Food fraud vulnerability assessment: Towards a global consensus on procedures to manage and mitigate food fraud

Anticounterfeiting and Fraud Mitigation Solutions for High-value Food Products

Globalization and the increasing complexity of supply chains have allowed food fraud to expand to a great extent. Some of the most serious effects of these deceitful activities are damage to a brand's reputation and trust, economic losses, and public health risks. The usual victims of food fraud are dairy, meat, fish, and seafood products, as well as fats/oils and alcoholic drinks. The purpose of this review paper is to present an updated analysis of the currently available anticounterfeit technologies and their application to the four most fraud-affected food supply chains. An assessment that was conducted to determine when the adoption of a combination of technologies could enhance food safety and brand protection is also provided. The obtained results indicate that electronic and data-driven technologies (RFID devices and digital traceability systems) are still in their infancy in the food sectors that are subjected the most to fraudulent activities. Research is necessary to develop innovative digital and physical technologies to "outsmart" such fraudsters and to prevent their illicit actions in the food sector.

Database of Food Fraud Records: Summary of Data from 1980 to 2022

Food fraud prevention and detection remains a challenging problem, despite recent developments in regulatory and auditing requirements. In 2012, the United States Pharmacopeial Convention created a database of food ingredient fraud. The objective of this research was to report on updates made to the database structure and to provide an updated analysis of food fraud records. The restructured database was relational and included four tables: ingredients, adulterants, adulteration records, and references. Four adulteration record types were created to capture the variety of information that can be found in public food fraud reports. Information was searched and extracted from the peer-reviewed scientific literature, media publications, regulatory reports, judicial records, trade association reports, and other public sources covering 1980-present. Over an almost seven-year data entry period, a total of 15,575 records were entered, sourced primarily from the peer-reviewed literature and media reports. The percentage of records that included at least one potentially hazardous adulterant ranged from 34% to 60%, depending on the record type. The ingredients with the highest number of incident and inference records included fluid cow's milk, extra virgin olive oil, honey, beef, and chili powder. The ingredient groups with the highest number of incident and inference records included Dairy Ingredients, Seafood Products, Meat and Poultry Products, Herbs, Spices, and Seasonings, Milk and Cream, and Alcoholic Beverages. This database was created to serve as a standardized source of information about publicly documented occurrences of food fraud and other information relevant to fraud risk to support food fraud vulnerability assessments, mitigation plans, and food safety plans. These data support the contention that food fraud presents a public health risk that should continue to be addressed by food safety systems worldwide.

The Impact of Harvesting Mechanization on Oolong Tea Quality

Mechanization is the inevitable future of tea harvesting, but its impact on tea chemistry and quality remains uncertain. Our study examines untargeted metabolomic data from 185 oolong tea products (Tieguanyin) made from leaves harvested by hand or machine based on UPLC-QToF-MS analysis. The data revealed a minimum 50% loss for over half of the chemicals in the machine-harvested group, including catechins, theaflavin, gallic acid, chlorogenic acid, and kaempferol-3-gluocside. Integrating sensory evaluation, OPLS-DA identified the six most important metabolites as significant contributors to sensory decline caused by harvesting mechanization. Furthermore, our research validates the possibility of using DD-SIMCA modelling with untargeted metabolomic data for distinguishing handpicked from machine-harvested tea products. The model was able to achieve 93% accuracy. This study provides crucial insights into the chemical and sensory shifts during mechanization, along with tools to manage and monitor these changes.

Incidents and Potential Adverse Health Effects of Serious Food Fraud Cases Originated in Asia

Food fraud has long been regarded as a major issue within the food industry and is associated with serious economic and public health concerns. Economically motivated adulteration, the most common form of food fraud, has consequences for human health, ranging from mild to life-threatening conditions. Despite the potential harm and public health threats posed by food fraud, limited information on incidents causing illness has been reported. Enhancing the food control system on the Asian continent has become crucial for global health and trade considerations. Food fraud databases serve as valuable tools, assisting both the food industry and regulatory bodies in mitigating the vulnerabilities associated with fraudulent practices. However, the availability of accessible food fraud databases for Asian countries has been restricted. This review highlights detrimental food fraud cases originating in Asian countries, including sibutramine in dietary supplements, plasticizer contamination, gutter oil, and the adulteration of milk. This comprehensive analysis encompasses various facets, such as incident occurrences, adverse health effects, regulatory frameworks, and mitigation strategies.

Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art

There is a necessity to protect the quality and authenticity of herbs and spices because of the increase in the fraud and adulteration incidence during the last 30 years. There are several aspects that make herbs and spices quite vulnerable to fraud and adulteration, including their positive and desirable sensorial and health-related properties, the form in which they are sold, which is mostly powdered, and their economic relevance around the world, even in developing countries. For these reasons, sensitive, rapid, and reliable techniques are needed to verify the authenticity of these agri-food products and implement effective adulteration prevention measures. This review highlights why spices and herbs are highly valued ingredients, their economic importance, and the official quality schemes to protect their quality and authenticity. In addition to this, the type of frauds that can take place with spices and herbs have been disclosed, and the fraud incidence and an overview of scientific articles related to fraud and adulteration based on the Rapid Alert System Feed and Food (RASFF) and the Web of Science databases, respectively, during the last 30 years, is carried out here. Next, the methods used to detect adulterants in spices and herbs are reviewed, with DNA-based techniques and mainly spectroscopy and image analysis methods being the most recommended. Finally, the available adulteration prevention measurements for spices and herbs are presented, and future perspectives are also discussed.

Food fraud and mitigating strategies of UK food supply chain during COVID-19

Covid-19 had shown the vulnerability of the food supply chain and fraudsters may take advantage of the pandemic whilst the population needed a continuous supply of safe and quality food. The lack of monitoring and policing in the food supply chain may encourage fraudsters to upscale their operations. Previous studies had warned of a surge in fraudulent products due to COVID-19. This raised the question on whether food fraud had increased during the pandemic? This study aims to investigate food fraud during COVID-19 and how the food supply chain develops mitigating strategies against fraudulent activities. A mixed-method approach including survey and semi-structured interviews were conducted among UK food businesses. Two hundred and two agri-food businesses responded to the survey and 15 semi-structured interviews were conducted. The majority of the food businesses did not experience an increase of food fraud activities during COVID-19. Two thematic domains and ten sub-themes were identified from the data set. There was a heightened sense of anticipation and preparation for increased fraudulent activities during the pandemic. The main risk mitigating strategies included horizon scanning; developing and maintaining supplier relationship and assurance; understanding product characteristics, testing capabilities, conducting vulnerability assessments and training. Practical and cost-effective strategies for small and medium food businesses were recommended. This is the first empirical study on food fraud and mitigating strategies of the UK food supply chain during the pandemic. Our findings provide evidence for informing the policies and practices of the food regulatory authorities as well as best practices to protect the UK food supply chain against food fraud during exogenous shocks like COVID-19.

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Willingness to Pay for Food Labelling Schemes in Vietnam: A Choice Experiment on Water Spinach

The growing concern for food safety and quality motivates governments and private sectors to improve consumers’ confidence in food systems, such as through adopting certifications and traceability systems. The recent emergence of diverse food labelling schemes and the turbulence in food systems in emerging countries have sparked questions about consumers’ valuation of such labels. Nonetheless, little is known on how the familiarity with, trust in and knowledge of these food labelling schemes affect consumers’ willingness to pay for labelling schemes in emerging market contexts. This study aims to address these literature gaps by investigating consumers’ valuation of existing certifications, branding and traceability labelling schemes in Vietnam. A face-to-face survey was conducted, including a discrete choice experiment on water spinach in Ho Chi Minh City, Vietnam. The findings indicated that Vietnamese consumers are generally willing to pay price premia for food labelling schemes, such as VietGAP certification, EU and USDA organic certifications, private branding and traceable Quick Response (QR) coding. While familiarity and understanding had no significant impact on Vietnamese consumers’ valuation, trust was found to be a critical factor shaping willingness to pay for products bearing VietGAP label. Policy implications and marketing strategies for organic certifications and traceability schemes in Vietnam are discussed.

