indonesia; cDepartment of Nutrition and Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; dDivision of Animal Products Technology, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; eInternational Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), 53100 Jalan Gombak, Selangor, Malaysia; fDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia *E-­mail: [email protected]   Food Chemistry, Function and Analysis No. 32 Advanced Spectroscopic Techniques for Food Quality Edited by Ashutosh Kumar Shukla © The Royal Society of Chemistry 2022 Published by the Royal Society of Chemistry, www.rsc.org

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8.1  Introduction to Virgin Coconut Oil Virgin coconut oil (VCO) is a functional food oil that is widely used and distributed throughout the world. VCO is commonly used in foods, toiletries, and some industrial applications. The demand for VCO continues to increase each year. The global market for VCO in 2018 was US$2.7 billion and it is predicted to increase to US$4.8 billion by 2024. VCO is obtained directly from the extraction of fresh mature coconut meat and is not subjected to any kind of chemical treatment such as refining, bleaching, and deodorizing.1 According to Srivastava et al.,2 VCO is defined as the oil obtained from the fresh and mature kernel of coconuts using natural means or mechanical treatment with or without heating and not using chemical treatments. Studies showed that extraction methods using natural means do not cause alterations of the natural oil content. VCO contains saturated fats in high concentrations of up to >90%. The fatty acid composition of VCO is shown in Table 8.1. The high content of saturated fats is believed to have a role in increasing high blood cholesterol levels, hence there has been some concern that the use of coconut oil may have adverse effects.3 However, preclinical and clinical trials conducted in the past few years demonstrated that VCO has some advantages for human health that might counter the argument that VCO consumption is undesirable.4 VCO has a rich content of lauric acid, which is categorized as a medium-­chain triglyceride (MCT). This MCT is the main fatty acid compound in VCO and it can be hydrolyzed and absorbed more easily than long-­chain triglyceride (LCT) in the human gastrointestinal tract.5 The triacylglycerol (TAG) content in VCO is presented in Table 8.2. VCO also exhibits several biological activities, such as antifungal, antiparasitic, antibacterial, antiviral, antioxidant, cardioprotective, antidiabetic, hepatoprotective, and hypolipidemic activity.6 It has been demonstrated that VCO can lower the level of low-­density lipoprotein (LDL), very low-­density lipoprotein (VLDL), TAGs, phospholipids, and total cholesterol in the body.1 Table 8.1  Fatty  acid composition of virgin coconut oil from various sources.a Fatty acid composition/%

CODEXb

APCCc

Marina et al.7 Dia et al.8

C6 (caproic acid) C8 (caprylic acid) C10 (capric acid) C12 (lauric acid) C14 (myristic acid) C16 (palmitic acid) C18 (stearic acid) C18:1 (oleic acid) C18:2 (linoleic acid) C18:3 (linolenic acid)

nd–0.70 4.60–10.00 5.0–8.0 45.10–53.20 16.80–21.00 7.50–10.20 2.00–4.00 5.00–10.00 1.00–2.50 nd–0.20

0.40–0.60 5.00–10.00 4.50–8.00 43.00–50.00 16.00–21.00 7.50–10.00 2.00–4.00 5.00–10.00 1.00–2.50 0.995). The recoveries obtained in the application of the developed method were 101.3 and 91.2% for added concentrations of lard of 2.5 and 5%, respectively, in VCO brand A and 104.8 and 104.4% in VCO brand B.22

8.3.2  A  uthentication Analysis of VCO Using NMR Spectroscopy Nuclear magnetic resonance (NMR) spectroscopy is commonly used in many fields of analysis, including the analysis of food and pharmaceutical products. The principle of NMR spectroscopy is based on the interaction of samples with radiofrequency waves and determines the net absorption of energy in the radiofrequency region of the electromagnetic spectrum by the nuclei of those elements which have a magnetic moment and spin angular momentum.23 However, not all nuclei are able to absorb radiofrequency waves, therefore they cannot be analyzed using NMR spectroscopy. The common nuclei that are active in absorbing radiofrequency waves include 1H, 13C, 31P, and 19F. 1H and 13C NMR methods are the most commonly available and are applied in many analyses. NMR spectroscopy provides information about the number of magnetically distinct atoms and atomic nuclei have an intrinsic quantum number called spin.24 1H has a natural abundance of 99.86%, 13C 1.11%, and 31P 100%. The relative sensitivity of 31P is 5680 relative to 1H and 377 relative to 13C. Therefore, 31P possesses the highest sensitivity for analysis compared with other NMR techniques. Owing to the very low abundance of 13C, 13C NMR spectroscopy is not as sensitive as 1H and 31P NMR spectroscopy. This means that a larger quantity of sample is required when distinguishing different samples using 13 C NMR spectroscopy.25 NMR spectroscopy is an important technique in the field of analytical chemistry, owing to the information provided on chemical shifts and spin– spin coupling constants.26 It also provides information on how many hydrogen environments there are in the molecules. The signal-­to-­noise ratio (SNR) in NMR spectroscopy is proportional to the magnetic field strength to the power of 3/2 to 7/4. The higher the frequency of the NMR instrument, the better is the SNR. For example, the SNR of an NMR spectrometer with a frequency of 800 MHz (18.8 T) is three times higher than that of a 400 MHz instrument (9.4 T). Therefore, improving the frequency is aimed at improving the sensitivity of NMR analysis. NMR has some advantages such as easy sample preparation steps, rapid analysis, e.g. 1H NMR analysis requires

