Target and Non-Target Approaches for Food Authenticity and Traceability

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.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Who uses food barcode scanner apps and why? Exploration of users' characteristics and development of the Food Barcode Scanner App Questionnaire

BACKGROUND

Food barcode scanner apps (FBSAs) are increasingly being used to verify food quality. By scanning a product's barcode, they can provide a range of information, including nutritional quality or information on the toxicity of food components. Although they seem to be widely used, no study has yet examined their use in the general population. The objectives of this study were therefore twofold: (a) to identify who the users of FBSA are and (b) to evaluate behaviours and cognitions associated with use of these apps through the development and validation of the Food Barcode Scanner App Questionnaire (FBSAQ).

METHOD

A total of 1626 women (average age of 37.51 years; SD = 12.67) from the general population were included in this study, with 25.7% reporting themselves as using at least one FBSA. Participants completed questionnaires assessing socio-demographic and health characteristics, the use of health apps and the FBSAQ, when relevant.

RESULTS

The users of FBSAs did not differ from nonusers in regard to key socio-demographic characteristics, but they were more likely to use healthcare services and other health apps than nonusers of FBSAs. Psychometric analyses allowed validation of the FBSAQ through three factors: pathological use, dietary concerns and exclusion of unhealthy components.

CONCLUSION

Data showed that the use of FBSAs can be beneficial for many individuals, as they help with food choices. However, some user may develop more problematic behaviours and have difficulties in not using these apps.

Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea

The geographic origin of agri-food products contributes greatly to their quality and market value. Here, we developed a robust method combining metabolomics and machine learning (ML) to authenticate the geographic origin of Wuyi rock tea, a premium oolong tea. The volatiles of 333 tea samples (174 from the core region and 159 from the non-core region) were profiled using gas chromatography time-of-flight mass spectrometry and a series of ML algorithms were tested. Wuyi rock tea from the two regions featured distinct aroma profiles. Multilayer Perceptron achieved the best performance with an average accuracy of 92.7% on the training data using 176 volatile features. The model was benchmarked with two independent test sets, showing over 90% accuracy. Gradient Boosting algorithm yielded the best accuracy (89.6%) when using only 30 volatile features. The proposed methodology holds great promise for its broader applications in identifying the geographic origins of other valuable agri-food products.

The "Crosstalk" between Microbiota and Metabolomic Profile of Kefalograviera Cheese after the Innovative Feeding Strategy of Dairy Sheep by Omega-3 Fatty Acids

The purpose of this study was to examine the effects of two different feeding systems, a control or a flaxseed and lupin diet (experimental), for a sheep flock, on the microbiota and metabolome of Kefalograviera cheese samples produced by their milk. In particular, the microbiota present in Kefalograviera cheese samples was analyzed using 16S rRNA gene sequencing, while ultra-high performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) was applied to investigate the chemical profile of the cheeses, considering the different feeding systems applied. The metagenomic profile was found to be altered by the experimental feeding system and significantly correlated to specific cheese metabolites, with Streptococcaceae and Lactobacillaceae establishing positive and negative correlations with the discriminant metabolites. Overall, more than 120 features were annotated and identified with high confidence level across the samples while most of them belonged to specific chemical classes. Characteristic analytes detected in different concentrations in the experimental cheese samples including arabinose, dulcitol, hypoxanthine, itaconic acid, L-arginine, L-glutamine and succinic acid. Therefore, taken together, our results provide an extensive foodomics approach for Kefalograviera cheese samples from different feeding regimes, investigating the metabolomic and metagenomic biomarkers that could be used to foresee, improve, and control cheese ripening outcomes, demonstrating the quality of the experimental Kefalograviera cheese.

Classification of Prunus Genus by Botanical Origin and Harvest Year Based on Carbohydrates Profile

The 1H-NMR carbohydrates profiling was used to discriminate fruits from Rosaceae family in terms of botanical origin and harvest year. The classification was possible by application of multivariate data analysis, such as principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson analysis. Prior, a heat map was created based on 1H-NMR signals which offered an overview of the content of individual carbohydrates in plum, apricot, cherry and sour cherry, highlighting the similarities. Although, the PCA results were almost satisfactory, based only on carbohydrates signals, the LDA reached 94.39% and 100% classification of fruits according to their botanical origin and growing season, respectively. Additionally, a potential association with the relevant climatic data was explored by applying the Pearson analysis. These findings are intended to create an efficient NMR-based solution capable of differentiating fruit juices based on their basic sugar profile.

