Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures

Area of Science:

• Food microbiology and predictive modeling
• Quantitative risk assessment for foodborne pathogens

Background:

• Listeria monocytogenes is a significant public health concern due to its high fatality rate.
• This pathogen's ability to grow at refrigeration temperatures poses a risk for ready-to-eat foods.
• Accurate modeling of L. monocytogenes growth is crucial for shelf-life studies, particularly concerning storage temperatures.

Purpose of the Study:

• To compare the efficacy of omnibus modeling versus conventional sequential modeling for L. monocytogenes growth data.
• To evaluate the performance of a mixed-effects nonlinear regression approach (omnibus modeling) in analyzing growth across multiple conditions.

Main Methods:

• Growth data from five L. monocytogenes strains were analyzed.
• Omnibus modeling combined a Huang primary model with secondary models for growth rate and lag phase duration.
• This approach utilized a mixed-effects nonlinear regression framework to model data from all experimental conditions simultaneously.

Main Results:

• Both first-order (sequential) and omnibus modeling showed broad agreement in growth rate estimates.
• First-order modeling identified small, significant strain-dependent differences in growth rate across temperatures.
• Omnibus modeling indicated smaller differences, suggesting a more nuanced interpretation of strain variability.

Conclusions:

• Omnibus modeling provides a unified approach to analyzing pathogen growth data across diverse environmental conditions.
• The mixed-effects nonlinear regression framework allows for the identification and exploration of potential data errors.
• This method enhances the reliability and comprehensiveness of shelf-life studies for Listeria monocytogenes.