Tarlak, FatihYucel, Ozgun2024-06-132024-06-1320221336-86721338-42602-s2.0-85144220362https://hdl.handle.net/11501/1488The main aim of the present study was to develop a prediction tool to describe Listeria monocytogenes behaviour in milk by employing traditionally used models (the re-parametrized Gompertz, Baranyi and Huang models) and an alterna-tively proposed machine learning-based regression model. The fitting capability of both groups of models was evaluated and compared considering their statistical indices (coefficient of determination R-2, root mean square error RMSE). The machine learning-based regression model provided better predictions (with R-2 of 0.958 and RMSE of 0.407) than the traditionally used models. The prediction capability of both methodologies was tested considering externally collected data from the literature. The machine learning-based regression model in the validation process gave satisfactory sta-tistical indices (bias factor of 1.016 and accuracy factor of 1.056), which is better prediction power than the traditionally used models. These results indicated that the machine learning-based regression method can be reliably employed as an alternative way of describing the growth behaviour of L. monocytogenes in milk. Therefore, the software developed in this work has a significant potential to be used as an alternative simulation method to traditionally used approach in the field of predictive microbiology.eninfo:eu-repo/semantics/closedAccessData MiningPrediction ToolGaussian Process RegressionPredictive MicrobiologyStep Modeling ApproachBacterial-GrowthChicken MeatLag PhasePredictionEnvironmentValidationKineticsArticle3884Q338061WOS:000897993300008Q4