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just ∼10 min for each sample measurement, high reproducibility, and high robustness. Moreover, it offers simultaneous analysis to detect either primary or secondary metabolites in certain samples comprehensively. The 1H NMR signals have a large number of measurable properties. Proton is the ideal nucleus for most NMR measurements because most samples contain protons in high abundance. It is highly reproducible, non-­destructive, and it can be used for quantitative measurements because the signal intensities are correlated with the concentration of compounds/molecules in the samples.27 It is possible to obtain the relative abundances of different molecules in mixtures if the absolute concentration of a molecule such as an internal standard is known. The signal of the internal standard should not overlap with the signals of targeted compounds. NMR spectra contain a wealth information about the identity of the molecules in the samples. Some of the information provided by NMR measurements includes frequency, intensity, chemical shift, line width, line shape, and relaxation time.28 NMR spectroscopy is very effective in discriminating between group variations of the spectral data. Numerous statistical tools have been used to study the large dataset generated from NMR measurements.29 There are some critical points in NMR analysis such as sample preparation, solvent selection, and pH control. In order to obtain reproducible results, sample preparation must be performed carefully. One of the important factors in sample preparation is solvent selection, where a number of parameters should be considered, including polarity, selectivity, boiling point, cost, and toxicity and environmental considerations.30 Solvents that are commonly used in NMR analysis include deuterated methanol (CD3OD), deuterated water (D2O), deuterated chloroform (CDCl3), and deuterated dimethyl sulfoxide (DMSO-­d6). Deuterated methanol demonstrated a good extraction efficiency for extracting a wide range of metabolites, from polar to semi-­polar compounds. Combinations of these solvents are also often applied for certain samples to obtain optimum efficiency. The used of a buffer such as phosphate buffer in D2O (pH 6.0) has commonly been used and applied to extract many types of samples to stabilize the pH. For extraction of non-­polar samples such as oils and fats, CDCl3 and combined CDCl3–CD3OD have been widely used and provided good extraction efficiencies for the analysis of oils and fats. There are two types of NMR analysis, namely one-­dimensional (1D) and two-­dimensional (2D) NMR spectroscopy. 1D NMR is the most common NMR spectroscopic technique used for the analysis of many types of samples. 1D NMR spectroscopy is very useful for metabolite fingerprinting analysis because it provides a unique spectral pattern for each sample. The signals obtained are analyzed and fitted to patterns of signals corresponding to the metabolites expected to be present in the mixture. No two samples have the same 1H NMR spectra, therefore NMR spectroscopy is widely used for sample differentiation. However, sometimes the signals are overlapped because of the complexity of the metabolites present, and the 2D NMR approach can

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be used to resolve the signal overlapping for better identification. 2D NMR spectroscopy includes 1H–1H J-­resolved, 1H–1H COSY (correlated spectroscopy), 1H–13C HSQC (heteronuclear single quantum coherence), and 1H–13C HMBC (heteronuclear multiple bond correlation) techniques. These types of 2D NMR analysis have been proved to be very useful to for resolving the overlapping signals in crowded areas.31 13 C NMR spectroscopy in combination with untargeted chemometrics has been used for classifying VCOs. Classification was performed to differentiate VCO, RBD-­CO, VCO adulterated with RBD-­CO, and old VCO (the shelf life is more than 2 years). Sample extraction was carried out using deuterated chloroform (CDCl3) containing 0.5% v/v trimethylsilane (TMS). To the mixture was added 2.9% w/w 1,4-­dioxane as an internal standard to normalize the metabolite bucket integrations. NMR spectroscopy was operated at 100.097 MHz 13C. Chemometrics using multivariate analysis, namely unsupervised PCA using two principal components, PC1 and PC2, clearly separated VCO and RBD-­CO. However, for VCO and old VCO, some overlaps were seen and no clear separation was observed. PCA could differentiate adulterated VCO from pure VCO, but not all adulterated samples were clearly separated from pure VCO. VCO with lower adulterant concentrations of RBD-­CO still overlapped with pure VCO. Supervised chemometrics for pattern recognition was tried and it successfully classified VCO from others. Partial least-­squares discriminant analysis (PLS-­DA) demonstrated clear separations between VCO and RBD-­CO, VCO and adulterated VCO with RBD-­CO, and fresh VCO and old VCO. The optimum value of Q2 was obtained using four PLS-­DA components and the resulting overall accuracy was about 1. The orthogonal partial least-­squares discriminant analysis (OPLS-­DA) model was validated using a permutation test employing 1000 permutations and all models were confirmed to have valid results that were statistically significant (p < 0.001).32 31 P NMR spectroscopy has been used for the differentiation of VCO from RBD-­CO. Monoglycerides (MGs), diglycerides (DGs), free fatty acids, and sterols were used as target compounds to differentiate between VCO and RBD-­CO. These compounds were converted to dioxaphospholane derivatives prior to analysis using 31P NMR spectroscopy. The results showed that the composition of 1 MG was higher in VCO (0.027%) than in RBD-­CO (0.019%), the content of total DGs was lower in VCO (1.55%) than in RBD-­CO (4.10%), and total free fatty acids and total sterols were higher in VCO than in RBD-­CO. The free fatty acid composition in VCO was significantly higher (0.127%) than in RBD-­CO (0.015%). Moreover, the total sterol content in VCO was 0.096% whereas in RBD-­CO it was 0.032%. Chemometrics with PCA was used to differentiate between VCO and RBD-­CO. According to the PCA score plot, VCO and RBD-­CO could be clearly separated. The PCA loading plot showed that the variables 1,2-­DG, 1,3-­DG, and free fatty acids are the most important factors playing important roles in differentiating VCO and RBD-­CO.33

