Food authentication: Techniques, trends & emerging approaches

Assessing the impact of limited and extended oven heating exposure on the stable hydrogen and oxygen isotopic composition of wheat-processed products with varying formulations

This study aims to assess the effects of oven heating on the isotopic ratios of eight formulated wheat-processed products with different gluten-to-starch ratios. Two heating treatments were applied: limited heating in an oven with exposure to 100 °C for a specific time (cooking time-dependent) and extended heating in an oven with exposure to 100 °C, 180 °C and 260 °C for 6 min. Results showed limited heating exposure did not alter the δ2H and δ18O in the wheat-processed products. However, compared with unheated noodles, extended heating exposure caused a significant decrease (p<0.05) in the δ2H. A - 3.8 ‰ offset should be applied to δ2H of wheat-processed products heated between 100 and 260 °C (with 80 °C intervals). On the contrary, the offset of 1.3 ‰ for δ18O is only needed for temperatures between 180 and 260 °C. The results also indicate noodle formulation and heat treatment interact to influence the isotopic difference between heat-treated and untreated noodles.

Geographical differentiation between South Korean and Chinese onions using stable isotope ratios and mineral content analysis

Geographical origin authentication of onions has become significant owing to origin labelling fraud in South Korea. Various analytical techniques based on stable isotope ratios, organic and inorganic constituents, or their combinations, can distinguish agricultural products geographically. However, studies on the geographical classification of South Korean and Chinese onions using stable isotopes and minerals remain scarce. This study aimed to discriminate geographically between South Korean and Chinese onions using stable isotope ratios (δ13C, δ15N, and δ34S) and mineral contents (K, Ca, Mg, Na, P, Fe, Zn, Mn, Cu, and Sr) combined with multivariate statistical analysis. Fifty-eight onion samples cultivated in South Korea and China were collected in 2023. The two stable isotope ratios (δ15N and δ34S) and six minerals (K, Ca, Na, Fe, Zn, and Sr) significantly differed between these onions. These variables were applied in orthogonal partial least squares discriminant analysis to classify the onion samples regionally. The predictive ability and goodness-of-fit parameters (R2X and R2Y) were 0.671, 0.383, and 0.677, respectively. K, Sr, δ34S, and Na served as potential markers contributing to the classification. Therefore, stable isotopes and mineral elements may serve as effective indicators for the geographical discrimination of South Korean and Chinese onion samples using multivariate analyses.

Recent advances of machine learning in the geographical origin traceability of food and agro-products: A review

The geographical origin traceability of food and agro-products has been attracted worldwide. Especially with the rise of machine learning (ML) technology, it provides cutting-edge solutions to erstwhile intractable issues to identify the origin of food and agro-products. By utilizing advanced algorithms, ML can extract feature information of food and agro-products that is closely related to origin and, more accurately, identify and trace their origins, which is of great significance to the entire food and agriculture industry. This paper provides a comprehensive overview of the state-of-the-art applications of ML in the geographical origin traceability of food and agro-products. First, commonly used ML methods are summarized. The paper then outlines the whole process of preparation for modeling, model training as well as model evaluation for building traceability models-based ML. Finally, recent applications of ML combined with different traceability techniques in the field of food and agro-products are revisited. Although ML has made many achievements in solving the geographical origin traceability problem of food and agro-products, it still has great development potential. For example, the application of ML is yet insufficient in the geographical origin traceability using DNA or computer vision techniques. The ability of ML to predict the geographical origin of food and agro-products can be further improved, for example, by increasing model interpretability, incorporating data fusion strategies, and others.

Determination of nut varieties and their detection in festive cookies by MALDI-TOF mass spectrometry

RATIONALE

Nuts contain a large amount of essential fatty acids, amino acids, and whole range of minerals and vitamins valuable for human health, yet certain risks are associated with their consumption, of which allergic reaction is the most important. Considering the growing number of people suffering from allergies caused by allergens of protein origin, the aim of this work is to find out whether nuts can be distinguished from each other on the basis of contained proteins.

METHODS

A total of eleven raw and subsequently heat-treated nuts (almonds, Brazil nuts, cashews, coconuts, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios, and walnuts) were analyzed using MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry with the subsequent finding of characteristic m/z values for each analyzed nut. No previous method for protein extraction was used.

RESULTS

The characteristic values were used to verify the composition of seven types of festive cookies - six commercial products and one "unknown" cookie, where it was not known in advance, which nut it was made from.

CONCLUSIONS

The procedure, together with the found characteristic m/z values, could serve to rapidly identify the plant origin of nut products.

Wildlife trade investigations benefit from multivariate stable isotope analyses

The investigation of wildlife trade and crime has benefitted from advances in technology and scientific development in a variety of fields. Stable isotope analysis (SIA) represents one rapidly developing approach that has considerable potential to contribute to wildlife trade investigation, especially in complementing other methods including morphological, genetic, and elemental approaches. Here, we review recent progress in the application of SIA in wildlife trade research to highlight strengths, shortcomings, and areas for development in the future. SIA has shown success in species identification, determination of geographic provenance, and differentiating between captive-bred and wild individuals. There are also emerging applications of SIA in wildlife trade research including the use of labelling for traceability, more in-depth analyses such as compound specific isotope analysis (CSIA), the use of trace metal isotopes, and monitoring the health of individuals (e.g. dietary history and nutritional status). While these applications have shown the utility of SIA in wildlife trade investigations, there are a number of limitations and issues where standardisation of analytical procedures would improve the comparability and interpretation of results. First, there is high variation within many stable isotopes geographically and within tissues - this variation presents opportunities for tracking and monitoring but can also challenge detection of patterns when variation is high. Second, the choice of isotopes and tissues within an organism (and ideally, multiple isotopes and tissues) should be considered carefully as different isotopes and tissue types have variable strengths and weaknesses depending on the research question. Third, validation of SIA methods remains underutilised in the field but is critical for applying SIA broadly to wildlife trade investigations and, particularly, for applications in forensics and in court. Fourth, standards are essential for comparisons across studies. Fifth, while some reference databases exist for the use of SIA in wildlife trade research (e.g. ivory), there are still few comprehensive reference databases available. Development of robust reference databases should be a priority for advancing the use of SIA in wildlife trade research, and ecological study more broadly. Ultimately, further recognition of these primary challenges (and development of solutions) within wildlife SIA research will improve the potential for this technique in tackling the threat of overexploitation to global biodiversity - particularly in concert with the application of other investigative techniques such as genetics and elemental analysis.

