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Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain.

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Big data and digital technologies can enhance food safety microbiology through dynamic risk management. This approach improves microbial risk management and traceability for foodborne illnesses, despite data challenges.

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Area of Science:

  • Food safety microbiology
  • Quantitative risk assessment
  • Big data analytics

Background:

  • Foodborne pathogens cause significant global illness.
  • Quantitative risk-based approaches require data across the food value chain.
  • Digital transformation offers new data sources for food safety.

Purpose of the Study:

  • To explore the prospective use of big data for dynamic risk management in food safety microbiology.
  • To align big data applications with the International Commission on Microbiological Specifications for Foods (ICMSF) framework.
  • To demonstrate real-time hazard identification and control of Shiga toxin-producing Escherichia coli in leafy greens.

Main Methods:

  • Leveraging big data from digital transformational technologies like the Internet of Things (IoT).
  • Integrating data from precision agriculture, connected factories, logistics, and healthcare.
  • Utilizing interconnected public health databases, social media, e-commerce, and blockchain for traceability.

Main Results:

  • Big data enables a dynamic risk management concept for microbial food safety.
  • IoT and interconnected systems enhance the dynamism of microbial risk management.
  • Blockchain and other technologies improve traceability for managing foodborne cases.

Conclusions:

  • Big data offers significant potential for dynamic microbial risk management in food safety.
  • Challenges such as data ownership, interoperability, and accessibility need to be addressed.
  • A dynamic risk management system (DRMS) can be applied in real-time to control specific foodborne hazards like Shiga toxin-producing E. coli.