论文信息 - Modeling of Survival Curves in Food Microbiology Using Fuzzy Wavelet Neural Networks

Modeling of Survival Curves in Food Microbiology Using Fuzzy Wavelet Neural Networks

The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for methods to model highly nonlinear systems is long established. The architecture of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model is proposed. The objective of this research is to investigate the capabilities of the proposed scheme, in predicting the survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The proposed model is obtained from the Takagi---Sugeno---Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network. Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzy rules. The performance of the proposed scheme has been compared against neural networks and partial least squares models usually used in food microbiology.

Vassilis S. Kodogiannis | Ilias Petrounias

[1] M.A.J.S. van Boekel,et al. On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells , 2002 .

[2] E. H. Marth,et al. Listeria, Listeriosis, and Food Safety, Third Edition , 2007 .

[3] M. Amina,et al. Wavelet neural networks for modelling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk , 2010 .

[4] Ilias Petrounias,et al. A hybrid intelligent approach for the prediction of electricity consumption , 2012 .

[5] O. Nelles. Nonlinear System Identification , 2001 .

[6] M Hajmeer,et al. Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks. , 2006, Food microbiology.

[7] John N. Lygouras,et al. Identification of the Listeria monocytogenes survival curves in UHT whole milk utilising local linear wavelet neural networks , 2012, Expert Syst. Appl..

[8] J. Tissier,et al. Physiological effects of high hydrostatic pressure treatments on Listeria monocytogenes and Salmonella typhimurium , 2000, Journal of applied microbiology.

[9] D J Kilpatrick,et al. The combined effect of high hydrostatic pressure and mild heat on inactivation of pathogens in milk and poultry. , 1998, Journal of food protection.