Traceability of fruits and vegetables

Traceability of fruits and vegetables

Phytochemistry 173 (2020) 112291 Contents lists available at ScienceDirect Phytochemistry journal homepage: Revie...

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Phytochemistry 173 (2020) 112291

Contents lists available at ScienceDirect

Phytochemistry journal homepage:


Traceability of fruits and vegetables a,∗


Guyon Francois , Vaillant Fabrice , Montet Didier a b c

T b

Service Commun des Laboratoires, Laboratoire de Bordeaux/Pessac, 3 Avenue du Dr. A. Schweitzer, 33608, Pessac Cedex, France Qualisud, Univ Montpellier, CIRAD, Montpellier SupAgro, Univ d'Avignon, Univ de La Réunion, Montpellier, France AGROSAVIA (Colombian Corporation for Agricultural Research), C.I. La Selva, Km 7 via las Palmas, Rionegro, Antioquia, Colombia



Keywords: Fruits and vegetables traceability CE 178/2002 Food safety DNA fingerprinting Metabolomics profiling Stable isotope analysis Biological markers

Food safety and traceability are nowadays a constant concern for consumers, and indeed for all actors in the food chain, including those involved in the fruit and vegetable sector. For the EU, the principles and legal requirements of traceability are set out in Regulation 178/2002. Currently however the regulation does not describe any analytical traceability tools. Furthermore, traceability systems for fruits and vegetables face increasing competition due to market globalization. The current challenge for actors in this sector is therefore to be sufficiently competitive in terms of price, traceability, quality and safety to avoid scandal and fraud. For all these reasons, new, flexible, cheap and efficient traceability tools, as isotopic analysis, DNA fingerprinting and metabolomic profiling coupled with chemometrics are needed.

1. Introduction Fruit and vegetable traceability became mandatory for the EU market on January 1, 2005 with the implementation of European Regulation 178/2002, called Food Law. Article 18 of this regulation imposes administrative traceability on all foods, whether produced, imported or exported within the European Union. However, traceability methods are not described in the regulation thus allowing food chain actors (producers, food processors and vendors) freedom to apply or develop technologies able to ensure the traceability of foods (Montet and Ray, 2018). On the other hand, it is their responsibility to set up traceability systems that can be audited at any time (article 18, EU Regulation 178/2002). The fruit and vegetable sector is currently the only one that allows the introduction of genetically modified organisms (GMOs) into the international market, but while this may be of minor concern to the fruit (papaya in Thailand) and green vegetable, it is not true of cereals where there is a strong presence of GMOs. It is possible now to find GMOs in the markets for soya beans, wheat and soon maize, but they must comply with EU Directive 2001/18. In addition, their placing on the market requires national agencies to give their expert opinions individually to EFSA (European Food Safety Agency). In the EU, the traceability and labeling of GMOs are governed by Regulation (1830)/ 2003, though rules vary around the world. Thus for the EU, all authorized GMOs belong to a specified positive list that contains all seeds that will be allowed on the European market.

The EU has also created quality-labels for fruits and vegetables such as Protected Designation of Origin (PDO, EU Regulation 510/2006) for the protection of know-how linked to a terroir, for example, the “Pistacchio verde di Bronte” (Italy), the “Aceituna Aloreña de Málaga” (Spain), and the “Ananás dos Açores/São Miguel” (Portugal). Protected Geographical Indication (PGI), another EU quality label, protects knowhow as product development phases may not necessarily have originated from a specific terroir, but have a link to a territory and an established reputation, for example, “Plate de Florenville” (Belgium); “Salate von der Insel Reichenau” (Germany). An exhaustive list is provided on the EU DOOR Portal (DOOR Portal, 2019). Some countries have developed their own quality labels such as the French “Label Rouge”(INAO, 2019), created by the French framework law of 1960. Label Rouge is well represented in the French market, for example, the “ail rose de Lautrec” created in 1966. As all these quality labels are synonymous with production constraints and also higher prices, there is a potential and an incentive for forgery. As a result, all these labels must be traceable from production through to commercialization. Fruits and vegetable are also emblematic of the organic sector, and with, for example, nearly 50% of fruits and vegetables in France (conventional and organic) coming from imports, this sector requires also effective traceability to ensure product quality and to guarantee specific agricultural practices. Up to now, barcode technology has been successfully used worldwide for these specific chains, but despite the overall effectiveness of these systems, traceability is mainly guaranteed by administrative

