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Assessment of goat fat depots using ultrasound technology and multiple multivariate prediction models

2010Journal article Open access
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dc.contributor.authorPeres, António M.
dc.contributor.authorDias, L.G.
dc.contributor.authorJoy, Margalida
dc.contributor.authorTeixeira, Alfredo
dc.date.accessioned2011-06-15T13:23:04Z
dc.date.available2011-06-15T13:23:04Z
dc.date.issued2010
dc.description.abstractAssessment of fat depots for several goat body parts is an expensive and time-consuming task requiring a trained technician. Therefore, the establishment of models to predict fat depots based on data requiring simpler and easier procedures, such as ultrasound measurements, that could be carried out in vivo, would be a major advantage. An interesting alternative to the use of multiple linear regression models is the use of partial least squares or artificial neural network models because they allow the establishment of one model to simultaneously predict different fat depots of interest. In this work, the applicability of these models to simultaneously predict 7 goat fat depots (subcutaneous fat, intermuscular fat, total carcass fat, omental fat, kidney and pelvic fat, mesenteric fat, and total body fat) was investigated. Although satisfactory correlation and prediction results were obtained using the multiple partial least squares model (cross-verification and validation R2 and standard prediction error values between 0.66 and 0.98 and 247 and 2,168, respectively), the best global correlation and prediction performances were achieved with the multiple radial basis function artificial neural network (verification and validation R2 and standard prediction error values between 0.82 and 0.96 and 304 and 1,707, respectively). These 2 multiple models allowed correlating and predicting simultaneously the 7 goat fat depots based on the goat BW and on only 2 ultrasonic measures (lumbar subcutaneous fat between fifth and sixth vertebrae and the fat depth at the third sternebra). Moreover, both multiple models showed better results compared with those obtained with multiple linear regression models proposed in previous work.por
dc.identifier.citationPeres, A.; Dias, L.; Joy, M.; Teixeira, A. (2010). Assessment of goat fat depots using ultrasound technology and multiple multivariate prediction models. Journal of Animal Science. ISSN 0021-8812. 88:2, p. 572-580por
dc.identifier.doi10.2527/jas.2009-2195
dc.identifier.issn0021-8812
dc.identifier.urihttp://hdl.handle.net/10198/5197
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherAmerican Society of Animal Sciencepor
dc.relation.ispartofseries88;
dc.subjectArtificial neural networkspor
dc.subjectCarcass fat composition predictionpor
dc.subjectGoatpor
dc.subjectPartial least squarespor
dc.subjectUltrasound technologypor
dc.titleAssessment of goat fat depots using ultrasound technology and multiple multivariate prediction modelspor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage580por
oaire.citation.issue2por
oaire.citation.startPage572por
oaire.citation.titleJournal of Animal Sciencepor
person.familyNamePeres
person.familyNameDias
person.familyNameTeixeira
person.givenNameAntónio M.
person.givenNameLuís G.
person.givenNameAlfredo
person.identifier107333
person.identifier958487
person.identifier.ciencia-idCF16-5443-F420
person.identifier.ciencia-id2F11-9092-FAAF
person.identifier.ciencia-id2A1A-FF0C-185B
person.identifier.orcid0000-0001-6595-9165
person.identifier.orcid0000-0002-1210-4259
person.identifier.orcid0000-0003-4607-4796
person.identifier.ridI-8470-2012
person.identifier.ridG-4118-2011
person.identifier.scopus-author-id7102331969
person.identifier.scopus-author-id23569169900
person.identifier.scopus-author-id56195849200
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication7d93be47-8dc4-4413-9304-5b978773d3bb
relation.isAuthorOfPublicationeac8c166-4056-4ed0-8d8d-7ecb2c4481a5
relation.isAuthorOfPublication27cc89a2-6661-4d8d-a727-21109c04a74e
relation.isAuthorOfPublication.latestForDiscovery7d93be47-8dc4-4413-9304-5b978773d3bb

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