A strategy to establish Food Safety Model Repositories.

Area of Science:

• Food safety
• Microbiology
• Computational modeling

Background:

• Translating predictive microbiology models into practical food manufacturing applications remains a significant challenge.
• Existing models often lack accessibility, transparency, and ease of use for commercial and governmental sectors.

Purpose of the Study:

• To propose a strategy for creating open, community-driven, web-based predictive microbial model repositories.
• To enhance the transfer of knowledge from research to real-world food safety applications.
• To improve the efficiency, transparency, and usability of predictive microbial models.

Main Methods:

• Re-implementation of published Salmonella models in beef using the open-source PMM-Lab software.
• Publicly releasing these models in a Food Safety Model Repository.
• Demonstrating three approaches for model creation: from publications, parameter collections, and experimental data.

Main Results:

• Successfully created and shared predictive microbial models for Salmonella in beef.
• Demonstrated the feasibility of three distinct methods for populating model repositories.
• Established a sample Food Safety Model Repository and PMM-Lab software.

Conclusions:

• Open, collaborative model repositories can significantly improve the application of predictive microbiology in food safety.
• Standardized information exchange formats, potentially from Systems Biology, are key to this strategy.
• The proposed approach facilitates knowledge transfer and enhances model usability in food manufacturing.