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Development of an Electrochemical DNA Biosensor to Detect a Foodborne Pathogen
Published on: June 3, 2018
Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review
Maria Schirone1, Giovanni D'Ambrosio1, Antonello Paparella1
1Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via Balzarini 1, 64100 Teramo, Italy.
Foods (Basel, Switzerland)
|July 28, 2026
Summary
Artificial intelligence (AI) and machine learning (ML) show promise for foodborne pathogen detection across technologies and supply chains. However, rigorous external validation and regulatory harmonization are crucial for reliable real-world implementation.
Area of Science:
- Food Science
- Microbiology
- Data Science
Background:
- Foodborne pathogen detection is critical for public health and food safety.
- Existing detection methods face limitations in speed, accuracy, and scope.
- Advances in artificial intelligence (AI) and machine learning (ML) offer potential solutions.
Purpose of the Study:
- To systematically review recent advancements in AI/ML for foodborne pathogen detection.
- To cover diverse applications, analytical performance, and regulatory aspects.
- To identify challenges and future directions for AI/ML in food safety.
Main Methods:
- Systematic literature review following PRISMA 2020 guidelines.
- Searched Scopus, PubMed, and Web of Science (2010-2026).
- Included 152 studies focusing on AI/ML for pathogen detection in food or supply chains, excluding chemical-only or theoretical studies.
Main Results:
- AI-assisted microscopy (CNNs) achieved >99% accuracy for bacterial identification.
- Spectroscopy (SERS) combined with CNNs showed high accuracy for pathogens (98.68%) and resistant strains (99.85%).
- ML-driven biosensors demonstrated 80-100% accuracy, but performance dropped significantly on external validation (95% internal to 78-82% external).
Conclusions:
- AI/ML shows significant potential to enhance foodborne pathogen detection and food safety.
- Key challenges include data heterogeneity, validation standardization, and regulatory alignment.
- Integration with IoT, blockchain, and edge computing can improve real-time monitoring.
- AI serves as a decision-support tool, complementing existing controls, requiring external validation and regulatory harmonization for practical implementation.
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