Application of neural networks to simulate the growth profile of lactic acid bacteria in green olive fermentation

dc.contributor.author	Panagou, EZ	en
dc.contributor.author	Tassou, CC	en
dc.contributor.author	Saravanos, EKA	en
dc.contributor.author	Nychas, G-JE	en
dc.date.accessioned	2014-06-06T06:47:36Z
dc.date.available	2014-06-06T06:47:36Z
dc.date.issued	2007	en
dc.identifier.issn	0362028X	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/3712
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-34547757838&partnerID=40&md5=7a94d75c05444597df8e29d3c1fafd6a	en
dc.subject.other	area under the curve	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	bacterial count	en
dc.subject.other	biological model	en
dc.subject.other	fermentation	en
dc.subject.other	food control	en
dc.subject.other	growth, development and aging	en
dc.subject.other	kinetics	en
dc.subject.other	Lactobacillus	en
dc.subject.other	microbiology	en
dc.subject.other	olive tree	en
dc.subject.other	prediction and forecasting	en
dc.subject.other	time	en
dc.subject.other	Area Under Curve	en
dc.subject.other	Colony Count, Microbial	en
dc.subject.other	Fermentation	en
dc.subject.other	Food Microbiology	en
dc.subject.other	Kinetics	en
dc.subject.other	Lactobacillus	en
dc.subject.other	Models, Biological	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Olea	en
dc.subject.other	Predictive Value of Tests	en
dc.subject.other	Time Factors	en
dc.subject.other	Bacteria (microorganisms)	en
dc.subject.other	Lactobacillus paracasei	en
dc.subject.other	Lactobacillus plantarum	en
dc.subject.other	Lactobacillus sakei	en
dc.subject.other	Leuconostoc mesenteroides	en
dc.subject.other	Oleaceae	en
dc.title	Application of neural networks to simulate the growth profile of lactic acid bacteria in green olive fermentation	en
heal.type	journalArticle	en
heal.publicationDate	2007	en
heal.abstract	The growth profile of five strains of lactic acid bacteria (Lactobacillus plantarum ACA-DC 287, L. plantarum ACA-DC 146, Lactobacillus paracasei ACA-DC 4037, Lactobacillus sakei LQC 1378, and Leuconostoc mesenteroides LQC 1398) was investigated in controlled fermentation of cv. Conservolea green olives with a multilayer perceptron network, a combined logistic-Fermi function, and a two-term Gompertz function. Neural network training was based on the steepest-descent gradient learning algorithm. Model performance was compared with the experimental data with five statistical indices, namely coefficient of determination (R2), root mean square error (RMSE), mean relative percentage error (MRPE), mean absolute percentage error (MAPE), and standard error of prediction (SEP). The experimental data set consisted of 125 counts (CFU per milliliter) of lactic acid bacteria during the green olive fermentation process for up to 38 days (5 strains X 25 sampling days). For model development, a standard methodology was followed, dividing the data set into training (120 data) and validation (25 data) subsets. Our results demonstrated that the developed network was able to model the growth and survival profile of all the strains of lactic acid bacteria during fermentation equally well with the statistical models. The performance indices for the training subset of the multilayer perceptron network were R2 = 0.987, RMSE = 0.097, MRPE = 0.069, MAPE = 0.933, and SEP = 1.316. The relevant mean values for the logistic-Fermi and two-term Gompertz functions were R2 = 0.981 and 0.989, RMSE = 0.109 and 0.083, MRPE = 0.026 and 0.030, MAPE = 1.430 and 1.076, and SEP = 1.490 and 1.127, respectively. For the validation subset, the network also gave good predictions (R2 = 0.968, RMSE = 0.149 MRPE = 0.100, MAPE = 1.411, and SEP = 2.009). Copyright ©, International Association for Food Protection.	en
heal.journalName	Journal of Food Protection	en
dc.identifier.issue	8	en
dc.identifier.volume	70	en
dc.identifier.spage	1909	en
dc.identifier.epage	1916	en