title: A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7.
abstract: An artificial neural network (ANN) model was developed to predict survival/death and growth/no-growth interfaces for Escherichia coli O157:H7 in a mayonnaise-type system. Temperature, pH, acetic acid, sucrose and salt were the inputs to a three-layer back-propagation neural network. The ANN model was trained using the data-set of McKellar et al. [2002. A probability model describing the interface between survival and death of E. coli O157:H7 in a mayonnaise model system. Food Microbiol. 19, 235-247] that consisted of 1,820 treatment combinations from controlled experiments with a cocktail of five strains of E. coli O157:H7. After training, the model correctly predicted the growth/no-growth in 1,810 combinations (99.5%) with 8 false positives and 2 false negatives, and survival/death in 1,804 combinations (99.1%) with 13 false positives and 3 false negatives. Classification accuracy was validated using additional literature data-sets for growth of E. coli O157:H7 under various environmental conditions. The ANN model accurately predicted the survival/death in 27 of 30 cases (90%) in experimental mayonnaise inoculated with E. coli O157:H7, with 3 fail-positive predictions and all observed growth (100%). Simulations were used to estimate the influence of incubation temperature on survival and growth for specific combinations of acetic acid, salt, pH and sucrose. The ANN model is recommended as an alternative tool for classification of survival and growth conditions in predictive microbiology.
authors.affiliation: School of Engineering, University of Guelph, Guelph, Ont., Canada N1G 2W1.
authors.forename: Ce
authors.initials: C
authors.lastname: Yu
authors.forename: Valerie J
authors.initials: VJ
authors.lastname: Davidson
authors.forename: Simon X
authors.initials: SX
authors.lastname: Yang
mesh_terms: D015169:Colony Count, Microbial; D019453:Escherichia coli O157 / Q000254:growth & development*; D005188:False Negative Reactions; D005189:False Positive Reactions; D005506:Food Contamination / Q000032:analysis*; D005516:Food Microbiology; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D013696:Temperature