Corresponding author. Contact author. E-mail address: [email protected] (G. Francois). Received 5 March 2019; Received in revised form 27 January 2020; Accepted 1 February 2020 0031-9422/ © 2020 Elsevier Ltd. All rights reserved.

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means (especially through EU regulation 178/2002). It is thus important to develop reliable analytical tools to ensure food traceability and so, food safety and quality, notably for foods originating from the fruit and vegetable sector (Montet and Ray, 2018). Traceability techniques will need to be highly flexible, reliable, rapid and cost effective. This paper covers traceability techniques currently applied in the fruit and vegetable sector and some that could become important in the near future as they allow traceability biomarkers to be identified. Geographical origin and some types of adulterations can be traced by stable isotope analysis. Cultural practices can be verified by stable isotopes and microbial DNA analysis. Compliance with EU legislation in terms of GMO's in food require DNA analysis. Some guidelines will also be provided for these data applications through multidimensional treatments.

combining stable isotope content (apples in Magdas et al., 2012; Mimmo et al., 2015; bell peper in Krauβ and Vetter, 2019; coconut in Psomiadis et al., 2018; Lentils in Longobardi et al., 2015; oranges in Zhong et al., 2014) along with trace metal concentration (pistachio in Perez et al., 2006; berries in Anderson and Smith, 2005; Perini et al., 2018; oranges in Rummel et al., 2010; tomatoes in Mahne Opatic et al., 2018; vegetables in Cristea et al., 2017). However, studies on the geographical origin of fruits and derived products are relatively scarce, because commercialized products usually do not mention their geographical origin. Nonetheless, stable isotopes are used to control label claims such as “pure juice”, “no added sugar”, “additive-free”. δ18O measured on the fruit juice provides information on water addition, and δ13C measurements, on added organic compounds (sugars, organic acids).

2. Stable isotopes ratios at natural abundance

2.1. δ18O applications

Stable isotopes ratios at natural abundance are a powerful tool for geographical origin traceability and authenticity of food product for various reasons (Rossmann, 2001; Ghidini et al., 2006; Förstel, 2007; Camin et al., 2016, 2017). The term stable isotope of a chemical element refers to its nonradioactive isotopes, the relative abundances of which can be influenced by natural fractionation. This fractionation can be characterized using a stable isotope ratio, the relative amount of the two atoms in the same sample. By convention, the stable isotope ratio is expressed with the most abundant isotope in the denominator; and is expressed against a relative reference according to:

The oxygen stable isotope ratio of water is measured by equilibration between injected CO2 and H2O of the juice placed in a sealed vial. After an equilibration time, CO2 gas, calibrated against V-SMOW, is analyzed by an isotope ratio mass spectrometer (IRMS) providing the δ18O value of the juice water. All these steps can be automatically performed by commercially available systems. The 18O/16O ratio is used because of its depletion in tap-water compared with the values found in fruits in which evapotranspiration leads to the concentration of 18O (Dunbar and Wilson, 1983). As a result, a clear distinction can be observed between δ18O values of juices not-from-concentrate and juices reconstituted from concentrate by adding water. Some minimum δ18O values in fruit juices have been defined in the code of practice of the representative association of the fruit juice industry in the European Union (AIJN, 2019). However, performing verification on this basis is limited as the oxygen isotope ratio also depends on harvest period, meteorological conditions and particularly on geographical origin. Therefore, accurate verification requires databanks elaborated for each kind of fruit over several years so that seasonal variability and geographical origin are taken into account. To improve the detection of water addition without the need for a data-base, alternative methods have been developed coupling juice water δ18O value with juice sugars or citric acid δ18O value (Houerou et al., 1999) or with δ18O of ethanol derived from the fermentation of juice sugars (Jamin et al., 2003). It appears that δ18O values of organic compounds, quantified using the coupling pyrolysis-IRMS, can act as an internal reference. δ18O analysis of fruit water has also been used to differentiate the Pruneaux d'Agen mi-cuits (France, dried plums) from regular dried plums, by performing CO2 gas equilibrium directly on the fruit. The main difference between these two products came from their drying process: the “Pruneaux d'Agen mi-cuits” are dried to 35% water content before commercialization while regular plums are rehydrated to this water content after being dried to 25% of humidity. As a result, the drop in δ18O values allows the two types of plums to be distinguished (Guyon et al., 2015a, 2018).

