| dc.contributor.author |
Amina, M |
en |
| dc.contributor.author |
Kodogiannis, VS |
en |
| dc.contributor.author |
Panagou, EZ |
en |
| dc.contributor.author |
Nychas, G-JE |
en |
| dc.date.accessioned |
2014-06-06T06:49:48Z |
|
| dc.date.available |
2014-06-06T06:49:48Z |
|
| dc.date.issued |
2010 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1109/IJCNN.2010.5596880 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/4795 |
|
| dc.subject.other |
Dynamic neural networks |
en |
| dc.subject.other |
Food industries |
en |
| dc.subject.other |
Food microbiology |
en |
| dc.subject.other |
High hydrostatic pressure |
en |
| dc.subject.other |
Inactivation kinetics |
en |
| dc.subject.other |
Listeria monocytogenes |
en |
| dc.subject.other |
Process condition |
en |
| dc.subject.other |
Statistical models |
en |
| dc.subject.other |
Survival curves |
en |
| dc.subject.other |
Wavelet neural networks |
en |
| dc.subject.other |
Whole milk |
en |
| dc.subject.other |
Curve fitting |
en |
| dc.subject.other |
Diseases |
en |
| dc.subject.other |
Hydrostatic pressure |
en |
| dc.subject.other |
Listeria |
en |
| dc.subject.other |
Microorganisms |
en |
| dc.subject.other |
Nonlinear systems |
en |
| dc.subject.other |
Power transformers |
en |
| dc.subject.other |
Neural networks |
en |
| dc.title |
Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks |
en |
| heal.type |
conferenceItem |
en |
| heal.identifier.primary |
10.1109/IJCNN.2010.5596880 |
en |
| heal.identifier.secondary |
5596880 |
en |
| heal.publicationDate |
2010 |
en |
| heal.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 ""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. © 2010 IEEE. |
en |
| heal.journalName |
Proceedings of the International Joint Conference on Neural Networks |
en |
| dc.identifier.doi |
10.1109/IJCNN.2010.5596880 |
en |