Foodomics as a Tool for Evaluating Food Authenticity and Safety from Field to Table: A Review

The globalization of the food industry chain and the increasing complexity of the food supply chain present significant challenges for food authenticity and raw material processing. Food authenticity identification now extends beyond mere adulteration recognition to include quality evaluation, label compliance, traceability determination, and other quality-related aspects. Consequently, the development of high-throughput, accurate, and rapid analytical techniques is essential to meet these diversified needs. Foodomics, an innovative technology emerging from advancements in food science, enables both a qualitative judgment and a quantitative analysis of food authenticity and safety. This review also addresses crucial aspects of fully processing food, such as verifying the origin, processing techniques, label authenticity, and detecting adulterants, by summarizing the omics technologies of proteomics, lipidomics, flavoromics, metabolomics, genomics, and their analytical methodologies, recent developments, and limitations. Additionally, we analyze the advantages and application prospects of multi-omics strategies. This review offers a comprehensive perspective on the food chain, food safety, and food processing from field to table through omics approaches, thereby promoting the stable and sustained development of the food industry.

Overview of industrial food fraud and authentication through chromatography technique and its impact on public health

Food fraud is widespread nowadays in the food products supply chain, from raw materials processing to the final product and during storage and transport. The most frequent fraud is practiced in staple food commodities like cereals. Their origin, variety, genotype, and bioactive compounds are altered to deceive consumers. Similarly, in various food sectors like beverage, baking, and confectionary, items like melamine, flour improver, and food colors are used in the market to temple consumers. To tackle food fraud and authentication, non-destructive techniques are being used. These techniques have limitations like lack of standardization, interference from multiple absorbing species, ambiguous results, and time-consuming to perform, depending on the type, size, and location of the system proved difficult to quantify the samples of adulteration. Chromatography has been introduced as an effective technique. It serves to safeguard public health due to its detection capabilities. Chromatography proved a crucial tool against fraudulent practices to preserve consumer trust.

A synergistic solution for fighting fraudulent practices in squid using light stable isotope ratios and lanthanide tracers

To identify a novel optimized strategy for preventing fraudulent substitutions of squid species and origins, forty European squids (Loligo vulgaris) and forty flying squids (Todarodes sagittatus) from the Mediterranean Sea and Atlantic Ocean were analyzed for δ13C, δ15N, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Yb, and Lu using isotope ratio mass spectrometry and inductively coupled plasma-mass spectrometry. While δ13C and δ15N variations were mainly species-related, they alone could not reliably distinguish samples. To address this issue, decision rules were developed using Classification and Regression Tree analysis. Threshold values for δ13C (-19.91‰), δ15N (14.87‰), and Pr (0.49 μg kg-1) enabled successful discrimination among Mediterranean European squids, Atlantic European squids, Mediterranean flying squids, and Atlantic flying squids, achieving over 90% accuracy, 81% precision, 80% sensitivity, and 93% specificity. This method holds promise for enhancing traceability and safety in the seafood industry, ensuring product integrity and consumer trust.

Fusing 1H NMR and Raman experimental data for the improvement of wine recognition models

The present study proposes the development of new wine recognition models based on Artificial Intelligence (AI) applied to the mid-level data fusion of 1H NMR and Raman data. In this regard, a supervised machine learning method, namely Support Vector Machines (SVMs), was applied for classifying wine samples with respect to the cultivar, vintage, and geographical origin. Because the association between the two data sources generated an input space with a high dimensionality, a feature selection algorithm was employed to identify the most relevant discriminant markers for each wine classification criterion, before SVM modeling. The proposed data processing strategy allowed the classification of the wine sample set with accuracies up to 100% in both cross-validation and on an independent test set and highlighted the efficiency of 1H NMR and Raman data fusion as opposed to the use of a single-source data for differentiating wine concerning the cultivar and vintage.

Handheld NIR Spectroscopy Combined with a Hybrid LDA-SVM Model for Fast Classification of Retail Milk

The EU market offers different types of milk, distinguished by origin, production method, processing technology, fat content, and other characteristics, which are often detailed on product labels. In this context, ensuring the authenticity of milk is crucial for maintaining standards and preventing fraud. Various food authenticity techniques have been employed to achieve this. Among them, near-infrared (NIR) spectroscopy is valued for its non-destructive and rapid analysis capabilities. This study evaluates the effectiveness of a miniaturized NIR device combined with support vector machine (SVM) algorithms and LDA feature selection to discriminate between four commercial milk types: high-quality fresh milk, milk labeled as mountain product, extended shelf-life milk, and TSG hay milk. The results indicate that NIR spectroscopy can effectively classify milk based on the type of milk, relying on different production systems and heat treatments (pasteurization). This capability was greater in distinguishing high-quality mountain and hay milk from the other types, while resulting in less successful class assignment for extended shelf-life milk. This study demonstrated the potential of portable NIR spectroscopy for real-time and cost-effective milk authentication at the retail level.

High-precision identification of highly similar Pinelliae Rhizoma and adulterated Rhizoma pinelliae pedatisectae through deep neural networks based on vision transformers

Pinelliae Rhizoma is a key ingredient in botanical supplements and is often adulterated by Rhizoma Pinelliae Pedatisectae, which is similar in appearance but less expensive. Accurate identification of these materials is crucial for both scientific and commercial purposes. Traditional morphological identification relies heavily on expert experience and is subjective, while chemical analysis and molecular biological identification are typically time consuming and labor intensive. This study aims to employ a simpler, faster, and non-invasive image recognition technique to distinguish between these two highly similar plant materials. In the realm of image recognition, we aimed to utilize the vision transformer (ViT) algorithm, a cutting-edge image recognition technology, to differentiate these materials. All samples were verified using DNA molecular identification before image analysis. The result demonstrates that the ViT algorithm achieves a classification accuracy exceeding 94%, significantly outperforming the convolutional neural network model's 60%-70% accuracy. This highlights the efficiency of this technology in identifying plant materials with similar appearances. This study marks the pioneer work of the ViT algorithm to such a challenging task, showcasing its potential for precise botanical material identification and setting the stage for future advancements in the field.

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products

The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords "authentication" AND "food", "authentication," AND "beverage", from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.

Rogers K, Zhang Y, Yuan Y. Chemometric Discrimination of the Geographical Origin of Rheum tanguticum by Stable Isotope Analysis

Rheum tanguticum is one of the primary rhubarb species used for food and medicinal purposes, and it has recently been gaining more attention and recognition. This research represents the first attempt to use stable isotopes and elemental analysis via IRMS to identify the geographical origin of Rheum tanguticum. A grand total of 190 rhubarb samples were gathered from 38 locations spread throughout the provinces of Gansu, Sichuan, and Qinghai in China. The carbon content showed a decreasing trend in the order of Qinghai, followed by Sichuan, and then Gansu. Nitrogen content was notably higher, with Qinghai and Sichuan displaying similar levels, while Gansu had the lowest nitrogen levels. Significant differences were noted in the δ13C (-28.9 to -26.5‱), δ15N (2.6 to 5.6‱), δ2H (-120.0 to -89.3‱), and δ18O (16.0‱ to 18.8‱) isotopes among the various rhubarb cultivation areas. A significant negative correlation was found between %C and both longitude and humidity. Additionally, δ13C and δ15N isotopes were negatively correlated with longitude, and δ15N showed a negative correlation with humidity as well. δ2H and δ18O isotopes exhibited a strong positive correlation with latitude, while significant negative correlations were observed between δ2H and δ18O isotopes and temperature, precipitation, and humidity. The LDA, PLS-DA, and k-NN models all exhibited strong classification performance in both the training and validation sets, achieving accuracy rates between 82.1% and 91.7%. The combination of stable isotopes, elemental analysis, and chemometrics provides a reliable and efficient discriminant model for accurately determining the geographical origin of R. tanguticum in different regions. In the future, the approach will aid in identifying the geographical origin and efficacy of rhubarb in other studies.

