Data driven food fraud vulnerability assessment using Bayesian Network: Spices supply chain Article Swipe
Yamine Bouzembrak
,
Ningjing Liu
,
Wenjuan Mu
,
Anand Gavai
,
Louise Manning
,
Francis Butler
,
H.J.P. Marvin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.foodcont.2024.110616
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.foodcont.2024.110616
Recognizing the vulnerabilities that arise in the spices' supply chain for food fraud, determining which products and food fraud types to assess is crucial for ensuring food quality and food safety. In this study, we developed a data driven food fraud vulnerability assessment approach based on a Bayesian Network (BN) and Failure Modes and Effects Analysis (FMEA) to predict the food fraud vulnerability level for products entering Europe including the food fraud types and potential adulterants for each step in the supply chain. The BN model was developed using a dataset based on spice-related fraud cases reported in the European Union (EU) Rapid Alert System for Food and Feed (RASFF) over the period 2005-2020. Three use cases were explored in the study: chilli, black pepper, and turmeric. The model showed a prediction accuracy higher than 95%. The vulnerability factors in the spices' supply chain having the highest prediction accuracy for fraud are closely associated with the product concerned, the site of intervention and the country of origin of the product. A food fraud vulnerability assessment approach developed in this study could support the food industry and authorities to be more efficient in resource allocation for monitoring and verification whilst maximising their opportunity in detecting a fraudulent product.
Related Topics
Concepts
Vulnerability (computing)
Product (mathematics)
Business
Vulnerability assessment
Supply chain
Food safety
Food security
European union
Bayesian network
Food chain
Risk analysis (engineering)
Computer science
Computer security
Marketing
Food science
Artificial intelligence
Geography
Mathematics
Geometry
Chemistry
Biology
Economic policy
Psychotherapist
Paleontology
Psychological resilience
Psychology
Archaeology
Agriculture
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.foodcont.2024.110616
- OA Status
- hybrid
- Cited By
- 11
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399169710
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399169710Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.foodcont.2024.110616Digital Object Identifier
- Title
-
Data driven food fraud vulnerability assessment using Bayesian Network: Spices supply chainWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-30Full publication date if available
- Authors
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Yamine Bouzembrak, Ningjing Liu, Wenjuan Mu, Anand Gavai, Louise Manning, Francis Butler, H.J.P. MarvinList of authors in order
- Landing page
-
https://doi.org/10.1016/j.foodcont.2024.110616Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.foodcont.2024.110616Direct OA link when available
- Concepts
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Vulnerability (computing), Product (mathematics), Business, Vulnerability assessment, Supply chain, Food safety, Food security, European union, Bayesian network, Food chain, Risk analysis (engineering), Computer science, Computer security, Marketing, Food science, Artificial intelligence, Geography, Mathematics, Geometry, Chemistry, Biology, Economic policy, Psychotherapist, Paleontology, Psychological resilience, Psychology, Archaeology, AgricultureTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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