Direct infusion-multiple reaction monitoring cubed (DI-MRM3) enables widely targeted bi-omics of Colla Corii Asini (Chinese name: Ejiao)

Food authenticity is extremely important and widely targeted bi-omics is a promising pipeline attributing to incorporating metabolomics and peptidomics. Colla Corii Asini (CCA, Ejiao) is one of the most popular tonic edible materials, with counterfeit and adulterated products being widespread. An attempt was devoted to develop a high-throughput and reliable DI-MRM3 program facilitating widely targeted bi-omics of CCA. Firstly, predictive MRM program captured metabolites and peptides in trypsin-digestive gelatins. After data alignment and structure annotation, primary parameters such as Q1 → Q3 → QLIT, CE, and EE were optimized for all 17 metabolites and 34 peptides by online ER-MS. Though a single run merely consumed 6.5 min, great selectivity was reached for each analyte. Statistical results showed that nine peptides contributed to distinguish CCA from other gelatins. After cross-validation with LC-MRM, DI-MRM3 was justified to be reproducible and high-throughput for widely targeted bi-omics of CCA, suggesting a meaningful tool for food authenticity.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Amplification-free quantitative detection of genomic DNA using lateral flow strips for milk authentication

With the globalization and complexity of the food supply chain, the market is becoming increasingly competitive and food fraudulent activities are intensifying. The current state of food detection faced two primary challenges. Firstly, existing testing methods were predominantly laboratory-based, requiring complex procedures and precision instruments. Secondly, there was a lack of accurate and efficient quantitative detection methods. Taking cow's milk as an example, this study introduced a novel method for nucleic acid quantification in dairy products, based on lateral flow strips (LFS). The core idea of this method is to design single-stranded DNA (ssDNA) probes to hybridize with mitochondrial genes, which are abundant, stable, and species-specific in dairy products, as detection targets. Drawing inspiration from the principles of nucleic acid amplification, this research innovatively established a new DNA hybridization method, named LAMP-Like Hybridization (HybLAMP-Like). Leveraging the denaturation and DNA polymerization functions of the bst enzyme, efficient binding of the probe and template strand was achieved. This method eliminated the need for nucleic acid amplification, simplifying the procedure and mitigating aerosol contamination, thereby ensuring the accuracy of the detection results. The method exhibited exceptional sensitivity, capable of detecting extremely low to 12.5 ng in visual inspection and 3.125 ng when using a reader. In terms of practicality, it could achieve visual detection of cow's milk content as low as 1% in adulterated dairy products. When combined with a portable LFS reader, it also enabled precise quantitative analysis of milk adulteration.

Development and validation of sensitive and rapid CRISPR/Cas12-based PCR method to detect hazelnut in unlabeled products

Hazelnut, one of the most popular tree nuts, is widely found in processed food and even very small amounts can trigger severe allergic reactions in susceptible people. Herein, we developed a sensitive and rapid method based on CRISPR and qPCR capable of detecting low-abundance hazelnut in processed food. The assay, known as CRISPR-based nucleic acid test method (Crinac) can detect 1 % of hazelnut in a mixture and allows the species to be identified in a complex processed sample. The detection process can be completed within 60 min. Contributed to amplification via PCR and CRISPR/Cas12a, enables end-fluorescence measurement for the quantification of hazelnut, thus reducing assay time and eliminating the need for costly real-time fluorescence PCR instruments. The assay based on CRISPR/Cas12 and PCR has potential as a sensitive and reliable analytical tool for the detection of food authenticity.

Combined quantitative lipidomics and back-propagation neural network approach to discriminate the breed and part source of lamb

The study successfully utilized an analytical approach that combined quantitative lipidomics with back-propagation neural networks to identify breed and part source of lamb using small-scale samples. 1230 molecules across 29 lipid classes were identified in longissimus dorsi and knuckle meat of both Tan sheep and Bahan crossbreed sheep. Applying multivariate statistical methods, 12 and 7 lipid molecules were identified as potential markers for breed and part identification, respectively. Stepwise linear discriminant analysis was applied to select 3 and 4 lipid molecules, respectively, for discriminating lamb breed and part sources, achieving correct rates of discrimination of 100 % and 95 %. Additionally, back-propagation neural network proved to be a superior method for identifying sources of lamb meat compared to other machine learning approaches. These findings indicate that integrating lipidomics with back-propagation neural network approach can provide an effective strategy to trace and certify lamb products, ensuring their quality and protecting consumer rights.

Stable isotope ratios and current-use pesticide levels in edible insects: Implications on chemical food safety

In the past years, the European Union (EU) has added edible insects to the list of novel foods, allowing an increasing number of insect-based products into the European market. With insects gaining more popularity in the Western world, it is crucial to investigate their chemical food safety. This study aimed at investigating possible isotopic patterns in different edible insect species (n = 52) from Asia, Africa and Europe using stable isotope ratio analysis (SIRA) to provide a framework for future investigations on food authenticity and traceability. Additionally, complementary mass-spectrometric screening approaches were applied to gain a comprehensive overview of contamination levels of current-use pesticides (CUPs) in edible insects, to assess their chemical food safety. SIRA revealed significant differences between countries in δ13CVPDB- (p < 0.001) and δ15Nair- (p < 0.001) values. While it was not possible to distinguish between individual countries using principal component analysis (PCA) and linear discriminative analysis (LDA), the latter could be used to distinguish between larger geographical areas (i.e. Africa, Europe and Asia). In general, African samples had a more distinct isotopic profile compared to European and Asian samples. When comparing the isotopic compositions of samples containing pesticides with samples with no detected pesticides, differences in sulphur compositions could be observed. Additionally, LDA was able to correctly classify the presence of pesticides in a sample with 76% correct classification based on the sulphur composition. These findings show that SIRA could be a useful tool to provide a framework for future investigations on food authenticity and traceability of edible insects. A total of 26 CUPs were detected using suspect screening and an additional 30 CUPS were quantified using target analysis, out of which 9 compounds had a detection frequency higher than 30%. Most detected pesticides were below the maximum residue levels (MRLs) for meat, suggesting low contamination levels. However, dichlorvos and fipronil could be detected in the same order of magnitude as the MRLs, even in samples purchased in Europe. These findings indicate a limited chemical risk for edible insects regarding pesticide contamination. Nevertheless, the study also highlights that further and more extensive investigations are needed to give a comprehensive assessment of the chemical risk of edible insects as a novel food source in Europe. With insects recently being potentially more incorporated into daily diets, more attention should be paid to possible chemical hazards to accurately assess their risk and to ensure food safety.