Robust chemical analysis with graphene chemosensors and machine learning

Ion-sensitive field-effect transistors (ISFETs) have emerged as indispensable tools in chemosensing applications1-4. ISFETs operate by converting changes in the composition of chemical solutions into electrical signals, making them ideal for environmental monitoring5,6, healthcare diagnostics7 and industrial process control8. Recent advancements in ISFET technology, including functionalized multiplexed arrays and advanced data analytics, have improved their performance9,10. Here we illustrate the advantages of incorporating machine learning algorithms to construct predictive models using the extensive datasets generated by ISFET sensors for both classification and quantification tasks. This integration also sheds new light on the working of ISFETs beyond what can be derived solely from human expertise. Furthermore, it mitigates practical challenges associated with cycle-to-cycle, sensor-to-sensor and chip-to-chip variations, paving the way for the broader adoption of ISFETs in commercial applications. Specifically, we use data generated by non-functionalized graphene-based ISFET arrays to train artificial neural networks that possess a remarkable ability to discern instances of food fraud, food spoilage and food safety concerns. We anticipate that the fusion of compact, energy-efficient and reusable graphene-based ISFET technology with robust machine learning algorithms holds the potential to revolutionize the detection of subtle chemical and environmental changes, offering swift, data-driven insights applicable across a wide spectrum of applications.

Effect of the Bioprotective Properties of Lactic Acid Bacteria Strains on Quality and Safety of Feta Cheese Stored under Different Conditions

Lately, the inclusion of additional lactic acid bacteria (LAB) strains to cheeses is becoming more popular since they can affect cheese's nutritional, technological, and sensory properties, as well as increase the product's safety. This work studied the effect of Lactiplantibacillus pentosus L33 and Lactiplantibacillus plantarum L125 free cells and supernatants on feta cheese quality and Listeria monocytogenes fate. In addition, rapid and non-invasive techniques such as Fourier transform infrared (FTIR) and multispectral imaging (MSI) analysis were used to classify the cheese samples based on their sensory attributes. Slices of feta cheese were contaminated with 3 log CFU/g of L. monocytogenes, and then the cheese slices were sprayed with (i) free cells of the two strains of the lactic acid bacteria (LAB) in co-culture (F, ~5 log CFU/g), (ii) supernatant of the LAB co-culture (S) and control (C, UHT milk) or wrapped with Na-alginate edible films containing the pellet (cells, FF) or the supernatant (SF) of the LAB strains. Subsequently, samples were stored in air, in brine, or in vacuum at 4 and 10 °C. During storage, microbiological counts, pH, and water activity (aw) were monitored while sensory assessment was conducted. Also, in every sampling point, spectral data were acquired by means of FTIR and MSI techniques. Results showed that the initial microbial population of Feta was ca. 7.6 log CFU/g and consisted of LAB (>7 log CFU/g) and yeast molds in lower levels, while no Enterobacteriaceae were detected. During aerobic, brine, and vacuum storage for both temperatures, pathogen population was slightly postponed for S and F samples and reached lower levels compared to the C ones. The yeast mold population was slightly delayed in brine and vacuum packaging. For aerobic storage at 4 °C, an elongation in the shelf life of F samples by 4 days was observed compared to C and S samples. At 10 °C, the shelf life of both F and S samples was extended by 13 days compared to C samples. FTIR and MSI analyses provided reliable estimations of feta quality using the PLS-DA method, with total accuracy (%) ranging from 65.26 to 84.31 and 60.43 to 89.12, respectively. In conclusion, the application of bioprotective LAB strains can result in the extension of feta's shelf life and provide a mild antimicrobial action against L. monocytogenes and spoilage microbiota. Furthermore, the findings of this study validate the effectiveness of FTIR and MSI techniques, in tandem with data analytics, for the rapid assessment of the quality of feta samples.

Comprehensive and heart-cutting multidimensional liquid chromatography-mass spectrometry and its applications in food analysis

In food analysis, conventional one-dimensional liquid chromatography methods sometimes lack sufficient separation power due to the complexity and heterogeneity of the analyzed matrices. Therefore, the use of two-dimensional liquid chromatography (2D-LC) turns out to be a powerful tool to consider, especially when coupled to mass spectrometry (MS). This review presents the most remarkable 2D-LC-MS food applications reported in the last 10 years, including a critical discussion of the multiple approaches, modulation strategies as well as the importance of the optimization of the different analytical aspects that will condition the 2D-LC-MS performance. The presence of contaminants in food (food safety), the food quality, and authenticity or the relationship between the beneficial effects of food and human health are some of the fields in which most of the 2D-LC-MS applications are mainly focused. Both heart-cutting and comprehensive applications are described and discussed in this review, highlighting the potential of 2D-LC-MS for the analysis of such complex samples.

Influence of wheat content and origin on the volatilome of craft wheat beer: An investigation by combined multivariate statistical approaches

A deeper knowledge of the effect of wheat origin on the volatile organic compounds (VOCs) profile of craft wheat beer is crucial for its quality improvement and local product valorisation. The VOCs profile of 17 craft wheat beers obtained by common and durum, heritage and modern, wheat varieties grown in different fields sited at different altitudes was analysed. Data were processed by multivariate analysis using different approaches. Partial least square (PLS) analysis evidenced that wheat concentration was the highest source of VOCs variance, followed by, wheat species, wheat ancientness, and altitude of cultivation. An insight into the effect of wheat concentration was given by sparse PLS analysis (sPLS). The effect of wheat variety was explored by linear discriminant analysis (LDA), which permitted to correctly classify craft beers made with wheat of different origin (species and variety) on the basis of their VOCs profile. sPLS regression analysis permitted to find a combination of VOCs able to predict the altitude of wheat cultivation as well as to correctly classify wheat beers made with wheat cultivated at different altitudes. A further 'one versus all' approach by Soft Independent Modelling of Class Analogies (SIMCA) permitted to correctly authenticate beers made with different cereal species. Finally, shape analysis by generalized Procrustes analysis (GPA) revealed that the differences among samples were conserved and reflected from wheat kernels to wheat beers. This study suggests a promising use of volatiles fingerprinting with a combination of different statistical approaches to authenticate beer made with wheat of different origin and cultivated at different altitudes, thus stressing out the importance of territory in craft beer production, which, until now, was a neglected topic.

