Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures.

Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 °C. Repeated E. coli O157 enumeration was performed at 0-96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination (R) and mean squared error (MSE) are two essential criteria used to evaluate the model performance regarding the comparison between the observed value and the prediction made by the model. RF model showed superior performance with 0.98 R and 0.08 MSE values for predicting the growth performance of E. coli O157 at different temperatures. MLR model predictions were obtained further from the observed values with 0.66 R and 2.7 MSE values. Our results indicate that ML methods can predict of E. coli O157:H7 growth in ground beef at different temperatures to strengthen food safety professionals and legal authorities to assess contamination risks and determine legal limits and criteria proactively.