δ (‰) = [(Rs / Rstd ) − 1] × 1000 where Rs is the measured isotope ratio of the sample and Rstd the isotope ratio of internationally accepted standards. In the field of food analysis, isotopes ratios are usually expressed in δ ‰ versus V-PDB (Vienna - Pee Dee Belemnite) for δ 13C, atmospheric nitrogen for δ 15N, V-SMOW (Vienna-Standard Mean Ocean Water) for δ18O and in ppm for δ2H. Deuterium (D or 2H) content is usually used to determine sugar origin in fruit juices. The measurements, performed on ethanol recovered by distillation after juice fermentation, are preformed using 2H – NMR technique and the (D/H) ratio is expressed in ppm. Most of the methods used to measure stable isotope ratios in fruits and vegetables have been standardized (Table 1). Stable isotopes ratios are of interest for establishing the authenticity of fruits and vegetables for several reasons. Firstly, the non-homogeneous repartition of oxygen-18 (δ18O) and deuterium (δ2H) over the globe linked to the hydrological cycle (Craig, 1961; Eriksson, 1965; Kendall and Coplen, 2001) provides information on plant growth localization and meteorological conditions. Secondly, plant metabolism has a determining impact on carbon-13 (δ13C) and deuterium (D/H) stable isotope ratios in the molecules metabolized by plants. Because of soil fertilization, the nitrogen stable isotope ratio (δ15N) can be a marker for the differentiation of conventional and organic agriculture. Studies on fruit geographical traceability are based on models

2.2. δ13C applications Table 1 List of standardized methods for stable isotopes measurements. Method




OIV-MA-AS2-12 ENV 12141 OIV-MA-AS311-09



OIV-MA-AS312-06 ENV 12140, ENV 13070 AOAC 2004.01 OIV-MA-AS311-05 AOAC 995,17


Wine, must Fruit juice Wine, must and concentrate must Wine, must Fruits & Vegetables

“ 2 H-NMR “

“ (D/H) “

Fruit Juice & maple syrup Wine, must Fruit juice






The 13C/12C ratio of a raw organic compound is performed by the coupling of an elemental analyzer (EA) with an IRMS. The elemental analyzer acts as a combustion step and the generated CO2 is analyzed by IRMS. Before 2013, fruit juices were allowed to be supplemented either for acidification (commonly by citric acid) or sweetening (commonly sugars) but not simultaneously. Moreover, this addition had to be mentioned on the label. After 2013, the new regulation prohibits such additions with only some exceptions (e.g. Tartaric acid in grape juice, and malic acid in pineapple juice) (EU Directive, 2012/112/EC). These compound additions can be detected using the δ13C ratio mainly to detect adulteration with compounds synthesized by “C4” type plants (i.e. maize, and sugar-cane) (Fig. 1). C3 type plants (most fruits) 2

