Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tholozan, J.L., Ritz, M., Jugiau, F., Federighi, M., Tissier, J.P.: Physiological effects of high hydrostatic pressure on Listeria monocytogenes and Salmonella Typhimurium. J. Appl. Microbiology 88, 202–212 (2000)
Patterson, M.F., Kilpatrick, D.J.: The combined effect of high hydrostatic pressure and mild heat on inactivation of pathogens in milk and poultry. J. Food Prot. 61, 432–436 (1998)
Ryser, E.T., Marth, E.H.: Listeria, Listeriosis and Food Safety. Marcel Dekker Inc., New York (1999)
Hajmeera, M., Basheer, I., Cliver, D.O.: Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks. Food Microbiology 23, 561–570 (2006)
Van Boekel Martinus, A.J.S.: On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells. International journal of food microbiology 74(1-2), 139–159 (2002)
Amina, M., Kodogiannis, V.S., Petrounias, I., Lygouras, J.N., Nychas, G.-J.E.: Identification of the Listeria Monocytogenes survival curves in UHT whole milk utilising Local Linear Wavelet Neural Networks. Expert Systems and Applications 39(1), 1435–1450 (2012)
Amina, M., Panagou, E.Z., Kodogiannis, V.S., Nychas, G.-J.E.: Wavelet Neural Networks for modeling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk. Chemometrics and Intelligent Laboratory Systems 103(2), 170–183 (2010)
Amina, M., Kodogiannis, V.S., Petrounias, I., Tomtsis, D.: A hybrid intelligent approach for the prediction of electricity consumption. International Journal of Electrical Power & Energy Systems 43(1), 99–108 (2012)
Nelles, O.: Nonlinear system identification. Springer (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kodogiannis, V.S., Petrounias, I. (2013). Modeling of Survival Curves in Food Microbiology Using Fuzzy Wavelet Neural Networks. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-38682-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38681-7
Online ISBN: 978-3-642-38682-4
eBook Packages: Computer ScienceComputer Science (R0)