Digital twin-centered food safety management systems: A review of IoT, AI, and blockchain integration for bacterial...

The digitalization of food safety management systems (FSMS) represents a crucial strategy for mitigating persistent pathogen contamination and foodborne disease outbreaks. The ISO 22000-based FSMS, incorporating hazard analysis and critical control points (HACCP), relies on periodic verification, retrospective microbiological testing, and manual records, leading to reactive risk management, delayed corrective actions, and limited adaptability to evolving contamination dynamics. Key Industry 4.0 technologies, including digital twins (DT), the Internet of Things (IoT), artificial intelligence (AI), and blockchain, have individually demonstrated the capacity to simulate pathogen contamination dynamics (DT), monitor pathogens in real-time with high sensitivity and predictive accuracy (IoT), enable predictive risk assessment (AI), and reduce traceback from days to seconds (blockchain). However, empirical applications remain limited, with most studies addressing them individually or in pairwise combinations and focusing primarily on supply chain logistics, authenticity, or quality assurance rather than their convergent role in combating foodborne pathogens and supporting HACCP implementation. Following a PRISMA methodology, this review critically examines the potential of DT-centered integration of IoT, AI, and blockchain for pathogen-focused FSMS, which remains underexplored. A unified DT-centered framework linking IoT-based sensing, AI-driven predictive analytics, and blockchain-enabled traceability enables continuous monitoring of critical process and environmental parameters, simulation of contamination dynamics, early risk detection, predictive risk assessment, and enhanced traceability. However, widespread implementation depends on addressing challenges, including heterogeneous data synchronization, interoperability, validation, cybersecurity, implementation costs, and regulatory alignment. Overall, this study provides a pathogen-focused assessment of DT-based systems and outlines future directions for building an intelligent farm-to-fork FSMS.