| dc.contributor.author |
Panagou, EZ |
en |
| dc.contributor.author |
Kodogiannis, V |
en |
| dc.contributor.author |
Nychas, GJ-E |
en |
| dc.date.accessioned |
2014-06-06T06:47:53Z |
|
| dc.date.available |
2014-06-06T06:47:53Z |
|
| dc.date.issued |
2007 |
en |
| dc.identifier.issn |
01681605 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.ijfoodmicro.2007.03.010 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3837 |
|
| dc.subject |
Artificial neural networks |
en |
| dc.subject |
Monascus ruber |
en |
| dc.subject |
Polynomial regression |
en |
| dc.subject |
Predictive mycology |
en |
| dc.subject |
Radial basis function network |
en |
| dc.subject.other |
accuracy |
en |
| dc.subject.other |
article |
en |
| dc.subject.other |
Ascomycetes |
en |
| dc.subject.other |
binding kinetics |
en |
| dc.subject.other |
fungus growth |
en |
| dc.subject.other |
fungus isolation |
en |
| dc.subject.other |
growth rate |
en |
| dc.subject.other |
heat tolerance |
en |
| dc.subject.other |
mathematical model |
en |
| dc.subject.other |
Monascus |
en |
| dc.subject.other |
monascus ruber |
en |
| dc.subject.other |
nerve cell network |
en |
| dc.subject.other |
nonhuman |
en |
| dc.subject.other |
olive |
en |
| dc.subject.other |
pH measurement |
en |
| dc.subject.other |
radial basis function neural network |
en |
| dc.subject.other |
root mean square error model |
en |
| dc.subject.other |
sensitivity analysis |
en |
| dc.subject.other |
standard error of prediction |
en |
| dc.subject.other |
surface property |
en |
| dc.subject.other |
temperature measurement |
en |
| dc.subject.other |
training |
en |
| dc.subject.other |
Area Under Curve |
en |
| dc.subject.other |
Colony Count, Microbial |
en |
| dc.subject.other |
Food Contamination |
en |
| dc.subject.other |
Food Microbiology |
en |
| dc.subject.other |
Hydrogen-Ion Concentration |
en |
| dc.subject.other |
Kinetics |
en |
| dc.subject.other |
Models, Biological |
en |
| dc.subject.other |
Models, Statistical |
en |
| dc.subject.other |
Monascus |
en |
| dc.subject.other |
Neural Networks (Computer) |
en |
| dc.subject.other |
Predictive Value of Tests |
en |
| dc.subject.other |
Temperature |
en |
| dc.subject.other |
Water |
en |
| dc.subject.other |
Fungi |
en |
| dc.subject.other |
Monascus ruber |
en |
| dc.subject.other |
Oleaceae |
en |
| dc.title |
Modelling fungal growth using radial basis function neural networks: The case of the ascomycetous fungus Monascus ruber van Tieghem |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.ijfoodmicro.2007.03.010 |
en |
| heal.publicationDate |
2007 |
en |
| heal.abstract |
A radial basis function (RBF) neural network was developed and evaluated against a quadratic response surface model to predict the maximum specific growth rate of the ascomycetous fungus Monascus ruber in relation to temperature (20-40 °C), water activity (0.937-0.970) and pH (3.5-5.0), based on the data of Panagou et al. [Panagou, E.Z., Skandamis, P.N., Nychas, G.-J.E., 2003. Modelling the combined effect of temperature, pH and aw on the growth rate of M. ruber, a heat-resistant fungus isolated from green table olives. J. Appl. Microbiol. 94, 146-156]. Both RBF network and polynomial model were compared against the experimental data using five statistical indices namely, coefficient of determination (R2), root mean square error (RMSE), standard error of prediction (SEP), bias (Bf) and accuracy (Af) factors. Graphical plots were also used for model comparison. For training data set the RBF network predictions outperformed the classical statistical model, whereas in the case of test data set the network gave reasonably good predictions, considering its performance for unseen data. Sensitivity analysis showed that from the three environmental factors the most influential on fungal growth was temperature, followed by water activity and pH to a lesser extend. Neural networks offer an alternative and powerful technique to model microbial kinetic parameters and could thus become an additional tool in predictive mycology. © 2007 Elsevier B.V. All rights reserved. |
en |
| heal.journalName |
International Journal of Food Microbiology |
en |
| dc.identifier.issue |
3 |
en |
| dc.identifier.volume |
117 |
en |
| dc.identifier.doi |
10.1016/j.ijfoodmicro.2007.03.010 |
en |
| dc.identifier.spage |
276 |
en |
| dc.identifier.epage |
286 |
en |