Evaluating Source Complexity in Blended Milk Cheese: Integrated Strontium Isotope and Multi-Elemental Approach to PDO Graviera Naxos

Graviera Naxos, a renowned cheese with Protected Designation of Origin status, is crafted from a blend of cow, goat, and sheep milk. This study focused on assessing the Sr isotopic and multi-elemental composition of both the processed cheese and its ingredients, as well as the environmental context of Naxos Island, including samples of milk, water, soil, and feed. The objective was to delineate the geochemical signature of Graviera Naxos cheese and to explore the utility of Sr isotopes as indicators of geographic origin. The 87Sr/86Sr values for Graviera Naxos samples ranged from 0.70891 to 0.70952, indicating a relatively narrow range. However, the Sr isotopic signature of milk, heavily influenced by the feed, which originates from geologically distinct areas, does not always accurately reflect the local breeding environment. Multi-elemental analysis revealed variations in milk composition based on type and season; yet, no notable differences were found between raw and pasteurized milk. A mixing model evaluating the contributions of milk, sea salt, and rennet to the cheese's Sr isotopic signature suggested a significant average contribution of 73.1% from sea salt. This study highlights the complexities of linking dairy products with their geographical origins and emphasizes the need for sophisticated geochemical authentication methods.

Monitoring the Bioprotective Potential of Lactiplantibacillus pentosus Culture on Pathogen Survival and the Shelf-Life of Fresh Ready-to-Eat Salads Stored under Modified Atmosphere Packaging

Globally, fresh vegetables or minimally processed salads have been implicated in several foodborne disease outbreaks. This work studied the effect of Lactiplantibacillus pentosus FMCC-B281 cells (F) and its supernatant (S) on spoilage and on the fate of Listeria monocytogenes and Escherichia coli O157:H7 on fresh-cut ready-to-eat (RTE) salads during storage. Also, Fourier transform infrared (FTIR) and multispectral imaging (MSI) analysis were used as rapid and non-destructive techniques to estimate the microbiological status of the samples. Fresh romaine lettuce, rocket cabbage, and white cabbage were used in the present study and were inoculated with L. pentosus and the two pathogens. The strains were grown at 37 °C for 24 h in MRS and BHI broths, respectively, and then were centrifuged to collect the supernatant and the pellet (cells). Cells (F, ~5 log CFU/g), the supernatant (S), and a control (C, broth) were used to spray the leaves of each fresh vegetable that had been previously contaminated (sprayed) with the pathogen (3 log CFU/g). Subsequently, the salads were packed under modified atmosphere packaging (10%CO2/10%O2/80%N2) and stored at 4 and 10 °C until spoilage. During storage, microbiological counts and pH were monitored in parallel with FTIR and MSI analyses. The results showed that during storage, the population of the pathogens increased for lettuce and rocket independent of the treatment. For cabbage, pathogen populations remained stable throughout storage. Regarding the spoilage microbiota, the Pseudomonas population was lower in the F samples, while no differences in the populations of Enterobacteriaceae and yeasts/molds were observed for the C, F, and S samples stored at 4 °C. According to sensory evaluation, the shelf-life was shorter for the control samples in contrast to the S and F samples, where their shelf-life was elongated by 1-2 days. Initial pH values were ca. 6.0 for the three leafy vegetables. An increase in the pH of ca. 0.5 values was recorded until the end of storage at both temperatures for all cases of leafy vegetables. FTIR and MSI analyses did not satisfactorily lead to the estimation of the microbiological quality of salads. In conclusion, the applied bioprotective strain (L. pentosus) can elongate the shelf-life of the RTE salads without an effect on pathogen growth.

Analytical insights for ensuring authenticity of Greek agriculture products: Unveiling chemical marker applications

Food authentication, including the differentiation of geographical or botanical origin, the method of production i.e. organic vs. conventional farming as well as the detection of food fraud/adulteration, has been a rapidly growing field over the past two decades due to increasing public awareness regarding food quality and safety, nutrition, and health. Concerned parties include consumers, producers, and legislators. Thus, the development of rapid, accurate, sensitive, and reproducible analytical methods to guarantee the authenticity of foods is of primary interest to scientists and technologists. The aim of the present article is to summarize research work carried out on the authentication of Greek agricultural products using spectroscopic (NIR, FTIR, UV-Vis, Raman and fluorescence spectroscopy, NMR, IRMS, ICP-OES, ICP-MS) and chromatographic (GC, GC/MS, HPLC, HPLC/MS, etc.) methods of analysis in combination with chemometrics highlighting the chemical markers that enable product authentication. The review identified a large number of chemical markers including volatiles, phenolic substances, natural pigments, elements, isotopes, etc. which can be used for (i) the differentiation of botanical/geographical origin; conventional from organic farming; production procedure and vintage year, etc. and (ii) detection of adulteration of high quality plant and animal origin foods with lower value substitutes. Finally, the constant development of reliable analytical techniques in combination with law enforcement authorities will ensure authentic foods in terms of quality and safety for consumers.

The "Dry-Lab" Side of Food Authentication: Benchmark of Bioinformatic Pipelines for the Analysis of Metabarcoding Data

Next Generation Sequencing Technologies (NGS), particularly metabarcoding, are valuable tools for authenticating foodstuffs and detecting eventual fraudulent practices such as species substitution. This technique, mostly used for the analysis of prokaryotes in several environments (including food), is in fact increasingly applied to identify eukaryotes (e.g., fish, mammals, avian, etc.) in multispecies food products. Besides the "wet-lab" procedures (e.g., DNA extraction, PCR, amplicon purification, etc.), the metabarcoding workflow includes a final "dry-lab" phase in which sequencing data are analyzed using a bioinformatic pipeline (BP). BPs play a crucial role in the accuracy, reliability, and interpretability of the metabarcoding results. Choosing the most suitable BP for the analysis of metabarcoding data could be challenging because it might require greater informatics skills than those needed in standard molecular analysis. To date, studies comparing BPs for metabarcoding data analysis in foodstuff authentication are scarce. In this study, we compared the data obtained from two previous studies in which fish burgers and insect-based products were authenticated using a customizable, ASV-based, and command-line interface BP (BP1) by analyzing the same data with a customizable but OTU-based and graphical user interface BP (BP2). The final sample compositions were compared statistically. No significant difference in sample compositions was highlighted by applying BP1 and BP2. However, BP1 was considered as more user-friendly than BP2 with respect to data analysis streamlining, cost of analysis, and computational time consumption. This study can provide useful information for researchers approaching the bioinformatic analysis of metabarcoding data for the first time. In the field of food authentication, an effective and efficient use of BPs could be especially useful in the context of official controls performed by the Competent Authorities and companies' self-control in order to detect species substitution and counterfeit frauds.