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8.3.3  A  uthentication Analysis of VCO Using Chromatography-­ based Techniques Chromatography is a separation technique for the analysis of compounds present in certain samples, and the results are presented in the form of chromatograms that contain much useful information, mainly indirect information about the authenticity of samples. The sample is inserted via an injector into the chromatographic column for separation. The separation step in the column is a critical stage in chromatographic analysis. After the analytes have been separated, they are directed to a detector and recorded as a chromatogram.34 The two main types of chromatographic techniques commonly used for analysis are liquid chromatography (LC) and gas chromatography (GC). LC separates analytes based on the interaction of the analytes between the mobile phase and the stationary phase. The separation is based on the polarity of the analytes. There are two types of LC, namely normal-­phase and reversed-­phase formats. Normal-­phase LC uses a polar stationary phase and a non-­polar mobile phase whereas reversed-­phase LC uses a non-­polar stationary phase and a polar mobile phase. Reversed-­phase high-­performance liquid chromatography (RP-­HPLC) is widely used in food analysis for food authentication purposes.35 Several types of detectors are used in LC, such as the refractive index detector, electron-­capture detector, charged aerosol detector, ultraviolet–visible (UV–VIS) detector, fluorescence detector, evaporative light scattering detector, and mass detector (mass spectrometer). The most commonly used detectors for food authentication purposes are UV–VIS, fluorescence, and mass spectrometric (LC-­MS) detectors.36 The RP-­HPLC system is preferred for food analysis purposes because of its better signal acquisition system than in a normal HPLC system. For authentication analysis of edible oils, the common targets are TAGs and fatty acids (FAs). For comprehensive and complex data, chemometrics is also required to manage the data.37 GC is applied for the separation of compounds using an inert gas as mobile phase with a stationary phase in a capillary column. The sample must be volatile in order to be analyzed using GC. Therefore, not all types of sample can be analyzed using GC, and non-­volatile compounds require a derivatization step prior to GC analysis. Separation between analytes is based on the different partitioning of the analytes between the stationary phase and mobile phase. The compounds must be stable or not decompose at the temperatures of injection and column separation.38 Several types of detectors are used in GC; flame ionization detection (FID) and MS detection are commonly used for food authentication purposes, including the authentication of edible oils. In recent years, a new type of GC technique has been applied for food analysis purposes, namely two-­dimensional GC (GC × GC) coupled with either FID or MS detection. This type of GC is more powerful than one-­dimensional GC for in-­depth investigations of

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190 39

complex samples. FID and MS detectors are useful for identifying chemical markers, fingerprinting, and profiling approaches for authentication purposes. For GC-­MS analysis, the data obtained are more comprehensive and therefore powerful statistical tools such as chemometrics with multivariate analysis are required. This is very useful when applying GC-­MS for fingerprinting and profiling purposes. HPLC has been widely used for the authentication of VCO. Authentication of VCO using HPLC has been performed for the analysis of the TAG composition of authentic VCO and VCO adulterated with palmolein and palm kernel oil (PKO). Authentication was also performed to quantify palmitolein and PKO in VCO. To detect adulteration with PKO, three major TAG molecular species, namely dilaurin–monocaprin/myristin–caprylin–laurin (C34), trilaurin (C36), and dilaurin–monomyristin (C38), in coconut oil were determined to establish the degree of adulteration of VCO with PKO. To investigate the adulteration of VCO with palmolein, the area ratio of dipalmitin–monoolein (POP) to trilaurin (LaLaLa) was used as the detection index for quantification of palmolein. Measurement of % C36 (x-­axis) and % PKO (y-­axis) showed a linear correlation with a coefficient of determination (R2) of 0.9917. Moreover, the correlation between % palmolein in VCO and the area ratio of POP to LaLaLa was also linear with R2 = 0.99694. These results indicated that the method provided high accuracy for the analysis of palmolein and PKO adulterants in authentic VCO.40 HPLC with evaporative light-­scattering detection (ELSD) has been used for the analysis of modified VCO obtained from glycerolysis of VCO by determining its lauric acid, monolaurin, dilaurin, and trilaurin contents. Analysis was carried out by RP-­HPLC with a C18 column, using as mobile phase acetonitrile with 0.01% of acetic acid (A) and acetone (B). All standards exhibited good accuracy and showed a high coefficient of determination (R2 > 0.99). The method is sensitive enough to determine lauric acid, monolaurin, dilaurin, and trilaurin. The limit of detection (LOD) and limit of quantification (LOQ) obtained were in the ranges 0.040–0.421 and 0.122–1.227 mg mL−1, respectively. The developed method is more suitable for the determination of monolaurin, dilaurin, and trilaurin than lauric acid.41 HPLC with MS detection is also widely used for authentication analysis, including edible oil analysis. HPLC with direct infusion electrospray ionization mass spectrometry (ESI-­MS) has been reported for the analysis of soybean oil (SyO) in VCO with different concentration levels of SyO ranging from 2% to 70%. Authentication was carried out by analyzing the TAG markers. TAG compositions in VCO were LaLaLa, followed by CLaLa, CCLa, LaLaM, and LaMM, where C = capric acid, M = myristic acid, and La = lauric acid. The TAG of LLnP as its ammonium adduct [LLnP + NH4]+ (m/z 870.9), where L = linoleic acid, Ln = linolenic acid, and P = palmitic acid, was used as a TAG marker for SyO. The correlation of LLnP (x-­axis) with SyO (y-­axis) was linear with R2 = 0.994. Therefore, the concentration of LLnP could be used as

