Improving microbial growth prediction by product unit neural networks

Abstract

This article presents a new approach to the Artificial Neural Networks (ANN) modeling of bacterial growth; using Neural Network models based on Product Units (PUNN) instead of on sigmoidal units (multilayer perceptron type [MLP]) of kinetic parameters (lag‐time, growth rate, and maximum population density) of Leuconostoc mesenteroides and those factors affecting their growth such as storage temperature, pH, NaCl, and NaNO2 concentrations under anaerobic conditions.