title: Product unit neural network models for predicting the growth limits of Listeria monocytogenes.
abstract: A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.
authors: Department of Food Science and Technology, University of Cordoba, Campus Rabanales, Edif. Darwin, 14014 Córdoba, Spain. bt2vadia@uco.es
authors: A
authors: A
authors: Valero
authors: C
authors: C
authors: Hervás
authors: R M
authors: RM
authors: García-Gimeno
authors: G
authors: G
authors: Zurera
mesh_terms: D015169:Colony Count, Microbial; D005516:Food Microbiology; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D008089:Listeria monocytogenes / Q000254:growth & development*; D016015:Logistic Models; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods*; D013696:Temperature