Application of artificial neural networks to describe the combined effect of pH and NaCl on the heat resistance of Bacillus stearothermophilus

• Published in: International journal of food microbiology Vol. 106; no. 2; pp. 153 - 158
• Main Authors: Esnoz, A., Periago, P.M., Conesa, R., Palop, A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2006; Elsevier
• Subjects: Bacillus stearothermophilus; bacterial contamination; Bacterial spores; Biological and medical sciences; culture media; food contamination; Food industries; Food Microbiology; food pathogens; Fundamental and applied biological sciences. Psychology; Geobacillus stearothermophilus - growth & development; Geobacillus stearothermophilus - physiology; heat stress; heat tolerance; Hot Temperature; Hydrogen-Ion Concentration; inactivation temperature; microbial growth; Models, Biological; Neural networks; Neural Networks (Computer); Predictive microbiology; Predictive Value of Tests; salt concentration; sodium chloride; Sodium Chloride - pharmacology; spore-forming bacteria; Spores, Bacterial - growth & development; Thermal inactivation; viability; Bacterial spores; Bacillus stearothermophilus; Neural networks; Thermal inactivation; Predictive microbiology; Heat resistance; Neural network; Bacillaceae; Inactivation; Spores; Bacillales; pH; Bacteria; Application
• ISSN: 0168-1605; 1879-3460
• DOI: 10.1016/j.ijfoodmicro.2005.06.016

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.