Food Research Institute

Tarlak, F. – Yücel, Ö.
Assessment of robustness of machine learning-assisted modelling approach to describe growth kinetics of microorganisms using Monte Carlo simulation


Fatih Tarlak, Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Cumhuriyet Street 2254, 41400 Gebze, Kocaeli, Turkey. E-mail: ftarlak@gtu.edu.tr

Original article
Received 9 May 2024; 1st revised 18 June 2024; accepted 9 July 2024; published online 16 August 2024

Summary: Understanding the growth behaviour of microorganisms is crucial for various fields such as microbiology, food safety and biotechnology. Traditional modelling approaches face challenges in accurately capturing the dynamic and complex nature of microbial growth especially when high variation is seen. In contrast, machine learning techniques offer a promising avenue for creating more accurate and adaptable models. This study aimed to develop a new modelling method, machine learning-assisted modelling approach, and compare the robustness of machine learning-assisted and traditional modelling approaches in describing microbial growth behaviour, employing Monte Carlo simulation. The research involved subjecting both machine learning-assisted and traditional modelling approaches to 10, 50 and 500 trials. The results showed that the machine learning approach led to more robust results than the traditional modelling approach providing higher adjusted coefficient of determination (R²_adj) value than 0.919 and lower root mean square error (RMSE) value than 0.319. These findings suggest that the machine learning-assisted modelling approach, particularly with Gaussian process regression, has the potential to serve as a highly reliable prediction method for describing the growth behaviour of microorganisms in frames of predictive food microbiology. The study provides insights into practical application of machine learning in enhancing our understanding and predictive capabilities of microbial growth dynamics.

Keywords: machine learning regression; model robustness; growth parameters; Monte Carlo simulation

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