A review of the emerging technologies and systems to mitigate food fraud in supply chains.

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification.

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

The high-resolution molecular portrait of coffee: A gateway to insights into its roasting chemistry and comprehensive authenticity profiles.

The direct-infusion of 130 coffee samples into a Fourier-transform ion cyclotron mass spectrometer (FT-ICR-MS) provided an ultra-high resolution perspective on the molecular complexity of coffee: The exceptional resolving power and mass accuracy (± 0.2 ppm) facilitated the annotation of unambiguous molecular formulas to 11,500 mass signals. Utilizing this molecular diversity, we extracted hundreds of compound signals linked to the roasting process through guided Orthogonal Partial Least Squares (OPLS) analysis. Visualizations such as van Krevelen diagrams and Kendrick mass defect analysis provided deeper insights into the intrinsic compositional nature of these compounds and the complex chemistry underlying coffee roasting. Predictive OPLS-DA models established universal molecular profiles for rapid authentication of Coffea arabica versus Coffea canephora (Robusta) coffees. Compositional analysis revealed Robusta specific signals, indicative of tryptophan-conjugates of hydroxycinnamic acids. Complementary LC-ToF-MS2 confirmed their compound class, building blocks and structures. Their water-soluble nature allows for application across raw and roasted beans, as well as in ready-made coffee products.

The Diversity of N-Glycans of Chlorella Food Supplements Challenges Current Species Classification.

N-glycans have recently emerged as highly varied elements of Chlorella strains and products. Four years and eighty samples later, the increasing N-glycan diversity calls for a re-examination in the light of concepts of species designations and product authenticity. N-glycans of commercial products were analyzed by matrix-assisted time-of-flight mass spectrometry (MALDI-TOF MS) supported by chromatography on porous graphitic carbon with mass spectrometric detection. Although 36% of 172 products were labeled C. vulgaris, only 9% presented what could be taken as a C. vulgaris type N-glycan pattern. Respectively, 5 and 20% of the products matched with C. sorokiniana strains SAG 211-8k and SAG 211-34, which, however, carry entirely different structures. Furthermore, 41% presented with one of four frequently occurring glyco-types while 26% of the samples showed unique or rare N-glycan patterns. These glycan signatures thus profoundly challenge the stated species designations. By no means do we want to question the presumed health benefits of the products or the sincerity of manufacturers. We rather aim to raise awareness of the fascinating but also concerning diversity of microalgal N-glycans and suggest it as a means for defining product identity and taxonomic classifications.

Revealing the sterilization impact on paprika fingerprint: Key markers identified using untargeted metabolomics by liquid chromatography-Orbitrap high-resolution mass spectrometry.

Paprika (Capsicum annuum L.) is a popular spice known for its unique properties. Spices are susceptible to microbiological risks arising from harvest factors such as high moisture or environmental contamination. To ensure microbiological safety, post-harvest processing based on heat sterilization, free of chemicals and radiation, is becoming essential in the European market. This study introduces a novel metabolomics approach using ultra-high performance liquid chromatography (UHPLC) coupled with quadrupole-Orbitrap-high-resolution mass spectrometry (HRMS) to assess the sterilization impact on paprika's metabolomic composition. Sterilized and untreated samples were distinguished by OPLS-DA, achieving perfect predictability with high-quality parameters (R2Y = 0.988, Q2 = 0.904). The methodology identified 19 key markers, including fatty acids, amino acids, etc. Sterilization reduced fatty acids such as linoleic acid but increased other metabolites such as DL-malic acid and flazin. This research introduces new metabolomics strategies to ensure paprika quality and other valuable spices, focusing on unexplored sterilization processes.

DNA Metabarcoding Approach as a Potential Tool for Supporting Official Food Control Programs: A Case Study.

Food authentication significantly impacts consumer health and the credibility of Food Business Operators (FBOs). As European regulations mandate the verification of food authenticity and supply chain integrity, competent authorities require access to innovative analytical methods to identify and prevent food fraud. This study utilizes the DNA metabarcoding approach on meat preparations, sampled during an official control activity. It assesses animal and plant composition by amplifying DNA fragments of the 12S rRNA and trnL (UAA) genes, respectively. The results not only confirmed the declared species but also revealed undeclared and unexpected taxa in products labelled as containing a single animal species and various unspecified plant species. Notable findings such as the presence of Murinae, Sus scrofa, Ovis aries, and Pisum sativum could raise public health concerns, compromise consumer choices made for ethical or religious reasons, and reflect the hygienic conditions of the processing plant. This study demonstrates that the DNA metabarcoding approach looks to be a promising support tool for official control authorities to ensure food authenticity and safety, and to develop risk profiles along the supply chain.

