An intelligent based prediction of microbial behaviour in beef

The main purpose of this work was to develop different machine learning-based regression methods referred to as decision tree regression (DTR), generalized additive model regression (GAMR) and random forest regression (RFR) to predict bacterial population on beef. For this purpose, 2654 bacterial data points of Listeria monocytogenes, Escherichia coli and Pseudomonas spp. Which are the most investigated bacterial genera in beef were collected from the ComBase database (www.combase.cc). Temperature, salt concentration, water activity and acidity were used as the main predictor variables to estimate the growth or survival behaviour of the microorganisms in beef. The hyperparameters are optimized for proposed machine learning-based regression methods with nested cross-validation. The fitting capabilities of the proposed machine learning algorithms were compared considering their statistical indices (coefficient of determination “R2” and root mean square error “RMSE). Each regression method provided satisfactory predictions with being 0.931