Generative and Foundation-Model Artificial Intelligence in Food Safety Analysis, Authenticity Identification, and Quality Control: A Systematic Review of Validation and Decision Readiness.
- 1Department of Industrial Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.
- 2CESI LINEACT, 15C Avenue Albert Einstein, 69100 Villeurbanne, France.
Abstract
Generative and foundation-model artificial intelligence can strengthen food analysis, but computational performance does not establish decision readiness. This systematic review examines applications and validation requirements in food safety, authenticity, and quality control. A Scopus search on 13 June 2026 identified 438 records; 65 publications were retained, including 41 direct analytical reports and 24 supporting publications. Independent dual screening and coding supported thematic synthesis and descriptive evidence mapping. The direct core comprised 18 safety-related, 4 authenticity, 9 quality-related, and 10 adjacent-domain reports. Of these, 23 reported experimental or reference-method validation, 13 dataset/model evaluation, and 5 human, sensory, or expert assessment. None of the 41 direct reports documented prospective evaluation in a real industrial setting. This finding remained unchanged in narrower-scope analyses, although the balance among experimental, dataset/model, and human-assessment validation changed. The generated food-evidence transfer gap integrates established transfer problems with food-specific decision requirements. Five cumulative gates organize provenance, technical validity, food-science validity, human calibration, and deployment governance. The framework connects generative-AI evidence requirements to established analytical standards rather than replacing them. Decision readiness requires risk-proportionate evidence for a specified use, including applicable reference-method and regulatory requirements. These findings characterize the captured literature, not the complete industrial deployment landscape.
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BibTeX
@article{bahroun2026generative,
title = {{Generative and Foundation-Model Artificial Intelligence in Food Safety Analysis, Authenticity Identification, and Quality Control: A Systematic Review of Validation and Decision Readiness.}},
author = {Zied Bahroun and Mohamed Amine Abdeljaouad},
journal = {Foods (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/foods15193420},
url = {https://europepmc.org/article/MED/42883527},
note = {CC BY},
}
RIS
TY - JOUR
TI - Generative and Foundation-Model Artificial Intelligence in Food Safety Analysis, Authenticity Identification, and Quality Control: A Systematic Review of Validation and Decision Readiness.
AU - Zied Bahroun
AU - Mohamed Amine Abdeljaouad
PY - 2026
DA - 2026/09/24
JO - Foods (Basel, Switzerland)
DO - 10.3390/foods15193420
UR - https://europepmc.org/article/MED/42883527
AB - Generative and foundation-model artificial intelligence can strengthen food analysis, but computational performance does not establish decision readiness. This systematic review examines applications and validation requirements in food safety, authenticity, and quality control. A Scopus search on 13 June 2026 identified 438 records; 65 publications were retained, including 41 direct analytical reports and 24 supporting publications. Independent dual screening and coding supported thematic synthesis and descriptive evidence mapping. The direct core comprised 18 safety-related, 4 authenticity, 9 quality-related, and 10 adjacent-domain reports. Of these, 23 reported experimental or reference-method validation, 13 dataset/model evaluation, and 5 human, sensory, or expert assessment. None of the 41 direct reports documented prospective evaluation in a real industrial setting. This finding remained unchanged in narrower-scope analyses, although the balance among experimental, dataset/model, and human-assessment validation changed. The generated food-evidence transfer gap integrates established transfer problems with food-specific decision requirements. Five cumulative gates organize provenance, technical validity, food-science validity, human calibration, and deployment governance. The framework connects generative-AI evidence requirements to established analytical standards rather than replacing them. Decision readiness requires risk-proportionate evidence for a specified use, including applicable reference-method and regulatory requirements. These findings characterize the captured literature, not the complete industrial deployment landscape.
SN - 2304-8158
ER -