Traceability and authentication in agri-food production: A multivariate approach to the characterization ofthe Italian food excellence elephant garlic (Allium ampeloprasum L.), a vasoactive nutraceutical

A research platform for food authentication was set up by combining stable isotope ratio analysis, metabolomics by gas and liquid mass-spectrometry and NMR investigations, chemometric analyses for food excellences. This multi-analytical approach was tested on samples of elephant garlic (Allium ampeloprasum L.), a species belonging to the same genus of common garlic (Allium ampeloprasum L.), mainly produced in southern Tuscany-(Allium ampeloprasum). The isotopic composition allowed the product to be geographically characterized. Flavonoids, like (+)-catechin, cinnamic acids, quercetin glycosides were identified. The samples showed also a significant amount of dipeptides, sulphur-containing metabolites and glutathione, the latter of which could be considered a molecular marker of the analyzed elephant garlic. For nutraceutical profiling to reach quality labels, extracts were investigated in specific biological assays, displaying interesting vasorelaxant properties in rat aorta by mediating nitric oxide release from the endothelium and exhibited positive inotropic and negative chronotropic effects in rat perfused heart.

Convergent technologies to tackle challenges of modern food authentication

The authentication process involves all the supply chain stakeholders, and it is also adopted to verify food quality and safety. Food authentication tools are an essential part of traceability systems as they provide information on the credibility of origin, species/variety identity, geographical provenance, production entity. Moreover, these systems are useful to evaluate the effect of transformation processes, conservation strategies and the reliability of packaging and distribution flows on food quality and safety. In this manuscript, we identified the innovative characteristics of food authentication systems to respond to market challenges, such as the simplification, the high sensitivity, and the non-destructive ability during authentication procedures. We also discussed the potential of the current identification systems based on molecular markers (chemical, biochemical, genetic) and the effectiveness of new technologies with reference to the miniaturized systems offered by nanotechnologies, and computer vision systems linked to artificial intelligence processes. This overview emphasizes the importance of convergent technologies in food authentication, to support molecular markers with the technological innovation offered by emerging technologies derived from biotechnologies and informatics. The potential of these strategies was evaluated on real examples of high-value food products. Technological innovation can therefore strengthen the system of molecular markers to meet the current market needs; however, food production processes are in profound evolution. The food 3D-printing and the introduction of new raw materials open new challenges for food authentication and this will require both an update of the current regulatory framework, as well as the development and adoption of new analytical systems.

A one-class classification approach for authentication of specialty coffees by inductively coupled plasma mass spectroscopy (ICP-MS)

Due to the lucrative nature of specialty coffees, there have been instances of adulteration where low-cost materials are mixed in to increase the overall volume, resulting in illegal profit. A widely used and recommended approach to detect possible adulteration is the application of one-class classifiers (OCC), which only require information about the target class to build the models. Thus, this work aimed to identify adulterations in specialty coffees with low-quality coffee using multielement analysis determined by ICP-MS and to evaluate the performance of one-class classifiers (dd-SIMCA, OCRF, and OCPLS). Therefore, authentic specialty coffee samples were adulterated with low-quality coffee in 25 % to 75 % (w/w) proportions. Samples were subjected to acid decomposition for analysis by ICP-MS. OCPLS method presented the best performance to detect adulterations with low-quality coffee in specialty coffees, showing higher specificity (SPE = 100 %) and reliability rate (RLR = 94.3 %).

Evaluating Commercial Loop-Mediated Isothermal Amplification Master Mixes for Enhanced Detection of Foodborne Pathogens

Loop-mediated isothermal amplification, LAMP, is nowadays the most popular isothermal nucleic acid amplification technique, and as such, several commercial, ready-to-use master mixes have flourished. Unfortunately, independent studies to determine their performance are limited. The current study performed an independent evaluation of the existing ready-to-use commercial LAMP master mixes WarmStart® LAMP Kit, LavaLAMP™ DNA Master Mix, Saphir Bst Turbo GreenMaster, OptiGene Fast Master Mix ISO-004, and SynLAMP Mix. To reduce bias, three different genes, namely ttr (Salmonella spp.), rfbE (E. coli O157), and hly (Listeria monocytogenes), were targeted. The comparison was based on amplification speed, performance with decreasing DNA concentrations, and the effect of five typical LAMP reaction additives (betaine, DMSO, pullulan, TMAC, and GuHCl). Significant differences were observed among the different master mixes. OptiGene provided the fastest amplification and showed less detrimental effects associated with the supplements evaluated. Out of the chemicals tested, pullulan provided the best results in terms of amplification speed. It is noteworthy that the different additives impacted the master mixes differently. Overall, the current study provides insights into the performance of commercial LAMP master mixes, which can be of value for the scientific community to better select appropriate reagents when developing new methods.

Food adulteration: Causes, risks, and detection techniques-review

Food adulteration is the intentional addition of foreign or inferior substances to original food products for a variety of reasons. It takes place in a variety of forms, like mixing, substitution, hiding poor quality in packaging material, putting decomposed food for sale, misbranding or giving false labels, and adding toxicants. Several analytical methods (such as chromatography, spectroscopy, electronic sensors) are used to detect the quality of foodstuffs. This review provides concise but detailed information to understand the scope and scale of food adulteration as a way to further detect, combat, and prevent future adulterations. The objective of this review was to provide a comprehensive overview of the causes, risks, and detection techniques associated with food adulteration. It also aimed to highlight the potential health risks posed by consuming adulterated food products and the importance of detecting and preventing such practices. During the review, books, regulatory guidelines, articles, and reports on food adulteration were analyzed critically. Furthermore, the review assessed key findings to present a well-rounded analysis of the challenges and opportunities associated with combating food adulteration. This review included different causes and health impacts of food adulteration. The analytical techniques for food adulteration detection have also been documented in brief. In addition, the review emphasized the urgency of addressing food adulteration through a combination of regulatory measures, technological advancements, and consumer awareness. In conclusion, food adulteration causes many diseases such as cancer, liver disease, cardiovascular disease, kidney disease, and nervous system-related diseases. So, ensuring food safety is the backbone of health and customer satisfaction. Strengthening regulations, taking legal enforcement action, enhancing testing, and quality control can prevent and mitigate the adulteration of food products. Moreover, proper law enforcement and regular inspection of food quality can bring about drastic changes.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model. Multiblock SIMCA is shown to be useful for variable selection, and Tucker 3 SIMCA to select the optimal model complexity when applying multiway data decomposition and to assess the role of individual samples in the classification model. Both approaches, together with the two-way DD-SIMCA version applied to the unfolded data, are compared regarding the analysis of an experimental data set including genuine and adulterated blueberry extract samples. The latter were employed to produce matrix spectral-time data matrices per sample within a flow injection system, taking advantage of the spectral changes in the sample constituents as a function of the pH of the carrier phase. The need to employ the Tucker 3 model instead of a trilinear decomposition is supported by a discussion on the lack of the trilinearity property of the studied data.

