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Lamb meat quality assessment by support vector machines

dc.contributor.authorCortez, Paulo
dc.contributor.authorPortelinha, Manuel
dc.contributor.authorRodrigues, Sandra
dc.contributor.authorCadavez, Vasco
dc.contributor.authorTeixeira, Alfredo
dc.date.accessioned2008-09-16T14:14:50Z
dc.date.available2008-09-16T14:14:50Z
dc.date.issued2006
dc.description.abstractThe correct assessment of meat quality (i.e., to fulfill the consumer's needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner-Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches.en
dc.identifier.citationCortez, P.; Portelinha, M.; Rodrigues, Sandra; Cadavez, Vasco; Teixeira, Alfredo (2006). Lamb meat quality assessment by support vector machines. Neural Processing Letters. ISSN 1370-4621. 24:1, p. 41-51en
dc.identifier.doi10.1007/s11063-006-9009-6
dc.identifier.issn1370-4621
dc.identifier.urihttp://hdl.handle.net/10198/858
dc.language.isoengen
dc.publisherSpringeren
dc.subjectRegressionen
dc.subjectMultilayer perceptronsen
dc.subjectSupport vector machinesen
dc.subjectMeat qualityen
dc.subjectData miningen
dc.subjectFeature selectionen
dc.titleLamb meat quality assessment by support vector machinesen
dc.typejournal article
dspace.entity.typePublication
person.familyNamePortelinha
person.familyNameRodrigues
person.familyNameCadavez
person.familyNameTeixeira
person.givenNameManuel
person.givenNameSandra
person.givenNameVasco
person.givenNameAlfredo
person.identifierR-000-HDG
person.identifier958487
person.identifier.ciencia-id7610-3BAB-B4C1
person.identifier.ciencia-id651F-D964-32E1
person.identifier.ciencia-id441B-01AB-A12E
person.identifier.ciencia-id2A1A-FF0C-185B
person.identifier.orcid0000-0003-3301-1729
person.identifier.orcid0000-0002-3077-7414
person.identifier.orcid0000-0003-4607-4796
person.identifier.ridC-6486-2008
person.identifier.ridA-3958-2010
person.identifier.ridG-4118-2011
person.identifier.scopus-author-id14048942500
person.identifier.scopus-author-id9039121900
person.identifier.scopus-author-id56195849200
rcaap.rightsopenAccess
rcaap.typearticleen
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relation.isAuthorOfPublication684ab6a0-e3ea-4b6e-9874-d20d71e2a3f5
relation.isAuthorOfPublication57b410e9-f6b7-42ff-ab3d-b526278715eb
relation.isAuthorOfPublication27cc89a2-6661-4d8d-a727-21109c04a74e
relation.isAuthorOfPublication.latestForDiscovery27cc89a2-6661-4d8d-a727-21109c04a74e

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