Free amino acid profile in Mimosa scabrella honeydew honey from Brazil and chemometric analysis for geographical discrimination

In this study, twenty free amino acids (FAA) were investigated in samples of bracatinga (Mimosa scabrella) honeydew honey (BHH) from Santa Catarina (n = 15) and Paraná (n = 13) states (Brazil), followed by chemometric analysis for geographic discrimination. The FAA determination was performed by gas chromatography-mass spectrometry (GC-MS) after using a commercial EZ:faast™ kits for GC. Eight FAA were determined, being proline, asparagine, aspartic and glutamic acids found in all BHH, with significant differences (p < 0.05). In addition, with the exception of proline, the others FAA (asparagine, aspartic and glutamic) normally showed higher concentrations in samples from Santa Catarina state, being that in these samples it was also observed higher FAA sums (963.41 to 2034.73 mg kg-1) when compared to samples from Paraná state. The variability in the results did not show a clear profile of similarity when the heatmap and hierarchical grouping were correlated with the geographic origin and the concentration of eight determined FAA. However, principal component analysis (PCA) demonstrated that serine, asparagine, glutamic acid, and tryptophan were responsible for the geographic discrimination among samples from Santa Catarina and Paraná states, since they were the dominant variables (r > 0.72) in the PCA. Therefore, these results could be useful for the characterization and authentication of BHH based on their FAA composition and geographic origin.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

MALDI-ToF MS and chemometric analysis as a tool for identifying wild and farmed salmon

In this study, the difference between wild and farmed salmon production was successfully profiled and differentiated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) combined with chemometric analysis. The established method based on multivariate analysis mainly involved principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) as the screening and verifying tools to provide insights into the distinctive features found in wild and farmed salmon products, respectively. The discrimination between farmed and wild salmon was accomplished with 100% classification accuracy using chemometric models, 100% identification accuracy was also achieved in distinguishing wild Salmo salar and Oncorhynchus nerka samples. The results of the present work suggest that the proposed method could serve as a reference for detecting salmon fraud relating to wild or farmed production and expand the application of MALDI-ToF technology further into food authenticity applications.

A robust nanoLC high-resolution mass spectrometry methodology for the comprehensive profiling of lactic acid bacteria in milk kefir

Consumer attention to functional foods containing probiotics is growing because of their positive effects on human health. Kefir is a fermented milk beverage produced by bacteria and yeasts. Given the emerging kefir market, there is an increasing demand for new methodologies to certify product claims such as colony-forming units/g and bacterial taxa. MALDI-TOF MS proved to be useful for the detection/identification of bacteria in clinical diagnostics and agri-food applications. Recently, LC-MS/MS approaches have also been applied to the identification of proteins and proteotypic peptides of lactic acid bacteria in fermented food matrices. Here, we developed an innovative nanoLC-ESI-MS/MS-based methodology for profiling lactic acid bacteria in commercial and artisanal milk kefir products as well as in kefir grains at the genus, species and subspecies level. The proposed workflow enables the authentication of kefir label claims declaring the presence of probiotic starters. An overview of the composition of lactic acid bacteria was also obtained for unlabelled kefir highlighting, for the first time, the great potential of LC-MS/MS as a sensitive tool to assess the authenticity of fermented foods.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Characterizing wine terroir using strontium isotope ratios: a review

This paper presents a detailed review of the use of 87Sr/86Sr isotope systematics for wine provenance studies. The method is based on the principle that the Sr isotope ratio in wine reflects that of the labile fraction of the vineyard soil from which the wine is produced. The review encompasses 87Sr/86Sr data from wine samples published between 1993 and 2021 from terroirs in 22 different countries. The analytical procedures and techniques adopted by the different authors and the range of isotope ratios obtained in the different studies are discussed and evaluated. This study provides a bibliometric analysis of the 87Sr/86Sr isotope approach for wine authentication at different scales. Although limitations are evident when implemented at large (global) scales, we demonstrate that the 87Sr/86Sr isotope tracing technique remains a powerful and reliable tool for determining the geographical origin of wine when combined with detailed knowledge of the geological and soil characteristics of the substrata. For example, this combination of data allows the wines grown in the volcanic soils of Central and Southern Italy to be unambiguously fingerprinted. We present a detailed protocol for the application of the Sr isotope technique to wine authentication.

Interpreting the variation in particle size of ground spice by high-resolution visual and spectral imaging: A ginger case study

High-resolution (HR) visual imaging and spectral imaging are common computer vision-based techniques used for food quality analysis and/or authentication based on the interaction of light and material surface and/or composition. The particle size of ground spices is an important morphological feature that affects the physico-chemical properties of food products containing such particles. This study aimed to interpret the impact of particle size of ground spice on its HR visual profile and spectral imaging profile using ginger powder as a representative spice powder model. The results revealed an increase in the light reflection with the decrease of particle size of ginger powder, which was manifested by the lighter colour (higher percentage of the colour code with lighter yellow colour) of the HR visual image and stronger reflection with spectral imaging. The study also revealed that, in spectral imaging, the influence of the particle size of ginger powder increased with rising wavelengths. Finally, the results indicated a relationship between spectral wavelengths, ginger particle size, and other natural variables of the products which might be generated from cultivation to processing. Ultimately, the impact of natural variables arising during the food production process on the physico-chemical properties of the product should be fully considered or even additionally evaluated prior to the application of specific food quality and/or authentication analytical techniques.