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a detection index for the quantification of SyO in VCO. ESI-­MS is a very sensitive method that can detect of 2% of adulterant (SyO) in VCO.42 GC has been widely used for the analysis of edible and vegetable oils. It is commonly used for the authentication analysis of vegetable oils by determining the fatty acid composition. GC-­FID has been used for the analysis of fatty acids to detect and determine palmolein as an adulterant in VCO. Derivatization was carried out to obtain the fatty acid methyl ester (FAME). Analysis was combined with chemometrics utilizing multivariate analysis, namely PCA. The variables used for PCA analysis were the area of each fatty acid. PCA could differentiate between authentic VCO and VCO adulterated with palmolein. Analysis using PCA loading scores indicated that the fatty acids lauric, palmitic, and oleic acid were the most influential parameters for discriminating authentic VCO and VCO adulterated with palmolein. Ten variables were found to have a high correlation with increasing concentrations of palmolein in VCO, with high R2 values.43 Mansor et al.44 studied the application of fast GC with a surface acoustic wave (SAW) detector to the authentication of lard adulteration in VCO, using mixtures of VCO samples containing lard at concentrations of 1–50% v/v. The chromatogram obtained showed 10 peaks that were identified as adulterants. One of the peaks was found to have the best relationship with a high coefficient of determination (R2 = 0.9344). GC-­SAW is intended to imitate the human olfactory system. The principle is to analyze the inputs through chromatograms which can further be translated into a radial plot of the SAW detector response. The retention time is denoted an angular variable and can be used for the analysis of particular analytes such as volatile compounds that are important for sample differentiation. This can be used to search for marker compounds to differentiate between authentic and adulterated VCO. Analysis of medium-­chain fatty acids for authentication of VCO has also been carried out using GC. The fatty acids were analyzed in FAME form. Derivatization was performed using three different methods, namely acid-­ catalyzed, base-­catalyzed, and acid boron trifluoride-­catalyzed procedures. The acids found were caprylic, capric, lauric, myristic, palmitic, stearic, oleic, and linoleic acids. The results showed that the kinetics of the esterification of the fatty acids were not same for each different derivatization method. Base-­catalyzed derivatization using was observed to be the best method.45 GC-­FID has been used for the analysis of lauric acid, monolaurin, dilaurin, and trilaurin in VCO modified by glycerolysis. Derivatization was performed using the silylation method and pyridine was used as an internal standard. GC-­FID could be used for this quantitative analysis with a high coefficient of determination (R2 > 0.99). The LOD and LOQ were found to be in the ranges 0.033–0.260 and 0.099–0.789 mg mL−1, respectively. The results showed that GC-­FID was most suitable for the quantification of lauric acid, monolaurin, and dilaurin.41

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GC-­MS is also widely used for the authentication analysis of edible oils. Both one-­ and two-­dimensional GC are commonly used for the analysis of oils. Two-­dimensional GC in combination with time-­of-­flight (TOF) MS was successfully used for the analysis of VCO adulterated with animal fats, namely lard (LD), chicken fat (CF), beef tallow (BT), and mutton tallow (MT). The analysis involved the separation and quantification of cholesterol and cholestanol derivatized as trimethyl silyl ethers (TMEs). With the application of two columns with different polarities, GC × GC gave the best separation of cholesterol trimethyl silyl ether (Che-­TME) and cholestanol trimethyl silyl ether (Cha-­TME). Owing to the structural similarity of Cha-­TME and Che-­ TME, one-­dimensional GC could not separate them, hence two-­dimensional GC is required. Using GC × GC-­TOF-­MS, the compounds could be separated into four peaks, namely CHe1, CHebI, CHebII, and CHe2, for Che-­TME and Cha-­TME was also separated into four peaks (Cha1, CHabI, CHabII, and CHa2). These compounds were used for quantification analysis to determine the authenticity of VCO. This study indicated that quantitative analyses of cholesterol levels in VCO could be used as a reliable technique for the detection of animal fats (LD, CF, BT, and MT or their mixtures) in VCO at levels as low as 0.25%.46

Acknowledgements The authors thank Universitas Gadjah Mada 2021 through the Twin Center Program between the Center of Excellence, Institute for Halal Industry and Systems, UGM, Indonesia, and the International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), Malaysia.

