Next-Generation Predictive Microbiology: A Software Platform Combining Two-Step, One-Step and Machine Learning...

Area of Science:

• Predictive Microbiology
• Computational Biology
• Food Science

Background:

• Microbial growth and inhibition are complex processes challenging traditional mechanistic models due to nonlinear interactions in food systems.
• Accurate prediction is crucial for food safety, shelf-life estimation, and antimicrobial testing.

Purpose of the Study:

• To develop a dynamic software platform integrating classical predictive microbiology models with machine learning (ML) methods.
• To enable direct comparison of one-step and two-step modeling approaches across various growth and inhibition models.
• To expand predictive capabilities using ML for microbial growth and inhibition beyond traditional parametric frameworks.

Main Methods:

• Integration of classical predictive microbiology models (one-step and two-step) with ML algorithms (Support Vector Regression, Random Forest Regression, Gaussian Process Regression).
• Implementation of four growth models (modified Gompertz, Logistic, Baranyi, Huang) and three inhibition models (Log-Linear, Log-Linear + Tail, Weibull).
• Validation using experimental and literature datasets to assess predictive accuracy and robustness.

Main Results:

• The developed platform demonstrated high predictive accuracy and robustness in modeling microbial growth and inhibition.
• Machine learning models, particularly Gaussian Process Regression and Random Forest Regression, outperformed classical predictive models.
• The platform facilitates flexible model evaluation and selection for diverse applications.

Conclusions:

• The novel software platform offers a powerful, data-driven decision-support tool for researchers, industry, and regulatory bodies.
• It enhances capabilities in food safety management, shelf-life estimation, and antimicrobial testing.
• Future work includes platform optimization, database integration, and expansion into emerging predictive microbiology fields.