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2.4. δ15N applications Nitrogen, an essential element for plant growth, can be supplied by animal and plant based manure and compost or synthetic fertilizers. The main industrial source of nitrogen is nitrogen gas from air that gives synthetic fertilizers a δ15N ranging from −4.0 to +4.0‰ while for manure δ15N is found in the range 0.0–16.0‰, with some exceptions (Rogers, 2008; Sturm and Lojen, 2011; Mukone et al., 2013). Use of the nitrogen stable isotope ratio, by itself, for the differentiation of organic and conventional agricultural practices is limited because of the overlap between the two ranges. The acceptable limit for organic product seems to be a δ15N ratio higher than +4‰ (Rogers, 2008; Camin et al., 2007). Conclusions can be confirmed by combining the nitrogen isotope ratio with other stable isotope ratios (Luo et al., 2015), elemental markers (Magdas et al., 2018) or by analyzing it on specific markers such as amino acids (Paolini et al., 2015).

Fig. 1. Typical natural δ13C ratios according to the plant metabolism.

synthesize sugars from atmospheric CO2 via the Calvin cycle, while in C4 type plants it is via the Hatch & Slack cycle (Hatch and Slack, 1970). These two cycles lead to synthesized molecules with δ13C in the range −24 to −33‰ in C3 plants, and −10 to −16‰ in C4 plants (O'Leary, 1988). A third biochemical pathway called Crassulacean acid metabolism (CAM) is found in a few plants (e.g. pineapple) giving δ13C value of −10 to −20‰ against V-PDB (O'Leary, 1988). As EA-IRMS is not selective, compound separation of a mixture is a prerequisite for compound-specific quantification of 13C/12C ratio. A protocol was developed for sugars and acid precipitation in order to separate and analyze them individually (Doner, 1985; Jamin et al., 1998a; Guillou et al., 1999). δ13C values provide accurate information in the case of C4 type compound addition, but only limited conclusions can be established for added C3 type compounds. In order to refine the interpretation of results, particularly in the case of C3 compound additions, some work has been devoted to find an internal standard that provides a reference value for δ13C. Fruit proteins, isolated through a bentonite precipitation, have been proposed as such a13C internal reference as a good correlation with the carbon-13 content of the given fruit sugars was observed. Therefore, the difference between the δ13C values of the two measurements increases the accuracy of data interpretation (Jamin et al., 1998b). 13 C-NMR of ethanol produced by juice fermentation has also been studied for the position-specific quantification of 13C on the carbonated ethanol skeleton (Gilbert et al., 2009, 2011; Guyon et al., 2015b). This technique allows differentiation of pure pineapple juices (CAM plant type) and pineapple juices supplemented by cane sugar (C4 type) which is not possible by EA-IRMS analysis (Thomas et al., 2010). High-performance liquid chromatography (HPLC) or ion chromatography (IC) can be coupled with IRMS using an interface that allows the chemical oxidation (co) of all organic matter to CO2 (Guyon et al., 2013, 2014). HPLC-co-IRMS and IC-co-IRMS systems allow organic acids (OA) and sugars (S) separation without any derivatization and a simultaneous analysis of δ13C ratios of eluted molecules. It appears that the ratio of isotope ratio of the organic acid and sugars (δ13COA/δ13CS) of an authentic fruit juice are constant and any deviation is the result of an adulteration.