Multielement and chemometric analysis for the traceability of the Pachino Protected Geographical Indication (PGI) cherry tomatoes

To prevent PGI (Protected Geographical Indication) cherry tomato of Pachino (Sicily, Italy) from frauds, an alternative method, which includes chemometric treatments, was proposed. The content of 32 inorganic elements (macro-micronutrients and lanthanides) present in 16 PGI and 24 not PGI cherry tomato samples cv Naomy, and in 16 PGI and 8 not PGI soil samples, was determined by Inductively Coupled Plasma - Mass Spectrometer (ICP-MS). To identify the elements able to differentiate PGI and not PGI cherry tomato samples, Principal Components Analysis (PCA) and Canonical discriminant analysis (CDA) were performed. The first two principal components (PC1-PC2) explain a total variance of 71,41% between PGI and not PGI group, whereas CDA showed Zn, Cd, Mn and Ca as inorganic markers able to correctly classify the 100% of samples. Furthermore, with a translocation factor (K), evaluated in soil/plant chain, the comparison of absorption trends for PGI and not PGI samples was realized.

A simple and fast triplex-PCR for the identification of milk's animal origin in Halloumi cheese and yoghurt

In this research communication we describe a straightforward triplex-PCR protocol able to differentiate the origin of milk from three closely related species (goat, sheep and cow) in Halloumi, a cheese with Protected Designation of Origin (PDO), and yogurts. Halloumi must contain at least 51% sheep or goat milk, therefore, the fraudulent adulteration of this cheese with excess of cow milk must be routinely tested. The assay employs one universal forward primer and three species-specific reverse primers giving rise to 287 bp (cow), 313 bp (goat), and 336 bp (sheep) amplicons, under the same amplification conditions. This protocol, when used to test a small number of Cyprus commercial products, correctly detected mislabeling in Halloumi (2 out of 6 samples were adulterated) and yogurt brands (1 out of 4 was adulterated). The suggested protocol is a reliable tool for identifying the origin of milk in Halloumi cheeses and yogurts and can be used in any laboratory equipped with a thermocycler and an agarose gel electrophoresis apparatus.

Development of a Ladder-Shape Melting Temperature Isothermal Amplification Assay for Detection of Duck Adulteration in Beef

ABSTRACT

Ladder-shape melting temperature isothermal amplification (LMTIA) is a newly developed technology, and the objective of this study was to establish its effectiveness for detection of duck adulteration in beef. LMTIA primers were designed with the prolactin receptor gene of Anas platyrhynchos as the target. The LMTIA reaction system was optimized, and its performance was compared with that of the loop-mediated isothermal amplification (LAMP) assay in terms of specificity, sensitivity, and limit of detection (LOD). Our results showed that the LMTIA assay was able to specifically detect 10 ng of genomic DNAs (gDNAs) of A. platyrhynchos, without detecting 10 ng of gDNAs of Bos taurus, Sus scrofa, Gallus gallus, Capra hircus, Felis catus, and Canis lupus familiaris. The sensitivity of the LMTIA assay was 1 ng of gDNAs of A. platyrhynchos; it was able to detect duck adulteration in beef with a 0.1% LOD. Although the LAMP assay could not clearly distinguish A. platyrhynchos from G. gallus, it had a sensitivity of 10 ng of gDNAs of A. platyrhynchos and a LOD of 1% duck adulteration in beef. This study may help facilitate the surveillance of commercial adulteration of beef with duck meat.

HIGHLIGHTS

Animal Species Authentication in Dairy Products

Milk is one of the most important nutritious foods, widely consumed worldwide, either in its natural form or via dairy products. Currently, several economic, health and ethical issues emphasize the need for a more frequent and rigorous quality control of dairy products and the importance of detecting adulterations in these products. For this reason, several conventional and advanced techniques have been proposed, aiming at detecting and quantifying eventual adulterations, preferentially in a rapid, cost-effective, easy to implement, sensitive and specific way. They have relied mostly on electrophoretic, chromatographic and immunoenzymatic techniques. More recently, mass spectrometry, spectroscopic methods (near infrared (NIR), mid infrared (MIR), nuclear magnetic resonance (NMR) and front face fluorescence coupled to chemometrics), DNA analysis (real-time PCR, high-resolution melting analysis, next generation sequencing and droplet digital PCR) and biosensors have been advanced as innovative tools for dairy product authentication. Milk substitution from high-valued species with lower-cost bovine milk is one of the most frequent adulteration practices. Therefore, this review intends to describe the most relevant developments regarding the current and advanced analytical methodologies applied to species authentication of milk and dairy products.