Elemental Fingerprinting of Pecorino Romano and Pecorino Sardo PDO: Characterization, Authentication and Nutritional Value

Sardinia, located in Italy, is a significant producer of Protected Designation of Origin (PDO) sheep cheeses. In response to the growing demand for high-quality, safe, and traceable food products, the elemental fingerprints of Pecorino Romano PDO and Pecorino Sardo PDO were determined on 200 samples of cheese using validated, inductively coupled plasma methods. The aim of this study was to collect data for food authentication studies, evaluate nutritional and safety aspects, and verify the influence of cheesemaking technology and seasonality on elemental fingerprints. According to European regulations, one 100 g serving of both cheeses provides over 30% of the recommended dietary allowance for calcium, sodium, zinc, selenium, and phosphorus, and over 15% of the recommended dietary intake for copper and magnesium. Toxic elements, such as Cd, As, Hg, and Pb, were frequently not quantified or measured at concentrations of toxicological interest. Linear discriminant analysis was used to discriminate between the two types of pecorino cheese with an accuracy of over 95%. The cheese-making process affects the elemental fingerprint, which can be used for authentication purposes. Seasonal variations in several elements have been observed and discussed.

Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning

This study assessed the combined utility of ATR-FTIR spectroscopy and machine learning (ML) techniques for identifying and classifying pure njangsa seed oil (NSO), palm kernel oil (PKO), coconut oil (CCO), njangsa seed oil-palm kernel oil (NSOPKO) and njangsa seed oil-coconut oil (NSOCCO) margarine. Additionally, it quantified the degree of adulteration in each oil and margarine using ML regression models and sunflower oil and canola-flaxseed oil margarine as adulterants. Fingerprints of the oils and the margarines derived in the spectra region 4000-600 cm-1 were combined with ML models. The first two principal components explained 99.4% and 98% of the variance of pure oils and margarines and 90.1, 88.3, 88, 97.3 and 98.3% of adulterated PKO, NSO, CCO, NSOCCO and NSOPKO, respectively while enabling visualization. Pure margarines were classified accurately (100%) in all models. KNN was most effective in classifying pure oil at 97% followed by LR (93%), SVM (83%), LightGBM (53%) and DT (50%). The R2 obtained from all the models for adulterated PKO, NSO, CCO, NSOPKO and NSOCCO ranged from 59-99%, 55-99%, 45-94%, 69-98% and 59-94%, respectively. SVM and DT underperformed, while KNN was the best model.

Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin? A systematic review

Food authentication using molecular techniques is of great importance to fight food fraud. Metabarcoding, based on the next-generation sequencing (NGS) technologies, allowing large-scale taxonomic identification of complex samples via massive parallel sequencing of fragments (called DNA barcodes) simultaneously, has become increasingly popular in many scientific fields. A systematic review to answer the question "Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin?" is presented. The inclusion criteria were focused on the selection of scientific papers (SPs) only applying metabarcoding to foodstuff of animal origin collected on the market. The 23 included SPs were first analyzed with respect to the metabarcoding phases: library preparation (target genes, primer pairs, and fragment length), sequencing (NGS platforms), and final data analysis (bioinformatic pipelines). Given the importance of primer selection, the taxonomic coverage of the used primers was also evaluated. In addition, the SPs were scored based on the use of quality control measures (procedural blanks, positive controls, replicates, curated databases, and thresholds to filter the data). A lack of standardized protocols, especially with respect to the target barcode/s and the universal primer/s, and the infrequent application of the quality control measures, leads to answer that metabarcoding is not ripe enough for authenticating foodstuff of animal origin. However, the observed trend of the SP quality improvement over the years is encouraging. Concluding, a proper protocol standardization would allow a wider use of metabarcoding by both official and private laboratories, enabling this method to become the primary for the authentication of foodstuffs of animal origin.

Rare Earth Element Variability in Italian Extra Virgin Olive Oils from Abruzzo Region

Extra virgin olive oil is a food product from the Mediterranean area that is particularly and continuously experiencing to increasing instances of fraudulent geographical labeling. Therefore, origin protection must be improved, mainly based on its intrinsic chemical composition. This study aimed to perform a preliminary chemical characterization of Abruzzo extra virgin olive oils (EVOOs) using rare earth elements (REEs). REEs were evaluated in EVOO samples of different varieties produced in different geographical origins within the Abruzzo region (Italy) in three harvest years using ICP-MS chemometric techniques. Principal component, discriminant, and hierarchical cluster analyses were conducted to verify the influence of the variety, origin, and vintage of the REE composition. The results of a three-year study showed a uniform REE pattern and a strong correlation in most EVOOs, in particular for Y, La, Ce, and Nd. However, europium and erbium were also found in some oil samples. Compared with cultivar and origin, only the harvest year slightly influenced the REE composition, highlighting the interactions of the olive system with the climate and soil chemistry that could affect the multielement composition of EVOOs.

Application of Microsatellites to Trace the Dairy Products Back to the Farm of Origin

The increasing number of food frauds, mainly targeting high quality products, is a rising concern among producers and authorities appointed to food controls. Therefore, the development or implementation of methods to reveal frauds is desired. The genetic traceability of traditional or high-quality dairy products (i.e., products of protected designation of origin, PDO) represents a challenging issue due to the technical problems that arise. The aim of the study was to set up a genetic tool for the origin traceability of dairy products. We investigated the use of Short Tandem Repeats (STRs) to assign milk and cheese to the corresponding producer. Two farms were included in the study, and the blood of the cows, bulk milk, and derived cheese were sampled monthly for one year. Twenty STRs were selected and Polymerase Chain Reactions for each locus were carried out. The results showed that bulk milk and derived cheese express an STR profile composed of a subset of STRs of the lactating animals. A bioinformatics tool was used for the exclusion analysis. The study allowed the identification of a panel of 20 markers useful for the traceability of milk and cheeses, and its effectiveness in the traceability of dairy products obtained from small producers was demonstrated.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Biomarker discovery and authentication of cold-slaughtered chicken through classical analytical procedures and mass spectrometry based proteomic approaches

1. This study evaluated the suitability of routine analytical procedures and used mass spectrometry-based proteomic approaches to distinguish meat from dead chicken/ cold-slaughtered birds (CS), electrically stunned and slaughtered birds, as per standard protocols (ES), and birds slaughtered according to halal guidelines (HS).2. Meat from CS birds had lower (P < 0.05) pH, water-holding capacity and higher (P < 0.05) lipid oxidation, haem iron content, residual blood and total viable counts relative to ES and HS meat indicating poor quality.3. The results demonstrated the presence of unique protein bands on SDS-PAGE only in CS meat that can be used for routine screening.4. Protein analysis using MALDI-TOF mass spectrometry identified haemoglobin subunit alpha-A and alpha-D; Adenylate kinase isoenzyme 1 as reliable and stable marker proteins for authentication of dead chicken meat under raw and cooked conditions and halal slaughtered chicken, respectively.5. The methods used may be employed by the food safety and regulatory agencies for regular screening of meat quality and to authenticate CS or HS chicken.

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.

