Application of artificial neural networks to describe the combined effect of pH and NaCl on the heat resistance of Bacillus stearothermophilus.
A model for prediction of bacterial spore inactivation was developed. The influence of temperature, pH and NaCl on the heat resistance of Bacillus stearothermophilus spores was described using low-complexity, black box models based on artificial neural networks. Literature data were used to build and train the neural network, and new experimental data were used to evaluate it. The neural network models gave better predictions than the classical quadratic response surface model in all the experiments tried. When the neural networks were evaluated using new experimental data, also good predictions were obtained, providing fail-safe predictions of D values in all cases. The weights and biases values of neurons of the neural network that gave the best results are presented, so the reader can use the model for their own purposes. The use of this non-linear modelling technique makes it possible to describe more accurately interacting effects of environmental factors when compared with classical predictive microbial models.
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Universidad Politécnica de Cartagena. Dpto. de Ingeniería de Alimentos y del Equipamiento Agrícola. P Alfonso XIII, 48 30203 Cartagena (Murcia) Spain. arturo.esnoz@upct.es
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D005516:Food Microbiology; D001411:Geobacillus stearothermophilus / Q000254:growth & development / Q000502:physiology*; D006358:Hot Temperature; D006863:Hydrogen-Ion Concentration; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012965:Sodium Chloride / Q000494:pharmacology*; D013171:Spores, Bacterial / Q000254:growth & development
D012965:Sodium Chloride