Digital Twin-Centered Food Safet A Review of IoT, AI, and Blockchain Integration
for Bacterial Pathogen Control
DOI:
https://doi.org/10.63053/ijhes.198Keywords:
Keywords: Digital twin; Food safety management systems; Bacterial pathogens; Internet of Things; Artificial intelligence; BlockchainAbstract
Abstract
Traditional food safety management systems (FSMS) are largely reactive, depending on end-product testing and periodic audits that fail to predict or prevent bacterial pathogen outbreaks. The convergence of the Internet of Things (IoT), Artificial Intelligence (AI), and blockchain with the emerging concept of digital twins (DTs) offers a paradigm shift. This review synthesizes literature from 2018 to 2026 to evaluate how DT-centered FSMS can enable real-time monitoring, predictive modeling of pathogen behavior (e.g., Listeria monocytogenes, Salmonella spp., Escherichia coli O157:H7), and immutable traceability. We analyze three core layers: (1) data acquisition via IoT biosensors, (2) AI-driven predictive microbiology and anomaly detection, and (3) blockchain for data integrity and recall management. Key findings indicate that while pilot projects demonstrate a 40–60% reduction in recall response times, significant barriers remain in data interoperability, sensor biofouling, and computational costs. We conclude by proposing a four-stage maturity model for DT adoption in food supply chains and outline critical research gaps for microbial risk assessment.
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