Evaluation of critical level in Bacillus Cereus growth curve in milk products with different conditions based on experimental data and ComBase Predictive Models

Predictive microbiology in food products empowered research application to achieve the high accurate evaluation of microbial risk in food products. According to previous research, Bacillus cereus selected as a serious problem in milk industry; because of the heat resistance characteristics which Bacillus cereus can causes two diverse syndromes of food poisoning (diarrhoea and vomiting), suspected foods contained between 10 and 0 cfu/g. In this study, two methods were used to evaluate, the first one is ComBase a Combined dataBase for predictive microbiology a common database on microbial responses to food environments and second one is NeuroXL Predictor ( neural network forecasting tool). The study went on the interaction of temperature (6, 7, 8 and 9 °C), pH (6.6, 6.7 and 6.8), according to raw milk pH on the probability of pathogen growth. Thus, Over 74 recorded sample in ComBase remarkable date were collected for comparison. The prediction model showed a good performance for validation data were correctly classified. The predictions indicated an abrupt growth/no growth interfaces occurred at low levels of temperature and pH which could in high risk of production of toxin in milk products (10 cfu/g).