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Title: Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products
Authors: Geeraerd, Annemie ×
Herremans, CH
Cenens, Chantal
Van Impe, Jan #
Issue Date: 1998
Publisher: ELSEVIER SCIENCE BV
Series Title: International journal of food microbiology vol:44 issue:1-2 pages:49-68
Conference: date:Katholieke Univ Leuven, Dept Food & Microbial Technol, BioTeC Bioproc Technol & Control, B-3001 Heverlee, Belgium
Abstract: 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. (C) 1998 Elsevier Science B.V. All rights reserved.
ISSN: 0168-1605
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division of Mechatronics, Biostatistics and Sensors (MeBioS)
Bio- & Chemical Systems Technology, Reactor Engineering and Safety Section
× corresponding author
# (joint) last author

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