Predicting Single Cell Lag Time and Maximum Specific Growth Rate of Proteus mirabilis using Curve Fitting Machine Learning Algorithm (MLA)

Abstract

The lack of adequate assessment methods for pathogens especially in food is a critical problem in microbiology. Traditional predictive methods are not able to accurately describe the trend of low-density bacterial growth behavior observed in the laboratory. The purpose of this study was to leverage state-of-the-art of machine learning algorithms (MLA) to develop a predictive model for bacterial growth of Proteus mirabilis after treatment of bay leaf extract. The experimental data are fitted to three models, namely logistic, Gompertz, and Richard models. These models are trained using simulation data and a curve-fitting optimization algorithm in MATLAB called fminsearch is applied to the data to obtain the optimal parameters of the models. The results show that this method provides a breakthrough in bacterial growth modeling. Various forms of mathematical models such as Gompertz, Richard, and others are no longer necessary to model bacterial behavior. Additionally, the generated model can help microbiologists in understanding the growth characteristics of bacteria after disinfectant treatment, and provides a theoretical reference and a method of risk management for better assessment of pathogens in food.