Predictive modeling in food mycology using adaptive neuro-fuzzy systems

Amina, Mahdi, Kodogiannis, Vassilis and Tarczynski, Andrzej (2009) Predictive modeling in food mycology using adaptive neuro-fuzzy systems. In: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA 2009). IEEE, pp. 821-828. ISBN 9781424438075


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Fungal growth leads to spoilage of food and animal feeds and to formation of mycotoxins and potentially allergenic spores. There is a growing interest in predictive modeling microbial growth as an alternative to time consuming traditional, microbiological enumeration techniques. Several statistical models have been accounted to describe the growth of different micro-organisms. However neural networks, as highly nonlinear approximator scheme, have the potential of modeling some complex, phenomena better than the others. The application of adaptive neuro-fuzzy systems in predictive microbiology is presented in this paper. This technique is used to build up a model of the joint effect of water-activity, pH level and temperature to predict the maximum specific growth rate of the Ascomycetous Fungus Monascus Ruber. The proposed scheme is compared against standard neural network approaches. Neuro-fuzzy systems offer an alternative and powerful technique to model microbial kinetic parameters and could thus become an efficient tool in predictive mycology.

Item Type: Book Section
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 25 Nov 2009 15:54
Last Modified: 11 Aug 2010 14:36

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