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21. R. Amit, S. Jamwal, A. S. Kumari, B. Dhaulaniya, S. Balan, A. Kelly, A. Cannavan and D. K. Singh, Utilizing ATR-­FTIR spectroscopy combined with multivariate chemometric modelling for the swift detection of mustard oil adulteration in virgin coconut oil, Vib. Spectrosc., February 2020, 109, 103066. 22. Shimadzu, Quantitative Determination of Lard Adulteration by FTIR Spectroscopy with Chemometrics Method – Virgin Coconut Oil, available at https://www.shimadzu.com/an/literature/ftir/apa218012.html. 23. D. Kealey and P. J. Haines, Analytical Chemistry, the Instant Notes Chemistry Series, Oxford, BIOS, 2022. 24. D. L. Pavia, G. M. Lampman, G. S. Kriz, and J. R. Vyvyan, Introduction to Spectroscopy, 4th ed. Belmont, CA, Brooks/Cole, Cengage Learning, 2009. 25. J. Li, T. Vosegaard and Z. Guo, Applications of nuclear magnetic resonance in lipid analyses: An emerging powerful tool for lipidomics studies, Prog. Lipid Res., 2017, 68, 37–56. 26. F. Van Der Kooy, F. Maltese, H. C. Young, K. K. Hye and R. Verpoorte, Quality control of herbal material and phytopharmaceuticals with MS and NMR based metabolic fingerprinting, Planta Med., June 2009, 75(7), 763–775. 27. J. L. Ward, et al., An inter-­laboratory comparison demonstrates that [1H]-­NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection, Metabolomics, June 2010, 6(2), 263–273. 28. D. Rolin, et al., High-­resolution 1H-­NMR spectroscopy and beyond to explore plant metabolome, Adv. Bot. Res., January 2013, 67, 1–66. 29. H. K. Kim, Y. H. Choi and R. Verpoorte, NMR-­based metabolomic analysis of plants, Nat. Protoc., 2010, 5(3), 536–549. 30. J.-­L. Xu, C. Riccioli and D.-­W. Sun, An Overview on Nondestructive Spectroscopic Techniques for Lipid and Lipid Oxidation Analysis in Fish and Fish Products, Compr. Rev. Food Sci. Food Saf., July 2015, 14(4), 466–477. 31. J. L. Markley, et al., The future of NMR-­based metabolomics, Curr. Opin. Biotechnol., 2017, 43, 34–40. 32. L. G. Lagurin, M. J. M. Garrovillas and F. M. Dayrit, The Application of 13C NMR and Untargeted Multivariate Analysis for Classifying Virgin Coconut Oil, Proceedings, 2020, 70(1), 54. 33. F. M. Dayrit, O. E. M. Buenafe, E. T. Chainani and I. M. S. De Vera, Analysis of monoglycerides, diglycerides, sterols, and free fatty acids in coconut (Cocos nucifera L.) oil by 31P NMR spectroscopy, J. Agric. Food Chem., July 2008, 56(14), 5765–5769. 34. L. Cuadros-­Rodríguez, C. Ruiz-­Samblás, L. Valverde-­Som, E. Pérez-­ Castaño and A. González-­Casado, Chromatographic fingerprinting: An innovative approach for food ‘identitation’ and food authentication -­ A tutorial, Anal. Chim. Acta, 2016, 909, 9–23.

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35. A. M. Jiménez-­Carvelo, M. T. Osorio, A. Koidis, A. González-­Casado and L. Cuadros-­Rodríguez, Chemometric classification and quantification of olive oil in blends with any edible vegetable oils using FTIR-­ATR and Raman spectroscopy, LWT -­Food Sci. Technol., 2017, 86, 174–184. 36. J. Zhou, L. Yao, Y. Li, L. Chen, L. Wu and J. Zhao, Floral classification of honey using liquid chromatography-­diode array detection-­tandem mass spectrometry and chemometric analysis, Food Chem., February 2014, 145, 941–949. 37. D. N. Vera, A. M. Jiménez-­Carvelo, L. Cuadros-­Rodríguez, I. Ruisánchez and M. P. Callao, Authentication of the geographical origin of extra-­ virgin olive oil of the Arbequina cultivar by chromatographic fingerprinting and chemometrics, Talanta, October 2019, 203, 194–202. 38. M. Esteki, B. Farajmand, Y. Kolahderazi and J. Simal-­Gandara, Chromatographic Fingerprinting with Multivariate Data Analysis for Detection and Quantification of Apricot Kernel in Almond Powder, Food Anal. Methods, October 2017, 10(10), 3312–3320. 39. F. Magagna, et al., Combined untargeted and targeted fingerprinting with comprehensive two-­dimensional chromatography for volatiles and ripening indicators in olive oil, Anal. Chim. Acta, September 2016, 936, 245–258. 40. A. C. Komaram, E. Anjaneyulu, K. Goswami, R. R. Nayak and S. Kanjilal, Detection and quantification of palmolein and palm kernel oil added as adulterant in coconut oil based on triacylglycerol profile, J. Food Sci. Technol., 2021, 58(11), 4420–4428, DOI: 10.1007/ s13197-­020-­04927-­z. 41. J. Ponphaiboon, S. Limmatvapirat, A. Chaidedgumjorn and C. Limmatvapirat, Optimization and comparison of GC-­FID and HPLC-­ELSD methods for determination of lauric acid, mono-­, di-­, and trilaurins in modified coconut oil, J. Chromatogr. B Anal. Technol. Biomed. Life Sci., November 2018, 1099, 110–116, DOI: 10.1016/j.jchromb.2018.09.023. 42. J. S. Pizzo, et al., Determination of coconut oil adulteration with soybean oil by direct infusion electrospray ionization mass spectrometry, J. Braz. Chem. Soc., 2019, 30(7), 1468–1474. 43. J. M. N. Marikkar, Principal Component Analysis of Fatty Acid Data to Detect Virgin Coconut Oil Adulteration by Palm Olein, CORD News, October 2018, 34(1), 9. 44. T. S. T. Mansor, Y. B. C. Man and A. Rohman, Application of Fast Gas Chromatography and Fourier Transform Infrared Spectroscopy for Analysis of Lard Adulteration in Virgin Coconut Oil, Food Anal. Methods, 2011, 4(3), 365–372DOI: 10.1007/s12161-­010-­9176-­y. 45. J. Pontoh, Gas chromatography analysis of medium chain fatty acids in coconut oil, J. Pure Appl. Chem. Res., 2016, 5(3), 157–161. 46. B. Xu, et al., Detection of virgin coconut oil adulteration with animal fats using quantitative cholesterol by GC × GC-­TOF/MS analysis, Food Chem., July 2015, 178, 128–135.

