Emerging Food Authentication Methodologies Using GC/MS 3031302877, 9783031302879

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Kristian Pastor   Editor

Emerging Food Authentication Methodologies Using GC/MS

Emerging Food Authentication Methodologies Using GC/MS

Kristian Pastor Editor

Emerging Food Authentication Methodologies Using GC/MS

Editor Kristian Pastor Department of Applied and Engineering Chemistry, Faculty of Technology Novi Sad University of Novi Sad Novi Sad, Serbia

ISBN 978-3-031-30288-6 ISBN 978-3-031-30287-9 https://doi.org/10.1007/978-3-031-30288-6

(eBook)

# The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

The world of packaged foods and beverages is one of the most challenging and interesting market fields, at present. This situation depends on the multifaceted and complex interconnection between different stakeholders, from food and beverage producers to packaging manufacturers, from commercial traders to regulatory agencies and governmental bodies, from private consultants and lawyers working in the evolving sector of food law to catering agencies, mass retailers, and the final consumers. It should be noted that food and beverage consumers are becoming aware of the benefits or negative impacts a specific food product could have on their health and wellbeing, because of the notable amount of available information. As a result, they can choose to buy healthier foods and beverages. On the other side, the search for similar products, certainly more expensive and less available than other foods and beverages without specific claims, ethical specifications, “strong” commercial brands, and other features, can cause the augment of prices. The transformation of the original edible food into packaged and preserved product cannot be achieved without an important knowledge contribution by food technologists, engineers, microbiologists, chemists, and so on. The obvious result is the fragmentation of food markets worldwide with a plethora of possibilities, from expensive and valuable foods and beverages on one side, to cheaper and low-quality products on the other. In this ambit, hygiene and safety problems have constantly increased in recent years. The awareness that all foods and beverages undergo different modifications of their chemical, microbiological, physical, sensorial, and structural features during time without exceptions has to be carefully evaluated, according to the first Parisi’s Law of Food Degradation1,2,3. Adulteration and food frauds for economically 1 Anonymous (2021) Parisi’s First Law of Food Degradation Valuable to Establish Adequate Protocols Concerning Food Durability. Inside Laboratory Management January/February 25, 1: 17. AOAC International, Rockville, MD 2 Parisi S (2002) I fondamenti del calcolo della data di scadenza degli alimenti: principi ed applicazioni. Ind Aliment 41, 417:905–919 3 Srivastava PK (2019) Status Report on Bee Keeping and Honey Processing. MSME – Development Institute, Ministry of Micro, Small & Medium Enterprises, Government of India 107, Industrial Estate, Kalpi Road, Kanpur-208012. Available http://msmedikanpur.gov.in/cmdatahien/

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motivated reasons can be a remarkable problem of integrity, and traceability issues have to be taken into account. The use of the “Hygiene/Integrity/Traceability/ Sharing” (HITS) approach, as discussed recently by Haddad and Parisi4,5, can be useful when speaking of the analysis of the current food market, taking into account the growth of food-serving channels without a physical and material market: the increasing value of food delivery transactions by means of dedicated web-based applications has to be considered carefully, and the same thing can be affirmed when speaking of charitable food sharing activities worldwide. As a result, official bodies and regulatory agencies are constantly working with the aim of assuring main safety, integrity (authenticity), and traceability of food products available to consumers, including also charitable food sharing circuits. New analytical approaches that would be able to authenticate food and beverage products would be really needed, provided that the following goals—enhanced and timesaving performances, lower price, user- and environment-friendly systems—are obtained. With reference to the analytical ambit, hyphenated gas chromatography–mass spectrometry (GC/MS) systems represent a powerful instrument. The aim of this edited book is to provide an overview of existing and emerging GC/MS methodologies for the analysis and characterization of a notable list of food and beverage categories, with the aim of contrasting food frauds. Part I provides a brief description of available GC/MS instrumentation (Chap. 1), and a reliable discussion with reference to statistical and mathematical models in food authentication (Chap. 2). The second part (Chaps. 3–9) shows the current situation of GG/MS applications for food authenticity assessment when speaking of different product categories (flour and bakery products, edible oils, milk and dairy products, fish and meat, honey, and others), while the discussion concerning beverages (coffee and tea, wine and beer, spirits, and others) is supplied in Part III (Chaps. 10–12). Finally, the fourth part is dedicated to concluding remarks about the most important and successful GC/MS applications when speaking of food authenticity assessment by different viewpoints, including new analytical possibilities. Because of the importance of this broad and complex argument, and also of the awareness that researchers and students in food science and separation techniques need a reliable knowledge

reports/diffIndustries/Status%20Report%20on%20Bee%20keeping%20&%20Honey%20 Processing%202019-2020.pdf 4 Haddad MA, Parisi S (2020) The next big HITS. New Food Magazine 23, 2:4 5 Parisi S (2022) The food packaging synergy: Advantages, safety and integrity risks, and possible solutions. Virtual Food Analysis Summit 2022, 04th October 2022, available https:// food_analysis_summit_2022.vfairs.com/en/

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base at present, we consider this book as an excellent work and instrument, wishing the Authors all the success this book deserves. Lourdes Matha Institute of Hotel Management and Catering Technology Thiruvananthapuram, Kerala, India

Salvatore Parisi

Contents

Part I 1

2

Introduction

Gas Chromatography and Mass Spectrometry: The Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kristian Pastor, Marko Ilić, Djura Vujić, Marijana Ačanski, Snežana Kravić, Zorica Stojanović, and Ana Đurović Statistical and Mathematical Models in Food Authentication . . . . . B. Dayananda and D. Cozzolino

Part II

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Authentication of Food

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Cereals, Pseudocereals, Flour, and Bakery Products . . . . . . . . . . . . Daniel Cozzolino

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Edible Oils and Fats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amani Taamalli, Ibrahim M. Abu-Reidah, and Hedia Manai-Djebali

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Milk and Dairy Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Havva Tümay Temiz, Akif Göktuğ Bozkurt, and Berdan Ulaş

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Meat, Eggs, Fish, and Seafood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Yasir A. Shah and Dirk W. Lachenmeier

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Honey and Bee Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Niki C. Maragou, Irini F. Strati, Panagiotis-Loukas Gialouris, Marilena Dasenaki, Vassilia J. Sinanoglou, Marijana Ačanski, Jaroslava Švarc Gajić, and Kristian Pastor

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Fruits, Vegetables, Nuts, and Fungi . . . . . . . . . . . . . . . . . . . . . . . . . 215 Lidia Montero, Ane Arrizabalaga-Larrañaga, and Juan F. Ayala-Cabrera

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Herbs and Spices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Aditi Negi and R. Meenatchi

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Part III

Authentication of Beverages

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Fruit Juices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Nur Cebi, Hatice Bekiroglu, Zeynep Hazal Tekin-Cakmak, Fatih Bozkurt, and Salih Karasu

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Coffee and Tea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thiago Ferreira and Adriana Farah

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Wine, Beers, and Alcoholic Beverages . . . . . . . . . . . . . . . . . . . . . . . 313 Oscar Núñez

Part IV 13

Outlook

Concluding Remarks and Future Perspectives . . . . . . . . . . . . . . . . 347 Saskia M. van Ruth and Sara W. Erasmus

Part I Introduction

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Gas Chromatography and Mass Spectrometry: The Technique Kristian Pastor, Marko Ilić, Djura Vujić, Marijana Ačanski, Snežana Kravić, Zorica Stojanović, and Ana Đurović

Abstract

Combining the techniques of gas chromatography and mass spectrometry provides conjunction with a confirmatory character, which has a great power of detection, identification, and quantification of a wide range of chemical compounds. This chapter serves as a brief description of gas chromatographymass spectrometry (GC/MS) instruments and their components, variations, and suitability for specific applications. Various gas chromatography techniques, such as standard GC or multidimensional GC, but also different types of mass spectrometers, ranging from classic quadrupole systems to high-resolution mass spectrometers, will be presented. The chapter encompasses corresponding merits and drawbacks, as well as references to subsequent chapters. Keywords

Gas chromatography · Injection · Columns · Mass spectrometry · Ion source · Mass analyzers · Detection · Confirmatory technique

List of Abbreviations CI GC GC×GC EI IR

Chemical ionization Gas chromatography Two-dimensional gas chromatography Electron ionization Isotope-ratio

K. Pastor (✉) · M. Ilić · D. Vujić · M. Ačanski · S. Kravić · Z. Stojanović · A. Đurović Department of Applied and Engineering Chemistry, Faculty of Technology Novi Sad, University of Novi Sad, Novi Sad, Serbia e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_1

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IT IUPAC LC M MS MS/MS m/z NCI PCI PTV SFC TOF Q

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Ion-trap The International Union of Pure and Applied Chemistry Liquid chromatography Magnetic sector mass analyzer Mass spectrometry Tandem or hybrid mass spectrometry Mass-to-charge ratio Negative chemical ionization Positive chemical ionization Programmed temperature vaporizing Supercritical fluid chromatography Time-of-flight Quadrupole

Introduction

Chromatography is a sophisticated analytical technique that is most widely used in chemical laboratories. Essentially, it is a physical method of separation in which the components of a mixture are separated by distribution between two immiscible phases, one of which is stationary while the other moves in a definite direction (the mobile phase) [1]. The power of chromatography stems from its ability to separate a certain mixture into its consisting components and determine their identity, chemical structure, and concentration [2]. Chromatography is usually classified into three basic types according to the mobile phase used: gas (GC), liquid (LC), and supercritical fluid chromatography (SFC). GC is principally used for the analysis of volatile, thermally stable organic substances; LC is convenient for the analysis of nonvolatile or thermally unstable substances, while SFC is employed for separations of compounds that cannot be separated by GC or LC. Generally, each type of chromatographic analysis consists of two basic steps: (1) chromatography itself, i.e. separation of individual components, and (2) detection and identification of each component. The GC technique is composed of the following steps: injecting a sample into an instrument; converting the sample to a gaseous state; transferring the vapor cloud in the column by carrier gas; separating the sample components in the column by their different interactions with the stationary phase; and detecting and recording results, i.e., chromatograms, which most ideally represent individual peaks of each component of an analyzed sample. The mass spectrometer has become a common detector in modern chromatography [2]. It is a settled opinion that no other detector can provide such a wealth of information [3]. This is an instrument that enables the identification of a certain component based on its mass spectrum. The analyzed sample is first ionized, and the formed ions are then separated by an electric and/or magnetic field and registered on

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the instrument’s detector [4]. Using computer mass spectra libraries of a large number of substances that are available today, the analyst has the option not to use the reference chemical standard. This especially plays an important role if the reference substance is not available or is, for example, very expensive [2]. Identification of an analyte using GC or MS technique separately requires an application of a pure analyte or a standard substance. In some cases, different analytes may have similar ionic fragments in their mass spectra, so the prior step of their separation would be necessary. By combining GC with MS, a coupled technique is obtained, which reduces the error possibility during identification [5]. This extremely powerful and confirmative conjunction of two techniques is the result of the combined ability to separate components, on the one hand, and to identify ionic fragments, on the other. In contrast, many other individual techniques and detectors have a suggestive character, which means that the analyst can never be absolutely sure of the identity of the analyzed compounds [2]. Gas chromatography combined with mass spectrometry (GC/MS) was first applied in 1957 by Holmes and Morrell. Years after that, GC/MS instruments were commercially produced and constantly developed. Today, a GC/MS technique is widely used in various fields of science, but for routine analysis as well [5]. Progressive development in GC instrumentation is reflected in the production of small and portable gas chromatographs [3]. Development of very efficient ionization techniques and novel, highly selective analyzer technologies, including hybrid and tandem mass spectrometers (MS/MS), lead to even greater progress in GC/MS instrumentation [6]. In the last decade, GC/MS supplanted many other techniques due to increased selectivity, which could be obtained even when applying a faster GC separation with a lower chromatographic resolution, due to the high-resolution of MS detectors. The field of application of GC/MS is limited to substances that are volatile enough to be analyzed by GC. Progress in the development of high-temperature resistant columns enables the analysis of high-boiling compounds. Additionally, controlled pyrolysis, separation, and detection of thermal decomposition products further expanded the application of GC/MS to involatile substances [7]. Consequently, GC/MS is nowadays widely used in analytical practice including the applications involving food, waste material, environmental, chemical, and pharmaceutical samples [7]. Depending on the sample, typical compounds analyzed by GC/MS include, but are not limited to, fatty acids [8–19], simple sugars [20, 21], flavor and aroma volatile components [22–25], contaminants and natural toxins [26, 27], metabolites [28], and many others.

1.2

Instrument Overview

Basic parts of each GC/MS system include a carrier gas (mobile phase) source, an inlet used to deliver the vaporized sample to the head of the column, a separation column inside a temperature-controlled oven, an interface (GC/MS connection), an ion source, a mass analyzer, an ion detector, a vacuum pumping system, and a data

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processor [3, 4]. Nowadays, highly developed computer systems are of utmost importance for data processing [4]. Figure 1.1 shows a scheme of a GC/MS instrumentation.

1.3

Carrier Gas

A carrier gas, which serves as the mobile phase, moves a sample from an inlet through a chromatographic column to a detector. Apart from purity, inertness, availability, affordable price, and safety, the carrier gas must also be free of contaminants and appropriate for an applied detector. The traces of hydrocarbons, water vapor, and oxygen may deteriorate polar stationary phases or reduce the sensitivity of detectors. Therefore, the carrier gas system includes filters with a molecular sieve intended to remove water and a reducing agent for other impurities [29]. The commonly used carrier gases for GC/MS are either helium or hydrogen. The carrier gas type and its column velocity have a significant impact on separation efficiency and analysis speed. The velocity where the maximum separation efficiency is reached depends on the gas used as the mobile phase because diffusion coefficient is related to the gas viscosity. Therefore, hydrogen as a mobile phase reaches maximum separation efficiency at a higher mobile phase velocity than helium and, thus, permits a shorter analysis time. However, hydrogen is flammable and not completely inert (e.g., reacts with some compounds at a higher temperature). MS detectors require a higher vacuum capacity and thus a more powerful pumping system [6], indicating that helium as a mobile phase is a better choice for this technique. However, the fact that a supply of natural helium is limited could increase its market price, which might cause hydrogen to be used as a suitable alternative after all [4, 30–32]. Sometimes even instrument suppliers advertise “hydrogen compatible” GC/MS instruments [33].

1.4

Inlet System

In all modern GC/MS instruments, a system for introducing samples in the capillary chromatography column is of fundamental importance for the quality of the analysis. Besides its role as an inlet for the sample, it needs to vaporize the sample, mix it with the carrier gas, and transport it into the column as a narrow band, having to keep a composition that is identical to the original sample. The narrow sample band at the start of the chromatographic process is crucial for achieving a good resolution, since the peak shape at the end of separation cannot be sharper, narrower, or more symmetrical than those at the beginning [34, 35]. Some general requirements that the inlet system should fulfil are the following: achieving optimal column efficiency and a high signal-to-noise (S/N) ratio; avoiding any changes in the qualitative and quantitative composition of the sample; avoiding sample components that cannot vaporize from reaching the column; enabling accurate and reproducible injections of small sample amounts; and being applicable for trace and undiluted sample analysis

Fig. 1.1 A scheme of a gas chromatograph coupled to a mass spectrometer

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Fig. 1.2 Split/splitless injector

[6, 35]. A universal inlet system has not been developed yet, so different types of injectors are used. The choice of injector depends on analytical column properties (internal diameter, nature of stationary phase, film thickness, capacity, etc.), sample properties, and the quantity of the analytes to be separated (a wide range of concentrations of individual components from highly to weakly volatile, different thermal stabilities, etc.). Most frequently, the sample solution is introduced by a micro-syringe through the septum into the injection chamber heated to a temperature that allows rapid evaporation of all sample components, which are carried to the column by the gas flow. The sample can be introduced into the GC/MS manually, using a conventional glass micro-syringe with stainless-steel needles. Alternatively, an autosampler is often employed, especially in the case when a large number of samples need to be analyzed in a short period of time, which is nowadays usually the case [36]. An autosampler, or automatic sampling device, is basically a part of every modern GC/MS system. It represents a robotic arm that automatically takes a sample from a certain vial and inserts it into an inlet of a gas chromatograph. The advantages of an autosampler over manual sampling are its repeatability and time optimization, which are important factors in determining the nature of an analyzed sample and its constituents. Furthermore, the depth of a syringe needle penetration can be easily adjusted by a computer [37]. It is also possible to choose the working program in order to rinse the injector needle between two injections—a solvent flush injection, then to re-sample from the same vial, as well as to periodically sample from the vial containing standard solutions with the aim of calibration [36]. In capillary gas chromatography coupled to mass spectrometry, commonly used injectors are split/splitless and programmable temperature vaporizing (PTV). Split/Splitless Injector The oldest and most utilized injector for capillary columns is the split/splitless injector, which is able to handle only a small sample capacity (Fig. 1.2). As its

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name implies, this injector can operate in two different modes: split (with flow splitting) and splitless (without flow splitting). Both injection modes imply hot isothermal injection processes. The injector is set at a temperature that is high enough to vaporize the solvent and the analytes, and this temperature is kept constant during the analysis [38]. Evaporation takes place in a tube specially fitted for this purpose (glass liner), after which the vapor is mixed with the carrier gas. The split injection mode divides the gas flow (evaporated sample mixed homogeneously with the carrier gas) into two unequal parts: a smaller part of the sample (usually 1–2%) is introduced into the column, while the larger part goes into the atmosphere through the outlet line (split vent). The splitting of the sample has two main functions: reducing the sample size to an amount compatible with the capacity of the capillary column and providing a small injection plug. In this way, chromatograms with better peak resolution are obtained. Typical split ratios are between 1:20 and 1:500, which means that only 1/20 or 1/500 part of the sample is introduced into the column. A split ratio that is too low results in column overloading and broader and asymmetrical peaks, while a high split ratio has an unfavorable effect on the detection limit. Therefore, the split ratio should be adapted to the sample concentration, the capacity of the capillary column, and the sensitivity of the detector [6]. The general advantages of split injections are the following: simplicity—the operator needs only to control the split ratio; small sample amounts that are being introduced into the column; and the fast flow rate up to the split point that results in high-resolution separations. Drawbacks of this technique are as follows: uncontrollable discrimination regarding the sample composition (particularly the samples with a wide boiling point range) and the fact that it is not suitable for trace analysis. The splitless injection uses the same hardware as a split injection (Fig. 1.2), but the split valve is initially closed and sample vapors are slowly transferred from the vaporizing chamber into the column. After most of the sample has been transferred (usually 15–60 s), the split valve is opened in order to purge the injector. Since the capillary column flows are typically low (1–2 ml/min), sometimes it can be difficult to transfer the sample to the column in a narrow band, leading to broadened or poor peak shapes, affecting quantitation and detection limits. To overcome these problems, it is necessary to condense the analytes at the top of the column, which is achieved by using a low initial column temperature. In that case, starting column temperature should be at least 10 °C below the boiling point of the solvent and should be held at least after the purge activation time. Under these conditions, the solvent condenses on the front of the column and traps the solute molecules, which focuses the sample into a narrow band [34]. If the analytes of interest have a substantially higher boiling point than the solvent, an alternative method is to set the initial column temperature below the boiling point of the earliest eluting analyte. The main advantage of splitless injection is the improved sensitivity; thus, it is a favorable technique for trace analysis [39]. In contrast to split injection, the implementation of this injection technique is more complicated. The operator should carefully select optimal conditions including splitless hold time, liner, inlet

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temperature, initial oven temperature, and solvent. Since the sample spends more time in the heated injector, thermal degradation is generally much more pronounced than split mode. Programmable Temperature Vaporizing (PTV) Injector PTV injector is conceptually similar to the conventional split/splitless injector, but the main difference is the ability to be heated very rapidly at a defined programmed temperature rate [40]. This injector represents the most versatile GC/MS inlet, offering a significant reduction of most problems typically present when using hot sample injection techniques [41]. Samples are introduced into the relatively cool injector. After the removal of the syringe needle, the inlet temperature is increased to ensure the evaporation of all sample compounds and the transfer of analytes into the column. Thermal discrimination is, thus, eliminated, solvents having low boiling points can be removed, and injection of large sample volumes is possible, which is desirable for improved detectability [29, 40]. The ability to inject larger sample volumes, up to hundreds of microliters, is the most important aspect of using a PTV inlet. Injecting the sample into a packed inlet liner at a temperature below the boiling point of the solvent, venting most of the solvent to waste, and then rapidly heating the inlet to transfer the remaining sample and solvent to the column, allows the inlet to accept much larger sample volumes [42]. In this way, lower detection limits of the analytical methods can be achieved, and sample preparation steps can be simplified or can, to a large extent, be integrated into instrumental analytical work [43]. Furthermore, the PTV inlet can serve as an interface for coupling GC with sample preparation methods, or other separation techniques, such as thermal desorption, headspace, solid-phase microextraction (SMPE), or supercritical fluid extraction (SFE) [42, 43]. The PTV injector can operate in common hot split/splitless, cold split/splitless, and large volume injection modes, so it is sometimes referred to as a “multi-mode” inlet. All of the mentioned injection modes could be achieved by controlling the inlet temperature, gas flow, and split valve positions throughout the sample transfer process. The choice of an appropriate injection technique for a certain application primarily depends on the sample concentration, boiling-point range, and thermal stability of analytes. Cold injection, followed by temperature programming, gives the best results regarding recovery of all analytes (e.g., narrow and wide boiling-point range, high and too low concentration) [38]. In order to achieve optimal performance for a particular PTV application, many parameters need to be optimized (e.g., initial and final injector temperature, inlet heating rate, venting time, flow and pressure, transfer time, injection volume, type of liner), so method development might be a very challenging task [41]. Therefore, cold injection techniques are primarily applied to samples with a wide boiling-point range of analytes; otherwise, hot injections are being used.

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Oven

Liquids and solids need to be converted to a gaseous state and then maintained in this state throughout the GC analysis in order to be able to be analyzed on this device. Therefore, gas chromatographs are equipped with thermostatically controlled ovens, which maintain the temperature of the column somewhere between 40 and 450 °C. The oven should have sufficient volume to hold one or two columns and the ability to keep column temperature to an accurate and reproducible value. Besides, the oven should meet these demands in an isocratic mode; it must enable the application of temperature programming with appropriate single or multiple gradients. Temperature programming mode is most commonly used, thus enabling the separation of substances having different vapor pressures during a single analysis, simultaneously shortening the total analysis time giving an optimal separation of the eluting components [3, 44]. In order to obtain reproducible separations, oven temperature must be controlled within 0.1 °C.

1.6

Capillary Columns

Capillary columns are long narrow open fused silica tubes with a thin layer of stationary phase deposited onto or bound to the interior surface. Fused silica tubing consists of ultra-pure silicon dioxide that is coated on the outer wall with a polyamide material to eliminate brittleness [38]. These columns are very flexible and very easily placed into the oven of a gas chromatograph [6]. Since the tube is open, and its resistance to gas flow is very low, long columns (even more than 100 m) can be used enabling high-efficient separations of complex sample mixtures. Due to the open flow path, capillary columns are also termed open tubular columns. According to the structure and layout of the liquid stationary phase, capillary columns are divided into three basic types: Wall Coated Open Tubular (WCOT), Porous Layer Open Tubular (PLOT), and Support Coated Open Tubular (SCOT) columns. As shown in Fig. 1.3, WCOT columns contain a liquid stationary phase as a thin film on the inner wall, PLOT columns have a layer of solid stationary phase (adsorbent) around the interior column surface, and SCOT columns contain a layer of small support particles coated with a liquid stationary phase on the inner wall. WCOT columns provide the highest resolution of all GC columns, and they are nowadays most frequently used, in more than 80% of all applications. WCOT columns are fabricated in a wide variety of dimensions. Generally, length varying from 5 to 100 m, the liquid stationary film thickness between 0.1 and 5 μm, and inner diameters of 0.10, 0.15, 0.18, 0.20, 0.25, 0.32, and 0.53 mm are commercially available [40, 45]. Critical column parameters that affect the separation include stationary phase composition, length, internal diameter, and film thickness. The structure of the stationary phase influences the separation of the compounds, and column dimensions primarily affect the resolution [46].

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Fig. 1.3 Types of capillary columns

Stationary Phase One of the most important factors when optimizing a gas chromatographic analysis is the selection of an analytical column, i.e., appropriate stationary phase. As previously mentioned, the stationary phase can be solid or liquid in nature, with the liquid being more frequently used. Polymers, such as polysiloxane and polyethylene glycol, are mostly used as materials for liquid stationary phases. Important properties of a liquid stationary phase are chemical inertness, thermal stability, low vapor pressure, and low viscosity, enabling a better mass transfer. In order to be effective, the stationary phase needs to have solubilization capabilities of the components of interest from the analyzed mixture, using the well-established principle like dissolves like. Therefore, polar phases are being used in the analysis of polar analytes and nonpolar ones in the analysis of nonpolar compounds. However, as the polarity of a particular molecule is theoretically difficult to determine due to the combined effects of intermolecular forces, the calculation of theoretical indices based on empirical measurements, such as Kovacs retention indices and Rohrschneider–McReynolds constants, is frequently used in practice. The abovementioned rule is not applied in the case of isomer analysis. These substances of similar structures, polarities, and boiling points demand the employment of the

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Table 1.1 Common capillary column liquid stationary phases and their properties

a

Composition 100% dimethylpolysiloxane

Polarity Nonpolar

5% diphenyl 95% dimethylpolysiloxane

Nonpolar

6% cyanopropylphenyl 94% dimethylpolysiloxane 14% cyanopropylphenyl 86% dimethylpolysiloxane

Midpolar

50% diphenyl 50% dimethylpolysiloxane

Midpolar

50% cyanopropylphenyl 50% dimethylpolysiloxane Polyethylene glycol

Polar

Midpolar

Polar

Representative applications Phenols Amines PCBs Pesticides Hydrocarbons Sulfur compounds Flavors and Fragrances Fatty acids Alkaloids Drugs Halogenated Compounds Pesticides Herbicides PCBs Alcohols Pesticides VOCs Pesticides Herbicides Trimethylsilyl sugar derivates PCBs Drugs Steroids Pesticides Glycols Fatty acids Alditol acetates Neutral sterols Free organic acids Alcohols Ethers Glycols Essential oils Solvents Flavors and fragrances

Approximate temperature rangea (°C) (isothermal/programmed) –60 – 325/350

–60 – 325/350

–20 – 280/300

–20 – 280/300

40 – 280/300

40 – 220/240

40 – 260/270

Temperature limits vary with column dimensions

opposite rule. Thus, in this specific case, polar stationary phases are used to separate nonpolar isomers [4, 47, 48]. Table 1.1 lists some common capillary column liquid stationary phases and their properties. Column bleed, i.e., thermal degradation of the stationary phase material, must be included in the criteria for deciding the choice of a column. Decomposition products of column bleed contribute to chemical noise within MS, causing interference with

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mass spectra of analytes, which may lead to a false qualitative result and reduced detection limit. Since coated capillary columns are characterized by slow bleed, columns with the stationary phase cross-linked and chemically bonded to the silica wall are being utilized [36]. These columns with immobilized stationary phases have better long-term stability, reproducibility, and performance. Column Length The most common lengths of capillary columns used for GC/MS applications are between 25 and 60 m, while longer ones are employed for the analysis of very complex mixtures or severe separation problems. In the case of isothermal mode of operation with twice increasing the length of the column: (1) the resolution is increased only by a factor of 1.4 (√2); (2) the price of the column is increased; and (3) retention times and total analysis time are doubled [6, 36]. In the case of the temperature-programmed mode of operation, longer columns offer an improved resolution at a minor increase in analysis time, but the extent strongly depends on the heating rate [33]. Since longer columns are expensive, it is generally recommended to use a column that is as short as possible. Inner Diameter An inner diameter of capillary columns in gas chromatography ranges from 0.1 mm (microbore), over 0.18 and 0.25 mm (narrow-bore), to standard columns with 0.32 mm or even up to 0.53 mm (megabore). In practice, only columns with an internal diameter of up to 0.32 mm are used for a direct connection to a mass spectrometer. As the inner diameter of the column decreases, and accordingly the film thickness: (1) the resolution; and (2) the total duration of the analysis increase [4, 6, 36]. Film Thickness As a rule, thicker films are being used in the analysis of very volatile compounds, and thinner films in the analysis of trace substances and compounds having a high boiling point. Column films with a thickness of 1.0 μm and more, very successfully separate components with extremely low boiling points. However, during the analysis at elevated temperatures, these can have a pronounced column bleed. For all other applications, thin film columns, about 0.1 μm thick, have been shown to be very effective in the GC/MS analysis. Such columns give narrow peaks, and even when used at high temperatures, they show no significant bleeding. The phase ratio of the capillary column is determined as the ratio of the volumes of the gaseous mobile phase (internal volume) and the volume of the coated stationary phase. High values of this ratio indicate a good separation. As the film thickness increases, the following factors increase as well: (1) the resolution of volatile substances; (2) the analysis time; and (3) the elution temperature [6, 36]. Sample Capacity The sample capacity is the maximum amount of an analyte that can be loaded on the stationary phase. Excessive amounts of a sample (overload) will cause peak

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asymmetry, having a slight gradient toward the front and a sharp slope from the back ( fronting). This can occur very easily if a column of the wrong polarity is chosen. It increases with (1) the column inner diameter, (2) the film thickness, and (3) the solubility of the sample [6]. Fast GC Using a fast GC technique, the GC analysis time can be substantially reduced compared to the typical analysis times of 10–60 min. The increase in analysis speed can be achieved in the following manners: using a shorter column, reducing column film thickness, increasing isothermal temperature, using the fast rate of temperature programming, and applying a higher carrier gas velocity, as compared to a conventional GC column with an optimal carrier gas flow. On the other hand, an increase in analysis speed generally leads to a decrease in separation efficiency and sample capacity. The acceptability of these losses has to be considered for each particular case separately, and the availability of compatible injection and detection techniques plays an important role in the selection of the analytical strategy [41]. It has been proven that maximal separation efficiency is not always required to meet the practical aims of certain GC/MS applications. It is not necessarily useful to separate compounds with baseline resolution if their mass spectra are sufficiently different to be easily resolved spectrally. With fast GC, the tendency is to sacrifice some separation efficiency, in order to reduce the retention time of the compounds of interest, while maintaining the requirements of the specific application by measuring the peaks of interest with sufficient accuracy. The analysis time of fast GC systems is typically a few minutes, very fast GC up to a couple of seconds, and ultrafast GC systems usually last less than one second [38]. Fast GC can be performed on conventional gas chromatographs, while very fast and ultrafast GC and GC/MS require specific instruments. Multidimensional Gas Chromatography The multidimensional gas chromatography (MDGC) technique is used to separate compounds present in complex mixtures, especially those with similar retention factors, by using the successive separation power of two or more different columns. Using dual- or multiple-column systems and by combining columns of different polarities, it is possible to perform the analysis of the samples comprising substances of a wide range of polarities. The vast majority of MDGC applications utilize two columns, so it is usually referred to as two-dimensional gas chromatography [49]. Two techniques are used for performing these separations: the heart-cutting method (GC-GC) and comprehensive two-dimensional gas chromatography (GC×GC). Schematic diagrams of these multidimensional gas chromatography systems are shown in Fig. 1.4. In heart-cutting, all components pass through a first column, and one or more fractions of closely eluting compounds that could not be separated is transferred (“cut”) from the first column to a second column capable of resolving them. This is accomplished by placing a valveless switching device (Deans switch) between two columns, which allows fast redirection of flow onto the second column. The choice

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Fig. 1.4 Schematic diagrams of multidimensional gas chromatography systems: (a) heart-cutting (GC–GC) and (b) comprehensive two-dimensional gas chromatography (GC×GC)

of column length is application-specific and does not interfere with the “cutting” process. The second column can be of the same dimension, but a different polarity is recommended [6]. As shown in Fig. 1.4a, the system includes two detectors (D1 and D2), so the complete first dimension separation is recorded at D1, while D2 generated a chromatogram of the heart-cut fraction. Commonly, a universal flame ionization detector (FID) is used to continually observe the separation on the first column, and an MS detector acquires data from the second column. The heartcutting technique is useful when one or a few target analytes have to be determined but cannot solve the analytical problems related to wide-ranging screening or non-targeted analysis. In addition, this approach is time-consuming as two fulllength conventional capillary columns are used. In contrast to heart-cutting, in comprehensive two-dimensional gas chromatography, all eluting compounds from the first column are transferred to the second column at regular intervals throughout the analysis. In other words, GC×GC aims to provide improved resolution for all sample constituents instead of a few selected [50]. Typically, a conventional-size nonpolar capillary column (15–30 m × 0.25–0.32 mm ID, 0.25 μm film thickness) is used as the first, and a short microbore polar column (1–2 m × 0.1 mm ID, 0.1 μm film thickness) as the second column/dimension. The modulator that connects the columns is the key component of the GC×GC system. Its fundamental functions are to accumulate or

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trap, refocus, and release narrow adjacent zones of the first column effluent rapidly into the second column, continuously throughout the analysis [51]. The time required to complete this process is defined as the modulation period, and it is similar to the widths of the peaks emerging from the primary column (generally in the 4–8 s range). This allows the majority of the resolution produced by the first column to be maintained but also means that the range of retention times in the second column must be less than the modulation period to avoid remixing of components separated by the primary column [49]. The three main types of modulators are thermal, valve-based, and flow modulators [52]. The high-speed conditions of the secondary fast chromatographic column result in very sharp peaks, with widths of 50–1000 ms at the baseline, and hence require fast detectors with an appropriate high detection rate. This means that the detector with a data acquisition rate of 50–200 Hz is necessary. Therefore, GC×GC/MS is most commonly performed with a time-of-flight (TOF) mass spectrometer due to its high dataacquisition speed, selectivity, reliable deconvolution of overlapping peaks, and the ability to scan a broad mass range [51]. Unfortunately, TOF-MS systems are very expensive. Rapid-scanning quadrupole MS is useful as a cost-effective alternative, but only for applications with a limited mass range, between 100 and 300 Da [50]. Finally, to complete the GC×GC process, data processing and visualization are required. Comprehensive GC separations provide the analyst with a tremendous amount of complex data, so sophisticated software is required to obtain useful information from this complex data. The high-resolution power and increased sensitivity make comprehensive two-dimensional gas chromatography advantageous over conventional one-dimensional GC for challenging food authentication assessments. On the other hand, optimization of the analysis requires a more complex approach. The changes in operational parameters, such as oven temperature or a carrier gas flow rate, have different impacts on the performance of separation columns since these differ both in their geometry and separation mechanism. Furthermore, the operation parameters of the modulator need to be optimized [41].

1.7

GC/MS Interface

As already mentioned, the basic principle of mass spectrometry is the generation of ions from inorganic or organic compounds by any driven ionization process, separation of these ions based on their mass to charge ratio (m/z), and their detection. To avoid collisions of formed ions passing from an ion source to a detector, a mass spectrometer must operate at a high vacuum. Uncontrolled collisions could cause further ion reactions, with large interferences, and therefore give false mass spectra [4, 36]. The interface has the function to transfer as much analytes as possible into the mass spectrometer, without imperiling the vacuum required for optimum ionization and mass analysis [53]. Nowadays, most GC/MS interfacing is done by simply inserting the capillary column directly into the ion source (direct coupling). In this case, the GC/MS interface is a heated metal tube equipped with a temperature

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controller. The interface is usually heated to 250–270 °C to prevent adsorption or condensation of sample components at the end of the column. This also facilitates sealing the capillary column to the mass spectrometer entry fitting [54].

1.8

Ion Source

An ion source should provide enough energy to ionize the analyte molecules. It should be borne in mind that some ionization techniques transfer a large amount of energy to a molecule and, thus, cause the formation of a large number of ion fragments, while soft ionization techniques usually create only molecular ions. Within a GC/MS instrument, electron ionization (EI) and chemical ionization (CI) are the most commonly used ionization techniques [36, 55–57]. Electron Ionization Electron ionization is a standard ionization process in most GC/MS instruments [6], and a schematic illustration of this ion source is given in Fig. 1.5. When employing electron ionization, ions are formed by directing an electron beam in a vapor of analyte molecules. The electron beam is created by heating the filament, directed through the ion source, and then collected in an electron trap. Magnets enable the helicoidal traveling path of electrons. Thus, their total trajectory increases, and accordingly the chance of an interaction between molecules and electrons. After the interaction, one electron (rarely more) is removed from the analyte, creating a free radical of the molecular cation. If the analyte has a high electron affinity, negative ions can form, although further use of the anion species is generally limited [58, 59]. All commercial devices available today employ the energy of 70 eV [6], which is far higher than the energy needed to ionize molecules. Excess energy is distributed between the bonds of the formed ions and often leads to their breakage,

Fig. 1.5 Schematic illustration of the electron ionization source

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resulting in ionic fragments. Fragmentation can be generally predicted, thus providing information on the structure of the analyte [58, 59]. The commonly used ionization energy of 70 eV, which has been applied for years, aims to give uniform and comparable mass spectra. This comparability of mass spectra is very important when creating mass spectra libraries for a wide range of chemical compounds. The mass spectra of all commercially available mass spectra libraries are obtained by applying this specific ionization energy. This enables the identification of an unknown component based on its characteristic ion fragments, naturally, only if this specific energy was used for the ionization process [4, 6, 36, 60]. Fragmentation can be more or less mitigated by reducing the energy of the electron beam, but the efficiency of ionization is, thus, also significantly reduced. The main disadvantage of electron ionization is that it is limited to molecules with a molecular weight of less than 1000 Da [58, 59]. Chemical Ionization The term chemical ionization, as opposed to electron ionization, refers to all soft ionization techniques that involve an exothermic chemical reaction in the gas phase, mediated by the gas reagent and its ions. Stable positively charged ions (positive chemical ionization—PCI) or negatively charged ions (negative chemical ionization—NCI) are obtained as products in this process [4, 6, 36, 60]. Negative ions are always formed during mass spectrometry, even during electron ionization, but they have no greater analytical significance because of an extremely low yield. However, in the case of negative chemical ionization, their higher production is intentionally induced [6, 60]. The most commonly used commercial GC/MS systems today typically detect only positive ions; thus, positive chemical ionization is more commonly used. In order to detect negative ions, special equipment is necessary to reverse the polarity potentials of the analyzer and the detector [4, 6, 36, 60]. The electron capture mechanism with the formation of negative ions is the process most commonly encountered in the negative chemical ionization technique, providing the lowest limits of detection in organic mass spectrometry [6]. In this way, compounds that contain electronegative elements in their structure, such as fluorine and chlorine, or acidic compounds (carboxylic acids, phenols, alcohols), can be ionized [55, 58, 61]. Chemical ionization takes place in two steps. First, the molecules of the gaseous reagent are ionized by electrons, and then the ions of the reagent transfer the charge to the analyte [59]. Methane, isobutane, and ammonia are most commonly used as gaseous reagents [58]. Therefore, chemical ionization is based on the ion-molecule reaction and involves the transfer of protons, electrons, or other ions, between the reactants: the neutral analyte molecule and the reagent ion. For this to be feasible, a sufficiently large number of collisions between the reactants must take place, which is achieved by a significant increase in the partial pressure of the gaseous reagent. Excess amounts of a gaseous reagent effectively protect analyte molecules from direct ionization by primary electrons, which is a prerequisite for combating competitive electron ionization of the analytes [61]. Therefore, instead of an open ion source, which is easily vacuumed, the closed ion volume is necessary for chemical

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ionization. This type of ionization is increasingly used in the analysis of trace substances, due to the technical solutions that allow the combination of ion sources and the expansion of the application spectrum of mass spectrometers with ion trap analyzers. The application of chemical ionization is important in determining the structure and molecular weight. In mass spectrometric detection, additional selectivity can be provided by using specific reagent gases in the ionization process [4, 6, 36, 60]. The reduction in the amount of energy transmitted to the analyte ions can be achieved by using chemical ionization sources operating at temperatures around 150 °C, and not at 250 °C, as in the case of electron ionization sources. Lower ion source temperatures can significantly reduce the fragmentation of some compounds [58]. Some instruments are equipped with an EI/CI combination source, which allows very fast switching from EI to CI operation without venting the instrument. The corresponding ion source design is a compromise achieving the highest sensitivity using both ionization techniques, and it is a very useful tool for comprehensive substance screening [53]. Field Ionization FI is a technique similar to CI, resulting in a high abundance of ions representing the intact molecule. Unlike CI, which produces even-electron protonated molecules (MH+), FI produces odd electron molecular ions (M+•). In FI, the ionization of an analyte molecule in the gas phase takes place in an electric field (107–108 V cm-1), maintained between two sharp points or edges of two electrodes [38]. Atmospheric Pressure Chemical Ionization APCI This type of ionization occurs outside of the vacuum of the mass spectrometer and, therefore, has less chance of fouling the ion optics just before the mass analyzer. APCI is the only atmospheric pressure ionization technique that is applicable to GC/MS. Ionized air (N2, O2, and H2O) undergoes ion/molecule reactions with gas-phase analytes at atmospheric pressure to produce protonated molecules, deprotonated molecules (negative-charge even-electron ions), and [M - H]+, formed by hydride abstraction and various adduct ions, especially in the presence of a dopant. APCI is considered to be the ionization technique that will provide the lowest detection limits for the widest varieties of analytes [38].

1.9

Mass Analyzers

After ion formation, these are separated based on their mass-to-charge ratio, m/z. The charge of ions, labeled with z, in most cases has a value of 1, either positive or negative. For that reason, the ratio m/z is usually considered a mass, m. A mass spectrum of an analyte is a result of the mass spectrometry. The mass spectrum is a two-dimensional graphical representation of the distribution of ions, which are separated and detected in accordance with their m/z values.

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Fig. 1.6 Schematic illustrations of mass analyzers: (a) quadrupole, (b) ion trap, (c) orbitrap, (d) magnetic sector, and (e) time-of-flight

One of the unique features of MS is that mass analysis can be done by different physical principles. There are several types of mass analyzers in CG/MS instruments: quadrupole (Q), ion trap (IT), magnetic sector (MS), time-of-flight (TOF), and the most recently invented orbitrap (OT) mass analyzer [4, 57, 58, 62]. Schematic representations of these mass analyzers are summarized in Fig. 1.6. Quadrupole Ever since the discovery of the quadrupole mass spectrometer was awarded the Nobel Prize, this type of instrument has been constantly gaining importance. Earlier systems had low separation power and low mass range, being able to work with m/z ratios between 1 and 200 m/z. Modern quadrupole analyzers cover a much wider range of masses, even over 2000 m/z [61]. Quadrupole (Q) analyzers (Fig. 1.6a), as their name suggests, consist of four rods that can be made of metal (molybdenum) or metal-coated ceramics. The rods are 10–20 cm long and about 10 mm in diameter. They are placed parallel to each other, forming a square where the opposite pairs are electrically connected. The rods must

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be precisely aligned to create the symmetrical electric field necessary to separate ions with different m/z ratios. The surface profile of the rods should be hyperbolic to obtain an ideal electric field, but it is often cylindrical in practice. One rod pair contains positive and the other negative direct current (DC). However, they are not constantly positive or negative. The radio frequency voltage (RF) causes the rods to oscillate between positive and negative polarities. Mass separation, also called mass filtering, is based on the fact that ions begin to oscillate when they enter a field produced by DC and RF voltages. At a certain quadrupole field voltage, only ions with one specific m/z value have a stable oscillatory path along the quadrupole axis to the detector. All ions with m/z values other than that develop unstable oscillatory motion and lose charge by colliding with rods or prefilters. The gradual change of DC and RF voltage, while maintaining their ratio constant, allows the passage of ions with specific m/z values, thus recording the mass spectrum [58]. The quadrupole mass analyzer can be operated in either the full scan mode or the selected ion monitoring (SIM) mode. In the full scan mode, the ions of a selected m/z range (e.g., m/z 50–450) will pass through the quadrupole sequentially to the detector; i.e., full mass spectrum is recorded. In contrast, in SIM mode, only a few selected m/z ions (typically 1–20) are monitored. The full scan mode of operation allows an unknown identification by library search, while SIM makes quantification more sensitive, but the spectral information is sacrificed. Therefore, SIM is primarily used for quantitative analysis of trace target compounds. The main advantages of quadrupole mass analyzers include simplicity, small size, high scanning speed, and low cost. They are, consequently, the most commonly used analyzers in GC/MS systems [4, 57]. Ion Trap The ion trap (IT) mass analyzers operate on a principle similar to a quadrupole. However, instead of ions passing through the quadrupoles, ions are stored in the trap for subsequent analysis. Therefore, IT can be used to perform sophisticated tandem mass spectrometry besides the conventional full scan mode of operation. There are multiple configurations of ion traps including a 3D ion trap (also called quadrupole ion trap), a linear quadrupole ion trap (2D trap), and an Orbitrap (OT). The first available ion trap is the 3D system which is composed of three electrodes, one ring and two hyperboloid end-cups, that create a 3D quadrupole field (Fig. 1.6b). The ions of selected m/z values are trapped, i.e., move stably in three-dimensional orbits, by applying an RF potential between the ring and endcaps electrodes. Subsequently, to record the mass spectrum, radio frequency is gradually increased and ions of a certain m/z value are destabilized and ejected via the hole in the exit endcap electrode to reach the detector of a mass spectrometer in increasing m/z order [55]. The main drawback of 3D ion traps is limited ion capacity leading to space-charge effects, which have adverse effects on sensitivity, mass accuracy, and mass resolution. Space-charge effects are typically eliminated by gain control systems.

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The construction of 2D ion traps is based on a quadrupole with four rods. At the end of the quadrupole, lenses are placed that reflect ions back and forth along the quadrupole [55]. The ion beam is reflected repeatedly between the two electrodes and a slot, made in one of the rods, allowing ions to be radially ejected. Considering its configuration, a linear ion trap can be used both as a quadrupole mass filter and an ion trap. In comparison to the 3D trap, which has a limited capacity for ion storage due to its small trapping volume, the 2D trap has almost one order of magnitude increase in ion capacity [53]. Thus, the space charge effect is weakened, and the specificity and sensitivity are greatly improved. The orbitrap represents a special case of an ion trap analyzer that is based on the separation of ions in an oscillating electric field. An orbitrap (Fig. 1.6c) consists of an outer barrel-like electrode and a coaxial inner spindle-like electrode. It traps ions in a stable flight path (orbit around the inner spindle) by an electrostatic field. The frequency of harmonic oscillations of the orbitally trapped ions around the inner electrode is related to the m/z of the ion. This oscillation can be detected as an image current on the two halves of an electrode encapsulating the orbitrap [63]. Fouriertransform of the signals provides the mass spectrum of the trapped ions. The merits of orbitrap are very high resolving power and sensitivity and high mass accuracy and mass range. Ion trap analyzers are quite cheap, simple, and very sensitive and therefore widespread in GC/MS systems [4, 57]. They are frequently combined with other mass analyzers in hybrid instruments and used to isolate ions of selected m/z to perform tandem mass spectrometry experiments. Magnetic Sector A magnetic sector (M) analyzer was the first developed type of mass analyzer and thus the basis of the mass spectrometry technique. A single-focusing magnetic sector analyzer (Fig. 1.6d) uses an electromagnet to separate ions in space according to the radius of their paths. In a typical magnetic sector analyzer, the strength of the magnetic field varies, directing the ion beam through a narrow slit through which ions with increasing or decreasing m/z values are being selected. In this way, a fullrange mass spectrum is obtained. However, there are small energy differences between ions of the same empirical formula, created during the ionization process. These energy differences can be eliminated by using an electrostatic analyzer, which separates ions based on differences in their kinetic energy—ions with higher energy have a longer path. An electrostatic sector can be installed before or after the magnetic sector to form double focusing magnetic sector mass analyzers. By combining a magnetic and electrostatic analyzer, ions with the same m/z ratio and close kinetic energy values are directed to the same position on the detector slot, resulting in higher mass resolving power [38, 58, 59]. Double-focusing magnetic sector mass spectrometers provide high sensitivity, high resolution, and reproducibility that is unrivaled in any other kind of mass analyzer. These instruments were the only ones capable of high-resolution measurement until the introduction of high-performance TOF. Compared to mass spectrometers with other mass analyzers, the size and cost of these instruments are considerably higher, so their use is nowadays limited.

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Nevertheless, it is still being used in high-resolution analysis, accurate mass measurements, and determination of isotopic ratio composition of the analytes. Time-of-Flight Time-of-flight (TOF) mass analyzer is based on a simple mass separation principle in which the m/z of an ion is determined by a measurement of its flight time over a known distance [5]. After a generation in the ion source, pulsed packets of ions are accelerated by an electric field into a flight tube such that all ions of the same charge obtain the same kinetic energy. Therefore, ions of different m/z values will have different velocities, ions with the lower m/z ratio will travel faster, and be the first ones to reach the ion detector, while those with the higher m/z ratio will arrive later, being the slower [58]. The mass spectrum is obtained by recording the arrival times of ions at the detector [38]. Unlike other mass analyzers, TOF theoretically has no mass limit for detection. An important source of error in the TOF experiment stems from small differences in the kinetic energies of ions of the same m/z resulting in reduced mass resolution and, at times, a lower sensitivity of detection. To overcome these differences, almost all TOF mass analyzers contain a reflectron (Fig. 1.6e). The reflectron typically consists of a series of lenses with increasing potential to push ions back at a slight angle into the direction of the detector [6]. With this kind of electrostatic mirror, ions of higher velocity (energy) will penetrate deeper into the decelerating zone than low-energy ions. In this way, the ions of the same m/z values with different initial energies are time focused and hit the detector at the same time. These improvements lead to significantly increased mass resolving power and mass accuracy. The advantages of the TOF analyzer include high sensitivity in full scan mode, fast data acquisition rates, high mass accuracy, virtually unlimited m/z range, and resolution higher than Q or IT mass analyzers. The employment of the TOF technique in GC/MS systems has gained considerable interest, primarily due to the scanning speed and the accuracy of the mass determination. Technical improvements have made GC/TOF-MS an alternative to magnetic sector instruments for accurate mass measurements [4, 57]. The fast data acquisition rates make the TOF analyzer the ideal mass detector for fast and comprehensive gas chromatography [6]. Table 1.2 summarizes the performance parameters of mass spectrometers with different mass analyzers. Hybrid and Tandem Analyzers It is nowadays common to combine two or more analyzers within one instrument (MS/MS) in order to improve and expand analytical capabilities. The same analyzers (tandem), for example, triple quadrupole (QqQ) and TOF-TOF, or different analyzers (hybrid) such as Q-TOF, Q-IT, IT-TOF, and IT-Orbitrap can be combined [5, 58]. It is important to mention here that the term triple quadrupole is somewhat misleading. The name implies that there are three quadrupoles in tandem. However, the instrument has two quadrupoles separated by a collision cell, which was constructed from a quadrupole device in the original design of the instrument.

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Table 1.2 Comparison of some common properties of mass spectrometers with different mass analyzers [4, 57] Feature Mass interval (Da) Spectrum recording Mass resolution Mass accuracy Limit of detection Scan MS/MS Cost

M 1–50 000 Full scan, SIM High 0.001 Accurate (1 ppm) 10–15–10–14 Slow None Very high

Q 2–4000 Full scan, SIM Mass unit Nominal

IT 10–2000 Full scan, SIM Mass unit Nominal

10–13–10–12 Fast None Low

10–13–10–12 Fast MSn Low

TOF No limit Full scan High Accurate (5 ppm) – Ultra-fast None High

Fig. 1.7 Overview of tandem mass spectrometry analysis

Therefore, a more appropriate description would be the tandem quadrupole mass spectrometer [38]. Tandem mass spectrometry has become the technique of choice for the analysis of trace substances, present in parts per billion (ppb) and parts per trillion (ppt) in complex matrices. Additionally, the determination of molecular structures is an important application of these coupled devices. The goal of coupled analyzers is to combine the strengths of each analyzer, while avoiding their drawbacks. Thus, better performances could be obtained using a combined instrument, compared to a single analyzer. Hybrids and tandems are characterized by combined abbreviations in the following order in which ions travel through the analyzers. Nowadays, almost all possible combinations of two different types of analyzers are commercially available. Soft ionization techniques, such as chemical ionization, are a preferred choice for MS/MS analysis, instead of electron ionization. They concentrate a few intense ions in the ion flow, which is usually a good starting point for MS/MS analysis. Such an analytical procedure takes place in several steps (Fig. 1.7). When a certain chemical compound, represented by a GC peak, reaches the ion source, it is firstly ionized. From obtained ion mixture, a precursor ion with a specific m/z value is selected in the first step (MS1). Through collisions of selected ions with neutral gas molecules (helium, nitrogen, argon, or xenon), fragmentation of precursor ions occurs, which has a specific pattern for each type of substance, thus forming a mixture of product ions with lower m/z values. A second mass analyzer (MS2) is required to analyze the ion masses of the product, after which the MS/MS spectrum of a particular

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Table 1.3 The scan modes with tandem MS/MS MS/MS mode Product (daughter) ion scan Precursor (parent) ion scan Selected reaction monitoring (SRM) Neutral loss scan

MS1 mode Single ion

MS2 mode Scan

Scan

Single ion

Single ion

Single ion

Scan

Scan

Application Structural determination, identification and confirmation of a compound Analysis of compounds with common structural features Quantify trace levels of target compounds Analysis of compounds with a common functional group

compound is obtained. By connecting MS1 and MS2, the opportunities rise for very specific and targeted analysis [6, 38, 60]. The various scan modes can be utilized with tandem mass spectrometry, whereby each mode can be used to obtain different information about the investigated sample. The main tandem MS/MS scan modes and their common applications are shown in Table 1.3. Tandem QqQ and hybrid Q-TOF analyzers are most frequently used in GC/MS/ MS systems. QqQ can achieve superior selectivity and sensitivity, but mass accuracy and resolution are relatively low. Therefore, it is commonly employed for target analyses. On the other hand, the Q-TOF mass analyzer offers high-resolution and mass accuracy; thus, it is used for the identification of unknown compounds in complex food matrices. Isotope-Ratio Mass Spectrometry Isotope ratio (IR) measurement was the first application of mass spectrometry. Today, GC-IRMS is an analytical approach for establishing precise signal ratios of stable isotopes of the following elements H, N, C, and O (H/D, 13C/12C, 15N/14N, 18 16 O/ O), since they are the main elements occurring in organic matter. The isotope signal ratios provide information on the physicochemical history, origin, or authenticity of the sample and, as such, are a key part of the research and process control in many areas, including food, forensics, material quality control, geology, or climate research [6, 60]. For isotope ratio measurement, the analyte must either be a simple gas, such as carbon dioxide or nitrogen, or must be converted into a simple gas, isotopically representative of the original sample, before entering the ion source of an IRMS [64]. Thus, IRMS determines the difference in isotope ratio between an analyte and a reference gas, or a standard, but not the absolute isotope ratio [65]. A dimensionless quantity (δ) is used to express the isotope ratio value in relation to the standard and is expressed in ‰. GC-IRMS instruments are equipped with a combustion interface placed between GC and the mass spectrometer, where the analytes (effluents from GC) are converted before they reach the ion source of the mass spectrometer. Typically, two types of combustion interfaces are mounted in parallel, a combustion reactor for carbon and

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nitrogen determination and a high-temperature thermal conversion reactor for hydrogen and oxygen determination. The combustion reactor consists of a capillary oxidation reactor, reduction reactor, and water trap. All compounds eluting from a GC column are oxidized in a capillary reactor and form CO2, N2, and H2O as a by-product at 940–1000 °C. The reduction reactor (typically comprised of copper material and operated at 650 °C) converts any produced NOx to N2 and removes potential oxygen bleed from the oxidation reactor [6]. A water trap removes water vapor produced by combustion, which could give isobaric interference in the CO2 measurements [65]. Mass spectrometers employed for isotope ratio measurements are non-scanning, static magnetic sector mass spectrometer systems. They employ an EI source, a singlefocusing magnetic sector analyzer, and an array of Faraday cups as an ion detector.

1.10

Ion Detectors

After separating ionic species in a mass analyzer, they need to be determined qualitatively and quantitatively [36]. The role of the detector is to convert ion energy into an electrical signal. When an ion reaches the detector, the ion impact energy causes the emission of secondary particles (electrons or photons) [59]. The detection consists of measuring an obtained abundance—a total ionic current. When the abundance is low (10–9–10–6 A), various direct current amplifiers, photomultipliers (photomultiplier conversion dynode), electron multipliers, and dynamic capacitors (vibrating-reed electrometer) are used. Fragments that pass through the analyzer are redirected from their path using an optical lens, before coming into contact with the detector surface. This avoids gamma rays, which are generated in the ionization chamber, to be registered on the surface of the detector giving false signals [36].

1.11

Conclusion

Gas chromatography with mass spectrometric detection is nowadays considered a technique that is commonly used in the instrumental analysis of chemical compounds. A mass spectrometer is considered the best-performing detector to be coupled to a gas chromatograph. In addition to frequently used one-dimensional GCs, two- and multidimensional systems are also employed today, in which two or more columns are used at the same time in order to analyze components of different polarities in one run. The most widely used mass analyzers in the MS technique coupled to a GC device are quadrupole, ion trap, and time-of-flight analyzers. Due to higher sensitivity, better resolution, and lower limit of detection and quantification, mass analyzers are often coupled in order to combine their advantages, resulting in hybrid and tandem mass spectrometers. Due to its characteristics, endless possibilities of work optimization, as well as modern technical solutions and improvements in instrument design, this combination of two powerful analytical

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techniques is frequently being used in the analysis of a wide range of volatile and semi-volatile chemical compounds today. Acknowledgment The authors would like to acknowledge the support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Program No. 451-03-68/2022-14/200134).