Multi-stable isotope and multi-element origin traceability of rice from the main producing regions in Asia: A long-term investigation during 2017-2020

Stable isotope and multi-element analytical techniques with chemometrics were developed to trace the origin authenticity of rice in China market. In the long-term study from 2017 to 2020, a total of 115 batches of rice samples from 8 main producing areas of 7 Asian countries were determined 5 stable isotope ratios and 18 elemental contents. One-way analysis of variance (ANOVA) and various multivariate modeling methods were performed for the origin discrimination. Supervised multivariate modeling including partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) can realize more satisfactory identification of 8 rice origins than ANOVA comparison and unsupervised methods, their leave-one-out cross-validation accuracies approach 85.0 % and 90.9 %, respectively. δ²H, δ¹³C, Ba, Al, Mg, δ³⁴S, Pb and δ¹⁸O were screened as the most important variables for rice origin traceability (VIP > 1 or AUC > 0.5). This analytical strategy combining maybe promising to ensure the origin authenticity and combat illegal mislabeling in rice trade.

Untargeted metabolomics-based approach using UHPLC-HRMS to authenticate carrots (Daucus carota L.) based on geographical origin and production mode

Carrots produced in different agricultural regions with organic or conventional mode were analyzed by untargeted UHPLC-HRMS using reversed-phase and HILIC modes. Data were first treated separately, and further combined to possibly improve results. An in-house data processing workflow was applied to identify relevant features after peak detection. Based on these features, discrimination models were built using chemometrics. A tentative annotation of chemical markers was performed using online databases and UHPLC-HRMS/MS analyses. An independent set of samples was analyzed to assess the discrimination potential of these markers. Carrots produced in the New Aquitaine region could be successfully discriminated from carrots originating from the Normandy region by an OLPS-DA model. Arginine and 6-methoxymellein could be identified as potential markers with the C18-silica column. Additional markers (N-acetylputrescine, l-carnitine) could be identified thanks to the polar column. Discrimination based on production mode was more challenging: some trend was observed but model metrics remained unsatisfactory.

E-eye, flash GC E-nose and HS-GC-MS combined with chemometrics to identify the adulterants and geographical origins of Ziziphi Spinosae Semen

Ziziphi Spinosae Semen (ZSS), a valuable seed food, has faced increasing authenticity issues. In this study, the adulterants and geographical origins of ZSS were successfully identified by electronic eye, flash gas chromatography electronic nose (Flash GC e-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS). As a result, there were color differences between ZSS and adulterants, mainly represented by the a* value of ZSS was less than adulterants. In ZSS, 29 and 32 compounds were detected by Flash GC e-nose and HS-GC-MS. Spicy, sweety, fruity and herbal were the main flavor of ZSS. Five compounds were determined to be responsible for flavor differences between different geographical origins. In the HS-GC-MS analysis, the relative content of Hexanoic acid was the highest in ZSS from Hebei and Shandong, while 2,4-Decadien-1-ol was the highest in Shaanxi. Overall, this study provided a meaningful strategy for addressing authenticity problems of ZSS and other seed foods.

Essential terminology and considerations for validation of non-targeted methods

Through their suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack." Instead, they exploit all the constituents of the haystack. This new type of analytical method is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing need to be propagated for the benefit of those associated with academic research, commercial development, or official control. This paper addresses frequently asked questions regarding terminology in connection with NTMs. The widespread development and adoption of these methods also necessitate the need to develop innovative approaches for NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap for approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions therefor.

Fingerprinting based on gas chromatography-Orbitrap high-resolution mass spectrometry and chemometrics to reveal geographical origin, processing, and volatile markers for thyme authentication

Thyme is an aromatic herb traditionally used for food purposes due to its organoleptic characteristics and medicinal properties, which is highly susceptible to food fraud. In this study, GC-HRMS-based fingerprinting was applied for the first time to determine the geographical traceability of thyme based on different origins (Spain, Poland, and Morocco), as well as to assess its processing by comparing sterilized vs. non-sterilized thyme. Unsupervised chemometric methods (PCA and HCA) revealed a predominant influence of the geographical origin on thyme fingerprints rather than processing effects. Supervised PLS-DA and OPLS-DA were used for discrimination purposes, revealing high predictive ability for further samples (100%), and allowing the identification of differential compounds (markers). A total of 24 markers were putatively identified (13 metabolites were confirmed) belonging to different classes: monoterpenoids, diterpenoids, sesquiterpenoids, alkenylbenzenes, and other miscellaneous compounds. This study outlines the potential of combining untargeted analysis by GC-HRMS with chemometrics for thyme authenticity and traceability.

