Amina, Mahdi and 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
Official URL: http://dx.doi.org/10.1109/AICCSA.2009.5069423
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|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||25 Nov 2009 15:54|
|Last Modified:||11 Aug 2010 15:36|
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