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dc.creatorLIMA, Alyne Cristina Sodré-
dc.creatorSOUSA, Marco Antonio Paula de-
dc.creatorARAÚJO, Jonas Carneiro-
dc.creatorNUNES, Manuella Paula de Mesquita-
dc.creatorMORAIS, Eziquiel de-
dc.creatorANDRADE, Stefano Juliano Tavares de-
dc.creatorCHAVES, Luciara Celi da Silva-
dc.creatorSILVA, André Guimarães Maciel e-
dc.creator.Latteshttp://lattes.cnpq.br/0601407560666791pt_BR
dc.date.accessioned2021-08-17T19:00:46Z-
dc.date.available2021-08-18-
dc.date.available2021-08-17T19:00:46Z-
dc.date.issued2017-06-01-
dc.identifier.citationLIMA, Alyne Cristina Sodré et al. Discrimination ability of Santa Inês and crossbred Santa Inês × Dorper lamb heavy carcasses by the Brazilian and European classification systemsRevista Brasileira de Zootecnia, vol. 46, n. 6, p. 527-536, 2017. Disponível em: https://www.scielo.br/j/rbz/a/kW6tvhQjYPSSFtCQBnyDZvp/?lang=en. Acesso em 17 ago. 2021.pt_BR
dc.citation.volume46pt_BR
dc.citation.issue6pt_BR
dc.citation.spage527pt_BR
dc.citation.epage536pt_BR
dc.identifier.doihttps://doi.org/10.1590/S1806-92902017000600008pt_BR
dc.identifier.issn1806-9290pt_BR
dc.identifier.urihttp://repositorio.ifap.edu.br:8080/jspui/handle/prefix/380-
dc.description.resumoThe ability of discriminating carcass characteristics of different fat cover scores of heavy carcasses, according to the European (EUS) and Brazilian (BRS) classification systems, was assessed. Fifty-six lambs, weighing between 26.25 and 46.15 kg, of the Santa Inês and crossbred Santa Inês × Dorper genetic groups were evaluated. The level of adiposity was assessed through color photography of the carcasses after refrigeration according to the EUS and BRS. The carcasses were assigned to four groups by cluster analysis according to 25 variables, namely, cold carcass weight; muscle; bone; fat; and muscle:fat and fat:bone ratios of the carcass and cuts (hindquarter, shoulder, back); kidney, pelvic, and inguinal fat; and subcutaneous fat thickness. Of the four groups obtained by cluster analysis, the scores according to the different classification systems only statistically differed between groups 1 and 4. The BRS had a higher number of variables well correlated with the scores by assessors than the groups classified by the EUS. The BRS was better correlated with tissue composition. However, most variables were better correlated with backfat thickness than the score obtained through the classification systems. Better results were obtained regarding the prediction of carcass fat by the BRS using backfat thickness or cold carcass weight. The Brazilian lamb carcass classification system better predicts tissue composition and is the best method to discriminate intermediate-fat classes when associated with cold carcass weight.pt_BR
dc.description.provenanceSubmitted by Borges Elinielle (elinielle.borges@ifap.edu.br) on 2021-08-17T18:59:51Z No. of bitstreams: 1 LIMA (2017).Discrimination ability...pdf: 1021541 bytes, checksum: b994d1897ab4a72221875cd1be53a0ed (MD5)en
dc.description.provenanceApproved for entry into archive by Borges Elinielle (elinielle.borges@ifap.edu.br) on 2021-08-17T19:00:46Z (GMT) No. of bitstreams: 1 LIMA (2017).Discrimination ability...pdf: 1021541 bytes, checksum: b994d1897ab4a72221875cd1be53a0ed (MD5)en
dc.description.provenanceMade available in DSpace on 2021-08-17T19:00:46Z (GMT). No. of bitstreams: 1 LIMA (2017).Discrimination ability...pdf: 1021541 bytes, checksum: b994d1897ab4a72221875cd1be53a0ed (MD5) Previous issue date: 2017-06-01en
dc.languageengpt_BR
dc.publisherInstituto Federal do Amapápt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.initialsIFAPpt_BR
dc.relation.ispartofRevista Brasileira de Zootecniapt_BR
dc.rightsAcesso Abertopt_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/br/*
dc.subjectConformation.pt_BR
dc.subjectNon-specialized breeds.pt_BR
dc.subjectTissue compositionpt_BR
dc.subject.cnpqCNPQ::CIENCIAS AGRARIASpt_BR
dc.titleDiscrimination ability of Santa Inês and crossbred Santa Inês × Dorper lamb heavy carcasses by the Brazilian and European classification systemspt_BR
dc.typeArtigo de Periódicopt_BR
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