Characterization of vanillin carbon isotope delta reference materials

Stable carbon isotope ratio measurements are used to investigate the provenance of vanillin. In this study, a variety of commercial vanillin samples and vanilla products were analyzed to provide a frame of reference for the variability of carbon isotope delta values in various vanillin samples, with the results ranging from -20.6 to -36.7‰ relative to the Vienna Peedee Belemnite (VPDB). We present information on the development of two synthetic vanillin reference materials, VANA-1 and VANB-1, prepared in 0.75 g units in glass vials, to be used for the calibration of carbon isotope delta measurements of vanillin and other easily combustible organic materials. Characterization of 40 vials each of VANA-1 and VANB-1 was performed by three laboratories over several measurement sequences. The certified carbon isotope delta values are -31.30 ± 0.06‰ (VANA-1) and -25.85 ± 0.05‰ (VANB-1). These uncertainties, for the 95% confidence level, include considerations for measurement uncertainty, coherence of the reference materials used for calibration, batch homogeneity, and stability during storage and transportation. The results are traceable to the VPDB through a set of nine reference materials (IAEA-CH-6, USGS65, IAEA-600, NBS22, USGS61, IAEA-603, IAEA-610, IAEA-611, and IAEA-612). For up to date certified values, users should refer to doi.org/10.4224/crm.2022.vana-1 and doi.org/10.4224/crm.2022.vanb-1.

gc-ims-tools - A new Python package for chemometric analysis of GC-IMS data

Due to its high sensitivity and resolving power, gas chromatography ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for non-target screening of complex sample materials. Given the wide range of applications, such as food authenticity, custom data analysis workflows are needed. As a common basis, they necessarily share many functionalities such as file input/output, preprocessing methods, exploratory or supervised analysis and visualizations. This study introduces a new open source, fully customizable Python package for handling and analysis of GC-IMS data. A workflow to classify olive oils by geographical origin exemplarily demonstrates functionality and ease of use. Key preprocessing steps, exploratory - and supervised data analysis and feature selections are visualized. All code and detailed documentation are freely available as open source under the BSD 3-clause license at https://github.com/Charisma-Mannheim/gc-ims-tools.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

Non-volatile molecular composition and discrimination of single grape white of chardonnay, riesling, sauvignon blanc and silvaner using untargeted GC-MS analysis

This study developed and applied a GC-MS method aiming at molecular fingerprinting of 120 commercial single grape white wines (Chardonnay, Riesling, Sauvignon Blanc and Silvaner) for possible authentication according to grape variety. The method allowed detection of 372 peaks and tentative identification of 146 metabolites including alcohols, organic acids, esters, amino acids and sugars. The grape variety effect explained 8.3% of the total metabolite variation. Univariate tests showed two-thirds of the metabolites being different between grape varieties. Partial least squares-discriminant analysis based classification models were developed for each grape variety and a panel of classifiers (42 metabolites) was established. All the classification models for grape variety showed a high certainty (>91%) for an independent test set. Riesling contained the highest relative concentrations of sugars and organic acids, while concentrations of hydroxytyrosol and gallic acid, common antioxidants in wine, decreased in the order of Chardonnay > Riesling > Sauvignon Blanc > Silvaner.

Classification of geographical origin of kimchi by volatile compounds analysis using an electronic nose

Food authenticity is one of the largest concerns in recent days. As kimchi has been a global food, its production origin has been important issue, particularly due to the large import from China. Among the potential methods, electronic nose which can measure volatile compounds in foods is considered to be a powerful device for identifying country of production. This study is to classify 69 kinds of kimchi produced in South Korea (39) and China (30) by analyzing volatile compounds in kimchi using electronic nose-mass spectrometry. Two widely used multivariate analyses, discriminant function analysis and principal component analysis, were used. Results showed that both multivariate analyses can completely separate Korean and Chinese kimchi using 10 kinds of molecular weights among 10-160 amu. The results indicate that the volatile compounds in kimchi are a suitable target to determine the geographical origin of kimchi.

A combined metabolomics and peptidomics approach to discriminate anomalous rind inclusion levels in Parmigiano Reggiano PDO grated hard cheese from different ripening stages

Parmigiano Reggiano is a hard cheese with a Protected Designation of Origin (PDO) certification that also applies to the grated product. The percentage of rind in grated Parmigiano Reggiano is regulated by the PDO production Specification and must not exceed the limit of 18% (w/w). The present study evaluates the potential of an untargeted foodomics approach to detect anomalous inclusions of rind in grated Parmigiano Reggiano cheese. In particular, a combined metabolomics and peptidomics approach was used to detect potential markers of counterfeits (rind > 18%). In the framework of realistic food integrity purposes, non-Parmigiano Reggiano grated samples and different ripening times were also considered. Untargeted metabolomics allowed detecting 347 compounds, with a prevalence of amino acids and peptide derivatives, followed by fatty acyls and other compounds (such as lactones, ketones, and aldehydes) typically related to proteolysis and lipolysis events. Overall, the unsupervised multivariate statistics showed that the ripening time plays a hierarchically higher impact than rind inclusion in determining the main differences in the chemical profiles detected. Interestingly, supervised statistics highlighted distinctive markers for ripening time and rind inclusion, with only 16 common discriminant compounds being shared between the two conditions. The best markers of rind inclusion > 18% were 2-hydroxyadenine (VIP score = 1.937; AUC value = 0.83) and the amino acid derivatives argininic acid (VIP score = 1.462; AUC value = 0.75) and 5-hydroxyindole acetaldehyde (VIP score = 1.710; AUC value = 0.86). Interestingly, the medium-chain aldehyde 4-hydroperoxy-2-nonenal was a common marker of both ripening time and anomalous rind inclusion (>18%), likely arising from the lipid oxidation processes. Finally, among potential marker peptides of rind inclusion, the alpha-S1 casein proteolytic product (F)FVAPFPEVFGK(E) could be identified.

Determining the presence of undeclared animal species using Real-time PCR in canned and ready-to-eat meat products in South Africa

DNA based PCR is the most widely used technique for the detection of animal species in processed meat products. However, the detection of animal species in highly processed meat products, specifically, canned meat, has been reported to be challenging due to the presence of highly degraded DNA and/or the inability to extract sufficient amount of amplifiable DNA. The aim of this study was to evaluate the use of Real-time PCR to detect animal species in ready-to-eat meat products which represent highly processed complex food matrices. DNA was extracted from a total of 44 ready-to-eat meat products purchased from supermarkets in South Africa. The extracted DNA was screened for the presence of commonly reported undeclared animal species using Real-time PCR. Real-time PCR successfully detected the animal species declared on the product label, thus demonstrating its suitability for highly processed complex food matrices. Undeclared animal species was detected in 27% of the meat products tested in this study. Surprisingly, four products marketed with a specific "no-pork" claim tested positive for pork. An additional eight products tested positive for undeclared chicken, beef and/or sheep. The presence of undeclared animal species indicates a need for food authenticity monitoring. In cases where it may not be practically feasible to prevent the adventitious presence of unintended animal species despite adhering to the highest standards of production, it may be advisable for manufacturers to implement precautionary labelling. The use of precautionary labelling for animal species in processed meat products is a new concept that requires further consideration.