Considering Fraud Vulnerability Associated with Credence-Based Products Such as Organic Food

Organic foods carry a premium price. They are credence-based foods, i.e., it is difficult for consumers to evaluate the premium aspects of organic food under normal use. In global supply chains, organic food is purchased on institutional trust (certification, logos, standards) rather than on relational trust. Relying on institutional trust makes consumers vulnerable to criminals who intentionally label conventional product as organic or develop sophisticated organized crime networks to defraud businesses and consumers. The aim of this research is to explore cases of organic fraud that are emergent from academic and gray literature searches to identify ways to strengthen future capabilities to counter illicit activities in a globalized food environment. Each case is considered in terms of perpetrator motivations (differentiated as economic, cultural, and behaviorally orientated drivers), the mode of operation (simple or organized), the guardians involved/absent, and the business and supply chain level vulnerabilities the cases highlight. The study finds that institutional trust is particularly vulnerable to fraud. Supply chain guardians need to recognize this vulnerability and implement effective controls to reduce the likelihood of occurrence. However, in some cases considered in the study, the guardians themselves were complicit in the illicit behavior, further increasing consumer vulnerability. Future research needs to consider how additional controls can be implemented, without increasing supply chain friction that will impact on food trade and supply, that can ensure consumers are purchasing what they believe they are paying for.

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into consideration by researchers. Due to the high nutritional value of turmeric among the spices as well as the fraudulent motives to gain economic profit from the selling of this product, its quality assessment is very important. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with turmeric in powder form and sold in the market. In this study, an improved convolutional neural network (CNN) was used to classify turmeric powder images to detect fraud. CNN was improved through the use of gated pooling functions. We also show with a combined approach based on the integration of average pooling and max pooling that the accuracy and performance of the proposed CNN has increased. In this study, 6240 image samples were prepared in 13 categories (pure turmeric powder, chickpea powder, chickpea powder mixed with food coloring, 10, 20, 30, 40 and 50% fraud in turmeric). In the preprocessing step, unwanted parts of the image were removed. The data augmentation (DA) was used to reduce the overfitting problem on CNN. Also in this research, MLP, Fuzzy, SVM, GBT and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that prevention of the overfitting problem using gated pooling, the proposed CNN was able to grade the images of turmeric powder with 99.36% accuracy compared to other classifiers. The results of this study also showed that computer vision, especially when used with deep learning (DL), can be a valuable method in evaluating the quality and detecting fraud in turmeric powder.

DNA-based techniques for seafood species authentication

Global trade of seafood has increased in the last decade, leading to significant concerns associated with seafood fraud. Seafood fraud involves the intentional misrepresentation of fish or shellfish for the purpose of economic gain and includes acts such as species substitution, illegal transshipment, overtreatment/short weighting, and mislabeling country of origin or production method. These fraudulent acts have had economic, environmental, and public health consequences on a global level. DNA-based techniques for seafood authentication are utilized by regulatory agencies and can be employed as part of a food fraud risk mitigation plan. This chapter will focus specifically on the use of DNA-based methods for the detection of seafood species substitution. Various methods have been developed for DNA-based species identification of seafood, including polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), species-specific PCR, real-time PCR, Sanger sequencing, microarrays, and high-resolution melting (HRM). Emerging techniques for seafood authentication include droplet digital PCR, isothermal amplification, PCR-enzyme-linked immunosorbent assay (ELISA), and high-throughput or next-generation sequencing. Some of these DNA-based methods target specific species, such as real-time PCR and droplet digital PCR, while other methods allow for simultaneous differentiation of a wide range of fish species, including Sanger sequencing and high-throughput sequencing. This chapter will begin with an introduction on seafood fraud and species substitution, followed by an analysis of the main DNA-based authentication methods and emerging techniques for species identification.