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Statistical and Mathematical Models in Food Authentication B. Dayananda and D. Cozzolino

Abstract

This chapter summarizes the standard statistical and chemometric procedures (hierarchical cluster analysis, principal component analysis, principal component regression, linear and quadratic discriminant analysis), including data preprocessing techniques and other algorithms (artificial neural networks, random forests and classification trees, support vector machines) that are commonly used for both food fraud detection and quantification using GC/MS data. The importance of chemometrics in the treatment and analysis of complex GC/MS datasets will also be highlighted. Keywords

Statistics · Chemometrics · Preprocessing · Targeted · Untargeted · GC/MS

2.1

Introduction

Food authentication is becoming increasingly important to consumers [1, 2]. Food authenticity is an integral part of food safety as it is a critical process to satisfy legislation, regulations, and consumer demands [1, 2]. Hence, during the last decades, mathematical modeling and statistical techniques have been utilized to optimize the different steps of the food authentication processes in order to provide B. Dayananda School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, Australia D. Cozzolino (✉) Queensland Alliance for Agriculture and Food Innovation (QAAFI), Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, Australia e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_2

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validated tools [1–6]. In order to achieve this, statistical techniques need to be applied from the designing stage of the process (e.g., experimental design, data collection), where an experienced data analyst should be incorporated in this process to understand what the final analysis results are to be implemented in the decisionmaking process [3–10]. This process and tasks will reduce uncertainty and increase the levels of confidence in the tools developed. The rapid development of analytical techniques, including GC/MS, together with mathematical modeling and statistical techniques, plays a major role in the development of new applications, including food authentication [5–9, 11]. However, experience and knowledge behind the computer screen are essential to understand the process and useful to incorporate it into the decision-making processes beyond the so-called “black box” application [3, 4, 6]. Chemometric applications are common in food authentication, which are often characterized by multidimensional data as a result of the different instrumental techniques used [3, 4, 7–9, 12]. This data has complex hidden relationships between variables and advanced mathematical modeling and statistical techniques are essential to extract valuable information [3–5, 7–9]. The fundamental purpose of statistical analyses is to make conclusions about the population based on the sample data collected during an experiment. However, traditional statistical methods, such as t-tests and analysis of variance (ANOVA), are inadequate or not sufficient to obtain accurate and in detail interpretation of the multidimensional data collected during the use of modern instrumental techniques (e.g., spectra, chromatograms) [3–5, 7–9, 13]. In this chapter, we introduce and discuss some of the most common chemometric methods, including pattern recognition analysis as well as preprocessing methods used to analyze GC/MS data in food science applications related to food authenticity [14–17].

2.2

Untargeted and Targeted Approaches

In food authenticity, two main strategies are used: the so-called target quantitative analysis and the untargeted analysis or fingerprinting profiling [18–20]. Targeted approaches select a predefined group of compounds or properties for which standards are available, requiring the optimization of sample preparation (e.g., need of standards, calibration curves). This strategy has been used for the analysis of specific metabolites or compounds [18–20]. On the other hand, the objective of untargeted analysis is on the simultaneous measurement of the whole chromatogram as well as many unknown compounds as possible in a biological system, enabling a more exhaustive illustration of the changes of the complete profile based on the identification of compounds affected by an external variable [18–20]. Non-targeted data analysis is more data analysis demanding compared to that required in classical targeted approaches. In targeted analysis, results can be analyzed and evaluated (e.g., compound-by-compound) using univariate statistics [18–20]. Data collected for non-targeted approaches typically needs to be evaluated using multivariate statistical models or chemometric approaches [18–20].

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2.3

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Chemometric and Machine Learning Tools in Food Authentication

The chemometric methods used in pattern recognition include various multivariate techniques, where clustering and ordination are the two main classes that chemometric researchers often employ [3, 4, 7–9, 13, 21–26]. The clustering techniques reduce the multidimension of data to one dimension, whereas ordination techniques reduce the multidimensional data to two or three dimensions [7–9, 13]. However, most importantly both clustering and ordination techniques (dimension reduction) depend on the distance measure [3, 4, 7–9, 13, 21–27]. There are several categories of clustering methods available, and among these methods, hierarchical clustering and K-means clustering methods are commonly used in chemometric approaches applied in food authentication studies [28, 29]. Ordination is a set of powerful techniques used to investigate, summarize, and visualize multivariate data. One of the main objectives of ordination methods is to understand the multidimensional data in a reduced low-dimensional space [3, 4, 30]. Ordination techniques can be divided into two broad categories: constrained and unconstrained ordinations. In general, unconstrained ordination uses a single data table, whereas constrained ordination uses it to identify relationships between two or more data tables. However, most importantly, all these ordination techniques are based on similarity distance matrices and different ordination methods use different similarity matrices [3, 4, 30]. In chemometrics, unconstrained ordination techniques are commonly used for pattern recognition, and the most popular techniques are principal component analysis (PCA), principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and correspondence analysis (CA). Moreover, in general, pattern recognition methods used in chemometrics can be divided into two main sub-categories: supervised and unsupervised pattern recognition. Supervised learning techniques use labeled datasets, and the algorithm knows the structure and pattern of data and what needs to be detected or classified [7–9, 13, 14, 31, 32]. The main supervised learning techniques are classification and regression. Unsupervised learning techniques do not use labeled data, and algorithms divide the dataset based on the similarity of the properties of the dataset. The main unsupervised learning techniques are based in clustering, association, and dimensionality reduction [7–9, 13, 14, 31, 32].

2.3.1

Unsupervised Tools

2.3.1.1 Cluster Analysis (CA) Cluster analysis is a multivariate data analysis technique that groups multivariate data (objects) based on the measure of similarity. Such groups are called clusters, and the clustering algorithms find similar objects to create clusters [8–10, 13, 31]. This is an unsupervised pattern recognition technique where clustering methods do not find labels; instead, it groups objects which are close together. In chemometrics,

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hierarchical clustering and K-means clustering are widely applied methods for pattern recognition [7–9, 13, 17, 31, 33, 34]. Hierarchical Clustering This is the most common clustering method used in authentication studies. Clustering results are visualized in a tree-like structure called dendrograms. When running the hierarchical clustering, it calculates distances or (dis)similarities; then, it checks for the two most similar clusters (shortest pairwise distances) and combines them [9, 27, 31]. Based on how the distance between clusters is measured, the hierarchical clustering method has four different clustering algorithms: “average,” “complete,” “single” linkage methods, and Ward’s clustering method. In the single-linkage algorithm, it joins clusters based on the shortest distance between clusters; however, some objects in each cluster can be distant from each other [7, 9, 31]. However, in complete-linkage clustering, clusters group when the distance is farthest away from each other, which is opposite to single-linkage. The average-linkage clustering is an intermediate method between single-linkage and complete-linkage algorithms, which group clusters based on the mean distance between clusters [7, 9, 31]. The Ward’s clustering (or the so-called Ward’s minimum variance clustering) uses the least squares method, which minimizes within-cluster sums of squares. The main difference between Ward’s clustering and linkage clustering is that Ward’s clustering algorithms consider the assessment as an analysis of variance technique, while linkage clusters use distance matrices [7, 9, 17]. However, it is common that Ward’s clustering and complete linkage have similar performance. K-Means Clustering This is a non-hierarchical approach suitable for large datasets having a lot of samples. At first, the K-means algorithm partitions the items into initial clusters (K numbers) [7, 9, 17, 31]. Then, it calculates the distance of items and assigns each item to the closest center. To apply K-means procedure, it is necessary to define how many clusters are to be used (k) first; however, there is no fixed rule or prior knowledge for cluster specification [7, 9, 27, 28, 31].

2.3.1.2 Principal Component Analysis (PCA) PCA is an unsupervised dimensionality reduction technique, and it is one of the most popular exploratory approaches to analyze the interrelationships among the large number of variables [7, 9, 15, 30, 32, 35]. The PCA method constructs new variables that are calculated and derived from the original large number of variables with minimum loss of information. In other words, the original dataset contains items (rows) and variables (columns) and PCA constructs new dimensions/axis (generally two or three) [15, 27, 32]. PCA performs a rotation of the original data to create new axes (principal components), which are orthogonal to each other [7, 9, 32]. The principal components are the linear combinations of the original variables that account for the variance in the data set [7, 9, 15]. The results of a PCA can be represented on a diagram called a biplot, and axes correspond to the new system of coordinates [7, 9, 15, 17, 32].

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2.3.1.3 Principal Coordinate Analysis (PCoA) PCoA is also referred to as metric multidimensional scaling. The main difference between PCA and PCoA is that PCoA has the flexibility in choosing the distance method [7, 9, 33, 34]. Therefore, PCoA and PCA return the same results when Euclidean distance is applied to the same dataset [7, 9, 27, 32].

2.3.2

Supervised Tools

2.3.2.1 Linear Discriminant Analysis (LDA) Linear discriminant analysis (LDA) is a supervised pattern recognition used to understand patterns between classes using dimension reduction and classification techniques [7, 9, 27, 31]. Based on the between-group variance and the within-group variance ratio, LDA shows the differences among samples in groups [30, 31]. LDA is originally described as a two-class method; however, multiple discriminant analysis was also introduced later, although both are referred to as Linear Discriminant Analysis. However, LDA cannot handle multicollinearity and need to be used with care [7, 9, 27, 31]. 2.3.2.2 Partial Least Squares Regression (PLS) and -Discriminant Analysis (PLS-DA) Partial least squares (PLS) regression can be applied when the data has high collinearity (multicollinearity) [8, 36–38]. The PLS technique does not directly use original predictor variables but instead uses a smaller set of uncorrelated predictors [8, 36, 37]. The PLS-DA technique is a supervised pattern recognition technique based on PLS regression. PLS-DA is highly popular in food authentication and has the ability to analyze highly collinear and noisy data [8, 36, 37]. Furthermore, PLS-DA is the best option to use with datasets with a smaller number of samples than variables [8, 36, 37]. 2.3.2.3 Soft Independent Modeling of Class Analogy (SIMCA) SIMCA is a supervised pattern recognition technique based on principal component analyses. The SIMCA method first proceeds with creating separate PCA models for each group, and then classification rules are built for each group [36]. 2.3.2.4 K-Nearest Neighbor (KNN) K-Nearest Neighbor also known as KNN is a type of supervised learning technique used for both regression and classification. KNN is a simple but powerful algorithm in pattern recognition of arranged data based on the selected features [9, 28]. Another advantage of KNN is that it can be used for nonparametric data [9, 28]. The parameter k (positive integer) refers to the number of nearest neighbors to use, and the algorithm classifies data based on a similarity measure.

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2.3.2.5 Support Vector Machines (SVMs) Support vector machines are one of the most popular supervised learning techniques in pattern recognition and can be used for classification and regression issues [39]. The SVM algorithm finds a hyperplane in multidimensional space that separates the features of the data [30, 39, 40]. Most applications of SVM are applied to datasets with a relatively small number of variables to those typically obtained in analytical chemistry. Although there is no reason why SVM cannot be extended to highly multivariable datasets, the application of SVM often requires a prior variable reduction step (e.g., PCA) [39]. This technique is widely used and is most commonly applied in machine learning [30, 39]. 2.3.2.6 Artificial Neural Networks (ANNs) Artificial neural networks (ANNs) have evolved from the way the human brain neurons have interconnected with each other. Similarly, ANN creates many individual elements (nodes or neurons), which are interconnected with each other in a pattern to communicate between the elements [41]. In general, individual elements work together to gather information by detecting the patterns and relationships of the data. Every element relates to others through a connection link and is associated with a weighted input that has information fed into an activation function. The output of the activation rule is then passed to the next element in the network. Figure 2.1 shows an example of the utilization of preprocessing and chemometrics methods to classify GC-MS data.

2.4

Validation

After a model is developed, it must be validated. This step is very important when chemometric methods are applied to any multivariate data set (e.g., classification, regression) [42–44]. Most of the applications of GC/MS reported in the literature have used cross-validation as the preferred method to validate the models developed. However, proper validation should be achieved using an independent data set. An

Fig. 2.1 Example of the use of preprocessing and chemometrics to classify GC-MS data

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independent data set must be created using samples that are like those analyzed or utilized during the development of the model and should have similar characteristics and properties [44]. Samples from different batches, experiments, harvest conditions, and processes should be considered and included. In proof-of-concept applications, the availability of independent samples to be selected as training and validation set is rare and costly. Therefore, the use of cross-validation can be justified [44–46]. The most widely used internal validation method is cross-validation [44– 47]. Under the banner of cross-validation, leave-one-out, k-fold cross-validation, segment cross-validation, and random subsampling are examples of the existing and commonly used ones [44]. The selection and use of a given cross-validation method mostly depend upon its availability in the commercial software used to analyze the data (e.g., chemometric software). In most applications, the leave-one-out crossvalidation method is the most commonly utilized [44]. Particularly, this method has been applied in cases where the number of samples is limited or when preliminary studies have been evaluated and described (e.g., a new technique, new compound) [44]. However, it is important to note that the sole utilization of cross-validation might provide over-optimistic results [44]. It has been reported that the misuse of cross-validation can generate models that are susceptible to being overfitted [44]. Overfitted models can be the consequence of using the same samples employed during the calibration development without testing the robustness of such models using a proper validation protocol (e.g., external validation) [44]. It has also been reported that the overuse of cross-validation can deliver over-optimistic models where the performance of such models is overrated [44]. To overcome some of these issues with cross-validation, different strategies have been evaluated and recommended. For example, in addition to the classical internal validation approaches (e.g., cross-validation), other methods might be utilized such as bootstrapping. This method has been shown to offer an unbiased estimate of the predictive accuracy of the model with low variance [44]. In addition to bootstrapping, resampling is another method that has been suggested by different researchers [44]. During the application of resampling, a subgroup of samples can be withdrawn from the original data set, where one subgroup is selected as calibration and the other subgroup is selected as validation [44]. Other methods, such as Jackknifing, have also been suggested to improve the issues associated with crossvalidation [48, 49].

2.5

Variable Preprocessing

The direct interpretation of the data collected during the utilization of GC/MS without any preprocessing is not the best-advised approach to follow when chemometric tools are to be implemented [37, 38, 50–53]. One reason for this is that the data can be influenced by day-to-day variations in the collection of information. Examples of this variation can be associated with changes in the instrument performance that might cause baseline effects, drifts, non-linearities, changes in the

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Fig. 2.2 Summary of the most important steps required during the development of a classification model

solvent used, changes in the column, or other specific issues associated with the signal-to-noise ratio [37, 38, 50, 52]. These issues can also be magnified if different information is to be combined, where each of the variables can be collected from different units of scale, days, etc. [37, 38, 50, 52]. Therefore, as stated above, variable preprocessing is a very important step before using chemometrics. Variables can be standardized using the ratio between each variable over the standard deviation (1/STD). Using standardization, variables will have the same influence (weight) in the model. However, an important aspect to consider is that the interpretation of the results, such as loadings, will also be influenced by the preprocessing technique utilized [45, 46, 51]. Different data processing techniques or methods have also been proposed and utilized in practice. For example, processing of the signal using derivatives (e.g., first, second), smoothing techniques, normalization, peak alignments, baseline corrections, Savitzky–Golay (S-G) filter, wavelet filtering, and Fourier transformation are a few of the most commonly used preprocessing methods applied and reported [37, 38, 50, 52]. A summary of the most important steps required during the application of chemometrics methods to develop a classification model is shown in Fig. 2.2.

2.6

Chemometrics Software

In recent decades, chemometric data analysis has greatly enhanced due to the development of computing power, as well as a wide range of chemometrics applications. Although it is relatively easy to understand and use such programs, basic principles of mathematics and statistics are highly required to turn instrumental data into information and decisions. Model calibration and predictions are the key

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principles of chemometric software and mathematics, and statistics are often applied to these two steps. For example, spectral or signal preprocessing algorithms and multivariate data analysis algorithms are the commonly used chemometric applications of such algorithms, based on the technical knowledge and application of various baseline corrections, smoothing, scatter correction, and standardization, as discussed above. Hence, solely mastering a given software does not help solve the problems in practical applications and one potentially incorrect step could create a case of garbage in, garbage out. Nowadays, several different software tools, spanning the range from commandline to completely menu-driven, are used in chemometric data analysis. In particular, some commercial instrument manufacturers have developed their own chemometric software, which can be used with their products, such as OPUS (Bruker), WinISI (FOSS), TQ Analyst Pro (Thermo Fisher Scientific), etc. In addition, there are popular commercially available chemometrics software, such as The Unscrambler, SIMCA, MATLAB, Pirouette, InStep, and LineUp, which have presented a friendly and easy-to-use interface. Moreover, programming languages R and Python have been significantly popular and provide great convenience for the implementation of machine learning algorithms.

2.7

Final Considerations

Several publications and reports in food science using GC/MS targeting authenticity, contamination, fraud, origin, and traceability of foods have highlighted the importance of using chemometric methods for data mining and interpretation. The application of chemometric methods requires the preprocessing of the signal (e.g., chromatogram), as well as the validation of the obtained models. Overall, the combination of chemometrics with GC/MS data has proved that robust tools can be developed to target authenticity issues in food and beverages.

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34. Pastor K, Vujasinovic V, Marjanovic Jeromela A, Vujic D, Jovanovic D, Acanski M (2019) Gas chromatography–mass spectrometry system applied to determine botanical origin of various types of edible vegetable oils. J Serb Chem Soc 84(9):1019–1025 35. Pastor K, Pezo L, Vujić Đ, Jovanović Đ, Ačanski M (2018) Discriminating cereal and pseudocereal species using binary system of GC/MS data—pattern recognition approach. J Serb Chem Soc 83(3):317–329 36. Bylesjö M, Rantalainen M, Cloarec O, Nicholson JK, Holmes E, Trygg J (2006) OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemometr 20(8–10):341–351 37. Mehmood T, Hovde K, Snipen L, Saeo S (2012) A review of variable selection methods in partial least squares regression. Chemom Intel Lab Syst 118:62–69 38. Skov T, Honore AH, Jensen HM, Naes T, Engelsen SB (2014) Chemometrics in foodomics: handling data structures from multiple analytical platforms. Trends Anal Chem 60:71–79 39. Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135:230–267 40. Pastor K, Ačanski M, Vujić D, Kojić P (2019) A rapid discrimination of wheat, walnut and hazelnut flour samples using chemometric algorithms on GC/MS data. J Food Meas Char 13: 2961–2969 41. Baş D, Dudak FC, Boyacı IH (2007) Modeling and optimization III: Reaction rate estimation using artificial neural network (ANN) without a kinetic model. J Food Eng 79(2):622–628 42. Alewijn M, van der Voet H, van Ruth S (2016) Validation of multivariate classification methods using analytical fingerprints—concept and case study on organic feed for laying hens. J Food Compos Anal 51:15–23 43. Perez-Guaita D, Kuligowski J, Lendl B, Wood BR, Quint G (2018) Assessment of discriminant models in infrared imaging using constrained repeated random sampling-Cross validation. Anal Chim Acta 1033:156–164 44. Westad F, Marini F (2015) Validation of chemometric models: A tutorial. Anal Chim Acta 893: 14–23 45. Badertscher M, Pretsch E (2006) Bad results from good data. Trends Anal Chem 25:1131–1138 46. Kjeldhal K, Bro R (2010) Some common misunderstanding in chemometrics. J Chemometr 24: 558–564 47. Naes T, Isaksson T, Fearn T, Davies T (2002) A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, UK 48. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 71:1–26 49. Efron B (1992) Jackknife-after-bootstrap standard errors and influence functions. J R Stat Soc B 54:83–127 50. Engel J, Gerretzen J, Szymanska E, Jansen JJ, Downey G, Blanchet L, Buydens LMC (2013) Breaking with trends in pre-processing. Trends Anal Chem 50:96–106 51. Oliveri P, Malegori C, Simonetti R, Casale M (2019) The impact of signal pre-processing on the final interpretation of analytical outcomes e A tutorial. Anal Chim Acta 1058:9–17 52. Rinnan A, van denBerg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem 28:1201–1222 53. Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44:1–12

Part II Authentication of Food

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Cereals, Pseudocereals, Flour, and Bakery Products Daniel Cozzolino

Abstract

Guaranteeing the authenticity and origin of both cereals and cereal products is a priority for consumers, the food industry, and authorities. Cereals, cereal ingredients, and by-products might be either deliberately or unintentionally adulterated as a consequence of mislabeling or by the substitution of a highvalue ingredient (e.g., premium quality) with a similar but of lower-quality or cheap value, ultimately leading to intentional or unintentional fraud. This chapter presents and reviews current applications employing gas chromatography-mass spectrometry (GC/MS) that have been utilized by different researchers in the field to determine the authenticity of cereal grains such as wheat, rye, triticale, barley, oats, corn, and rice, as well as pseudocereal grains (e.g., buckwheat, quinoa, and amaranth). Applications related to the authenticity of cereal and pseudocereal flours and pasta and bread made from these flours that are available in the food market (e.g., bread, pastas, cookies, etc.) will also be reported. Sample preparation, pre-processing methods, and data processing techniques (e.g., chemometrics) will also be discussed. Keywords

Cereals · Pseudocereals · Flour · Bakery products · GC/MS · Chemometrics · Fraud · Authentication

D. Cozzolino (✉) Queensland Alliance for Agriculture and Food Innovation, Centre for Nutrition and Food Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, Australia e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_3

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Introduction

Changes in food production systems as well as in the food supply and value chains will be affected by the growth in world population, which is estimated to be above nine billion people by the middle of this century [1–5]. Other issues, such as climate change, pandemic episodes, and political tensions between countries, will also affect the sustainability of the food production systems. These scenarios will determine an increase in the number of food security cases that will be intimately associated with food authenticity, provenance, and origin. Therefore, these issues will become of primary importance, as they will influence consumers’ choices and confidence about food ingredients and foods [6–13]. Tackling these issues requires the development of innovative, rapid, economical, and objective analytical methods or techniques able to evaluate and monitor food safety and security [7–9, 11–14]. Modern analytical and quality control systems are intimately integrated with big data, and they are utilized to trace and monitor the authenticity, origin, and provenance of both food ingredients and products along the supply and value chains [7–9, 11–13]. These control and monitoring systems are considered on the top of the list by the food industry, playing an important role in delivering useful information about the composition and quality along the supply and value chains of a wide range of food ingredients and products, including cereals and cereal by-products [7–9, 11– 13]. Guaranteeing the authenticity and origin of cereals, cereal ingredients, and by-products is a priority for consumers, the cereal industry, and the government [15–17]. Nowadays, consumers require accurate and comprehensive information regarding the products that they purchase and consume. This has become more prevalent as an increase in the number of reported cases associated with the intentional or unintentional adulteration of food ingredients and foods [7, 11, 15, 18–22]. As defined by other authors, adulteration and fraud might occur either by the mislabeling of a product or by the substitution of a high-value ingredient with a similar but of lower-quality or cheap counterpart, ultimately leading to cases of intentional or unintentional fraud [7, 11, 18–22]. Consequently, assuring the authenticity of food ingredients and products is critical not only for preventing economic fraud but also for reducing the negative impact of these issues on both the consumers (e.g., health or even death) and the food industry as a whole (e.g., stakeholders trust, image). Recent food security issues (e.g., beef contaminated with horse meat, the melamine contamination in China) have shown that no stated, mislabeled, or even fake food ingredients represent a significant risk to the health and safety of consumers. For example, no declared allergens in food, due to mislabeling or adulteration, might cause serious health issues to the consumer [7, 11, 18–22]. Despite the implementation of strict regulations and labeling protocols by various regulatory authorities and governments, both food safety and security remain of an international concern for the food industry [7, 11, 15, 18–22]. Recently, a number of reviews have been published focusing on the current state of the art on the technology available, the different applications and approaches (e.g., different methods and

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techniques, food matrices), and chemometric methods, utilized to target food authentication, adulteration, provenance, and fraud in different foods [7, 11, 18–22]. More recently, disruptions in the food industry associated with different and new consumer demands and trends and the effect of foreign matters on the food industry and food supply chains, as demonstrated during the COVID pandemic, have created among consumers awareness about food security and sustainability issues [23– 26]. Therefore, food authenticity and provenance have become a topic of increasing interest not only for the consumers but also for the research community and the food industry as a whole [27, 28]. As many other foods and commodities, cereals and their sub-products are not exempt against issues such as adulteration, fraud, and contamination. Adulteration can be simply performed by the addition of either cheap chemicals or food waste products (e.g., dilution), such as the addition of foreign materials [29, 30]. Adulteration can trigger a wide range of disruptions in the entire value chain and create fear among the consumers of such foods [29]. Thus, with the economic importance of cereal and cereal by-products, they will become a target for adulteration or fraud. Furthermore, it might be possible that high-quality or premium ingredients or products will be replaced by cheaper or counterfeit materials, which may influence their nutritive value, functionality, and composition [29]. Therefore, there is a high interest in both the cereal and grain industries in detecting contamination and, more recently, monitoring authenticity and fraud [29]. The grain industry is also worried about the potential of cross-contamination of ingredients that might increase the prevalence of food allergies or other health issues along the supply and value chains [29]. Several methods and techniques have been used to measure and monitor authenticity, fraud, and provenance in several food ingredients and products, as well as agricultural commodities, including cereals [31–34]. These methods and techniques include the utilization of high-performance liquid chromatography (HPLC), isotope analysis [35–38], mass spectrometry (MS), the analysis of macro- and microelements using ICP-MS, the utilization of vibrational spectroscopy techniques (e.g., near- and mid-infrared, Raman) [7, 18–22, 39–43], and more recently the use of nanotechnology and DNA analysis [44]. This chapter presents and reviews existing applications employing gas chromatography and mass spectrometry (GC/MS) that have been evaluated by different researchers in the field to determine the authenticity of cereal grains, such as wheat, rye, triticale, barley, oats, corn, and rice, as well as pseudocereal grains (e.g., buckwheat, quinoa, and amaranth). Applications related to the authenticity of cereal and pseudocereal flours, bakery, and confectionary products made from the flours of these cereals, which are available in the food market (e.g., bread, pastas, cookies, etc.), will also be reported. Sample preparation and pre-processing methods and data processing techniques (e.g., chemometrics) will be discussed.

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Gas Chromatography

Gas chromatography (GC) is one of the most utilized analytical techniques applied to evaluate, measure, and study volatile compounds in several food ingredients and products [33, 45]. This technique analyzes volatile, semi-volatile, and aromatic compounds, as well as contaminants, such as pesticides, present in the sample matrix [33, 45]. In this analytical technique, a sample mixture of different components is analyzed by injecting it into the GC instrument, where the mixture is changed into vapors without modifications in a heated chamber [33, 45]. Then, the gas mixture goes through the GC column where they interact with the coating of the column, where the volatile components become separated [33, 45]. Most of the GC columns consist of a liquid stationary phase, which is adsorbed onto the surface of a thin fused-silica capillary tube. Usually, an inert carrier gas (e.g., helium) transports the sample through the column [33, 45]. Several detectors can be incorporated and used in a GC instrument. These detectors might include electron capture detectors (ECDs), flame ionization detectors (FIDs), mass spectrometry (MS), photoionization detectors (PIDs), flame photometric detectors (FPDs), thermionic detectors (TDs), and atomic emission detectors (AEDs) [45]. The selection of a detector depends on the nature of the compound or mixture of compounds to be analyzed [33, 45]. For example, during the analysis of volatile organic compounds, FIDs showed better performance with high sensitivity, low noise, and large linear response range compared with other types. It is well known that MS is regarded as a very powerful analytical tool, mostly used in food research to analyze and identify a wide range of compounds and molecules present in food ingredients and products [33, 45]. The combination of GC with MS allows for the qualitative and quantitative analysis of complex mixtures containing different components [33, 45]. The quantitative and qualitative analysis of chromatographic profiles of different food ingredients, food products, and commodities provide valuable insights for the determination of the authenticity and geographical origin of these products [33, 45]. However, applications related to authenticity, fraud, origin (e.g., botanical, geographical, or varietal origin), and provenance require the use of data mining techniques and chemometrics, where targeted and untargeted methods are the most widely used by numerous researchers in the field [46, 47].

3.1.2

Chemometrics

As described above, the utilization of both GC and GC/MS as analytical techniques targeting issues related to authenticity, adulteration, and fraud requires the utilization and incorporation of data mining and chemometric techniques. Food authenticity and fraud issues are analyzed using both targeted and non-targeted methods [47]. Chemometrics defines a family of statistical and mathematical methods and techniques that are different to the classic statistics, as they can analyze multiple variables simultaneously, taking collinearity into account (the variation in one variable or group of variables in terms of covariation with other variables) [18, 19,

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47–49]. Most of these techniques are important to handle several variables at a time, such as GC/MS chromatograms. Different tools are used to analyze these data sets and to develop specific applications [18, 19, 47–50]. Among them, principal components analysis (PCA) is the most utilized method [18, 19, 48, 49]. The utilization of PCA has been reported as a tool for screening, extracting, and compressing multivariate data (e.g., GC/MS chromatograms), and it is generally considered an unsupervised method [48, 51]. PCA can be performed on either a data matrix or a correlation matrix, depending on the type of variables being measured. However, in a case where the original variables are nearly noncorrelated, nothing can be gained by using a PCA analysis compared with classic statistics, such as analysis of variance (ANOVA). Other tools commonly utilized in food authenticity, fraud, and provenance are discriminant analysis (DA) and partial least squares discriminant analysis regression (PLS-DA) [48, 51]. In these applications, instead of calibrating for a continuous variable, the calibration is developed, taking into consideration a group membership (e.g., categories, origin, species). The resulting models are evaluated in terms of their predictive ability (e.g., coefficient of determination, standard error in cross-validation or prediction, etc.) as well as on their ability to correctly classify samples belonging to each category or to identify unknown samples (e.g., outliers) [48, 51]. Another discriminant technique extensively used is linear discriminant analysis (LDA), which is defined as a supervised classification technique. The criterion of LDA for the selection of latent variables is maximum differentiation between the categories and minimal variance within categories [48, 51–53]. This method produces several orthogonal linear discriminant functions, equal to the number of categories minus one, which allow the samples to be classified in one or another category [54]. In recent years, the utilization of machine learning strategies, such as artificial neural networks (ANNs), has been reported in several applications related to food provenance. This method is characterized by its analogy with the biological neuron [54]. Unlike PLS-DA regression, ANN can deal with nonlinear relationships between the variables analyzed [53, 54]. Many other classification methods or techniques are utilized, such as support vector machine (SVM), k-nearest neighbor algorithm (k-NN), and soft independent modeling by class analogy (SIMCA), to mention a few [48, 51, 52, 54]. Figure 3.1 provides an example of the combination of GC/MS and chemometrics in the classification of cereals.

3.2

Applications

The following section reports and discusses some of the recent applications on the utilization of GC/MS as a technique to evaluate and monitor issues associated with food safety and security, such as authenticity and provenance (e.g., origin) in different cereals and cereal by-products.

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Fig. 3.1 An example of the combination of GC/MS with chemometrics to classify cereal samples according to provenance

3.2.1

Cereal Grains

Barley (Hordeum vulgare) Low alcohol hulless barley wine (HW) is a popular beverage among the highland areas in China [55]. This by-product is recognized to have numerous health benefits due its high content of β-glucan, as well as antioxidant compounds [55]. In this study, samples from the highland areas of Sichuan province and Tibet were analyzed for total β-glucan content, total phenolic compounds (TPCs), and flavonoids (TF), as well as by GC/MS to determine several volatile compounds [55]. These researchers reported that 46 volatile aroma compounds were identified and used to characterize the HW samples [55]. Principal component analysis (PCA) was used to classify the HW samples into three distinctive groups associated with the geographical origin (Sichuan province and Tibet) based on their volatile fingerprint [55]. These results demonstrated how the combination of PCA with GC/MS as an analytical technique can be utilized to classify HW samples from two different geographical origins [55]. Rice (Oryza sativa) As discussed in the previous sections, food mislabeling for economic gain is an increasing issue of concern for traders, producers, and consumers. To target authenticity issues regarding cereals and cereal by-products, systematic and high throughput traceability methods are of importance, not only to be used in research but also to be implemented by the industry. Lim et al. [56] reported the utilization of headspace solid-phase microextraction (SPME) combined with GC/MS (HS-SPME GC/MS) to measure volatile organic compounds to distinguish between white rice samples sourced from two countries (China and Korea). The GC/MS data were analyzed using PLS-DA regression, where very good classification rates were reported for the discrimination of white rice samples sourced from both China and Korea [56]. These authors reported that the main loadings (e.g., main variables used by the model) included volatile compounds, such as hexanal, 1-hexanol, and hydrocarbons, which

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have been known to be associated with different geographical regions or countries [56]. The utilization of the SPME technique combined with GC/MS was also reported to characterize the cooking quality properties of seven aromatic and one non-aromatic rice genotype grown in Brazil [57]. This study showed that 23 volatile compounds were identified using SPME GC/MS [57]. The use of both PCA and PLS-DA regression allowed for the classification of aromatic and non-aromatic rice genotypes [57]. PLS-DA analysis showed that six compounds were the most important and used by the model to discriminate between the rice groups, including 2-acetyl-1-pyrroline (2-AP), decanal, 2-hexanone, 2-pentylfuran, 1-hexanol, and hexanal. It was highlighted by the researchers that 2-AP was detected only in aromatic genotypes, where the concentration varied from 0.21 to 0.57 μg/g [57]. The authors also indicated that 2-AP was identified only in samples belonging to the aromatic genotypes, where some rice genotypes (e.g., BR5) have the best performance since their volatile compounds results might indicate less off-flavors (hexanal) [57]. The authors of this study concluded that these outcomes would contribute to selecting novel genotypes of aromatic rice to be grown in Brazil [57]. The presence of yellowed rice results in serious economic losses to the grain industry in China [58]. The profile of volatile compounds before and after-yellowing rice of five commercial Chinese cultivars was achieved by using HS-GC combined with ion mobility spectrometry (IMS) and HS-SPME GC/MS [58]. The GC/MS data were analyzed using PLS-DA regression, where good classification rates were reported for the discrimination of white and yellowed rice samples. The authors reported that yellowed rice samples released more aldehydes, alcohols, and furan and less ester than the white rice samples analyzed [58]. Overall, hexanal, nonanal, octanal, 1-pentanol, and 2-pentyl-furan were selected as the potential markers by the variable importance in projection (VIP) variable selection method [58]. It has been highlighted by the authors of this research that these markers were associated with fatty acid oxidation [58]. The profile of volatile compounds, chemical parameters, and the identification of specific biomarkers measured in the different cultivars of Chinese yellowed rice samples allowed the researchers to define the main mechanism involved in the yellowing of rice [58]. These results indicated that GC/MS combined with chemometrics is a useful method, not only to reveal the rice yellowing mechanism but also to classify samples according to their aroma fingerprint [58]. Rice is one of the most important cereals for human nutrition and is a basic staple food for half of the global population [59]. The assessment of rice’s geographical origin in terms of its authenticity was reported [59]. In this study, HS GC/MS was used to characterize and quantify volatile organic compound (VOC) profiles to distinguish rice samples from different countries, such as China, India, and Vietnam [59]. In this study, PLS-DA regression was utilized as a supervised method where good discrimination rates (R2 = 0.98, accuracy = 1.0) for the classification of rice samples sourced from China, India, and Vietnam were reported [59]. The authors also reported that the utilization of soft independent modelling of class analogy (SIMCA) and K-nearest neighbors as data mining techniques yield good

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classification results in identifying the geographical origin of the samples with 100% specificity and 100% accuracy [59]. It was concluded by the authors of this study that established VOC fingerprinting based on the utilization of GC/MS is highly efficient to identify the geographical origin of rice [59]. In China, green food is defined or associated with a wide range of certified agricultural and processed food ingredients that are strictly produced following defined standard protocols and branded with a specified Green Food label [59]. According to these authors, the demand for green-branded rice is rapidly growing due to its higher quality and adherence to safety standards compared to conventional rice [59]. In this context, chemical, physical, and nutritional composition of green rice samples demands to be further evaluated to fulfil and guarantee consumers’ needs and requirements [60]. In this study [60], a well-known commercial type of green rice (Daohuaxiang 2) was analyzed. Before GC/MS analysis, the samples were also evaluated using routine compositional and industrial parameters used by the cereal industry, such as thousand kernel weight, chalkiness, amylose content, and rheological properties [60]. The utilization of GC/MS as a metabolomics tool combined with chemometrics allowed the authors of this study to identify 15 metabolites that were used to differentiate between green and conventional rice samples [60]. The authors reported that ten of these metabolites were present in high concentrations in the green rice samples compared with the conventional varieties, where some amino acids, sugars, and fatty acids were the most prevalent [60]. The authors of this research indicated that the superior quality green rice types might boost green rice production and consumption in China [60]. Wheat (Triticum aestivum) Starr et al. reported the analysis of volatile compounds on several wheat (Triticum aestivum) cultivars and landraces (n = 81), grown under controlled conditions, to explore the ability of GC/MS to discriminate wheat varieties based on the profile of volatile compounds [61]. In this study, wheat samples were analyzed using dynamic headspace extraction GC/MS and the data were analyzed and interpreted using chemometrics [61] The results of this research suggested that esters, alcohols, and furans were used as the main variables able to classify between landraces and modern varieties, whereas differences in modern varieties were characterized by terpenes, pyrazines, and straight-chain aldehydes [61]. The combination of chemometrics and GC/MS to analyze grain wheat (Triticum aestivum) samples sourced from different farming systems was also reported [62]. Learning algorithms were used to classify the GC/MS data of the samples in groups, such as conventionally grown or organically grown wheat samples, as well as the identification of different cultivars [62]. Wheat samples (n = 11) collected during three consecutive years and sourced from organic and conventional farming systems were analyzed. More than three hundred volatiles were collected and later analyzed using chemometrics [62]. Unsupervised (e.g., PCA) and supervised methods, such as SVM, were compared and evaluated for sample visualization and classification [62]. This research showed that both harvest year and variety

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have a great influence on the metabolic profile of the wheat samples analyzed [62]. This study also highlighted that for a given year and cultivar, organic and conventional cultivation can be distinguished using a combination of GC/MS and chemometrics [62]. The discrimination of Chinese winter wheat (Triticum aestivum) cultivars (n = 10) sourced from three distinctive geographical origins was reported using SPME GC/MS [45]. Peak areas obtained from 32 main volatile compounds were selected and analyzed using PCA and multivariate analysis of variance (MANOVA) [45]. Significant differences were observed between the peak areas of the samples analyzed and sourced from the different geographical regions and cultivars [45]. Classification was achieved using LDA, where correct classification percentages of 90% and 100% for cultivar and region were achieved, respectively [45]. The utilization of SPME GC/MS was also reported to evaluate the effect of sample preparation (e.g., method and quantity), extraction temperatures and times, desorption times, and oven programs on the properties of wheat (Triticum aestivum) varieties [63]. More than hundred VOCs were reported after analyzing six wheat cultivars harvested in different geographical areas, where more than 90 were never reported before [63]. The PCA showed that the geographical area explained the highest source of variability in the data set while PLS-DA regression correctly classified wheat based on their cultivation area and species [63]. It was concluded by the researchers that these results were promising to evaluate the effect and influence of geographical origin on wheat quality [63]. These authors highlighted that comprehensive knowledge of the main variables that influence the concentration of VOCs in wheat is of relevant importance for quality improvement and control of its derivatives [63]. In a similar study, the same authors reported the profiles in VOCs found in common and durum wheat kernels grown in different farms located at different altitudes over two consecutive harvests [64]. Both PCA and PLS-DA regression were used to analyze the GC/MS data [64]. The PCA analysis showed that harvest (year of cultivation) contributed to explaining most of the variability in the concentration of VOC profiles [64]. The PLS-DA regression correctly classified the wheat samples sourced from different geographical origins based on the VOC profiles [64]. The authors emphasized that the different variables influence the VOC profiles where the year of cultivation was the highest, followed by field of cultivation, species, and altitude [64]. Overall, it was concluded that environmental conditions were more relevant than species in the determination of the VOC profiles in the wheat samples analyzed [64].

3.2.2

Pasta and Pasta Products

One of the most common Italian pasta brands is Pasta di Gragnano with protected geographical indication (PGI) [65]. Authentic Pasta di Gragnano and other traditional pasta samples were analyzed using GC/MS [65]. Different chemometric tools were applied and compared to discriminate pasta samples based on their chemical

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information provided from 20 target flavor compounds, as well as Maillard reaction compounds and lipid oxidation products [65]. PCA and LDA results showed a natural grouping of samples according to the drying process adopted for their production (e.g., the traditional Cirillo method versus a high-temperature method) [65]. SIMCA and unequal dispersed classes (UNEQ) were also utilized to create class models using 95% confidence and 100% sensitivity levels [65]. Both algorithms showed good performances using cross-validation, where the correct classification rates were 57.01% for SIMCA and 86.60% for UNEQ [65]. This research highlighted the importance of the combination of chemometrics with GC/MS to analyze and interpret the flavor profiles and how this information can be utilized to evaluate the authenticity of Pasta di Gragnano PGI samples [65]. It was concluded that GC/MS coupled with chemometric techniques could be used as a tool to protect Pasta di Gragnano PGI from brand frauds by confirming whether samples fulfil legal declarations regarding the drying process conditions as indicated by the product specifications [65].

3.2.3

Cereal and Pseudocereal Flour

In recent years, the so-called metabolomic approaches utilized in food research are at the state of the science and are applied as robust and reliable techniques to identify, quantify, and characterize the biochemical profiles of a wide range of food ingredients and products [66]. The utilization of both GC/MS and electronic nose instruments were reported to collect the so-called volatilome of different bakery products, obtained from mature and immature grains (Khorasan and durum wheat), as well as the corresponding sourdough made of Lactobacillus spp. and Saccharomyces cerevisiae [66]. This study demonstrated that the techniques applied could distinguish Khorasan doughs fermented industrially at the fully ripe stage, the same doughs at the milky stage, and Khorasan sourdough at the fully ripe stage [66]. In this study, the electronic nose allowed for the discrimination between different types of flours, and GC/MS indicated the volatilome of sourdough Khorasan [66]. The effects of various bran sources, such as wheat, barley, and rice, on the composition and quality of volatile compounds of Egyptian Balady bread (Fino) were reported [67]. The authors of this research reported that at least 36 volatile compounds can be identified in the bread samples using GC/MS, including 5 alcohols, 6 pyrazines, 2 acids, 9 aldehydes, 5 ketones, 3 esters, and 6 sulfurcontaining compounds [67]. The alcohols were the predominant volatile compounds accounting for 58.30, 61.57, 59.08 and 56.15% in the control and in the bread samples prepared with bran sourced from rice, barley and wheat, respectively [67]. Pastor et al. described an innovative and fast approach to classify different types of gluten and non-gluten cereal flour (wheat, rye, triticale, barley, oats, and corn) into groups according to their botanical origin [68–72]. In this study, liposoluble compounds were extracted from flour samples, derivatized and analyzed using GC/MS [68–72]. The unprocessed GC/MS chromatogram was used directly for data analysis as they represent the unique fingerprint for each class studied [68–

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72]. The authors evaluated the implementation of an automatic machine learning framework for classification where the algorithm was able to evaluate and explore each of the 39 classifiers provided by the software using cross-validation [68– 72]. The model resulted in 85.71% correct classification according to the origin of the samples (e.g., cereal species) [68–72]. The authors of this study highlighted that this non-targeted strategy supports the use of artificial intelligence in developing methods for flour authentication [68–72]. A wide range of cereal and pseudocereal species such as wheat, barley, rye, oats, triticale, spelt, corn, amaranth, and buckwheat samples were milled into flour, extracted using n-hexane, derivatized with trimethyl sulfonium hydroxide solution, and analyzed using GC/MS [70]. Fatty acid methyl esters and non-saponifiable compounds (phytosterols, alpha-tocopherol and squalene) were identified by comparing their mass spectra with the Wiley MS library [70]. The main lipids that were present in all analyzed flour samples were removed from further data analysis, leaving only those that represent the unique pattern or trace to differentiate the flour samples according to the corresponding cereal and pseudocereal species [70]. Cluster analysis (CA) and PCA were utilized to identify groupings as well as the separations between the samples analyzed. The authors of this study concluded that this approach enables the rapid differentiation of flour samples made from various cereal and pseudocereal species according to either their origin or gluten content. The same authors reported the potential use of fatty acid profiles of several crops to discriminate them based on their botanical origin [71]. Cereals, such as corn, wheat, barley, and oats, grown in Serbia, were milled into flour and lipid fractions were extracted using n-hexane [71]. In this study, the peaks of molecular ions of methyl esters of nine dominant fatty acids were extracted, and their abundances were used to create numerical matrices for further data processing. PCA, CA, principal coordinate analysis (PCoA), and LDA were used to isolate the main and significant variables, visualize discriminations, and classify samples [71]. This approach has enabled the classification of cereals into three groups, such as corn, oats, and the samples of small grains—wheat and barley [71]. The authors concluded that this study showed a new concept of the application of fatty acid analysis in developing authentication methods for industrial crops and their staple food products [71].

3.2.4

Bread

Regardless the importance of bread in human nutrition, only one study was found reporting the use of GC/MS. In this study, the analysis of carbohydrates in bread crusts made from artificial mixtures of buckwheat and wheat flour using GC/MS was reported [72, 73]. Mono- and disaccharides and heterocyclic compounds derivatized into their corresponding trimethylsilyl ethers were analyzed using GC/MS [72, 73]. The utilization of PCA and dendrograms highlighted the difference between the different mixtures of buckwheat flour used to make the bread. The authors of this study showed that the content of buckwheat flour in the bread made of

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a mixture of buckwheat and wheat flour can be determined by applying chemometrics to the GC/MS profile. The analysis of the GC/MS data showed that the method was able to classify samples depending on the mass proportion of buckwheat in its mixture with wheat flour. The authors of this study concluded that the method could be considered relatively easy to perform and could be useful for the quality control of buckwheat food products [72, 73]. A targeted chemometric approach based on fatty acid profiles was applied in order to differentiate bread crumbs according to the applied fermentation process and used flour type: spontaneous sourdough versus yeast fermentation and wholegrain versus refined wheat flour content [74], [68, 69]. The fatty acid profiles were determined using gas chromatography with flame ionization detection. Bread crumbs, crusts, and whole slices were analyzed separately. Principal component analysis was applied to give an insight into the potential of a fatty-acidomic approach for authentication purposes. The similarity percentage (SIMPER) test allowed the selection of fatty acids with a high discrimination potential, which was further employed as matrices for the construction of a 100% correct LDA classification model [68, 69].

3.2.5

Distillers’ Dried Grains with Solubles

Demand for ethanol-substituted fuels from the utilization of cereal-based biofuel has resulted in an overproduction of dried distillers’ grains with solubles (DDGS) [74]. The DDGS are available on the animal feed market; however, they are in oversupply [74]. Due to the high availability of these by-products, there is a potential variability in the nutritional value of DDGS and possible risks of feed contaminants [74]. Authentication and traceability of alternative animal feed sources have become of high priority for the animal feedstock industry [74]. A study that is part of the EU research project Quality and Safety of Feeds and Food for Europe (QSAFFE FP7-KBBE-2010-4) was reported to classify the geographical origin of cereal grains used in the production of DDGS material [74]. DDGS materials sourced from wheat and corn were obtained from Europe, China, and the USA [74]. Volatile fingerprints were measured by GC/FID as well as using rapid proton transfer reaction mass spectrometry (PTR-MS) [74]. The combination of GC/FID with chemometrics allowed the researchers to correctly classify the geographical and botanical origin of the cereals used in the production of DDGS [74].

3.3

Challenges and Limitations of These Studies

The applications and examples reported in the previous sections have demonstrated and highlighted the existing state of the art as well as the advances in the utilization of GC/MS combined with chemometrics (e.g., PCA, CA, PLS-DA, SIMCA) to authenticate several cereals, cereal products, and by-products. These applications have shown that GC/MS is capable of measuring a wide range of compounds and is

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used as a fingerprint method, sometimes even without the need to identify single compounds. However, it is important to highlight some limitations of these studies. Most of the published studies were based on the utilization of small data sets where few samples were analyzed. The classification or predictive models were only internally validated, where the utilization of different cross-validation techniques, such as leave-one-out and random full cross-validation, have been the most prevalent, but no external or independent validation of the models was reported.

3.4

Conclusions

The integration of GC/MS with data mining techniques is determining a shift in the way cereals, cereal products and by-products can be evaluated and monitored along the supply and value chains. This new approach allows one to better analyze and understand the complexity of issues associated with food security, such as authenticity, contamination, fraud, traceability, and provenance in these foods [75–80]. Modern food supply and value chains are shifting into a new digital edge where advances in management decision systems and an increase in the number of sensing techniques, such as wireless sensor networks, are showing promising results. The incorporation of digital and technological innovations in the cereal industry is determining a rise in the amount of information generated along the supply and value chains. Yet, knowledge and understanding of issues related to food safety and security, as well as the complex relationships that determine the origin of foods (e.g., biochemical, chemical properties, the effect of climate change, etc.), is still lacking. Despite these issues, the potential demonstrated by the utilization of analytical technologies, such as GC/MS combined with data mining and chemometric methods, provides new tools that can be incorporated by the food industry and research and development organizations. Most of the applications and examples showed in this chapter demonstrated that challenges and issues associated with authenticity, provenance, and fraud are beyond the simple measurement of a chemical property or volatile compound. These have limited the understanding and ability to rapidly target all the subtle changes that influence food production systems and value chains. The incorporation of modern technologies and the combination of these technologies with chemometrics prepare the food industry with a new set of toolboxes to rapidly respond to these challenges. Ensuring the safety and security of foods from different origins and provenances along the different steps of the food supply and value chain needs a different approach. It has been proposed that taking a forensic approach [75], where the analysis of the food, together with the analysis of the environment and the different steps in the supply and value chain, may be useful for retrospective determination of food provenance or to target fraud. The increased demand for foods with unique geographic designations together with external variables, such as seasonal variations (e.g., climate change), will also influence the biochemistry and the profile of volatile compounds of the cereals, cereal products, and by-products. These issues or complexity will demand that additional measurements, such the evaluation of specific

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biomarkers or precursors of volatile compounds, have different characteristics and properties. The existence of such complexity will add to the challenges in how to authenticate or discriminate foods produced within close geographical or climatic conditions, where subtle differences might be the ones that explain the observed differences or similarities in the food ingredients. Acknowledgments The support of QAAFI and the University of Queensland is acknowledged.

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4

Edible Oils and Fats Amani Taamalli, Ibrahim M. Abu-Reidah, and Hedia Manai-Djebali

Abstract

Consumer interest in safety, authenticity, and quality of food products is constantly increasing. Food products can be verified based on their chemical composition, botanical sources, specified geographical origin, or possible adulterations by modern analytical methods. Many analytical techniques that are able to detect adulteration and consequently guarantee food quality and authenticity have been developed. Considering edible oils and fats, gas chromatography combined with mass spectrometry (GC-MS) was applied and coupled with various chemometric tools. This chapter represents an overview of the published research employing GC/MS techniques that deal with edible oils and fats authentication and adulteration detection. Keywords

Authentication · Adulteration · Gas chromatography · Mass spectrometry · Edible oils · Lipids

A. Taamalli (✉) Department of Chemistry, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia Laboratory of Olive Biotechnology, Center of Biotechnology of Borj Cedria, Hammam Lif, Tunisia I. M. Abu-Reidah Memorial University of Newfoundland, St. John’s, NL, Canada H. Manai-Djebali Laboratory of Olive Biotechnology, Center of Biotechnology of Borj Cedria, Hammam Lif, Tunisia # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_4

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Abbreviations 3-MCPD CE/UV EVOO FAAEs FAESs FAMEs FID GC×GC/MS HCA HS GC/IMS HS-SPME GC/MS HS UV/IMS LDA MANOVA MS OPLS-DA PCA PCO PLS-DA QDA SLDA TGA-GC/MS TOF

4.1

3-monochloropropane-1,2-diol Capillary electrophoresis coupled to UV detector Extra-virgin olive oil Fatty acid alkyl esters Fatty acid ethyl esters Fatty acid methyl esters Flame ionization detector Two-dimensional gas chromatography/mass spectrometry Hierarchical cluster analysis HS-gas chromatography coupled to IMS with tritium source Headspace solid phase microextraction gas chromatography with mass spectrometry Headspace ultraviolet ion mobility spectrometry Linear discriminant analysis Multivariate analysis of variance Mass spectrometry Orthogonal partial least squares discriminant analysis Principal component analysis Principal coordinate analysis Partial least squares discriminant analysis Quadratic discriminant analysis Stepwise linear discriminant analysis Thermogravimetric-gas chromatography/mass spectrometry Time of flight

Introduction

The authentication of foodstuffs and associated fraud are a concern in modern society [1] since adulteration can have serious consequences on human health and affects market growth by destroying consumer confidence [2]. Edible fats and oils are considered important components of food products [3]. Vegetable oils, animal fats, salad and cooking oils, margarine and butter, edible oils, and fats are classified as the foods that are most frequently susceptible to adulteration [4]. Therefore, the authentication analysis of fats and oils is very important to ensure that the fats and oils are authentic and free from adulteration practice. A wide variety of methods and analytical techniques have been developed to evaluate the quality of foods and detect fraud. The application of sophisticated instrumentation with this aim is thus a valuable tool for food processors, retailers, and consumers, but also for regulatory authorities [2].

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Several techniques, such as mid-infrared (MIR), near-infrared (NIR), Fourier transform infrared (FT-IR), Raman, UV-Vis, nuclear magnetic resonance spectroscopy (NMR) and fluorescence spectroscopy, high-performance liquid chromatography (HPLC), gas chromatography (GC), and mass spectrometry (MS) have been applied to food-authentication studies and can show high accuracy, speed, and non-destructive analysis and have been used for various foods of high value [5]. DNA-based techniques, such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), and also electronic noses have more recently been employed by the industry, offering high accuracy and certain benefits [6], while chemometrics supplement the methods by allowing the effective management of complex databases and their interpretation [4]. Many applications of chemometrics, coupled with both conventional and innovative analytical measurements, have been proposed and applied in the last decade to solve technological and legislative food control problems [7]. Among the most reliable methods that can be used to confirm the authenticity of food products, HPLC and GC chromatographic techniques are considered. Gas chromatography coupled with mass spectrometry (GC/MS) demonstrates great potential in various analytical fields. This analytical technique has powerful applications in food characterization and authentication [8]. It is highly effective and robust with a great number of advantages, including the convenience of use and high capability for compound identification. Chemical derivatization is required before analysis since the analyzed compounds need to be volatile [9]. This chapter represents an overview of the analytical methods employing GC/MS techniques that deal with edible oils and fats authentication and adulteration detection. The methods described have been published in the scientific literature in the last 5 years.

4.2

Olive Oil

Olive oil is a highly nutritious food, a good source of vitamins, proteins, and natural antioxidants (e.g., phenolics, phytosterols, tocopherols, carotenoids, chlorophyll, and squalene) [10]. Different grades of olive oil can be produced, and extra virgin olive oil is the purest and most expensive grade. Due to its unique sensorial qualities and nutritional properties, virgin olive oil has a high economic value not only for consumers in producing countries but also for consumers worldwide. The globalization of the olive sector has resulted in a very competitive worldwide marketplace, where fraudulent practices undermine public confidence [11]. Adulteration of olive oil can occur by either mixing it with other vegetable oils or by adding cheaper olive oil to more expensive grades [12]. The criteria to define the authenticity of olive oil depend on the designation and definition of each oil category. Any fairly broad definition leaves an empty space for highly sophisticated fraudulent practices. Nevertheless, several types of olive oil frauds can be easily detected using standard and regular analytical methods. Trade standards established by the international organizations European Commission, International Olive

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Council, and Codex Alimentarius Commission are updated constantly to regulate the quality and authenticity of olive oil. Most of the official methods are based on the gas chromatography technique [10]. The scientific effort today aims at improving legislated and standard methods and developing at the same time new, rapid, reliable, and cost-effective analytical approaches for the measurement against more and more sophisticated fraudulent practices [13]. A variety of methods are currently used to verify the authenticity of olive oil and detect adulterants or contaminants, including chromatographic (e.g., HPLC, GC), spectroscopic (e.g., UV-Vis, NIR, MIR, FT-IR), or DNA-based methods (e.g., PCR). A series of methodologies combining the above-mentioned techniques have recently been trialed, leading to potential improvements in the accuracy and effectiveness of the authentication processes. VOO authenticity is associated with genetic variety, geographical origin, and/or quality grade [9]. Minor components of olive oils can be good markers for their authenticity [14].

