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A novel modelling approach for predicting microbial growth in a raw cured meat product stored at 3 degrees C and at 12 degrees C in air

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dc.contributor.author Aggelis, G en
dc.contributor.author Samelis, J en
dc.contributor.author Metaxopoulos, J en
dc.date.accessioned 2014-06-06T06:43:30Z
dc.date.available 2014-06-06T06:43:30Z
dc.date.issued 1998 en
dc.identifier.issn 0168-1605 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/1317
dc.subject predictive microbiology en
dc.subject modelling en
dc.subject raw cured meat en
dc.subject sausage en
dc.subject.classification Food Science & Technology en
dc.subject.classification Microbiology en
dc.subject.other ESCHERICHIA-COLI O157-H7 en
dc.subject.other LISTERIA-MONOCYTOGENES en
dc.subject.other BACTERIAL-GROWTH en
dc.subject.other SODIUM-CHLORIDE en
dc.subject.other WATER ACTIVITY en
dc.subject.other SHELF-LIFE en
dc.subject.other TEMPERATURE en
dc.subject.other PH en
dc.subject.other SAUSAGES en
dc.subject.other LACTOBACILLUS en
dc.title A novel modelling approach for predicting microbial growth in a raw cured meat product stored at 3 degrees C and at 12 degrees C in air en
heal.type journalArticle en
heal.language English en
heal.publicationDate 1998 en
heal.abstract To predict microbial growth during chill storage of a traditional Greek raw sausage, a numerical model was developed and validated. In our novel approach, the specific growth rate of each microbial population was calculated on the basis of the main microbial populations grown in the sausage. In addition, the specific destructive effect of the sausage ecosystem was introduced to evaluate microbial growth. The model was integrated by the Runge-Kutta method and the parameter values were optimised by the least squares method. Fitting of the model to the experimental data derived from four sausage batches stored aerobically at 3 and 12 degrees C successfully described the microbial growth kinetics in the sausage niche. Finally, the parameter values estimated by the fitting of the model on the data set from each batch were used to predict microbial growth in the other batches at both storage temperatures. (C) 1998 Elsevier Science B.V. All rights reserved. en
heal.publisher ELSEVIER SCIENCE BV en
heal.journalName INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY en
dc.identifier.issue 1-2 en
dc.identifier.volume 43 en
dc.identifier.isi ISI:000076033200006 en
dc.identifier.spage 39 en
dc.identifier.epage 52 en


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