2.5. δ34S applications The sulphur isotope ratio (δ34S) can also be quantified by IRMS. This ratio is expressed against the international reference material, which is Canyon Diablo Triolite (CDT, meteoritic FeS). Studies on δ34S are scarce, the main reason being the sulphur concentration in food products (< 1%) which can be a measurement issue (Tcherkez and Tea, 2013; Krivachy et al., 2015). Therefore the δ34S is usually measured on purified fractions of the product, proteins for example. δ34S has been used to determine the geographical origin of fruits and vegetables such as asparagus (Schlicht et al., 2006), and oranges (Rummel et al., 2010). 3. DNA fingerprinting approaches Technologies based on deoxyribonucleic acid (DNA) analysis are often applied in various fields such as the medical, and food sectors. Firstly, it provides deeper levels of discrimination for species identification (Madesis et al., 2014) and secondly, DNA is usually highly resistant to industrial processing, being relatively thermostable and generally highly conserved compared to other molecules (Galimberti et al., 2015). For these reasons, amplification by Polymerase Chain Reaction (PCR) quickly became a ubiquitous tool in the field of food authenticity testing (Palmieri et al., 2009; Galimberti et al., 2013). DNA analysis can be used for plant and animal variety identification (Palmieri et al., 2009), for product-specific micro-organism studies (Ercolini, 2004; Hamdouche et al., 2015), for plant disease diagnosis (e.g. by identifying the DNA material of a particular pathogen), and for traceability purposes (Dufossé et al., 2013; Di Rienzo et al., 2016). DNA fingerprinting or barcoding approaches, also called molecular typing methods, allow genome nucleotide sequence variations (human, plant, animal and microbial) to be measured (Nocke et al., 2007), and are widely used in the food sector (Durand et al., 2013). The technique consists of targeting and analyzing specific regions of DNA (or RNA) by combining PCR amplification assays with electrophoretic techniques (Alvarez-Rivera et al., 2018; Böhme et al., 2019). The molecular markers, that is the genome targeted regions, depend on the research objective. Applying to fruits and vegetables, research has focused on finding markers allowing the discrimination of variety, geographical origin, mode of production, and the identification of GMOs or allergens and food additives (Martins-Lopes et al., 2013). GMOs are complex to identify and characterize as the number of species and countries involved are increasing in a globalized market (HolstJensen et al., 2012). GMO regulations for foods differ among countries. In the European Union, it is mandatory to label the GMO content if the food contains more than 0.9% of transgenic DNA (EU regulation 1830/ 2003). The GMO detection standard is PCR-based analysis, especially quantitative PCR (qPCR), despite its ability to detect only known GMOs. As not all GMOs are fully characterized, more sensitive and informative approaches are required (Wilkes et al., 2016).

2.3. (D/H) applications The δ2H of water can be used to detect fruit juice adulteration by added water (Sieper et al., 2006), although this method has been practically abandoned in food control laboratories in favor of δ18O quantification. The stereospecific quantification of deuterium concentration is usually performed on the ethanol resulting from fruit juice sugars fermentation by yeast addition. Once the fermentation ended, the alcoholic solution is distilled using a Cadiot column to separate the ethanol from water. The isolated ethanol is analyzed by EA-IRMS for δ13C quantification and by 2H-NMR to quantify the deuterium content of each isotopomers, i.e. CH22HCH2OH (so called (D/H)I) and CH3CH2HOH (so called (D/H)II). The analysis by 2H-NMR allows the detection of exogenous C3 and C4-plant sugars (Yunianta et al., 1995; Ogrinc et al., 2003; Jamin et al., 2005; Bontempo et al., 2014).


