Food fraud detection using explainable artificial intelligence Article Swipe
Okan Buyuktepe
,
Cagatay Catal
,
Gorkem Kar
,
Yamine Bouzembrak
,
H.J.P. Marvin
,
Anand Gavai
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/exsy.13387
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/exsy.13387
Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/exsy.13387
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.13387
- OA Status
- hybrid
- Cited By
- 59
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381989074
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381989074Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/exsy.13387Digital Object Identifier
- Title
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Food fraud detection using explainable artificial intelligenceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-25Full publication date if available
- Authors
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Okan Buyuktepe, Cagatay Catal, Gorkem Kar, Yamine Bouzembrak, H.J.P. Marvin, Anand GavaiList of authors in order
- Landing page
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https://doi.org/10.1111/exsy.13387Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.13387Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.13387Direct OA link when available
- Concepts
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Computer science, Harm, Artificial intelligence, Food supply, Scalability, Food safety, Deep learning, Supply chain, Population, Machine learning, Risk analysis (engineering), Business, Database, Marketing, Agricultural science, Demography, Medicine, Pathology, Sociology, Environmental science, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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59Total citation count in OpenAlex
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2025: 37, 2024: 19, 2023: 3Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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