An optimization algorithm for estimation of microbial survival parameters during thermal processing.

Area of Science:

• Microbiology
• Food Science
• Mathematical Modeling

Background:

• Microbial inactivation kinetics are crucial for predicting shelf-life and ensuring food safety.
• Isothermal survival curves are commonly used, but non-isothermal data offers more comprehensive insights.
• Existing models often require extensive parameter estimation, limiting their practical application.

Purpose of the Study:

• To develop and validate a novel algorithm for estimating microbial survival parameters from non-isothermal data.
• To improve the accuracy and reliability of microbial inactivation modeling.
• To enable the generation of accurate non-isothermal survival curves from known kinetic parameters.

Main Methods:

• Development of an algorithm based on the steepest descent optimization method.
• Minimization of the sum of squared differences between experimental and model-predicted data.
• Utilization of data from multiple non-isothermal processes for parameter estimation, unlike traditional methods.

Main Results:

• The algorithm successfully estimated Salmonella survival parameters using the Weibull model from non-isothermal data.
• Accurate parameter estimation requires a sufficiently large number of data points, achievable through multiple curves or numerous endpoints.
• The developed algorithm is mathematically versatile and applicable to various microbial inactivation kinetics without pre-assumption.

Conclusions:

• The novel algorithm provides a robust method for estimating microbial survival parameters from non-isothermal data.
• Utilizing extensive data points from multiple non-isothermal treatments enhances the statistical soundness of parameter values.
• This approach offers a significant advancement in microbial inactivation modeling for applications in food safety and preservation.