4.2.1

Varietal Origin

In a study based on the profile of volatile aroma compounds of the most economically important and widespread Croatian native olive varieties, Lukić et al. [15] applied comprehensive two-dimensional gas chromatography for the differentiation of virgin olive oils according to variety and geographical origin. Among identified volatile compounds, 131 were significantly different across monovarietal VOOs, while 60 were found useful for discrimination according to geographical origin. In another study, four monovarietal Italian extra-virgin olive oils (EVOOs) (Dolce Agogia, Frantoio, Leccino, and Moraiolo) could be classified on the basis of volatile compounds using SPME GC/MS with a chemometric tool—linear discriminant analysis (LDA) [16]. Considering Greek cultivars, Kosma and collaborators focused on a total of 167 virgin olive oil samples that were collected during the harvest years 2012–2013 and 2013–2014 from various regions in Greece belonging to ten different cultivars, namely Ladolia Kerkyras, Galano, Adramitiani, Samothraki, Athinolia, Hontrolia, Koutsourelia, Kolovi, Topiki Makris, and Manaki. The HS-SPME GC/MS volatile compound data from the different cultivars were combined and analyzed using multivariate analysis of variance (MANOVA) and LDA [17] to differentiate olive oils and identify marker volatile compounds that would enable differentiation of botanical origin. More recently, to investigate the volatile profile of virgin olive oils as a suitable tool to discriminate monocultivar virgin olive oils, 72 volatile compounds determined by HS-SPME GC/MS for 320 monocultivar samples from 4 crop seasons and several geographic areas were considered [18]. Partial least squares discriminant analysis (PLS-DA) model was successfully validated, with percentages of correct classification up to 95.9% after external validation. When investigating a higher number of samples, a fingerprinting approach using the sesquiterpene hydrocarbon profile of 404 virgin olive oils from 6 countries and 38 different cultivars and coupages analyzed by HS-SPME GC/MS was successfully

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applied to build a binary PLS-DA model capable of discriminating virgin olive oil samples of the Arbequina cultivar from those produced from other cultivars [19]. Moreover, the individual and combined forms of fatty acid alkyl esters (FAAEs) and waxes can be considered good candidates for authentication markers of the botanical origin of olive oils [14]. In this context, fatty acid ethyl esters (FAEEs), fatty acid methyl esters (FAMEs), and wax esters determined with the GC/MS method were investigated to authenticate 91 olive oils from various parts of the Aegean Region of Turkey during two consecutive harvest year with respect to variety. The research revealed that FAAEs and waxes together could be a promising alternative for the authentication of olive oils according to the olive cultivar [14].

4.2.2

Geographical Origin

False declaration of origin is one of the counterfeiting practices affecting virgin olive oil. A current European Union regulation states geographical origin as mandatory for virgin olive oils, even though an official analytical method is still lacking [20]. The volatile profile of virgin olive oils has been reported to be affected by the growing area and used for proposing approaches for authentication of the geographical origin. A recent work focused on the authentication of the geographical origin of 1217 oil samples from the main worldwide producing countries from three consecutive olive oil crops. Cecchi et al. [21] used quantitative data collected by analyzing the volatile fraction with an HS-SPME GC/MS combined with LDA chemometric method. The approach applied showed reliable results and represents a very useful and easily applicable tool for the olive oil sector, helping in protecting consumers and producers from frauds. In another study aiming to contribute to a deeper investigation of the understudied Greek olive oil cultivars Megaritiki and Amfissis, SPME GC/MS and chemometrics were applied for the differentiation of the Greek olive oil cultivars Koroneiki, Megaritiki, and Amfissis originated from Thrace, Thessaly, Central Greece, Attica, Peloponnese, and Crete [22]. Five hydrocarbons and one ester were selected by the forward stepwise algorithm as the ones that best discriminated the olive oil samples. SPME GC/MS combined with chemometrics proved to be a powerful tool for the authentication of Greek olive oil, and the proposed methodology can be used in an industrial setting for the determination of the Greek olive oil botanical origin. Sesquiterpene hydrocarbons have been reported to be suitable virgin olive oil geographical markers given that they are highly dependent on the olive trees’ cultivar and growing area. Quintanilla-Casas et al. [23] aimed to discriminate among EVOOs from distinct Catalan PDOs and verify the belonging of EVOO samples to a given Catalan PDO against EVOO from different geographical areas. For this purpose, the sesquiterpene hydrocarbon fingerprint was determined by HS-SPME GC/MS. The proposed strategy allowed discrimination of each Catalan PDO from non-PDO samples produced in different geographical areas with an efficiency between 95 and 99% [23]. In a further study, Quintanilla-Casas et al. [20] applied HS-SPME GC/MS together with the PLS-DA chemometric approach

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for virgin olive oil geographical authentication to discriminate between EU and non-EU olive oils. They obtained successful results (an average of 92.2% of correct classification for EU and 96.0% for non-EU countries) [20].

4.2.3

Adulteration with Other Edible Oils

The identification and determination of adulterations in oils and fats is not a simple task. The chromatographic methods used for the detection of EVOO adulteration are based on the quantification of specific marker compounds [24]. The marker compounds, such as FAMEs, sterols (campesterol and stigmasterol), triacylglycerols, stigmastadiene, and volatile compounds, were usually analyzed by gas chromatography. Several studies reported the use of GC/FID for the detection of olive oil adulteration with other edible oils [25–28], and in other studies, a combination of mass spectrometry with gas chromatography was carried out. Soybean oil is a typical additive for the adulteration of olive oils due to its low price [29]. Based on a study in 2021, thermogravimetric-gas chromatography/mass spectrometry (TGA-GC/MS) combined with chemometrics was applied for the first time for the determination of olive oil adulteration with soybean oil [30]. This analytical method permitted us to identify the markers that differentiate olive oil from soybean oil. Studying 42 olive oil samples composed of unrefined olive oil (including samples of 20 extra virgin olive oil) and refined olive oil (including 15 samples of olive oil grade and 7 samples graded as olive pomace oil) to determine the degree to which olive oils have been refined, Huang and his collaborators [31] used 3-monochloropropane-1,2-diol (3-MCPD) esters as a target compound and a mean to differentiate extra virgin olive oil (low 3-MCPD ester content) from olive oil (medium 3-MCPD ester content) and olive pomace oil (high 3-MCPD ester content). To evaluate the quality of some edible oils, among which olive oils, the research screened the fatty acids and secondary metabolites composition using GC/MS analysis, thereby identifying the adulterants in comparison between the unrefined and refined oils. The fatty acid profiles of the unrefined oils were found to be following the literature survey results and CODEX standards, whereas the commercially available refined oils were mainly adulterated with palmitic acid, palmitoleic, stearic, and myristic acids [32]. A novel mathematical model applied to determine the content of high-oleic sunflower oil blended in various proportions with extra-virgin olive oil based on the peak signals of dominant fatty acids determined using GC/MS was proposed by Ilić et al. [33]. The novel mathematical model was applied without the application of multivariate statistics, and the results were compared with theoretical values. The proposed approach revealed as low as 10% mass ratio of the substitution of extravirgin olive oil with a chemically similar high-oleic sunflower oil [33] (Table 4.1).

Geographical origin

Verifying EU and single country labeldeclaration Verification of PDO compliance

PCA, SLDA, and hierarchical clustering

SPME GC/MS HS-SPME GC × GC/ TOF-MS and 1D-GC/ MS

GC/MS

Volatile compounds Volatile compounds

Fatty acid alkyl esters and waxes Volatile compounds

Sesquiterpene hydrocarbons Sesquiterpene hydrocarbons

Volatile compounds

LDA

HS-SPME GC/MS

Volatile compounds

PLS-DA

HS-SPME GC/MS HS-SPME GC/MS

PLS-DA

OPLS-DA

LDA QDA PCA PLS-DA

SPME GC/MS ATR-FTIR

HS-SPME GC/MS

PLS-DA

HS-SPME GC/MS

MANOVA LDA

Chemometric technique PLS-DA

(continued)

[23]

[20]

[22]

[21]

[14]

[15]

[16]

[17]

[18]

References [19]

Authentication/ adulteration Variety Analytical technique HS-SPME GC/MS

Table 4.1 Virgin olive oil authentication using GC/MS methodologies Analytes Sesquiterpene hydrocarbons Volatile compounds

Edible Oils and Fats

Purpose of the study Efficiency of sesquiterpene fingerprinting for Mediterranean Arbequina oils Volatile compounds for authentication of virgin olive oils (Olea europaea L.) according to cultivars Recognition of specific markers for cultivar differentiation of Greek virgin olive oil samples Varietal authentication of extra virgin olive oils by triacylglycerols and volatiles analysis Combined targeted and untargeted profiling of volatile aroma compounds with comprehensive two-dimensional gas chromatography for differentiation of virgin olive oils according to variety and geographical origin Fatty acid alkyl ester and wax compositions of olive oils as varietal authentication indicators Authentication of the geographical origin of virgin olive oils from the main worldwide producing countries Greek olive oil botanical origin discrimination

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Adulteration

Authentication/ adulteration

Determination of triacylglycerols by HT-GC/ FID as a sensitive tool for the identification of rapeseed and olive oil adulteration

[26]

[27]

– –

SPE GC/FID

Free and esterified hydroxylated minor compounds of Triacylglycerols

HT-GC/FID

[31]

ANOVA

GC/MS

Monochloropropane-1,2diol ester

[32]

[33]

[30]

GC/MS

Mathematical model ANOVA

PCA

[34]

References [22]

Fatty acids and secondary metabolites

TGA-GC/MS

HS-SPME GC/MS

Chemometric technique PCA LDA QDA PLS-DA

GC/MS

Sesquiterpene hydrocarbons Volatile oxidation compounds

Profiling versus fingerprinting analysis of sesquiterpene hydrocarbons In-situ assessment of olive oil adulteration with soybean oil based on thermogravimetricgas chromatography/mass spectrometry combined with chemometrics Detection of adulteration of extra-virgin olive oil with high-oleic sunflower oil Detection of adulterants from common edible oils by GC/MS Srividya Identification of 3-monochloropropane-1,2diol esters to verify the adulteration of extra virgin olive oil Detection of adulteration of olive oil with sunflower oil

Analytical technique SPME GC/MS

Dominant fatty acids

Analytes Volatile compounds

Purpose of the study Discrimination of botanical origin of olive oil from selected Greek cultivars

Table 4.1 (continued)

72 A. Taamalli et al.

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4.3

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Other Edible Oils Authentication by GC/MS

Edible oils, including cooking oils, margarine, shortening, and butter, are classified as the foods most frequently counterfeited and with the most sophisticated frauds. There are two major adulterations in edible oils: ad-mixing a cold press oil with a refined one and replacing more expensive oils and fats with cheaper ones [35]. Based on the growing market of vegetable oils, their authenticity has become an essential question in terms of health and commercial perspectives—the differential between the price of the oils (i.e., argan adulterated with cheaper seed or vegetable oils). Several analytical techniques have been applied to detect adulteration or confirm authentication of the botanical origin of vegetable oil mixings. The suggested approach to the issue can be summarized as a combination of two-step analytical methods. The general screening methods utilize fingerprinting spectroscopic methods, such as Raman and Fourier-transform infrared spectroscopies (FTIR). Moreover, the authenticity of edible oils is commonly evaluated using analytical methods for the determination of fat phase components. Analysis of the fatty acid composition by gas chromatography is the most widely used routine analytical method, mainly, GC/FID/MS [35, 36]. The analyses of the authenticity of seed/vegetable oils using a GC technique usually focus on the determination of adulteration of the oils with cheaper or lower quality ones, i.e., avocado oil mixed with soybean oil or Kosher foods [37]. The PCA of gas chemometric fingerprints was used to show the separation of different oils to detect adulteration as well as the quality parameters. Table 4.2 illustrates an in-order list of examples of authentication and adulteration detection procedures for various types of oils and derivatives. Rydlewski et al. [37] evaluated the authenticity of avocado oil-based Brazilian products using gas chromatography with a flame ionization detector for fatty acid composition and lipid profile analysis using direct infusion electrospray ionization mass spectrometry (ESI-MS). PCA was also used together with ESI-MS to test the authentic and fraudulent samples, confirming the efficiency of lipid profile analysis. It was found that out of eight brands examined, three were authentic and three were adulterated with soybean oil, as presented by their lipid profile. The results showed the incidence of fraud by the addition of a cheaper oil (soybean oil), which may compromise the quality and therapeutic effectiveness of these oils and coproducts thereof [37]. A new GC/MS strategy for discrimination of 59 samples of various cold-pressed, virgin, and refined edible vegetable oils belonging to 17 plant species: olive, sunflower, safflower, flax, pumpkin, sesame, hemp, walnut, hazelnut, almond, grape, black cumin, apricot, plum, soybean, wheat, and rapeseed, according to the corresponding botanical origin was proposed by Pastor et al. [1]. A GC/MS device performing in selected ion monitoring mode, combined with multivariate clustering, was employed in the analysis. The discriminations between species were based on marker peaks of molecular ions of the following dominant fatty acid methyl esters (FAMEs), which were chosen as descriptors: m/z 268, 270, 292, 294, 296, 298,

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Table 4.2 Other edible oils and fats authentication Purpose of the study Other edible oils Determination of edible oils Marine oils (fish oils) and vegetable oils determination

Analytical technique

Chemometric technique

GC×GC/MS HS-SPME GC/TOF-MS

– 3-way PLS regression model – OPLS-DA OPLS-DA and DD-SIMCA LDA, PCA, SIMCA, HCA sPLS-DA

[38] [39]

–

[45]

PCA

[46]

PCA

[47]

HCA

[1]

PCA CA PCO LDA PCA, RDA CA PC

[48]

Edible oil adulteration Argan oil authentication Authentication of fish oil (omega-3) supplements

GC/MS GC/FID GC×GC/MS

Adulteration detection of black cumin seed oil, sunflower oil, hazelnut oil, soybean oil Walnut oil determination

EI-MS; GC/FID HS-SPME GC/MS GC/MS

Camellia seed oil adulteration with soybean oil detection Evaluation of the adulteration of edible and cosmetic sunflower oils Discrimination of Amazonian oils Differentiation between cold-pressed, virgin, and refined edible vegetable oils belonging to 17 plant species: olive, sunflower, safflower, flax, pumpkin, sesame, hemp, walnut, hazelnut, almond, grape, black cumin, apricot, plum, soybean, wheat, and rapeseed Differentiation between cold-pressed oils of rape, flax, safflower, and pumpkin

Evaluation of seeds oil authenticity Detection of vegetable oils’ adulteration Detection of sesame oil adulteration Authenticity vegetable oils Fresh milk authentication Fats Discrimination of butter and butter adulterated with lard Discrimination of Amazonian butter

GC/FID and ESI-MS GC/MS and ESI-MS SIM-GC/MS

SIM-GC/MS

GC/FID IC-GC/MS GC/FID and GC/TOF-MS GC/MS/MS GC/FID

Ref.

[40] [41] [42] [43] [44]

[41] [1] [49]

– PCA, OPLSDA

[50] [51]

GC/MS

PCA, PLS-DA

[52]

GC/MS and ESI-MS

PCA

[47]

324, 326, and 354. Dendrogram obtained after performing cluster analysis shows clear discriminations of the analyzed samples based on the belonging botanical origin [1]. A similar GC/MS approach was applied to differentiate between the cold-pressed oils of rape, flax, safflower, and pumpkin. Chemometric multivariate

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data analysis tools—principal component analysis (PCA), cluster analysis (CA), principal coordinate analysis (PCO), and linear discriminant analysis (LDA)—were used to extract significant variables, visualize discriminations, and classify between analyzed plant samples [48]. Based on the measuring of the volatile odors of edible vegetable oils, a new and fast detection method via an electronic nose was proposed based on the odor fingerprint [53]. The odor profiles were obtained for different blends of sunflower and canola oil added to sesame oil and analyzed by GC/MS. The chemometric methods, including PCA, LDA, etc., were used to analyze the signals from the electronic nose. By using the electronic nose, it was useful to detect the levels of fraud with very high accuracy. This potential to detect and quantify edible oil fraud can be used to improve the efficiency and quality control of oils and to ensure the safety of consumption of edible oils [53]. Authenticity covers many aspects, including characterization, misleading origin, adulteration, and mislabeling, among others. Edible oils authentication is carried out using instrumental techniques that deliver data about the qualitative and quantitative composition of these oils. As a result of the advances in analytical methods, or the new challenges created by fraudsters, official methods and trade standards are periodically revised and upgraded. Comprehensive two-dimensional (GC×GC-MS) fingerprints of fish oil supplements were evaluated by Lima et al. [42]. The study aimed at building predictive models for the automated authentication of common products. The authentication process depends on a one-class classifier model using DD-SIMCA. The compositional analysis showed a significant variation in the samples, which validated the need for reliable statistical models. Although the DD-SIMCA algorithm is still emerging in GC×GC studies, it has shown to be an excellent tool for authentication purposes, attaining chemometric models with high sensitivity, specificity, and accuracy for fish oil authentication. By using OPLS-DA, it was found useful to distinguish the features that characterized the groups, which supports the DD-SIMCA model results that IFOS-certified oils are positively correlated to omega-3 fatty acids, including EPA and DHA [42].

4.4

Fats Authentication by GC/MS

Milk and dairy co-product authentication using GC, for instance, is usually based on the determination of the fat content of samples: triacylglycerols (TAGs) and/or fatty acids (FAs). Then, it is usually enough to combine GC with FID, to perform a successful routine analysis by using commercial standards [54]. In some cases, GC/MS is also an instrument of choice in the identification process of new and unknown compounds. Methods described in the literature rarely use chemometric data analysis, in some cases principal component analysis (PCA), and PLS-DA, but rather rely on the application of standard statistics. Papers describing the authentication of milk and dairy derivatives typically deal with discriminating organic from conventionally produced ones, discriminating samples according to geographical origin and according to the animal breed they are produced of.

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Since butter has similar characteristics to lard, it makes lard a desirable adulterant in butter due to economic advantages [52]. The presence of lard as an adulterant in butter has been determined using GC/MS and NMR spectroscopy with the aid of chemometrics: PCA and discriminant analysis (DA). The DA model classified 100% of 17 investigated samples accurately according to its belonging group (butter and butter adulterated with animal fats), meaning that no samples were misclassified [52]. In the work of Fasciotti et al. [47], the composition of different Amazonian oils and butter were studied by using GC/MS and ESI-HRMS. About 70 TAGs were tentatively annotated per sample; complex TAGs were considerably found to be abundant in the samples analyzed. By using PCA for comparison with common edible oils, such as soybean, corn, etc., this study provided profiles that ensure Amazonian oils’ and butter’s quality and authenticity. The comprehensive set of data on the TAG composition of Amazonian oils and butter innovated might help guide the use and applications of different fats and oils, providing consumers with the health benefits from a nutritional perspective. Besides, by using the database, adulterations could be more easily detected based on the chemical composition of the certified samples [47].

4.5

Conclusion

The interest of researchers in the authentication of vegetable oils has led to an improvement in the control of adulteration. One- or two-dimensional gas chromatography with different coupling techniques is considered a common tool in most analytical labs worldwide and can be successfully used in the authentication and detection of adulteration of various food and beverage products, such as olive oil and other edible vegetable oils, meat types, buttermilk, and dairy products, as well fish and seafood. In this manner, a gas chromatograph can be attached to a flame ionization (FID) detector or single/tandem mass spectrometers. Chromatographic techniques could be used to quantify those chemical compounds that are markers for adulteration, and their results could be statistically distinguished and classified. In conclusion, the application of a GC analytical tool in the development of authentication methods could provide us with significant findings, besides indicating a substantial impact on this emerging field in the time to come.

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34. Quintanilla-Casas B, Bertin S, Leik K, Bustamante J, Guardiola F, Valli E, Bendini A, Toschi TG, Tres A, Vichi S (2020) Profiling versus fingerprinting analysis of sesquiterpene hydrocarbons for the geographical authentication of extra virgin olive oils. Food Chem 307. https://doi.org/10.1016/j.foodchem.2019.125556 35. Hong E, Lee SY, Jeong JY, Park JM, Kim BH, Kwon K, Chun HS (2017) Modern analytical methods for the detection of food fraud and adulteration by food category. J Sci Food Agric 97: 3877–3896 36. Tan CH, Kong I, Irfan U, Solihin MI, Pui LP (2021) Edible oils adulteration: A review on regulatory compliance and its detection technologies. J Oleo Sci 70:1343–1336. https://doi.org/ 10.5650/jos.ess21109 37. Rydlewski A, Pizzo J, Manin L, Galuch M, Santos P, Zapiello C et al (2020) Evaluation of possible fraud in avocado oil-based products from the composition of fatty acids by GC-FID and lipid profile by ESI-MS. Chem Pap 74(9):2799–2812. https://doi.org/10.1007/s11696-02001119-z 38. Zhu G, Liu F, Li P, He S, Zhu S, Gao Q, Feng Y (2019) Profiling free fatty acids in edible oils via magnetic dispersive extraction and comprehensive two-dimensional gas chromatographymass spectrometry. Food Chem 297:124998. https://doi.org/10.1016/j.foodchem.2019.124998 39. Gómez-Cortés P, Camiña JM (2019) Oxidomics on the omega-3 volatile degradation pattern to determine differences between vegetable and marine oils. Food Res Int 122:10–15. https://doi. org/10.1016/j.foodres.2019.03.064 40. Pulassery S, Abraham B, Ajikumar N, Munnilath A, Yoosaf K (2022) Rapid iodine value estimation using a handheld Raman spectrometer for on-site, reagent-free authentication of edible oils. ACS Omega 7:9164–9171. https://doi.org/10.1021/acsomega.1c05123 41. Miklavčič MB, Taous F, Valenčič V, Elghali T, Podgornik M, Strojnik L, Ogrinc N (2020) Fatty acid composition of cosmetic argan oil: provenience and authenticity criteria. Molecules 25. https://doi.org/10.3390/molecules25184080 42. Lima RAM, Ferraz SMM, Cardoso VGK, Teixeira CA, Hantao LW (2022) Authentication of fish oil (omega-3) supplements using class-oriented chemometrics and comprehensive two-dimensional gas chromatography coupled to mass spectrometry. Anal Bioanal Chem. https://doi.org/10.1007/s00216-022-04428-2 43. Kenar A, Çiçek B, Arslan FN, Akin G, Elmas ŞNK, Yilmaz I (2019) Electron impact–mass spectrometry fingerprinting and chemometrics for rapid assessment of authenticity of edible oils based on fatty acid profiling. Food Anal Methods 12:1369–1381. https://doi.org/10.1007/ s12161-019-01472-0 44. Kalogiouri NP, Manousi N, Rosenberg E, Zachariadis GA, Paraskevopoulou A, Samanidou V (2021) Exploring the volatile metabolome of conventional and organic walnut oils by solidphase microextraction and analysis by GC-MS combined with chemometrics. Food Chem 363: 130331. https://doi.org/10.1016/j.foodchem.2021.130331 45. Xie J, Liu T, Yu Y, Song G, Hu Y (2013) Rapid detection and quantification by GC-MS of camellia seed oil adulterated with soybean oil. JAOCS J Am Oil Chem Soc 90:641–646. https:// doi.org/10.1007/s11746-013-2209-0 46. Manin LP, Pizzo JS, Rydlewski AA, Santos PDS, Galuch MB, Zappielo CD, Santos OO, Visentainer JV (2021) Evaluation of the adulteration of edible and cosmetic sunflower oils by GC-FID and ESI-MS. J Braz Chem Soc 32:2086–2094. https://doi.org/10.21577/0103-5053. 20210101 47. Fasciotti M, Monteiro TVC, Rocha WFC, Morais LRB, Sussulini A, Eberlin MN, Cunha VS (2020) Comprehensive triacylglycerol characterization of oils and butters of 15 Amazonian Oleaginous species by ESI-HRMS/MS and comparison with common edible oils and fats. Eur J Lipid Sci Technol 122:2000019. https://doi.org/10.1002/ejlt.202000019 48. Pastor K, Ilić M, Vujić D, Jovanović D, Ačanski M (2020) Characterization of fatty acids in cereals and oilseeds from the Republic of Serbia by gas chromatography – mass spectrometry (GC/MS) with chemometrics. Anal Lett 53(8):1177–1189

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5

Milk and Dairy Products Havva Tümay Temiz, Akif Göktuğ Bozkurt, and Berdan Ulaş

Abstract

This chapter summarizes recent applications of the gas chromatography/mass spectrometry (GC/MS) technique to authenticate and detect adulteration of milk and dairy products. Reported studies were categorized under five different classes, according to the product under study, which are milk, cheese, yoghurt, butter, ghee, and milk powder. Milk authentication and adulteration studies have mostly investigated the effect of feeding regime, animal type and animal species on the molecular composition of the final products. Cheese authentication has focused on the ability and limitations of GC/MS-based techniques to provide data for the Protected Designation of Origin of cheese samples. Yoghurt, butter, ghee, and milk powder samples are the other dairy products on which limited number of studies were reported in the literature with regard to their authentication. Keywords

Food authentication · GC/MS · Milk · Cheese · Butter · Dairy · Chemometrics

H. T. Temiz (✉) Ülker Bakery R&D Center, Kocaeli, Turkey Northstar Innovation Company, Yıldız Holding, Istanbul, Turkey A. G. Bozkurt Department of Food Technology, Vocational School of Technical Sciences, Ardahan University, Ardahan, Turkey e-mail: [email protected] B. Ulaş Faculty of Engineering, Department of Mining Engineering, Van Yüzüncü Yıl University, Van, Turkey e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_5

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List of Abbreviations CLAs CPFAs GC GLM MS MUFA PCA PDO PLS-DA PUFA TAGs ToF

5.1

Conjugated linoleic acids Cyclopropane fatty acids Gas chromatography General linear model Mass spectrometry Monounsaturated fatty acids Principal component analysis Protected designation of origin Partial least squares discriminant analysis Polyunsaturated fatty acids Triacylglycerols Time of flight

Introduction

The food integrity term has been raised by Elliot in the aftermath of the 2013 horsemeat scandal. In the related article, food integrity term has been described as “the nature, substance and quality and safety of food” as well as “the way it has been sourced, procured, and distributed and being honest about those areas to consumers.” Food authenticity and food fraud have been counted among the pillars of food chain integrity along with food safety, food quality, food transparency, and food crime [1, 2]. Assurance of these pillars will be possible through the control of food integrity elements, which are defined as product integrity, process integrity, people integrity, and data integrity [3]. In this context, there has always been a pressing need for standardized techniques to detect food authenticity and food adulteration. Chromatographic analysis relies on the separation of chemically similar compounds and obtaining chemical fingerprints of food. On the other hand, identification of minimal analytical differences and extraction of specific marker compounds from the highly complex food matrices are major challenges faced by chromatographic methods. Two-dimensional chromatography (GCxGC) systems coupled with a time of flight (ToF) detector enhanced the separation efficiency and allowed the simultaneous analysis of hundreds of components [4]. High-resolution chromatographic techniques, such as gas chromatography (GC) coupled with mass spectrometry (MS) and double MS (triple quadrupole), i.e., GC/MS/MS, are the emerging solutions to overcome these limitations. GC/MS provides a holistic approach to obtaining the volatile compounds profile of the analyzed food sample [5]. Hence, it has been widely used in metabolomics studies and regarded as the gold standard for analysis, as it provides a fingerprint chromatogram based on both a retention time and a mass spectrum. Thanks to constant electron ionization, highly

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reproducible and instrument-independent spectral databases can be generated. Recent advances in ionization methods have increased the reproducibility of the fragmentation spectra and lessen the matrix effects [6]. However, usage of GC/MS is limited to compounds that are volatile or that can be made volatile after the application of proper derivatization. Additionally, GC/MS obligates the elimination of all nonvolatile compounds from the matrix prior to the analysis. Also, this technique requires the use of standard compounds and solutions and the application of predefined procedures for sample preparation [7]. Advanced technological developments in chromatography result in hyphenated techniques whose high dimensional data require the application of chemometric techniques to extract meaningful information. Analytical advantages, such as reduced analysis time, reduced consumption of chemicals, and a minimal number of pre-processing steps, are provided by multi-way chemometric data analysis [8, 9]. The recent review article by Bos et al. (2020) has discussed the advantages and limitations offered by chemometrics for one- and two-dimensional chromatography. The authors have comprehensively covered the developments in pre-processing methods, namely baseline correction, retention-time-alignment techniques, signal deconvolution, and resolution enhancement; peak detection, peak properties, and extraction of the information; supervised and unsupervised classification methods; quantification; and optimization methods [10]. This chapter summarizes the recent studies that have employed GC/MS-based techniques to determine adulteration or to authenticate milk and dairy products.

5.2

Authentication and Adulteration Detection of Milk

There is an increasing consumer awareness on ethical-based practices, e.g., resource conservation, biodiversity, and animal-friendly approaches, to increase the sustainability of dairy production [11]. Among the efforts to increase the sustainability of dairy production, food fraud and adulteration should also be included, as the loss of confidence by investors, customers, consumers, and authorities is considered a direct threat to sustainability [12, 13]. Milk and dairy products are among the most adulterated food products owing to their irreplaceable nutritional composition, increased global demand, as well as unmet need for authentication methods [14, 15]. There have been promising advances in GC-based techniques to fulfill this need. In recent years, numerous studies have been reported that employ GC-based techniques to analyze milk and dairy products. Studies have been focused on the determination of marker molecules from different fractions of analyzed samples. The feeding system is reported to be the main factor affecting the quality traits and the composition of milk in terms of fatty acids, fat-soluble vitamins, N-compounds, organic acids and volatile aromatic compounds (terpenes and carotenoids) is responsible for the flavor [16]. Milk fat is known to be affected by diet composition and to provide information about ruminal fermentation patterns [16, 17].

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Hay milk is a relatively new term described by the Austrian dairy farming body and registered as “traditional specialty guaranteed” (TSG) by the European Union (EC, 2016). It is characterized by a distinct herb aroma assured by seasonal feeding instead of silage feeding. The study by Paredes et al. (2018) is based on the usability of differences determined by GC in milk fatty acid profiles to discriminate between different feeding regimes [18]. Complementary to the study by Werteker et al. [19], the authors reported that discrimination between hay milk (HM) and conventional milk (CM) is possible by using GC with a flame ionization detector (FID) coupled with partial least square discriminant analysis (PLS-DA). However, this discrimination required the use of different PLS-DA models depending on the production season, as the feeding regime in the summer period is almost the same for both HM and CM [18]. Imperiale et al. (2021), have employed GC/MS for cyclopropane fatty acid (dihydrosterculic acid, DHSA) detection, which is accepted as the indicator of maize silage feeding. The authors have emphasized the limitation of the GC/MS method in terms of sensitivity and obtained a better classification through TAG profiling of the samples using high-performance liquid chromatography with highresolution mass spectrometry (HRMS). The need for further validation with a higher sample size and sampling from different seasons have been indicated in the study as well [20]. In a recent study, the authors underlined the extreme variability in the fatty acid profile of milk arising from the differences in genetics, season and stage of lactation, and feeding regime. They have compared the effect on the milk fatty acids of high and medium maize silage, mixed crop silages, grass and lucerne hays, and greengrass feeding. Fatty acid composition of milk from silage diets of hays and fresh grass was different in terms of higher saturated fatty acids, lower conjugated linoleic acids and odd chain fatty acids, and long-chain polyunsaturated fatty acids (PUFAs). The potential of C16:1 c-9, C17:0, C18:0, C18:3 c-9, c-12, c-15, C18:1 cis9, C18:1 trans11, and C20:0 fatty acids to be considered as biomarkers of different feeding systems was described in the study by Riuzzi et al. [21]. Vicente et al. (2017), also reported lower concentrations of C18:3, C18:1 cis9, and C18:1 trans11 fatty acids in milk samples of maize silage-fed cows [22]. The differences in fatty acids and metabolomic profiles of milk samples from cows fed with maize silage, grasslegume, maize silage, and grass and lucerne hay were compared through chemometric analysis of GC/MS and proton nuclear magnetic resonance (1H NMR) data. The changes in fatty acid composition and NMR metabolite profiles were only significant in the case of the total replacement of maize silage [23]. In the study by Al-Shamsi et al., the fatty acid profiles (g/100 g fat) of lipids from cow and camel milk were determined. It has been reported that short-chain fatty acids, i.e., butyric, caproic, and caprylic acids were not present in camel milk, compared with the substantial amounts (~8%) found in cow milk. The lower amounts of saturated fatty acids (~68% and ~74%) and higher amounts of unsaturated fatty acids (~32% and ~26%) were determined in camel milk compared with cow milk. The results of the authors were in accordance with those of previous studies [24–26] on the same subject. Although the main focus was the comparison of

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protein characterization, the results on the fatty acid profile show that this significant difference may be potentially used for species discrimination [27]. Bakry et al. (2021) performed a very detailed study on camel milk. The authors reported the data on the composition of camel milk by compiling the studies to date, considering the fat globule membrane, fat globule, cholesterol, fatty acids, triacylglycerols (TAGs), and phospholipids, and the environmental and physiological factors affecting them. The usability of TAGs for species discrimination was emphasized in the study [28]. Smiddy et al. (2012) used GC for TAG analysis of milk fat from cow, goat, sheep, water buffalo, donkey, horse and camel, and camel milk was differentiated from the other species by its C48, C50, and C52 content. The authors have underlined the usability of TAG profiling for fingerprinting and determination of adulteration [29]. A detailed review on the comparative use of direct (GC- and LC-based) and indirect (enzymatic-instrumental and chemical-instrumental) methods for TAG profiling of milk fat from various mammalian species may be found in Cossignani et al. [30].

5.3

Authentication and Adulteration Detection of Cheese

Protected designation of origin (PDO) is the focus of authentication issues in the dairy sector. Higher prices of certified and high-added-value products have increased consumer awareness and the frequency of counterfeit practices simultaneously. The need for advanced authentication techniques with high sensitivity and reproducibility is of immense importance, as there are numerous factors, such as differences in the feeding system, geographical and process parameters, that can be monitored through these techniques and may be defined as the marker compounds. Eisenstecken et al. (2021) employed GC/FID and GC/MS simultaneously to profile fatty acid composition and to perform an untargeted analysis of milk and the related ripened cheese samples collected from Austria, France, Germany, Italy, the Netherlands, and Slovakia. PCA and PLS-DA were used to analyze the collected data. The authors reported significant changes in fatty acid profiles (odd- and branched-chain FA, long-chain PUFA, and MUFA) of milk samples from different geographical areas. The need for sampling from a wider range of geographical areas and multiple sampling times to increase the precision of the developed method was emphasized, as fatty acid composition shows a strong dependency on feeding regime, animal species, lactation period, and ruminal fermentation [31]. The term Protected Designation of Origin (PDO) describes a food that is produced, processed, and prepared in a specific geographical area through the application of a recognized methodology using local ingredients. From the perspective of producer, consumer, and regulatory institutions, there is an emerging need for methods to determine fraud and confirm the geographical origin of the product [32]. In addition to the fulfillment of processing and ripening conditions specified, dairy farming and milk production systems may cause significant variability in the quality of the final product [33].

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In the study by Segato et al. (2017), lipid samples extracted from Asiago PDO cheese produced by three different production systems were analyzed by two-dimensional GC. The applied production systems were different in terms of dairy farm location, rearing and feeding systems, as well as diet compositions. The high potential of PUFAs, conjugated linoleic acids (CLAs), and fat-soluble vitamin contents to strongly differentiate pasture milk processing upland cheeses, and cyclopropane FA and C9:0 as specific biomarkers for maize silage-based diets in the lowland were reported [34]. The results of Segato et al. (2017) were in accordance with those of the previous studies, which reported the usability of cyclopropane fatty acids (CPFAs), i.e., DHSA and lactobacillic acid, and ω-cyclohexyl FAs, i.e., ω-cyclohexyl tridecanoic and ω-cyclohexyl undecanoic acids, as the indicators of maize silage and cereal grain inclusion into the diet. In the study by Caligiani et al. (2016), more than – 300 PDO cheese samples— Parmigiano Reggiano (Italy), Grana Padano (Italy), Fontina (Italy), Comté (France), and Gruyère (Switzerland)—were analyzed by GC/MS to establish the usability of CPFAs as a quality control indicator of PDO cheeses, whose processes inhibit the use of silage feedings. The developed method was also able to quantify the adulteration of Parmigiano Reggiano with Grana Padano at 10% and above. It is known that Grana Padano cheeses contain CPFAs, within the range 300–830 mg/kg of fat, as their production permits the use of silages. Currently, GC/MS-based CPFA quantification has been validated as an official method and included in ParmigianoReggiano Product Specification Rules among the official controls [35, 36]. Bhandari et al. (2016) used a portable gas sensor to profile the volatile organic compounds (VOCs) of Parmigiano Reggiano cheese and analyzed the correlation of results with those of GC/MS. The high potential of the developed sensor and its further adaptation to cheese-chain productions were emphasized by the authors, whereas GC/MS was used as the reference method for VOC profiling [37]. Solid-phase microextraction (SPME) coupled with GC/MS has been widely employed for the authentication of different food samples. Providing a solvent-free extraction along with being low cost and simple [38], the use of SPME for milk and dairy product analysis has been reported by numerous authors [39, 40]. Cozzolino et al. (2021) employed HS-SPME GC/MS to distinguish Pecorino di Carmasciano (PdC) cheese samples from commercial Pecorino cheese samples based on their VOCs. PdC is an ewe-milk cheese specific to the Avellino province of Campania Region in Southern Italy. In the study, authors mentioned possible factors characterizing PdC, namely, highly acidic soils of the Carmasciano area, strong proteolysis activity, and a unique microbial community of lamb paste chymosin. The profiling of major molecular aroma compounds was not enough to discriminate PdC samples and the authors have controverted the common belief about the dominance of sulfur-containing VOCs in PdC [41]. Authentication of dairy products through volatile fingerprinting by GC has been utilized in many studies, but in recent years high cost, long duration of experiments, and low adaptability of the technique to production monitoring have been discussed [42]. Danezis et al. (2020) combined the physicochemical data and FA profiling approach to classify 112 samples from 21 Greek PDO, non-PDO, and potential

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PDO cheese samples by label (cheese identity), cheese type (hard, semi-hard, soft, spread, and whey), and milk type (cow, goat, sheep, and combinations). They have built a library for Greek PDO cheeses and recommended the use of different parameters to classify samples in accordance with the above-mentioned criteria. C10:0, C11:0, C14:1, C18:2n-6c, C20:0, C20:3n-6, and SFA were reported to be the potential biomarkers for milk-type determination [43]. Formaggioni et al. (2020), have coupled FA profiling with sensorial data to authenticate Formaggella della Valle di Scalve cheese. The cheese fat from stall milk was found to contain a higher amount of medium-chain fatty acids and a lower amount of long-chain fatty acids than pasture milk. The mountain pasture and low=altitude pasture were differentiated in terms of oleic and omega-3 fatty acid contents. The authors indicated a possible correlation between sensorial properties and VOC profiles of cheese samples [44]. The artisanal cheeses from different regions of Brazil were analyzed for FA profiles and compositional information. FA profiles of the samples have shown promising results to discriminate samples based on their geographical origin and the contribution of these data to the PDO status of the samples was emphasized [45]. The authors of this chapter have performed a comprehensive literature and database search to visualize the current situation of PDO cheeses in different countries. The results were summarized in Table 5.1. The data for registered cheese samples were obtained from eAmbrosia (europa.eu) [202]. As can be seen from Table 5.1, there have been extensive studies on PDO and PGI cheeses belonging to Italy, Greece, Spain, and France. Although there are numerous cheese samples registered in Germany, Austria, Poland, Netherlands, and the UK, there are few or no studies reporting the use of GC/MS-based techniques for authentication of these cheeses. It should be emphasized that the complicated terminology used in the current literature may have misdirected the authors. The use of the correct terminology, i.e., the exact term used in the PDO certificate, would increase visibility in the literature search.

5.4

Authentication and Adulteration Detection of Other Dairy Products

5.4.1

Yoghurt

Yogurt and milk samples from sheep and goat breeds in Sardinia, Italy, were analyzed by GC/MS and multivariate data analysis in terms of their polar metabolite profiles. A comparison of metabolite profiles of milk and yoghurt samples resulted in six metabolites: β-hydroxy-isobutyric acid, urea, isocitric acid, tyrosine, gluconic acid, and saccharide, that were found only in milk whereas 15 metabolites: α-hydroxy-isobutyric acid, butanoic acid, glycolic acid, α-hydroxy-isocaproic acid, isoleucine, adipic acid, γ-aminobutyric acid, pimelic acid, lysine, and saccharide, were found only in yoghurt samples. Additionally, free amino acids, g-aminobutyric acid, pyroglutamic acid, and b-phenyl lactic acid contents were higher in goats’ milk yoghurt. On the other hand, higher levels of myoinositol, N-acetyl-galactosamine,

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Table 5.1 Protected designation of origin (PDO) and protected geographical indication (PGI) cheeses of different countries Country European Union Belgium Czechia Denmark

Germany

Product type Cheeses (type of registration year) Fromage de Herve (PDO) Olomoucké tvarůžky (PGI) Jihočeská Zlatá Niva (PGI) Havarti (PGI-2019) Danbo (PGI-2017) Esrom (PGI-1996) Danablu (PGI-1996) Allgäuer Sennalpkäse (PDO-2016) Weißlacker (PDO-2015) Holsteiner Tilsiter (PGI-2013) Hessicher Handkäse (PGI-2010) Nieheimer Käse (PGI-2010) Odenwälder Frühstückskäse (PDO-1997) Altenburger Ziegenkäse (PDO-1997) Allgäuer Bergkäse (PDO-1997) Allgäuer Emmentaler (PDO-1997)

Ireland

Imokilly Regato (PDO-1999)

Greece 22 PDO & 1 PGI cheeses

Arseniko Naxou (PDO-2020)

Tiri tis Possias (PGI-2019)

Xigalo Siteias (PDO-2003)

Feta (PDO-2002)

Katiki Domokou (PDO-1996)

Graviera Kritis (PDO-1996) Kopanisti (PDO-1996)

Total publication on Web of Science GC/MS studies Basic search: 1 Basic search: 8 Basic search: 4 Basic search: 22 Basic search: 25 Basic search: 0 Basic search: 20 Basic search: 0 Basic search: 4 Basic search: 23 Basic search: 2 Basic search: 0 Basic search: 1

– – – – [46–48] – [49–52] – – – – – –

Basic search: 0

–

Basic search: 0 Basic search: 0 Keywords: (Emmentaler cheese)163 Basic search: 0 Keywords: (Regato cheese)-0 Basic search: 0 Keywords: (Arseniko Naxou cheese)-0 Basic search: 0 Keywords: (Tiri tis Possias cheese)-0 Basic search: 2 Keywords: (Xygalo Siteias cheese)-2 Basic search: 557 Keywords: (Feta cheese Greece)-215 Basic search: 4 Keywords: (Katiki Domokou cheese)-4 Basic search: 15 Basic search: 15

– [53, 54]

– – –

[43]

[55–59]

[43]

[43, 60] [43, 61, 62] (continued)

5

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89

Table 5.1 (continued) Country

Product type Graviera Agrafon (PDO-1996)

Total publication on Web of Science Basic search: 1

Xynomyzithra Kritis (PDO-1996)

Basic search: 1

Batzos (PDO-1996) Ladotyri Mytilinis (PDO-1996) Kefalograviera (PDO-1996) Galotyri (PDO-1196)

Basic search: 13 Basic search: 2 Basic search: 54 Basic search: 20 Keywords: (Galotyri cheese)-19 Basic search: 37 Keywords: (Manouri cheese)-19 Basic search: 39 Keywords: (Kasseri cheese)-38 Basic search: 7 Keywords: (Anevato cheese)-7 Basic search: 1 Basic search: 3 Basic search: 2 Basic search: 2 Basic search: 1

Manouri (PDO-1996)

Kasseri (PDO-1996)

Anevato (PDO-1996)

Sfela (PDO-1996) Kalathaki Limnou (PDO-1996) Graviera Naxou (PDO-1996) Metsovone (PDO-1996) Formaella Arachovas Parnassou (PDO-1996) Pichtogalo Chanion (PDO-1996) Spain26 PDO& 3 PGI Cheeses

Queso Castellano (PGI-2020)

Queso Los Beyos (PGI-2013)

Queso Camerano (PDO-2012

Queso Casín (PDO-2011)

Queso de Guía (PDO-2010) Arzùa-Ulloa (PDO-2008)

Basic search: 3 Basic search: 0 Keywords: (Queso Castellano cheese)-0 Basic search: 0 Keywords: (Queso Los Beyos cheese)-0 Basic search: 0 Keywords: (Queso Camerano cheese)-0 Basic search: 0 Keywords: Queso Casín cheese)-0 Basic search: 1 Basic search: 11

[43]{Danezis, 2020 #290} [43]{Danezis, 2020 #290} [43] [43] [43, 63, 64] [43, 65, 66]

[43, 67–69]

[43]

[43]

[43] [43] [43, 60] [43, 70] [43]{Danezis, 2020 #290} [43] – – – – –

[71] [72, 73] {RodriguezAlonso, 2009 #320;RodriguezAlonso, 2011 #321} (continued)

90

H. T. Temiz et al.

Table 5.1 (continued) Country

Product type San Simón da Costa (PDO-2008) Cebreiro (PDO-2008) Afuega’l Pitu (PDO-2008) Gamonedo (PDO-2008) Queso Ibores (PDO-2005) Queso de Valdeón (PGI-2004)

Queso de Valdeón (PDO-2003)

Queso de la Palma (PDO-2002) Queso de Murcia al vino (PDO-2002) Queso de Murcia (PDO-2002) Queso de l’Alt Urgell y la Cerdanya (PDO-2000)

Queso Majorero (PDO-1999) Roncal (PDO-1996) Quesucos de Liébana (PDO-1196) Queso Zamorano (PDO-1996) Queixo Tetilla (PDO-1996) Queso Manchego (PDO-1996) Queso de La Serena (PDO-1996) Queso Nata de Cantabria (PDO-1996) Picón Bejes-Tresviso (PDO-1996) Mahón-Menorca (PDO-1996) Cabrales (PDO-1996) France 49 PDO&10 PGI Cheeses

Brousse du Rove (PDO-2020) Brillat-Savarin (PGI-2017) Soumaintrain (PGI-2016)

Raclette de Savoie (PGI-2016) Charolais (PDO-2014)

Total publication on Web of Science Keywords: (San Simón da Costa)-6 Basic search: 19 Basic search: 6 Basic search: 1 Basic search: 0 Keywords: (Queso de Valdeón cheese)-0 Basic search: 0 Keywords: (Queso de Valdeón cheese)-0 Basic search: 1 Basic search: 1 Basic search: 1 Basic search: 0 Keywords: (Queso de l’Alt Urgell y la Cerdanya cheese)-0 Basic search: 1 Keywords: (Roncal cheese)-32 Basic search: 1 Basic search: 0 Basic search: 0 Basic search: 7 Basic search: 1 Basic search: 0 Basic search: 4 Keywords: (MahónMenorca cheese)-2 Keywords: (Cabrales cheese)-71 Basic search: 0 Keywords: (BrillatSavarin cheese)-0 Keywords: (Soumaintrain cheese)0 Basic search: 0 Basic search: 5

[74] – [75, 76] [77] [78] – – – – – –

– [79–84] – – – [78]{Delgado, 2011 #347} – – – – [77, 85–87] – – – – – – (continued)

5

Milk and Dairy Products

91

Table 5.1 (continued) Country

Product type Saint-Marcellin (PGI-2013) Rigotte de Condrieu (PDO-2013) Gruyère (PGI-2013) Mâconnais (PDO-2010) Banon (PDO-2007) Tome des Bauges (PDO-2007) Chevrotin (PDO-2005) Valençay (PDO-2004) Morbier (PDO-2002) Pélardon (PDO-2001) Bleu du Vercors-Sassenage (PDO-2001) Rocamadour (PDO-1999) Fourme d’Ambert (PDO-1996) Fourme de Montbrison (PDO-1996) Picodon (PDO-1996) Bleu de Gex Haut-Jura/Bleu de Septmoncel (PDO-1996) Brie de Meaux (PDO-1996) Camembert de Normandie (PDO-1996) Cantal/Fourme de Cantal (PDO-1996) Chaource (PDO-1996) Chabichou du Poitou (PDO-1996) Comté (PDO-1996) Crottin de Chavignol/Chavignol (PDO-1996) Époisses (PDO-1996) Laguiole (PDO-1996) Langres (PDO-1996) Livarot (PDO-1996) Maroilles/Marolles (PDO-1996) Mont d’Or/Vacherin du HautDoubs (PDO-1996)

Total publication on Web of Science Keywords: (SaintMarcellin)-1 Basic search: 0 Keywords: (Gruyere cheese)-165 Keywords: (Mâconnais cheese)-0 Keywords: (Banon cheese)-0 Basic search: 1 Basic search: 1 Basic search: 1 Basic search: 1 Basic search: 4 Basic search: 0

– – [36, 88–90] – – – – – – [91] –

Keywords: (Rocamadour cheese)-3 Basic search: 9 Basic search: 5

[92, 93] [94, 95] [96]

Basic search: 1 Basic search: 0

– –

Basic search: 2 Basic search: 5

– –

Basic search: 0

–

Basic search: 3 Basic search: 1 Keywords: (Comté cheese)-199 Basic search: 9

– – [36, 97–100]

Keywords: (Epoisses cheese)-16 Basic search: 3 Keywords: (Langres cheese)-0 Basic search: 20 Basic search: 11 Basic search: 5

–

–

– – [101] [102] – (continued)

92

H. T. Temiz et al.

Table 5.1 (continued) Country

Product type Munster/Munster-Géromé (PDO-1996) Neufchâtel (PDO-1996) Ossau-Iraty (PDO-1996) Pouligny-Saint-Pierre (PDO-1996) Pont-l’Évêque (PDO-1996) Reblochon/Reblochon de Savoie (PDO-1996) Roquefort (PDO-1996) Saint-Nectaire (PDO-1996) Sainte-Maure de Touraine (PDO-1996) Salers (PDO-1996) Selles-sur-Cher (PDO-1996) Brocciu Corse/Brocciu (PDO-1996) Abondance (PDO-1996) Beaufort (PDO-1996) Bleu d’Auvergne (PDO-1996) Bleu des Causses (PDO-1996) Tomme des Pyrénées (PGI-1996) Tomme de Savoie (PGI-1996) Emmental de Savoie (PGI-1996) Emmental français est-central (PGI-1996) Brie de Melun (PDO-1996)

Croatia 1 PDO&2PGI Italy

Lički škripavac (PGI-2021) Bjelovarski kvargl (PGI-2020) Paški sir (PDO-2019) Caciottone di Norcia (PGI-2021) Burrata di Andria (PGI-2016) Canestrato di Moliterno (PGI-2010) Mozzarella di Gioia del Colle (PDO-2020) Provola dei Nebrodi (PDO-2020)

Total publication on Web of Science Basic search: 49

[103]

Keywords: (Neufchâtel cheese)-4 Basic search: 12 Basic search: 1 Basic search: 6 Basic search: 2

–

Keywords: (Roquefort cheese)-79 Keywords: (SaintNectaire cheese)-31 Basic search: 3

[85, 106–109]

Keywords: (Salers cheese)-56 Keywords: (Selles-surCher)-0 Basic search: 0

[112]

[104] – [105] –

[110, 111] –

– –

Basic search: 17 Keywords: (Beaufort cheese)-23 Basic search: 8 Basic search: 3 Basic search: 1 Basic search: 4 Basic search: 2 Basic search: 0

[113] [97, 113]

Keywords: (Brie de Melun)-0 Basic search: 1 Basic search: 0 Basic search: 2 Basic search: 0 Basic search: 1 Basic search: 4

– – – – – – [114, 115]

Basic search: 2

[116]

Basic search: 8

[117–120]

[95] – – – – –

(continued)

5

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93

Table 5.1 (continued) Country

Product type Pecorino del Monte Poro (PDO-2020) Ossolano (PDO-2017)

Silter (PDO-2015) Pecorino delle Balze Volterrane (PDO-2015) Pecorino Crotonese (PDO-2014) Strachitunt (PDO-2014) Pecorino di Picinisco (PDO-2013) Puzzone di Moena/Spretz Tzaorì (PDO-2013) Squacquerone di Romagna (PDO-2012) Nostrano Valtrompia (PDO-2012) Salva Cremasco (PDO-2011) Formaggella del Luinese (PDO-2011) Piacentinu Ennese (PDO-2011) Vastedda della valle del Belìce (PDO-2010) Piave (PDO-2010) Provolone del Monaco (PDO-2010) Formaggio di Fossa di Sogliano (PDO-2009) Casatella Trevigiana (PDO-2008) Pecorino di Filiano (PDO-2007) Stelvio/Stilfser (PDO-2003) Spressa delle Giudicarie (PDO-2003) Raschera (PDO-1996) Bra (PDO-1996) Caciocavallo Silano (PDO-1996) Castelmagno (PDO-1996) Fiore Sardo (PDO-1996) Monte Veronese (PDO-1996)

Total publication on Web of Science Basic search: 0 Basic search: 2

–

Basic search: 3 Basic search: 0

[121, 122] {Barile, 2006 #436;Zeppa, 2003 #437} – –

Basic search: 4 Basic search: 2 Basic search: 0 Basic search: 3

[123, 124] [125, 126] – –

Basic search: 0

–

Basic search: 1 Basic search: 0 Basic search: 0

– – –

Basic search: 12 Basic search: 9

[127–129] [130–133]

Basic search: 2 Basic search: 6

– [134, 135]

Basic search: 1

–

Basic search: 0 Basic search: 4 Basic search: 1 Basic search: 0

– – – –

Basic search: 5

[136] – – – [82, 137–140] –

Pecorino Sardo (PDO-1996)

Basic search: 7 Basic search: 19 Basic search: 54 Keywords: (Monte Veronese cheese)-2 Basic search: 26

Pecorino Toscano (PDO-1996)

Basic search: 16

[82, 138, 139, 141, 142] [138, 143, 144] (continued)

94

H. T. Temiz et al.

Table 5.1 (continued) Country

Product type Robiola di Roccaverano (PDO-1996) Toma Piemontese (PDO-1996) Bitto (PDO-1996) Valtellina Casera (PDO-1996) Ragusano (PDO-1996) Asiago (PDO-1996) Canestrato Pugliese (PDO-1996)

Cyprus Lithuania

Hungary

Total publication on Web of Science Basic search: 9 Basic search: 6 Basic search: 9 Basic search: 4 Keywords: (Ragusano cheese)-72 Keywords: (Asiago cheese)-39 Keywords: (Canestrato Pugliese)-33

Casciotta d’Urbino (PDO-1996) Fontina (PDO-1996) Formai de Mut dell’Alta Valle Brembana (PDO-1996) Gorgonzola (PDO-1996) Grana Padano (PDO-1996)

Basic search: 1 Basic search: 32 Basic search: 0

Montasio (PDO-1996) Mozzarella di Bufala Campana (PDO-1996) Murazzano (PDO-1996) Parmigiano Reggiano (PDO-1996)

Basic search: 51 Basic search: 45

Pecorino Romano (PDO-1996) Pecorino Siciliano (PDO-1996) Provolone Valpadana (PDO-1996) Quartirolo Lombardo (PDO-1996) Taleggio (PDO-1996) Valle d’Aosta Fromadzo (PDO-1996) Hellim (PDO-2021) Liliputas (PGI-2015) Džiugas (PGI-2019) Lietuviškas varškės sūris (PGI-2013) Győr-Moson-Sopron megyei Csemege sajt (PGI-2020)

Basic search: 83 Basic search: 251

Basic search: 0 Basic search: 461 Basic search: 35 Basic search: 45 Basic search: 2 Basic search: 0 Basic search: 56 Basic search: 3 Keywords: (Halloumi cheese)-91 Basic search: 0 Basic search: 1 Basic search: 0 Basic search: 0

[145] [146] [147–150] – [151–154] [155–157] [139, 158–160] {Di Cagno, 2003 #521;Di Marzo, 2006 #522; Piombino, 2008 #523;Claps, 2016 #525} [161] [36, 97, 153, 162] – [107, 163–168] [36, 157, 169– 174] [175–178] [179–181]

[36, 37, 169–171, 174, 182–192] [139, 142, 193] [138, 194, 195] [196] – – [197]{Dolci, 2020 #629} [198–200] – – – – (continued)

5

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95

Table 5.1 (continued) Country Netherland 4 PDO& 3 PGI

Austria

Poland

Portugal

Product type Hollandse geitenkaas (PGI-2015) Edam Holland (PGI-2010) Gouda Holland (PGI-2010) Kanterkaas/Kanternagelkaas/ Kanterkomijnekaas (PDO-2000) Boeren-Leidse met sleutels (PDO-1997) Noord-Hollandse Gouda (PDO-1996) Noord-Hollandse Edammer (PDO-1996) Ennstaler Steirerkas (PDO-2021) Tiroler Almkäse/Tiroler Alpkäse (PDO-1997) Vorarlberger Alpkäse (PDO-1997) Tiroler Bergkäse (PDO-1997) Vorarlberger Bergkäse (PDO-1997) Gailtaler Almkäse (PDO-1997) Tiroler Graukäse (PDO-1996) Ser koryciński swojski (PGI-2012) Redykołka (PDO-2009) Wielkopolski ser smażony (PGI-2009) Oscypek (PDO-2008) Bryndza Podhalańska (PDO-2007) Queijo mestiço de Tolosa (PGI-2000) Queijo do Pico (PDO-1998) Queijo de Cabra Transmontano / Queijo de Cabra Transmontano Velho (PDO-1996) Queijo de Évora (PDO-1996) Queijo Serpa (PDO-1996) Queijo S. Jorge (PDO-1996) Queijo de Nisa (PDO-1996) Queijo Serra da Estrela (PDO-1996) Queijo da Beira Baixa (PDO-1996) Queijo de Azeitão (PDO-1996) Queijo Terrincho (PDO-1996) Queijo Rabaçal (PDO-1996)

Total publication on Web of Science Basic search: 0 Basic search: 2 Basic search: 1 Basic search: 0

– – – –

Basic search: 0

–

Basic search: 0

–

Basic search: 0

–

Basic search: 0 Basic search: 0

– –

Basic search: 0 Basic search: 0 Basic search: 0

– – –

Basic search: 0 Basic search: 0

– – – – – – – – – – – – – – – – – – – (continued)

96

H. T. Temiz et al.

Table 5.1 (continued) Country Romania

Slovenia

Slovakia 8 PGI Cheeses

Sweden

Non-EU Countries United Kingdom 10 PDO& 7 PGI Cheeses

Product type Caşcaval de Săveni (PGI-2021) Telemea de Sibiu (PGI-2019) Telemea de Ibăneşti (PDO-2016) Mohant (PDO-2013) Bovški sir (PDO-2012) Tolminc (PDO-2012) Nanoški sir (PDO-2011) Klenovecký syrec (PGI-2015) Zázrivské vojky (PGI-2014) Zázrivský korbáčik (PGI-2011) Tekovský salámový syr (PGI-2011) Oravský korbáčik (PGI-2011) Slovenský oštiepok (PGI-2008) Slovenská bryndza (PGI-2008) Slovenská parenica (PGI-2008) Svecia (PGI-1997) Jämtländsk vit källarlagrad getost (PGI-2020) Wrångebäcksost (PDO-2022) Vrigstad Hemost (PGI-2019) Sörmlands Ädel (PGI-2020) Cheeses (reg type-year) Traditional Welsh Caerphilly/ Traditional Welsh Caerffili (PGI-2018) Traditional Ayrshire Dunlop (PGI-2015) Yorkshire Wensleydale (PGI-2013) Orkney Scottish Island Cheddar (PGI-2013) Staffordshire Cheese (PDO-2007) Exmoor Blue Cheese (PGI-1999) Dorset Blue Cheese (PGI-1998) Teviotdale Cheese (PGI-1998) Swaledale ewes’ cheese (PGI-2018) White Stilton cheese/Blue Stilton cheese (PDO-1996) West Country farmhouse Cheddar cheese (PDO-1996)

Total publication on Web of Science Basic search: 2 Basic search: 1 Basic search: 2

– – – [201] – – – – – – – – – – – – –

Total publication of WOS

– – – GC/MS studies – – – – – – – – – – – (continued)

5

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Table 5.1 (continued) Country

Product type Beacon Fell Traditional Lancashire cheese (PDO-1996) Single Gloucester (PDO-1996) Swaledale cheese (PDO-1996) Bonchester cheese (PDO-1996) Buxton Blue (PDO-1996) Dovedale cheese (PDO-1996)

Total publication on Web of Science – – – – – –

and N-acetyl-glucosamine were determined in yoghurt produced from sheep’s milk [203]. The metabolite profile differences between sheep’s and goat’s milk were analyzed by GC/MS-based untargeted metabolomics. Arabitol, citric acid, α×ketoglutaric acid, glyceric acid, myoinositol, and glycine contents were higher in sheep’s milk, whereas higher levels of mannose×6×phosphate, myoinositol isomaltulose, valine, pyroglutamic acid, leucine, and fucose were detected in goat’s milk [204]. In a similar study, metabolite profiles of cow’s and goat’s milk yoghurt were compared during low-temperature storage for up to 28 days. This study was conducted as an extension of the previous study by the authors [205] to enhance the knowledge of the regulation of amino acid and fatty-acid metabolisms during storage. PLS-DA was applied to the metabolites of cow and goat yoghurts on day 14, as the most significant difference between the two samples was obtained on this day. Up-regulation of amino acids and dipeptides, down-regulation of tri-peptides, and higher pH; up-regulation of sebacic acid, higher contents of medium-chain fatty acids and carboxylic acids; and higher contents of linolenic acid and n-butyrylhomoserine lactones were observed for goat yoghurt at day 14 [206].

