Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products.
In many chilled, prepared food products, the effects of temperature, pH and %NaCl on microbial activity interact and this should be taken into account. A grey box model for prediction of microbial growth is developed. The time dependence is modeled by a Gompertz model-based, non-linear differential equation. The influence of temperature, pH and %NaCl reflected in the model parameters is described by using low-complexity, black box artificial neural networks (ANN's). The use of this non-linear modeling technique makes it possible to describe more accurately interacting effects of environmental factors when compared with classical predictive microbiology models. When experimental results on the influence of other environmental factors become available, the ANN models can be extended simply by adding more neurons and/or layers.
Geeraerd
A H
AH
BioTeC-Bioprocess Technology and Control, Department of Food and Microbial Technology, Katholieke Universiteit Leuven, Heverlee, Belgium.
Herremans
C H
CH
Cenens
C
C
Van Impe
J F
JF
D001419:Bacteria / Q000254:growth & development*; D005516:Food Microbiology; D006863:Hydrogen-Ion Concentration; D008954:Models, Biological; D016571:Neural Networks, Computer; D013696:Temperature