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

Jan Mei Soon,Ikarastika Rahayu Abdul Wahab

doi:10.3390/foods11030328

Jan Mei Soon, Ikarastika Rahayu Abdul Wahab

Open Access

https://doi.org/10.3390/foods11030328

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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%).

Highlights

The increasing scale, the complexity of food supply networks, and current disruptions due to COVID-19 and climate variability can lead to food and drink products becoming more vulnerable to fraud
The Bayesian network (BN) model provides the distribution of probabilities for food fraud type as mislabelling (20.70%), followed by artificial enhancement (17.20%), substitution (16.36%), counterfeit (13.56%), and dilution (11.47%) (Figure S1)
A BN model was developed based on the Food Adulteration Incidents Registry (FAIR) database which represents a snapshot of global food fraud incidences

Summary

Introduction

The increasing scale, the complexity of food supply networks, and current disruptions due to COVID-19 and climate variability can lead to food and drink products becoming more vulnerable to fraud. The type of adulterants used in dairy products includes nitrogen sources (e.g., ammonium salts, melamine, urea, and non-dairy proteins) [8,9] to mask the reduction of dairy protein content caused by dilution. Substances such as formaldehyde, hydrogen peroxide, hypochlorite, and salicylic acid are added to enhance product shelf-life [8,10]

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