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of the complexity of the molecular set (metabolome) in a biological medium. Therefore, as long as the composition of the metabolome is modulated by genotype, environment, crop management, processing and their interactions, a metabolomic approach is relevant for assessing food traceability. Two main metabolomic approaches are used. The first, an untargeted analysis, consists of obtaining a sample fingerprint without further elucidation as the raw signal only is needed for comparisons and classification using multivariate statistical methods. The second, a targeted approach, consists of screening and identifying discriminant compounds between different states of biological matters. After unsupervised statistical analysis, such as PCA (principal component analysis) or HCA (hierarchical cluster analysis), which yields a simplified description of complex data, supervised statistics are then used. Among other statistical tools, partial least squares discriminant analysis (PLS-DA) projection to latent structures is mainly used for this purpose and allows the spectral features that most contribute to the observed separation between groups to be extracted. Multivariate methods can be used for the selection of the most discriminant signal and/or molecule. In all cases, the ultimate objective is to assign class membership to biological samples, but this requires large data sets collected over different periods of time to validate robustness against variability of non-controlled factors (Esslinger et al., 2014). This is the main limitation of the approach, but once statistical robustness has been validated through training and appropriate blind-tests, subsequent analytical protocols can be simple, sensitive, efficient, rapid and costeffective. A review of different applications shows that the most investigated food matrices were honey, virgin olive oil and wine with relatively little having been done on other fruits and vegetables (Cubero-Leon et al., 2014; Esslinger et al., 2014). Near infrared spectroscopy (NIRS) signal patterns combined with multivariate analysis has been used to discriminate between geographical origins of olive oil (Bevilacqua et al., 2012) and wine (Versari et al., 2014). Mass spectrometry (MS) metabolomic fingerprinting has been applied to assess the authenticity of different fruit juice (Vaclavik et al., 2012). For metabolomics studies, two analytical platforms have mainly been used, namely nuclear magnetic resonance (NMR) spectroscopy and MS. 1H-NMR untargeted metabolomic approach has been applied to the discrimination of potato and tomato varieties grown under organic or conventional management (Pacifico et al., 2013; Hohmann et al., 2015) and for the discrimination of olive oils from 7 regions in the Mediterranean area (Longobardi et al., 2012). In this latter case, classification relied on the relative composition of fatty acids whose concentration appeared to be modulated by environmental factors. NMR-based metabolomics is particularly appropriate when primary metabolites such as lipids, protein, sugars or other main components are affected, but this technique suffers from a lack of sensitivity. As a result, metabolomic approaches based on mass spectrometry are increasingly employed for food traceability, at least for the first step of biomarker identification (Castro-Puyana and Herrero, 2013; Cubero-Leon et al., 2014). The most recent examples concern application MS metabolomic for traceability of rice (Uawisetwathana & Karoonuthaisiri, 2019), buffalo milk (Salzano et al., 2020), saffron (Senizza et al., 2019) and extra-virgin olive oil (Ghisoni et al., 2019). For instance, Citrusin D, a secondary metabolite, was shown to be a powerful discriminant of sweet orange from Valencia's region (Díaz et al., 2014). Usually, one particular metabolite is not enough to ensure robust classification. For instance, Indian citrus authentication study (Jandrić et al., 2017), the authors used the ratio between two biomarkers (limonin glucoside and hesperidin) showing that discrimination can rely on several, including their respective concentrations and the mathematical relationships between them. To summarize, methodological approaches for food traceability systems based on metabolomic data should include a first step of identifying biomarkers using untargeted metabolomic applied to NMR data if the biomarkers are primary metabolites or MS data if the biomarkers are secondary metabolites. The suitability of biomarkers, the