Mass spectrometry-based protein and peptide profiling for food frauds, traceability and authenticity assessment

The ever-growing use of mass spectrometry (MS) methodologies in food authentication and traceability originates from their unrivalled specificity, accuracy and sensitivity. Such features are crucial for setting up analytical strategies for detecting food frauds and adulterations by monitoring selected components within food matrices. Among MS approaches, protein and peptide profiling has become increasingly consolidated. This review explores the current knowledge on recent MS techniques using protein and peptide biomarkers for assessing food traceability and authenticity, with a specific focus on their use for unmasking potential frauds and adulterations. We provide a survey of the current state-of-the-art instrumentation including the most reliable and sensitive acquisition modes highlighting advantages and limitations. Finally, we summarize the recent applications of MS to protein/peptide analyses in food matrices and examine their potential in ensuring the quality of agro-food products.

PNA-functionalized magnetic microbeads as substrates for enzyme-labelled voltammetric genoassay for DNA sensing applied to identification of GMO in food

A novel enzyme-labelled voltammetric magnetogenoassay for DNA sensing based on the use of carboxyl-surface coated magnetic microbeads functionalized with PNA probes and subsequent read-out on screen-printed electrode (SPE) substrates was developed. The assay was validated for determination of non-amplified genomic DNA from genetically modified Roundup Ready soy. Outstanding performance with respect to other genoassays requiring preliminary amplification of target DNA via PCR was demonstrated. The analytical performance was also improved compared to previous methods based on the immobilization of the same PNA probes on SPE substrates, since the method was found capable of achieving LOD and LOQ of 415 fM and 995 fM, respectively. The ability of the magnetogenoassay to detect the presence of Roundup Ready soy DNA sequence was tested on genomic DNA extract from European Reference Material soy flours, demonstrating the capability of the method to match the European Union regulation for labelling of food containing a percentage of GM products greater than 0,9%.

Characterization of the microbiota of commercially traded finfish fillets

The profile of human gut microbiota is known to be affected by diet and is linked to human health. Seafood is a highly consumed food and it accounts for a large proportion of food-borne illness. The objective of this study is to characterise the microbiota of fish fillets of various species sold in the Canadian market. We test 19 fish fillet samples from nine species in five fish families, ten of which were previously determined to be mislabeled as different species. The microbiota profiles were characterized using 16S rRNA gene high-throughput sequencing. Despite the complexities of the supply chain to produce these fillets, the major microbial groups were fairly consistent across samples. Significant differences in microbial taxa were observed between species, families, and based on labelling accuracy. Several putative spoilage and putative pathogenic taxa were identified. Studying food-associated microbiota can provide comprehensive information on food safety, authenticity, and traceability.

Capillary electromigration methods for fatty acids determination in vegetable and marine oils: A review

Fatty acids determination is of paramount importance for quality control and suitable labeling of edible oils, required by regulatory agencies in several countries, and fast methods for this determination are worldly desired. This review article aimed to explore the available analytical methods for vegetable and marine oils analyses employing CE, which can be a straightforward and faster alternative than GC methods for fatty acid determination, considering some purposes. CE usually offers the possibility of a rapid analysis with a simple preparation of the sample, without requiring specific columns, which are inherent advantages of the technique. Instrumental conditions and the key points about fatty acids determination employing the technique are highlighted, and the main challenges and perspectives are also approached. Potential use of CE for edible oil analyses has been demonstrated for research and routine, which can be of interest for industries, regulatory agencies, and edible oil researchers. Therefore, we have explored the analytical approaches described in the last decades, intending to spread the interest of CE methods for fatty acid monitoring, label accuracy assessment, and food authenticity evaluation of edible oils.

A barcode-DNA analysis method for the identification of plant oil adulteration in milk and dairy products

In the present work, a barcode-DNA analysis method is described for the detection of plant oil adulteration in milk and dairy products. The method relies on the fact that plant DNA should not be present in readily detectable amounts in a dairy product unless it contains undeclared plant material. Thus, a universal plant barcode is chosen as the target to be amplified from dairy samples. Accordingly, barcode PCR-CE (PCR-capillary electrophoresis) assays are described, which do not require preliminary information on the species source of the adulterant oil type. Two PCR-CE assays, one operating on the plastid trnL (UAA) intron and the other targeting its inner P6 loop in nested format, were shown to detect corn, soybean, rapeseed and sunflower oils in clarified butter, milk and yogurt. Both barcodes are robustly amplified with extremely conserved primers. While the intron provides the species discrimination ability, the P6 loop provides superior detection sensitivity.

A novel duplex SYBR Green real-time PCR with melting curve analysis method for beef adulteration detection

An efficient and reliable duplex SYBR Green real-time quantitative PCR (qPCR) method for beef products adulteration detection was developed based on bovine specific and vertebrate universal primers. By analyzing the numbers, positions (Tm value) of melting curve peaks of the duplex PCR products, we simultaneously identified bovine and preliminary screened non-bovine in samples, and also semi-quantified the bovine percentage according to the area ratios of peaks. All of these were necessary for adulteration determination. The specific and universal primers were designed based on mitochondrial genes ND4 and 16S rRNA respectively, their amplicons Tm values were 72.6 ± 0.5 °C and 79-81 °C. There might be some other peaks at 74-78 °C and above 81 °C if non-bovine components existed. Thelimit of detectionwas 1 pgforbovineDNA, and1 - 30 pg fornon-bovineDNAbasedon differentspecies.