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47. Codex Alimentarius Commission, Codex Standard for Named Vegetable Oils, 2nd edn, Revised, CX-­Stan, 2001, pp. 210–1999. 48. Bureau of Product Standards, Philippine National Standard for Virgin Coconut Oil (VCO), PNS/BAFPS 22, Department of Trade and Industry, Philippine, 2004.

Chapter 9

Application of Molecular Spectroscopy and Chromatography in Combination with Chemometrics for the Authentication of Cod Liver Oil Agustina A. M. B. Hastutia,b and Abdul Rohman*a,b a

Center of Excellence Institute for Halal Industry and Systems, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; bDepartment of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia *E-­mail: [email protected]

9.1  Introduction In the past few decades, fish oils, including cod liver oil (CLO), have been acknowledged as functional oils owing to their capability to provide some health benefit effects.1 CLO is also known as a good source of omega-­3 and omega-­6 fatty acids, such as eicosapentaenoic acid (EPA, C20:5 ω-­3) and docosahexaenoic acid (DHA, C22:6 ω-­3).2 CLO contains some fat-­soluble vitamins and is considered to be a source of vitamin A and vitamin D, which are widely   Food Chemistry, Function and Analysis No. 32 Advanced Spectroscopic Techniques for Food Quality Edited by Ashutosh Kumar Shukla © The Royal Society of Chemistry 2022 Published by the Royal Society of Chemistry, www.rsc.org

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used in food and pharmaceutical products in oral dosage forms such as suspension or capsule formulations. In addition, CLO is also formulated as an ointment exhibiting good activity against strains of Gram-­positive bacteria such as Enterococcus faecalis, Staphylococcus aureus, Streptoccoccus pyogenes, and Streptoccoccus pneumoniae,3 and formulated in poly(lactic acid)–chitosan nanoscaffolds with wound-­healing activity.4 In the fats and oils industry, CLO had a high commercial price, hence it is often subjected to adulteration by substituting or adding lower priced oils, either other fish oils or vegetable oils. Fish oil authentication requires verification that the fish oil complies with its labeled information, such as origin, production method, and composition.5 Although the majority of fish oil adulterations do not pose a health risk to the consumer, in some cases adulteration can cause serious harm to human health, e.g. if the adulterant oil is derived from species associated with certain types of food poisoning or allergens. However, even when the adulteration practice does not cause any hazardous health effects, such fraud can undermine consumer confidence and can affect fish oil consumption.6 Therefore, the development of rapid, reliable, and simple authentication analytical methods is very important to ensure that fish oils are authentic. Several instrumental methods combined with multivariate data analysis (MDA) or chemometrics have been applied for the authentication of CLO, mainly based on molecular spectroscopy-­ and chromatography-­based techniques. Molecular spectroscopy, including Fourier transform infrared (FTIR), nuclear magnetic resonance (NMR), and Raman spectroscopy, has been reported for the detection of adulteration of CLO. Chromatographic methods such as gas chromatography (GC) and liquid chromatography (LC) in combination with mass spectrometry (MS) have also been widely applied for the authentication of CLO by identifying the specific components present in CLO and its adulterants. In the authentication analysis of edible fats and oils, including CLO, using chemical and biological methods, three approaches are commonly used, namely (1) determining the ratio values between chemical constituents, while assuming that these ratios are constant for CLO; (2) identifying specific markers present in CLO, such as eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and minor components which may be found at different levels in authentic CLO; and (3) employing analytical methods based on physical, chemical, and biological properties to evaluate the effects of adulteration on CLO.7 Chromatography-­and spectroscopy-­based methods applied in CLO authentication are described in the following sections.

