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Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food.

Abstract：

:The application of Artificial Neural Networks (ANN) in predictive microbiology is presented in this paper. This technique was used to build up a predictive model of the joint effect of NaCl concentration, pH level and storage temperature on kinetic parameters of the growth curve of Lactobacillus plantarum using ANN and Response Surface Model (RSM). Sigmoid functions were fitted to the data and kinetic parameters were estimated and used to build the models in which the independent variables were the factors mentioned above (NaCl, pH, temperature), and in some models, the values of the optical densities (OD) vs. time of the growth curve were also included in order to improve the error of estimation. The determination of the proper size of an ANN was the first step of the estimation. This study shows the usefulness of an ANN pruning methodology. The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity. The best architecture has been sought using genetic algorithms (GA) in conjunction with pruning algorithms and regularization methods in which the initial distribution of the parameters (weights) of the network is not uniform. The ANN model has been compared with the response surface model by means of the Standard Error of Prediction (SEP). The best values obtained were 14.04% of SEP for the growth rate and 14.84% for the lag estimation by the best ANN model, which were much better than those obtained by the RSM, 35.63% and 39.30%, respectively. These were very promising results that, in our opinion, open up an extremely important field of research.

journal_name

Int J Food Microbiol

journal_title

International journal of food microbiology

authors

García-Gimeno RM,Hervás-Martínez C,de S

doi

10.1016/s0168-1605(01)00608-0

subject

Has Abstract

pub_date

2002-01-30 00:00:00

pages

19-30

issue

1-2

eissn

0168-1605

issn

1879-3460

pii

S0168-1605(01)00608-0

journal_volume

pub_type

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Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food.