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.

Phishing vulnerability compounded by older age, apolipoprotein E e4 genotype, and lower cognition.

With technological advancements, financial exploitation tactics have expanded into the online realm. Older adults may be particularly susceptible to online scams due to age- and Alzheimer's disease-related changes in cognition. In this study, 182 adults ranging from 18 to 90 years underwent cognitive assessment, genotyping for apolipoprotein E e4 (APOE4), and completed the lab-based Short Phishing Email Suspicion Test (S-PEST) as well as the real-life PHishing Internet Task (PHIT). Across both paradigms, older age predicted heightened susceptibility to phishing, with this enhanced susceptibility pronounced among older APOE4 allele carriers with lower working memory. Additionally, performance in both phishing tasks was correlated in that reduced ability to discriminate between phishing and safe emails in S-PEST predicted greater phishing susceptibility in PHIT. The current study identifies older age, APOE4, and lower cognition as risk factors for phishing vulnerability and introduces S-PEST as an easy-to-administer, ecologically valid tool for assessing phishing susceptibility.

Historical evolution of basic characteristics, underlying causes, and management tools of food fraud in China: 1949-2022.

The emergence and development of food fraud are closely related to a country's economic system and social development. It has distinct characteristics in different historical stages, and an inherent historical logic links different historical stages. Following the thread of "what", "why", and "what to do", this study uses a broad perspective and comparative historical approach to examine the evolution of the basic characteristics, underlying causes, and management tools of food fraud in China at different historical stages over 70 years from 1949 to 2022. This study argues that the historical evolution of food fraud in China has characteristics unique to China as well as features similar to those in other countries. It provides a window for academics to understand the historical evolution of food fraud in China. It also provides valuable insights for other countries, especially developing countries, for objectively understanding the evolution of food fraud during their economic development process, and how to address it.

Microbiome signatures associated with flavor development differentiate Protected Designation of origin water Buffalo Mozzarella cheese from different production areas.

Water Buffalo Mozzarella (BM) is a typical cheese from Southern Italy with unique flavor profile and texture. It is produced following a traditional back-slopping procedure and received the Protected Designation of Origin (PDO) label. To better understand the link between the production area, the microbiome composition and the flavor profile of the products, we performed a multiomic characterization of PDO BM collected from 57 different dairies located in the two main PDO production area, i.e. Caserta (n = 35) and Salerno (n = 22). Thus, we assessed the microbiome by high-throughput shotgun metagenomic sequencing and the Volatile Organic Compounds (VOCs) by gas chromatography/mass spectrometry (GC/MS). Streptococcus thermophilus, Lactobacillus helveticus, and Lactobacillus delbrueckii subsp. delbrueckii were identified as the core microbiome present in all samples. However, the microbiome taxonomic profiles resulted in a clustering of the samples based on their geographical origin, also showing that BM from Caserta had a greater microbial diversity. Consistently, Caserta and Salerno samples also showed different VOC profiles. These results suggest that the microbiome and its specific metabolic activity are part of the terroir that shape BM specific features, linking this traditional product with the area of production, thus opening new clues for improving traceability and fraud protection of traditional products.

Addressing the Marketing Practices of Medicare Advantage Plans.

The Medicare Advantage (MA) Program, home to nearly half of the eligible Medicare population, has recently come under increased scrutiny. Recent investigations conducted by the United States Senate Committee on Finance and Centers for Medicare & Medicaid Services (CMS) have uncovered marketing practices of MA insurance agents that "were not complying with current regulation and unduly pressuring beneficiaries, as well as failing to provide accurate or enough information to assist a beneficiary in making an informed enrollment decision." These findings come at a time in which MA programs are under investigation for denials of prior authorization requests that fall within Medicare guidelines for covered health services. In this Commentary we consider the backdrop for the growing scrutiny of the MA program and the implications thereof to its future trajectory.

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.

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.

Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches.

