Name
Title | ||
|---|---|---|
Modeling the Listeria monocytogenes survival/death curves using wavelet neural networks |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596880 | Neural Networks |
Keywords | Field | DocType |
dairy products,feedforward neural nets,food processing industry,optimisation,wavelet transforms,Listeria monocytogenes survival/death curves,UHT whole milk,feedforward neural networks,food industry,food microbiology,nonlinear systems,pressure inactivation kinetics,wavelet neural networks | Feedforward neural network,Wavelet neural network,Nonlinear system,Biological system,Computer science,Listeria monocytogenes,Artificial intelligence,Statistical model,Artificial neural network,Machine learning,Hydrostatic pressure,Wavelet transform | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahdi Amina | 1 | 32 | 2.75 |
| Vassilis S. Kodogiannis | 2 | 1 | 0.73 |
| E. Z. Panagou | 3 | 7 | 1.86 |
| George E. Nychas | 4 | 0 | 1.35 |