Chemometrics methods, sensory evaluation and intelligent sensory technologies combined with GAN-based integrated deep-learning framework to discriminate salted goose breeds.

The authenticity of salted goose products is concerning for consumers. This study describes an integrated deep-learning framework based on a generative adversarial network and combines it with data from headspace solid phase microextraction/gas chromatography-mass spectrometry, headspace gas chromatography-ion mobility spectrometry, E-nose, E-tongue, quantitative descriptive analysis, and free amino acid and 5'-nucleotide analyses to achieve reliable discrimination of four salted goose breeds. Volatile and non-volatile compounds and sensory characteristics and intelligent sensory characteristics were analyzed. A preliminary composite dataset was generated in InfoGAN and provided to several base classifiers for training. The prediction results were fused via dynamic weighting to produce an integrated model prediction. An ablation study demonstrated that ensemble learning was indispensable to improving the generalization capability of the model. The framework has an accuracy of 95%, a root mean square error (RMSE) of 0.080, a precision of 0.9450, a recall of 0.9470, and an F1-score of 0.9460.

Food profiling goes green: Sustainable analysis strategies for food authentication.

Omics technologies, such as genomics, proteomics, metabolomics, isotopolomics, and metallomics, are important tools for analytical verification of food authenticity. However, in many cases, their application requires the use of high-resolution technological platforms as well as careful consideration of sample collection, storage, preparation and, in particular, extraction. In this overview, the individual steps and disciplines are explained against the background of the term "Green Chemistry," and the various instrumental procedures for the respective omics disciplines are discussed. Furthermore, new approaches and developments are presented on how such analyses can be made sustainable in the future.

Rapid detection of murine-derived ingredients in meat products using real-time enzymatic recombinase amplification.

BACKGROUND: Accurate identification of meat species is critical to prevent economic fraud and safeguard public health. The use of inappropriate meat sources, such as murine, poses significant health risks because of potential contamination with pathogens and allergens, leading to foodborne illnesses. The present study aimed to develop a novel real-time enzymatic recombinase amplification (ERA) method for the rapid and specific detection of murine DNA in meat products. RESULTS: A novel ERA primer and probe set was designed, targeting a murine-specific single-copy nuclear gene identified through bioinformatics analysis. The assay demonstrates high specificity, showing no amplification in commonly consumed meats, other animals or major crops. Additionally, it exhibits remarkable sensitivity, detecting as few as five copies of murine genomic DNA. For practical application, the ERA method could effectively identify mouse DNA in laboratory-prepared samples at concentrations as low as 0.5% and also quantify samples with mouse DNA content as low as 5%. It also accurately detects the presence of murine-derived ingredients in commercially available meat products. The detection process is straightforward, utilizing a simple isothermal device for incubation, blue light excitation and a smartphone camera for result interpretation. This rapid analysis can be completed within 20 min. CONCLUSION: The newly developed real-time ERA method provides a valuable tool for standardizing meat trade practices, promoting food safety and enhancing consumer confidence in the authenticity of meat products. © 2024 Society of Chemical Industry.

Research advances in detection of food adulteration and application of MALDI-TOF MS: A review.

Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.

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.

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 â†’ Q3 â†’ 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.

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning.

Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

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.

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.

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.

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.

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.

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.

Recent and Advanced DNA-Based Technologies for the Authentication of Probiotic, Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) Fermented Foods and Beverages.

The authenticity of probiotic products and fermented foods and beverages that have the status of protected designation of origin (PDO) or geographical indication (PGI) can be assessed via numerous methods. DNA-based technologies have emerged in recent decades as valuable tools to achieve food authentication, and advanced DNA-based methods and platforms are being developed. The present review focuses on the recent and advanced DNA-based techniques for the authentication of probiotic, PDO and PGI fermented foods and beverages. Moreover, the most promising DNA-based detection tools are presented. Strain- and species-specific DNA-based markers of microorganisms used as starter cultures or (probiotic) adjuncts for the production of probiotic and fermented food and beverages have been exploited for valuable authentication in several detection methods. Among the available technologies, propidium monoazide (PMA) real-time polymerase chain reaction (PCR)-based technologies allow for the on-time quantitative detection of viable microbes. DNA-based lab-on-a-chips are promising devices that can be used for the on-site and on-time quantitative detection of microorganisms. PCR-DGGE and metagenomics, even combined with the use of PMA, are valuable tools allowing for the fingerprinting of the microbial communities, which characterize PDO and PGI fermented foods and beverages, and they are necessary for authentication besides permitting the detection of extra or mislabeled species in probiotic products. These methods, in relation to the authentication of probiotic foods and beverages, need to be used in combination with PMA, culturomics or flow cytometry to allow for the enumeration of viable microorganisms.