Towards a Food Safety Knowledge Base Applicable in Crisis Situations and Beyond

Area of Science:

• Food Safety
• Risk Assessment
• Computational Modeling

Background:

• Timely intervention is essential to mitigate public health impacts from food chain contamination.
• Predictive modeling of contaminant behavior in food aids risk assessors and decision-makers.
• Accessible, adaptable models are needed for rapid response during food safety crises.

Purpose of the Study:

• To demonstrate the utility of a food safety knowledge base concept for crisis management.
• To apply modular, open-source software tools (PMM-Lab and FoodProcess-Lab) for simulating contamination scenarios.
• To model an intentional ricin contamination in beef salami production.

Main Methods:

• Reviewed predictive models for ricin inactivation.
• Implemented relevant inactivation models in PMM-Lab.
• Conducted simulations of residual ricin in beef salami using FoodProcess-Lab.

Main Results:

• Successfully modeled ricin inactivation and simulated residual toxin levels in beef salami.
• Demonstrated the adaptability of PMM-Lab and FoodProcess-Lab for various food safety scenarios.
• Validated the concept of a food safety knowledge base for crisis situations.

Conclusions:

• Modular, open-source tools like PMM-Lab and FoodProcess-Lab facilitate rapid simulation of food contamination events.
• The food safety knowledge base approach enhances risk assessment and mitigation strategies.
• These tools are valuable for both crisis management and routine food quality and safety investigations.