9.2  Cod Liver Oil CLO has been utilized for treatment purposes for centuries. Historically, communities along the coastline of northern Europe used CLO, extracted from Gadus morhua (Atlantic cod), to treat people with poor health.8 It was also used to treat rheumatism, gout, rickets, and tuberculosis during the

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18th and early 19th centuries. The high concentrations of vitamins A, D, and E and the long-­chain ω-­3 fatty acids EPA and DHA account for the therapeutic attributes of cod liver oil.10 CLO is now also obtained from Pacific cod (Gadus macrocephalus).11 CLO is characterized by an iodine value of 159–166 g I2 per 100 g, a slip point of 0.99, RMSEC = 0.04– 0.82% v/v, and RMSEP = 1.75% v/v (CaO), 1.39% v/v (CO), 1.35% v/v (SO), and 1.37% v/v (WO). DA was successful for the classification of CLO and CLO mixed with these vegetable oils using nine principal components

25

PLS calibration was accurate for the prediction of CLO; the equation for the correlation between the actual value of CLO (x-­axis) and FTIR-­predicted value (y-­axis) is y = 0.9938x + 0.1567 with R2 = 0.9962 PLS using normal spectra was successful for the prediction of CLO in binary mixtures with CO with quantification R2 > 0.9999 and RMSEC = 0.48% v/v. The equation obtained is y = 0.9982x + 0.0750 PLS calibration revealed a good relationship between the actual and FTIR-­predicted values of CLO (% v/v) in GSO in the concentration range 0–50% v/v in GSO with R2 = 0.9999 and RMSEC = 0.52% PLS using normal spectra was successful for the prediction of CO in CLO with R2 = 0.9999, RMSEC = 0.051, and RMSECV = 3.22% v/v

26

27

27 27

27 28

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Table 9.1  (continued) Adulterant

FTIR conditions and chemometrics used

Beef fat (BF)

1200–1000 cm−1 for quantification with PLS

Mutton fat (MF)

Combined regions of 3010–2995 and 1500–900 cm−1 for quantification with PLS and DA

Results

Ref.

PLS was successfully developed for the quantification of BF in CLO. The values of R2 and RMSECV in cross-­validation were 0.998 and 1.02% v/v, respectively. RMSEC = 0.55% and RMSEP = 0.82% v/v The PLS calibration model revealed a good correlation between the actual value of BF and the predicted value, with R2 = 0.992 and RMSEC = 1.31%. DA could classify CLO and CLO adulterated with MF

30

31

a

 A, discriminant analysis; PCR, principal component regression; PLS, partial least squares; D RMSEC, root mean square error of calibration; RMSECV, root mean square error of cross-­ validation; RMSEP, root mean square error of prediction.

the wavenumber region selected was based on its capability to give the highest accuracy rates between the authentic group and the adulterated group of CLO.33 For quantitative analysis, the absorbance values at the selected fingerprint region of 1035–1030 cm−1 were selected, which resulted in the equation y = 0.944x – 0.605 for the relationship between actual values (x-­axis) and FTIR-­predicted values (y-­axis) using PLS calibration of lard in CLO with the statistical parameters R2 for calibration of 0.996 and RMSECV = 1.04. External validation using internal validation was also acceptable, as indicated by an R2 for validation of 0.987 with the equation y = 0.872x – 0.392. In addition, discrimination between authentic CLO and CLO adulterated with lard was carried out at wavenumbers 1500–1030 cm−1 with an accuracy level of 100%, indicating that all samples (authentic and adulterated CLO) were classified correctly without any misclassification being observed.25 The application of FTIR spectroscopy combined with chemometrics for the authentication of CLO adulterated with chicken fat (CF) was reported.34 The PLS calibration model was successfully applied for the prediction of CF levels as an adulterant in CLO employing the wavenumber region 1500–900 cm−1. The PLS calibration model provided R2 = 0.999 and RMSEC = 0.346. The PLS calibration model was further evaluated using a validation model to confirm its validity. The results of the validation model demonstrated a high R2 for validation of 0.996 and a low RMSEP value of 0.513, supporting the validity of the PLS calibration model. FTIR spectroscopy combined with chemometrics using PLS was successful for the authentication analysis of CLO to distinguish it from the vegetable oil adulterants sunflower oil (SFO), corn oil (CO), and grape seed oil (GSO). The selection of these vegetable oils as adulterants for CLO was based on close score plots (PC1 and PC2) during principal component analysis (PCA), indicating that these vegetable oils had a close similarity to CLO in

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35

terms of their FTIR spectra. The PLS calibration model was applied successfully for the quantitative analysis of SFO, CO, and GSO in binary mixtures with CLO. Analysis of CLO in mixtures with SFO was carried out using the FTIR normal spectra at wavenumbers of 1250–950 cm−1, resulting in R2 = 0.9962. The PLS model in the wavenumber region 1350–900 cm−1 using normal spectra demonstrated that it was a good calibration model for predicting CLO in binary mixtures with CO with high R2 (>0.9999) and RMSEC = 0.48%. The wavenumber region 1350–900 cm−1 using normal spectra was used for the quantitative analysis of CLO in mixtures with GSO with R2 = 0.9999 and RMSEC = 0.52%. The models were validated using independent samples, resulting in R2 values of >0.99 with RMSEP values of 95% for class prediction. Trout, salmon, and cod oils were completely and correctly classified.38 13C NMR spectroscopy combined with chemometrics using Bayesian belief networks (BBN) was also successful for the authentication of different fish oils, including CLO, with an accuracy level of 100%.39