5.4.2

Butter, Ghee

In the study by Kazazic et al. (2021), adulteration of butter through the addition of margarine or pork fat was determined by using both GC and near-infrared (NIR) spectroscopy. PCA performed on GC data showed that the adulteration ratio has a strong dependence on lauric, palmitic, stearic, palmitoleic, oleic, and linoleic acid contents. NIR spectroscopy enabled the authors to obtain the fatty acid fingerprint of the samples, and the high potential of NIR spectroscopy to be used instead of GC was shown in the study [207]. Ghee can be described as the clarified form of butter, which is prepared by the removal of water (80%) & Kovats Index (criteria: < 20 units difference in comparison to published data) HS-SPME GC/MS Fiber: DVB/CAR/PDMS Equilibration time (min): 30 Extraction temp (°C): 60 Extraction time (min): 30 Column: Restek Rtx-5MS Run time (min): 42.3 min Mass range: 35–650 m/z Peak identification: Wiley 275 mass spectra library and linear retention indices (LRI) HS-SPME GC/MS Fiber: CAR/PDMS/ DVB Extraction temp (°C): 25 Extraction time (min): 180 Column: Rtx-Wax column Run time (min): 32.9 min Mass range: 30–350 m/z Peak identification: (NIST) MS spectral database and pure compounds when available Xagoraris et al. [43]

Karabagias et al. [41, 42]

Honey and Bee Products (continued)

PCA: The first two Borgonovo et al. [44] principal components accounted for the 92.6% of the total variance

Stepwise LDA: Classification rate: 82.3% with crossvalidation method

MANOVA ( p < 0.05) SLDA k-NN: classification rate 89.5% for crossvalidation

7 153

Cotton (n = 5) Greece Erica (n = 9) Greece Fir (n = 6) Pine (n = 19) Greece

Honey botanical origin (number of samples) Geographical origin Bell heather (n = 6) Carob tree (n = 5) Chestnut (n = 2) Eucalyptus (n = 5) Incense (n = 4) Lavender (n = 8) Orange (n = 9) Rape (n = 2) Raspberry (n = 2) Rosemary (n = 1) Sunflower (n = 3) Strawberry tree (n = 4) Portugal Thyme (n = 13) Greece

Table 7.1 (continued)

Non-targeted (124 volatiles identified)

Targeted/nontargeted Targeted (192 volatiles identified)

α-pinene, octane, nonanal

Benzeneacetaldehyde, benzealdehyde, and benzyl nitrile 2-methyl-1-butanol, 4-methyl-1-pentanol Isophorone and furfural

Marker compounds n-nonadecane, nheneicosane, n-tricosane, npentacosane, palmitic, linoleic and oleic acids, cisand trans-linalool oxide (furanoid), hotrienol, α-isophorone, benzene acetaldehyde, and 3,4,5trimethylphenol.

Dilution of honey (10 g) in water IS: styrene

Sample preparation Hydrodistillation (HD) Dilution of honey (50 g) in water Time: 1 h Recovery of the analytes with pentane Concentration under a stream of nitrogen Analytical method GC/MS Column: DB-1 and a DB-17HT Run time (min): 61.7 min Peak identification: retention indices (RIs) & GC/MS spectra from a laboratory-made library based upon the analyses of reference essential oils, laboratory-synthesized components and commercially available standards Purge and Trap GC/MS Trap polymer: Tenax TM TA Column: HP-5MS Run time (min): 60.2 Peak identification: Electronic libraries and tables of retention times and spectra kept in the Laboratory of ApicultureSericulture & RI MANOVA ( p < 0.05) SLDA: The original group cases were correctly classified at a 94.2% rate, whereas the crossvalidated group cases were correctly classified at a 92.3% rate

Data analysis Hierarchical clustering Dendrogram Classification Tree The classification tree needed 12 volatiles for the full discrimination of the 11 varieties

Tananaki et al. [46]

Refs. Machado et al. [45]

154 N. C. Maragou et al.

Non-targeted (92 volatiles identified)

Jujube (n = 10) China Amorpha (n = 12) China Elaeagnus (n = 10) China Apocynum (n = 12) China Retama Non-targeted sphaerocarpa (67 volatiles (n = 7), Retem identified) honey Algeria Eruca sativa (n = 5) Harra honey Algeria Atractylis serratuloides (n = 11) Sor honey Algeria

Lavender(n = 12) China

1,6,10-dodecatrien-3ol,3,7,11-trimethyl-, (E); 1,6-octadien-3-ol,3,7dimethyl; phenol, 2-methoxy and 2-naphtalene methanol, decahydro-α,α,4atrimethyl-8-methylene-, [2R(2α,4aα,8a,8aβ)]

Dimethyl trisulfide

Menthol, 2-nonen-1-ol and 2,3-dimethyldodecane Lilac aldehyde and lilac aldehyde D

Benzyl alcohol and nonane

Marker compounds: hexanal, hexanol, heptanol, and methyl enanthate 2-octenal and 2-ethylhexanol 1-octen-3-ol and teaspirane

HS-SPME GC/MS Fiber: DVB/CAR/PDMS Equilibration time (min): 15 Extraction temp (°C): 45 Extraction time (min): 30 Column: HP-5MS Run time (min): 57 min Mass range: 50–350 m/z Peak identification: (NIST search 2.3) and HMDB database & RI HS-SPME GC/MS Fiber: PDMS/DVB Extraction temp (°C): 50 Extraction time (min): 60 Column: ZB-5MSi column Run time (min): approx. 65 Peak identification: NIST Library and linear retention indices

Dilution of honey (2 g) in water Addition of NaCl IS: Benzophenone

Dilution of honey (7.5 g) in 30% NaCl solution

[47]

[48] PCA (First two components 87.1% variability)

ANOVA PCA OPLS-DA ( p > 0.01) VIP (≥ 1)

7 Honey and Bee Products 155

156

N. C. Maragou et al.

compounds using head-space solid-phase microextraction (HS-SPME). HS-SPME is an easy and green technique since it includes only dilution of honey in water or in sodium chloride solution or just exposure of the SPME fiber to the vial headspace while heating the sample [32, 34, 44]. It is also a suitable technique for the analytes of interest, which belong to the volatile and semi-volatile compounds that contribute significantly to the aroma of honey and which can be used for its authentication. The technique usually requires a small quantity of sample (1–7.5 g), while even sample masses as low as 50 and 250 mg have been reported for one- and two-dimensional HS-SPME GC/MS methods [34]. The material of the SPME fiber is in most cases DVB/Carboxen/PDMS. Apart from static headspace-gas chromatography (HS-GC), dynamic HS-GC has also been applied for the isolation of the volatile compounds of honey in botanical authentication studies. In particular, purge and trap-GC/MS with a sample concentrator containing the porous polymer Tenax TA was used for the discrimination of acacia, sunflower and tilia [27], lavender and thyme [33], and thyme, cotton, erica, fir, and pine honey [46]. In dynamic HS-GC, sample preparation comprises capturing the volatiles by a gaseous effluent passed through or over the matrix onto a suitable trapping system, such as cryotraps, solid adsorbents, liquid stationary phases, or selective reagents for a given class (or classes) of compounds, coated on a solid support. The trapped volatiles are then recovered through heat or solvent elution either on-line or off-line to the gas chromatograph [49]. Hydrodistillation [45], liquid–liquid extraction with methylenechloride [40], and sample preparation techniques that require larger samples of 50 g and more toxic solvents, especially for liquid–liquid extraction, have also been applied. Regarding the gas chromatographic separation, different columns have been applied, as shown in Table 7.1, but HP-5MS columns have been mostly used. The mass spectrometry was exclusively applied in a full-scan mode, screening a wide range of m/z (24–650 m/z). The identification of the compounds was based on (i) the linear retention indices, (ii) the comparison of the obtained mass spectra of the chromatographic peaks to those registered in databases, such as NIST (National Institute of Standards and Technology), and (iii) reference standards when available. It is noted that the term “targeted” used in Tables 7.1–7.7 stands for the screening methodology that uses reference standards for the identification and quantification of the analytes, while “non-targeted” stands for the screening methodology where no reference standards have been used and the identification was based on the mass spectra and the retention indices, following in a way the approach of the corresponding environmental screening analysis [88]. Some of the GC/MS methodologies applied for the botanical authentication of honey samples employ a simple comparison of the concentrations of the identified compounds found in the tested samples originated from different plants [25, 26, 32, 36]. However, the emerging GC/MS methodologies for the botanical authentication of honey import the generated GC/MS data along with the appropriate sample information to sophisticated chemometric tools, such as unsupervised tools for the clustering of the tested samples originated from different plants [27, 33–35, 44, 48] and supervised machine learning applications for the generation of a prediction

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model that could be used for the identification of unknown samples [30, 31, 38, 39, 41–43, 46, 47]. As the Mediterranean basin is an area of excellence for beekeeping, several studies concerning the Mediterranean honey varieties have been reported in the literature during the last years. Particularly, Greek fir, pine, thyme, and citrus monofloral honeys, being the most commonly available Greek varieties, have been intensively studied [38, 39, 41–43, 89]. The analytical methodology applied in these studies included either HS-SPME GC/MS [38, 39, 41–43] or purge and trap GC/MS analysis [89] followed by tentative identification of the determined analytes using NIST database and retention index (RI) values. Semi-quantification of all compounds was performed using the internal standard (benzophenone) and afterward multivariate statistical techniques were applied to the semi-quantitative data, such as MANOVA, LDA, and k-NN. Satisfactory discrimination of these varieties, along with other studied varieties, such as chestnut, clover, eucalyptus, and heather, was achieved in almost all studies, with classification rates varying from 77.3% [38, 39] to 96.8% [41, 42]. Several markers indicating honey botanical origin were revealed, such as salicylaldehyde and beta-thujone of pine honey, methylanthranilate and anethofuran of citrus honey, and pentanoic acid and phenylacetonitrile of thyme honeys. Portuguese eucalyptus, chestnut, and heather honey volatile profiles were studied using HS-SPME GC/MS, and several compounds were tentatively identified [36]. The authors used ANOVA and t-test to demonstrate differences in terms of both botanical and geographical origin; however, the sample set was very low (7 samples) and no undisputable markers could be confided. A very interesting study on the authentication of 12 Portuguese honey varieties was reported in 2021 by Machado et al. [45]. In this study, the volatile profiles of bell heather, chestnut, rape, eucalyptus, incense, orange, strawberry, raspberry, rosemary, sunflower, carob, and lavender were assessed using hydrodistillation as the extraction technique and GC/MS for compound identification. Retention indices and GC/MS spectra of the analytes were evaluated and compared to laboratory-synthesized and commercially available standards, and GC/FID was used for quantification. Hierarchical cluster analysis (HCA) indicated two main clusters, and a dendrogram was built using 12 compounds that were found to be enough to fully discriminate 11 out of the 12 honey types. These compounds were benzaldehyde, benzene acetaldehyde, β-copaene, cis-linalool oxide, n-decane, ethyl hexadecanoate, α-eudesmol, 2-furfural, heptacosene, hotrienol, n-tricosane, and one unknown that could not be identified [45]. The influence of the botanical origin in the composition of volatiles of acacia, sunflower, and tilia honeys from different countries (Spain, Romania, and Czech Republic) was also investigated [27]. Purge and trap GC/MS analysis was performed, and 51 compounds were identified using authentic standards or MS spectra that match to the NIST database. The honey botanical type was found to have by far the greatest influence on the differentiation of honeys, and several floralorigin markers were disclosed, such as carvacrol and α-terpinene for tilia honey, α-pinene and 3-methyl-2-butanol for sunflower honey, and cis-linalool oxide for

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acacia honey. Thus, the determination of volatile compounds could present an efficient complementary tool to the pollen analysis to distinguish between these types of honey [27]. Moreover, volatile analysis using GC/MS could also be used to distinguish honey botanical origin when pollen content is under-represented, such as in the case of lavender honey, indicating its strong correlation to the honey’s sensory profile [33]. In a novel study by Yang et al. [30], Corsican PDO honeys were characterized in terms of volatile compounds and differentiated according to their botanical origin. HS-SPME GC/MS and GC/FID were used for the analysis, and target and non-target screening of the samples was performed (e.g., identification with either authentic standards or commercial libraries). Therefore, 105 compounds were determined in 269 honey samples, establishing an exceptional data set for the differentiation of chestnut grove, spring clementine, spring maquis, summer maquis, autumn maquis, and honeydew maquis honeys using supervised and unsupervised chemometric techniques. Corsican honeydew honey was found to contain much greater amounts of 3-furaldehyde compared to blossom honey. Several characteristic markers were identified for different botanical origins of blossom honey, such as 2-aminoacetophenone for chestnut grove honey, p-anisaldehyde and 4-npropylanisole for spring maquis, and isophorone and 3,4,5-trimethylphenol for autumn maquis honey [30]. Moreover, Italy offers a wide selection of regionally produced honeys with over 40 monofloral varieties. Aromatic profiles of Italian sainfoin and eucalyptus honeys were evaluated using an electronic nose with volatile compound profile analyses using HS-SPME GC/MS [44]. A clear discrimination using PCA was achieved (92.6% of the variance with the first two PCs). Moving on to other European honey varieties, a comprehensive GC × GC/MS approach was developed by Siegmund et al. [34] to investigate the volatile profiles from 8 monovarietal honeys from dandelion, fir tree, linden tree, chestnut tree, robinia, orange, lavender, and rape from Austria and Croatia. The high separation power of two-dimensional GC/MS enabled the distinct identification of coeluting compounds, and 76 analytes were identified in all honey samples. PCA and cluster analysis of the volatile compounds showed a high correlation with the PCA obtained from sensory evaluation, thus indicating the potential of profiling techniques to accurately classify unifloral honey types. Furthermore, several odor-active compounds, such as eucalyptol, eugenol, and thymol, among others, were determined, most possibly deriving from sources other than the respective honeyflow. This could be attributed to the use of essential oils in apiculture [34]. Lacy phacelia, rape, and willow honey varieties from Poland were also studied utilizing chromatographic fingerprinting. HS-SPME GC/MS analysis demonstrated that trans-linalool oxide, hotrienol, cis-linalool oxide, and cis-epoxylinalool were the predominant compounds of pure phacelia honeys [37]. Chiral volatile compounds, such as linalool, linalool oxides, hotrienol, lilac aldehydes, 4-terpineol, and α-terpineol, have been reported to be major constituents of different honeys; however, according to Špánik et al. [26], the distribution of enantiomers depends on the honey botanical origin, with a within-class variation up to 10%. Multidimensional gas chromatography in separate GC runs was used for the

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determination of enantiomer ratios of target analytes in monofloral honey samples from various European countries (acacia, rapeseed, sunflower, linden, chestnut, and orange honeys). Although Europe produces a variety of high-quality apicultural products that are obliged to fulfill specific rules on quality and labeling, Asia is by far the world’s biggest producer of honey, particularly China, which accounts for about a quarter of global output [90]. However, studies dealing with botanical origin authentication using GC/MS are rather limited. Chen et al. [31] reported the development of a non-target screening workflow for the differentiation of acacia, linden, vitex, and rape Chinese honeys using HS-SPME GC/MS and chemometrics. A series of statistical steps were followed to reduce the number of variables from 2734 to 70 and classification and prediction models were constructed using PLS-DA, NB, and BP-ANN analysis. The models demonstrated excellent performance with 100% accuracy. Moreover, validation of the models was performed by an independent set of additional 20 authentic honey samples, which were all accurately classified. Aroma profiles of Sudanese Acacia nilotica, Acacia seyal, Ziziphus spina-christi, Amaranthus graecizan, Eucalyptus spp., and multifloral honeys were investigated using SPME GC/MS, thereby identifying 58 aroma compounds by collating results to NIST database and retention indices [29]. Twenty of the identified compounds were chosen as key compounds for honey aroma characterization, and multivariate analysis (HCA and PCA) was subsequently applied for botanical origin discrimination. In addition, the relationship between sensory descriptors and aroma compounds was demonstrated using Partial Least-Squares Regression (PLSR). The correlation between volatile compounds and aroma and floral origin was the objective of a comprehensive study of Zhu et al. [47], revealing 27 volatiles with significant aroma contribution to an intense fruity odor of apocynum, jujube, and amorpha honey, together with mint and anise, rosaceous and citrus odor to elaeagnus honey, and green, balsamic, and jasmine flavor to lavender honey. Chemometric analysis evidenced important correlations between volatiles and floral origin, proving that volatiles and aroma features can offer compelling evidence for honey authentication. Chemical fingerprints were studied for five unifloral (litchi, eucalyptus, lemon, neem, and ginger) and three multifloral Indian honeys using GC/MS, among other analytical techniques [40]. Tentative identification of volatiles was performed using spectral libraries and literature data, and the analytes were semi-quantified using 2-pentanol as an internal standard. Validation data were also obtained for some compounds in terms of linearity, recoveries, and repeatability. Analysis of volatiles led to the first-time determination of odor-active compounds, such as azadirachtin for neem and zingiberene in ginger honey. Moreover, PCA was performed with the first two components explaining 71.21% of the total variance. Significant variations in the volatile compounds were also determined in longan, sunflower, coffee, wild flowers, and lychee honeys from Thailand; 32 analytes were identified as potential markers of botanical origin, suggesting that differences in the honey volatile profile are attributed not only to floral source but also to honeybee species [32]. Different bee species provide an entirely different volatile fingerprint of honey, as also indicated by da Costa et al. [35] who studied the aroma compounds of Brazilian

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monofloral honeys produced by Melipona subnitida (jandaíra) and Meliponas cutellaris (uruçu) stingless bees. In this work, the HS-SPME GC/MS methodology for the determination of volatiles was optimized, validated, and applied for the characterization of chanana, malícia, algaroba, and angico honeys. The results revealed discriminating compounds and possible floral markers, such as linalool for malícia honey, D-sylvestrene for chanana honey, rose oxide for algaroba honey, and benzenethanol for angico honey. Finally, the famous New Zealand manuka honey was discriminated from its pollen-identical kanuka honey and an Australian jelly bush honey through their volatile profiles using HS-SPME GC/MS [25].

7.3

Authentication of Geographical Origin of Honey

Along with the declaration of the honey’s botanical origin, the declaration of its geographical origin is also of high importance for its fair trade. According to Directive 2001/110/EC [1] amended by DIRECTIVE2014/63/EU, the country or countries of origin where the honey has been harvested shall be indicated on the label. If the honey originates from more than one member state or from a third country, that indication may be replaced with the words “blend of EU honey,” “blend of non-EU honey,” or “blend of EU and non-EU honey.” Similarly, according to the International Standard for Honey by Codex Alimentarius (CXS 12-1981, amended in 2019), honey may be designated by the name of the geographical or topographical region if the honey was produced exclusively within the area referred to in the designation. Taking into consideration the above and the interest of the consumers for honey of Protected Designation of Origin (PDO) and Protected Geographical Identification (PGI), which possess unique characteristics, the development of analytical methodologies that can guarantee the geographical authentication of this product is of raised interest. In recent years, several varieties derived from different countries and different regions of the same country have been analyzed in terms of characterization and authenticity assessment. Table 7.2 summarizes the most recent GC/MS studies on the investigation of the geographical origin of honey. It is observed that the dominant methodology for the sample preparation is the isolation of volatile and semi-volatile compounds using either just HS-GC or more frequently HS-SPME [38, 39, 41, 42, 50–53, 56–59] as observed in the GC/MS studies for the authentication of botanical origin. The methods required small quantities of the sample (1–5 g), while even a miniaturized solid-phase microextraction methodology (mini-SPME), using only 200 mg of honey sample mixed with 200 μL of water, has recently been developed [59]. For gas chromatographic separation, different columns have been applied, as shown in Table 7.2, but columns DB-5MS and HP-5MS have been mostly used [38, 39, 41, 42, 50–54, 57]. The mass spectrometry is applied exclusively in a full-scan mode, screening a wide range of m/z (28–650 m/z), and the identification of the compounds is based on (i) the linear retention indices, (ii) the comparison of the obtained mass spectra of the chromatographic peaks to those registered in

Non-targeted (33 volatiles identified)

Greece (n = 16) Citrus

Morocco (n = 6) Citrus Spain (n = 8) Citrus

Non-targeted (47 volatiles identified)

Greece Thyme Irakleio (n = 10) Hania (n = 10) Kefalonia (n = 8) Symi (n = 7) Lakonia (n = 7)

p-cymene, linalool, nonanal, 2-cyclohexene-1-propanal and 3-cyclohexene-1- propanal, ethyl octanoate, and ethyl nonanoate herboxide(Isomer II) and cislinalool oxide, ethyl acetate hotrienol

formic acid ethyl, formic acid, acetic, 1-hydroxy-2-propanone, octane, terpinen-4-ol, decanal, decanoic acid ethyl ester, 4,7,7trimethyl-bicyclo[3,3,0]-octan-2one

Dilution of honey (2 g), in water and addition of NaCl IS: Benzophenone

HS-SPME GC/MS Fiber: DVB/CAR/ PDMS Equilibration time (min): 15 Extraction temp (° C): 45 Extraction time (min): 30

Analytical method HS-SPME GC/MS Fiber: DVB/CAR/ PDMS Equilibration time (min): 15 Extraction temp (° C): 45 Extraction time (min): 30 Column: HP-5MS Run time (min): 36.5 Peak identification: MS data base Wiley 7, NIST 2005 and Retention Indices

Data analysis MANOVA ( p < 0.05) LDA Linear discriminant analysis Classification rate: 84.6% with the cross-validation method MANOVA ( p < 0.05) LDA Linear discriminant Analysis Classification rate: 64.3% with the cross-validation method MANOVA ( p < 0.05) LDA Linear discriminant Analysis Classification rate: 86.5% with the cross-validation method.

(continued)

[52]

[51]

Refs [50]

Honey geographical origin (number of samples) Botanical origin Greece Pine Halkidiki (n = 10) Evia (n = 12) Thassos (n = 10) Samos (n = 7) Sample preparation Dilution of honey (2 g), in water and addition of NaCl IS: Benzophenone

Table 7.2 GC/MS studies for the authentication of the geographical origin of honey

Marker compounds Hexanoic acid ethyl ester, 2,3 butanediol, decane, β-thujone, heptanoic acid ethyl ester, 1-methyl-4-(1-methylethenyl) benzene, nonanal, and ethyl-1hexanol

Honey and Bee Products

Targeted/nontargeted Non-targeted (55 volatiles identified)

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Spain (n = 10) Thyme Brazil (n = 17) Guaraqueçaba (east region), Prudentópolis (southeast region) Cambará (north pioneer region)

Greece (n = 12) Thyme Egypt (n = 7) Thyme

Honey geographical origin (number of samples) Botanical origin Egypt (n = 7) Citrus Marocco (n = 5) Thyme

Table 7.2 (continued)

octane, hexanoic acid ethyl ester, octanoic acid ethyl ester, sid-linalool oxide, decanoic acid ethyl ester benzeneacetaldehyde, benzaldehyde, octanal, 1-methyl-4-(1-methylethyl)benzene, α-terpinolene, linalool, borneol, terpinen-4-ol, 2-methoxy-4-(2-propenyl)phenol hotrienol, lilac aldehyde C

hotrienol, epoxylinalol, benzaldehyde and TDN, thuja-2,4 (10)-diene, ethyl hexanoate, p-cymene, 2- heptanol, and 2-heptanone

Non-targeted (44 volatiles identified)

Marker compounds 2-methyl-butanal, heptane

Non-targeted (62 volatiles identified)

Targeted/nontargeted

Addition of honey (2 g) to saturated NaCl solution

Sample preparation Analytical method

Static HS GC/MS Equilibration time (min): 10 Extraction temp (° C): 60 Column: HP-5MS Run time (min): 18.08 Mass range: 50–550 m/z Peak identification:

Column: HP-5MS Run time(min): 36.5 Peak identification: MS data base Wiley 7, NIST 2005 (similarity criteria ≥80%) and Linear Retention Indices (Kovats indices)

PCA LDA (85.7% of correct predictions samples from Guaraqueçaba, 80.0% Cambará and 100% from Prudentópolis (100% of correct predictions)

MANOVA ( p < 0.05) LDA Linear discriminant analysis Classification rate: 88.2% with the cross-validation method

Data analysis

[54]

[53]

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Non-targeted (110 volatiles identified)

Non-targeted. (25 volatiles identified)

Argentina Patagonian Forest (n = 7) Espinal (n = 6) Pampa (n = 6) Parana Delta and Island (n = 6)

Cyprus Multifloral Limassol (n = 5) Larnaca (n = 10) Nicosia (n = 5)

6-methyl-5-hepten-2-one, 2,6-dimethylphenol, terpinolene and nonanal Ethyl 2-phenylacetate and naphthalene 3,8-p-menthatriene, cyclopropylidenemethylbenzene, 1,1,6-trimethyl-1,2dihydronaphthalene, 1,2,4trimethylbenzene and α-pinene 3-methyloctane, Isopropyl 2-methylbutanoate, 2,6-dimethyl1,6-octadiene, unknown compound 4, cymene 1-(2-furanyl-ethanone), cis-linalool oxide, para-cymenene Dilution of honey (2 g), in water and addition of NaCl IS: Benzophenone

Dilution of honey (10 g) in water IS: styrene Samples were heated at 40 °C and then directly purged for 30 min

HS-SPME GC/MS Fiber: DVB/CAR/ PDMS Equilibration time (min): 15 Extraction temp (° C): 45 Extraction time (min): 30 Mass range: 50–550 m/z Column: DB-5MS Run time (min): 36.5 Peak identification:

Linear Retention Index & NIST library Purge and trapGC/MS Trap polymer: Tenax TM TA Column: SGE BPX5 Run time (min): 55.6 Peak identification: Linear Retention Index & NIST and Wiley libraries (Match criteria >80%) & Retention Indices MANOVA ( p < 0.05) DFA Discriminant function analysis Classification rate 70% with crossvalidation method

PCA RDA Redundancy analysis LDA (96% prediction success)

Honey and Bee Products (continued)

[38, 39]

[55]

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Romania (n = 50) Acacia Transilvania, (Zone 1) Southern Romania (Zone 2) East Romania (Zone 3).

Honey geographical origin (number of samples) Botanical origin

Table 7.2 (continued)

Non-targeted (79 volatiles identified)

Targeted/nontargeted

Zone 1: -methyl-3-buten-1-ol Zone 2: ethanol, acetic acid, 5-ethenyldihydro-5-furanone Zone 3: acetone, 3-methyl-3buten-1-ol, trans-linalool oxide, benzemethanol

Marker compounds

Addition of honey (4 g) in a headspace vial

Sample preparation MS database Wiley 7, NIST 2005 (similarity criteria ≥90%) and Linear Retention Indices (Kovats indices) HS-SPME GC/MS Fiber: DVB/CAR/ PDMS Equilibration time (min): 30 Extraction temp (° C): 40 Extraction time (min): 30 Column: SP-5DBWAX Run time (min): 47.6 Peak identification: retention time, & comparison of obtained mass spectra with standard mass spectra from the “Pal 600” spectra library

Analytical method

Comparison of GC/MS chromatograms and normalized peak areas

Data analysis

[56]

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Portugal (n = 10) honeydew, chestnut, heather & multifloral Greece (n = 6) honeydew, oak, thyme plus herbs, orange blossom & multifloral New Zealand & Australia (n = 2) thyme & eucalyptus plus ground flora Malaysia (n = 2) multifloral Greece Aitoloakarnania (n = 9) Central Greece (n = 8) Thessaly (n = 10) Macedonia (n = 7) Quercus ilex honey

Non-targeted

Non-targeted

eucalyptol, 1-decanol, tetradecanoic acid ethyl ester

linalool

no strong marker

Nonane and α-isophorone

1-hydroxy-2-propanone

Dilution with water Addition of NaCl + Benzophenone (IS)

Dilution of honey (2 g) in water Addition of NaCl IS: Benzophenone

HS-SPME GC/MS Equilibration time (min): 15 Extraction temp (° C): 45 Extraction time (min): 30 Column: DB-5MS Mass range: 50–550 m/z Run time (min): 36.5 Peak identification: Linear Retention Index & NIST 2005

Retention indices (RI) HS-SPME GC/MS Peak identification: Linear Retention Index & NIST 2005 (Similarity criteria >80%)

MANOVA ( p < 0.05) PCA (98% of total variance) SLDA (classification rate 80%) PLS-VIP (scores >1)

PCA (90% of total variance) ROC Receiver operating characteristic curve

Honey and Bee Products (continued)

[57]

[41, 42]

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Turkey (n = 23) Pine 23 stations from 7 Aegean and Mediterranean regions

Honey geographical origin (number of samples) Botanical origin

Table 7.2 (continued)

Targeted and non-targeted

Targeted/nontargeted

nonanal, nonanol, octanol, decanal, phenylacetaldehyde, benzaldehyde, octanal, α-pinene, 4-oxoisophorone, methyl salicylate, isopropyl myristate, limonene, and β-damascenone

Marker compounds

Dilution in NaCl solution

Sample preparation Similarity criteria >80% Occurrence >50% of samples HS-SPME GC/IT-MS Equilibration time (min): 30 Extraction temp (° C): 50 Extraction time (min): 30 Column: n Rxi-5Sil MS (Restek) Run time (min): 65 Mass range: 28–650 m/z Peak identification: Retention indices, computer matching with the Wiley, ADAMS, and NIST 08 MS databases, comparison of fragmentation patterns of the mass spectra reported in

Analytical method

PCA HCA Hierarchical clustering analyses

Data analysis

[58]

Refs

166 N. C. Maragou et al.

Spain Galician honeys (n = 12) honeydew, multifloral, chestnut, blackberry & heather Greece (n = 3) multifloral Kazakhstan (n = 2) multifloral & thyme Italy (n = 2) chestnut, orange France (n = 1) Lavender Spain Galician honeys (n = 12) honeydew, multifloral, chestnut, blackberry & heather Greece (n = 3) multifloral Kazakhstan (n = 2) multifloral & thyme

δ-decalactone, 4-hydroxy-4methyl-2-pentanone

Thymol, carvacrol benzothiazole Benzothiazole, 1,2-dihydrolinalool,

Non-targeted

Non-targeted

Non-targeted

phenylethyl alcohol, benzyl alcohol, β-damascenone, cislinalool oxide, β-linalool, 1,1,5trimethyl-1,2-dihydronaphtalene, 1-(2,3-dimethylphenyl)ethanone, α-Ionene, megastigmatrienone A, B and C, α-gurjunene

Targeted

Dilution with water IS: 1-Octanol and celestolide

the literature, reference standards for some of the compounds mini HS-SPME GC/MS Fiber: DVB/CAR/ PDMS Equilibration time (min): 2 Extraction temp (° C): 100 Extraction time (min): 30 Column: DB-WAX Run time (min): 32 Mass range: 35–400 m/z Peak identification: NIST v2 Similarity criteria >80%. Confirmation with reference standards for nine out of the 24 targeted compounds Validation data for nine compounds in terms of linearity, precision and accuracy) Comparison of GC/MS chromatograms

Comparison of GC/MS chromatograms and the determined concentrations of the analytes

(continued)

[59]

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Brazil (n = 30) Santa Catarina, Bocaina do Sul (BS), Bom Retiro (BR), Lages (LG), Urubici (UB), Urupema (UP)

Honey geographical origin (number of samples) Botanical origin

Table 7.2 (continued)

Targeted

Targeted/nontargeted

epoxylinalool, 1,4-dimethylindanyl acetate free amino acids: ser, pro, asn, asp, and glu

Marker compounds

Dilution of honey in water IS: Removal of proteins with solid phase extraction Derivatization of amino acids Extraction, evaporation, dilution of the residue

Sample preparation

Liquid Injection GC/MS Column: Zebron ZB-AAA Run time: 7 min Mass range: 45–450 m/z Peak identification: Reference standards Validation data available

Analytical method

PCA ( p < 0.05) Cluster analysis (82% of the total variance)

Data analysis

[60]

Refs

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databases, such as NIST, and (iii) reference standards when there was availability similarly to the authentication of botanical origin studies. The comparison of the quantified or semi-quantified compounds found in the tested samples originated from different geographical regions is performed [56, 59]. However, the emerging GC/MS methodologies for the geographical authentication of honey make use of unsupervised data clustering of the tested samples originated from different regions of the same country [58, 60] and supervised machine learning applications for the generation of a prediction model that could be used for the identification of unknown samples [38, 39, 41, 42, 50–53, 55, 57]. A characteristic example of the latest methodology employs discriminant function analysis (DFA) for the geographical differentiation of Cypriot multifloral honeys. In particular, 25 volatile compounds of different classes were identified by HS-SPME GC/MS in 20 samples originated from three different regions of Cyprus by 100% frequency rate and semi-quantified using the internal standard of benzophenone. Application of multivariate analysis of variance (MANOVA) showed that 3 volatiles [1-(2-furanyl-ethanone), cis-linalool oxide, para-cymenene] recorded significant variations (p < 0.05) according to geographical origin. Discriminant function analysis (DFA) classified honeys according to geographical origin, providing a total classification rate of 85% using the original and 70% the cross validation method based on the aforementioned volatiles. The same study also indicated that the lower the altitude of harvesting the richer the aroma of honeys [38, 39]. The same methodology was followed for the geographical discrimination of thyme honeys originated from Mediterranean countries of Marocco, Greece, Egypt, and Spain [52], and from different regions of Greece [51]; of citrus honeys of the same Mediterranean countries (Marocco, Greece, Egypt and Spain) [53]; and of pine honeys of different regions of Greece [50]. A similar methodology was applied for the identification of volatile indicators of the provenance of Greek Quercus ilex honey from four different geographical regions of Greece. PCA, MANOVA, and stepwise linear discriminant analysis (SLDA) showed that provenance affected the volatile composition of Quercus ilex honey and taking into consideration the complementary results of PLS-VIP (partial least squares analysis with variable importance in projection) eucalyptol, 1-decanol, and tetradecanoic acid ethyl ester were proposed as volatile indicators of the provenance of this honey type [57]. Another classification technique, the receiver operating characteristic (ROC) curve has been applied to HS-SPME GC/MS data for the geographical origin characterization of Portuguese, Greek, Oceanian, and Asian honey, in combination with PCA [41, 42]. ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings (independent variables). Between the identified analytes, higher amounts of nonane and α-isophorone were observed in Greek samples, while 1-hydroxy-2-propanone was increased in Portuguese samples and linalool in honeys from Malaysia [41, 42].

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A relatively quick and simple HS-GC/MS method, with a run time of approximately 18 min was applied for the comparative analysis of the volatile composition of honeys from different Brazilian regions produced by stingless bees. Unsupervised (PCA) and supervised (LDA) tools were applied for the identification of distinctive minor volatile components and classification of the samples, accordingly [54]. Apart from the static HS-GC, dynamic HS-GC has also been applied for the isolation of the volatile compounds of honey in geographical authentication studies. In particular, purge and trap GC/MS with a sample concentrator containing the porous polymer Tenax TM TA was used for the classification of Argentinean honeys according to their geographical origin [55]. In dynamic HS-GC, the sample is obtained by capturing the volatiles in a gaseous effluent passed through or over the matrix onto a suitable trapping system, such as cryotraps, solid adsorbents, liquid stationary phases, or selective reagents for a given class (or classes) of compounds, coated on a solid support. The trapped volatiles are then recovered through heat or solvent elution either online or offline to the gas chromatograph [49]. In this study, the GC/MS data were used in PCA, redundancy analysis (RDA), and LDA with a prediction success of 96%. RDA is a constrained ordination analysis that displays how the response variables (in this case, the volatile composition of the samples) correlate with the explanatory variables (in this case, the geographical or the floral origin of the honeys) [55]. Regarding the quantitation of the identified compounds, the use of internal standards, such as benzophenone, 1-octanol, celestolide, and styrene, has been reported, while there are limited cases where reference standards have been used for the quantification of the analytes and for the validation of the method in terms of recovery, precision, linearity, and limit of detection/quantification. One study reporting the use of reference standards for the part of the identified compounds investigates the volatile compounds of pine honeys from 23 stations from 7 regions in Turkey’s Aegean and Mediterranean regions by SPME GC and time of flight mass spectrometry. A total of 32 volatile compounds were identified using linear retention indices and comparison of mass spectra with WILEY, ADAMS, and NIST 08 MS databases. In some cases, additional identification was based on comparison with standard compounds. Classification based on the geographical origin of pine honey samples was determined using PCA and HCA [58]. A mini-SPME, using only 200 mg of honey sample mixed with 200 μL of water, and followed by GC/MS, was applied for the simultaneous analysis of 24 volatile and semivolatile compounds in honeys from European and Asian countries. The selection of 24 compounds was based on their high abundance in tested multifloral honeys and on literature data. The GC/MS method applied full-scan screening, and identification was based on comparison (match >80%) between the obtained experimental MS spectra and those provided by the commercial spectral library database (NIST), use of internal standards, and use of reference standards for nine of the identified compounds for method validation. The 24 target compounds were identified in the 20 analyzed honey samples, and the results were expressed as normalized areas. Non-target species identification was also performed, and some extra compounds were proposed to be specific markers of the geographical and

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botanical origin of the honey. Nevertheless, as the authors comment, a larger panel of samples and a rigorous statistical approach would be necessary for marker identification and classification purposes [59]. Finally, a different methodology for geographical classification using GC/MS for the determination of free amino acids (FAA) in bracatinga honeydew honey (Mimosa scabrella Bentham) from different regions of Brazil is proposed by Azevedo et al. [60]. The sample preparation consists of multiple steps, including removal of proteins with solid-phase extraction, derivatization of the amino acids, extraction, evaporation, and dilution of the final residue, which is subsequently injected into the GC/MS. The results showed that proline is provided exclusively by Apis mellifera bees, and this honey could be differentiated according to geographic regions based on the FAA profile. PCA identified the main FAA responsible for clustering of the samples in these regions (the sum of the first two principal components accounted for 82% of the total variance) and provided a similar discrimination of the geographical location map, particularly with regard to the northern and southern geographical orientations.

7.4

Authentication of Entomological Origin of Honey

Honey samples produced by two different tribes of bees, both belonging to Apidae family, have been studied in the framework of the entomological authentication by GC/MS analysis, the Apini and the Meliponini tribe. There are two bee species of the Apini tribe that are used in commercial apiculture, Apis mellifera and Apis cerana. Apis mellifera is known as the Western Honey Bee [91]. It has a native distribution in Europe, the Middle-East, and Africa, and it has been transported by humans to America, Asia, and Australia [92]. Apis cerana is known as the Eastern or Asian Honey Bee [91]. Apis mellifera and Apis cerana are also called honey bees. According to the Research Report of the International Centre for Integrated Mountain Development (ICIMOD) on Pro-Poor Value Chain Development for Apis cerana Honey [93], mountain communities of the Himalayas have a rich tradition of beekeeping and honey hunting with indigenous honeybee Apis cerana. Honey harvested from these bees is an important source of income for households in the mountain areas. Honey collected from these areas is in great demand, and good quality Apis cerana honey fetches a much higher price than Apis mellifera (European honeybee) honey [93]. Regarding the Meliponini tribe species, these are raised by indigenous cultures in many regions of the world, like Brazil, Malaysia, and Indonesia. They are also called stingless bees. Meliponinae honey or stingless bee honey is highly valued as it is characterized as medicinal honey, used in the treatment of a number of respiratory, dermatological, and gastrointestinal disorders, increasing its value in comparison with the honey of Apis mellifera [94, 95]. It is worth noting that although the honey produced by both tribes of bees (Apini and Meliponini) is valued and used, the International Standard for Honey by Codex Alimentarius (CXS 12–1981, amended in 2019) defines honey as the natural sweet

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substance produced by “honey bees,” a term that is often used for the Apini tribe. In Europe, the official definition of honey is clearly limited to the natural sweet substance produced by Apis mellifera bees according to Council Directive 2001/ 110/EC [1]. The review of the definition of honey and the elaboration of standards, regarding the authentication of honey based on the entomological origin, would contribute to a fair universal trade of honey, with special attention to the authentication of honey produced by indigenous limited bee species of each country/region. Advanced GC/MS methodology comes to serve as a significant tool for the reliable determination of the volatile profile and characteristic compounds of honey samples produced by different bee species and, with the aid of chemometric tools, for the classification of honey samples according to their entomological origin. The GC/MS methodologies that have been applied for the identification of the entomological origin of honeys are summarized in Table 7.3. It is observed that they encompass full-scan GC/MS analysis of ether extracts of honey [61, 63], static HS-SPME GC/MS analysis of diluted honey [54], and the HS-SPME GC/MS analysis of diluted honey, preceded by untargeted metabolomics by headspace GC ion mobility spectrometry (HS GC/IMS) [62]. The majority of the GC/MS methodologies are combined with chemometric tools for the discrimination of the tested groups. There are two recent GC/MS studies that investigated the possibility of discrimination between honey samples produced by two different species of Apini tribe, Apis mellifera and Apis cerana. One of these studies includes the GC/MS analysis of petroleum ether extracts of honey samples produced by Apis mellifera and Apis cerana in order to identify compound markers belonging to the beeswax of the two different species [61]. It is believed that in honeybee colony, where honey is stored in the comb, tiny beeswax fragments are inevitably existing in honey due to the behavior of honeybees and the process of harvesting honey. The full-scan GC/MS detection (30–550 m/z) of the petroleum ether extracts of the two kinds of honey showed that 17-pentatriacontene and hentriacontane were the characteristic constituents of Apis cerana honey and Apis mellifera honey, respectively. The results were confirmed by analyzing the respective beeswax samples. The identification of the compounds was based on the fragmentation pattern by the NIST database but was also confirmed with reference standards increasing the reliability of the identification of the marker compounds. In addition, validation data on the limit of detection, linearity, and repeatability are provided for the developed GC/MS-MS method. The discrimination between A. cerana and A. mellifera honey samples was also investigated by the application of untargeted metabolomic analysis based on volatile fractions in honey from A. cerana and A. mellifera using HS-GC/IMS with no pretreatment and combined with a chemometric method consisting of principal component analysis, and orthogonal partial least-squares discriminant analysis [62]. The variable importance in projection (VIP) was used to explore tentative markers and, subsequently, these tentative markers were confirmed based on their retention index (RI), retention time, and mass fragments by GC/MS. In the framework of the same study, a targeted method was established using the HS-SPME

Targeted and nontargeted

1-nonanol 1-heptanol phenethyl acetate Benzaldehyde Heptanal Phenylacetaldehyde

Hentriacontane

Dilution with water Addition of NaCl

HS GC/IMS: untargeted metabolomics HS-SPME GC/MS: targeted method Fiber type: DVB/CAR/PDMS Equilibration time (min): 20 Extraction temp (°C): 40 Extraction time: 20 min Column: HP-DB-5 Mass range: 15–200 m/z Peak identification: Retention Index & NIST 14 & reference standards

Analytical method GC/MS Column: HP-5 MS Run time: 37.5 min. Mass range: 30–550 m/z Peak identification: NIST 11 & Confirmation with reference standards

PCA OPLS-DA VIP score > 1.5 SIMCA

[62]

Refs [61]

(continued)

Data analysis Comparison of GC/MS chromatograms

Honey entomological origin Subfamily Species Country (number of samples) Apini Apis cerana China (n = 34), Vietnam (n = 3) Apini Apis mellifera China (n = 37), Australia (n = 3), Brazil (n = 3), South Africa (n = 2) Apini Apis cerana China (n = 20) Apini Apis mellifera China (n = 20) Sample preparation Dilution with water Paper filtration Extraction of analytes from filter paper with Petroleum Ether 60–90 °C Concentration

Table 7.3 GC/MS studies for the authentication of the entomological origin of honey

Marker compounds 17-pentatriacontene

Honey and Bee Products

Targeted/ nontargeted Targeted

7 173

Meliponini Heterotrigona bakeri Malaysia (n = 5) Meliponini Tetragona clavipes Fab. Brazil (n = 2) Meliponini Scaptotrigona bipunctata Lep. Brazil (n = 2)

Honey entomological origin Subfamily Species Country (number of samples) Meliponini Geniotrigona thoracica Malaysia (n = 5) Meliponini Tetrigona binghami Malaysia (n = 5)

Nontargeted

Targeted/ nontargeted Nontargeted

Table 7.3 (continued)

No strong marker compound

Ethyl octanoate Ethyl decanoate

2,6,6-trimethyl-1cyclohexene-1carboxaldehyde; 2,6,6-trimethyl-1cyclohexene-1acetaldehyde; Ethyl 2-(5-methyl-5 vinyltetrahydrofuran-2yl)propan-2-yl carbonate No strong marker compound

Marker compounds Copaene

Dissolution in saturated NaCl

Sample preparation Extraction with diethyl ether Rotary evaporation at 35 ° C Drying over anhydrous sodium sulfate – removal of moisture Dilution with ethyl acetate

Static HS GC/MS HS incubation temp/time: 60 °C/ 10 min Column: HP-5MS Mass range: 50–550 m/z Peak identification: Linear Retention Index & NIST library

Analytical method GC/MS Column: HP-5MS Mass range: 30–600 m/z Peak identification database: NIST 11 (similarity index >80%)

PCA LDA

Data analysis PCA HCA PLS-DA VIP score > 0.8 SVM

[54]

Refs [63]

174 N. C. Maragou et al.

Melipona bicolor Lep. Brazil (n = 2) Melipona marginata Lep. Brazil (n = 2) Melipona scutellaris Lat. Brazil (n = 2) Melipona quadrifasciata Lep. Brazil (n = 3) Tetragonisca angustula Lat. Brazil (n = 3) Scaptotrigona xanthotricha Mou. Brazil (n = 1) Meliponini Melipona subnitida Ducke (jandaíra) Brazil Meliponini Melipona Scutellaris Latrelle (uruçu) Brazil

Nontargeted

No strong marker compound

Dilution with water

HS-SPME-GC/MS Fiber type: PDMS/DVB Equilibration time (min): 15 Extraction temp (°C): 45 Extraction time (min): 45 Column: HP-5MS Run time (min): 69.5 min Mass range: 29–400 m/z Peak identification: NIST/EPA/ NIH mass spectral database or published spectra & Linear Retention Index

PCA

[35]

7 Honey and Bee Products 175

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GC/MS to quantitate the markers and verify the effectiveness of these markers to differentiate A. cerana honey from A. mellifera honey. The final marker compounds for A. cerana honey were 1-nonanol, 1-heptanol, and phenethyl acetate, whereas benzaldehyde, heptanal, and phenylacetaldehyde were determined as markers of A. mellifera honey. The method was validated in terms of the limit of quantification, linearity, repeatability, and recovery. It is noted that the two aforementioned GC/MS studies resulted in different marker compounds for A. cerana and A. mellifera honey, which is anticipated taking into consideration the different extracts analyzed by GC/MS, which were ether extracts versus volatile and semi-volatile compounds obtained by HS-SPME, as well as the different m/z ranges scanned. Similar GC/MS methodologies have been applied in order to investigate the possibility of discrimination of honey samples produced by different species of stingless bees, which belong to the subfamily Meliponini. A study aiming at differentiation of Malaysian stingless bee honey from three different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica, and Tetrigona binghami) applied GC/MS to analyze diethyl ether extracts of honey for the determination of volatile compound profiles in combination with PCA and other statistical tools in order to identify potential chemical biomarkers [63]. The identification of the chemical biomarkers was based on the comparison of their mass spectra to the NIST mass spectral library with a similarity index criterion of >80%. It was found that the profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, which was characterized by high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1cyclohexene-1-acetaldehyde, and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl) propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. An HS-GC/MS analysis combined with a chemometric approach, to diluted honey samples produced by eight different species of stingless bees in South Brazil, succeeded in identifying ethyl octanoate and ethyl decanoate as compound markers for Borá species (Tetragona clavipes Fab.) [54]. In addition, the study of the volatile profile of monofloral honey samples of different botanical origin, produced by two stingless bee species of the Melipona genus: (i) Melipona subnitida Ducke (jandaíra) and (ii) Melipona Scutellaris Latrelle (uruçu) from the semiarid region of north-eastern Brazil, determined by HS-SPME GC/MS and principal component analysis did not reveal specific marker compounds for the differentiation of the entomological origin [35]. Instead of that, it was concluded that the botanical source had a strong influence on the volatile profile of monofloral honeys produced by the two stingless bees’ species (jandaíra and uruçu). The existing GC/MS studies for the entomological authentication of honey are limited and further research on this issue is required, focusing on the use of a larger sample size, reference standards of the proposed marker compounds, quantitation of the marker compounds, and interlaboratory validation of the developed methods in order to be adopted as enforcement methods.

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177

Authentication of Honey Production

The honey production process includes several steps, i.e., initial extraction, dehumidification, liquefaction and mixture, heating, pasteurization, crystallization, and final packaging. There are several adulteration issues related to honey production that should be taken into consideration. More specifically, the adulteration of honey production is related to: (a) Direct (incorporation) or indirect (bee-feeding) adulteration with exogenous sugar syrups (b) Harvesting unripe honey (c) Inappropriate handling and storage (d) Mixing honeys (e) Honey contamination (contact of bees with polluted water, air, and plants, contamination with pesticides, heavy metals, microorganisms, or genetically modified organisms [GMO]) GC/MS has been applied in a number of studies aiming at the development of analytical methodologies suitable for the authentication of honey related to the addition of exogenous sugar syrups, the authentication of honey ripeness, the presence of potential migrants from processing and packaging, and finally the authentication of organic and conventional honey, which is mainly related to the presence of pesticides and veterinary drugs.

7.5.1

Adulteration of Honey with Exogenous Sugar Syrups

Table 7.4 presents the GC/MS studies related to the adulteration of honey with exogenous sugar syrups. GC/MS has been used to analyze mono-, di-, and tri-saccharides in honey with a relatively high resolution and sensitivity, which aids the detection of adulteration [64]. Other studies analyzed difructose anhydrides (DFAs) by GC/MS after conversion to their trimethylsilyl (TMS) derivatives, suggesting them as chemical markers of adulteration of honeys with acid caramels [65] or as good indicators of honey adulteration with high fructose corn syrup (HFCS) and inverted syrup (IS), in concentrations as low as 5% [66]. Additionally, other GC/MS methods have been developed for the detection of honey adulteration with high fructose inulin syrups (HFIS), suggesting to use inulotriose as a marker for honey adulteration with HFIS [67], and for the determination of carbohydrate composition of honeys produced by bees fed with high fructose corn syrup (HFCS) [68]. In the latest study, syrups were used to feed caged bees and the resulting honeys were analyzed by GC/MS in order to determine their influence on carbohydrate composition. Fructosyl-fructoses were mainly detected in honeys from bees fed with high-fructose corn syrups (HFCS) but not from those honeys coming from free-flying bees or bees fed with sucrose syrups (SS).

difructose anhydrides (DFAs)

difructose anhydrides (DFAs)

Targeted

Direct adulteration of honeys with high fructose corn syrup (HFCS) and inverted syrup (IS)

Marker compounds 16 disaccharides and 9 trisaccharides

Targeted and non-targeted

Targeted/nontargeted Targeted

Direct adulteration of honeys with fructose, sucrose, and glucose caramels

Authentication issue Direct adulteration with exogenous sugar syrups (detection of unknown honey carbohydrates)

Sample preparation (number of samples) Dilution of honey samples with ethanol and mixing with phenyl-β-d-glucoside. Formation of trimethylsilyl (TMS) derivatives (oximes and ethers) (n = 10) Dilution of honey samples and caramels in methanol. Formation of trimethylsilyl (TMS) derivatives (n = 20) Treatment of 20% (w/v) water solutions of samples with 1% (w/v) aqueous solution of Saccharomyces cerevisiae at 30 °C. Two-step derivatization procedure (oximation and trimethylsilylation) (n = 20)

Table 7.4 GC/MS studies for the determination of honey adulteration with exogenous sugar syrups

GC/MS Peak identification: Reference standards & Mass spectral data for confirmation of peak identities

GC/MS Peak identification: Reference standards & Mass spectra data & literature

Analytical method GC/MS Peak identification: Comparison with reference standards

Refs Sanz et al. [64]

Montilla et al. [65]

RuizMatute et al. [66]

Data analysis Determination and comparison of marker concentrations

Determination and comparison of marker concentrations Determination and comparison of marker concentrations

178 N. C. Maragou et al.

Targeted and non-targeted

Targeted and non-targeted

Targeted and non-targeted (119 volatile organic compounds detected)

Direct adulteration of honeys with high fructose syrups from inulin

Indirect adulteration of honeybees with high fructose corn syrup (HFCS)

Direct adulteration of honeys with inverted sugar syrup at different concentration levels (3–39%, at 3% intervals)

Undecane; undecane, furfural, 5-methyl-2furancarboxaldehyde and 5-hydroxymethylfurfural

Fructosyl-fructoses

inulotriose

Dilution of honey in water at a ratio of 5:1 (v/w) Sonication 30 min IS: 3-heptanone (n = 136)

Dilution of honey samples in 80% ethanol/water. Two-step derivatization procedure (oximation and trimethylsilylation) (n = 107) Derivatization of carbohydrates of syrups to their trimethylsilyl oximes (TMSO) prior to analysis (n = 9) GC/MS Peak identification: Standard compounds; mass spectral data were used to confirm peak assignation and to tentatively identify those peaks that were not available as commercial standards SPME GC/MS Fiber: DVB/Carboxen/ PDMS Peak identification: Retention time and mass spectra comparison to reference standards and NIST/EPA/NIH Mass Spectral Library & RI & APCI-MS

GC/MS Peak identification: Reference standards and mass spectral to identify peaks that were not available as commercial standards

ElMasry et al. [69]

RuizMatute et al. [68]

Determination and comparison of marker concentrations

PLSR Model validation and prediction of adulteration level

RuizMatute et al. [67]

Determination and comparison of marker concentrations

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More recently, the ability of HS-SPME GC/MS and atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to identify the presence of unexpected compounds in honey originated from sugar syrup was examined in pure and adulterated honey samples. The key volatile compounds were identified and quantified in pure and adulterated honey (with inverted sugar syrup) using HS-SPME GC/MS. It was observed that most of the substances identified in syrup headspace (undecane, furfural, 5-methyl-2-furancarboxaldehyde and 5-hydroxymethylfurfural) were also found in the samples of authentic honey, indicating that the concentrations – and not just the presence – of these compounds can be the key parameter in detecting the addition of syrup in the counterfeit honey samples. The PLS regression model developed on the whole volatile profile provided an accurate prediction of the adulteration level in honey samples [69].