Stable isotope analysis of non-exchangeable hydrogen in carbohydrates derivatised with N-methyl-bis-trifluoroacetamide by gas chromatography - Chromium silver reduction/High temperature Conversion-isotope ratio mass spectrometry (GC-CrAg/HTC-IRMS)

A novel procedure for the rapid isotope analysis of the carbon-bound non-exchangeable (CBNE) hydrogen in mono and disaccharides has been developed to demonstrate the feasibility of detecting undeclared addition of exogenous sugar products in foods and beverages susceptible to economically motivated adulteration. The procedure utilizes a simple one-step reaction, with the derivatising agent N-methyl-bis-trifluoroacetamide, to substitute the exchangeable hydroxyl-hydrogens with trifluoroacetate derivatives that are sufficiently volatile to be separated and measured by a gas chromatograph coupled to an isotope ratio mass spectrometer. The conversion of the derivatised sugars into the measuring gas is achieved using a high temperature chromium-silver reactor that retains carbon, oxygen and fluorine whilst releasing hydrogen gas for stable isotope measurement. The new procedure has advantages over existing methods in terms of ease of use, analysis time and compound-specific information. Sugars from fruit juice and honey have been measured to demonstrate the feasibility of using this technique.

LC-QTOF-MS identification of rabbit-specific peptides for authenticating the species composition of meat products

Rabbit is a healthy meat, with low allergenicity and excellent nutritional properties. The global popularity of rabbit meat makes it a target for food fraud. We present a LC-QTOF-MS/MS approach for detecting and identifying rabbit-specific peptide-markers from thermally processed meat products to differentiate rabbit from other commonly-consumed animal species. We identified 49 heat-stable specific peptides. We selected the most stable markers for testing complex meat matrices by analysing pâtés-type products with a rabbit meat content ranging from 5% to 85%. Of the 49 heat-stable peptides detected in pure cooked rabbit meat, three were consistently detected in all investigated pâté samples i.e., SSVFVADPK, AFFGHYLYEVAR and PHSHPALTPEQK. Monitoring meat species other than rabbit in the examined pâtés using pork-, lamb- and chicken-specific peptides identified the presence of undeclared chicken in two samples. The results confirm that LC-QTOF-MS/MS is a suitable tool for multi-species detection in processed meat products, particularly for authentication purposes.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.

DNA barcoding and mini-barcoding in authenticating processed animal-derived food: A case study involving the Chinese market

This study used DNA barcoding and DNA mini-barcoding to test a variety of animal-derived food products sold in the Chinese market for potential mislabeling. Samples (52) including meat, poultry, and fish purchased from retail and online sources were examined. Regions of cytochrome C oxidase I (COI) gene (~650 bp) and 16S rRNA (~220 bp) were used as full- and mini-barcode markers, respectively. Approximately 94% (49 of 52) of the samples generated barcode sequences. The failure rate for full COI full-barcodes was 44%, but we obtained the 16S rRNA mini-barcode from 87% of the COI-failed cases. Overall, the survey revealed that 23% (12 of 52) of animal-derived products were mislabeled and, in most cases, contain undeclared species. Thus, regulatory measures and continuous monitoring for mislabeling of animal-derived products should be conducted.

Characterization of Brazilian coffee based on isotope ratio mass spectrometry (δ13C, δ18O, δ2H, and δ15N) and supervised chemometrics

Authentication of ground coffee has become an important issue because of fraudulent activities in the sector. In the current work, sixty-seven Brazilian coffees produced in different geographical origins using organic (ORG, n = 25) and conventional (CONV, n = 42) systems were analyzed for their stable isotope ratios (δ¹³C, δ¹⁸O, δ²H, and δ¹⁵N). Data were analyzed by inferential analysis to compare the factors whereas linear discriminant analysis (LDA), k-nearest neighbors (k-NN), and support vector machines (SVM) were used to classify the coffees based on their origin. ORG and CONV cultivated coffees could not be differentiated according to C stable isotope ratio (δ¹³C; p = 0.204), but ORG coffees presented higher values of the N stable isotope ratio (δ¹⁵N; p = 0.0006). k-NN presented the best classification results for both ORG and CONV coffees (87% and 67%, respectively). SVM correctly classified coffees produced in São Paulo (75% accuracy), while LDA correctly classified 71% of coffees produced in Minas Gerais.

The magic angle view to food: magic-angle spinning (MAS) NMR spectroscopy in food science

Nuclear Magnetic Resonance (NMR) spectroscopy has been used in food science and nutritional studies for decades and is one of the major analytical platforms in metabolomics. Many foods are solid or at least semi-solid, which denotes that the molecular motions are restricted as opposed to in pure liquids. While the majority of NMR spectroscopy is performed on liquid samples and a solid material gives rise to constraints in terms of many chemical analyses, the magic angle thrillingly enables the application of NMR spectroscopy also on semi-solid and solid materials. This paper attempts to review how magic-angle spinning (MAS) NMR is used from 'farm-to-fork' in food science.

Absolute quantification of targeted meat and allergenic protein additive peptide markers in meat products

We present an implementation of the absolute quantification (AQUA) method for monitoring of peptide abundance in complex mixtures of processed proteins. Specific peptide markers from meats (chicken, duck, goose, pork and beef) and common protein allergenic additives (soy, milk and egg white preparations) were chosen and synthesised with stable isotopes (13C and 15N) for use as internal standards. A wide range of food samples, from cooked or raw meat to sterilised pâté, was analysed by a triggered multiple reaction monitoring mode experiment and triple quadrupole mass spectrometry for the direct measure of tryptic peptides representing the amounts of specific proteins. Considerable differences among the abundances of meat and non-meat proteins were observed, and illegal addition and replacement of ingredients were discovered, i.e. undeclared addition of pork and egg white proteins, and illegal substitution of veal, goose and duck meat with cheaper pork.

