Amina, Mahdi and Kodogiannis, Vassilis and Revett, Kenneth and Lygouras, John N. (2010) Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks. In: 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 18-23 July 2010. IEEE, pp. 1-8. ISBN 9781424469161
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Official URL: http://dx.doi.org/10.1109/IJCNN.2010.5596880
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 “intelligent” methods to model highly nonlinear systems is long established. Feed-forward neural networks have been successfully used for modeling of nonlinear systems. The objective of this research is to investigate the capabilities of a new wavelet neural network, to predicting of survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure in UHT whole milk. The performance of the proposed scheme has been compared against a dynamic neural network and classic statistical models used in food microbiology.
|Item Type:||Book Section|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||11 Nov 2010 12:11|
|Last Modified:||11 Nov 2010 12:11|
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