Background: Some COVID-19 testing centres have reported manipulated test numbers for antigen tests/rapid tests. This study compares statistical approaches with traditional fraud detection methods. The extent of agreement between traditional and statistical methods was analysed, as well as the extent to which statistical approaches can identify additional cases of potential fraud. Methods: Outlier detection marking a high number of tests, modeling of the positivity rate (Poisson Regression), deviation from distributional assumptions regarding the first digit (Benford's Law) and the last digit of the number of reported tests. The basis of the analyses were billing data (April 2021 to August 2022) from 907 testing centres in a German city. Results: The positive agreement between the conventional and statistical approaches ('sensitivity') was between 8.6% and 24.7%, the negative agreement ('specificity') was between 91.3% and 94.6%. The proportion of potentially fraudulent testing centres additionally identified by statistical approaches was between 7.0% and 8.7%. The combination of at least two statistical methods resulted in an optimal detection rate of test centres with previously undetected initial suspicion. Conclusions: The statistical approaches were more effective and systematic in identifying potentially fraudulent testing centres than the conventional methods. Testing centres should be urged to map paradata (e.g. timestamps of testing) in future pandemics.

Unlocking food safety: a comprehensive review of South Africa's food control and safety landscape from an environmental health perspective.

Food fraud (often called fake food in South Africa) the deliberate misrepresentation or adulteration of food products for financial gain, is a growing problem in South Africa (SA) with severe public health and financial consequences for consumers and businesses. The recent public outcry against food fraud practices especially in communities that have lost loved ones due to the consumption of allegedly adulterated foodstuffs, highlights the grave danger that food fraud poses to consumers and the potential for significant reputational damage to food manufacturers. Despite the risks, food fraud often goes undetected, as perpetrators are becoming increasingly sophisticated. The precise magnitude of food fraud remains obscure, as incidents that do not cause consumer illnesses are frequently unreported and, as a result, are not investigated. Food fraud costs the global economy billion annually. This cost is borne by consumers, businesses, and the government. Food fraud can occur at any stage of the food supply chain, from production to processing to retailing or distribution. This is due in part to the limitations of current analytical methods, which are not always able to detect food fraud. This review of food fraud in SA looks at several factors that may be contributing to epidemic of food fraud, including inadequate penalties, inadequate government commitment, a complex labelling regulation, emerging threats such as e-commerce, and shortage of inspectors and laboratories. The review recommends establishing a single food control/safety authority, developing more food safety laboratories, and adopting innovative technologies to detect and prevent food fraud. SA faces a serious food fraud crises unless decisive action is taken.

An imbalanced learning method based on graph tran-smote for fraud detection.

Fraud seriously threatens individual interests and social stability, so fraud detection has attracted much attention in recent years. In scenarios such as social media, fraudsters typically hide among numerous benign users, constituting only a small minority and often forming "small gangs". Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to insufficient representation of fraud characteristics. To address these issues, the tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph neural network extractor, these features are integrated with attribute features using transformer technology, and the node's information representation is enriched, thereby addressing the issue of inadequate feature representation. Additionally, this approach involves setting a feature embedding space to generate new nodes representing minority classes, and an edge generator is used to provide relevant connection information for these new nodes, alleviating the class imbalance problem. The results from experiments on two real datasets demonstrate that the proposed GTS, performs better than the current state-of-the-art baseline.

Twenty-three years of PCR-based seafood authentication assay development: What have we learned?