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

Application of multiple stable isotopes to aid identification of the origin of regional and organic animal products in Hesse, Germany

There is an increasing global demand for regional and organic produce. However, the growth of these markets depends on consumers' trust. Thus, novel methods must be developed to aid the verification of the origin of produce. We built on our previous study to identify the geographical origin and production method of animal-derived food products. Thirty-samples of eggs, 99 of milk, 34 of beef, and 62 of pork were collected from different regions in central Germany and analysed for their stable isotopic composition. The analysis followed a single-variate authentification approach using five isotope signatures, δ18O, δ2H, δ13C, δ15N, and δ34S. The best-performing indicators for verification of the geographical origin were δ15N and δ34S for beef; δ18O, δ2H, and δ13C for milk, and δ2H and δ13C for pork. These tracers indicated statistically significant differences among regions with the exception of pork; the results recorded for eggs were inconclusive. It was possible to distinguish between production methods by means of δ15N and δ34S (beef); all five tracers (eggs), and δ13C, δ15N, and δ34S (milk). This study demonstrated how the analysis of stable isotopes can be employed to determine the geographic region of origin and production method of animal-derived products in Germany.

Isotope Fingerprinting as a Backup for Modern Safety and Traceability Systems in the Animal-Derived Food Chain

In recent years, due to the globalization of food trade and certified agro-food products, the authenticity and traceability of food have received increasing attention. As a result, opportunities for fraudulent practices arise, highlighting the need to protect consumers from economic and health damages. In this regard, specific analytical techniques have been optimized and implemented to support the integrity of the food chain, such as those targeting different isotopes and their ratios. This review article explores the scientific progress of the last decade in the study of the isotopic identity card of food of animal origin, provides the reader with an overview of its application, and focuses on whether the combination of isotopes with other markers increases confidence and robustness in food authenticity testing. To this purpose, a total of 135 studies analyzing fish and seafood, meat, eggs, milk, and dairy products, and aiming to examine the relation between isotopic ratios and the geographical provenance, feeding regime, production method, and seasonality were reviewed. Current trends and major research achievements in the field were discussed and commented on in detail, pointing out advantages and drawbacks typically associated with this analytical approach and arguing future improvements and changes that need to be made to recognize it as a standard and validated method for fraud mitigation and safety control in the sector of food of animal origin.

Evaluation of IMS drift tube temperature on the peak shape of high boiling fragrance compounds towards allergen detection in complex cosmetic products and essential oils

Gas chromatography-ion mobility spectrometry (GC-IMS) has recently gained increasing attention for the analysis of volatile compounds due to its high sensitivity, selectivity, and robust design. Peak shape distortion, including peak tailing or broadening, are well known challenges in chromatographic analysis that result in peak asymmetry and decreased resolution. However, in IMS analysis peak tailing, which is independent on the column separation technique, have also been observed. As high boiling substances, such as monoterpenes, are mainly affected by enlarged peak tailing in GC-IMS, we propose that condensation or adsorption effects within the "cold" IMS cell, which is commonly operated at 45 °C-90 °C, are the root cause. To avoid condensation and to decrease peak tailing, we used a prototypic high temperature ion mobility spectrometry (HTIMS) in this work, which allows an increase of the IMS drift tube temperature up to 180 °C. This HTIMS was coupled to a GC column separation and used to analyse the peak shape of homologues series of ketones, alcohols, aldehydes, as well as high boiling fragrance compounds, such as monoterpenes and phenylpropanoids. While we were able to show that an increased IMS drift tube temperatures correlates well with improved peak shapes, the GC parameters of the HS-GC-HTIMS method, however, were found to have little effect on the peak shapes in IMS spectra. In particular monoterpenes, which display intense peak tailing at lower IMS drift tube temperatures, show significant improvement of the peak shape at higher IMS drift tube temperatures. This leads to the assumption that high boiling substances indeed undergo condensation effects within the IMS cell at low drift tube temperatures. For many separation tasks, such as the separation of the phenylpropanoids eugenol and isoeugenol, comparably low IMS temperatures of 120 °C are already sufficient to achieve a resolution above 1.5. However, the optimal drift tube temperature is dependent on the substance class. While the aspect ratio increases steadily for most monoterpenes, phenylpropanoids and aldehyde monomer peaks investigated, an optimal aspect ratio was found for ketones between 140 °C and 160 °C and alcohols between 120 °C and 140 °C. Lastly, the change of the reduced mobility K₀ with the increase of drift tube temperature was analysed. Compounds with similar chemical structure, such as the alcoholic monoterpenes citronellol and geraniol or the phenylpropanoids eugenol and isoeugenol show similar shifts of the K₀ value. Substances which differ in their chemical structure, such as the aldehyde monoterpenes citral and cinnamal have substantially different shifts of the K₀ value. With a future large substance database, the temperature dependant slope of the K₀ value of a substance could be used to identify the substance groups of unknown molecules. Furthermore, substances with the same drift time but different chemical composition could be separable through a change in drift tube temperature.

Geographical Classification of Saffron (Crocus Sativus L.) Using Total and Synchronous Fluorescence Combined with Chemometric Approaches

There is an increasing interest in food science for high-quality natural products with a distinct geographical origin, such as saffron. In this work, the excitation-emission matrix (EEM) and synchronous fluorescence were used for the first time to geographically discriminate between Moroccan saffron from Taroudant, Ouarzazate, and Azilal. Moreover, to differentiate between Afghan, Iranian, and Moroccan saffron, a unique fingerprint was assigned to each sample by visualizing the EEM physiognomy. Moreover, principal component analysis (LDA) and linear discriminant analysis (LDA) were successfully applied to classify the synchronous spectra of samples. High fluorescence intensities were registered for Ouarzazate and Taroudant saffron. Yet, the Azilal saffron was distinguished by its low intensities. Furthermore, Moroccan, Afghan, and Iranian saffron were correctly assigned to their origins using PCA and LDA for different offsets (Δλ) (20-250 nm) such that the difference in the fluorescence composition of the three countries' saffron was registered in the following excitation/emission ranges: 250-325 nm/300-480 nm and 360-425 nm/500-550 nm. These regions are characterized by the high polyphenolic content of Moroccan saffron and the important composition of Afghan saffron, including vitamins and terpenoids. However, weak intensities of these compounds were found in Iranian saffron. Furthermore, a substantial explained variance (97-100% for PC1 and PC2) and an important classification rate (70-90%) were achieved. Thus, the non-destructive applied methodology of discrimination was rapid, straightforward, reliable, and accurate.

