Predictive Model for Listeria monocytogenes in RTE Meats Using Exclusive Food Matrix Data.

Area of Science:

• Food safety microbiology
• Predictive modeling in food science
• Quantitative risk assessment for foodborne pathogens

Background:

• Listeria monocytogenes contamination is a significant safety concern for ready-to-eat (RTE) meat products.
• Mathematical models offer efficient methods for predicting pathogen behavior, shelf life, and safety in food products.
• Accurate predictive models are crucial for risk management in the RTE meat industry.

Purpose of the Study:

• To develop and validate a comprehensive mathematical model for predicting Listeria growth rates in RTE meat products.
• To assess the influence of key environmental factors (temperature, pH, water activity, and preservatives) on Listeria growth.
• To provide a tool for enhancing the safety and shelf-life assessment of RTE meat products.

Main Methods:

• Collected Listeria growth data from 731 datasets in RTE beef, pork, and poultry products from literature and databases.
• Utilized a logistic-with-delay primary model to estimate growth parameters.
• Developed a secondary gamma model using 80% of the data for training and validated it on the remaining 20% testing dataset.

Main Results:

• The developed secondary gamma model demonstrated good fit (R²=0.86, RMSE=0.06) during development.
• Model validation showed an RMSE of 0.074 (μmax), with bias and accuracy factors of 0.95 and 1.50, respectively.
• Approximately 81% of predictions fell within acceptable error limits; lag time prediction accuracy was lower (accuracy factor 2.23).

Conclusions:

• The developed model effectively predicts Listeria growth rates in RTE meat products under various conditions.
• While lag time prediction requires further refinement, the model is a valuable tool for shelf-life assessment and safety decisions.
• This predictive model can aid in the formulation and safety management of RTE meat products to control Listeria contamination.