9.4  Authentication of CLO Using Chromatography Authentication of CLO using chemometrics requires the analysis of many compounds in the samples, and the capability of chromatographic techniques to separate and detect many components makes them suitable for this purpose. Authentication and characterization of CLO have been performed using, but not limited to, GC, LC, and thin-­layer chromatography (TLC) with various detection methods. GC has commonly been employed for fatty acid analyses in CLO. The fatty acids in CLO are a mixture of free fatty acids, saturated fatty acids longer than C11, monosaturated fatty acids, ω-­3 and ω-­6 PUFAs, DHA, and EPA.40 Owing to the low volatility of long-­chain fatty acids, prior derivatization is required to produce fatty acid methyl esters (FAMEs). Quantitative conversion to FAMEs is critical to ensure accurate and precise analysis. Incomplete reaction, degradation of the products, and evaporation due to compound volatility reduce the derivatization yield. An internal standard, such as a C21:0 methyl ester or a C6:0 methyl ester, could be added to compensate for the loss.41,42 ISO 12966 : 2 provides several procedures for the preparation of FAMEs.43 Rapid transmethylation could be achieved by adding anhydrous methanol and alkaline catalysts, such as KOH, NaOH, and CH3ONa. However, free fatty acids could not be converted into FAMEs and the presence of water could hydrolyze the FAME products. A general methylation procedure could be applied for a wide range of fatty acids under sequential alkaline and acidic conditions. However, it is not suitable for lauric oils and functional groups could be decomposed during the process. For gas–liquid chromatography (GLC), transesterification with trimethylsulfonium hydroxide (TMSH) and base catalysts could rapidly produce FAMEs.44 GC analysis is performed using FID for routine analysis. MS is used for the identification and quantification of fatty acids. The fatty acid compositions are usually presented as a percentage with respect to total fatty acids or relative to a standard FAME mixture.41,42

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While fatty acid analysis is usually conducted with GC, the analysis of triacylglycerols is preferably conducted by LC. A reversed-­phase column with a polar mobile phase is normally used for separations of fatty acids in fish oils.45 Zeng et al. analyzed triacylglycerols in CLO using liquid chromatography-­ electrospray ionization tandem mass spectrometry (LC-­ESI-­MS/MS).46 ESI was operated in the positive ion mode, resulting in [M + NH4]+ or [M + Na]+ as the triacylglycerol adducts. The mobile phase was delivered in gradient mode using 10 mM isopropyl alcohol–ammonium acetate (90 : 10 v/v), acetone, and acetonitrile. This procedure produced reproducible retention times and peak areas of the compounds. The tandem MS detector not only provides structural information about the peaks but also reveals more information about the unresolved peaks. Some peaks that were overlapped but exhibited similar fragmentation patterns were revealed to be stereoisomers and structural isomers.46 Alternative chromatographic methods for characterizing lipid contents and other compounds in marine oil samples are available. TLC was used to characterize lipid contents in marine oils, but the capability to separate compounds was poorer than that of GC and LC, and only a limited number of compounds (i.e. lipid classes, such as triacylglycerols, free fatty acids, diacylglycerols, sterols, phospholipids, and ethyl esters) can be analyzed.47 Profiling of metal-­containing species, such as arsenic species, in LC was performed by LC coupled with inductively coupled plasma mass spectrometric (ICP-­MS) detection. Fifteen lipid-­soluble arsenic-­containing compounds and three water-­soluble arsenic-­containing compounds were detected in CLO.48 Arsenic species can potentially be used for the authentication of CLO and this approach has been successfully applied to the authentication of other products.49,50 A combination of chromatographic techniques and chemometrics was used to distinguish CLO derived from wild or farmed cod obtained in Norway and Scotland. Twenty-­one fatty acids were determined using GC-­FID. In the PCA plot, CLO from farmed fish was clustered in the same area. In contrast, the CLO from wild fish was widely distributed in the PCA plot, which could be explained by the various foods consumed in an open ecosystem. Whereas samples from the same farm were grouped in the same area, no clear grouping was observed between different countries. Supervised classification with LDA gave a correct classification rate of 97% for the differentiation of CLO between wild and farmed fish. However, classification of the samples based on geographical origin had only a 63% correct classification rate.41 CLO is potentially adulterated with other marine oils and plant-­based oils. The lipid profiles of each oil are unique, hence they can be used to detect the adulteration of oils. PCA of the FAME profiles derived from GC-­FID showed that marine oils were well separated from plant-­based oils. Marine oils (i.e. cod liver, whale, seal, and salmon oils) were grouped in the same area in the PC1 and PC2 score plots, which accounted for 41.91 and 24.44% of the variance, respectively.51 The triacylglycerol profiles analyzed by LC-­ESI-­MS and LC-­ESI-­MS/MS showed a distinctive pattern for marine oils and plant-­ based oils. The PCA plots based on triacylglycerol compositions and total ion

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current profiles gave a more satisfactory discrimination between marine and plant-­based oil, compared with PCA plots based on single and tandem total mass spectral profiles. The marine oils, including CLO, were hardly distinguished in all profiles.46,52 Giese et al. used ANN models to predict the level of adulteration of fish oils with CaO and SFO.6 The best regression model based on fatty acid profiles determined by GC-­FID gave an RMSEP of 1.1% and an LOD of 0.81%. The predictive ability was better than the best regression model based on 1H NMR spectroscopy (RMSEP = 2.7%, LOD = 3.0%) but lower than that obtained by FTIR spectroscopy (RMSEP = 0.86%, LOD = 0.22%). For the classification model, the best model was established using repeated incremental pruning to produce error reduction (RIPPER) based on the level of linoleic and oleic acids. The RIPPER model considered CLO to be a pure sample when it contained