A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration

Abstract

Primary and secondary food processing had been identified as areas vulnerable to fraud. Besides the food processing area, other stages within the food supply chain are also vulnerable to fraud. This study aims to develop a Bayesian network (BN) model to predict food fraud type and point of adulteration i.e., the occurrence of fraudulent activity. The BN model was developed using GeNie Modeler (BayesFusion, LLC) based on 715 notifications (1979–2018) from Food Adulteration Incidents Registry (FAIR) database. Types of food fraud were linked to six explanatory variables such as food categories, year, adulterants (chemicals, ingredients, non-food, microbiological, physical, and others), reporting country, point of adulteration, and point of detection. The BN model was validated using 80 notifications from 2019 to determine the predictive accuracy of food fraud type and point of adulteration. Mislabelling (20.7%), artificial enhancement (17.2%), and substitution (16.4%) were the most commonly reported types of fraud. Beverages (21.4%), dairy (14.3%), and meat (14.0%) received the highest fraud notifications. Adulterants such as chemicals (21.7%) (e.g., formaldehyde, methanol, bleaching agent) and cheaper, expired or rotten ingredients (13.7%) were often used to adulterate food. Manufacturing (63.9%) was identified as the main point of adulteration followed by the retailer (13.4%) and distribution (9.9%).

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

“…Additionally, 62% of the participants were post-processors and were less vulnerable to food fraud. Previous studies had identified primary and secondary processing as more vulnerable to fraudulent activities since these were the points where original food materials were altered (e.g., mincing, filleting, grinding) making them indistinguishable from other similar products (Robson et al, 2022; Soon & Abdul Wahab, 2022 ).…”

Bibliometric Analysis of Food Fraud

“…Food counterfeiting commonly uses chemicals (21.7%) such formaldehyde, methanol, bleach, and cheaper, expired, or damaged products (13.7%). Manufacturing (63.9%), merchants (13.4%), and distribution (9.9%) are the primary counterfeiting points [16]. Data consistency and security are crucial in the livestock-derived food component supply chain.…”

Assessment of cheese frauds, and relevant detection methods: A systematic review

“…1 ). Soon and Wahab (2020) estimated the impact of fraud on the food industry to be >$50 billion annually ( Soon & Wahab, 2022 ). Montgomery, Haughey, and Elliott (2020) reported that cheese had the highest adulteration rate among dairy products.…”