Application of Stable Isotope Analysis for Detecting the Geographical Origin of the Greek Currants "Vostizza": A Preliminary Study

There is a plethora of food products with geographical indications registered in the European Union without any study about their discrimination from other similar products. This is also the case for Greek currants. This paper aims to analyze if stable isotope analysis of C, N, and S could discriminate the Greek currants "Vositzza", registered as a product of Protected Designation of Origin, from two other currants registered as products of Protected Geographical Indication coming from neighboring areas. The first results show that the stable isotope ratio of sulfur is not detectable due to the very low sulfur content in the samples, and the analysis should be based on the stable isotope ratios of carbon and nitrogen to discriminate these products. The mean value of δ¹⁵N (1.38‱) of PDO "Vostizza" currants is lower than that of currants grown outside the PDO zone (2.01‱), while the mean value of δ¹³C of PDO "Vostizza" currants is higher (-23.93‱) in comparison to that of currants grown outside the PDO zone (-24.83‱). Nevertheless, the results indicate that with only two isotopic ratios, discrimination could not be achieved, and further analysis is required.

Comparison of Lycium barbarum fruits polysaccharide from different regions of China by acidic hydrolysate fingerprinting-based HILIC-ELSD-ESI-TOF-MS combined with chemometrics analysis

INTRODUCTION

Lycium barbarum is an edible fruit widely used in herbal medicines and as a functional food. Polysaccharide is one of the most important active ingredients. Only L. barbarum grown in the Ningxia region of China are officially recognised as suitable for use in traditional Chinese medicine, but the systematic comparison of L. barbarum polysaccharide between Ningxia and the other growing regions of China has been rarely reported.

OBJECTIVE

To compare the difference of L. barbarum polysaccharide from different grown regions of China.

METHODS

A chemical fingerprint of L. barbarum polysaccharide hydrolysates was established based on controlled acidolysis combined with hydrophilic interaction liquid chromatography-evaporative light scattering detection-electrospray ionisation-time-of-flight-mass spectrometry (HILIC-ELSD-ESI-TOF-MS). Then, it was employed for the comparison of L. barbarum samples from different geographical origins of China combined with chemometrics analysis.

RESULTS

Six monosaccharides [rhamnose (Rha), xylose (Xyl), arabinose (Ara), mannose (Man), glucose (Glu), galactose (Gal)] were qualitatively and quantitatively determined and four glycoconjugates were preliminarily identified from the hydrolysates. Content determination for the polysaccharide and monosaccharide indicated obvious geographical features. The HILIC-ELSD fingerprint combined with partial least squares-discriminant analysis (PLS-DA) was able to differentiate L. barbarum samples from Ningxia, Xinjiang, Gansu and Qinghai regions with 89.19% classification accuracy. Orthogonal projection to latent structure discriminant analysis (OPLS-DA) was able to differentiate between samples from Ningxia and those from the other three growing regions, polysaccharide and Ara were the potential chemical markers.

CONCLUSIONS

These findings form the basis of a reliable method to trace the region of origin of L. barbarum sample and thereby, improve the quality control of L. barbarum therapeutic polysaccharides.

Quality analysis of genomic DNA and authentication of fisheries products based on distinct methods of DNA extraction

Molecular genetic techniques are an effective monitoring tool, but high-quality DNA samples are usually required. In this study, we compared three different protocols of DNA extraction: NaCl (saline); phenol-chloroform and commercial kit (Promega)-from three biological tissues of five individuals of Lutjanus purpureus under two methods of storage. The evaluated items included DNA concentration and purity, processing time and cost, as well as the obtaining of functional sequences. The highest average values of DNA concentration were obtained using the saline procedure and the commercial kit. Pure DNA was only obtained using the saline protocol, evaluated by the ratio of 260/280. The saline and phenol-chloroform protocols were the least expensive methods. The commercial kit costs are counterbalanced by the short time required. The procedure based on phenol-chloroform presented the worst results regarding DNA yield and the time required to perform all steps. The saline and commercial kit protocols showed similar results concerning the amount and quality of extracted DNA. Therefore, the final choice should be based on the available financial resources and the available time for carrying out each procedure of DNA extraction.

TaqMan Probes for Plant Species Identification and Quantification in Food and Feed Traceability

In the last few years, the traceability and labeling of processed food and feeds have gained increasing importance due to the impact that mislabeling and product fraud may have on human/animal health or on the quality of final products, such as milk, cheese, and meat, as a consequence of animal dietary. The presence of contaminants or possible frauds due to the use of alternative plant materials in food and feeds can greatly impact the economy; therefore, they are becoming important targets for product certification by competent institutional services. This is especially relevant when complex matrixes are considered, in which the visual identification of the different components is quite difficult or even impossible. Despite the existence of mandatory traceability requirements for the analysis of feed/food composition addressed by European Community regulations, the labels do not always provide a sufficient guarantee about the ingredients and additive composition of those products. In this sense, the development of new methodologies that aim to assess the traceability of feed and food complex matrixes is crucial. In this chapter, a general protocol is presented for the establishment of quantitative real-time PCR-based techniques based on TaqMan assays applied to feed/food traceability, with a special focus on applications in the areas of food and feed security (e.g., for the detection of plant species involved in allergenic reactions), fraud detection (e.g., genetically modified organisms), and certification (e.g., protected denomination of origin).

NMR-Based Metabolite Profiling and the Application of STOCSY toward the Quality and Authentication Assessment of European EVOOs

Extra virgin olive oil (EVOO) possesses a high-value rank in the food industry, thus making it a common target for adulteration. Hence, several methods have been essentially made available over the years. However, the issue of authentication remains unresolved with national and food safety organizations globally struggling to regulate and control its market. Over the course of this study, the aim was to determine the origin of EVOOs suggesting a high-throughput, state-of-the-art method that could be easily adopted. A rapid, NMR-based untargeted metabolite profiling method was applied and complemented by multivariate analysis (MVA) and statistical total correlation spectroscopy (STOCSY). STOCSY is a valuable statistical tool contributing to the biomarker identification process and was employed for the first time in EVOO analysis. Market samples from three Mediterranean countries of Spain, Italy, and Greece, blended samples from these countries, as well as monocultivar samples from Greece were analyzed. The NMR spectra were collected, with the help of chemometrics acting as "fingerprints" leading to the discovery of certain chemical classes and single biomarkers that were related to the classification of the samples into groups based on their origin.

Using HPLC with In-Column Derivatization to Authenticate Coffee Samples

Coffee is one of the world's most popular beverages, with the global coffee capsule market worth over USD 4 billion and growing. The incidence of coffee fraud is estimated to be up to one in five coffees being contaminated with cheaper blends of coffee. Given the worsening extent of climate change, coffee crop yields are harder to maintain, while demand is increasing. The 2021 Brazil frost delaying or destroying many coffee crops is an example. Hence, the incidence of coffee fraud is expected to increase, and as the market becomes more complex, there needs to be faster, easier, and more robust means of real-time coffee authentication. In this study, we propose the use of novel approaches to postcolumn derivatization (termed herein as in-column derivatization) to visualize the antioxidant profiles of coffee samples, to be later used as indicators for authentication purposes. We propose three simple mathematical similarity metrics for the real-time identification of unknown coffee samples from a sample library. Using the CUPRAC assay, and these metrics, we demonstrate the capabilities of the technique to identify unknown coffee samples from within our library of thirty.