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dc.contributor.authorMasferrer, Gerard
dc.contributor.authorCarreras, Ricard
dc.contributor.authorFont-i-Furnols, Maria
dc.contributor.authorGispert, Marina
dc.contributor.authorSerra, Moises
dc.contributor.authorMarti-Puig, Pere
dc.contributor.otherIndústries Alimentàriesca
dc.date.accessioned2019-06-05T13:32:45Z
dc.date.available2020-04-24T22:01:12Z
dc.date.issued2019-04-25
dc.identifier.citationMasferrer, Gerard, Ricard Carreras, Maria Font-i-Furnols, Marina Gispert, Moises Serra, and Pere Marti-Puig. 2019. "Automatic Ham Classification Method Based On Support Vector Machine Model Increases Accuracy And Benefits Compared To Manual Classification". Meat Science 155: 1-7. Elsevier BV. doi:10.1016/j.meatsci.2019.04.018.ca
dc.identifier.issn0309-1740ca
dc.identifier.urihttp://hdl.handle.net/20.500.12327/415
dc.description.abstractThe thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting.ca
dc.format.extent17ca
dc.language.isoengca
dc.publisherElsevierca
dc.relation.ispartofMeat Scienceca
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAutomatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classificationca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/acceptedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.subject.udc663/664ca
dc.identifier.doihttps://doi.org/10.1016/j.meatsci.2019.04.018ca
dc.contributor.groupQualitat i Tecnologia Alimentàriaca


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/