Seafood is a prime target for fraudulent activities due to the complexity of its supply chain, high demand, and difficult discrimination among species once morphological characteristics are removed. Instances of seafood fraud are expected to increase due to growing demand. This manuscript reviews the application of DNA-based methods for commercial fish authentication and identification from 2000 to 2023. It explores (1) the most common types of commercial fish used in assay development, (2) the type of method used, (3) the gene region most often targeted, (4) provides a case study of currently published assays or primer-probe pairs used for DNA amplification, for specificity, and (5) makes recommendations for ensuring standardized assay-based reporting for future studies. A total of 313 original assays for the detection and authentication of commercial fish species from 191 primary articles published over the last 23 years were examined. The most explored DNA-based method was real-time polymerase chain reaction (qPCR), followed by DNA sequencing. The most targeted gene regions were cytb (cytochrome b) and COI (cytochrome c oxidase 1). Tuna was the most targeted commercial fish species. A case study of published tuna assays (n = 19) targeting the cytb region found that most assays were not species-specific through in silico testing. This was conducted by examining the primer mismatch for each assay using multiple sequence alignment. Therefore, there is need for more standardized DNA-based assay reporting in the literature to ensure specificity, reproducibility, and reliability of results. Factors, such as cost, sensitivity, quality of the DNA, and species, should be considered when designing assays.

Machine Learning Models and Applications for Early Detection.

From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

Portable and miniature sensors in supply chain for food authentication: a review.

Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.

What the fish? Tracing the geographical origin of fish using NIR spectroscopy.

Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling.

Using a Centroid-based approach for a reliable identification of morels (Morchella spp.): A case study for food authentication.

Food fraud is a problematic yet common phenomenon in the food industry. It impacts numerous sectors, including the market of edible mushrooms. Morel mushrooms are prized worldwide for their culinary and medicinal use. They represent a taxonomically complex group in which food fraud has already been reported. Among the methods to evaluate food fraud, some rely on comparisons of genetic sequences obtained from a sample to existing databases. However, the quality and usefulness of the results are limited by the type of comparison tool and the quality of the database used. The Centroid-based approach is applied by SmartGene in a proprietary artificial intelligence-based method for the generation of automatically curated reference databases that can be further expert curated. In this study, using sequences of the ribosomal internal transcribed spacer (ITS) of the genus Morchella (true morels), we compared this approach to the traditional pairwise alignment tool using two other databases: UNITE and Mycobank (MLST). The Centroid-based approach using an expert-curated database was more performant for the identification of 53 representative ITS sequences corresponding to validated species (83% accuracy, compared to 36% and 47% accuracy for UNITE and MLST, respectively). The Centroid method also revealed an inaccurate taxonomic annotation for sequences of commercial cultivars submitted to public databases. Combined with the web-based commercial software IDNS® available at Smartgene, the Centroid-based approach constitutes a valuable tool to ensure the quality of morel products on the market for actors of the food industry. PRACTICAL APPLICATION: The Centroid-based approach can be used by agri-food actors who need to identify true morels down to the species level without any prior taxonomical knowledge. These include routine laboratories of the food industry, food distributors, and public surveillance agencies. This is a reliable method that requires minimal skills and resources, therefore being particularly adapted for nonspecialists.

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning.

Privacy Enhancing Technologies (PETs) have the potential to enable collaborative analytics without compromising privacy. This is extremely important for collaborative analytics can allow us to really extract value from the large amounts of data that are collected in domains such as healthcare, finance, and national security, among others. In order to foster innovation and move PETs from the research labs to actual deployment, the U.S. and U.K. governments partnered together in 2021 to propose the PETs prize challenge asking for privacy-enhancing solutions for two of the biggest problems facing us today: financial crime prevention and pandemic response. This article presents the Rutgers ScarletPets privacy-preserving federated learning approach to identify anomalous financial transactions in a payment network system (PNS). This approach utilizes a two-step anomaly detection methodology to solve the problem. In the first step, features are mined based on account-level data and labels, and then a privacy-preserving encoding scheme is used to augment these features to the data held by the PNS. In the second step, the PNS learns a highly accurate classifier from the augmented data. Our proposed approach has two major advantages: 1) there is no noteworthy drop in accuracy between the federated and the centralized setting, and 2) our approach is flexible since the PNS can keep improving its model and features to build a better classifier without imposing any additional computational or privacy burden on the banks. Notably, our solution won the first prize in the US for its privacy, utility, efficiency, and flexibility.

The influence of personal and economic oppression on a country's corruption levels worldwide.