7.5.2

Authentication of Honey Ripeness and Proper Handling

Another adulteration practice in honey production consists of harvesting unripe honey, mainly by identifying changes in volatile organic components (VOCs) at different maturity stages. Researchers have applied GC/MS methodology (Table 7.5) for the analysis of the type and relative content of VOCs extracted from honey through the HS-SPME method, thus allowing the discrimination of re-ripe from unripe honey [70] or the identification of the maturity stage of buckwheat honey [71]. Lately, Sun et al. [72] investigated the use of GC/MS in detecting decenedioic acid, the bee-derived component, and confirmed that it could be used as a potential indicator for distinguishing mature from immature honey. Additionally, the presence of potential migrants from honey processing and packaging was addressed as a complementary issue ensuring honey safety and authenticity. More specifically, GC/MS analysis was applied to determine 26 plasticizers and bisphenol A (BPA) in honey samples. The method was validated in terms of linearity, sensitivity, repeatability, and matrix effect [73]. More recently, other researchers [74] developed a sensitive and reliable dispersive liquid–liquid microextraction (DLLME) GC/MS method in order to evaluate the migration of chemical compounds from Polyethylene Terephthalate (PET) and Polystyrene (PS) containers to honey. This validated method allowed the quantification of 15 target compounds that were expected to migrate into food and, simultaneously, identified untargeted species (other plastic additives and their degradation products) (Table 7.6).

7.6

Authentication of Organic and Conventional Honey

Another important authenticity issue, also recognized as an emerging risk for honey safety and associated with human health, is the contamination of honey by anthropogenic chemicals from the surrounding environment and beekeeper’s management practice. The contamination concerns mainly the pesticides’ residues in honey,

Nontargeted

Targeted

Discrimination of immature (IMH) from mature honey (MH)

Targeted/ nontargeted Nontargeted

Identification of honey maturity stage

Authentication issue Discrimination of re-ripe from non-ripe honey

identification of 168 VOCs (including alcohols, esters, aldehydes, acids and lactones) decenedioic acid

Marker compounds VOCs (acids, alcohols, aldehydes, esters, ketones)

Dilution of honey (10 g) with water. Extraction with n-hexanediethyl ether solution (v: v, 2:1) Concentration Derivatization of fatty acids. (n = 3)

Sample preparation (number of Samples) Dilution of honey (8 g) with saturated NaCl Three sets of re-ripe honey samples (500 g/sample) from Apis cerana colonies and three sets of non-ripe honey samples (500 g/ sample) from Apis mellifera ligustica colonies (n = 14 samples of buckwheat honey)

Table 7.5 GC/MS studies for the authenticity of honey ripeness

GC/MS Peak identification: Reference standard and MetaScope and database

HS-SPME GC/MS

Analytical method HS-SPME GC/MS Peak identification: MS NIST 14 database

TaoHong et al. [71]

Sun et al. [72]

t-test ( p < 0.05) The chromatograms of the FAs were extracted by MassHunter Qualitative Analysis software Quantitative analysis through the standard curves of the peak area (R2 ≥ 0.99)

Ref Guo et al. [70]

n.a.

Data analysis One-way ANOVA ( p < 0.05)

7 Honey and Bee Products 181

Identification of potential migrants from PET and PS containers to honey

Authentication issue Determination of migrants from processing and packaging

Targeted and nontargeted

Targeted/ Nontargeted Targeted

Target compounds (styrene, p-TBP, DEP, DBP, NP, oleamide, BPA, BBP, and DEHP) Untargeted compounds (iBP, squalene, hexadecanoic acid and its derivative methyl palmitate, glycerol-β-monostearate, hexadecanal, as well as nonanoic, decanoic acid, stearic acid, methyl stearate, phthalic anhydride, and 2,4-di-tert butylphenol and 9-di-tertbutyl-1-oxaspiro[4.5] deca-6,9-diene-2,8-dione)

Marker compounds 26 plasticizers and bisphenol A (BPA)

Sample preparation (number of samples) Dilution of honey in water, spiked with DBP-d4 and DEHP-d4, at a concentration level of 1 mg L-1. Solid phase extraction with Oasis HLB (n = 39) Preconcentration technique: Dispersive liquid-liquid microextraction (DLLME) (n = 8)

Table 7.6 GC/MS authenticity studies related to honey processing and packaging

Analytical method GC/MS Full scan & SIM mode (3 m/z, one quantitative analysis and two qualitative) Peak identification: Reference standards Validated method GC/MS Peak identification: Identification of the target compounds based on their standard retention times and MS spectra libraries Identification of untargeted compounds based on their characteristic m/z ions, by the NIST and Wiley MS libraries

Refs Lo Turco et al. [73]

Peñalver et al. [74]

Data analysis Quantitation of the concentration of the analytes with calibration curves

Identification, quantitation and semi-quantitation of the detected compounds

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which are originated either from the application of plant protection products or from the use of veterinary drugs to bees. Plant protection products do not reach only the treated plants. Through the spray drift, they travel and deposit in surroundings where they can come into contact with bees and, subsequently, they can be transferred to the produced honey. In addition, pesticides are used in veterinary practices to protect bees from insects. In conventional production, honeybees are exposed to acaricides, such as amitraz and coumaphos, whereas in the case of organic honey production, only formic acid, lactic acid, acetic acid, and oxalic acid, as well as menthol, thymol, eucalyptol or camphor, may be used in cases of infestation with Varroa destructor as laid down in Regulation (EC) 889/2008. Other anthropogenic contaminants that could be found in honey through the environmental pollution are the polycyclic aromatic hydrocarbons (PAHs) and the poly chlorinated biphenyls (PCBs). Furthermore, pyrrolizidine alkaloids (PAs) are potential natural environmental contaminants of honey originated from plants. They are secondary metabolites produced by plants as a chemical defense against herbivores. Plants containing PAs are widely distributed in almost all geographical regions, and they are considered to be the most widely spread toxins of natural origin (Ewelina [75]). The high production cost and the consumer perception of organic honey as a healthier option are the main reasons for the increased price of organic honey compared to that of conventional one. The presence of several contaminants’ residues in honey can be minimized by good beekeeping practices and by complying with the requirements for organic production. In this aspect, GC/MS methodology has been applied for the determination of pesticides’ residues, namely the active substances of plant protection products and active substances of veterinary drugs, polycyclic aromatic hydrocarbons (PAH), polychlorinated biphenyls (PCBs), or toxicologically relevant secondary plant metabolites in honey, as presented in Table 7.7. In most of the aforementioned studies, the isolation of the compounds of interest was performed with the modified QuEChERs method using acetonitrile. The methodology followed mainly targeted screening of specific compounds using reference standards for the identification and the quantification of the determined analytes, while the majority of the methods were validated. Despite the numerous studies that have been conducted for the monitoring of pesticides and other contaminants in honey from different geographical areas and contamination sources, limited information exists on the methodology for the discrimination between organic and conventional honey based on their contaminants’ levels [79, 85], although being of the utmost interest for consumers.

7.7

Authentication of Bee Products Other Than Honey

7.7.1

Beeswax

Apart from its use for foundations in beekeeping, beeswax is used in cosmetics, pharmacy, candle making, and art and for many other purposes. It is an extremely complex material, consisting of more than 300 different substances, but mainly of

Benzo[a]pyrene, benzo[a] anthracene, benzo[b] fluoranthene, and chrysene

16 polycyclic aromatic hydrocarbons (PAHs)

58 insecticides, 16 fungicides, 14 herbicides and 8 breakdown products/metabolites, 33 polycyclic aromatic hydrocarbons

6 indicator PCBs Non-polar pesticides

Polycyclic aromatic hydrocarbons

Pesticides and Polycyclic aromatic hydrocarbons

Pesticides and PCBs

Screening compounds (detected) Pyrrolizidine alkaloids

Polycyclic aromatic hydrocarbons

Contaminant category Toxicologically relevant secondary plant metabolites in honey

Dilution of honeys in 20% methanol/deionized water solution, solid phase extraction and eluted with ethyl acetate hexane (n = 212) Gel permeation chromatography (n = 61) Certified organic (n = 16)

Sample preparation (number of samples) Solid phase extraction with MCX cartridges. Elution with a mixture of ethyl acetate, methanol, ammonia, and triethylamine. Reduction to their common backbone structures and derivatized with heptafluorobutyric anhydride Liquid/liquid extraction with cyclohexane/ethyl acetate (50/50, v/v) mixture. SPE Honey samples from eight colonies in each apiary (six apiaries in total) QuEChERS method (n = 50)

GC/MSMS

GC/MSMS

GC/MSMS

GC/MSMS

Analytical method GC/MS

Method validated with reference standards

Method validated (European Union Commission Regulation No. 836/2011) Method validated (National Association of Testing Authorities guidance document)

Monitoring two ions (MRM transitions) Method validated

Data analysis Method validated according to SANTE 2015 Selected ion monitoring mode of four ions

Lazarus et al. [79]

Hungerford et al. [78]

Toptanci et al. [77]

Lambert et al. [76]

Refs Ewelina Kowalczyk et al. [75]

Table 7.7 GC/MS studies related to the determination of anthropogenic, environmental contaminants of honey – organic and conventional production

184 N. C. Maragou et al.

Pesticides (veterinary drugs, acaricides) Pesticides (veterinary drugs, acaricides)

Pesticides

Pesticides

Pesticides

Pesticides

34 pesticide (bifenthrin, methidathione, triazophos, metconazole, and cypermethrin GC determined) 24 pesticides (dichlorvos, monocrotophos, profenofos, permethrin, ethion and lindane) 10 pesticide/pesticide metabolites (amitraz metabolites and coumaphos determined) Screening 61 pesticides plus 3 metabolites (Carbendazim pyrethroids determined) Coumaphos and two transformation products of amitraz (DMF and DMPF) (a) amitraz and all metabolites containing the 2,4-dimethylaniline moiety (b) thymol, chlorfenvinphos and coumaphos (c) 75 active substances GC/MS

GC/MSMS

modified QuEChERS method (n = 483) Modified QuEChERS method including ultrasound-assisted extraction (UAE) (a) Hydrolysis and extraction with n-hexane (b) + (c) Extraction with mixture of acetone, dichloromethane and petroleum ether (n = 60) Organic honeys (n = 22) Conventional honeys (n = 38)

GC/MS

GC/MSMS

GC/MS

GC/MSMS

modified QuEChERS method (n = 32)

QuEChERS method (n = 100)

Conventional honeys (n = 45) QuEChERS method (n = 15)

For each active substance, one target and 2 or 3 qualifier ions were used Calibration was performed to matrix matched standards

Use of reference standards and quality assurance control Method validated (SANTE/ 11945/2015)

One quantifier ion and one qualifier ion

Method validated

Method validated

Honey and Bee Products (continued)

Baša Česnik et al. [85]

Lozano et al. [84]

Xiao et al. [83]

Bommuraj et al. [82]

Kumar et al. [81]

Bargańska et al. [80]

7 185

Pesticides (organochlorines)

Contaminant category Pesticides (organochlorines)

Table 7.7 (continued)

20 organochlorine pesticides (4,4-DDD, 4,4-DDT, dieldrin, α-endosulfan, and β-endosulfan determined)

Screening compounds (detected) hexachlorobenzene

Sample preparation (number of samples) Dilution with water. Extraction with acetonitrile. Evaporation reconstitution with ethyl acetate (n = 14) QuEChERS method (n = 90) GC/MSMS

Analytical method GC/MS

Use of reference standards

Data analysis n.a.

Mulugeta and Tadese [87]

Refs Alghamdi et al. [86]

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esters of higher fatty acids and alcohols [96]. Various Pharmacopeias, such as International Pharmacopeia, European Pharmacopeia, United States Pharmacopeia, etc., give a definition of beeswax, providing reference measurement guidelines for the determination of its general physical and chemical characteristics. Besides being considered a pharmaceutical product, it is also an authorized food additive (E901) used as a glazing agent and colors and flavors carrier [97]. The main beeswax quality issues are related to its adulteration, primarily with paraffin and stearic acid/stearin, and its contamination, primarily with acaricides – pesticides most commonly used in beekeeping [96]. Physico-chemical properties of paraffin (inertness, odorless, colorless) make it almost ideal for adulteration, while other adulterants are observed sporadically [97]. Apart from measuring classical sensorial, physico-chemical, and spectroscopic properties, gas chromatography presents a technique of choice for verifying wax authenticity [96]. In 2020, EFSA came to the conclusion that beeswax purity testing should include at least two physico-chemical parameters complemented with advanced analytical methods for a reliable and sensitive detection (limit of detection 60 min Peak identification: NIST 05 and RI Internal standard: eicosane

Refs [99]

[100]

[101]

Data analysis Comparison of GC/MS chromatograms

Comparison of GC/MS chromatograms

One-way ANOVA Student– Newman– Keuls test

188 N. C. Maragou et al.

Dissolving in chloroform

palmitic and stearic acid

Targeted

Determining geographical origin Terengganu vs Cameron highland (n = 7) Malaysia

GC/MS Column: DB-5 Mass range: 40–650 m/z Run time: 56 min NIST library SIM mode GC/MS

Dissolving in chloroform

n-alkanes palmitic acid methyl ester and octacosane

Targeted

HT-GC/MS Column: ZB-5 Inferno Mass range: 40–850 m/z Run time: 55 min SIM mode

Dissolving in chloroform

GC/MS Column: HP-5MS Mass range: 50–550 m/z Run time: >30 min Internal standard: squalene

GC/MS Column: UNIMETAL Mass range: 50–800 m/z Run time: 3 min

Dissolving in chloroform; no derivatization

Dissolving in petroleum ether

Nontargeted

Adulteration with paraffin Apis mellifera Virgin beeswax (n = 12) Comb beeswax (n = 26) Foundation beeswax (n = 33) Portugal Adulteration with paraffin Apis mellifera Apis cerana (n = 6) China Adulteration with paraffin (n = 15) Czech Republic

Nontargeted

selecting m/z fragments measured in SIM mode: for stearin wax 284.3; for paraffin wax 501.4, 515.4, 531.4, 545.4, 558.5; and for beeswax 592.6 profiles of more than 50 chemical compounds: odd and even hydrocarbons, oleofin, palmitate, oleate, and hydroxypalmitate monoesters hydrocarbons: eicosane, pentacosane, hexacosane, octacosane, tetratriacontane, and pentatriacontane

Targeted

Adulteration with paraffin and stearin

Honey and Bee Products (continued)

[106]

[105]

Ratios of palmitic acid methyl ester to octacosane

Ratios of palmitic and stearic acids

[104]

[103]

[102]

PCA PLS

CA PCA LDA

Comparison of GC/MS chromatograms

7 189

Authentication aim Geographical origin Pesticide contamination USA

Table 7.8 (continued)

Targeted/ nontargeted Targeted

Marker compounds chlorothalonil, chlorpyrifos, bifenthrin, lambda-cyhalothrin, coralox, permethrin, coumaphos, cyfluthrin, cypermethrin, taufluvalinate, and atrazine

Sample preparation solvent extraction; GPC; cleanup; dispersive SPE with Z-Sep

Analytical method GC/MS Column: HP-5MS Run time: 26 min EI and NCI mode Internal standards: PCB 204, chlorpyrifos d10, coumaphos d10, flucynthrinate, andatrazine d5

Data analysis Instrument detection limits (IDL) Method detection limits (MDL)

Refs [108]

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and supervised (LDA) pattern recognition procedures to differentiate authentic and paraffin adulterated beeswaxes based on the profiles of more than 50 chemical compounds, belonging to the following classes: odd and even hydrocarbons, oleofin, palmitate, oleate, and hydroxypalmitate monoesters. The LDA model was validated by leave-one-out cross-validation and showed good recognition and prediction abilities of 100% and 99% [103]. After dissolving in petroleum ether, a GC/MS coupled to chemometric analysis (PCA and PLS) can be applied to detect and quantify the paraffin in beeswax of both Apis mellifera and Apis cerana bees by using the peaks of hydrocarbons: eicosane, pentacosane, hexacosane, octacosane, tetratriacontane, and pentatriacontane as discriminating variables [104]. A GC/MS approach of measuring ratios of palmitic acid methyl ester to octacosane was also applied to detect the presence of paraffin in beeswax. It was concluded that an increase in chromatographic peak heights of even-numbered n-alkanes could point to the addition of paraffin to beeswax, but the ratio of palmitic acid methyl ester and octadecane and CPI values were even more useful [105]. A thesis by Iskandariah [106] suggests the potential of fatty acid ratios (palmitic/ stearic) in the differentiation of beeswax samples from various geographical locations. On the contrary, a study of saturated hydrocarbon profiles of beeswax from northern and central Mozambique and other geographical regions (Spain and Honduras) showed that these compounds do not contain enough information for their geographical differentiation [107]. Li et al. [108] proposed two multi-residue methods based on different extraction and cleanup procedures for the determination of 11 relevant pesticides in honeybees, pollen, and beeswax randomly collected from apiaries located in Virginia, USA in 2014, by using GC/MS. These included chlorothalonil, chlorpyrifos, bifenthrin, lambda-cyhalothrin, coralox, permethrin, coumaphos, cyfluthrin, cypermethrin, tau-fluvalinate, and atrazine. Sample preparation included solvent extraction followed by gel permeation chromatography (GPC) cleanup and cleanup using a dispersive solid-phase extraction with zirconium-based sorbents (Z-Sep). Atrazine was quantified in electron impact (EI) mode, while all of the other target pesticides were quantified in negative chemical ionization (NCI) mode. The separation was performed with a HP-5MS (30 m × 0.25 mm × 0.25 μm film thickness) column [108].

7.7.2

Propolis

Many authors performed both volatile and non-volatile profiling, i.e., characterization, via targeted and non-targeted approaches, of propolis samples originating from Ecuador [109], Ethiopia [110], Morocco [111], Oman [112], China [113, 114], Brazil [115, 116], Uruguay, Estonia [117], Malta [118], Nigeria [119], Italy [120], Vietnam [121], Greece [122], Venezuela, Argentina [123], South Africa [124], and other regions of the world, Table 7.9. However, studies specifically focusing on developing methodologies for authentication of propolis botanical and geographical origin employing GC/MS instrumentation are scarce.

GC/FID GC/MS Column: DB-1 Scan range: 40–300 m/z EI 70 eV RI GC/MS Column: DB-5MS EI 70 eV RI and comparing mass spectra with literature data TLC NMR RP/HPLC GC/MS

Nontargeted

Characterization and geographical origin Ecuador n=3

Methanol extracts

Extracts of a mixture of dichloromethane and methanol

triterpenoids (mainly α-, β-amyrins and amyryl acetates), n-alkanes, n-alkenes, methyl n-alkanoates, and longchain wax esters lanosterol and its isomers, β-amyrone, lupeol and its isomers, β-amyrin, lanosta-9 (11), 24 dienacetate, 24-methylene-9,19ciclolanostan-3β-ol, lupeol acetate

Nontargeted

Characterization of Ethiopian propolis Ethiopia n=4

Ethanol extracts

Analytical method GC/MS Column: HP-5MS EI 70 eV RI and comparing mass spectra with commercial libraries and literature LC/ELSD/UV/MS GC/MS NMR

Type Ethanol extracts

Hydrodistillation

Marker compounds diterpene compounds; sugars and sugar derivatives; mono- and sesquiterpenyl esters; daucane diterpene esters of hydroxybenzoic acids major compounds: phenolics and triterpenoids

Major compounds: β-eudesmol, cedrol, n-tricosane, ar-curcumene

Nontargeted

Targeted/ nontargeted Nontargeted

Nontargeted

Characterization of Nigerian propolis Nigeria n = 12 Characterization of Moroccan propolis Morocco n = 24

Authentication aim Geographical origin Characterization of Maltese propolis Malta n = 17

Table 7.9 GC/MS studies for the authentication of propolis

One-way ANOVA Student’s t-test

Common statistics

HCA

PCA PLS

Data analysis Pearson’s correlation coefficients

[109]

[110]

ElGuendouz et al. [111]

[119]

Refs [118]

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flavonoids, terpenoids, acids, esters, hydrocarbons, phenols and alcohols, aldehydes and ketones, and others

Volatile compounds

triterpenoids, prenylated isoflavonoids, stilbenoid

alk(en)ylresorcinols anacardic acids triterpenes

sugars, polyols, hydroxy acids, fatty acids, cardanols and cardols, anacardic acids, flavan derivatives, triterpenes, prenylated flavanones and chalcones Black propolis: flavanones and glycosyl flavones (Piauı and Goias states)

Nontargeted

Nontargeted

Nontargeted

Nontargeted

Nontargeted

Nontargeted

Characterization of Vietnamese stingless bee propolis Vietnam

Characterization with possible botanical origin Oman (n = 7)

Geographical origin and characterization of black and green propolis

Characterization and potential for geographical origin China n=5 Characterization and geographical origin Brazil n=4 Characterization and geographical origin African continent n = 22

Methanol and chloroform extracts

Ethanol extracts

Ethanol extracts

Ethanol extracts

Powdered samples

Powdered samples

LC/UV LC/ELSD LC/HRMS GC/MS LC/DAD/HRMS-MS GC/MS Column: DB-5MS EI 70 eV Commercial libraries, literature data and/or comparison with mass spectra of reference compounds 1 H NMR (600 MHz) 13 C NMR (150 MHz) GC/MS HP5-MS capillary column Tentative identification and semi-quantification HPLC/DAD/ESI/MS GC/MS

HS-SMPE GC/MS

Py-GC/MS with a vertical microfurnace pyrolyzer

CA

PCA

Common statistics

PCA

PCA

Common statistics

Honey and Bee Products (continued)

[127]

[112]

[121]

[128]

[115]

[114]

7 193

Nontargeted

Nontargeted

Nontargeted

Geographical origin Venezuela n=7

Geographical origin South Africa n = 39

Targeted/ nontargeted

Geographical origin Brazil Estonia China Uruguay n=6

Brazil n=8

Authentication aim Geographical origin

Table 7.9 (continued)

Green propolis: Prenylated phenylpropanoids and caffeoylquinic acids (Bahia, Minas Gerais, Sao Paulo, Parana states) α-Pinene and β-pinene (Brazil, Uruguay, Estonia); β-methyl crotonaldehyde (Brazil); limonene (Uruguay); eucalyptol (Estonia); 3-methyl- 3-buten-1-ol, 3-methyl-2-buten-1-ol, 4-penten-1-yl acetate (China) Volatiles: limonene, beta-caryophyllene and nerolidol (Venezuela); prenyl acetate, benzyl acetate, and 2-phenylethyl acetate (Agentina) dehydrosabinene, isopropentyltoluene, p-cymene, acetophenone and α-thujene (Northern Cape); λ-terpinene, propanoic acid, furfural, 2-methoxy benzyl alcohol and hexanoic acid

Marker compounds

HS GC/MS RTX-5MS column EI 70 eV Mass range 33–350 m/z Identification NIST v1.7 (>90%)

SPME GC/MS DHS GC/MS Column: TR-5MS DSQII electron ionization NIST 8 and RI GCxGC/ToF–MS First Column: Rxi-5SilMS Second Column: Rxi-5SilMS Run time: 21 min Mass range: 35–400 m/z NIST 11 (similarity ≥800) Authentic standards

Powdered samples

Powdered samples

Analytical method

Powdered samples

Type

PCA CA Heat-map

CA

PCA HCA KMCA

Data analysis

[124]

[123]

[117]

Refs

194 N. C. Maragou et al.

Botanical origin of brown propolis Brazil n=7

Botanical origin Europe and Asia n = 37 Botanical origin Greece n=6

Geographical origin China n = 12 Characterization of red propolis Brazil

Nontargeted

Targeted

Nontargeted

Nontargeted

Nontargeted

volatiles, among which major are monoterpenes and sesquiterpenes

Isoflavonoids as major compounds: 3-hydroxy-8,9 dimethoxypterocarpan and medicarpin Extracting 219 m/z fragment in SIM mode for isolation of phenylpropenoids diterpenes

methyl ester (Gauteng); prenal, cinnamaldehyde styrene, 1,8-cineole, decanal, prenyl acetate and butanoic acid (Western Cape Province) acids, esters, alcohols, terpenes, aromatics

Powdered samples

Ethanol extracts

Ether extracts

Ethanol extracts

Powdered samples

GC/MS Column: HP-5MS EI 70 eV RI and mass spectral fragmentation SHS GC/MS Column: Rtx-5MS Run time: 26 min EI 70 eV Mass range: 45–450 m/z LRI NIST 11, Wiley 8 and Wiley FFNSC libraries

GC/MS

UV/VIS RP-HPLC/PDA GC/MS

DHS-GC/MS E-nose

PCA CA Heat-map

Common statistics

(continued)

[126]

[122]

[125]

[116]

Common statistics

Common statistics

[113]

PCA

7 Honey and Bee Products 195

Authentication aim Geographical origin Determination of allergenic esters Italy n=5 Seasonal variation of Brazilian green propolis Brazil n=8

Table 7.9 (continued)

Nontargeted

Targeted/ nontargeted Targeted

volatiles polyphenols

Marker compounds benzyl salicylate benzyl cinnamate

Powdered samples

Type Ethanol extracts

HS GC/MS ESI-MS

Analytical method UV-VIS GC/MS HPLC/UV-VIS PCA

Data analysis Common statistics

[130]

Refs [120]

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In the study by Isidorov et al. [125], samples of Eurasian propolis (from 11 countries of Europe and Asia) were characterized in terms of their botanical origin by comparing their GC/MS chemical profiles with those of buds extracts of their principal plant precursors. In the majority of the analyzed cases, the propolis turned out to be of mixed origin [125]. Popova et al. [122] used GC/MS for diterpene profiling in six Mediterranean propolis samples from different Greek regions. The flora of these locations is typical of Greece, with most abundant being the conifer trees (Pinus sp. and Cupressus sempervirens). More than 30 diterpenes, including new propolis constituents, were identified and characterized and could serve as a basis for rapid GC/MS profiling of this propolis type and for revealing its plant sources [122]. The same authors also used PCA to compare chemical profiles of Omani propolis extracts obtained by GC/MS and concluded that propolis samples from Oman exhibit unique chemical properties compared to the others. They demonstrated that, although Oman is not a large country, the plant sources of propolis varied significantly. Furthermore, new plant sources of propolis were discovered: Azadiracta indica (neem tree) and Acacia spp. (most probably A. nilotica) [112]. Volatiles of 7 Brazilian samples of organic and non-organic brown propolis were profiled using static HS GC/MS coupled to PCA, CA, and heat-mapping. Nighty nine volatiles were tentatively identified, with monoterpenes and sesquiterpenes being the most abundant chemical classes. Multivariate analysis of volatile profiles shed light on propolis botanical origins, which, according to the authors, is important for the further development of authentication protocols for propolis [126]. The 39 propolis samples collected from various regions of South Africa (Gauteng, Northern Cape, and Western Cape Provinces) were evaluated in terms of their volatile profiles using HS GCxGC/ToF–MS [124]. PCA, HCA, and heatmapping demonstrated that headspace volatiles of propolis varied between locations: dehydrosabinene, isopropentyltoluene, p-cymene, acetophenone, and α-thujene were markers for the Northern Cape propolis; λ-terpinene, propanoic acid, furfural, 2-methoxy benzyl alcohol, and hexanoic acid methyl ester for Gauteng propolis; and prenal, cinnamaldehyde styrene, 1,8-cineole, decanal, prenyl acetate, and butanoic acid markers for Western Cape Province [124]. Cheng et al. [113] and Yang et al. [114] investigated the potential of GC/MS in Chinese propolis profiling with the aim to authenticate the region of origin. The GC/MS and eNose coupled to PCA were able to successfully distinguish 12 representative raw propolis samples from different geographical regions of China. The samples have been assigned to four large groups according to their vegetal sampling locations [113]. Yang et al. [114] investigated the potential of pyrolysis GC/MS with a vertical microfurnace pyrolyzer to characterize propolis from different regions of China, whereby they identified 76 compounds grouped into 8 chemical classes, such as flavonoids, terpenoids, acids, esters, hydrocarbons, phenols and alcohols, aldehydes and ketones, and others. The contents of these compounds in propolis samples were very different, with the highest difference observed between triterpenoid contents [114].

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A study on four brown propolis samples from different states of Brazil (Bahia, Minas Gerais, Parana, and Sergipe) enabled the differentiation of the investigated samples according to the geographical origin using PCA on HS-SPME GC/MS volatile data. A total of 315 volatile compounds were identified and found to be dependent on the geographical area of propolis collection [115]. Silva et al. [116] utilized GC/MS, among other techniques, to characterize Brazilian red propolis and made the first report on the leguminous species being the source of propolis (Dalbergia ecastophyllum (L.) Taub). Righi et al. [127] found differences in chemical compositions between black and green Brazilian propolis samples, stemming from various regions, using both GC/MS and LC/MS. Two chemical profiles were observed between the analyzed samples: black propolis, which was characterized by flavanones and glycosyl flavones (Piaui and Goias states); and green propolis, characterized by prenylated phenylpropanoid and caffeoylquinic acids (Bahia, Minas Gerais, Sao Paulo, and Parana states) [127]. Multivariate tools, such as PCA, HCA, and KMCA, on the volatile profiles determined using a static HS GC/MS enabled the efficient clustering of six propolis samples from Brazil, Estonia, China, and Uruguay, according to the geographical origin [117]. Monoterpenes (α- and β-pinenes) were predominant in all samples, except for the sample from China. This sample was distinguished by the alcohols 3-methyl-3-buten-1-ol and 3-methyl-2-buten-1-ol and ester 4-penten-1-yl acetate. α-Pinene and β-pinene were dominant volatiles in Brazilian, Uruguayan, and Estonian propolis. Brazilian propolis was distinguished by a high amount of β-methyl crotonaldehyde, Uruguayan by limonene, and the Estonian sample by a high amount of eucalyptol [117]. Seven propolis samples from Venezuela, Brazil, and Argentina were evaluated in terms of volatile composition using both dynamic HS and SPME GC/MS analysis [123]. In the case of Venezuelan propolis, sesquiterpenes, esters, aromatic compounds, and aliphatic hydrocarbons were identified. Limonene was found only in Venezuelan samples, while prenyl acetate, benzyl acetate, and 2-phenylethyl acetate were detected only in samples from Argentina. Cluster analysis allowed us to relate the propolis volatile profiles to their provenance of origin [123]. On the other hand, no clear geographic delineation was observed in the classification of propolis from different African countries, except for the one uncommon propolis sample from southern Nigeria, which stood out from the others [128]. Other authors, such as Falcão et al. [129], used GC/MS coupled to CA to analyze volatiles from essential oils of 36 propolis samples from 6 different geographical locations in Portugal (mainland, Azores archipelago, and Madeira Island) and also concluded that obtained clusters were not related to sample site collection. Similarly, El-Guendouz et al. [111] used cluster analysis on volatile data of 24 propolis samples from different regions of Morocco, thereby establishing two uncorrelated clusters. However, despite the obtained results, the authors concluded that the volatile profiles of these samples were distinct from those of Algeria, Canary Islands, and Ethiopia [111]. According to Nunes and Guerreiro [130], seasonal variations between eight samples of Brazilian green propolis harvested in spring, summer, and autumn

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could be detected by determining the composition of volatile and non-volatile compounds using HS GC/MS and electrospray ionization–mass spectrometry (ESI-MS). Within each season, high-quality commercial classes of propolis exhibited similar properties, while the low-quality classes exhibited distinct characteristics. In spring and summer, propolis of the trimming class, commonly considered of low quality by beekeepers, presented a composition similar to the superior quality propolis [130]. A new GC/MS methodology for the quantitative analysis of two allergenic esters in propolis specimens, benzyl salicylate and benzyl cinnamate, was proposed by Aliboni et al. [120]. The authors tested the proposed methodology on specimens from different locations in central Italy.

7.7.3

Bee Pollen

Environmental conditions are generally not suitable for harvesting monofloral bee pollen. Beekeepers, therefore, collect polyfloral pollen in most cases, which is difficult to characterize. Pollen from different plant species differ largely in their nutritional, sensory, and food safety attributes. Thus, identification and characterization are crucially important for food development and public health [131]. Microscopic analysis of pollen grains is still a precise method for identifying pollen origin [132]. However, it must be noted that microscopy-based identification of pollen is slow, has low taxonomic resolution, and has few expert practitioners [133]. As being indicated in the study on standard methods for pollen research by Campos et al. [132], besides the identification of pollen floral origin by microscopy, the phenolic and polyphenolic profiling using HPLC with a diode-array detector (DAD) and DNA meta-barcoding using polymerase chain reaction (PCR) are also being employed to serve this aim. Lately, other studies are reporting the application of other techniques combined with various multivariate data processing tools to verify the botanical origin of pollen, such as capillary electrophoresis system equipped with a DAD and coupled to PCA data analysis [134]; high-performance liquid chromatography electrospray ionization tandem mass spectrometry [135]; attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with PCA, HCA, and LDA [136, 137]; Fourier transform near-infrared, middle-infrared and Raman spectroscopy with PCA [138]; and E-nose and E-tongue combined with PCA and LDA [131]. Moreover, an approach combining two reliable and non-destructive spectroscopy methods, ED-XRF and ATR–FTIR, was proposed to characterize bee pollen and, when coupled to PCA and HCA, allowed to reveal the dependence on the date and location of pollen harvest [139]. Although it is a technique widely used in laboratories, only a few publications demonstrated the potential of GC/MS instrumentation in developing methods for pollen authentication. It has mostly been applied for the characterization of volatile profiles of pollen samples belonging to various floral sources and geographical regions. However, in order to fully characterize pollen taking into account both polar and non-polar constituents, the authors mostly used a combined approach of

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two complementary techniques: gas and liquid chromatography [140]. Carotenoids, fatty acids, and main lipid classes of 16 fresh collected bee pollen samples from Romania were determined by Mărgăoan et al. [140], where it was shown that β-criptoxanthin and β-carotene were present in a wide range of amounts, which was correlated with predominant botanical origin. High amounts of lutein indicated the presence of Callendula officinalis, Taraxacum officinale, and Anthylis sp., while the highest amount of total lipids was found in samples where pollen from Brassica sp. was predominant. Lipids were extracted using a chloroform/methanol mixture, derivatized using an acid-catalyzed transesterification procedure, and separated on a Supelcowax 10 capillary column. Identification was achieved by comparing retention times with those of known standards and by comparing mass spectra with NIST database. ANOVA and PCA were used for data processing [140]. The HS-SPME GC/MS system was applied by various authors in order to evaluate the volatile profiles of multifloral and unifloral bee pollen samples from Lithuania [141, 142], Northwestern Greece [143], and continental, mountain, and Adriatic regions of Croatia [144]. Employing unsupervised (PCA and HCA) and supervised (KMCA and LDA) data processing tools, Kaškonienė et al. [141] demonstrated the potential of volatile compounds to discriminate between pollen samples. Despite the classification method used, the results showed that not only the samples formed different numbers of clusters, but also the observed dependence of the sample data on the geographical origin was not clearly exposed in all cases. However, the K-means clustering analysis (KMCA) revealed the impact of the pollen collection region on the classification of the pollen in regard to the volatile profile and phenolic acid and flavonoid composition. It was concluded that the analyzed samples formed Lithuanian, Latvian, and non-Baltic region groups [142]. Using a DB-5MS column and a DVB/CAR/PDMS fiber, Karabagias et al. [143] showed that the volatile pattern of Greek bee pollen is dominated by aldehydes, ketones, terpenoids, and other classes of volatile compounds in minor quantities. With the help of ANOVA, Prđun et al. [144] found remarkable differences between bee pollen volatile fractions. The major compounds identified were lower aliphatic compounds, monoterpenes (mainly linalool derivatives, especially in Prunus mahaleb and Prunus avium species), and benzene derivatives (mainly benzaldehyde in T. officinale and Salix spp.). Aldehydes C9 to C17 were present in almost all samples. The authors used a DVB/CAR/PDMS fiber to extract volatile compounds and HP-5MS capillary column for their separation. The identification was based on the comparison of the retention index (RI) with those reported in the literature and the internal database, as well as of their mass spectra with the NIST 17 mass spectral library [144]. One of the hypotheses that may help explain the loss of honey bee colonies worldwide is their exposure to various contaminants, such as pesticides [108]. Two multi-residue methods based on different extraction and cleanup procedures have been developed and compared for the determination of 11 relevant pesticides in honey bees and pollen by GC/MS. Sample preparation included solvent extraction followed by gel permeation chromatography (GPC) cleanup and cleanup using a dispersive solid-phase extraction with zirconium-based sorbents (Z-Sep). Atrazine was quantified in EI mode, while other target pesticides were in NCI mode. Analysis

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was performed on an HP-5MS column using a quadrupole mass analyzer. Chlorothalonil, chlorpyrifos, bifenthrin, lambda-cyhalothrin, coralox, permethrin, coumaphos, cyfluthrin, cypermethrin, tau-fluvalinate, and atrazine were identified and quantified in pollen samples [108]. А non-targeted screening approach for the detection and quantitation of contaminants in 22 pollen samples using GC/MS has been reported by Hakme et al. [145]. These included insecticides/acaricides (chlorpyrifos, coumaphos, fluvalinate-tau, chlorfenvinphos, pyridaben, and propyl cresol), herbicide (oxyfluorfen), and a growth regulator hormone (methoprene). The analytes were separated by fused silica HP-5MSUI capillary column with a capillary flow technology (CFT) and EI fragments were analyzed on a TOF mass analyzer [145]. Rodríguez-Carrasco et al. [146] employed GC tandem triplequadrupole MS after QuEChERS extraction procedure to identify and quantify eight selected Fusarium toxins in 15 commercial pollen samples from Spain: type A and type B trichothecenes, deoxynivalenol, 3-acetyldeoxynivalenol, fusarenon-X, diacetoxyscirpenol, nivalenol, neosolaniol, HT-2, and T-2, where 2 of 15 samples showed quantifiable values for neosolaniol and nivalenol [146].

7.7.4

Royal Jelly

Although it is a valuable and expensive product, not many studies exist in the scientific literature dealing with the authentication of royal jelly using GC/MS instrumentation. Royal jelly originating from Malaysia was analyzed using a GC/MS system in order to compare phytochemical composition between samples from Penang and Johor in the north and south regions of Malaysia [147]. Samples were dissolved in methanol, and the compounds were separated on an HP-5 capillary column and identified using both NIST and Wiley mass spectra libraries. A total of 24 and 21 volatile compounds were identified, respectively, with major compounds being trans-10-hydroxy-2-decanoic acid, 10-hydroxydecanoic acid, and 5-hydroxymethyl-2-furancarboxaldehyde. Royal jelly samples originating from Penang had more volatile compounds than those from Johor, thus suggesting that the variation in the composition may be related to both geographical and floral origins [147]. In another study, volatile profiles of royal jelly samples harvested from 10 nectar plants in flowering seasons were analyzed using HS-SPME GC/MS [148]. The DB-WAX column was used for the separation, after which the eluting compounds were identified using the NIST library and RI. Hierarchical clustering was used for processing semi-quantitative data. The samples were shown to be rich in different contents of acids, esters, and aldehydes, with acetic acid, benzoic acid methyl ester, hexanoic acid, and octanoic acid being those that can be used for distinguishing royal jelly harvested in the seasons of different nectar plants [148]. Royal jelly produced from tea tree, the so called tea royal jelly, is a popular bee product in China due to its multiple health-beneficial properties [149]. An HS-SPME/GC-MS method combined with chemometric analysis (both unsupervised – PCA and HCA; and supervised – PLS-DA with VIP scores) was

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used to reveal the differences in volatile compounds between royal jelly produced from tea tree and other plants. Among 66 volatile compounds identified in tea royal jelly, including alkanes, aldehydes, alcohols, ketones, esters, lactones, phenols, organic acids, and others, the 35 compounds were shown to be significant in detecting the differences between royal jelly produced from tea and pagoda trees. The volatile compounds were extracted with a DVB/Carboxen/PDMS fiber and separated on a DB-5MS capillary column. The mass scan range was set from 35 to 450 m/z using a full-scan mode, and eluting compounds were identified using authentic standards and NIST 14 library with a match score of more than 70 [149]. A study on the volatile composition of 17 royal jelly samples that could have the potential to easily and efficiently assess the freshness and authenticity of this commercial product was reported by Isidorov et al. [150]. The method was based on HS-SPME GC/MS analysis of diethyl ether and methanol royal jelly extracts. The separation was carried out on an HP-5MS fused silica column, and compounds were identified using RI and by comparing mass spectral data with literature. The authors were able to isolate as many as 185 organic compounds [150]. Qi et al. [151] employed chemometric tools – PCA and HCA – on HS-SPME GC/MS volatile data and concluded that honeybee stock selected for increasing RJ yields (royal jelly bees) has shaped distinct volatile component profile compared with the unselected Italian bees. This was based on 37 identified compounds that could distinguish three royal jelly samples, including 5 aldehydes, 5 esters, 5 alkanes, 14 alcohols and phenols, 4 ketones, and 4 others. The method employed a DVB/Carboxen/PDMS fiber for extraction of volatile compounds, a DB-5MS capillary column for separation, and NIST 14 library and RI data for their identification. This was the first report on the characterization of volatile components in royal jelly that were able to uncover the differences among samples secreted by different bee stocks [151]. The GC/MS methodology is a standard method of choice when it comes to analyzing pesticides in royal jelly, such as frequently used amitraz (N-methylbis (2,4-xylyliminomethyl)amine) and 2,4-DMA (2,4-dimethylaniline) [152]. Sample treatment, including extraction and clean-up, is the key step for acaricide analysis to reduce the matrix interference and increase the sensitivity. To achieve these goals, SPME and HS-SPME are lately used. The following columns might be used for the analysis: CP-Sil8, SE-54, HP-5MS, or HP-1MS, and the following ions should be monitored utilizing mass spectrometry in the SIM mode: 293, 147, 121, 120, and 106 m/z [152].

7.7.5

Bee Venom

By analyzing a thorough review study published by de Graaf et al. [153], standardized methods for honey bee venom characterization and research employ various modifications of liquid chromatography technique, most frequently coupled to a mass spectrometric detector. This is because bee venom, also denoted as apitoxin, consists predominantly of various peptides, enzymes, and amino acids,

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which are compounds more amenable to liquid chromatography [153]. Two complementary mass spectrometry methods, MALDI-TOF and nanoESI-QqTOF-MS, were used to study 41 honeybee venom samples of different bee strains, countries of origin (Poland, Georgia, and Estonia), year, and season of the venom collection. Statistical analysis (ANOVA) has shown that there are qualitative and quantitative differences in the composition between honeybee venom samples collected over different years. It has also been demonstrated that MALDI-TOF spectra can be used as a protein fingerprint of honeybee venom in order to confirm the identity of the product [154]. No authentication studies based on GC/MS instrumentation were found by the authors.

7.8

Conclusions

Honey and other bee products, such as bee venom, Royal jelly, pollen, beewax, and propolis, are valuable products from many aspects, such as nutritional, medicinal, cosmetic, etc. As such, these products are among the most frequently adulterated, considering their high value on the market. Honey adulteration is most frequently related to altered technology of conventional honey production, such as sugar addition, feeding honey bees with high fructose syrup, or stating its “organic” origin or degree of ripeness. Honey itself is often falsely declared in respect to its botanical, geographical, or entomological origin. Currently, there is no available standardized analytical procedure for the detection of honey and bee products fraud, bringing a serious challenge to analysts of the research field of identifying adulteration. In general, the analysis of honey and bee products consists of multiple steps, which usually include sample preparation (SPME, head space, purge and trap, extraction with organic solvents, etc.) for the isolation of a specific group of analytes or possible markers, a separation technique (GC, HPLC, CE) combined with selective detection, such as tandem mass spectrometry, and afterward the generated data are chemometrically treated in order to classify the samples, a laborious and often time-consuming procedure that requires advanced chemometric skills and tools. Thus, it becomes obvious that drawing conclusions about the origin and adulteration of honey and bee products is not straightforward, but an analytical task extremely difficult and challenging. In particular, for the authentication of the botanical, geographical, and entomological origin of honey samples, gas chromatography or two-dimensional gas chromatography in combination with headspace gas chromatography (either HS SPME or purge-and-trap) is frequently applied by many authors to detect specific volatile profiles, afterward treating data with different chemometric approaches, such as HCA (hierarchical cluster analysis), LDA (linear discrimination analysis), FA (factor analysis), or PCA (principal component analysis). The less usual approach moves the focus from volatile profiles to free amino acid profiles that could enable the clustering of samples with the same geographical origin. Other instrumental analytical tools are also used in a search for adulteration of honey or honey bee products. HPLC has been used in the analysis of monoterpenes,

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diterpenes, sesquiterpenes, triterpenoids, bioflavonoids, and stilbenoids, in combination with multivariate analysis, such as PCA and HCA, for detecting the botanical and geographical origin of propolis. In pollen analysis, spectroscopic analysis might be applied, including microscopy and more advanced spectroscopic analysis, such as Raman spectroscopy or ATR-FTIR, ED-XRF, and NMR. Bee venom is one of the most expensive products produced by bees. Its value on the market is really high since it is known for its strong anti-inflammatory potential and other medicinal and cosmetic uses. The analysis of bee venom, being composed of a mixture of enzymes and other proteins, is mostly oriented toward protein fingerprinting by MALDI TOF. Beewax, if adulterated, is usually done by the addition of stearic acid or paraffins; thus, hydrocarbon profiles, specific alkanes, alkenes, and diens, can indicate it. In conclusion, all bee products have been reported to be adulterated, and a number of methodologies, including GC/MS at the top of them, have been applied by several research groups for the authentication of these products. It is noted that there is a high demand for the development of analytical tools that will reveal adulteration due to the high commercial value of all bee products. However, confirmation and analysis of bee products are not straightforward, and the absence of standardized analytical methods renders this analytical task extremely challenging and demanding, requiring good knowledge of bee products’ chemistry, technology, most frequent adulteration pathways, sample preparation, and use of advanced analytical tools. The future perspective of the field of honey and bee product authentication could include the establishment of analytical protocols for the determination of specific marker compounds after their validation and standardization by interlaboratory trials. Acknowledgments The authors would like to acknowledge the support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Program no. 451-03-68/2022-14/200134), and the Science Fund of the Republic of Serbia (CleanNanoCatalyze, Grant No 7747845 “In situ pollutants removal from waters by sustainable green nanotechnologies”-CleanNanoCatalyze).

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Fruits, Vegetables, Nuts, and Fungi Lidia Montero, Ane Arrizabalaga-Larrañaga, and Juan F. Ayala-Cabrera

Abstract

Food fraudulent activities are creating more concern in the society. The increased interest of consumers in the nutritional, sensorial, and visual values of the products has also led to an increment of counterfeiting practices. Thus, fruits and vegetables, as well as nuts and fungi, are among the products that have notified a higher number of adulterations because of their high nutritional value and the economic gain of some specific products. To face out these problems, analytical methodologies for the authentication of these food products are strongly required. Gas chromatography coupled to mass spectrometry (GC/MS) has demonstrated to be a reliable tool in the food authentication field by using fingerprinting strategies or by monitoring specific biomarkers related with the quality of the food. Here, we present an overview of GC/MS methodologies dealing with the authentication of fruits, vegetables, nuts, and fungi. The sample extraction, the GC/MS analytical determination, and data analysis strategies have been critically evaluated and the most relevant markers for different fraudulent activities (false declarations of cultivar varieties, geographical origin, processing and post-processing, etc.) have been thoroughly discussed to help on the authentication of these valuable food products.

L. Montero Institute of Food Science Research (CIAL), CSIC-UAM, Madrid, Spain A. Arrizabalaga-Larrañaga Wageningen Food Safety Research (WFSR), Part of Wageningen University & Research, Wageningen, The Netherlands J. F. Ayala-Cabrera (✉) Department of Analytical Chemistry, University of the Basque Country, Leioa, Spain Research Centre for Experimental Marine Biology and Biotechnology (PiE), University of the Basque Country, Plentzia, Spain e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_8

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Keywords

Gas chromatography · Mass spectrometry · Food analysis · Authenticity · Fraud · Fruits · Vegetables · Nuts · Fungi

8.1

Introduction

Food products are complex mixtures constituted by a great variability of endogenous organic nutrients, such as proteins, fats, carbohydrates, vitamins, and volatile organic compounds. However, food products can also contain exogenous (xenobiotic) substances, due to the use of agrochemicals and technological processes in the food industry. Moreover, the objective of obtaining more economic benefit from some food products, especially those of greater economic value, has sometimes led to the fraudulent introduction of some banned substances. Therefore, the integrity of food is under persistent threat from numerous fraudulent imitations. Thus, assuring consumers and stakeholders about the quality, authenticity, and safety of food is of leading importance for the agri-food economy [1]. As consumers become more interested in how their food is grown, manipulated, and delivered to market, the food system must ensure that what is being done in the food supply chain is appropriate and reliable. Under this scenario, to enhance citizens’ trust and increase the efficiency of the official control of the agri-food chain, the development of platforms that guarantee the integrity of food has been promoted. As an example, in the European Union, the Rapid Alert System for Food and Feed [2] and the Food Fraud Network [3] were developed. In these platforms, national authorities exchange information regarding any possible detection of a public health risk and fraudulent practice in the food chain. The most common economically motivated intentional adulteration practices are (1) mislabeling, (2) replacement/dilution/addition/removal in food product, (3) unapproved treatment and/or process, (4) documents (absent or falsified), and (5) intellectual property rights infringement. During the last 5 years, according to these platforms and literature, the food products that have shown the most adulteration notifications are fats and oils, fruits and vegetables, nuts, poultry, fish, milk and dairy products, coffee, and organic products among others [4]. The most appreciated quality attributes by consumers in fruits and vegetables are related to the organoleptic properties of the product, such us color, shape, size, texture, taste, and flavor. Many of these organoleptic attributes are closely related to the physico-chemical properties of the fruits and vegetables. These properties are usually related with their geographical origin, type of variety, or harvest conditions, among others, giving an extra value for the consumer’s perception and, thus, also from economic point of view. For these reasons, the main adulterations notified for most fruits and vegetables are the mislabeling and intellectual property rights infringement. On the other hand, nuts are also characterized by a high nutritional value and beneficial health effects, such as prevention of cardiovascular diseases [5]. Unfortunately, these excellent properties have also led to food fraud activities,

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such as mislabeling, replacement, and document falsification. However, as it is wellknown, nuts also involve allergenic properties which are mainly related with particular proteins or glycoproteins [6]. Thereby, these fraudulent practices might also suppose a risk for human health, thus merging food authentication and food safety issues [7]. Additionally, the high variety of fungi with different nutritional and organoleptic properties and even with high bioactive characteristics for human health are very attractive for consumption. Currently, truffles are one of the most appreciated gourmet products in the gastronomy industry, which made this valuable product very expensive and exclusive and therefore prone to suffer fraud and counterfeiting practices [8]. The main reason for the high demand of truffles is their unique organoleptic properties, mainly related to their special aroma, and their limited offer because they grow wildly under very specific climatological conditions and their harvest also requires special and laborious strategies, for instance, the use of trained dogs or pigs. Therefore, the aforementioned fraudulent practices in these food products need to be tracked due to their high economic impact and the human health problems they can cause. In the literature, among the analytical methods published for authentication of food products, chromatographic separation techniques, such as liquid chromatography (LC) and gas chromatography (GC) mainly coupled to mass spectrometry (MS), are the most used [1, 9–13]. These techniques permit the detection and characterization of the analytes present in food, and in many cases, the information obtained is subsequently treated with multivariate methods, such as ANOVA, principal component analysis (PCA), and partial least square discriminant analysis (PLS-DA) to address the authentication of the food. In GC/MS methods, equilibrium-based extraction techniques, such as static headspace extraction (HS) and solid-phase microextraction (SPME), have been widely used for the determination of volatile biomarkers in a wide variety of liquid and semi-solid (slurry) food samples [14]. SPME is considered one of the most versatile and popular non-exhaustive sample preparation techniques currently available, since it is a quick, inexpensive, sensitive, and efficient single-step solvent-free sample preparation technique [15]. On the other hand, in the case of non-volatile biomarkers determined by GC/MS, liquid-liquid extraction (LLE) techniques are usually employed. Among the most common biomarkers determined by GC/MS for authentication purposes of many food products, volatile organic compounds (VOCs), sugars, amino acid derivates, and fatty acid derivates are the most relevant metabolites [16]. In particular, the aroma is a highly important quality parameter that affects the acceptability of many food products by the consumer. Aroma is composed by a complex mixture of different families of volatile organic compounds, including esters, terpenes, alcohols, aldehydes, and ketones. The VOCs profile is a characteristic of each product and, even, of each variety or cultivar. Therefore, the same class of food product may present different aroma profiles. The main reason for this variability in the volatile profile is that the VOC composition is highly affected by several parameters, such as the climatological conditions (geographical origin, year, ground), the specific crop or variety, or harvest conditions (organic or conventional farming methods) [17]. Thus, the volatile profile can be used as a fingerprint that is

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the result of all the environmental and intrinsic parameters of each cultivar. For this reason, many authentication studies have been focused on the analysis of VOCs to establish the distinctive compounds that differentiate their quality. This chapter gives an overview of the analytical strategies available in the literature employing GC/MS to authenticate different food products, such as fruits, vegetables, nuts, and fungi.