In the literature, the efficiency of single nucleotide polymorphisms (SNPs), simple sequence repeats (SSRs), internal transcribed spacer (ITS) and universal chloroplast primers have been tested mainly in order to identify different cultivars due to their high level of polymorphism and high reproducibility (Galimberti et al., 2014; Scarano et al., 2014; Barcaccia et al., 2016; Di Rienzo et al., 2016). Such markers have been successfully employed for fruit and vegetable varietal traceability and the detection of adulteration (e.g. olive oil, wines, tomatoes, fermented table olives, and fruit juices) (Turci et al., 2010; Sardaro et al., 2013; Pasqualone et al., 2013; Montemurro et al., 2015; Barcaccia et al., 2016). As an example, the use of universal primers targeting regions on the chloroplast genome (e.g. plastid markers such as trnL genes) demonstrated the ability to detect the addition of a cheaper fruit juice. Their discriminatory power is especially enforced when used in combination to target several markers or when DNA barcoding is combined with a HRM (High Resolution Melting) approach (Ganopoulos et al., 2013; Galimberti et al., 2014; Barcaccia et al., 2016; Bazakos et al., 2016). Other types of markers have been investigated and have demonstrated their efficacy for traceability aims. Indeed, rather than targeting DNA regions specific to fruit and vegetable genomes, some research teams showed that microbial DNA associated with fruits and vegetables can be used to trace them (Le Nguyen et al., 2008; El Sheikha et al., 2009; Tatsadjieu et al., 2010; Dufossé et al., 2013). These studies showed that a global microbial fingerprint was linked not only to the geographical origin of foods, but also to their mode of production (organic versus conventional, Bigot et al., 2015) and post-harvest process (Cocolin et al., 2013; Hamdouche et al., 2015). It was also demonstrated that a more targeted analysis could be more effective in discriminating organic from other fruits. Interestingly, these studies performed on apples and bananas from different geographical origins (Bigot et al., 2015), harvest times and varieties, showed that there are not only specific microbial groups for organic fruits but there are also discriminant microbial groups for conventional fruits as well. The use of the two types of discriminant microbial markers (from organic and conventional fruits) together would be more effective as discriminating and tracing organic fruits. The microbial composition of these discriminant groups may change during post-harvest handling, so, further studies are needed to verify the robustness of these markers. However, the results obtained up to now showed their efficacy and their potential to create an analytical tool to trace organic fruits and vegetables. Recent technical advances in DNA sequencing (with Next Generation Sequencing, NGS, approaches) will allow the identification of new DNA markers (e.g. for GMOs), notably for verifying the traceability. The combination of fingerprinting and NGS approaches could permit the use of robust DNA markers to ensure effective and reliable traceability. The current challenges today are mainly optimizing DNA extraction methods (varying efficiency according to the type of food, difficulties in analyzing degraded DNA, the presence of various inhibitors, etc.) and limiting bias linked to PCR amplification (Turci et al., 2010; Martins-Lopes et al., 2013; Barcaccia et al., 2016). In addition, as for metabolomic and isotopic analysis, NGS approaches (e.g. MiSeq from Illumina (USA); PGM from Ion Torrent (USA); MinION from ONT (GB)) rely on the availability and reliability of databases (Galimberti et al., 2014; Di Rienzo et al., 2016). As many organisms are not available on databases, or have their genomes only partially sequenced, it is often difficult to identify all the organisms present in a complex sample. That is why a fraction of read data from NGS analysis may remain “unclassified”. 4. Metabolomic approaches Metabolomics is a tool that combines high throughput analytical methods and multivariate statistical analysis of the generated data, to study small molecular weight metabolites (often between 50 and 1500 Da) in biological media. It aims to enable a better understanding 4

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concentration threshold between classes and their relationship needs to be validated on a large scale through blind-test training to achieve a robust statistical model. This work is usually accomplished by academic researchers who need to think, in a second step, of the best analytical instrument able to detect the specific signals of the biomarkers that can be run fast, accurately, repeatedly and cost effective. The third step is the implementation of the ready-to-use combination technique and multidimensional model in food control laboratories.

