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

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 adulterat...

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Autores principales: Soon, Jan Mei, Abdul Wahab, Ikarastika Rahayu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834205/
https://www.ncbi.nlm.nih.gov/pubmed/35159479
http://dx.doi.org/10.3390/foods11030328
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author Soon, Jan Mei
Abdul Wahab, Ikarastika Rahayu
author_facet Soon, Jan Mei
Abdul Wahab, Ikarastika Rahayu
author_sort Soon, Jan Mei
collection PubMed
description 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%).
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spelling pubmed-88342052022-02-12 A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration Soon, Jan Mei Abdul Wahab, Ikarastika Rahayu Foods Article 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%). MDPI 2022-01-25 /pmc/articles/PMC8834205/ /pubmed/35159479 http://dx.doi.org/10.3390/foods11030328 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Soon, Jan Mei
Abdul Wahab, Ikarastika Rahayu
A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration
title A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration
title_full A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration
title_fullStr A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration
title_full_unstemmed A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration
title_short A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration
title_sort bayesian approach to predict food fraud type and point of adulteration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8834205/
https://www.ncbi.nlm.nih.gov/pubmed/35159479
http://dx.doi.org/10.3390/foods11030328
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