This empirical research study endeavors to analyze the indirect association between oppression and corruption in 147 nations around the globe to answer one question: "Why does oppression drive corruption in many nations?" The author used secondary data from two different resources. The first source is the Corruption Perception Index (CPI), created by Transparency International (TI) in 2020, and the Human Freedom Index (HFI), co-published by the Cato Institute. In addition, the second source is the Political Stability Index in 2020 to test the three research hypotheses using the R-square, and Anova shows that the model is personal and economic oppression explains 53.5 percent of the variance. In addition, the Weighted Least Squares Regression Analyses imply that there is a positive and meaningful connection between personal oppression (ß = 3.028, p < 0.000) and corruption and economic oppression and corruption (5.203, p < 0.000). This study's findings confirmed the theoretical and conceptual relationship between oppression and bribery and identified personal and economic oppression factors as the leading causes of corruption in many countries. The study findings also contribute to the literature and the industry as well. Theoretically, the study results help researchers to understand why oppression causes corruption at the country level. Practically, the study results help policymakers, educators, and international business planners to consider roots when making successful strategic decisions in the era of the globalized world. The author also discussed the research limitations and practical and theoretical implications.

How to fight fake papers: a review on important information sources and steps towards solution of the problem.

Scientific fake papers, containing manipulated or completely fabricated data, are a problem that has reached dramatic dimensions. Companies known as paper mills (or more bluntly as "criminal science publishing gangs") produce and sell such fake papers on a large scale. The main drivers of the fake paper flood are the pressure in academic systems and (monetary) incentives to publish in respected scientific journals and sometimes the personal desire for increased "prestige." Published fake papers cause substantial scientific, economic, and social damage. There are numerous information sources that deal with this topic from different points of view. This review aims to provide an overview of these information sources until June 2024. Much more original research with larger datasets is needed, for example on the extent and impact of the fake paper problem and especially on how to detect them, as many findings are based more on small datasets, anecdotal evidence, and assumptions. A long-term solution would be to overcome the mantra of publication metrics for evaluating scientists in academia.

Myoglobin as a molecular biomarker for meat authentication and traceability.

The global incidence of economically motivated meat adulteration represents a crucial issue for the food industry. Undeclared addition of cheaper or low-quality species to meat products of high commercial value has become a common practice that needs to be countered with specific measures. In this framework, myoglobin (Mb) is a sarcoplasmic haemoprotein, primarily responsible for meat colour and has been successfully used in meat fraud authentication. Mb is highly soluble in water, easily monitored at 409 nm and species-specific. Knowing that various analytical DNA-based and protein-based methods, as well as spectroscopic techniques have been developed over the years for the detection of meat fraud, the aim of the present review is to take stock of the situation regarding the possible use of Mb as a molecular biomarker for the easy and rapid detection of undeclared species in meat products, avoiding the need of sophisticated or expensive equipment and specialised operators.

Health Claims for Sports Drinks-Analytical Assessmentaccording to European Food Safety Authority's Scientific Opinion.

In Europe, sports food supplements (SSFs) are subject to specific laws and regulations. Up to 70% of athletes are highly influenced by the information on the label or the advertisement of the SSF, which often does not correspond to the scientific evidence, such as health claims. The aim is to analyze such claims relating to sports drinks (SDs) in commercial messages. To this end, an observational and cross-sectional study was conducted based on the analysis of the content and degree of adequacy of the health claims indicated on the labelling or technical data sheet of the SDs with those established by the European legislation in force according to the European Food Safety Authority (EFSA). The SSFs were searched for via Amazon and Google Shopping. A total of 114 health claims were evaluated. No claim fully conformed with the recommendations. A total of 14 claims (n = 13 products) almost conformed to the recommendations; they were "Maintain endurance level in exercises requiring prolonged endurance", "Improve water absorption during physical exercise", and "Improved physical performance during high intensity, high duration physical exercise in trained adults", representing 12.3% of the total (n = 114). The vast majority of the claims identified indicated an unproven cause-effect and should be modified or eliminated, which amounts to food fraud towards the consumer.