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5. Conclusions The aim of this paper was to present an overview of current methods employed to trace food products, especially from the fruit and vegetable sectors, but also to describe by giving some examples of applications, innovative technologies proposed by the scientific community. Fruits and vegetables are among the most difficult food to preserve due to their perishable nature, and so, among the most difficult to export. It is thus essential to develop tools able to trace them in the most accurate way to avoid health hazards, quality lost and unfair competition. Stable isotope ratios at natural abundance are a powerful and acknowledged tool to detect certain frauds like water addition or sugar supplementation. They can also provide valuable information regarding the geographical origins of fruits and vegetables, and to some extent to discriminate between organic and conventional agricultural practices. They are not however useful for detecting GMOs which can only be done using DNA. The study of microbial DNA is a new and promising tool for fruit and vegetable traceability. Food traceability usually requires a multi-technique approach as single methods do not generally produce sufficiently discriminating factors. Supplementary information can be provided through spectrophotometric and spectroscopic techniques, and heavy isotopes ratios. As a result, the quantity of information produced is substantial and data treatment requires multidimensional analysis in order to elaborate models for authentication. This requirement raises questions of model validity in terms of the authenticity and representativeness of the samples used in the model. Finally, as food fraud is an international plague, an interesting proposition would be the creation of a community using standardized methods to elaborate and continuously add to a standard database useable, notably, by food control bodies. Declaration of competing interest None Acknowledgment The authors want to thank Guy Self for his constructive remarks and for reviewing the English text of this manuscript. References AIJN - European Fruit Juice Association, 2019. Code of Practice. Alvarez-Rivera, G., Cifuentes, A., Puyana, M.C., 2018. Electrophoretic technique: Capillary zone electrophoresis. Modern Techniques for Food Authentication, Second ed. Da-Wen Sun, pp. 659–685. 00016-5. Anderson, K.A., Smith, B.W., 2005. Use of chemical profiling to differentiate geographic growing origin of raw pistachios. J. Agric. Food Chem. 53, 410–418. 10.1021/jf048907u. Barcaccia, G., Lucchin, M., Cassandro, M., 2016. DNA barcoding as a molecular tool to track down mislabeling and food piracy. Diversity 8, 2. d8010002. Bazakos, C., Spaniolas, S., Kalaitzis, P., 2016. DNA-based approaches for traceability and authentication of olive oil. In: Boskou, D., Clodoveo, M.L. (Eds.), Products from Olive Tree. InTech. Bevilacqua, M., Bucci, R., Magri, A.D., Magri, A.L., Marini, F., 2012. Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: a case study. Anal. Chim. Acta 717, 39–51. Bigot, C., Meile, J.C., Kapitan, A., Montet, D., 2015. Discriminating organic and conventional foods by analysis of their microbial ecology: an application on fruits. Food


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Dr. François Guyon is expert in food authenticity. After his Ph-D in organic synthesis and electrochemistry in Brest (France), he worked, in Geneva, as a post-doctoral fellow on the study of metal transport mechanism through supported liquid membrane. Then he spent one year in Los Alamos (New Mexico, USA) as a temporary staff member on a fuel cell program. In 2001, he joined the Service Commun des Laboratoires in Bordeaux (merger of customs and antifraud laboratories) to supervise the isotopic unit. Since then, his work is devoted to the control of beverages authenticity (wines, spirits and fruit juices) and to the development of stable isotope applications in food authenticity. He co-authored more than 35 papers and book chapters. He participated in the European Project Authent-Net and he is a French expert at the organisation Internationale de la Vigne et du Vin (OIV).

Fabrice Vaillant (PhD) is a research scientist with the Department of Food Quality within the International Research Center in Agronomy for Development (CIRAD). He is currently hosted by the Colombian Research Corporation for Agriculture (AGROSAVIA). Within his research unit, he works on the characterization of tropical fruits, their potential health benefits using untargeted metabolomic approaches and the development of innovative processes that preserve high quality and are viable for small-scale sustainable rural agro-industries. He has authored/co-authored more than 60 scientific publications and book chapters in these fields, including 5 patents.

Dr. Didier Montet is a senior researcher and expert in food safety. He created and led the team of Control of contaminants along the food chain (food safety team) at CIRAD in Montpellier, France. Cirad is an international research center devoted to development. He got his Ph.D. in Food microbiology in 1984 at the University of Montpellier. He is doing expertise for more than 13 years at the National French Agency for food-safety (Anses) where he was vicechair of the Biotechnology group. His main research topic concerns the understanding of the microbial ecology of food and food-safety (origin determination, discrimination of organic food, hazard points, mycotoxinogenic fungi). He has published nearly 187 papers in the field of food. He coedited 7 books. He was food expert for the French Embassy in South-East Asia and was professor at the Asian Institute of Technology in Thailand (1997–1999). He just finished a Europe Aid Project in Ivory Coast that conducted to the creation of their national food safety agency and participates in different European projects (Collab4safety, Autent-net, Dafrali) and international expertises (FAO, Philipino government). He is also elected at the Scientific Council of Cirad.