8.2

Authenticity of Fruits

In the last 5 years, the GC/MS methodologies developed for the evaluation of fruit authentication have been focused on three main objectives, i.e., authentication of fruits regarding the geographical origin, classification of fruit variety, and differentiation between conventional and organic farming products. Table 8.1 summarizes the most recent GC/MS applications for the authenticity determination in fruits. The use of GC/MS methods to find distinctive markers that enable the authentication of specific fruit varieties has been one of the most used strategies in the last years. In particular, the classification of apples varieties has raised considerable interest. The volatile profile of the apple peels has been used as an authenticity parameter for the classification of apple cultivars. Yang et al. [18] studied the volatile composition of the peel of 40 apple cultivars. Seventy-eight compounds were identified and quantified in all the samples, being esters the main group, followed by aldehydes, and to a lesser extent, alcohols, ketones, and acids. The content of these compounds varied between the cultivars. Although hexyl-2-methylbutyrate was the most abundant ester in the samples, and hexanal and (E)-2-hexanal were the predominant aldehydes, there were some compounds, like (E,E)-2,4-heptadienal, that were found only in the Indo and Granny Smith varieties. The chemometric analysis of these data, using PCA, allowed the classification of the 40 cultivars in 5 groups, being the esters hexyl butyrate, hexyl 2-methylbutyrate, and hexyl hexanoate, together with the aldehydes hexanal, (E) 2-hexanal, and the alcohol 1-hexanol, and terpenes estragole and α-farnesene, the responsible compounds for the classification. A new application for a non-destructive in situ sampling method of the metabolite profile of apple peels was developed by Barberis et al. [30]. The sampling consisted of a functionalized strip placed in the apple surface that adsorbed the metabolites present in the apple peel. The functionalized strip was based on ethyl-vinyl acetate with mix-bed cation/anion exchange and C8 resins. After the sample preparation, the strip was desorbed and analyzed by comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer (GC × GC/QTOF MS). In the first dimension of the GC × GC separation, an Rxi-5Sil (30 m × 0.25 mm × 0.25 μm) column was used, while an Rxi-17Sil (2 m × 0.25 mm, 0.25 μm) column was used as second dimension. This method was used for the classification of 44 apples from 5 different apple cultivars. The high separation power offered by the multidimensional separation produced the identification of approximately 800 metabolites from the apple surface, among them not only volatile compounds like esters, alcohols, terpenes, alkanes, and aldehydes were determined

Not reported

MS system EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) n.r.a EI-Q (full scan) EI-QTOF (full scan)

GC/MS determination Column HP-INNOWAX (60 m × 0.25 mm × 0.25 μm) HP-INNOWAX (60 m × 0.25 mm × 0.25 μm) HP-INNOWAX (30 m × 0.25 mm × 0.15 μm) VF-WAXms (30 m × 0.25 mm, 0.25 μm) VF-WAX MS (30 m × 0.25 mm × 0.25 μm) HP-5MS (30 m × 0.25 mm × 0.25 μm) DB-5MS (60 m × 0.25 mm × 0.25 μm) DB-624 (30 m × 0.25 mm × 1.4 μm) DB-WAX (30 m × 0.25 mm × 0.25 μm) DB-WAX (30 m × 0.25 mm × 0.25 μm) SLB-5 (30 m × 0.25 mm × 0.25 μm) HP-5MS (30 m × 0.25 mm × 0.25 μm) DB-wax (30 m × 0.25 mm × 0.25 μm) DB-5MS (60 m × 0.25 mm × 0.25 μm) 1 D: Rxi-5Sil (30 m × 0.25 mm × 0.25 μm) 2 D: Rxi-17Sil (2 m × 0.25 mm, 0.25 μm)

Extraction HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS)

HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS)

HS-SPME (DVB/CAR/PDMS)

HS-SPME (DVB/PDMS) HS-SPME (DVB/PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/PDMS)

SPME (DVB/PDMS) HS-SPME (DVB/CAR/PDMS)

HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/PDMS) HS-SPME (DVB/CAR/PDMS) Noninvasive functionalized strim

Matrix Apple peel Apple pulp Apple Apple

Olives

Mango Plum Raspberry Winter jujube Peach Palm fruit (dates) Melon Papaya Orange Apple peel

PLS-DA PCA, HCA, OPLS-DA PLS-DA PCA, PLS-DA PCA, PLS-DA PLS-DA, HCA, machine learning

PLS-DA Data base creation PCA, HCA, PLS-DA PCA, HCA, DA PLS-DA PCA, CA LDA

Data analysis PCA PCA

[28] [29] [23] [30]

[26] [27]

[22] [23] [24] [25]

[21]

[19] [20]

Ref. [18] [17]

Fruits, Vegetables, Nuts, and Fungi

a

Metabolites (VOCs, sugars, fatty acids, waxes)

Analytes VOCs

Table 8.1 GC/MS methodologies for the authentication of fruits

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but also sugars and lipids (fatty acids and waxes). PLS-DA, VIP scores, and hierarchical clustering, together with machine learning techniques, allowed the classification of the five apple cultivars. In particular, the varieties Gala and Golden (which are genetically related) were statistically associated and the wax heneicosane3-methyl, the fatty acid 11,14-eicosadienoic acid, the aldehydes 1-udecanal and 10-nonacosanal, the anthranilic acid, and, as described also in the work from Yang et al. [18], the terpenoid α-farnesene were distinctive compounds between the five varieties. Besides the peel, the volatile profile of apple pulp has also been used for the differentiation between apples varieties. The GC/MS analysis of VOCs of 85 pulp apple cultivars grown in China revealed caprylate as a potential marker of the apple variety Jiguan, while furfural was a distinctive compound for the Jazz cultivar. Moreover, α-farnesene together with 1-hexanol was related to the Honeycrisp apple, which is recognized as a high-quality cultivar with pleasant aroma. Other markers found in different apple varieties were hexyl acetate and butyl acetate [17]. In relation to the authentication of other fruit varieties, Valdés-García et al. [24] developed a HS-SPME GC/MS method for the differentiation of “Adelita” raspberry cultivar, known for its high nutritional and commercial quality, from other raspberry varieties. In this work, the HS-SPME sample preparation process was optimized to maximize the VOCs recovery using a response surface methodology (RSM), considering the sample weight, time, and extraction temperature as factors. The temperature resulted to be the factor with higher impact in the extraction of volatiles with a positive effect. After the HS-SPME optimization and the GC/MS analysis, decanal, nonanal, hexanal, α- and β-ionone, and linalool were the main responsible compounds for the statistical separation of the Adelita from the other raspberry cultivars. Other studies that have been using the potential of GC/MS for the differentiation between fruit varieties by their VOC profiles have been used for the differentiation between climacteric and non-climacteric varieties [28] and fruit varieties with different pulp organoleptic characteristics, like color and flavor [26]. Due to the close relationship between the aroma and the consumer quality perception, some works combine the analytical GC/MS information with sensorial analysis. This was the case of the authentication of 24 Spanish-style green table olives according to their variety and to the sampling time during the elaboration process. The sensorial panel described nine major aromas (lactic, green fruit, ripe fruit, grass, hay, musty, lupin, wine, and alcohol). The GC/MS analysis identified 133 volatiles that were used for the multivariate analysis. The PCA analysis showed a clustering trend of the samples according to the sampling time during the process, for example, it was observed that 1,4-dimethoxybenzene, pseudocumene, heptanoic acid, octanoic acid, nonanal, and phenylacetaldehyde were markers of the olives sampled after the packing procedure, though PCA did not provide a good separation of the sample varieties. However, thanks to the combination of sensorial and GC/MS analyses data, four of the nine sensorial descriptors were detected as more relevant in the results. These four descriptors (lupin, lactic, alcohol, and wine) were considered for performing PLS-DA, and then, a better classification of the varieties was achieved. That way, a correlation between the sensorial perception and the analyte

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responsible for this effect was achieved. The lupin and lactic perceptions were correlated to 3-hexanoate and methyl hydrocinnamate compounds, respectively, while the alcohol and wine feelings were strongly associated between each other and related to two volatile compounds, namely, methyl hexanoate and benzaldehyde [21]. In the same way, the mango variety known as Harumanis has been subjected to authenticity evaluation due to its organoleptically higher quality in comparison to other mango cultivars. The authentication of this special variety is necessary because it has been a target of adulteration and frauds with cheaper and lower-quality mango varieties. The GC/MS analysis of VOCs preceded by a sensorial analysis allowed the complete authentication of Harumanis mango from other two varieties usually used for adulteration, achieving 100% correct classification of the three varieties. Three analytes could be detected as authentication markers of the Harumanis variety (β-ocimene, trans-β-ocimene, and allo-ocimene) [22]. The correlation between the chemical volatile composition of four papaya varieties and the organoleptic properties of each variety has also been used for the variety differentiation. In this work, PLS-DA provided better statistical separation of the samples than PCA analysis. Two varieties were not possible to be separated by the volatile compound profile, due to their very similar composition. PLS-DA improved the separation and established a relationship between the “pleasant retronasal odor impression” of SH-5 papaya variety with its higher terpene content [29]. As mentioned above, another authenticity evaluation with a high socio-economic impact is the search of markers for the differentiation between organic and conventional farming processes. Fruit VOCs are proposed to find differences in the volatile profile between the two farming methods, and, therefore, GC/MS analysis has an important role in the authentication of conventional and organic crops. Several esters and acids, like ethyl acetate, propanoic acid, 2-methyl-, ethyl ester, or butanoic acid, among many others, were some of the compounds found in higher concentration in organic apple cultivars [19]. On the other hand, the GC in combination to combustion-isotope ratio mass spectrometry (GC-C-IRMS) method developed by Strojnik et al. [20] also revealed hexyl butyrate as a compound that occurred in higher concentration in organic apple crops in comparison to the corresponding conventional apples. The use of GC/MS for the differentiation between organic and conventional management systems has also been used for orange samples. A HS-SPME GC/MS method detected that conventional oranges showed higher content of geranyl-diphosphate derivates, like neryl and geranyl acetate, while the organic fruits were richer in acetates and terpenoids. The PLS-DA analysis of the orange volatile compounds detected by GC/MS classified accurately the samples from each farming system, and the VIP scores showed ethyl acetate, ethyl propanoate, methyl butanoate, ethyl isobutyrate, ethyl butanoate, and ethyl 2-methylbutanoate as discriminant compounds between the two harvest alternatives [23]. The aroma compounds of plums harvested under organic and conventional systems have also been evaluated for the possible differentiation between the two farming systems; however, the chemometric analysis of the identified and quantified volatile compounds did not provide a clear prediction of the effect of management system. Thus, only statistical differences in the aldehyde content, especially the

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hexanal and (E)-2-heptenal concentrations (two compounds related with soapy and fatty odors), were found with higher content in the conventional plum samples and were confirmed as parameters affected by the cultivation system. Finally, the last GC/MS applications that have been developed for the evaluation of fruit authenticity are related to the determination of the geographical origin accordingly to quality parameters, like the fruit aroma. Giannetti et al. [19] developed a HS-SPME GC/MS for the differentiation of the flavor composition of 42 apple varieties, including ancient, non-native, and new hybrid apples from different regions of Northeast Italy. In this study, apples with different geographical origin were well classified (prediction accuracy of 91.3%) according to their volatile composition, and this classification could also be related to the specific climatological conditions of the regions, to the ground of the production area, and to the acquired resistance of the varieties to adverse conditions. Ethyl acetate, ethanol, and butanoic acid ethyl ester were detected as discriminant compounds for the Friuli Venezia Giulia region, while 1,5-pentanediol and the esters were more characteristics of the Adige-South Tyrol region. Quiao et al. [25] used a multi-GC platform consisted of GC/IMS and HS-SPME GC/MS as well as electronic nose (E-nose) analysis for the authentication of winter jujube fruits from eight Chinese regions. GC/IMS and E-nose were used for a non-targeted analysis of VOCs to provide a fast classification of the samples according to their geographical origin. Then, targeted GC/MS analysis of the most relevant compounds was performed. The PLS-DA analysis of the GC/MS data amplified the statistical differentiation between the groups. VIP scores revealed 1-peten-3-ol, acetone, 2-methoxyphenol, methyl laurate, 3-pentanone, 2-formyltoluene, and ethyl hexanoate as the most influencing compounds for the discrimination between the eight regions. Besides these three authenticity objectives used in fruits, the detection of natural and artificial aromas in fruits is an important task to ensure the consumer confidence. In this regard, GC-C-IRMS has been used for the identification and authentication of apple natural aromas and their comparison with synthetic aromas. GC-C-IRMS is a very specific method for the determination of food authenticity and for the detection of the addition of synthetic aromas. The large specificity provided by this technique is based on the ability to distinguish the slight differences that exist between the δ13C values of artificial compounds (synthesized from coal and petroleum) from the δ13C of modern plants [31]. In GC-C-IRMS methods, it is crucial to minimize any contamination source and isotopic fractionations that contribute to false results. In this work, HS-SPME was used for the sample preparation with a divinylbenzene/ carboxen/polydimethylsiloxane (DVB/CAR/PDMS) SPME fiber. To improve the synthetic aroma detection in real samples, a database of δ13C values of synthetic aromas was created, and these values were compared with the extracted aromas from the natural apples. With this method, the authentication of the aroma origin of apples was established [20].

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Authenticity of Vegetables

The essential role of vegetables in a healthy and balanced diet has increased the demand for this food for consumption throughout the world. Today, due to the development of the food production system, the types and amounts of available vegetables have been expanded globally, and consumers can obtain products from all over the world. This development has also carried out fraudulent practices, as mentioned in the introduction, that involved the mislabeling and intellectual property rights infringement. For this reason, consumers prefer to buy vegetables from farmers and/or those under well-known brand names, despite the higher prices comparing to those imported. Under this scenario, several studies have been carried out to assess markers that enable the authentication of vegetables according to vegetable variety, geographical origin, and production system and processing. Therefore, the recently developed GC/MS methodologies to assess these authenticity approaches are discussed in this section. Table 8.2 includes a summary of the GC/MS methods developed for the authentication of vegetables. As can be seen in Table 8.2, most of the works focus on the determination of VOCs in matrices such as peppers and tomatoes. The most used analytical strategy for the analysis of VOCs is the combination of HS-SPME using DVB/CAR/PDMS fibers with GC/MS system and post data analysis with multivariate methods, such as PCA. Additionally, few studies determine other metabolites, such as sugars and phenolic compounds in several vegetables. Aguiar et al. [32] studied the volatile profile of different vegetables from regular consumption, such as tamarillo, tomato, broccoli, white and orange carrot, beetroot, and spinach, among others. The analysis of the samples was carried out by HS-SPME (DVB/CAR/PDMS) GC/MS, using a polar BP-20 column. Among all the samples studied, 320 metabolites were identified in total, of which terpenes and organosulfurs were the main groups, followed by esters, aldehydes, ketones, and alcohols. As it is expected, the content of VOCs varied between vegetables. The most abundant VOCs in beetroot and carrots were terpenes, in onion and garlic organosulfurs, and in spinach and broccoli, alcohols and aldehydes were the predominant. These VOC profiles may be very useful for authentication purposes, since these compounds differ between varieties and can also be affected by both environmental and agronomic conditions, as well as the processing method. In this context, Zhou et al. [42] investigated the physicochemical and antioxidant properties of three pumpkins (Cucurbita maxima, Cucurbita moschata, and Cucurbita pepo) to obtain a sensory comparative description with 24 parameters in which VOCs were present. The VOC profile was determined by HS-SPME (DVB/CAR/PDMS) GC/MS, using a non-polar DB-5 column and electron ionization with a single quadrupole mass analyzer in full scan mode. Authors identified 93 aroma compounds by comparing the retention indices and the obtained mass spectra. The three varieties showed the presence of common VOCs, such as 1-hexanol, 2-ethyl-, eucalyptol, propanoic acid, 2-methyl-, 3-hydroxy-2, 4,4-trimethylpentyl ester, and butylated hydroxytoluene. Additionally, the authors found that different VOCs predominated in different

Sugars Phenolic compounds

Analytes VOCs

Matrix Vegetables Bell pepper Red pepper Paprika Paprika Tomato Tomato Tomato Tomato Tomato Pumpkin Asparagus Corn Vegetables

Extraction HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (CAR/PDMS) HS-SPME (CAR/PDMS) Distillation HS-SPME (DVB/PDMS) HS-SPME (DVB/PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS) Distillation Hexane LC-18 SPE (2 g)

GC/MS determination Column BP-20 (30 m × 0.25 mm × 0.25 μm) VF5-ms (30 m × 0.25 mm × 0.25 μm) DB-wax (30 m × 0.25 mm × 0.25 μm) DB-5 (30 m × 0.32 mm × 1.05 μm) DB-5MS (60 m × 0.25 mm × 0.25 μm) ZB-5MS (30 m × 0.25 mm × 0.25 μm) DB-5 (30 m × 0.25 mm × 0.25 μm) ZB-wax (30 m × 0.25 mm, 0.25 μm) DB-1 (60 m × 0.25 mm × 0.25 μm) DB-wax (30 m × 0.25 mm × 0.25 μm) DB-5 (30 m × 0.25 mm × 0.25 μm) INNO-WAX (60 m × 0.25 mm × 0.5 μm) RTX-5MS (30 m × 0.25 mm × 0.25 μm) RTX-5MS (30 m × 0.25 mm × 0.25 μm)

Table 8.2 GC/MS methodologies for the authentication of vegetables MS system EI-Q (full scan) EI-IT (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-IT (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan)

Data analysis PLS-DA PCA PCA, LDA, SIMCA PCA, OPLS, O-PLS HSD, ANOVA PCA, ANOVA PCA, HCA, OPLS-DA PCA, ANOVA PCA SAS

Ref. [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45]

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varieties, such as alcohols, aldehydes and ketones in C. maxima pulp, and alcohol esters and alkenes in both C. moschata pulp and C. pepo pulp. The use of these VOC profiles, together with other physicochemical and antioxidant properties, enabled authors to separate varieties by PCA multivariate approach, showing that the selected parameters were good discriminant factors between different varieties of pumpkins. In relation to the authentication of fresh hot peppers (Capsicum chinense), Gahungu et al. [34] developed a HS-SPME GC/MS method for the characterization of VOC profiles in peppers at red stage. In this study, 70 VOCs were identified, which included aliphatic esters (ethyl isopentanoate, pentyl 3-methylbutanoate, and hexyl pentanoate), alcohols (10-undecenol, 3,3-dimethyl cyclohexanol), terpenes (β-chamigrene), and fatty acids ((E)-9-tetradecenoic acid, pentadecanoic acid, and hexadecanoic acid, among others). In addition to VOCs, the potential of other metabolites, such as sugars [44] and phenolic compounds [45] in vegetables, has been studied for authentication purposes. In a study by Pastor et al. [44], authors aimed to develop a fast method for corn and small grain flour authentication. For this purpose, simple sugars were extracted with ethanol and derivatized with TMSO to be analyzed by GC/MS system with electron ionization and single quadrupole mass analyzer. Once authors performed the creation of a binary simple sugar profile, exploratory data analysis by hierarchical cluster analysis (HCA), principal coordinate analysis, and PCA was carried out. The obtained results in all cases showed that corn samples were strongly separated from other grain species enabling the authentication of corn/small grain mixtures. On the other hand, Tiveron et al. [45] evaluated the potential of phenolic compounds as biomarkers for authentication purposes in vegetables, such as artichoke, broccoli, carrot, pumpkin, and cucumber among others, cultivated in Brazil. For this purpose, samples were submitted to a solid phase extraction (SPE) procedure with LC-18 SPE cartridges to remove sugars that might interfere with the subsequent derivatization step for the formation of trimethylsilyl derivatives (TMS). Afterwards, the analysis of the obtained extracts was performed by using a RTX-5MS low-polar column and electron ionization in full scan mode. The identification of target compounds was carried out by comparing their retention time and ion fragmentation with available analytical standards and Wiley library. In this research, authors identified several compounds derived from benzoic acid (2,5-dihydroxybenzoic, 3-hydroxybenzoic) and cinnamic acid (caffeic acid, sinapic), where in the case of spinach, caffeic acid was the most abundant one. Additionally, flavonoids, such as quercetin, were identified in asparagus, lettuce, and escarole. In this way, as can be observed, several phenolic compounds related to the high antioxidant activity were identified and could be useful as potential biomarkers for their authentication. GC/MS authentication methodologies to differentiate the geographical origin of vegetables have also been proposed. These methods are based on the VOCs profiling in tomatoes [37] and bell pepper [33]. Lo Feudo et al. [37] evaluated the discrimination between fresh tomatoes from different Italian production areas (Emilia Romagna, Basilicata, Calabria, and Puglia) collected in the same harvest period (August 2007). The analysis of the samples was carried out by HS-SPME

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Fig. 8.1 PLS-DA analysis: score plot for the geographical discrimination of bell pepper (Capsicum annuum) species by HS-SPME GC/MS. Empty and filled symbols represent training (tr.) and test samples, respectively (Reprinted from [33], Copyright (2021), with permission from MDPI)

(CAR/PDMS) GC/MS, using a non-polar ZB-5MS column. Pattern recognition analysis was done using two supervised chemometric techniques, linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA), to obtain classification rules for distinguishing between the production areas. Both models allowed the differentiation by the geographical origin achieving a very good percentage of prediction (96% for LDA and 94% for SIMCA). Among the compounds that show a high discriminant power, there are some strongly related with the tomato flavor, such as 3-methylbutanal, 2-isobutylthiazole, and hexanal, as well as other compounds including furans (2,3-dihydrofuran, 2-methylfuran), other aldehydes (2-methylbutanal, heptanal, octanal), p-cymene, and alcohols (1-pentanol, 5-methyl-5-nonanol), among others. On the other hand, Reale et al. [33] assessed the geographical discrimination of bell pepper species from diverse Italian areas (Abbruzo and Lucania) by monitoring the VOC profiles obtained by HS-SPME (DVB/CAR/PDMS) GC/MS. The separation of the analytes was achieved in a non-polar VF5-MS column. The aroma profile led to the identification of 59 VOCs. By applying a PLS-DA, all the samples could be correctly classified as can be observed in Fig. 8.1. This approach led to the selection of 16 compounds (3-carene, p-cymene, (-)limonene, (+)-carvone, estragole, α- and β-copaene, β-elemene, tetradecane, dehydro isolongifolene, γ-selinene, eremophila-9(10),11(12)-diene, eremophilene, β-bisabolene, guaia-1(10),11-diene, and β-sesquiphellandrene) as the most discriminant markers (VIP > 1) to achieve the geographical origin discrimination of bell peppers. As it is well-known, the production process affects the final quality of the food, and in many cases a specific procedure has an added value, and, therefore, the price of the product is increased. Unfortunately, in some foods, such as vegetables, in

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order to have an economic gain, cases of mislabeling have been detected in reference to the vegetable production system. For this reason, the search of markers for the differentiation between organic and conventional production processes has gained importance. For instance, Lee et al. [39] carried out metabolomic studies of VOCs in different tomatoes that were grown in net-house and open-field conditions. Three Texas (TAMU) and five commercial tomato varieties were characterized by HS-SPME (DVB/CAR/PDMS) GC/MS (DB-Wax column), and the data was evaluated by multivariate analysis, in order to distinguish between genotypes and production systems, as well as to identify potential biomarkers. Among the VOC profiles, hexanal, p-cymene, and (E)-2-hexenal abundance vary from TAMU samples to commercial tomato samples, and the presence of 16 metabolites was affected by the production system, being higher in those from net-house. Additionally, it was reported that flavor related volatiles, such as 6-methyl-5-penten-3-one, 1-penten-3-one, (E)-2-heptenal, (E)-3-hexen-1-ol, and 2-isobutylthiazole, have been affected by the production system. Similarly, Lijima et al. [38] aimed to identify the factors that affect the flavor characteristic of tomatoes combining both the VOC profile and sensory evaluation. The VOC profile was extracted from tomatoes by HS-SPME (DVB/ PDMS) before the determination by GC/MS. In total, 160 compounds were detected but 89 identified by library matching tool. As expected, hexanal, cis-3-hexenol, and 6-methyl-5-hepten-2-one were the most abundant in all samples. However, if the VOC profiles among different samples were compared, it could be observed that the VOC profile was influenced by both the type of tomatoes used and the processing conditions. Additionally, once the VOC profile of fresh market processing tomatoes was determined, both volatile and sensorial descriptors were evaluated by PCA, and the results showed that the obtained values were negatively correlated (Spearman’s correlation coefficient was -0.880) indicating that they both were directly related. Within this study, the importance of the selection of variety and production system to preserve desired aromas based on VOC profiles could be observed. The type of processing carried out in some vegetables may change their metabolite profile, and, thus, its characterization is very useful in terms of authentication. Due to the relation of the processing steps with the aroma and flavor, the developed methodologies mainly focused on the VOC profile of the vegetables [35, 36, 41, 43]. For instance, Martin et al. [35] evaluated the impact of VOC profiles on the sensorial attributes of dried paprikas, whereas Kevresan et al. [36] studied the influence of the post-harvest ripening conditions in the VOC profile of paprika. In both cases a DB-5 column was used for the determination of VOCs by GC/MS. Martin et al. [35] investigated the VOC profile of smoked, oven-dried, and sun-dried paprikas, and, in total, 124 compounds were identified in which 72 belong to sun-dried paprikas and 60 to oven-dried ones. Among the identified VOCs, acids were the most abundant in all cases, followed by alcohols, although other compounds, such as furans, phenols, ketones, terpenes, and aldehydes, were also identified. In this context, the smoked paprikas had higher amount of alcohols, phenols, and pyrroles, while aldehydes and terpenes were characteristics of ovendried samples. In addition to the VOC profiles, authors also performed a sensorial

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evaluation of the samples, which related the oven dried paprikas with a fruity odor and the sun-dried paprikas with straw aromas. With the data collected in these studies, PCA analysis was carried out, and the scores plot showed that the three paprika types could be authenticated based on their VOC profiles. On the other hand, in the study developed by Kevresan et al. [36] regarding the post-harvest ripening, 97 VOCs were detected in samples without post-harvest ripening, 96 VOCs in those ripened in the dark, and 89 VOCs in those ripened on a daily light. Authors highlighted that these results may be related to the increase of the temperature during the drying process, which is needed to obtain high-quality paprika with high aromatic properties. In general, the main identified compounds were fatty acids and their esters, terpenes, terpenoids, aldehydes, and ketones, regardless of the ripening stage. However, results showed that the ripening in the dark favored the decrease of VOCs, such as fatty acids and their esters, in the sample. On the contrary, an increase of terpenes and terpenoids was observed in the same conditions, while the presence of aldehydes and ketones was favored under daylights.

8.4

Authenticity of Nuts

The vast varieties of nuts include almonds, cashew nuts, chestnuts, hazelnuts, walnuts, pistachios, peanuts, or pine nuts, among others. Since nuts are among the most appreciated goods by consumers, there is a high demand on the food market. For this reason, the production of nuts has significantly grown from 80 up to 95 million tons in the last years (2015–2020) [46]. As mentioned in the introduction, nuts could suffer from fraud activities (i.e., mislabeling, replacement, and falsified documents) that might even pose a risk for human health, due to their well-known allergenic properties. Thereby, a thorough monitoring of the potentially fraudulent activities is required. Thus, this section describes GC/MS methodologies recently proposed for the authentication of nuts and closely related products. Table 8.3 summarizes different reported approaches using GC/MS for the authentication of nuts. In general, it can be observed that the profiling of VOCs is the method of choice for the characterization and authentication of a large number of nuts. For this purpose, HS-SPME using fibers with multiple adsorbents such as DVB/CAR/PDMS is usually considered for nuts authentication, since they cover a wider range of interesting compounds, thus avoiding the discriminations usually observed in solvent-based extraction techniques. Moreover, the range of columns used for the determination of VOCs is quite broad, although some trends towards the use of non-polar stationary phases can be observed. Besides VOCs, fatty acids are also frequently determined for the authentication of nuts. In this case, the extraction is usually carried out using organic solvents/mixtures (hexane, chloroform/methanol, etc.) or cold-pressing processes. After their derivatization into fatty acid methyl esters (FAMEs), their separation is generally achieved in non-polar columns based on 5% diphenyl-95% polydimethylsiloxane stationary phases. Moreover, more polar biomarkers, like phytosterols, polyphenols, sugars or alcohols, have also been

Analytes VOCs

HS-SPME (DVB/CAR/PDMS)

HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS)

Hazelnuts (high-quality, PDO)

Baru nuts

Almonds

Pistachio

HS-SPME (DVB/CAR/PDMS)

Hazelnuts

Hazelnuts

HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/PDMS)

HS-SPME (DVB/CAR/PDMS) HS-SPME (CAR/PDMS)

Areca nuts

Peduncles of cashew clones Pine nuts

Extraction HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/CAR/PDMS)

Matrix Torreya yunnanensis nuts Torreya yunnanensis nuts

1 D: CW20 M (30 m × 0.25 mm, 0.25 μm) 2 D: OV1701 (1 m × 0.1 mm, 0.10 μm) 1 D: SolGel-wax (30 m × 0.25 mm × 0.25 μm) 2 D: OV1701 (1 m × 0.1 mm × 0.10 μm) Sapiens-5MS (30 m × 0.25 mm × 0.25 μm) Rxi-1301SilMS (30 m × 0.25 mm × 1 μm) DB-WAX (30 m × 0.25 mm × 0.25 μm)

Rtx-5 (30 m × 0.25 mm × 0.25 μm)

DB-5(60 m × 0.25 mm × 0.25 μm)

ZB-wax (60 m × 0.25 mm × 0.25 μm)

1 D: SR-5MS (30 m × 0.25 mm × 0.25 μm) 2 D: DB-17MS (1.295 m × 0.18 mm × 0.18 μm) SLB-5MS (30 m × 0.25 mm × 0.25 μm)

GC/MS determination Column Rtx-5MS (30 m × 0.25 mm × 0.25 μm)

Table 8.3 GC/MS methodologies for the authentication of nuts and related products

EI-Q (full scan) EI-Q (full scan) EI-QqQ (full scan)

EI-Q (full scan)

EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan)

MS system EI-Q (full scan) EI-QTOF (full scan)

PCA/PLS/ ANOVA

Fruits, Vegetables, Nuts, and Fungi (continued)

[56]

[55]

[54]

– ANOVA

[53]

[52]

[51]

[50]

[49]

[48]

[47]

Ref. [47]

PCA/heat map

ANOVA/ MSTAT-C –

PCA/ANOVA

PCA/PLS

Venn diagram

PCA/ANOVA

Data analysis PCA/ANOVA

8 229

Almonds, Brazil nuts, cashews, peanuts, hazelnuts, pecans, pine nuts, and walnuts Tiger nut flour

Cashews, peanut, tiger nut, and coconut Wheat, walnut, and hazelnut flour Almond, walnut, and hazelnut oils Walnut oil (African)

Terminalia catappa nuts

Fatty acids

Fatty acids and non-polar metabolites

Almonds (Californian) Almonds Pecans (Georgian)

Matrix Almonds

VOCs

Analytes

Table 8.3 (continued)

EI-Q (full scan) EI-Q (SIM)

EI-Q (full scan) EI-Q (full scan) EI-Q (full scan) EI-Q (full scan)

DB-5MS (30 m × 0.25 mm × 0.25 μm) DB-5MS (30 m × 0.25 mm × 0.25 μm) DB-5 (15 m × 0.25 mm × 0.25 μm) Rxi-5MS (30 m × 0.25 mm × 0.25 μm)

Hexane

Cold-pressing

Hexane

1. UAE (CH3OH/H2O, 3:7 v/v) 2. LLE (hexane)

EI-Q (full scan) n.r.a EI-Q (full scan) EI/SPIb-TOF (full scan)

MS system EI-Q (full scan)

INNO-WAX (30 m × 0.25 mm × 0.25 μm) DB-23 (30 m × 0.25 mm × 0.25 μm)

BPX35 (3 m × 0.25 mm × 0.25 μm)

DB-1(60 m × 0.32 mm × 0.25 μm) HP-5MS (30 m × 0.25 mm × 0.25 μm)

DB-WAX (30 m × 0.25 mm × 0.25 μm)

GC/MS determination Column SPB-5 (30 m × 0.25 mm × 0.25 μm)

HS-SPME (DVB/CAR/PDMS) CH3Cl/CH3OH (2:1 v/v)

Extraction 1. Fat extractor (petroleum ether) 2. HS-SPME (DVB/CAR/ PDMS) HS-SPME (DVB/CAR/PDMS) HS-SPME (DVB/PDMS) HS-SPME (DVB/CAR/PDMS) Thermal analysis (250 °C)

–

–

[66]

[65]

[64]

[63]

[62]

–

PCA/PCoA/ HCA/SVM CA

[61]

[7]

[59] [60]

[58]

Ref. [57]

Heat map

–

MANOVA ANOVA

PLS/ANOVA

Data analysis CA/PCA

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Coconut water

Almonds, pistachios, macadamia nuts, walnuts, hickory nuts, chestnuts, cashews, pine nuts, hazelnuts, and peanuts Pistachio

Not reported Single photon ionization

b

a

Sugars, alcohols, organic, and fatty acids

Polyphenols

Phytosterols

1. Acetone 2. LLE (CH3OH/H2O, 8:2 v/v) LLE (ethyl acetate)

1. Soxhlet (petroleum ether) 2a. KOH (1 M in EtOH) and LLE (CH2Cl2) 2b. SPE (strata NH2, 1 g)

EI-Q (full scan) EI-Q (full scan)

Rtx-5MS (30 m × 0.25 mm × 0.25 μm) HP-5MS (30 m × 0.25 mm × 0.25 μm)

EI-Q (SIM)

DB-5MS (30 m × 0.25 mm × 0.25 μm)

Deconvolution/ PLS-DA/VIP

PCA

Heat map

[69]

[68]

[67]

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considered for the authentication of nuts by GC/MS, although the number of applications is quite reduced compared with VOCs and fatty acids. The authentication methodologies proposed for nuts could be classified in the following groups: nut types, cultivar and varieties, geographical origin, processing procedures (fresh and roasted nuts, roasting conditions, roasting evolution, etc.), and post-processing (post-harvest, industrial storage, etc.). Phytosterols [67] and fatty acids [63, 64] have been considered as biomarkers for discrimination of different types of nuts. Wang et al. [67] used the free and esterified phytosterol profiles to differentiate nuts and seeds commonly consumed in China by GC/MS using a DB-5MS column. A Soxhlet extraction with petroleum ether was carried out to extract nut and seed oils. Then, oil extract was either saponified (KOH in ethanol) and extracted with dichloromethane for the total phytosterol content, or submitted to a solid phase extraction (SPE) with Strata NH2 cartridges to determine the free phytosterol profile. Eight kinds of 4-desmethyl sterols, as well as three kinds of 4,4-dimethyl sterols in both free and esterified forms, were detected in the samples. The different phytosterol contents allowed the clustering of nuts and seeds in four different groups: (1) peanuts and sunflower seed kernel (high contents of campesterol, stigmasterol, and 24-methylene cycloartenol); (2) pistachios and pine nuts (high contents in plant stanols and campesterol); (3) chestnuts, almonds, macadamia nuts, walnuts, hickory nuts, cashews, and hazelnuts (all of them are tree nuts, although chestnuts had a much lower content of phytosterols); and (4) watermelon seed kernel and pumpkin seed kernel (high contents of α-spinasterol and cycloartenol). Moreover, the fatty acid profile has also been considered for the discrimination of flours [63] and oils [64] obtained from different types of nuts. For the analysis of nut flours, the nut grains were milled and extracted with hexane before being derivatized with trimethylsulfonium hydroxide (TMSH) to form the FAMEs. The extracts were analyzed by GC/MS, and FAMEs were identified using the NIST and WILEY mass spectral libraries with a match quality over 90%. The GC/MS data were analyzed by PCA and heat maps to select relevant variables for the cultivar origin of the flours, while hierarchical clustering analysis (HCA) and principal coordinated analysis (PCoA) were carried out to test the potential of unsupervised discriminations in this field. The authors found that palmitic, linoleic, oleic, and stearic acids were able to discriminate between wheat, hazelnut, and walnut flours after applying a PCoA and a support vector machine (SVM) classification (coefficient of determination 98.6) [63]. When the authentication methods were focused on the discrimination of different varieties from the same nut species, VOCs were considered as potential biomarkers. For instance, the VOC profile has been used to discriminate between eight cultivars of fresh pistachio nuts [55]. The samples were extracted by HS-SPME (DVB/CAR/ PDMS) and analyzed by GC/MS using a Rxi-1301SilMS cyano-stationary phase. The tentative identification was carried out by determining the Kovat’s index (KI) and by comparison with the NIST spectra library. Thus, 13 VOCs, including 2 alcohols, 2 aldehydes, 6 terpenes, 1 pyrrole, acetic acid, and benzyl acetate, were isolated being α-pinene and 1-methylpyrrole the most abundant substances in all the

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samples and, thus, key compounds in the odor of unroasted pistachios. On the other hand, the lower concentrations of hexanal, β-pinene, and limonene showed to be statistically different to explain the more complex sensory profile of some varieties. For example, the high flavor intensity of the Kerman variety might be linked with the higher concentration of limonene in this cultivar. King et al. [56] also proposed a methodology combining sensory analysis and GC/MS to discriminate different sweet almond varieties from two growing seasons (2015–2016). A PCA biplot overlaying both sensory and chemical analyses showed that moisture, amygdalin, and several VOCs were important in modelling the sensory profile. The classification of almond cultivars (Marcona, Guara and Butte) has also been proposed by analyzing the VOC profile obtained from an oil extract [57]. The VOCs were extracted from the almond oil by HS-SPME (DVB/CAR/PDMS) before the analysis by GC/MS. In general, it was observed that the volatile content of Spanish almond cultivars (Marcona and Guara) was higher than the Californian almond cultivar (Butte). Oleic acid derivatives, octanal, and nonanal, were diminished in the American cultivar. Using 12 VOCs (including C8–C14 aldehydes, linear-chain alkanes, carboxylic acids, and 1,3-dimethylbenzene), the almond cultivars could be discriminated by linear discriminant analysis, allowing the correct classification of all the samples (100%). De Freitas et al. [49] also analyzed the VOC profile of peduncles of cashew clones by HS-SPME (DVB/CAR) and GC/MS using a polyethylene glycol stationary phase (ZB-WAX). The identified VOCs consisted mainly of esters (50%) followed by alcohols (15%), ketones (10%), terpenes (8%), aldehydes (8%), acids (6%), and one disulfide. Among them, methyl butanone, methyl 3-methylbutanoate, ethyl 2-methylbutanoate, methyl 2-butenoate, methyl 3-methylpentanoate, 3-carene, methyl (E) - 2-methyl-2-butenoate, ethyl 4-methylpentanoate, 2-hexenal, butyl 3-methylbutanoate, butyl pentanoate, and 3-methyl butanoic acid, were the most important chemical biomarkers to differentiate between the aroma and flavor of the different peduncles of the evaluated cashew clones. The geographical origin of specific varieties of nuts has also been considered, since it could affect the quality and therefore the cost of the products. For instance, Saitta et al. [68] determined the polyphenolic profile to differentiate pistachios (Pistacia Vera L.) from Italy (Bronte) and Turkey by GC/MS (Rtx-5MS column) after trimethylsilyl (TMS) derivatization. A PCA was conducted to discriminate between both groups using variables that provided a significant differentiation, such as methyl-(3,4-dihydroxybenzoate), ethyl-(3,4,5-trihydroxybenzoate), (+)-catechin, 4-hydroxybenzoic acid, syringaldehyde, hydroxytyrosol, cis-ferulic acid, and 4-coumaric acid ( p values 0.05) to establish a clear classification. In the same way, the GC/MS method developed for the differentiation between the three desert truffles Picoa lefebvrei, Tirmania nivea Trappe, and Terfezia boudieri showed that Tirmania nivea was the most aromatic truffle, followed by Terfezia boudieri and Picoa lefebvrei. In accordance with the work of Farag et al. [70], 1-octen-3-ol was a compound characteristic of Terfezia boudieri. Although this compound was also a marker of the Tirmania nivea, this variety contained other amino acid derivatives-related markers, like 2and 3-methylbutanal, benzaldehyde, benzeneacetaldehyde, methional, and dimethyl disulfide. Moreover, the axenically culture of Terfezia boudieri was compared with its corresponding wild truffle. The mycelia grown in axenic conditions presented much lower content of VOCs and fatty acid derivative volatile compounds [73]. The authentication of truffles by geographical origin is of special importance, since the limited production of truffles in classical regions cannot fulfil the high

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demand in the market. Therefore, new truffles grown in new areas with lower quality are appearing in the market [85]. The identification of markers of origin from the volatile profile of truffles is a promising but challenging authentication strategy, since truffle sampling along the places of interest and the growing conditions of those areas can strongly affect the results. For example, the aroma profile of nine Tuber species from 11 countries (Slovenia, Croatia, Bosnia, Macedonia, Italy, Spain, France, United Kingdom, Germany, Poland, and China) was analyzed by GC/MS, establishing clear separations for both Tuber species and geographical origin authentication aims, obtaining an overall correct classification of 97%. Moreover, several volatile markers were identified. For instance, Tuber magnatum variety grown in Slovenia presented a characteristic profile of the following volatile compounds, ethanol, benzaldehyde, 2-methyl-1-butanol, and dimethyl sulfide, while the same variety grown in Italy could be differentiated by the content on anisole, 1,4-dimethoxy-benzene, 1-methoxy-3-methyl-benzene, 1-octen-3-ol, 3-octanone, and 2-methylbutanal [77]. However, another study focused on the differentiation of the same Tuber variety (Tuber magnatum) also grown in different countries (Italy, Croatia, Hungary, and Serbia), but did not achieve a clear separation of the different origins, even by using some of the most relevant VOCs for truffle classification (i.e., 1-octen-3-ol and benzaldehyde) [75]. Sterols are other important metabolites present in fungi that have been used for the authentication of truffles. In this sense, the sterol content of five truffles (genus Tuber) from nine countries was evaluated as possible marker of geographical origin to avoid food fraud. Ergosterol and brassicasterol were the main sterols found in all the samples. The ratio between these sterols was characteristic for each variety and allowed the differentiation between European and Chinese truffles. However, this was not enough to completely differentiate the origins. Thereby, the minor sterol pattern including 25 minor sterols with 27–31 carbons and 1–4 double bounds were selected, providing more information about the locality of the samples [85]. Other interesting authenticity factors that have been studied using the GC/MS analysis are the effect of the harvesting time of truffles and of the freeze-dried storage process on the volatile profile of truffles. These methods could be implemented as food fraud detection as well. Harvesting time in truffles is a critical parameter to achieve a complete mature product that meets the quality expectations. The genus Tuber needs a very long growing time to reach the complete maturity. For this reason, one common food fraud practice is the appearance of unripe truffles with lower organoleptic quality in the market. A GC/MS approach has been used to evaluate the differences in the VOC content of truffles at different harvest times reporting that 5-methyl-2-phenyl-2-hexenal and 1-tridecene could be markers of unripe truffles [74]. On the other hand, conservation processes, like freeze-drying, are done to elongate the lifetime of truffles, but are linked to a loss of the native aroma profile of the product, since the elimination of water produces critical structure and physicochemical modifications in the native matrix, such as the inactivation of enzymes responsible for the formation of VOCs characteristic of truffles. Šiškovič et al. [76] evaluated the alteration in the volatile profile of four varieties of Tuber samples after freeze-drying. The MDA analysis of VOCs revealed

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that the drying process affected the native aroma in comparison to the fresh truffles. The parameters that were identified as markers to differentiate fresh and freeze-dried products were the increment in the content of 2- and 3-methylbutanal and the decrease of 2-methylbutan-1-ol and sulfur-containing compounds, like (methylsulfanyl)methane, which is one of the main compounds responsible of truffle aroma.

8.6

Conclusions

In general, GC/MS methods reported for the evaluation of fruit, vegetables, nuts, and fungi authentication can be classified according to the aim of the authenticity evaluation in four groups: authentication of cultivars, differentiation of geographical origin, establishment of differences between organic and conventional crops, and evaluation of processing (roasting, etc.) and post-processing (post-harvest, storing conditions, etc.) activities. These methodologies have highlighted different groups of metabolites as useful markers for authenticity purposes. Among them, VOCs and lipids are the most common biomarkers, although in some cases, sugars, amino acids, and organic acids also played an important role in the differentiation of diverse samples. Among the sample preparation steps and GC/MS techniques employed, HS-SPME combined with GC and single quadrupole MS is the preferred analytical strategy to perform the authenticity distinctions of these food products. Regarding the authentication of fruits and vegetables, aroma is the main organoleptic parameter that has been widely employed for the GC/MS authenticity evaluation. In the case of fruits, the authenticity of apples has shown the highest interest, and the analysis of VOCs from the peel, pulp, and the whole fruit has been used to establish differentiations between apple cultivars, growing areas, and pharming processes. The terpene α-farnesene has shown to be one of the most relevant markers in the differentiation between apple varieties. Besides, it can generally be concluded that fruits growing under organic processes are richer in terpenes and aldehydes. Moreover, the combination of GC/MS and sensorial analyses is very frequent in both fruit and vegetable authenticity evaluations, due to the high impact of the different VOCs in the organoleptic characteristics of the products. For the authentication of nuts, the differentiation between nut types as well as related products (cold-pressed oils or flours) is also of great concern, due to their allergenic properties. These types of discriminations have been possible by monitoring phytosterol and fatty acid profiles and a further data treatment with multivariate analysis techniques. Moreover, authentication methodologies dealing with the roasting processing have been widely studied, since it clearly affects the sensorial properties of nuts. With this aim, the monitoring of the VOC profiles extracted by HS-SPME has been applied. The formation of pyrazines and furans has been proposed for monitoring of good roasting practices, while the excessive roasting (200–230 °C) can be detected by the reduction of the VOC profile or the formation of guaiacol. Besides, good practices on post-processing activities (post-harvest drying,

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storing conditions) can be monitored by a high presence of lipid oxidation products in the VOC profiles. Finally, the authentication of edible mushrooms and truffles has in common that the most appreciated characteristics of these two fungi classes are their volatile and lipid profiles. Therefore, the GC/MS methodologies used for the authentication of fungi rely on establishing differences between VOC, fatty acid, and sterol profiles. More relevant are the GC/MS methods developed for the authentication of truffles, due to the high price that these valuable products have in the market, making them a common target for counterfeiting practices. Among the truffles, the authenticity of Tuber genus has been widely evaluated by GC/MS. Besides the variety and growing area, other economically important parameters for the market and consumers, which can be evaluated by GC/MS to ensure the authenticity and good practices, are the maturity and the storage process.

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9

Herbs and Spices Aditi Negi and R. Meenatchi

Abstract

Spices and herbs are the most in-demand and expensive food commodities exported and imported by foreign countries, owing to their flavor and preservative properties. The spice and herb market is expanding globally, because of its enormous health benefits, including antioxidant, antimicrobial, and antiinflammatory effects and potential protection from cancer, diabetes, and infectious diseases. The extensive use of herbs and spices, increased consumer demand, and widespread possible use lead to chances of adulteration. It is essential to check their authenticity and traceability. Technological advancement in spectroscopic methods replaced several physical, chemical, and analytical techniques. The advancement in gas chromatography/mass spectrometry (GC/MS) leads to its use to identify adulterants faster and more cost-effectively, thus helping the herb and spice industry, as well as consumers, to afford goodquality herbs and spices. Keywords

Spices · Herbs · Health benefits · Adulteration · GC/MS · Authenticity

Abbreviations GC/MS HS-SPME

Gas chromatography-mass spectrometry Headspace solid-phase micro-extraction

A. Negi · R. Meenatchi (✉) Department of Primary Processing, Storage & Handling, National Institute of Food Technology, Entrepreneurship and Management (NIFTEM-T), Formerly Indian Institute of Food Processing Technology, Ministry of Food Processing Industries, Govt. of India, Thanjavur, Tamil Nadu, India e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_9

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SIMCA TD-GC/MS

9.1

Soft independent modelling by class analogy Thermal desorption GC/MS

Introduction

Herbs and spices are more beneficial to health and wellness and are in high demanded worldwide. They have various functionalities, such as flavoring of food, perfumery, cosmetics, pharmaceuticals, and preservative properties [1]. According to the Food and Drug Administration (FDA), spices are aromatic plant materials used in the whole or powdered form for food seasoning rather than nutrition [2]. Spices and herbs are an integral part of cooking and provide color and flavor, even when added in small quantities. The spices can be differentiated from herbs by the fact that herbs consist of only the leafy part of the plant (fresh or dried), whereas spices come from plant parts other than leaves, such as seeds, fruits, buds, stems, roots, and bark. Herbs and spices have been used since 1500–2000 BC (according to Indian Vedic literature) in Mediterranean countries and the Middle East or Asia, where they were traded in the form of whole fresh commodities. Herbs and spices play an essential role in ancient systems of medicine, such as Ayurveda, Siddha, Unani, Chinese, and others; because of their medicinal values, they act as a bioavailability enhancer. They are being used for making infusions, decoction, maceration, tincture, nutraceuticals, fluid extracts, etc. They possess therapeutic qualities, such as diuretic, digestive stimulant, anti-inflammatory, appetite stimulant, and cholesterol reducer [3].

9.2

Common Adulterants in Spices and Herbal Products

Over the past few decades, several health benefits attributed herbs and spices significantly increased the trade market and research focus. Spices and herbs are high-value commodities and are under constant threat from adulteration, by incorporating low-grade or low-quality products or similar material or extraneous material, the addition of dyes, etc. Adulteration reduces the quality and affects consumers’ health [1]. Based on the presence of impurities in herbs and spices, they are classified into different groups, as shown in Table 9.1.

9.3

Types of Fraudulent Adulterations in Spices and Herbs

Spices and herbs possess bright, vibrant colors that attract adulteration. The raw and processed products from spices and herbs have a high value by weight, thus being prone to adulteration, and consumers have a limited capacity to detect adulteration [4, 5]. The mixing or substituting of similarly low-quality material to increase the

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Table 9.1 List of common herbs and spices with their common adulterants Description and origin Small shrub Asia and Africa

Parts used Fresh leaves, young stems

Bay leaves Cinnamomum tamala

Medium tree Mediterranean

Fresh or dried leaves

Dill Anethum graveolens

Tall shrub Southwest Asia

Fresh and dried leaves, dried seeds

Carminative, stomachic, antipyretic

Fennel Foeniculum vulgare

Tall shrub Mediterranean

Fresh and dried leaves, fresh stem base, dried seeds

Garlic chives Allum tuberosum

Small clump Southwest Asia

Fresh leaves flowers, buds, flower stems

Marjoram and oregano Origanum spp.

Spreading clumps Mediterranean to East Asia

Fresh and dried leaves

Treating digestive, endocrine, reproductive, and respiratory systems, ailments, galactagogue Antimicrobial, diuretic, diaphoretic, antiflatulence, cholesterol lowering, antiinflammatory Carminative, expectorant, tonic, astringent

Mint Mentha spp.

Spreading clumps

Fresh and dried leaves

Name Basil Ocimum species

Medicinal uses Stomachic, anthelmintic, diaphoretic, expectorant, antipyretic, carminative, stimulant, diuretic, demulcent Stimulant, narcotic

Stimulant, stomachic,

Common adulterant Chinese chaste tree (Vitex negundo); similar Ocimum spp.

Similar spp. Cinnamomum malabatrum; Mexican bay leaf, (Laurelillo); West Indian bay leaf (pimento); Indonesian bay leaf (Dawn salam); Californian bay leaf (Oregon myrtle) Fennel or cumin seeds or inferiorquality seed of the same shape Anethum graveolens (dill) fruit; Cuminum cyminum (cumin) fruit Corn starch; wheat flour; rice flour; peanut butter powder; talc

Sumac; olive leaves; myrtle leaves; Satureja montana L.; Origanum majorana L.; Cistus incanus L.; Rubus caesius L.; Rhus coriaria L. Similar species Mentha arvensis (continued)

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Table 9.1 (continued) Name

Description and origin

Parsley Petroselinum crispum

Europe, Asia, Africa Low-growing clump Europe

Parts used

Fresh and dried leaves

Medicinal uses carminative, antiseptic Stimulant, diuretic, carminative, emmenagogue, antipyretic, antiinflammatory Mild irritant, carminative, stimulant, diaphoretic Antispasmodic, carminative, emmenagogue, anthelmintic, spasmodic, laxative, stomachic, tonic, vermifuge Stimulant, digestive, carminative

Rosemary Rosmarinus officinalis

Medium bush Mediterranean

Fresh and dried leaves

Thyme Thymus vulgaris

Spreading clump Central and Eastern Europe, Southern Russia

Fresh and dried leaves

Allspice Pimenta dioica

Evergreen tree America and West Indies

Cardamom Elettaria cardamomum

Medium clump India

Dried and cured unripe berries, whole and ground Dried pods and seeds, fresh leaves

Chili Capsicum spp.

Small bushes to small trees USA

Fresh and dried fruits and seeds

Carminative, antirheumatic

Cassia Cinnamomum aromaticum

Tall evergreen tree Burma

Dried bark, whole and ground

Cinnamon Cinnamomum zeylanicum

Medium evergreen tree Southern India and Sri Lanka

Dried bark, whole and ground

Stimulant, carminative, astringent, aphrodisiac, antiinflammatory Stimulant, carminative, astringent, aphrodisiac, antiinflammatory

Antidepressive, carminative, appetizer, diuretic

Common adulterant

Thyme; celery leaf (Apium graveolens)

Lemongrass oil; peppermint oil

Thymus, Satureja, Origanum spp.; Triticum spp.

Powdered cloves; larger and less aromatic berries of the Mexican Myrtus tobasco Seeds of large cardamom; cereal and pulse flours; extracted ginger Ziziphus nummularia fruits; plant husks; rice powder; sawdust; stone powder; azo dyes Coffee husk

Pepper powder; cinnamon oil; clove powder; clove oil; preparations containing eugenol and cinnamaldehyde; (continued)

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Table 9.1 (continued) Name

Description and origin

Parts used

Coriander Coriandrum sativum

Small shrub Europe

Dried seeds, whole and ground

Cumin Cuminum cyminum

Small shrub Mediterranean

Dried seeds, whole and ground

Ginger Zingiber officinale

Spreading clump Asia

Fresh and dried, whole and ground roots

Black cumin Nigella sativa

Medium, erect Southwest Asia

Seeds

Nutmeg and Mace Myristica fragrans

Evergreen medium tree Indonesia

Nutmeg seeds, Maceplacental seed coverings, dried, whole or ground

Black pepper Piper nigrum

Climber Southern India and Sri Lanka

Dried or pickled fruits

Saffron Crocus sativus

Small, bulbous Probably Greece

Dried stigmas

Medicinal uses

Carminative, diuretic, tonic, stimulant, stomachic, aphrodisiac, analgesic, antiinflammatory Stimulant, carminative, stomachic, astringent, antiseptic Carminative, antinauseant, diuretic, antiflatulence, antihistaminic, aphrodisiac, cholesterol lowering

Carminative, antiinflammatory, indigestion, bloating, skinrelated disorders Stimulant, carminative, astringent, aphrodisiac, antiinflammatory

Carminative, antipyretic, diuretic, anthelmintic, antiinflammatory, antiepileptic Stimulant, stomachic, anticarcinogenic

Common adulterant C. cassia and C. malabatrum Saw dust; rice flour

Almond; Peanut; tree nuts; peach and cherry; fennel seeds; peanut shell Lime; capsaicin; exhausted ginger; grains of paradise; Z. zerumbet (pinecone, bitter or ‘shampoo’ ginger); Z. cassumunar (cassumunar or plai (Thai) ginger) Onion seeds; other low-grade seeds

Coffee husks; wild species Macassar (Myristica argentea); Bombay nutmeg (Myristica malabarica); Myristica otoba Chili; millets; buckwheat; papaya seeds

Saffron of unknown origin labeled as being cultivated in the PDO region in Spain can be used (continued)

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Table 9.1 (continued) Name

Star anise Illicium verum

Turmeric Curcuma longa

Vanilla Vanilla planifolia

Description and origin

Parts used

Medicinal uses

Common adulterant for substitution; beet; pomegranate fibers; dyed corn stigmas; red dyed silk fibers; safflower; marigold to red stigma; stigma of other saffron types; starch Japanese star anise and other Illicium species

Evergreen, small tree China and Vietnam Leafy clump India

Dried fruit, seed

Antifungal, antibacterial, antiinflammatory

Fresh and dried, whole and ground roots

Climbing orchid Florida, West Indies, Central and South America

Dried and cured fruits (pods)

Carminative, antibiotic, antiflatulence, antiseptic, antiinflammatory Fever, spasms, dysmenorrhea, blood clotting, gastrointestinal distress

Curcuma zedoaria; Curcuma malabarica; chalk powder Tonka beans (Dipteryx odorata); Dipteryx oppositifolia; vanillin (Vanilla pompona); little vanilla (Selenipedium chica); leaves of orchid Angreacum fragrans and Orchis fusca; ladies’ tresses (Spiranthes cernua); “vanilla plant” (Trilisa odoratissima); “herb vanilla” (Nigritella angustifolia)

Sasikumar et al. [3], Osman et al. [6]; https://www.encyclopedia.com/plants-and-animals/botany/ botany general/herbs-and-spices

bulk or the addition of dyes or colors to increase organoleptic properties in spices and herbs could be categorized into incidental and intentional adulteration. Incidental or unintentional adulteration covers all the processes where foreign material may be added to food by an accidental event, such as ignorance, negligence, or inadequate processing and handling facilities of spices or herbs. This adulteration

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may be acquired during harvesting, including pesticide residues, rodents’ and birds’ excreta, bacterial spoilage, fungi, harmful chemicals from packaging, or the presence of a similar natural variety of the herb or spice [7]. Accidental adulteration also includes insects, larvae, insect excreta, fragments, and secretion, promoting microbial activity and spoilage. Spices and their products are more prone to being adulterated by insects owing to their moisture content. In the past few years, 7% of spice lots have been rejected because of accidental adulteration (https://www. nytimes.com/2013/10/31/health/12-percent-of-us-spice-imports-contaminated-fdafinds.html). Metallic contamination also comes under accidental adulteration as heavy metals, such as arsenic by sprays, mercury from polluted water, or barium from rodent baits are introduced to the food commodity. Intentional adulteration entails the inferior materials deliberately added to spices and herbs to mimic their properties and can also be known as economic adulteration. Such examples cover the addition of synthetic dyes to saffron, brick powder to chili powder or coal tar to asafetida to enhance the quality appearance and value for economic gain. Other than this, sand, marble, sawdust, coloring, mineral oil, and floral parts may be added or similar species materials mixed to increase the bulk and reduce the cost of production of the spice or its products. The processed powdered spice and herb products are more prone to being adulterated by the foreign substance and are challenging to detect, thus creating a significant health risk to humans [8].

9.4

GC/MS as an Emerging Technique for Food Authentication

The global spice and herb industry will grow by 7.1% from 2020 to 2025 (https:// www.mordorintelligence.com/industry-reports/spice-and-herb-extracts-market). The global market of herbs and spices has witnessed many food fraud incidents. Thus, consumers, spice industries, and importing and exporting authorities are now demanding excellent controls on food quality, authenticity, and traceability for quality and safety. Several traditional physical, chemical, and analytical methods are available and widely utilized, such as microscopic, ultraviolet spectroscopy, infrared, Fourier transform spectroscopy, nuclear magnetic resonance, highperformance liquid chromatography and gas chromatography, mass spectrometry coupled with chromatography, immunological, and molecular techniques [7, 9]. Among all the techniques mentioned above, the chromatographic approach is the most common method of choice as it can be applied to detect adulteration and verify authenticity [10]. The introduction of hyphenated techniques such as liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) coupled with multivariate data analysis, allows the characterization of a wide range of volatile components in a very complex food matrix. The aroma and volatile compounds in spices and herbs generate a specific chemical fingerprint, which is the key to product quality control. Thus, the use of a GC/MS technique provides:

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• Excellent separation capabilities; • Appropriate and convenient methodologies with high precision; • Reproducibility and accuracy for detection of volatiles. This chapter reviews the analytical methods that employ emerging GC/MS methodologies to authenticate and detect adulteration in spices and herbs. The practices described have been published in the scientific literature in the last decade.

9.5

Use of GC/MS for the Detection of Adulterants in Spices and Herbs

As the name explains, GC/MS is composed of two main components, the gas chromatograph and the mass spectrometer. The gas chromatograph consists of a capillary column where the separation of molecules takes place depending on the column’s length, diameter, film thickness, and stationary phase properties. Different molecules present in a mixture interact differently, and based on their relative affinity for the column’s stationary phase, separation of volatile molecules takes place throughout the length of the column. Based on their retention and elution time from the column, the molecules enter the ionization chambers, where molecules get ionized and fragmented. The accelerated ions are then rapidly deflected by using a magnetic or electric field and then the sorted ions are detected by their mass to charge (m/z) ratio in the mass analyzer [11]. Aromatic oils present in spices are usually analyzed using GC, as it is able to separate the volatile compounds with high resolution and sensitivity. The spice powders or a mixture of spices or paste are often prone to adulteration and it is difficult to test the purity and quality of spices mixed in a complex food matrix. To overcome such problems in commercial spices, GC or LC are usually coupled with MS. Nowadays, advanced versions of triple quadruple mass spectrometers, such as GC/MS-MS and LC/MS-MS, are being widely used and frequently combined with various multivariate data processing approaches.

