A novel approach to predicting microbial inactivation kinetics during high pressure processing.

Area of Science:

• Food Microbiology
• Food Engineering
• Mathematical Modeling

Background:

• High Pressure Processing (HPP) is an effective microbial inactivation method.
• Existing models for HPP inactivation kinetics often rely on curve fitting rather than prediction.
• Accurate predictive models are crucial for optimizing HPP parameters and ensuring food safety.

Purpose of the Study:

• To develop a novel predictive model for simulating Escherichia coli inactivation kinetics during HPP.
• To establish a reliable method for predicting microbial inactivation under diverse pressure conditions.
• To advance the field of predictive modeling for HPP applications.

Main Methods:

• Examined inactivation kinetics of Escherichia coli (ATCC 25922) under HPP (200-400 MPa, 15°C).
• Calculated maximum inactivation rate (k(max)) and analyzed its relationship with treatment pressure.
• Developed a differential equation model incorporating the square root function of k(max).

Main Results:

• A linear relationship (R²=0.99) was found between the square root of k(max) and treatment pressure.
• The developed model accurately simulated inactivation kinetics under constant, dynamic, and pulsed pressure conditions.
• Model predictions showed comparable accuracy to Weibull and modified Gompertz models.
• The model successfully described independent data from other studies.

Conclusions:

• The novel differential equation model provides accurate predictions of microbial inactivation during HPP.
• This model advances predictive capabilities for HPP, applicable to various pressure profiles.
• The findings contribute to the development of robust predictive tools for food safety and processing.