Stochastic and deterministic model of microbial heat inactivation

Area of Science:

• Microbiology
• Mathematical Modeling
• Food Science

Background:

• Microbial inactivation kinetics are crucial for food safety and sterilization processes.
• Existing models often assume specific kinetic orders or mechanisms, limiting their applicability.
• Diverse survival patterns observed in microbial inactivation experiments require a more generalized modeling approach.

Purpose of the Study:

• To develop a unified model for microbial inactivation based on changing survival probabilities.
• To explain various inactivation kinetics (log-linear, Weibullian, etc.) using a single framework.
• To provide a method for deducing inactivation mechanisms from experimental survival curves.

Main Methods:

• A stochastic and deterministic model based on individual cell/spore survival probabilities.
• Application of the model to both isothermal and dynamic heat treatments.
• Regression analysis to estimate survival parameters from experimental data.

Main Results:

• The model generates first-order (log-linear) kinetics when mortality probability is constant.
• It accurately describes complex survival curves like Weibullian, tailing, and sigmoid patterns.
• The model's equation is consistent for different heat treatment conditions and does not require prior knowledge of kinetic order.

Conclusions:

• The developed model offers a unified approach to understanding microbial inactivation kinetics.
• It connects macroscopic survival curves to underlying cellular-level probabilities.
• The model is valuable for simulating irregular inactivation patterns and potentially for nonthermal inactivation methods.