9.6

Role of Gas Chromatographic Fingerprints and NIST Library

Several libraries of printed and digital spectrum databases are currently accessible. These spectra can be used to compare fragment masses as well as intensities. Once the molecules are identified with the possible match, the identical molecule can be run to validate the identity using GC retention time and mass spectra. The fact that the mentioned library spectra are run on various mass spectrometers and under multiple conditions complicates this matching. Large numbers of spectra can be examined and compared relatively quickly using contemporary computer database searching techniques. This gives the unskilled spectroscopist confidence in using GC/MS for compound identification. Target mass pieces distinctive of each

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molecule may be selected using these spectra, allowing it to be identified among similarly eluting compounds in the chromatogram. Compounds can be employed as standards for quantitative examination of mixtures of compounds once they have been identified. By using spectrum comparison and library searching, unknown substances observed in quantitative analysis of mixtures may be highlighted and recognized. Spectra obtained from scans of the chromatography peak fronts and tails can be used to validate purity or detect contaminants [11].

9.7

Authentication and Adulterant Detection in Herbs and Spice

9.7.1

Traded Black Pepper

Black pepper, Piper nigrum L. (Family Piperaceae), is made of dried mature fruits (berries), also referred to as the “King of Spices.” It is the most common spice used for seasonings, beverages, sauces, curry powders, etc. It possesses antimicrobial, antioxidant, anti-inflammatory, and antitoxic activity and it is an essential ingredient for the treatment of many diseases in the Indian system of Ayurveda, Siddha, and Unani medicines [12, 13]. Currently, Vietnam is the world’s largest producer and second exporter of black pepper, after Ethiopia, followed by Brazil, India, and Indonesia (2019–2020). The annual trade in black pepper is valued at 3903 million US dollars in 2021 on the global market annually. Black pepper is merchandised as whole dried berries and value-added forms, such as white pepper, ground pepper/ black pepper powder, dehydrated green pepper, fried dried green pepper, pepper oil, and oleoresin. Thus, the high commercial value and gap in processed forms are responsible for the adulteration. The black pepper is often adulterated with other plant materials of similar shape, size, and color, such as dried papaya seeds [14]. Berries of wild piper species P. attenuatum and P. galeatum, and non-timber forest-produced dried fruits of Lantana camara, Embelia ribes, seeds of Mirabilis jalapa, and berries of Schinus molle are being used as adulterants [15, 16]. Black pepper powder is also adulterated with black pepper stems and colored starches. Several methods, such as floatation test followed by visual and microscopic detection, staining, and molecular techniques are used to differentiate papaya seeds in black pepper [1, 7]. Although several techniques are available for detecting adulteration in black pepper, GC/MS helps to identify and analyze significant volatiles and semi-volatile organic compounds present in it. Essential oil from black pepper consists of aromatic monoterpene hydrocarbons (59.2–80.1%), sesquiterpene hydrocarbons (17.0–37.7%), and oxygenated terpenoids (1.3–2.7%). The major volatiles identified in essential oil are camphene, δ-3-carene, p-cymene, limonene, α- and β-phellandrene, α- and β-pinene, and β-caryophyllene [17]. Other compounds that black pepper contains are volatiles eugenol, methyl eugenol, benzaldehyde, trans-anethole, myristicin, safrole, piperonal, and phenolic compounds, such as quercetin, isoquercetin, isorhamnetin 3-β-D-rutinoside, and kaempferol 3-arabinoside, which possess myriad biological activities [18].

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Gopalakrishnan et al. [19] analyzed the composition of black pepper oil from different cultivars for three consecutive seasons using GC/MS, employing a nonpolar capillary column (methyl silicone). Oils of these cultivars possess α-pinene within the range 5.07–6.18%, β-pinene 9.16–11.08%, sabinene 8.50–17.16%, limonene 21.06–22.71%, and β-caryophyllene 21.52–27.70%, with some new sesquiterpenes identified. This helps in selection of the suitable black pepper for specific flavors. A significant change in the chemical composition of essential oil when extracted from fresh and dried black pepper fruit was reported [20]. The major volatiles found in fresh pepper oil were trans-linalool oxide and α-terpene, whereas dried pepper oil contained α- and β-pinene, d-limonene and β-caryophyllene, as the main volatile compounds. Sixty-four percent oxygenated compounds were reported to be in fresh pepper oil compared with 17% in dried pepper oil. Similar studies for the five new genotypes of white pepper (Piper nigrum L.) were performed by Liu et al. [21] using solid-phase micro-extraction (SPME) fiber coated with divinylbenzene/carboxen/polydimethylsiloxane, and GC/MS equipped with nonpolar HP-5MS fused capillary column used for quantification and identification of volatiles by their retention indices (RIs) and National Institute of Standards and Technology (NIST) library matching. It was observed that white pepper had a poor composition compared with black pepper. The different genetic varieties (hybrid origins) of white pepper can be identified using GC/MS based on piperine and essential oils. A GC/MS method was used to differentiate methanolic extracts of black pepper adulterated with papaya seed. The profiles of volatile phyto-constituents were compared. Clear differences were observed between volatile constituents of papaya seed and black pepper extract. Papaya seed extract showed the presence of the following major phytochemicals: 2-propyl-1-pentanol, benzyl nitrile, 4H-pyran-4one, 2,3-dihydro-3,5-dihydroxy-6-methyl-benzene, and n-hexadecanoic acid. Black pepper extract contained 3-cyclohexene-1-methanol, 4-trimethylacetate, caryophyllene, cyclohexane methanol, and piperine. Employing GC/MS analysis, the presence of piperine in pepper extract could be used as a chemical marker to distinguish the pepper from its herbal adulterant papaya seed. This method appears to be quick and efficient, but more sophisticated instrumentation is required to identify the herbal adulterants [22]. A GC/MS was also employed for metabolic profiling of the fruits of P. nigrum and seeds of C. papaya and observed 84 and 61 chemically different metabolites in both samples. Thus, multivariate data analysis and clustering analysis tools can be applied to the phytochemical fingerprint of a specific spice to detect the adulteration [23].

9.7.2

Capsicum

The genus Capsicum is well known for its color, pungency, flavor, and texture. It is commercially used in food processing, owing to its vitamin content and health benefits, such as pain relief and repellency. Five well-known species are Capsicum

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annuum, C. baccatum, C. chinense, C. frutescens, and C. pubescens, with C. annum being most commonly used in food products. The paprika, oleoresin, fresh fruits, frozen fruits (whole, diced, and pureed) and dried chili (whole, powdered, and smoked) are the major products traded in the world market. Fruits of capsicum species have a low content of volatile oil, ranging from 0.1 to 2.4% in paprika. Using GC/MS it was determined that 1-methoxy-isobutyl pyrazine is the major component responsible for the characteristic aroma and flavor of Californian bell pepper [24]. The major adulterants reported in whole chili were calyx pieces, loose tops, dirt, stones, insects and their fragments, and mold growth. In processed spice powders and paste more adulterants were found, owing to the presence of filth, dust, mold development, coloring, added flavoring agents, insect infestation, increased moisture, fermentation, and pesticides, compared with whole spices. To analyze the volatile organic components of healthy and infested powdered chili pepper adulterated with insects a solvent-free solid injector was used in conjunction with GC/MS. About 43 volatile compounds were detected in both healthy and infested chili powder. Major volatile compounds found in the healthy chili powder were acetic acid (13.77%), propanal (2.477%), N-methyl pyrrole (1.986%), and 2-methyl-propanal (1.768%). In infested samples, 9,12octadecadienoic acid, ethyl ester (15.984%), acetic acid (11.249%), hexadecanoic acid, methyl ester (3.3%), N-methyl pyrrole (3.221%), and 2-furan methanol (2.629%) were found. Trimethylamine and isosorbide were found in moderately and severely infected chili, but not in healthy samples. Thus, these volatiles can be used as biomarkers to differentiate between healthy and contaminated chili [25]. Synthetic dyes/colorants offer a brighter and longer-lasting color to foods than natural dyes, resulting in adulteration by dyes in spices and herbs. Synthetic dyes are less expensive, more readily accessible, are longer lasting, and provide natural hues that are not possible with natural colorants. Synthetic dyes, such as azo dyes, contain azo-group (-N=N-) in their molecular structures, which when cleaved, results in the formation of amines, which are known to possess mutagenic and carcinogenic properties. In Europe, the USA, and many other countries, the samples adulterated with azo dyes and Sudan I–IV, para red, rhodamine b, and orange 2 are outlawed. Multiple techniques are available to detect the dyes in herbs and spices. Liquid chromatography is a favorable technique for detecting challenging compounds, such as Sudan I–IV or para red dyes [26]. GC/MS is used to detect aromatic amines generated from azo dyes in paprika samples found illegally in food, which are known for their carcinogenic properties. Extraction was performed with dichloroethane, the limits of detection (LODs) ranged from 10.6 to 84.4 ng/mL. The accuracy for 23 azo dye breakdown products ranged from 90 to 104%, and the relative standard deviation (RSD)% for the analysis of 2.4 g/mL of each component was less than 4.6%. The recovery of azo dyes in paprika samples ranged from 71.2 ± 3.5% (benzidine) to 118.9 ± 2.5% (para-cresidine). According to their findings, azo dye breakdown products in 60–240 g/kg paprika can be quantified. This advanced GC/MS method simultaneously enabled the precise and accurate diffuse orange 3 (azo dye) measurement [26].

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GC/MS was also used as an indirect analytical screening method for detecting Sudan dyes in red paprika extract. Red paprika samples were extracted with acetone. Supernatants were collected followed by buffering and a reduction step (sodium dithionite), after which the generated amine products were detected by GC/MS. The apparent recovery values for Sudan I, II, III, and IV dyes were 77.6, 69.4, 51.3, and 42.5%, with RSD of 31.8, 20.4, 20.3, and 31.2%, respectively [27].

9.7.3

Cardamom

Cardamom is renowned as the “Queen of Spices” and it is one of the costliest spices in the world. Because of its therapeutic and healing effects, it is employed in Ayurvedic medicine formulations and used in processed foods, fragrances, and oleoresins. In order of significance, six primary components of cardamom essential oil have been identified: 1,8-cineole, α-terpinyl acetate, linalool, linalyl acetate, α-terpineol, and terpin-4-ol. These six oxygenated molecules account for over 90% of the aromatic compounds in cardamom essential oil [28]. Decorticated seeds can be mixed with lower-grade seeds and large cardamom seeds, owing to their similar shape, size, and color. With its inferior quality, low volatile content and intensity, immature cardamom can be identified by pale brown seeds. Large cardamom seeds have a lower volatile oil concentration and a completely different composition and scent than tiny cardamom seeds. High concentrations of 1,8-cineole and terpene hydrocarbon levels are biomarkers that show the presence/mixing of large cardamom in the small cardamom seeds [28]. Jabbar and Ghorbaniparvar [29] determined volatile components of black cardamom essential oil by GC/MS with the help of a multivariate curve resolution (MCR) approach. The major volatiles reported were 1,8-cineole (36.66%), β-pinene (8.55%), α-terpineol (8.44%), 1Rα-pinene (5.10%), and limonene (4.51%). Similarly, Gurudutt et al. [30] also used GC/MS to analyze black cardamom essential oil extracted by steam distillation. The major compounds reported were 1,8-cineole (61.31%), β-pinene (8.85%), α-terpineol (7.92%), α-pinene (3.79%), and alloaromadendrene (3.17%), whereas α-terpinyl acetate was absent. The presence of α-terpinyl acetate and 1,8-cineole imparts good aroma and flavor [30, 31].

9.7.4

Saffron

Saffron (Crocus sativus) is the costliest spice in the world, worth more per gram than gold and thus the primary target for adulteration. Adulteration in saffron occurs in both the whole and powdered forms of the spice. The stigmas of the Crocus flower are the saffron threads. The low quantity per flower and labor-intensive handpicking contribute to a high price. Partial substitution with older, deteriorated saffron, root hairs, stamens, and styles or stigmas from other Crocus species are prevalent types of saffron adulteration [32].

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The aroma profiles of genuine saffron (Crocus sativus L.) and saffron artificially adulterated with calendula, safflower, and powdered turmeric rhizomes, were analyzed using GC/MS combined with headspace (HS) SPME. The multivariate statistical methods were used to differentiate genuine saffron samples produced in three different Italian regions from artificially adulterated samples at 2–5% w/w contamination levels. Thirty samples of real saffron and 30 samples of counterfeited saffron (10 for each type of adulterant) were tested. To aid in selecting the GC/MS data pre-treatment a principal component analysis was used, whereas the classification of genuine and adulterated saffron samples was performed using partial least squares-discriminant analysis (PLS-DA). Only a saffron sample contaminated with safflower was incorrectly allocated to the group of actual saffron samples in calibration, even though all other samples were accurately categorized regardless of the kind of contamination. The soft independent modeling by class analogy (SIMCA) of genuine saffron had a high sensitivity and 100% specificity for externally contaminated samples [33]. Combined with chemometrics, a GC/MS technique was employed to determine saffron purity, authenticity, adulterant detection, and roasting indices of its aroma. Safranal and 2-caren-10-al were identified as discriminatory markers for saffron for its freshness, ageing markers, and volatile aroma [34]. Online coupling of thermal desorption (TD) to GC/MS (TD-GC/MS) has been applied, among others, to determine the authenticity of Spanish saffron (Crocus sativus L.) [35].

9.7.5

Vanilla

Vanilla is the second costliest spice after saffron and is extensively utilized in the food, beverage, cosmetic, pharmaceutical, and tobacco sectors, as a flavoring. Vanilla bean production is highly costly, as it is a labor-intensive operation with harvesting time 2–3 years after planting. It is often contaminated with a cheaper tonka bean extract, which smells and tastes like vanilla bean extract. Tonka beans contain the compound coumarin 1, which is banned for human consumption and absent in vanilla pods. Thus, the presence of coumarin is used as a marker of adulteration. A simple procedure of methylene chloride partition of the commercial extracts successfully separates vanillin and coumarin from the complex mixture of a crude extract, which were detected by capillary column GC/MS by Marles et al. [36]. GC/MS has been used to identify several benzyl ethers in the commercially manufactured pentane extract of vanilla [37]. The approach mentioned above was used to identify 54 hydrocarbons from three different vanilla species [38]. A GC/MS analysis allowed 65 volatile constituents of vanilla to be identified in pentane/ether extract, including 25 acids, 15 phenolic compounds, 10 alcohols, 4 aldehydes, 4 heterocyclic compounds, 4 esters, 2 hydrocarbons and 1 ketone, 26 of which were odor active, as determined by GC olfactometry analysis [39, 40]. In conjunction with SPME, GC/MS has been used to distinguish between various kinds of vanilla extracts and flavorings. The findings imply that the GC profile may detect the

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origin of natural extracts and detect distinctions between nature-identical and artificial flavors and natural extracts [41].

9.7.6

Fennel

Foeniculum vulgare fruit, commonly known as fennel, is widely used in traditional medicine. However, it is commonly adulterated with two major fruits: Anethum graveolens (dill) and Cuminum cyminum (cumin), owing to their similar appearance and odor. Bisht et al. [42] used a GC/MS method to detect adulteration of fennel seeds with cumin seeds. Detection was carried out using trans-anethole, which is reported to be a major marker compound in the essential oil of fennel seeds oil whereas it is absent in cumin seeds. The major compounds identified in the essential oil of fennel seeds were trans-anethole (50.4%), methyl chavicol (22.4%), limonene (11.4%) and fenchone (11.1%). In cumin seeds, γ-terpin-7-al (22.9%), γ-terpinene (22.6%), β-pinene (22.2%), and cumin aldehyde (13.1%) volatiles were identified. The method was able to identify adulteration in mixed samples with LOD up to 5%. As fennel is used in herbal medicine preparation, the extensive anatomical examination and chemical profiling are being carried out to differentiate the three herbal remedies accurately. The macroscopic and microscopic properties of mericarp, the presence or absence of reticulate cells and nonglandular hairs, and the fluorescence of the endocarp were shown to be diagnostic features. Ma et al. [43] also evaluated the essential oils qualitatively and semi-quantitatively using GC/MS and again observed that trans-anethole (83.20%) was the most abundant volatile compound in fennel, followed by estragole (5.03%) and limonene (3.45%). The main volatiles in dill were carvone, apiol, and limonene (42.58, 20.76, and 20.32%) respectively. Cuminaldehyde and 2-caren-10-al (36.00 and 23.25%) were the most abundant volatile compounds detected in cumin. Thus, substitution adulteration could again be detected based on GC/MS essential oil composition and quantity [43]. Estragole, a volatile phenylpropanoid present in various edible herbs and used as a flavoring ingredient, is genotoxic and carcinogenic and thus prohibited by European Union regulatory bodies. The presence of estragole was determined in infusions from several commonly used commercial herbal teas based on F. vulgare (fennel) seeds using HS-SPME GC/MS. The HS-SPME extraction conditions were improved by exposing a polydimethylsiloxane fiber to the herbal infusion, following by GC/MS analysis. The amount of estragole in commercial fennel seed tea infusions was reported to be between 50 and 250 μg per liter [44].

9.7.7

Cinnamon

The cinnamon species can also be recognized by analyzing the percentage composition of specific volatile oil components and comparing the differences in chromatographic patterns of their respective oils. According to chemical constituents,

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C. verum species possess higher euginol (3–16%), β-caryophyllene (8–14%) and linalool (5–8%) contents, whereas the species C. aromaticum is shown to be rich in o-methoxycinnamaldehyde (3–24%), cis-cinnamaldehyde (3–9%), and benzaldehyde (3–5%). In the species C. burmannii β-caryophyllene (3–13%), ciscinnamaldehyde (0–16%), α-terpineol, and cinnamyl acetate (0–8%) were detected as major volatiles, and in C. lourieroi these were cis-cinnamaldehyde (3–21%), O-methoxycinnamaldehyde (0–11%), and benzaldehyde (8–12%). The cases observed with changed volatile oil profiles that did not match one of the four species of six regions indicated sample adulteration or admixture of two or more species [45]. Garcia-Diez et al. [46] identified eugenol as a unique volatile present in cinnamon with 85.31%, with others being benzyl benzoate (4.92%), eugenol acetate (2.90%), β- caryophyllene (2.44%), isosafrole (1.44%), cuminlaldehyde (0.68%), and camphene (0.10%), β-bisabolene (0.10%). The identified compounds can be used as a baseline for future studies of these essential oils and to verify authenticity or detect adulteration.

9.7.8

Nutmeg

Nutmeg (Myristica fragrans) is a popular spice derived from the nutmeg tree characterized by its delicate aroma and strong flavor. Mace is the bright red or purple lacy coating on the pit, whereas nutmeg is the kernel inside the fruit pit (the aril). Mace and nutmeg are both dried and sold in whole or powdered form. They are mostly being adulterated with coffee husks, Bombay mace and other mace species (Myristica malabarica), extracted low-grade material, dried fruit pulp, etc. [47]. According to Krishnamoorthy and Rema [48], major volatile components in nutmeg oils from Indonesian nutmegs were rich in terpenes, such as sabinene (27%), α-pinene (18%), myristicin (14%), β-pinene (10%), and terpinen-4-ol (7%) dominating. Similarly, Garcia-Diez et al. [46] reported nutmeg volatiles rich in myristicin (43.55%) sabinene (23.28%), terpineol (4.86%), γ-terpinene (4.30%), satriole (3.74%), eugenol (2.84%), α-piene (2.56%), α-terpinollene (1.89%), elimicine (1.08%), methyleugenol (0.71%), and ocimene (0.49%).

9.7.9

Turmeric

Turmeric has been identified as a medicinal crop, and the essential oil and curcumin content are major bioactive components that make it beneficial. It is made from the roots of the turmeric plant (Curcuma longa). The GC/MS method was used to analyze the chemical fingerprint of the essential oils extracted from the rhizome of turmeric. Among 75 compounds, 67 of them were recognized, of which 98.59% accounted for essential oil with high phenolic content. There were 15 monoterpenes (5.58%), 43 sesquiterpenes (84.37%), and 10 nonterpenic components in the

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essential oil (8.64%). β-turmeron, α-turmeron, epi-α -patschutene, β-sesquiphellandrene, 1,4-dimethyl-2-isobutylbenzene, (±)-dihydro-ar-turmerone, zingiberene, E-α-atlantone, and (-)-caryophyllene oxide were identified [49]. Gas chromatography/mass spectrometry was also used to identify the phytochemical elements of Curcuma caesia Roxb. (black turmeric) using methanol as extraction medium. The major principal constituents identified were santalol (46.90%), retinal (10.72%), ar-tumerone (10.38%), alloaromadendrene (5.93%), megastigma3,7(E), 9-triene (4.80%), benzene, 1-(1,5-dimethyl- 4-hexenyl)-4-methyl (4.38%), 5,8,11,14, eicosapentaenoic acid, methyl ester (4.26%), and tricyclo [8.6.0.0(2,9)] trans-2,9-anti-9,10-trans-1,10hexadeca-3,15-diene (3.26%) [50].

9.7.10 Star Anise Star anise is a spice made from the fruit of the Chinese evergreen tree Illicium verum. This spice is often adulterated with Japanese star anise (Illicium anisatum) or other Illicium species. The adulterated species possess neurotoxic sesquiterpenes dilactone—anisatin, which may induce seizures, hallucinations, and nausea [51]. Chinese star anise may be distinguished from Japanese star anise by using TD-GC/MS where the volatiles desorbed from the pericarps of I. verum (Chinese star anise) were characterized by a high percentage of (E)-anethole (57.6–77.1%) and the presence of foeniculin, which was previously only found in the pericarps of I. lanceolatum. The percentage composition of (E)-anethole in the pericarps of all other species, such as Illicium anisatum, Illicium brevistylum, Illicium griffithii, Illicium henryi, Illicium lanceolatum, etc., were significantly lower (16.0%). Asaricin, methoxyeugenol, and two additional eugenol derivatives were found in the volatiles desorbed from the pericarps of the poisonous I. anisatum (Japanese star anise), none of which was found in any of the other species studied. TD-GC/MS allows for the direct study of volatile components from Illicium pericarps, which may help to distinguish the fruits of I. verum from those of other Illicium species, most notably the more poisonous I. anisatum [52].

9.7.11 Cumin Cumin (Cuminum cyminum L.) is used to flavor dishes and has a pleasant aroma. It is also employed in medical applications. India is the world’s largest producer of cumin. The most common adulteration in cumin seed is the addition of lower grade fennel (F. vulgare) and artificial coloring added to fennel seeds for an appearance similar to cumin. It is challenging to detect the adulteration of cumin with fennel seeds when added in powdered form. Bisht et al. [42] managed to detect an adulteration level of up to 5% in cumin seeds. The major compounds identified in the essential oil of cumin seeds were γ-terpin-7-al (22.9%), γ-terpinene (22.6%), β-pinene (22.2%), and cuminaldehyde (13.1%), whereas the major compounds identified in the essential oil of fennel seeds were trans-anethole (50.4%),

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methylchavicol (22.4%), limonene (11.4%) and fenchone (11.1%). The transanethole is considered a significant marker compound of the essential oil of fennel seeds, which is absent in the essential oil of cumin seeds, and thus forms the basis of adulteration detection. Tahri et al. [53] used SPME GC/MS in combination with different chemometric tools and sensors to accurately differentiate between numerous cumin samples of diverse geographical origins. The main volatile compounds detected in cumin (whole/powdered) were β-pinene, m-cymene, γ-terpinene, cuminaldehyde, and cuminic alcohol, present in different proportions depending on the geographical origin.

9.7.12 Nigella Nigella sativa L., commonly known as kalonji and also known as the black cumin seed, is one of the most revered medicinal seeds in history. Volatiles from Nigella seeds can be obtained by hot solvent extraction, by cold-pressing, supercritical CO2 extraction, and microwave-assisted extraction techniques. The major compound in N. sativa oil detected by GC/MS is thymoquinone (TQ), and it is the most biologically active compound. Other than TQ, carvacrol, γ-terpinene, carvone, limonene, and o-cymene are the main volatile compounds. The unsaturated fatty acids present are the following: linolenic, linoleic, oleic, arachidonic, and eicosadienoic acid [54]. The GC/MS analysis of N. sativa revealed the existence of the β-pinene, D-glucose 6-o-α-D-galactopyranosyl, o-cymene, D,L-arabinose, trans-4-methoxy thujane, 2-propyl-tetrahydropyran-3-ol, terpinene-4-ol, α-D-glucopyranoside, thymoquinone, 2-isopropylidene-5-methylhex-4-enal, limonene-6- ol, pivalate, longifolene, 2-(4-nitrobutyryl) cyclooctanone, β-bisabolene, 1,1-diphenyl-4phenylthiobut-3-en-1-ol, phenol, decyl oct-3-yl ester, 1,2-benzenedicarboxylic acid, bis (8- methylnonyl) ester, and stigmasterol in methanolic seed extracts of N. sativa [55]. Based on the TQ content and quality assessment, black cumin seed oil quality and adulteration can be detected [56].

9.7.13 Basil Basil is globally well-known for its culinary and medicinal properties. Chemical composition of basil essential oil studied by GC/MS showed following major volatiles: methyl chaviol (96.71%), 1,8-cineol (0.44%), methyl eugenol (0.30%), cis-ocimene (0.28%), and terpineol (0.27%), under total relative composition of essential oils [47]. Organic and conventionally cultivated basils were authenticated using GC/MS chemical profiles, based on pattern recognition. By using classification models, such as fuzzy rule-building expert system and the fuzzy optimal associative memory and control approaches, SIMCA and PLS-DA, the classification rates achieved for organic versus conventional basil were 100 ± 0%, 94.4 ± 0.4%, 93.3 ± 0.4%, and 100 ± 0% respectively [57].

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9.7.14 Dill Dill is a unique plant where both leaves and seeds are used as seasoning and are rich in antibacterial components. Zawirska-Wojtasiak et al. [58] used SPME to isolate volatiles from dill seeds. Carvone and limonene were the two major volatiles found in dill seeds. Six dill cultivars were analyzed using distillation and SPME and tested for their application to the chiral resolution of carvone and limonene enantiomers in dill seed oil. Direct enantiomer separation of carvone and limonene enantiomers in dill seed oil is helpful in the detection of adulteration and the presence of synthetic essential oil. El-Zaeddi et al. [59] investigated the effect of different harvest times on the composition and quantitation of dill essential oil, grown in Mediterranean regions of Spain employing GC/MS. The extraction was performed with the hydro-distillation method. The main components in the essential oil of dill shoots were α-phellandrene, dill ether, β-phellandrene, limonene, p-cymene, α-pinene, trans-β-ocimene, and myristicin with optimal harvest time 174 days after sowing for highest essential oil extraction. Matsushita et al. [60] identified total percentages of D-limonene (57.1%) and carvone (60.6%) in commercial dill samples, as distinctive markers that could be used for dill authentication. D-limonene is a compound contributing to the citrus odor and carvone is responsible for the herbaceous aroma.

9.7.15 Parsley El-Zaeddi et al. [59] studied the volatile composition of essential oils from parsley shoots and concluded that 1,3,8-p-menthatriene and β-phellandrene were the major volatiles detected by GC/MS. Similarly, Garcia-Diez et al. [46] identified other major volatiles in parsley, such as myristicin (44.88%), limonene (11.72%), α-piene (11.35%), and β-piene (5.38%), with a minor concentration of 1-butyin-3one, β-phellandrene, elimicin, α-terpinolene, and carvaxyl acetate, which were present in amounts of less than 3%. The presence or absence and change in the volatile concentration can be used as a marker for adulteration detection in parsley.

9.7.16 Bay Leaf The relative percentage of essential volatiles identified in bay leaves were calculated based on GC/MS peak areas, in which 1,8-cineole (58.20%) and α-terpineyl acetate (19.19%) were identified in the highest amounts. On the other hand, β-phellandrene (5.01%), terpineol (2.38%), α-piene (2.36%), cis-sabinene hydrate (2.01%), β-piene (1.02%) with traces of linalyl acetate, α–phellandrene, eugenol, sabimene, and methyleuginol, that were quantified as less than 0.2% [46].

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9.7.17 Coriander and Cilantro The fruit coriander and herb cilantro of Coriandrum sativum are widely used as seasonings in a variety of cuisines across the globe. Satyal and Setzer [61] used the GC/MS method to examine the essential oil compositions of commercial coriander and cilantro essential oil samples. In addition, chiral GC/MS was used to detect adulteration in coriander and cilantro commercial essential oil samples. Linalool (62.2–76.7%) dominates commercial coriander essential oil, with lower amounts of α-pinene (0.3–11.4%), γ-terpinene (0.6–11.6%), and camphor (0.0–5.5%). 2E-decenal (16.0–46.6%), linalool (11.8–29.8%), (2E)-decen-1-ol (0.0–24.7%), decanal (5.2–18.7%), (2E)-dodecenal (4.1–8.7%), and 1-decanol make up the majority of commercial cilantro essential oil (0.0–9.5%). In both coriander and cilantro essential oils, the enantiomeric distribution of linalool was 87%(+)-linalool:13%(-)linalool, whereas α-pinene was 93%(+):7%(-) in coriander, 90%(+):10%(-) in cilantro. The ratio of (+)-camphor:(-)-camphor was 13%: 87% in both essential oils. An adulterated coriander essential oil sample was detected using chiral GC/MS analysis. The information gathered in this research could serve as a benchmark for future evaluations of these essential oils and a check for authenticity or adulteration.

9.7.18 Mint Mentha has long been used in cooking as a source of minty flavor. Furthermore, essential oil adds value to various products, such as confectionery, cosmetics, and pharmaceuticals. A simple, sensitive enantioselective GC technique is reported for the quantitative measurement of terpenoid enantiomers in mint essential oils. In menthol, mint and peppermint oils, pure (-)-enantiomers were identified as limonene, menthone, menthol, and menthyl acetate, whereas pure (+)-enantiomers were identified as isomenthone, neomenthol, pulegone, and piperitone. For each enantiomer, selectivity and baseline resolution were established by a cyclodextrin phase with diethyl substitution, with selectivity ranging from 1.004 to 1.050. Furthermore, there was no preference for (+/-)-menthol enantiomers in permethylated or diacetylated cyclodextrin. For biosynthesis only, (-)-menthol enantiomer was found in the essential oils of Mentha species [62]. According to El-Zaeddi et al. [59], carvone and limonene are the two main volatile contents in the mint shoot, and high volatile content was obtained during the first harvest, during the second week of December, through GC/MS. High concentrations of isopulegol and another component, provisionally identified as neoisopulegol, were reported by Spencer et al. [63] as markers for the identification of corn mint oil (from Mentha arvensis) in the more expensive peppermint (Mentha piperita) oil. The methanolic extract of Mentha spicata was characterized by GC/MS analysis after comparing mass spectra with available libraries and the compounds detected along with their molecular formula, retention time, and peak area. The major volatile compounds identified were hexadecanoic acid, methyl ester (31.51%),

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9,12,15-octadecatrienoic acid, methyl ester (22.10%), 2-pentadecanone,6,10,14trimethyl (6.82%), tetramethyl-2-hexadecen-1-ol (6.20%), 9,12-octadecadienoic acid (Z,Z), methyl ester (6.18%), hexadecanoic acid (5.96%), and methyl stearate (4.49%) [64]. Coleman and Lawrence [65] developed a fast, sensitive, and miniaturized SPME GC/MS method to analyze the enantiomeric excess of chiral monoterpenes in peppermint, spearmint, and rosemary (Rosmarinus officinalis) essential oils from a different origin. Changes in enantiomeric patterns were used to ascertain the source and authenticity of these oils.

9.7.19 Rosemary The GC/MS analysis of the volatile composition of rosemary showed the following relative concentrations of camphor (22.4%), 1,8-cineole (11.34%), α-pinene (10.84%), β-cymene (5.25%), β-caryophyllene (3.90%), ocimene (3.47%), and bornyl acetate (3.20%), which are listed in essential oil analysis data.

9.7.20 Lemongrass Oil The online combination of GC with isotope ratio mass spectrometry (IRMS) has become more critical in the authenticity control of flavors. For origin-specific analysis and authenticity control of mandarin, lemon (Citrus limon), and lemongrass (Cymbopogon winterianus) essential oils, a combination of isotopic data (stable isotope ratios for carbon (13C), nitrogen (15N), and hydrogen (2H)) and GC data for characteristic aroma compounds has been reported [66].

9.7.21 Garlic Garlic is one of the most common spices used to season meat during the production of traditional meat products. The essential oil in garlic is well known for its antimicrobial effects. Diallyl disulfide (33.82%), diallyl disulfite (18.86%), methyl allyl di- and tri-sulfide (2.76 and 9.04%) are the major marker organosulfur compounds, present in garlic essential oils, possessing antimicrobial activity, which are also helpful for the authentication purposes [46].

9.7.22 Oregano Oregano is a complex matrix of essential oils, phytosterols, and pigments; its fragrance is derived from monoterpenes, diterpenes, and sesquiterpenes. In the study by Riccardino et al. [67] the volatiles were extracted using HS-SPME. Nontargeted analysis was used to evaluate the volatile profile of the oregano

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samples. A complete scan of data acquired for native oregano and adulterated samples was obtained in electron ionization mode. A mass spectral library match performed the spectral deconvolution, alignment, and compound identification. Multivariate statistical analysis extracted unique features contributing to the original and adulterated group differences. Additionally, positive chemical ionization acquired data to confirm compound identities from molecular ion information and assign chemical formulae. Fragments generated with additional MS/MS experiments were used to verify the molecular ion and support the proposed procedure. When comparing the full-scan total ion chromatograms of the native herbs (oregano, marjoram, thyme, and olive leaves) with the fake samples, differences in the chromatographic profiles of the native herbs were noticeable. Components putatively classified as α-pinene and camphene, based on the NIST library spectrum similarity index score came solely from thyme samples. Both native thyme and oregano contain thymol and carvacrol, but in varying quantities; thymol is prevalent in thyme, whereas carvacrol is the principal element of oregano aroma [67].

9.7.23 Multiple Spice Samples The study conducted by Ford et al. [68] describes a technique for automated chemical analysis of 170 terpenes in spices and other botanicals using GC, improving a successful identification system. The technology incorporates pattern recognition searches of spice databases to identify the origin of most spices, assisting in spice authentication and detecting artificial ingredients. Different vetted spices, with the number of distinct geographic locations, were collected and analyzed by GC, such as allspice (2), anise (2), basil (4), bay leaf (1), caraway (1), cardamom (1), cinnamon (4), clove (1), cumin (3), fennel (2), ginger (9), marjoram (2), mints (2), nutmeg (5), oregano (4), black pepper (5), white pepper (5), rosemary (5), saffron (1), sage (6), savory (3) and thyme (4). Samples were extracted assisted by ultrasound and identified by GC, GC/MS, and MIDI software. The results were interpreted using a chemometric tool SIMCA and a similarity index of the sample profile compared with the model built for the spice of interest. The similarity index is a statistical measure that ranges from 1.000 for a profile that perfectly matches the model’s centroid to 0.000 for a significantly different profile from the predicted terpene profile [68]. Furthermore, the presence of different plasticizers and their residues in ten different herb and spice samples from Tunisia and Italy was determined by GC/MS in black pepper, mint, caraway, coriander, oregano, rosemary, thyme, fennel, verbena, and laurel, representing incidental adulteration [69].

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Standards in the Authentication of Spice and Herb Products

International trade in spices, herbs, and flavorings has a long tradition. However, the quantity of food traded abroad has increased rapidly during the previous century. Even though the food trade has become more global, regulations remain predominantly country specific. Spices and herbs may be tested against various standards developed by different organizations—currently, most standards were set by trade organizations in the spice sector. The trade organization and regional regulations differ from the location and country, such as the European Union (EU), the Gulf Coast Countries (GCCs) in the Middle East, the Common Market of South America (MERCOSUR), and the Central American Council of Ministers of Economic Integration in Central America (COMIECO). As each spice and herb commodity has its characteristics, the grading standards also vary. Many organizations that provide quality standards also offer techniques for evaluating quality parameters in different varieties of herbs and spices [70]. The selection of herb and spice reference standards is very important during the method development, as reference information may not be comprehensive for each spice. Geographic conditions lead to variation in the quality of spice and herb essential oils, which is helpful for GC/MS analysis; thus, information may not be complete for each standard. Also, the available standards and references focus on analytical attributes essential for the spice/herb. There are currently no welldocumented/available standards that concentrate on adulterants, adulterant identification, or appropriate procedures for adulterant detection. Many research methodologies are available that focus on analytical testing for adulterant identification. Spices and herbs are often associated with standards of identification, such as the presence of an essential oil, which are mostly specified in publications. These guidelines may determine what constitutes the essential characteristics of a spice or herb and each variation from the standard suggests possible fraud or adulteration [71]. Since spices and herbs are well known for their aroma, flavor, and color, several trade organizations support the members navigating the local/regional/international regulations that apply in their local area, such as: US Flavor Extract Manufacturers Association (FEMA), the European Flavor and Fragrance Association, the ABIFRA—Brazilian Association of Flavors and Fragrances, the Japan Flavor and Fragrance Materials Association. These are the members of the International Organization of the Flavor Industry (IOFI). Similarly, to promote the global spice trade, the International Organization of Spice Trade Associations (IOSTA) consists of many local groups, such as the American Spice Trade Association (ASTA) and the Canadian Spice Trade Association (CSTA), Malaysia Pepper Board, Spices and Allied Products Association, Sri Lanka’s Product Producers, and Traders’ Associations, etc. The International Organization for Standardization (ISO) has published standards and defined what can be classified as a herb and a spice. The botanical nomenclature and physical description

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of herbs and spices, taste, color, cleanliness, and, in some circumstances, chemical properties, such as volatile oils and moisture, are covered by ISO standards [5]. The worldwide food standards, guidelines, and codes of practice developed by Codex Alimentarius contribute to the safety, quality, and fairness of the international spice and herbs trade. This committee’s mandate is to (1) establish global standards for dried and dehydrated spices and culinary herbs in whole, ground, cracked, or crushed form, and (2) interact with other international organizations as needed throughout the standards creation process to prevent duplication. The Codex established standards for black, white, and green pepper, cumin, and dried thyme. It provides international food standards, guidelines, and codes of practice that contribute to the safety, quality, chemical characteristics, spice grading classes, physical qualities, and general references for contaminants, hygiene, and labeling. The Codex standards for spices and herbs, similar to ISO standards, assist in precisely identifying specifications for spice and herb characterization to define minimum standards for the trade [70].

9.9

Conclusion

This chapter covers the methods of authentication of herbs and spices by the application of a GC/MS technique. Herbs and spices are rich in volatiles, therefore forming the basis for authentication and fraud detection by GC/MS methodologies. The utilization of GC/MS and chemometric tools contributes to the development of authentication methodologies for tracing adulterants in the supply chain and determining their origin and admixtures in spices. A GC/MS tool can help to further develop sensor-based E-nose assays, based on characteristic volatile profiles of herbs and spices. Acknowledgements The authors express their sincere gratitude to the Director, National Institute of Food Technology, Entrepreneurship and Management—Thanjavur, formerly (Indian Institute of Food Processing Technology) for providing technical facilities. The author Aditi Negi wishes to acknowledge the Department of Science and Technology for financial support through grant no. DST/WOS-B/2018/1108-HFN (G). Conflicts of Interest The authors declare that they have no conflicts of interest.

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Part III Authentication of Beverages

Fruit Juices

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Nur Cebi, Hatice Bekiroglu, Zeynep Hazal Tekin-Cakmak, Fatih Bozkurt, and Salih Karasu

Abstract

This chapter presents general applications of gas chromatography/mass spectrometry (GC/MS) for the determination of the authenticity and quality of fruit juices. The contents of the chapter are mainly based on the determination of the botanical origin, geographical origin, verifying organic cultivation, and detection of foreign matter in fruit juices. Today, the authenticity issue comes into prominence with especially financial and industrial concerns. The GC/MS technique provides high sensitivity, reliability, and precision for the quality control of fruit juices. In this chapter, the GC/MS provided information about the volatile compounds of popular fruits and fruit juices, such as apple juice, citrus juice, orange juice, and fashionable fruit juices. Presented applications highlighted the importance of the determination of volatile compounds and the volatile profile of fruits and fruit juices to maintain food integrity in the food supply chain from farm to fork. Keywords

Food authentication · GC/MS · Fruit · Fruit juices · Chemometrics

List of Abbreviations APCI-MS BMIS CBNE

Atmospheric-pressure chemical ionization mass spectrometry Beet medium invert syrup Carbon-bound non-exchangeable hydrogen

N. Cebi (✉) · H. Bekiroglu · Z. H. Tekin-Cakmak · F. Bozkurt · S. Karasu Chemical and Metallurgical Engineering Faculty, Food Engineering Department, Yıldız Technical University, İstanbul, Turkey e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_10

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GC/MS HCA HPLC HR-MS HS IRMS LDA NMR PCA PLS-DA SDE SLDA SPME VOCs

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Gas chromatography/mass spectrometry Hierarchical cluster analysis High-performance liquid chromatography High-resolution mass spectrometry Headspace Isotope-ratio mass spectrometry Linear discriminant analysis Nuclear magnetic resonance spectroscopy Principal component analysis Partial least squares discriminant analysis Simultaneous distillation extraction Stepwise linear discriminant analysis Solid-phase microextraction Volatile organic compounds

Introduction

Recently, there has been a renewed interest in the concepts, such as authenticity, food integrity, and food fraud. The public is facing a growing food market that includes a wide variety of food products across the world. People have sensitivity and concerns about the origin, authenticity, and quality of food products that are served on market shelves, because of the scaring food fraud cases, such as the milkmelamine crisis and the horsemeat scandal [1]. Similar fraud problems in a wide variety of food products, including olive oil, milk products, sweeteners, fruit juices, coffee and tea, spices, and organic foods, highlighted the vulnerability within the food supply chains [2]. Most importantly, food safety problems may threaten consumers’ health and may result in serious health problems. Similar food fraud and authenticity problems may cause a considerable impact in the global economy with product recalls, unfair commercial competition, and reduced quality. In the new global economy, determination of food authenticity is quite important to maintain food quality and safety. Food authenticity consists of several important concepts: (1) economically motivated adulteration of high-cost foods; (2) deceptions about the geographical and botanical origin of the foods; (3) disagreement with the legal legislations; (4) employment of inappropriate process conditions [3]. Previous studies have established that gas chromatography (GC) has been used for qualitative and quantitative analysis of food composition, natural products, food additives, flavor, and aroma components and contaminants [4]. Up to now, GC has been used as a well-known separation technique for authenticity determination and quality evaluation of a wide variety of food products. According to the working principle of GC, the sample or analyte is vaporized in an oven and then compounds are separated on the basis of their interaction with the column material. Gas chromatography/mass spectrometry (GC/MS) systems were reported as the “gold

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standard” because of the favorable analytical properties, such as high chromatographic resolution, reproducible separation, and standardized electron impact ionization conditions [5]. In other words, GC/MS systems provide an opportunity for the identification of unknown components with high accuracy and high resolution [6]. As a result, these systems provide an opportunity for robust evaluation of authenticity-related problems for a wide variety of food products. In previous studies, the GC/MS technique was combined with chemometric techniques, such as hierarchical cluster analysis, principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) for authenticity evaluation of date fruit, spices, and orange juice [7]. This chapter surveys recent scientific studies on the authenticity determination of fruit juices by using a GC/MS technique. Fruits can be considered one of the most attractive items on the food market, owing to their characteristic flavor, taste, nutritional composition, and biologically active ingredients, such as carbohydrates, sugars, organic acids, minerals, vitamins, and polyphenols [8]. Today, fruit juices are highly consumed because of their health benefits and favorable functional ingredients. Fruits are important sources of biologically active compounds, improving health, and decreasing the occurrence of chronic diseases. Especially polyphenols play an important role in the human biological system with their antioxidant, antimicrobial, and anti-carcinogenic properties [9]. There is an increasing trend in fruit and fruit juice consumption around the world, owing to the beneficial properties of plant-based fruit juices. It was reported that 9.3 billion liters of fruit juices and nectars were consumed in the EU in 2016 [10]. When we consider the size of the market and the economic importance of fruit juice trading, it can be understood that there is a need for effective control systems to protect the quality standards of the products and protect the consumer from counterfeit. Previous studies have reported that the adulteration of fruit juices occurs in the following ways: (1) dilution with water; (2) mixing with sugars, pulp, and other additives; and (3) juice-to-juice adulteration [10]. This chapter summarizes recent studies on the evaluation of the authenticity and quality of fruit juices by using a GC/MS technique.

10.2

Determining the Botanical Origin of Fruit Juices: Differentiation Between Fruit Species and Cultivars

Aroma is one of the most important parameters for the evaluation of the difference between fruit species and cultivars, as it is specific to each fruit species and directly affects the consumer’s perception. A GC/MS technique is widely used for the determination of aroma composition of fruit species [8]. However, a scarce amount of the published data deals with the differentiation between fruit juice species and cultivars. Cheng et al. [11] used GC headspace solid-phase microextraction (HS-SPME) system combined with chemometrics for differentiating Chinese bayberry (Myrica rubra) fruit cultivars stored under different storage conditions and PCA results showed that it was possible to group bayberry fruit into several clusters

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according to their cultivars and storage phases [11]. In another study, organically grown and conventionally farmed apples were discriminated by using PLS-DA based on their SPME GC/MS data. The apples were discriminated according to their growing conditions by using the volatile compound composition [12]. Ghaste et al. [13] reported that free and glycosidically bound aroma precursors showed different behavior for each different grape cultivar and species [13]. Another contribution evaluated the flavor compounds of different fresh orange cultivars by using a HS-SPME GC/MS methodology and some compositional differences were observed in conventional and organic orange samples [14]. Reinhard et al. [15] grouped the 76 commercial and 120 self-prepared citrus juices according to the fruit type and cultivar by using the volatile composition obtained from SPME GC/MS analysis.

10.3

Describing Methodologies that Determine the Geographical Origin of Fruits Used for Fruit Production or Fruit Juices

The geographical origin of fruit juice is one of the main authenticity issues. Because food with origin labeling is more likely to be a target for fraud, it is becoming increasingly important to identify the geographical origin of the food. Traceability confirming food authenticity, including exact labeling of origin, is essential for merchants, producers, and consumers, owing to the rise in illegal food traffic around the world. The claimed origin and quality must provide actual safety for consumers and dependable producers. The European Quality Policy recognizes and protects the names of goods connected to a particular location or production process, as geographical origin frauds are one of the most significant issues with food authenticity in Europe [15]. This recognition is transferred into high-grade emblems that allow for easy identification of these items and also ensure their authenticity and quality through certain food controls. Protected geographical indications link products to a location where at least one of the three manufacturing processes of production, transformation, or elaboration took place. Protected designation of origin links food products to the location where they were produced. Traditional production techniques are protected by the traditional specialties guaranteed certification [16, 17]. As a result, there is a high need for analytical techniques that can identify the geographical origin of food quickly and accurately. Because it enables the detection of as many metabolites as is practical and the classification of samples based on metabolite patterns, nontargeted analysis (often known as a “fingerprint”) in combination with chemometrics is a feasible method for food authentication. Chemometric methods with their multi-factor approach are widely used to evaluate the authenticity, quality, safety, and processing properties of various food products [18–20]. Consumers, agricultural growers, merchants, and administrative authorities are all in need of novel and more sophisticated procedures for establishing the geographical origin of agricultural goods. The geographical origin of numerous food matrices has been determined using a range of nontargeted analytical approaches, largely focused on vibrational spectroscopy, mass

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Table 10.1 Gas chromatography/mass spectrometry methods for determining the geographical origins of fruit juices Juice type Apple juice

Target markers Volatile organic compounds Alkyl esters, carbonyl compounds (hexanal, trans-2-hexenal) and alcohols (1-hexanol, 1-butanol, cis-3-hexenol) Stable isotopes Elemental composition Volatile organic compounds

Orange juice Pear juice

Chiral compounds Volatile organic compounds

Determination methods and chemometric tools GC/MS and SLDA APCI-MS and GC/MS with PLS-DA IRMS, NMR, TXRF, ICP–MS with PCA and LDA HS-SPME GC/MS, PCA and SLDA SDE GC/MS and SPME GC HS-SPME GC/MS

References Guo et al. [24] Gan et al. [25] Bat et al. [27] Guo et al. [28] Ruiz et al. [29] Karabagias et al. [30]

GC/MS gas chromatography/mass spectrometry, SLDA stepwise linear discriminant analysis, APCI atmospheric pressure chemical ionization, PLS-DA partial least-squares discriminant analysis, IRMS isotype-ratio mass spectroscopy, NMR nuclear magnetic resonance, TXRF total reflection X-ray fluorescence, ICP inductively coupled plasma, PCA principle component analysis, LDA linear discriminant analysis, SDE simultaneous distillation extraction, SPME solid-phase microextraction, HS headspace

spectrometry (MS), and nuclear magnetic resonance spectroscopy (NMR) [21, 22]. Owing to its numerous benefits in terms of speed, sensitivity, selectivity, and high-throughput analysis, nonchromatographic MS is a growing method for food authenticity research [22]. The aroma of freshly squeezed orange juice is influenced by a variety of volatile organic compounds (VOCs), comprising orange cultivar, environment, geographical origin, ripeness level, and storage conditions [14, 23]. Table 10.1 chronologically lists some literature examples describing GC/MS methodologies that determine the geographical origin of fruits used for fruit production or fruit juices. Although GC techniques utilizing conventional (non-MS) detectors are uncommon, the great majority of fruit juice authenticity examinations use GC/MS methodologies to evaluate the VOC composition of fruit juices. Apple, orange, pear, and pomegranate juices were examined using targeted and untargeted GC/MS techniques to determine their geographical origin. The development of chemometric models for identifying the geographical origin and a variety of food items has received more attention in recent years [31, 32]. This interest stems from two factors: (1) the need for additional information from customers (many nations’ laws require geographical origin labeling on food and drink products); and (2) the need to protect against fraud and label adulteration. Apple juice is the second most popular worldwide in terms of production and consumption [17]. Several quick, nondestructive techniques are now being

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developed for classifying foods chemically, including atmospheric-pressure chemical ionization mass spectrometry (APCI-MS). According to their botanical and geographical origins, different clarified monovarietal juices recovered from apples are categorized in the recent research using APCI-MS for VOC fingerprinting and chemometrics (PLS-DA). According to the findings, the categorization by cultivar and geographical origin had 100% and 94.2% accurate rates. Retention time parameters were acquired utilizing the chromatographic fingerprints of six different kinds of apple juice samples with a novel GC/MS untargeted method [24]. A classification model using stepwise linear discriminant analysis (SLDA) was created and successfully applied to categorize and predict different kinds of apple juice with a 100% accuracy. Ten VOCs were determined by SLDA and identified using GC/MS spectra. These ten retention times were chosen because they greatly aided variety discrimination. SLDA was employed to identify apple juice samples from four Chinese countries, with a recognition ability of 93.9% and a prediction ability of 89.8%, to categorize apple juices according to their geographical origin. It has been discovered that pentyl acetate, hexyl-2-methyl butanoate, (E)-2-hexenal, and butyl acetate, are useful for identifying apple juices by their geographical origin [24, 25]. To establish a connection between the phenolic content and the geographical origin of the apple juice samples, PCA and HCA were ineffective. The soft independent modeling by class analogy model demonstrated the highest prediction of geographical origin among the chemometric methods used, which also included k-nearest neighbors algorithm and PLS-DA [17, 32]. Orange juice is the most widely consumed fruit juice, and customers place a high priority on the authenticity and quality assurance of this product. Ruiz et al. [29] investigated the simultaneous distillation extraction (SDE) and SPME methods together with GC/MS analysis to evaluate the variability of the enantiomeric distributions of chiral terpenes in raw orange juices according to their geographical origins. In this study, they aimed to choose the most suitable technique for the determination of the enantiomeric compositions of chiral compounds and compared the results obtained with both sample preparation techniques. In conclusion, there are several benefits to using the SDE and SPME procedures for the enantiomeric analysis of chiral terpenes. They are both quick and cost-effective, whereas SPME seems to be easier and less time-consuming than SDE. Furthermore, because of its better selectivity, SPME is more appropriate for the search for specific elements, making SDE more intriguing when broad information on aroma-active components is required. However, if SDE is utilized, racemization studies are required that explicitly take into account the target chemicals and the matrix in which they occur, whereas SPME seems to be more trustworthy in that regard. In prior research, farnesyl acetone, which has a fruity flavor and is found in trace quantities in orange juice, was utilized to distinguish navel oranges based on their geographical origin [33]. Pear juice samples for each cultivar that referred to various market providers and geographical origins were chosen for GC/MS headspace analysis. VOCs including alcohols, aldehydes, hydrocarbons, and terpenoids present in pear juices were analyzed using HS-SPME linked to GC/MS by Karabagias et al. [30]. As a result

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of this study, when prickly pear juice samples were classified according to their geographical origin using linear discriminant analysis, a total of seven minerals and 21 volatile compounds provided satisfactory classification rates. These substances are now proposed as “geo-indicators” for prickly pear juice from the Peloponnese. Using prickly pear juice can help the local economy and help to create products with unique origin and added value.

