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Modeling and predicting spoilage of cooked, cured meat products by multivariate analysis

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dc.contributor.author Mataragas, M en
dc.contributor.author Skandamis, P en
dc.contributor.author Nychas, G-JE en
dc.contributor.author Drosinos, EH en
dc.date.accessioned 2014-06-06T06:48:01Z
dc.date.available 2014-06-06T06:48:01Z
dc.date.issued 2007 en
dc.identifier.issn 03091740 en
dc.identifier.uri http://dx.doi.org/10.1016/j.meatsci.2007.03.023 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/3918
dc.subject Meat product en
dc.subject Modeling en
dc.subject Multivariate analysis en
dc.subject Partial least square regression en
dc.subject Shelf life prediction en
dc.subject Spoilage en
dc.subject.other Bacteria (microorganisms) en
dc.title Modeling and predicting spoilage of cooked, cured meat products by multivariate analysis en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.meatsci.2007.03.023 en
heal.publicationDate 2007 en
heal.abstract A cooked, cured meat product is a perishable product spoiled mainly by lactic acid bacteria (LAB). LAB cause discoloration, slime formation, off-odors and off-flavors as the result of their metabolic activity producing various products. These microbial products in conjunction with the microbial population could be used to assess the degree of spoilage of this type of product. The spoilage evaluation was achieved by following a multivariate approach. Cluster analysis, principal component analysis and partial least square regression were employed to associate spoilage with microbiological and physicochemical parameters. The developed model was capable of giving accurate predictions of spoilage describing the spoilage associations. The study might contribute to the improvement of quality assurance systems of meat enterprises. © 2007 Elsevier Ltd. All rights reserved. en
heal.journalName Meat Science en
dc.identifier.issue 3 en
dc.identifier.volume 77 en
dc.identifier.doi 10.1016/j.meatsci.2007.03.023 en
dc.identifier.spage 348 en
dc.identifier.epage 356 en


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