Examining Patterns of Persistent Listeria Contamination in Packinghouses Using Agent-Based Models

Area of Science:

• Food safety
• Microbial contamination dynamics
• Computational modeling

Background:

• Persistent Listeria monocytogenes contamination poses a significant risk in produce packinghouses.
• Effective control requires understanding pathogen harborage and contamination patterns.

Purpose of the Study:

• To adapt agent-based models for long-term Listeria contamination simulation in packinghouses.
• To develop methods for identifying and analyzing persistent and transient contamination patterns.
• To evaluate the effectiveness of different corrective actions for Listeria control.

Main Methods:

• Adapted existing agent-based models for extended hourly simulation of Listeria contamination.
• Developed novel approaches to identify and analyze persistent and transient contamination patterns.
• Simulated and tested various corrective actions, including targeted sanitation strategies.

Main Results:

• Targeted, facility-specific, risk-based sanitation proved most effective in reducing Listeria contamination.
• Generic sanitation approaches are unlikely to be successful in controlling persistent contamination.
• Model simulations highlighted potential misinterpretation of contamination based on sampling frequency and detection limits.

Conclusions:

• Agent-based models are valuable decision support tools for identifying Listeria contamination patterns.
• Facility-specific root cause analysis and hygienic design are crucial for reducing Listeria persistence.
• Models can aid in assessing the viability of corrective actions to enhance food safety.