10.4

Verifying Organic Cultivation

Nowadays, healthy eating has become trendy and has created a consumer group that questions the characteristics of foodstuffs and their nutritive properties much more. The fact that the spread of many diseases, especially cancer, is closely related to bad eating habits and the rising awareness of the advantages of a healthy diet enables consumers to turn to organic foodstuffs. This increasing demand by consumers has led to the orientation toward sustainable agri-food and organic farming systems in the field of fruit juice, as in many other food sectors. Fruit juices produced from various types of fruit can satisfy the needs for vitamins, minerals, antioxidants, sugar, and dietary fiber, which are necessary for a healthy diet and have a wide consumer base. Fruit juice is preferred by all age groups and provides the opportunity to be consumed regardless of the season. It is crucial to identify and characterize the organic components to determine the pricing policy and the nutritional properties. Fruit juice problems are commonly associated with substitution with less expensive alternatives, such as the addition of water, sugar, pulp wash, or other affordable substitutes [34]. In addition to all these factors, determining the accuracy of products labeled as organic, which emerged in line with the aforementioned trends, is also very decisive in terms of consumer demand for fruit juices. Although there are overwhelming studies demonstrating the organic and conventional compositional properties of many other foodstuffs [35], there is a limited body of literature on fruit juice. Gas chromatography/mass spectrometry can be considered a key technology that can be used in the identification of fruit juices as well as other foodstuffs in recent years, especially to eliminate many of the consumer concerns mentioned above. These and related technologies allow for the definition and evaluation of a wide range of parameters, including geographical origin, imitation, adulteration, and fraudulent practices in fruit juices [36]. Fruit juices that are organic and those that are conventional are distinguished by the identification and classification of volatile components unique to each fruit type [37]. As it is known, volatile compounds in fruit juices are very sensitive to physical and chemical operations and vary depending on many factors, such as freshness, species, and origin. GC/MS and various combinations with chemometric methods can be defined as the most appropriate technique for the qualitative and quantitative determination of volatile compounds in fruit juices. GC/MS emerges as a system that can explain and identify even very small changes in volatile components, resulting from processes, counterfeiting, adulteration, and similar applications [36, 38]. In recent years,

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different chemometric algorithms used together with GC/MS permitted innovative and more realistic results to be obtained in the determination of organic components, thus organic fruit juices have been carried out. Innovative approaches, such as organic food authentication applications, are remarkable and shed light on the solution to a major problem in the food system. Cuevas et al. [36] examined the potential of MS techniques (high-performance liquid chromatography/highresolution mass spectrometry and HS-SPME GC/MS) using data fusion approaches to identify and evaluate the authenticity of premium organic orange juices. In this study, using advanced analytical techniques combining metabolomic fingerprinting, volatile profiling, and chemometrics, the first validation study of organic juices was performed and flavonoids, fatty acids, aldehydes, and esters were found to be potential markers responsible for the differentiation of organic juices. The characterization of the aroma profile depending on the volatile component content in several types of commercial fruit juices produced satisfactory results using the proposed HS-SPME and GC/MS method. In this study, conducted on apricot, peach, and pear juices, an accurate classification was determined in 94% of organic nectars and 92% of conventional nectars based on volatile components [37]. Alves Filho et al. [39] examined the effect of thermal and nonthermal processes on cashew apple juice composition by applying NMR, LC/MS, and GC/MS techniques, combined with an innovative chemometric approach. In this work, the decomposition of the food matrix was made possible by the application of various cutting-edge techniques for chemical characterization, including primary metabolites by NMR, secondary metabolites by LC/MS, and volatile organic compounds (odor) by GC/MS. Reinhard et al.’s study [15] for the classification of citrus juices based on their organic volatile component composition can be considered as another remarkable and innovative approach. Electronic nose and SPME GC/MS with LDA of volatile compounds of citrus juices was performed using a combination of citrus juices which were grouped by fruit type, variety, and processing. Volatile organic molecules, including alcohols, aldehydes, esters, ketones, terpenes and others, are known to be essentially what gives the juice its sensory qualities [40]. Therefore, the detection of variations in volatile organic molecules plays a crucial role as an indicator in the determination of many characteristics, such as whether it is an imitation, adulterated product, concentrate, or organic fruit juice [41–43]. Zhang et al. [44] developed a novel technique in line with this approach, which performed identity verification in nonconcentrated orange juices based on characteristic volatile component markers based on HS-SPME GC/MS. The findings from this authentication study showed that the volatile organic components in the PLS-DA model resulted in 95.5% and 100% confidence for prediction and calibration respectively. High sensitivity and accuracy results were obtained in the HS-SPME with GC/FID and GC/MS systems applied for the detection of organic volatile compounds in four different Brazilian fruits and juices, namely cashew nut, mango, guava, and pitanga [45]. Mutyam et al. [46] reported that the rapid quantitative evaluation of various organic acids in fruit juices can be carried out with good sensitivity and reproducibility using the established GC/MS-based ion-pair dispersive solid-phase extraction of the organic acids. The HS-SPME GC/MS method with

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chemometrics, which was developed to differentiate the concentrated and unconcentrated juice samples, which is a big problem for orange juice market, and to detect the components released in the process, has also been described as an innovative and powerful technique combination. Although this technique was described as a very powerful method for the detection and identification of organic volatile compounds, it was considered time-consuming [44]. The identification of the volatile organic components of the local apple juices of the island of Madeira, based on the geographical origin and species, was carried out using HS-SPME GC/MS combined with chemometric tools [47]. In this study, besides the determination of the organic components, the origin differentiation was successfully performed in four different apple species based on the concentrations of ethanol, ethyl butanoate, ethyl 2-methylbutanoate, and ethyl hexanoate. The main target in GC/MS-based chemometric methods is to develop tools that identify almost 100% of the organic volatile compounds specific to the fruit type and answer questions, such as fruit species, geographical origin, and whether or not it is organic, by giving results that are almost as confirmatory as a fingerprint region to this identification system (Table 10.2). Table 10.2 Selected studies on organic volatile component and organic juice detection analysis using gas chromatography/mass spectrometry with different chemometric techniques Type of fruit juice Pear, apricot, and peach Lemon, lime, grapefruit, mandarin, and orange Orange

Cashew apple Orange

Cashew nut, mango, guava, and pitanga Orange, apple, pomegranate, and grapes Apple

Parameters measured Flavor and aroma compounds

Analytical technique HS-SPME GC/MS

Volatile organic compounds

SPME GC/MS, electronic nose measurements

Flavonoids, fatty acids, aldehydes, and esters Flavor compounds Volatile organic components

HPLC/HR-MS and HS-SPME GC/MS NMR, LC/MS, and GC/MS HS-SPME GC/MS

Volatile organic components

SPME-HS, GC/FID, and GC/MS GC/MS, in situ butylation

Organic acids

Volatile organic compounds

GC/MS

Chemometric tool

References RiuAumatell et al. [37] Reinhard et al. [15]

PCA and HCA

Cuevas et al. [36]

PCA

Alves Filho et al. [39] Zhang et al. [44]

PCA, HCA, and OPLSDA

de Lourdes Cardeal et al. [45] Mutyam et al. [46] PCA and HCA

Medina et al. [47]

HS-SPME headspace solid-phase microextraction, HPLC high-performance liquid chromatography, HR high-resolution, NMR nuclear magnetic resonance, GC/MS gas chromatography/mass spectrometry, FID free induction decay, PCA principal component analysis, HCA hierarchical cluster analysis, OPLS-DA orthogonal projections to latent structures discriminant analysis

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Detecting the Addition of Foreign Matter

As the purposeful adulterants added to food increasingly resemble the original food, determining the authenticity is becoming more challenging. Table 10.3 illustrates various selected research for the identification of foreign objects in juices using gas chromatography/mass spectrometry combined with chemometrics. Fruit juices could be adulterated by adding less expensive carbohydrate syrups that closely approximate the primary carbohydrate content of the original juice. In a study conducted by Abrahim et al. [48], to demonstrate the viability of identifying the undeclared addition of exogenous sugar products in foods and beverages vulnerable to economically motivated adulteration, a novel method for the quick isotope analysis of the carbon-bound non-exchangeable (CBNE) hydrogen in mono- and disaccharides has been developed. In this method, the exchangeable hydroxylhydrogens are replaced with trifluoroacetate derivatives using a straightforward one-step reaction with the derivatizing agent N-methyl-bis-trifluoroacetamide. These derivatives are sufficiently volatile to be separated and measured using a gas chromatograph connected to an isotope ratio mass spectrometer. Using a hightemperature chromium-silver reactor, the derivatized sugars are transformed into the measuring gas, retaining carbon, oxygen, and fluorine, while releasing hydrogen gas for stable isotope detection. After extracting the crude sugars, cane sucrose was added at 10% w/w and 20% w/w of sucrose to the pineapple juice, and the matching δ2H value of CBNE hydrogen was obtained. The Pearson product-moment correlation coefficient (R2) was 0.9997, with a slope of 0.0040 and a shear of +0.6 between the measured and estimated sucrose δ2H values for 0% w/w, 10% w/w, 20% w/w, and 100% w/w sugar cane in pineapple juice. The observed CBNE δ2H values of various ratios of pineapple (CAM) sucrose and cane sugar (C4) sucrose and the related mixtures of sugar-trifluoroacetic acid derivatives also clearly showed a linear mixing relationship [48]. In another study, GC-isotope ratio mass spectrometry was improved to identify the addition of low-cost commercial sugar syrups to apple juices and similar

Table 10.3 Selected studies for foreign matter detection in juices using gas chromatography/mass spectrometry combined with chemometrics Type of fruit juice Apple Noni Orange Pineapple

Parameters measured δ13C and δ2H MS Flavor and odor index δ2H

Analytical technique GC/IRMS

Chemometric tool –

HS-SPME GC/MS GC/MS

PCA

GC/CrAg/ HTC-IRMS

–

–

Adulterants detected Beet medium invert syrup Benzoic acid and sorbic acid d-limonene and benzoic acid Cane sucrose

References Kelly et al. [49] Lachenmeier et al. [51] Bocharova et al. [50] Abrahim et al. [48]

GC/MS gas chromatography/mass spectroscopy, HTC hematocrit, IRMS isotope ratio mass spectrometry, HS-SPME headspace solid-phase microextraction, PCA principle component analysis

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products by measuring δ13C and δ2H isotope ratios. As it is not practical to alter the isotopic ratios of the sugars, isotopic techniques are frequently used to detect the addition of cheap sugars to fruit juices. The method makes use of the derivative hexamethylene tetramine, which is created by chemically altering a result of sugar breakdown and offers position-specific δ13C and δ2H ratios that are related to the parent sugar molecule. The results illustrated that the 100 real apple juices and the beet and cane commercial sugar syrups differed in their δ2H and δ13C values, making it possible to effectively identify their addition. Hexamethylene tetramine obtained from apple fructose and beet medium invert syrup (BMIS) had significantly different δ2H values. The variation between the lowest apple juice values and the observed values for beet medium invert syrup from the various geographic regions was between 40 and 70‰. The sensitivity of the approach was determined to be 0.66 per unit BMIS (%w/w) added by calculating the rate of change in the δ2H value with increasing BMIS concentration [49]. It has been reported that the volatile composition of fruit juices could change because of adulteration. In a study, results from a GC/MS analysis and those from a dilution approach were compared with regard to food flavors in orange juices that are marketed as “natural” in Ukrainian markets. Owing to the presence of d-limonene and benzoic acid, the legitimacy of 2 out of 12 different commercial orange juices were questioned. The presence of these compounds in fruit juices has been attributed to the addition of food flavorings during the restoration process or during concentrate production. The juice of freshly squeezed oranges had no d-limonene. The results produced by the dilution approach investigating the intensity of the odor of orange juice have been corroborated by the GC/MS analysis. Freshly squeezed fruit juices have been proven to eliminate the subjectivity factor when used as a control. Therefore, it was reported that the dilution index, which quantifies odor intensity, could be advised for a quick evaluation of orange juice authenticity in relation to added food flavors. It was discovered that the odor strength of orange juices made with and without added food flavors was two times greater and 1.5–2.5 times lower than that of freshly squeezed juice respectively [50]. Methods for assessing the validity of commercial Noni juices using HS GC/MS were developed by Lachenmeier et al. [51], enabling PCA to distinguish between authentic and adulterated products. The technique also made it possible to identify benzoic acid and sorbic acid as undeclared preservatives [51] (Table 10.3).

10.6

Conclusion

Today, it is essential to determine the authenticity of food products, among which fruit juices, which have an important place in the food market with their beneficial health properties. This chapter evaluated the capability of GC/MS methodologies for the determination of botanical origin and of geographical origin, verifying organic cultivation, and detection of the additional foreign matter in the composition of fruits and fruit juices. It can be concluded from past contributions that a GC/MS technique combined with multivariate statistics has a great capability for the authenticity

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evaluation of fruits and fruit juices. However, most of the studies focused on fruits. There is a need for further studies that will thoroughly investigate the authenticity and adulteration of fruit juices by using a GC/MS system on the basis of unique aroma characteristics. This chapter may shed light on the need for further studies that will explore challenging authenticity and traceability problems of fruits and fruit juices.

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Coffee and Tea

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Thiago Ferreira and Adriana Farah

Abstract

This chapter reports recent studies using state-of-the-art methodologies employing a gas chromatography-mass spectrometry technique followed by data treatment with chemometric tools in the authentication and fraud detection of coffee and tea. Origin discrimination has been the most recent issue regarding authentication of both products. However, considering the ever-changing processing methods and complexity of the beverages, especially in the case of coffee, only a few studies using the proper number of samples were able to discriminate origin, when other, simpler goals such as discriminating roasting degree and coffee species were more feasible. A few compounds pointed out by the authors as potential markers for coffee and tea differentiation are summarized. Keywords

Coffee · Tea · Authentication · Adulteration detection · GC/MS · Chemometrics

List of Abbreviations BHT EC FAO FDA FSA

Butylated hydroxy toluene European Commission Food and Agriculture Organization Food and Drug Administration Food Standards Agency

T. Ferreira · A. Farah (✉) Laboratório de Química e Bioatividade de Alimentos & Núcleo de Pesquisa em Café Professor Luiz CarlosTrugo—NuPeCafé, Instituto de Nutrição, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_11

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GC HCA k-NN LDA MS OPLS-DA PCA RF

11.1

Gas chromatography Hierarchical cluster analysis k-nearest neighbors Linear discriminant analysis Mass spectrometry Orthogonal partial least squares discriminant analysis Principal component analysis Random forest

Introduction

Since the industrial revolution, foods have been processed and manipulated in response to market demands. The rapid progress in food chemistry and technologies in the postwar period has allowed the development of a vast and extremely sophisticated repertoire of practices of foods and drinks adulteration [1]. As a response to this, academia, research centers, and regulatory agencies worldwide have published an extensive source of articles and regulatory documents to prevent, detect and investigate fraudulent practices. Generally, food fraud may be defined as “a collective term used to encompass the deliberate and intentional substitution, addition, tampering, or misrepresentation of food, food ingredients, or food packaging; or false or misleading statements about a product for economic gain” [2, 3]. From a common understanding, food fraud, food crime, and/or fraudulent practices in the food industry are considered synonymous; in addition, regulatory agencies, such as the Food Standards Agency (FSA), European Commission (EC), among others, classify food fraud into different types (Table 11.1). The key characteristics of food fraud are noncompliance with food law and/or misleading the consumer, performed intentionally and for reasons of financial gain [2, 3]. Considering that the food industry sector is regulated by specific requirements, food fraud also occurs when some aspect of the production violates these requirements or regulations [4]. It is estimated that the annual global trade in counterfeit foods and drinks ranges from USD 6.2 billion to USD 40 billion [5, 6]. Among the foods historically adulterated, coffee and tea are on the list of products most at risk of food fraud [7]. The first reports of tea and coffee adulterations date from the 1720s [1, 8]. This period is highlighted for a modest scale of food adulteration. However, it was considered a systemic practice in the early nineteenth century [1, 2, 9, 10]. In order to decrease food adulteration, some laws were proposed at that time, mainly in England [1]. Simultaneously, several chemical methods (today seen as ancient) were developed aiming to discourage such creativity [2, 11–13]. Nevertheless, they were largely ineffective and, apparently, intended to protect economic interests rather than public health [1, 14]. Naturally from Ethiopia, the coffee beverage migrated to Europe in the seventeenth century. During the early eighteenth century, coffee culture expanded in the

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Table 11.1 Types of food fraud according to regulatory agencies Agency FAO

Term Adulteration Tampering and mislabeling Over-run Theft Diversion Simulation Counterfeit

FSA

Theft Illegal processing Waste diversion Adulteration Substitution Misrepresentation Document fraud

EC

Dilution Substitution Concealment Unapproved enhancement Counterfeit Mislabeling

FDA

Gray market, forgery EMA

Definition A component of the finished product is fraudulent Legitimate products and packaging are used in a fraudulent way The legitimate product is made in excess of production agreements The legitimate product is stolen and passed off as being legitimately procured The sale or distribution of legitimate products outside of intended markets The illegitimate product is designed to look like but not exactly copy the legitimate product All aspects of the fraudulent product and packaging are fully replicated Dishonestly obtaining food, drink, or feed products to profit from their use or sale Slaughtering or preparing meat and related products in unapproved premises or using unauthorized techniques Illegally diverting food, drink, or feed meant for disposal, back into the supply chain Including a foreign substance that is not on the product’s label to lower costs or fake a higher quality Replacing a food or ingredient with another substance that is similar but inferior Marketing or labeling a product to wrongly portray its quality, safety, origin, or freshness Making, using or possessing false documents with the intent to sell or market a fraudulent or substandard product Process of mixing an ingredient with high value with an ingredient with a lower value Process of replacing a nutrient, an ingredient, a food, or a part of food with another one of lower value Process of hiding the low quality of food ingredients or products Process of adding unknown and undeclared compounds to food products in order to enhance their quality attributes Infringements to intellectual propriety rights False claims or distortion of the information provided on the label/packing Production, theft, diversion, etc. EMA occurs when someone intentionally leaves out, takes out, or substitutes a valuable ingredient or part of a food. EMA also occurs when someone adds a substance to a food to make it appear better or of greater value

FAO Food and Agricultural Organization, FSA Food Standards Agency, EC European Commission, FDA Food and Drug Administration, EMA economically motivated adulteration

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West, becoming essential in European and American households [12, 13, 15–17]. Today, coffee is appreciated and consumed on six continents, with Europe being the largest consumer, followed by Asia, Oceania, and North America [18, 19]. There is an old story that says that a legendary governor, ShenNung, who lived in China, discovered tea in 2732 BC, when the leaves from a fierce tea bush accidently dropped into boiling water in a vessel. DNA sequencing analysis has confirmed the hypothesis that the plant did indeed originate in the Southwestern region of China [20]. According to Kumar et al. [21], indigenous species of tea plant have also been discovered throughout the North-eastern region of India, primarily in Assam. These species were propagated throughout Southeast Asia. Tea cultivation began in Java and India around 1835. Cultivation in Sri Lanka began in the 1870s. Later, it spread from Southeast Asia to many tropical and sub-tropical regions in the world. Currently, the global coffee and tea market is valued at about USD 111.52 billion, with coffee comprising 92% of the total value [22, 23]. Additionally, global coffee consumption (9.9 million tons) was 35% higher than tea (6.4 million tons) in 2021 [22, 24]. Despite the existence of laws and developed methods of adulterant detection, because of their high market value, coffee and tea are still being subjected to fraud in the present day. Although coffee is prevalently adulterated by dilution with seeds/grains of lower value and coffee byproducts, tea tends to be diluted with different parts of the Camelia sinensis plant or with leaves from other species, which vary according to different countries. Currently, origin is a major concern and has been the target of the most recent studies on authenticity [25–27]. In the last few decades, several methods have been developed and used to differentiate coffee and tea from their adulterants in controlled assays and to verify the authenticity of tea and coffee, most of them using spectroscopy, chromatography, and mass spectrometry techniques, with or without chemometric post-analyses [2, 28, 29] with spectroscopic methods being the most studied. In this review, the most recent advances in coffee and tea authentication applying gas chromatography coupled with mass spectrometry (GC/MS), followed by chemometrics, will be reported. For this purpose, data searches were performed in the American Chemical Society, Science Direct, Springer, PubMed, and Wiley Online Library data bases, using the following keywords: coffee or tea, plus GC/MS, authenticity, adulteration, fraud, and origin, focusing on studies published from 2018 to 2022. Before we proceed with coffee and tea adulteration, let us define GC/MS and chemometrics, the latter often applied to treat complex data from a number of techniques, including GC/MS. GC/MS is the synergistic combination of two powerful microanalytical techniques. The gas chromatograph separates the components of a mixture in time, and the mass spectrometer provides information that aids in the structural identification of each component [30]. Taken together, the mass spectra and the chromatographic peaks allow unambiguous identification of each component. For an unknown mixture, the mass spectrum for each peak can narrow the possible identity of each component. Known standards can then confirm the identifications if both retention time and mass spectra match [30]. Nontargeted

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analysis with chemometrics is an alternative methodology often used for discrimination and identification of specific compounds in complex matrices. Chemometrics are not a single tool but a range of methods, including basic statistics, signal processing, factorial design, calibration, curve fitting, factor analysis, detection, pattern recognition, and neural networks [31]. Another interesting point is a new category of adulterants. In recent years, consumers’ interest in high-quality (gourmet) products in terms of health and sensory experience has been increasingly growing, as they become more and more sensitive to taste subtleties, meaning typical sensory attributes that can characterize terroirs for certain types of foods, for example, cheese, wine, coffee, and tea. These particularities derive from an association between animal or plant characteristics with soil composition (including microbiota), climate, and topography and their presence increases the product’s market value. Throughout time, such demands led to the creation of origin certificates, allowing consumers to predict the likelihood that a product from a given location will possess those specific characteristics. Therefore, traceability of geographical origin began to play an important role in the agrifood sector, becoming increasingly complex and specific. Since the onset of the European integration process [32], there have been plentiful laws in the European Union reserving specific names for foods and beverages of a particular quality or reputation [33]. In Europe, geographical origin is currently a major authenticity issue for food products, including coffee and tea [34]. According to the EC (2017), a geographical indication label is needed to protect producers because it authenticates and symbolizes the intellectual property right of a label owned collectively by all producers in a region. Labeling coffee and tea with a specific origin increases their commercial value and consequently, creates opportunities for adulteration with low-quality products, accompanied by the need for methods that can detect such adulteration, which, in this case, is not an easy task, given the subtleties of the differences between the original product and the adulterants. Because of these subtle differences among samples of different origin, the methods used to discriminate them must investigate a large number of samples, as opposed to specific markers and make use of chemometrics to identify compositional patterns. As a result, some recent studies applying GC/MS associated with chemometric methods to verify coffee and tea authenticity will be presented.

11.2

Coffee Authentication

In recent years, most coffee authentication studies using GC/MS were concerned with coffee geographical origin discrimination. In 2019, Mehari et al. developed a GC/MS method based on fatty acid profiles to discriminate coffee samples originated in Indonesia and Ethiopia. According to the authors, the intrinsic fatty acid profile reflects mainly genetic traits, agronomic practices, harvesting, postharvest conditions, and environmental factors. One hundred samples of green coffee (Coffea arabica L.) from four major producing areas in Ethiopia, named Northwest,

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West, East, and South were either commercially obtained from a commodity exchange or directly from producers. Principal component analysis (PCA) was used after chromatography to visualize data trends and linear discriminant analysis (LDA) was used to build classification models. Oleic, linoleic, palmitic, stearic, and arachidic acids were identified as the most discriminating compounds among the production regions. Fatty acid compositional data with LDA provided a classification model with high recognition and prediction abilities at a regional level. Also in 2019, Putri et al. assessed the variability of plant secondary metabolites among specialty Indonesian arabica and robusta coffees from different geographical origins by means of nontargeted GC/MS metabolite profiling. Green (n = 23) and roasted (n = 8) samples from Java, Sumatra, Mandheling, Bali, and Sulawesi Toraja were obtained at the Indonesian Coffee and Cocoa Research Institute and compared. Analysis of secondary metabolites was performed by GC/MS, followed by PCA to group the samples according to their botanical species and geographical origins. Samples could only be discriminated based on species, with PC2 explaining 19.3% variability. Metabolites shown in the loading plot of PC2 contributed to the separation between arabica and robusta. Arabica coffee contained a higher concentration of malic acid, whereas robusta showed a higher accumulation of caffeine, which is a well-known characteristic of this species [35–37]. Organic acids and amino acids were the most important metabolites for discriminating between green and roasted coffee beans. Concerning the classification of geographical origins, it was only possible to discriminate the origins when the species were compared separately; therefore, blending of the two species, which often happens in mainstream commercial coffee, would be a hamper. Metabolites showing a higher concentration in Sulawesi, Papua, Flores, and Sumatra samples were glycerol, glucuno-1,5-lactone, gluconic acid, and sorbitol. It is also interesting to note that there was a clear distinction in galactitol and galactinol concentration between all samples from the eastern part of Indonesia and the western and middle parts of Indonesia. The authors suggested a future study considering a larger sampling size. Using headspace solid-phase microextraction (SPME) GC/MS followed by PCA, Ongoa et al. [38] aimed to create a method of characterizing Philippine coffees and granting their authenticity, in standard and civet coffees. They compared the headspace volatile profiles of dark roasted civet coffees (excreted seeds from coffee fruits eaten by the Asian palm civet—Paradoxurus hermaphroditus), of higher market value, with regularly harvested seeds. Four civet coffee samples (two C. arabica and two C. canephora) and four regular samples (two C. arabica and two C. canephora) were compared. Their specific origin was not explained. With 30.70% variance, PCA data showed a clear separation between arabica and robusta samples. The main differences between the species were higher concentrations of acetic acid, furfural, 5-methylfurfural, 2-formylpyrrole, and maltol, and lower concentrations of 4-ethylguaiacol and phenol in arabica compared with robusta samples. The authors also aimed to compare the origins (C. arabica samples were from the Northern Philippines and C. canephora from the Southern Philippines), but because there were two variables (region and species) they did not succeed. It was not possible to clearly discriminate civet and noncivet coffees because of the small number of

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samples, especially because they were apparently not from the same trees. Furfural was the only volatile compound that showed a percentage increase in all civet samples. This compound originates mainly from the degradation of pentose sugars during the roasting process. Using headspace SPME GC/MS and chemometrics, Abdelwareth et al. [39] aimed to discriminate coffee species (C. arabica versus C. canephora), coffee processing (green versus roasted versus instant coffee), coffee brewing method (decoction versus infusion versus maceration and instant), and additives (pure coffee versus coffee with cardamom) using the volatile profiles of green (n = 2), groundroasted (n = 8), and commercial ground-roasted coffees from Brazil (Minas Gerais; n = 2), and the Middle East (n = 7). One sample of instant coffee was also evaluated. Roasting degree levels were characterized by melanoidin contents (ultraviolet spectroscopy). PCA, hierarchical cluster analysis (HCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), followed by radar plot were applied for samples classification. Ground-roasted samples from Brazil and the Middle East, instant coffee, and coffee blended with cardamon were successfully discriminated by PCA and HCA analysis, displaying a variance of 46% and 17% for PC1 and PC2 respectively. Instant coffee was considered an outlier in their model. Terpineol and cineol were considered potential markers for macerated commercial coffees, limonene for green and roasted arabica, eugenol for Brazilian coffees (arabica and robusta), whereas terpinyl acetate and octyl acetate were potential markers for other types of preparation methods in OPLS analysis. Using headspace SPME GC/MS with chemometrics, Zakidou et al. [40] analyzed volatile ground-roasted coffee profiles as a way of discriminating origin of the coffee and degree of roast. The author also aimed to match cup quality attributes with the respective volatile compounds. C. arabica samples from Colombia (dark roast), Honduras (medium roast), Mexico (light medium roast), El Salvador (light medium roasts), Peru (medium roast), Papua New Guinea (light medium roast), Ethiopia— Sidamo (light roast), Ethiopia—Harrar (dark medium roast), and Brazil (medium roast) were evaluated, totaling ten samples. Coffee cupping was performed according to cupping protocols of the Specialty Coffee Association in order to correlate the specific sensory brew characteristics with volatile profiles. PCA, HCA, and hierarchical cluster heatmap analysis were performed for origin classification. One hundred and thirty-eight volatile compounds were identified. About 25–39% were pyrazine derivatives. Two groups of coffee samples could be distinguished, with the first two principal components, accounting for 73.66% of the total variation, which was attributed mainly to the presence of higher amounts of furans and pyrazines, and of compounds from minor chemical classes (e.g., dihydrofuranone and phenol derivatives). Although PCA showed a clear separation of samples by degree of roast and an association with the desired brew characteristics, it was not able to discriminate samples based on their geographical origin because of the variability in degrees of roast and the small number of samples. More recently, Demianová et al. [41, 42] published two studies assessing the geographical origin of coffee. In the first study, the authors aimed to develop a statistical model using LDA to discriminate the origin based on fingerprinting of

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volatile compounds and the contents of caffeine and chlorogenic acids. Green arabica beans from Africa (Ethiopia, Burundi, Kenya, Uganda, Rwanda, n = 9), South America (Brazil, Bolivia, Colombia, n = 7), and Central America (Guatemala, Costa Rica, El Salvador, and Honduras, n = 7) from the 2018 crop were purchased in a coffee store in Slovakia. The authors considered the sampling authenticity according to their certificates. The volatile profiles were evaluated by headspace SPME GC/MS. Analyses of nonvolatile compounds were performed by highperformance liquid chromatography/diode-array detection (HPLC/DAD). More than 340 volatile compounds were identified in those samples. Volatile fingerprinting showed a better accuracy to discriminate the geographical origins than the HPLC/DAD data obtained. The volatile compounds correctly identified 100% of testing samples and predicted 86.96% accuracy in cross-validation. 91.17% of the variability among African, South American, and Central American coffees was explained based on ketones, aldehydes, organic acids and esters, nitriles, alcohols, and alkenes, with ketones appearing to be the strongest parameter among volatiles. In the second study, the authors also evaluated the volatile profiles of C. arabica green beans from Africa (n = 3), South America (n = 3), and Central America (n = 3) from some of the locations and sources of the first study and compared two crops (2018 and 2019). To create a model suitable for geographical origin identification, LDA was used. Analyzed samples from 2018 suggest that the most significant parameters among volatiles might have been organic acids and esters, alkenes, and ketones. According to LDA, these parameters explained most of the variability among African, South American, and Central American coffees. Samples from the same growing areas harvested in 2019 were added to the LDA model. The confusion matrix showed that training samples were correctly identified among all geographical groups regardless of the year’s harvest. However, leave-one-out testing showed several misclassified samples that lowered the model’s overall accuracy. Furthermore, based on LDA calculations the authors reduced input parameters and determined organic acids and esters, alkenes, terpenoids, aldehydes, and ketones to be most useful for geographical authentication regardless of the year of harvesting. Results showed that harvesting year may affect the volatile profile of coffee. More importantly, samples from both years suggest that the group of ketones might have been the most significant parameters in geographical origin identification regardless of the year. Therefore, the authors suggested the need for a more detailed investigation of ketones in green coffee, given that the group may contain individual markers suitable for origin identification that can simplify the whole identification process.

11.3

Tea Authentication

In 2021, Inarejos-García et al. used headspace SPME GC/flame ionization detectorMS methodology in order to identify defects and adulterants in 19 commercial tea extracts purchased in a Spanish health food store. Certified Chinese green and black tea leaves were obtained by ADM (Valencia, Spain) and were used as control samples. The identification of volatile compounds was performed by NIST 05 EI

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Database (National Institute of Standards and Technology, Gaithersburg, MD, USA) and confirmed by authentic reference standards. The authors also studied the effects of high-temperature treatment, normal pre-storage, and accelerated storage on the volatile profile by simulating these conditions. A high level of acetic acid was associated with prolonged storage conditions or high processing temperatures, whereas the presence of furfural (identified in 42% of the commercial samples) was linked to inadequate heat treatment during processing. Severe adulteration and falsification issues were identified. For instance, the functional tea extracts were claimed to be obtained by 100% water extraction. However, 66% of samples showed significant ethanol or ethyl acetate contents in their volatile composition. Compared with water, ethanol and ethyl acetate may improve the extraction of catechins, but these organic solvents were declared neither in the specifications nor in the certificate of analysis of the commercial tea extracts. In addition, other unexpected molecules, such as the synthetic antioxidant butylated hydroxy toluene (BHT), were identified in the green and black tea extracts. According to authors, BHT was added to these commercial tea extracts to increase the stability of the main catechin epigallocatechin gallate, ensuring the claimed shelf life of the catechins and preserving the functional properties of the product. Nevertheless, the addition should be labeled. Additionally, some commercial products did not show the characteristic Camellia sinensis profile at all. The authors concluded explaining that those GC profiles obtained for the powdered tea extracts could be used as an analytical tool, complementary to other reference methods, for product specifications in order to provide information on safety, authenticity, and product quality. In order to identify geographical origins, Yun et al. [43] proposed a headspace SPME GC/MS method followed by chemometrics to assess volatile compounds in 306 black tea samples from China, India, and Sri Lanka. The samples were collected from legal producers in Jinzipai (n = 30), Guxi (n = 75), Likou (n = 60), Guichi (n = 30), Dongzhi (n = 18), Changning (n = 15), Fujian (subdivided into Shaowu Lapsang Souchong-smoked, n = 21, and Wuyishan Lapsang Souchong non-smoked, n = 33), Darjeeling (n = 18), and Kandy (n = 24). The authors described the differences among the regions, including altitude, varieties, and processing types of teas produced in those regions. A complex and sophisticated model was built by using chemometrics (OPLS-DA based model). The model was first conducted considering the full-spectrum dataset. The differences in the content of major volatile compounds, such as alcohols, aldehydes, ketones, and esters, were used to identify black teas of different origins. Twenty-two compounds were selected as compounds commonly contained in all samples. Data from the full GC/MS spectrum and the 22 selected compounds were subjected to the k-nearest neighbor (k-NN) and random forest (RF) models. The discrimination rates were 100% using k-NN and 95% with RF for both datasets. The discrimination rates using two important aroma compounds (linalool and geraniol) analyzed by k-NN were 100% for most origins. Although the model has reached acceptable parameters, three samples from Guxi were misclassed as being from the Jinzipai area.

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Final Considerations and Conclusion

Most of the studies trying to use GC/MS and chemometrics to discriminate the geographical origin of coffee and tea, which is currently the main authenticity issue in the industries for these products, evaluated volatile profiles and, less commonly, lipid profiles. The main shortcomings of the majority of the studies was the lack of proper control samples and the small number of samples, which made origin and other types of discrimination difficult. The ever-increasing complexity of processing methods of coffee and tea (especially coffee because of the post-harvest methods and degrees of roast) increases the number of variables, demanding a very large number of samples from different sources and complex mathematical models to enable grouping and discriminating samples with common characteristics to their origin. Secondary goals such as species and degree of roast differentiation, however, were easier to achieve with GC/MS and simple chemometrics methods. In tea analysis, new authenticity issues were detected: organic solvents were used for polyphenol extraction when water extraction had been claimed on the label. In conclusion, for both coffee and tea, in most cases, GC/MS followed by chemometrics would be useful to support existing evidence, because even when properly using a large number of samples, some of them were misplaced in different regions. Table 11.2 summarizes the studies targeting the authenticity of coffee and tea using GC/MS and chemometric tools.

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Table 11.2 Studies using gas chromatography/mass spectroscopy-based methods coupled with chemometrics to assess coffee and tea authenticity Main volatile or lipid discriminative compounds Glycerol, glucuno-1,5lactone, gluconic acid, sorbitol galactitol, and galactinol Oleic, linoleic, palmitic, stearic, and arachidic acids

References Putri et al. [44]

Method GC/MS

Chemometrics tool PCA

Mehari et al. [25]

GC/MS

PCA and LDA

Ongoa et al. [38]

SPME GC/MS

PCA

Abdelwareth et al. [32]

SPME GC/MS

PCA, HCA, and OPLS-DA

Zakidou et al. [40]

SPME GC/MS

PCA, HCA, and HCA-Heatmap

Demianová et al. [41, 42]

SPME GC/MS

LDA

Demianová et al [41, 42]

SPME GC/MS

LDA

InarejosGarcía et al. [45]

SPME GC/FID-MS

Common statistics

Ethanol or ethyl acetate

Yun et al. [43]

HS GC/MS

OPLS-DA, kNN, and RF

Linalool and geraniol

Acetic acid, furfural, 5-methylfurfural, 2-formylpyrrole, maltol, 4-ethylguaiacol, and phenol Terpineol, cineol, limonene, eugenol terpinyl acetate, and octyl acetate Furans, pyrazines, dihydrofuranone, and phenol derivatives Ketones, aldehydes, organic acids and esters, nitriles, alcohols, and alkenes Organic acids and esters, alkenes, terpenoids, aldehydes, and ketones

Objective Geographical origin discrimination (coffee) Geographical origin discrimination (coffee) Geographical origin discrimination (coffee) Geographical origin discrimination (coffee) Geographical origin discrimination (coffee) Geographical origin discrimination (coffee) Geographical origin discrimination (coffee) Use of organic solvent for functional extract preparation (tea) Geographical origin discrimination (tea)

GC/MS gas chromatography/mass spectroscopy, SPME solid-phase microextraction, FID flame ionization detection, HS headspace, PCA principal components analysis, LDA linear discriminant analysis, HCA hierarchical cluster analysis, OPLS-DA orthogonal partial least squares discriminant analysis, k-NN k-nearest neighbor, RF random forest

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Acknowledgement The authors would like to acknowledge the support from the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro—FAPERJ, Brazil (Grant # E-26/203328/2017).

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Wine, Beers, and Alcoholic Beverages

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Oscar Núñez

Abstract

Food adulteration practices are potentially harmful to human health. Consequently, food safety and authenticity constitute an important issue in food science nowadays. Alcoholic beverages, such as wine, beer, and liquors/spirits, are among the most widely adulterated drinks because of their relatively high consumption and extensive global supply chains, their complex nature, and the ease to be adulterated. Besides, their relatively high cost in comparison to other widely consumed beverages makes possible to obtain a greater economic benefit from their adulteration, especially with wine and liquor or spirit beverages. These alcoholic beverages are characterized by important volatile profiles, consisting of a wide range of compounds (acids, alcohols, aldehydes, and others) and hundreds of flavor compounds that can be present at trace levels. Because of that, their volatilome is widely employed as sample chemical descriptor to assess authenticity, and gas chromatography (GC) is the technique of choice for that purpose. In the present chapter, the most widely employed GC methodologies to address wine, beer, and other alcoholic beverages’ authentication issues, such as GC coupled to low-resolution and high-resolution mass spectrometry, multidimensional GC, and GC coupled with isotope ratio mass spectrometry, will be addressed.

O. Núñez (✉) Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Barcelona, Spain Research Institute in Food Nutrition and Food Safety, University of Barcelona, Santa Coloma de Gramenet, Spain e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Pastor (ed.), Emerging Food Authentication Methodologies Using GC/MS, https://doi.org/10.1007/978-3-031-30288-6_12

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Keywords

Gas chromatography (GC) · Mass spectrometry (MS) · Alcoholic beverages · Wine · Beer · High-resolution mass spectrometry (HRMS) · Multidimensional chromatography · Isotope ratio mass spectrometry · Chemometrics · Authentication

Abbreviations 2D ANN-MLP ANOVA C CA Can CAR DO DA DVB EA EI EU FID FWHM GC × GC GC GC-HRMS GC-LRMS GC-MS GI HCA HRGC HRMS HS-SPME ID IRMS LDA LOD LRMS LZ MDGC MSD OPLS PC

Second dimension Artificial neural networks with multilayer perceptrons Analysis of variance Combustion Cluster analysis Canonical Carboxen Designation of origin Discriminant analysis Divinylbenzene Elemental analyzer Electron ionization European Union Flame ionization detector Full-width at half-maximum Multidimensional gas chromatography Gas chromatography Gas chromatography-high-resolution mass spectrometry Gas chromatography-low-resolution mass spectrometry Gas chromatography-mass spectrometry Geographical indication Hierarchical cluster analysis High-resolution gas chromatography High-resolution mass spectrometry Headspace solid-phase microextraction Internal diameter Isotope ratio mass spectrometry Linear discriminant analysis Limit of detection Low-resolution mass spectrometry Luzhoulaojiao Multidimensional gas chromatography Mass selective detector Orthogonal projections to latent structures Principal component

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PCA PDMS PDO PEG PGI PLS-DA Q Q-TOF SAB SBSE SCB SIM SPME TD TOF VIP YHRB

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Principal component analysis Polydimethylsiloxane Protected designation of origin Polyethylene glycol Protected geographical indication Partial least squares-discriminant analysis Quadrupole Quadrupole-time-of-flight Strong aroma-type baijiu Stir bar sorptive extraction Sichuan basin Selected ion monitoring Solid-phase microextraction Thermal desorption Time-of-flight Variable importance in projection Yangtze-Huaihe River Basin

Introduction

Today, the quality of the food and beverages we consume is of paramount importance, not only for the producing industries but also for consumers, who are willing to pay even more for products produced following certain quality standards. In this context, two concepts appear essential to guarantee consumers that a food product complies with the information included on the label and is, at the same time, safe and has not been adulterated: authenticity and traceability [1, 2]. A specific food or beverage is considered “authentic” if the product or its constituents correspond to the original condition and the information on the label. In this sense, a foodstuff is considered “authentic” if it has not been adulterated or submitted to fraudulent practices, not only regarding its composition, nature, and varietal purity but also with regard to any other “quality” attribute indicated in the label, such as the geographical origin, the manufacturing techniques, or the indication of ecological production, among others. Food traceability is the ability to follow the movement of a food product and its ingredients through all steps in the supply chain, both backward and forward. Traceability involves documenting and linking the production, processing, and distribution chain of food products and ingredients [3]. The protection of consumers against fraudulent practices by unethical businesses is a challenge that requires increased cross-border cooperation among administrations. Within the European Union (EU), food fraud refers to “any suspected intentional action by businesses or individuals for the purpose of deceiving purchasers and gaining undue advantage therefrom, in violation of the rules referred to in Article 1(2) of Regulation (EU) 2017/625 [4] (the agri-food chain

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legislation)” [5]. However, the definition of food fraud is very complex and may involve different aspects. Thus, regarding food fraud, we can refer to a counterfeit, adulterated, or altered food, depending on the situation [2]. For instance, counterfeit refers to a product that has been prepared or labeled to simulate another similar food product, or that the real composition does not correspond at all to that declared and commercially advertised in the product label. In contrast, a food product will be considered adulterated, when any substance (natural or synthetic) has been added, or subtracted, to change its composition, weight, or volume, with a fraudulent purpose or to conceal or correct any defects related to the final quality of the product (i.e., a lower quality or an altered one). And finally, a food product will be considered altered, when during any moment since its production to its consumption, and for reasons not deliberately intended, it suffered variations (organoleptic properties, composition, nutritional value, etc.), annulling, or clearly diminishing its aptitude as food, even if the final altered product remains harmless [2]. Adulteration is among the most common fraudulent practices in beverages, and is mainly carried out to increase volume, to mask the presence of inferior-quality components or to replace authentic substances for the seller’s economic gain. Nevertheless, food fraud is illegal worldwide, having not only economic consequences, but it may also represent important safety issues when prohibited (and in some cases toxic) substances are added to mask the adulteration by deceiving the organoleptic properties of the adulterated beverage, or when the adulterant may produce allergy episodes. Alcoholic beverages are among the most widely adulterated drinks, because of their relatively high consumption and extensive global supply chains, their complex nature and ease to be adulterated, and their relatively high cost in comparison to other widely consumed beverages, being possible to obtain a greater economic benefit from their adulteration. Besides, fraud can take place at several points in the production process and the supply chain, making detection and traceability more difficult. Within this context, the development of fast and reliable analytical methodologies to authenticate alcoholic beverages is necessary. In general, there are three main analytical strategies to approach the authentication of beverages and are based on chemical, biomolecular, and isotopic analyses [1]. Chemical approaches focus on the determination of the chemical composition of the analyzed samples and may be based on the qualitative or quantitative determination of chemical compounds that can be then employed as beverage authenticity markers. Biomolecular strategies authenticate beverages according to their DNA or protein composition, while isotopic analyses focus on the stable isotopic compositions of certain atoms. In addition, any of these analytical strategies can be employed within a targeted or a non-targeted approach [6]. Targeted approaches, also referred to as profiling approaches, focus on the determination (qualitative or quantitative) of a specific analyte (or group of analytes) or a specific sample attribute (i.e., a specific stable isotope ratio). These data are then employed as food features to address authenticity issues and are considered as food markers. If these markers directly link to solving the authenticity problem, for example, commonly known added illegal substances, they are considered primary markers complying with legal

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requirements regarding accepted limits. In contrast, secondary markers are defined when they indirectly allow to ensure authenticity of a given food, based on the variation of their content caused by the fraudulent adulteration practice. Non-targeted approaches, also known as fingerprinting approaches, consist of the analysis of instrumental responses, but without any previous knowledge about the sample composition [6–8]. These methodologies try to detect as much food features as possible by using unspecific sample treatments to avoid losing information, compared to targeted approaches, where performance depends on a correct sample preparation [9]. Both targeted, but especially non-targeted, approaches provide a multitude of data, and its correct interpretation is crucial to solve the authenticity problem. In order to correctly use these datasets to characterize, classify, and authenticate food products, chemometrics is required, and both non-supervised exploratory chemometric methods (i.e., principal component analysis (PCA), hierarchical cluster analysis (HCA), etc.) and supervised classification chemometric methods (i.e., linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), etc.) are employed [10]. Wine, beer, and spirits are characterized by important volatile profiles, consisting of a wide range of compounds (acids, alcohols, aldehydes, and others) and hundreds of flavor compounds that can be present at trace levels. Within this context, gas chromatography (GC) emerges as a powerful analytical separation technique in the analysis of alcoholic beverage products [11, 12]. In general, minimal sample preparation will be required, since samples are in the liquid state in mainly an alcohol or alcohol/water matrix. Although universal detectors, such as flame ionization detector (FID), are commonly being employed, more information-rich detectors based on mass spectrometry are lately preferred to solve authenticity issues. Thus, gas chromatography coupled with low-resolution mass spectrometry (GC/LRMS) and highresolution mass spectrometry (CG/HRMS) are among the most powerful techniques nowadays, to address the characterization and authentication problem of alcoholic beverages. Multidimensional gas chromatography (GC × GC) in combination with mass spectrometry techniques is also employed to solve authenticity issues of alcoholic beverages, taking advantage of their higher separation capabilities [13]. Besides, the determination of stable isotope ratios by means of GC coupled to isotope ratio mass spectrometry (IRMS) is also widely employed to address the authenticity issues of alcoholic drinks. In this chapter, chemical methodologies based on gas chromatography coupled to mass spectrometry to guarantee alcoholic beverages’ integrity and authenticity will be discussed. Wine, beer, and other alcoholic beverages, such as liquors, spirits, etc., will be addressed. Coverage of all kinds of applications is not intended; thus, the present contribution will focus on the most relevant applications published in the last years.

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Authenticity of Wine

Wine is an alcoholic drink typically made from fermented grapes. In the fermentation process, specific yeasts consume sugar present in grapes and convert it to ethanol and carbon dioxide, releasing heat in the process. Depending on the grape varieties and the strains of yeasts employed, different wines can be obtained. These differences result from the complex interactions between the biochemical development of grapes, the reactions involved in the fermentation process, the grape’s growing environment (terroir), and the wine production process, obtaining different wine typologies (white, rosé, red, sparkling wines, etc.) with a great variability of organoleptic properties. In addition, many countries enact legal appellations intended to define styles and qualities of wine. These typically restrict the geographical origin, permitted grape varieties, and other aspects of wine production. This is the case of the EU quality policy schemes aiming to protect the names of specific products to promote their unique characteristics, linked to their geographical origin as well as the traditional know-how. Product names can be granted a “geographical indication” (GI) if they have a specific link to the place where they are made. The GI recognition enables consumers to trust and distinguish quality products while also helping producers to market their products better. In the case of wines, two GIs are established: (1) protected designation of origin (PDO), where the grapes have to come exclusively from the geographical area where the wine is made, and (2) protected geographical indication (PGI), where at least 85% of the grapes used have to come exclusively from the geographical area where the wine is actually made [14]. In contrast to other foods with GIs, the use of PDO and PGI labels in wines is optional. Wine classification is also made by grape variety other than the geographical production region, especially for wines made outside the traditional wine regions of Europe. Wines not made from grapes involve fermentation of other crops, including rice wine and other fruit wines, such as plum, cherry, pomegranate, currant, and elderberry. As previously commented, society is increasingly interested in higher-quality products produced in specific regions or using traditional techniques. Considering the importance of this product, with a world wine production in 2020 estimated in 260 million hectoliters [15], and the complexity of the wine production chain, wine is among the most adulterated alcoholic beverages. The economic impact of fraud in the wine sector is estimated at €1.3 billion per year (3.3% of sales). Next, examples of GC/MS applications employed for the characterization, classification, and authentication of wine will be described, based on the most relevant adulteration and authenticity issues typically encountered with this alcoholic beverage.

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12.2.1 Authenticity Based on Geographical Region and Grape Variety 12.2.1.1 Low-Resolution Mass Spectrometry Techniques Gas chromatography coupled to low-resolution mass spectrometry (LRMS) instruments, mainly quadrupole analyzers, and frequently referred to by several authors as a mass selective detector (MSD), are widely employed to address authenticity issues related to wine geographical production region and grape variety [16– 21]. As expected, most of these methodologies are based on the determination of volatile compounds (the volatilome), mainly using targeted (profiling) strategies, although there are also certain non-targeted (fingerprinting) applications. In general, all of them have in common the use of capillary columns for the chromatographic separation, and electron ionization (EI) at 70 eV, as the ionization method that generates fragmented spectra, which are then monitored in the mass analyzer either in full scan mode or in selected ion monitoring (SIM) mode. The acquisition of full scan mass spectra under these standard conditions allows the use of spectral libraries for the subsequent identification of the volatile compounds detected. The targeted volatile compounds are then proposed as food markers to be monitored in order to solve the authenticity issue. As wines are in the liquid phase, and the target compounds are volatile, headspace solid-phase microextraction (HS-SPME) is the most widely used sample preparation methodology with GC/MS techniques. On the other hand, Springer et al. [18] proposed the use of an untargeted volatilome fingerprinting approach to verify the botanical origin of German white wines. For that purpose, a total of 198 white wines of different grape varieties (Riesling, Müller-Thurgau, Silvaner, Pinot Gris, and Pinot Blanc) were analyzed employing HS-SPME GC/MS. A polydimethylsiloxane (PDMS) fiber (100 μm thickness, 10 mm length) was employed for HS-SPME because of its low enrichment with ethanol, thus avoiding GC column overload. Extraction was carried out using 10 mL cooled wines in 20 mL headspace vials and for 45 min at a temperature of 40 °C. Desorption of the extracted analytes was performed by hot injection at 260 °C in splitless mode for 2 min. GC separation was carried out in a Agilent J&W DB 1701 (30 m length, 0.25 μm film thickness, and 0.25 mm internal diameter (I.D.)) capillary column, using He as carrier gas at a flow rate of 1.1 mL/min and under temperature gradient elution. MS acquisition was performed with a quadrupole (Q) instrument, in a full scan mode (m/z 35–320). The obtained untargeted volatilome fingerprints were then employed as sample chemical descriptors for varietal authentication by chemometrics. Figure 12.1 depicts the score plots of four-class PLS-DA training models. As can be seen, fairly good clustering of classes is obtained. On average, four PCs were necessary to model the differences between the studied classes, and all the training models were explained, on average, by 70% of the variance (R2), and the goodness of prediction achieved was on average 64% (Q2), which is among the expected results when volatilomics data is employed. As commented by the authors, because the presented models additionally showed a low difference between R2 and Q2, its fairly good prediction ability is clearly

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Fig. 12.1 Score plots of four-class PLS-DA training models. Classification of Pinot (Pin, triangles; Pinot Gris, and Pinot Blanc) vs. Silvaner (Silv, crosses) vs. Müller-Thurgau (MueTh, stars) vs. Riesling (Ries, circles). (a) Training model 2: N × M = 116 × 290, four PCs, 70.3% R2; 65.3% Q2. (b) Training model 7: N × M = 116 × 305, three PCs, 62.7% R2; 57.7% Q2. (c) Training model 10: N × M = 116 × 285, four PCs, 73.8% R2; 66.6% Q2. Reproduced with permission from reference [18]. Copyright (2014) American Chemical Society

demonstrated. During PLS-DA validation, external samples were correctly classified for 97% Silvaner, 93% Riesling, 91% Pinot Gris/Blanc, and 80% Müller-Thurgau. Among the detected analytes, the most discriminant compounds to build the chemometric models were related to monoterpenoids, C13-norisoprenoids, and esters. The authors showed that the employed strategy was reliable and relevant for white wine varietal classification, enhancing the superior classification performance of the Riesling class in comparison to the other white wine varieties. Stir bar sorptive extraction (SBSE) is another sample extraction procedure that has frequently been proposed for the determination of volatile components in wines, followed by GC/MS, to solve authenticity issues. In general, SBSE offers increased sensitivity compared to SPME because of an increased amount of stationary phase. As an example, Tredoux et al. [16] employed PDMS-coated stir bars of 10 mm length and 0.5 mm film thickness for the extraction of major wine volatile compounds. For that purpose, the stir bar was introduced in a vial containing 0.5 mL of wine and 10 mL of water, and extraction was performed by stirring (1200 rpm) for 1 h at 22 °C. Then, the stir bar was removed, washed and dried, and introduced in a thermal desorption tube for thermal desorption (TD)-GC/MS analysis. GC separation was performed in an INNOWAX (30 m length, 0.25 μm film

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Fig. 12.2 Total ion chromatogram for the SBSE TD-GC/MS analysis of a South African red wine (Shiraz 2000). For peak identification refer to the original reference. Reproduced with permission from reference [16]. Copyright (2008) American Chemical Society

thickness, and 0.25 mm I.D.) capillary column with helium as the carrier gas. The authors employed the developed methodology for the classification of 62 South African red and white wines according to their volatile composition by means of targeting 38 major volatile components, including alcohols, acids, esters, phenols, aldehydes, ketones, and lactones. As an example, Fig. 12.2 shows the total ion chromatogram obtained for the SBSE TD-GC/MS analysis of a South African red wine. A quadrupole mass analyzer in a full scan mode (m/z 30–350) was employed for data acquisition. The obtained targeted major volatile compound profiling was employed as wine chemical descriptors to assess their characterization and classification by PCA and cluster analysis (CA). Both chemometric methods showed that most of the variation in volatile composition between wine samples could be ascribed to differences in wine age, wood contact, and fermentation practices. However, despite the contribution of all these factors, the use of discriminant analysis (DA) allowed the classification of the analyzed South African red and white wines according to their cultivar, demonstrating that the proposed targeted SBSE TD-GC/MS major volatile profiling method was suitable for authentication purposes. The selection of specific volatile components as markers to solve authentication issues of alcoholic beverages is a trend employed by several authors with the aim of simplifying the proposed methodologies. For example, Dourtoglou et al. [17] studied the discrimination between wines of different grape varieties by determining only seven of the total extracted volatiles, mainly corresponding to higher alcohol and higher alcohol esters (3-methyl-1-butanol, 2,3-butanediol, ethyl lactate, 3-methyl-1-butyl acetate, 2-phenylethanol, phenyl ethyl acetate, and p-hydroxy phenyl ethanol). A simple liquid-liquid extraction procedure using a mixture of pentane and diethyl ether was proposed, and the obtained volatilome was analyzed by GC/MS using a mass selective detector and EI. GC separation was performed on

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Fig. 12.3 PCA score plot of PC1 vs. PC2 when using the selected seven volatile compounds as wine chemical descriptors for varietal wine discrimination. Group 1, Agiorgitiko; Group 2, Moschofilero; Group 3, Other; Group 4, Chardonnay. PC1 = 58.116% of total variance, PC2 = 20.356% of total variance. Adapted with permission from reference [17]. Copyright (2014) Elsevier

a HP-1 fused silica capillary column (30 m length, 0.25 μm film thickness, and 0.32 mm I.D.) with He as carrier gas (1.0 mL/min) under a gradient temperature program. As generally performed, mass spectra were interpreted by spectral matching either using the MS instrument data system library (NIST) or other collections of reference spectra. Discriminant analysis was then employed by the authors for the selection of the seven volatile markers that where then employed as key components to study sample discrimination by PCA. As can be seen in the obtained PCA score plot of PC1 vs. PC2 (Fig. 12.3), wine samples were discriminated according to their varietal composition. The groups of Agiorgitiko (Group 1), Moschofilero (Group 2), and Chardonnay wine (Group 4) were clearly discriminated one from another. However, the third group, labeled as “other” and consisting of wines from many different varieties, was clustered near the group of Agiorgitiko wines. Although these seven volatile components were not relevant to a single wine variety, they proved to be good sample chemical descriptors to address varietal wine discrimination.

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12.2.1.2 High-Resolution Mass Spectrometry Techniques The use of high-resolution mass spectrometry (HRMS) methodologies and accurate mass measurements has gained huge popularity in many application fields, because of the ability of these techniques to provide more comprehensive information regarding the exact molecular mass, elemental composition, and detailed molecular structure of a given compound. The main advantages in comparison to LRMS is that HRMS allows the differentiation of isobaric compounds (chemicals with the same nominal mass-to-charge ratio but different elemental compositions). In addition, the higher mass resolution attainable with HRMS instruments favors the simplification of sample treatment and preparation procedures, allowing the proposal of faster analytical methodologies with less sample manipulation. Besides, both screening and quantitation can be performed in a single run, including targeted, suspect, and non-targeted analyses. Finally, another important advantage of HRMS, especially when data is registered in full scan mode, is the possibility to perform retrospective analysis in a later stage, allowing for the identification and determination of new unknown or suspected compounds in previously analyzed samples. Gas chromatography coupled to high-resolution mass spectrometry (GC/HRMS) methodologies is therefore acquiring an important role in the characterization, classification, and authentication of wines based on their geographical origin and grape variety, especially when non-targeted metabolomic fingerprinting strategies are employed. Timeof-flight (TOF) or hybrid quadrupole-TOF (Q-TOF) instruments are the most widely employed for that purpose, and sample treatment and GC separation conditions follow the same trend as the previously commented with GC/LRMS applications. An interesting example was recently described by Khakimov et al. [22] that employed a non-targeted GC/TOF-MS metabolomic (non-volatile molecules) fingerprinting strategy for the discrimination of single grape white wines (Chardonnay, Riesling, Sauvignon Blanc, and Silvaner) to address their authentication according to grape variety. A total of 120 commercial white samples of different grape varieties were analyzed. For that purpose, 5 μL of wine sample was dried and submitted to a two-step derivatization procedure using first methoxyamine hydrochloride in dry pyridine and then trimethylsilyl cyanide. After derivatization, samples were immediately injected into the GC/TOF-MS and separated under temperature gradient elution using a Zebron ZB 5% Phenyl, 95% Dimethylpolysiloxane (30 m length, 0.25 μm film thickness, and 250 μm I.D.) column, and hydrogen as a carrier gas. Full scan acquisition within the range m/z 45–500 was performed. To prevent problems with the analyses, the proposed method switched off the MS detector when overloaded peaks of glycerol and unfermented residual sugars (glucose and fructose) eluted. Peak identification was performed using LECO-Fiehn and NIST11 metabolite libraries. A representative GC/HRMS total ion chromatogram obtained for a Chardonnay wine is shown in Fig. 12.4. The proposed methodology allowed the detection of 372 chemicals and the authors were able to tentatively identify 146 non-volatile metabolites including alcohols, organic acids, esters, amino acids, and sugars. They found that the grape variety effect explained 8.3% of the total metabolite variation. These fingerprints were then employed as wine chemical descriptors to address their classification

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Fig. 12.4 A representative GC/HRMS total ion chromatogram obtained for a Chardonnay wine. Reproduced with permission from reference [22]. Copyright (2022) Elsevier

according to the grape variety by means of PLS-DA. All the classification models employed for grape variety showed a high certainty (> 91%) for an independent test set. Wines of the Riesling variety were the ones showing the highest relative concentrations of sugars and organic acids. The authors also observed that two common antioxidants in wine, hydroxytyrosol, and gallic acid, decreased in the order of Chardonnay > Riesling > Sauvignon Blanc > Silvaner. The suitability of GC/MS non-volatile metabolome studies to address authentication issues in wines is therefore demonstrated, although the main drawback of these methodologies still resides in the need for time-consuming derivatization steps.

12.2.1.3 Multidimensional Gas Chromatography Techniques Multidimensional gas chromatography (MDGC) techniques offer excellent separation efficiency for the advanced characterization of volatiles and semi-volatiles in wine samples. It is nowadays becoming a popular technique for both non-targeted and targeted compound identification. The main benefit of these techniques, when addressing compound identification, is the use of orthogonal separations and different hyphenation possibilities, such as the coupling with mass spectrometry, allowing to expand the range of compounds that can be detected. However, the number of applications of these techniques to solve food authentication issues is still limited, although some examples focusing on wine samples can be found in the literature. The example is the work reported by Langen et al. [23] for the quantitative determination of α-ionone, β-ionone, β-damascenone, and the enantio-differentiation of α-ionone in wine for authenticity control. These compounds, belonging to the family of norisoprenoids, are substances that originate from carotenoid degradation, and

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