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.

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.

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.

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.

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.

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.

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.

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

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

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.

Identification of Cross-Species Marker Peptides for the Detection of Mammalian and Poultry Meat in Vegan and Vegetarian Foods.

Authentication of vegan and vegetarian foods is important since these increasingly popular food items could be adulterated with cheap meat to increase profit margins. In this study, nine marker peptides for the detection of meat (several species) were identified applying liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). These marker peptides enable the crucial differentiation of beef versus milk and chicken meat versus egg, demonstrated by the investigation of 19 commercial vegetarian meat substitutes containing milk and egg. Extensive experimental testing proved the presence of the cross-species meat marker peptides in 19 food-relevant types of mammals and poultry as well as their absence in more than 136 plant-based ingredients for the production of vegan and vegetarian foods. An authentic vegan sausage matrix based on an actual retail product was produced and spiked with 5.0%, w/w meat to confirm the high signal intensities and the heat stability of the marker peptides.

Characterizing wine terroir using strontium isotope ratios: a review.

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

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

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.

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

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