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.

[1]  K. Davey,et al.  Modelling the combined effect of temperature and pH on the rate coefficient for bacterial growth. , 1994, International journal of food microbiology.

[2]  M N Hajmeer,et al.  Computational neural networks for predictive microbiology. II. Application to microbial growth. , 1997, International journal of food microbiology.

[3]  Jan Van Impe,et al.  Mathematical concepts and techniques for validation of predictive models , 1998 .

[4]  A. N. Stokes,et al.  Model for bacterial culture growth rate throughout the entire biokinetic temperature range , 1983, Journal of bacteriology.

[5]  József Baranyi,et al.  A non-autonomous differential equation to model bacterial growth. , 1993 .

[6]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[7]  I A Basheer,et al.  Computational neural networks for predictive microbiology: I. Methodology. , 1997, International journal of food microbiology.

[8]  W. Garthright.  Refinements in the prediction of microbial growth curves , 1991 .

[9]  A. M. Marshall,et al.  The effects on temperature on growth in vitro of Pseudomonas syringae and and Xanthomonas pruni. , 1977, The Journal of applied bacteriology.

[10]  J Baranyi,et al.  A Combined Model for Growth and Subsequent Thermal Inactivation of Brochothrix thermosphacta , 1996, Applied and environmental microbiology.

[11]  J F Van Impe,et al.  Dynamic mathematical model to predict microbial growth and inactivation during food processing , 1992, Applied and environmental microbiology.

[12]  J F Van Impe,et al.  Predictive microbiology in a dynamic environment: a system theory approach. , 1995, International journal of food microbiology.

[13]  J. Ingraham.  GROWTH OF PSYCHROPHILIC BACTERIA , 1958, Journal of bacteriology.

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[15]  J G Phillips,et al.  Model for the combined effects of temperature, initial pH, sodium chloride and sodium nitrite concentrations on anaerobic growth of Shigella flexneri. , 1994, International journal of food microbiology.

[16]  J P Flandrois,et al.  Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth , 1995, Applied and environmental microbiology.

[17]  F. Rombouts,et al.  Modeling of the Bacterial Growth Curve , 1990, Applied and environmental microbiology.

[18]  J Baranyi,et al.  A predictive model for the combined effect of pH, sodium chloride and storage temperature on the growth of Brochothrix thermosphacta. , 1993, International journal of food microbiology.

[19]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[20]  Dan W. Patterson,et al.  Artificial Neural Networks: Theory and Applications , 1998 .

[21]  M R Adams,et al.  Modelling the effect of pH, acidulant and temperature on the growth rate of Yersinia enterocolitica. , 1991, The Journal of applied bacteriology.

[22]  Jan Van Impe,et al.  A prototype grey box model using neural networks for prediction of microbial growth , 1997 .

[23]  J P Flandrois,et al.  Accuracy of microbial growth predictions with square root and polynomial models. , 1995, International journal of food microbiology.

[24]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[25]  Tom Ross,et al.  Predictive Microbiology : Theory and Application , 1993 .

[26]  P. Davies,et al.  Intermediate Moisture Foods , 1976 .

[27]  M. A. Barber.  The Rate of Multiplication of Bacillus Coli at Different Temperatures , 1908 .

[28]  M H Zwietering,et al.  Modelling bacterial growth of Listeria monocytogenes as a function of water activity, pH and temperature. , 1993, International journal of food microbiology.

[29]  K van't Riet,et al.  Modeling of bacterial growth as a function of temperature , 1991, Applied and environmental microbiology.

[30]  D. Ohye,et al.  The temperature relations of Clostridium botulinum, types A and B. , 1953, Australian journal of biological sciences.

[31]  R. Buchanan,et al.  The effect of incubation temperature, initial pH, and sodium chloride on the growth kinetics of Escherichia coli O157:H7 , 1992 .

[32]  John G Phillips,et al.  Model for the Aerobic Growth of Aeromonas hydrophila K144. , 1991, Journal of food protection.