| dc.contributor.author |
Poirazi, P |
en |
| dc.contributor.author |
Leroy, F |
en |
| dc.contributor.author |
Georgalaki, MD |
en |
| dc.contributor.author |
Aktypis, A |
en |
| dc.contributor.author |
De Vuyst, L |
en |
| dc.contributor.author |
Tsakalidou, E |
en |
| dc.date.accessioned |
2014-06-06T06:47:58Z |
|
| dc.date.available |
2014-06-06T06:47:58Z |
|
| dc.date.issued |
2007 |
en |
| dc.identifier.issn |
00992240 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1128/AEM.01721-06 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3895 |
|
| dc.subject.other |
Computational methods |
en |
| dc.subject.other |
Dairy products |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
pH effects |
en |
| dc.subject.other |
Sodium chloride |
en |
| dc.subject.other |
Thermal effects |
en |
| dc.subject.other |
Toxic materials |
en |
| dc.subject.other |
Yeast |
en |
| dc.subject.other |
Bacteriocin |
en |
| dc.subject.other |
Biokinetic parameters |
en |
| dc.subject.other |
Kasseri cheese |
en |
| dc.subject.other |
Population behavior |
en |
| dc.subject.other |
Streptococcus macedonicus |
en |
| dc.subject.other |
Bacteria |
en |
| dc.subject.other |
bacteriocin |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
bacterium |
en |
| dc.subject.other |
experimental study |
en |
| dc.subject.other |
fermentation |
en |
| dc.subject.other |
food |
en |
| dc.subject.other |
modeling |
en |
| dc.subject.other |
yeast |
en |
| dc.subject.other |
article |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
cheesemaking |
en |
| dc.subject.other |
concept formation |
en |
| dc.subject.other |
fermentation |
en |
| dc.subject.other |
gamma concept |
en |
| dc.subject.other |
growth rate |
en |
| dc.subject.other |
mathematical model |
en |
| dc.subject.other |
milk |
en |
| dc.subject.other |
nonhuman |
en |
| dc.subject.other |
pH measurement |
en |
| dc.subject.other |
population dynamics |
en |
| dc.subject.other |
prediction |
en |
| dc.subject.other |
simulation |
en |
| dc.subject.other |
Streptococcus |
en |
| dc.subject.other |
Streptococcus macedonicus |
en |
| dc.subject.other |
Streptococcus macedonicus ACA DC 198 |
en |
| dc.subject.other |
temperature measurement |
en |
| dc.subject.other |
Animals |
en |
| dc.subject.other |
Bacteriocins |
en |
| dc.subject.other |
Cheese |
en |
| dc.subject.other |
Fermentation |
en |
| dc.subject.other |
Hydrogen-Ion Concentration |
en |
| dc.subject.other |
Milk |
en |
| dc.subject.other |
Models, Biological |
en |
| dc.subject.other |
Neural Networks (Computer) |
en |
| dc.subject.other |
Sodium Chloride |
en |
| dc.subject.other |
Streptococcus |
en |
| dc.subject.other |
Temperature |
en |
| dc.subject.other |
Streptococcus macedonicus |
en |
| dc.title |
Use of artificial neural networks and a gamma-concept-based approach to model growth of and bacteriocin production by Streptococcus macedonicus ACA-DC 198 under simulated conditions of Kasseri cheese production |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1128/AEM.01721-06 |
en |
| heal.publicationDate |
2007 |
en |
| heal.abstract |
Growth of and bacteriocin production by Streptococcus macedonicus ACA-DC 198 were assessed and modeled under conditions simulating Kasseri cheese production. Controlled fermentations were performed in milk supplemented with yeast extract at different combinations of temperature (25, 40, and 55°C), constant pH (pHs 5 and 6), and added NaCl (at concentrations of 0, 2, and 4%, wt/vol). The data obtained were used to construct two types of predictive models, namely, a modeling approach based on the gamma concept, as well as a model based on artificial neural networks (ANNs). The latter computational methods were used on 36 control fermentations to quantify the complex relationships between the conditions applied (temperature, pH, and NaCl) and population behavior and to calculate the associated biokinetic parameters, i.e., maximum specific growth and cell count decrease rates and specific bacteriocin production. The functions obtained were able to estimate these biokinetic parameters for four validation fermentation experiments and obtained good agreement between modeled and experimental values. Overall, these experiments show that both methods can be successfully used to unravel complex kinetic patterns within biological data of this kind and to predict population kinetics. Whereas ANNs yield a better correlation between experimental and predicted results, the gamma-concept-based model is more suitable for biological interpretation. Also, while the gamma-concept-based model has not been designed for modeling of other biokinetic parameters than the specific growth rate, ANNs are able to deal with any parameter of relevance, including specific bacteriocin production. Copyright © 2007, American Society for Microbiology. All Rights Reserved. |
en |
| heal.journalName |
Applied and Environmental Microbiology |
en |
| dc.identifier.issue |
3 |
en |
| dc.identifier.volume |
73 |
en |
| dc.identifier.doi |
10.1128/AEM.01721-06 |
en |
| dc.identifier.spage |
768 |
en |
| dc.identifier.epage |
776 |
en |