Application of a machine learning-based regression method to describe Listeria monocytogenes behaviour in milk
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Vup Food Research Inst, Bratislava
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Data Mining, Prediction Tool, Gaussian Process Regression, Predictive Microbiology, Step Modeling Approach, Bacterial-Growth, Chicken Meat, Lag Phase, Prediction, Environment, Validation, Kinetics
Kaynak
Journal of Food and Nutrition Research
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
61
Sayı
4