Species-specific peptide-based liquid chromatography-mass spectrometry monitoring of three poultry species in processed meat products

The detection of adulteration and mislabeling of food products, including intensively processed meat, is a challenge which needs urgent solutions to protect consumers' rights. The aim of the study was to demonstrate the feasibility of species-specific peptide-based LC-MS methods for monitoring duck, goose and chicken in processed meat products. Food commodities of various compositions, subjected to various treatments, including homogenization, cooking, roasting, drying, and sterilization during production, were examined to ensure that MS-based methods are resistant to matrix composition changes. A qualitative LC-QQQ multiple reaction monitoring (MRM) method was developed which allows high-confidence monitoring of duck, goose and chicken meat (ten specific peptides), simultaneously with beef and pork (seven peptides), in the presence of turkey meat, in highly processed food. The developed LC-MS methods can be used for food authentication, monitoring of the food composition conformity with label statements and detection of adulteration of poultry-containing food products.

Assessing fish authenticity by direct analysis in real time-high resolution mass spectrometry and multivariate analysis: discrimination between wild-type and farmed salmon

The constant increase in seafood consumption worldwide has led to a parallel growth of the incidence of products obtained by aquaculture on the market, but also of the fraudulent commercialization of farmed products as wild-type ones. A careful characterization of the lipid component of seafood products based on chromatography-mass spectrometry techniques has been reported as a promising approach to reliably differentiate farmed from wild-type products. In this context, a fast method based on Direct Analysis in Real Time (DART) coupled to High Resolution Mass Spectrometry (HRMS) based on a single stage Orbitrap mass analyzer, integrated by Principal Component Analysis (PCA), was developed in the present study and applied to scout for spectral features useful to discriminate wild-type from farmed salmon of Salmo salar species. In particular, normalized intensities obtained for the 30 most intense signals (all referred to fatty acids, FA) detected in negative ion DART-HRMS spectra of the lipid extracts of salmon fillets [26 wild-type from Canada, 74 farmed from Canada (25), Norway (25) and Chile (24)] were considered as the variables for PCA. The scatterplot referred to the first two principal components showed a clear distinction between wild-type and farmed salmon, which gathered as a unique cluster, despite the remarkable differences in their geographical origin. In accordance with previous studies based on more complex and time-demanding analytical approaches, three saturated (14:0, 16:0 and 18:0) FA, along with unsaturated ones having 20 or 22 carbon atoms, were found as the main discriminating variables for wild-type salmons, whereas FA with compositions 18:1, 18:2, 18:3 and several oxidized forms arising from them were found to have a significantly higher incidence in farmed salmon. The method was further validated by Discriminant Analysis (DA) performed on the same dataset used for PCA integrated by data obtained from 6 commercial samples, putatively referred to farmed Norwegian salmon. Results showed that 100% of the latter were correctly classified as farmed type. Relative abundances of DART-HRMS signals related to specific FA appear then very promising for the differentiation of wild-type salmon from farmed ones, a very relevant issue in the context of consumers' protection from seafood frauds.

Quantification of species-specific meat proteins in cooked and smoked sausages using infusion mass spectrometry

Label-free quantification combined with high-resolution infusion-based mass spectrometry (MS) was evaluated to authenticate ‘horse sausages’ made from horse meat and pork. Four types of industrially processed sausages, including cooked horse meat, pork and beef, and their mixtures were analysed. Quantitation and evaluation of the species composition were based on a set of 11 species-specific meat proteins and 14 unique heat-stable peptide markers. Using infusion MS, the highest distinguishing value was found in four proteins, namely, horse myosin-7 (MYH7_HORSE) and horse myoglobin (MYG_HORSE), porcine myosin-4 (MYH4_PIG) and bovine myoglobin (MYG_BOVIN). The limit of detection was 5% (w/w) for pork and beef in the three-component matrix and 1% (w/w) for horse meat. The proteins’ abundance was computed using a peak intensity measurement technique for precursor ions, based on the extracted ion currents/intensities of precursor ions. The procedure enabled discrimination between horse meat, pork and beef proteins, as well as estimation of the relative changes in protein abundance in all the examined samples. Substantial differences in the abundance of specific proteins were obtained from the pure meat samples, three-component mixtures and commercial sausages. The method may be useful in the preliminary screening of protein-rich food samples, aimed at fraud detection and estimation of the overall level of adulteration.

Development of a DNA metabarcoding method for the identification of fifteen mammalian and six poultry species in food

Meat products are prone to adulteration by the replacement of meat from more expensive animal species with meat from cheaper sources. We present a DNA metabarcoding method allowing the identification and differentiation of 15 mammalian and six poultry species in foodstuffs. The method, developed on the MiSeq^® platform, targets a mitochondrial 16S rDNA region recently found to be suitable for the differentiation of 300 mammalian species. We designed a novel primer pair for poultry and applied it in combination with the primer pair for mammalian species in a duplex assay. The applicability of the method was investigated by analysing DNA extracts from muscle, DNA extract mixtures and extracts from model sausages. Our results indicated that the species of interest can be identified, differentiated and detected down to a proportion of 0.1%. Since 96 samples can be sequenced in one run, the method has high potential for application in routine analysis.

Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy

Spectroscopic techniques for food quality analysis are limited to surface inspections and are highly affected by the superficial layers (skin or packaging material) of the food samples. The ability of spatially offset Raman spectroscopy (SORS) to obtain chemical information from below the surface of a sample makes it a promising candidate for the non-destructive analysis of the quality of packaged food. In the present study, we developed a line-scan SORS technique for obtaining the Raman spectra of packaged-food samples. This technique was used to quantify butter adulteration with margarine through two different types of packaging. Further, the significant commercial potential of the developed technique was demonstrated by its being able to discriminate between ten commercial varieties of butter and margarine whilst still in their original, unopened packaging. The results revealed that, while conventional backscattering Raman spectroscopy cannot penetrate the packaging, thus preventing its application to the quality analysis of packaged food, SORS analysis yielded excellent qualitative and quantitative analyses of butter samples. The partial least-square regression analysis predictive values for the SORS data exhibit correlation coefficient values of 0.95 and 0.92, associated with the prediction error 3.2 % and 3.9 % for cover-1 & 2, respectively. The developed system utilizes a laser line (ca. 14-cm wide) that enables the simultaneous collection of a large number of spectra from a sample. Thus, by averaging the spectra collected for a given sample, the signal-to-noise ratio of the final spectrum can be enhanced, which will then have a significant effect on the multivariate data analysis methods used for qualitative and/or qualitative analyses. This recently presented line-scan SORS technique could be applied to the development of high-throughput and real-time analysis techniques for determining the quality and authenticity various packaged agricultural products.

Untargeted metabolomics of fresh and heat treatment Tiger nut (Cyperus esculentus L.) milks reveals further insight into food quality and nutrition

Tiger nut (Cyperus esculentus L.) is a crop traditionally grown in Valencia Region (Spain) and other temperate and tropical regions in the world, where its tubers are commonly consumed as tiger nut milk (horchata). Because of their nutritive potential and original taste, these products are beginning to spread internationally and, as consequence, analytical procedures to assess nutritional profiles, quality control issues are acquiring increasing relevance. The main objective of this study was to use an advance analytical method and chemometrics tools to determine if the ultra-high temperature (UHT) treatment necessary to extend the shelf life of tiger nut milk would affect the profile of nutrients when compared to fresh product. A cold solvent extraction followed by liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS) was used. Datasets obtained from UHT and fresh tiger nut milk data were analyzed through an untargeted metabolomics approach to compare chemical patterns, highlighting differences in citric acid esters of mono- diglycerides (CITREM) and monoacylglycerol (MAG) used as emulsifiers of UHT products, and a remarkably higher abundance of biotin, phosphatidic acid (PA) and L-arginine in fresh products. These results showed that untargeted metabolomics through high resolution tandem mass spectrometry allowed fine differences between food products to be found, therefore, the nutrient lost caused by UHT treatment was clearly discerned.

Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial

Qualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers. Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes. Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed.

Assessing saffron (Crocus sativus L.) adulteration with plant-derived adulterants by diffuse reflectance infrared Fourier transform spectroscopy coupled with chemometrics

Saffron, the dried red stigmas of the plant Crocus sativus L., is well-known as one of the most important and expensive spices worldwide. It is thus highly susceptible to fraudulent practices that employ, among others, plant-derived adulterants. This study presents an application of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and chemometric techniques for evaluating adulteration of saffron with six characteristic adulterants of plant origin, i.e. C. sativus stamens, calendula, safflower, turmeric, buddleja, and gardenia. The proposed method involved a three-step process for the detection of adulteration as well as for the identification and quantification of adulterants. Partial least squares discriminant analysis (PLS-DA) was applied to perform authentication of saffron based on mid-infrared fingerprints (4000-600cm^-1), resulting in 99% correct classification of pure saffron and saffron adulterated at 5-20% (w/w) levels. Adulterant identification in positive samples was performed with high sensitivity and specificity by a six-class PLS-DA model, with spectroscopic data from the region 2000-600cm^-1. Subsequently, partial least squares (PLS) regression models were built for the quantification of each adulterant. By using synergy interval PLS (siPLS) for variable selection, models with improved performance were developed, with detection limits ranging from 1.0% to 3.1% (w/w). The results obtained illustrate that this strategy based on DRIFTS has the potential to complement existing methodologies for the rapid and cost-effective assessment of typical saffron frauds.

Development of a sensitive and specific multiplex PCR method for the simultaneous detection of chicken, duck and goose DNA in meat products

Identifying the origin of animal species in manufactured meat products is of considerable economic, religious and sanitary importance. In this study, we developed a multiplex PCR method to simultaneously detect chicken, duck and goose DNA in meat products derived from beef, pork, mutton or quail. The PCR primers were designed based on the sequence of mitochondrial genes of each avian species, and the amplicon sizes were 131, 283 and 387bp for chicken, duck and goose, respectively. The method had no cross-reaction with DNA isolated from beef, mutton, pork or quail, and generated products at a target DNA content as low as 0.05ng, or a target meat content of 1% of total meat weight. Moreover, screening of 24 commercial meat samples using this method indicated that six, two and one samples were contaminated with chicken, duck, or both, respectively, suggesting its usefulness for the simultaneous identification of chicken, duck and goose DNA in commercial meat products.

Evaluation of saffron (Crocus sativus L.) adulteration with plant adulterants by (1)H NMR metabolite fingerprinting

In the present work, a preliminary study for the detection of adulterated saffron and the identification of the adulterant used by means of (1)H NMR and chemometrics is reported. Authentic Greek saffron and four typical plant-derived materials utilised as bulking agents in saffron, i.e., Crocus sativus stamens, safflower, turmeric, and gardenia were investigated. A two-step approach, relied on the application of both OPLS-DA and O2PLS-DA models to the (1)H NMR data, was adopted to perform authentication and prediction of authentic and adulterated saffron. Taking into account the deficiency of established methodologies to detect saffron adulteration with plant adulterants, the method developed resulted reliable in assessing the type of adulteration and could be viable for dealing with extensive saffron frauds at a minimum level of 20% (w/w).