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Using Image Analysis Technique for Predicting Light Lamb Carcass Composition

2024Artigo científico Acesso aberto
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dc.contributor.authorAfonso, João J.
dc.contributor.authorAlmeida, Mariana
dc.contributor.authorBatista, Ana Catharina
dc.contributor.authorGuedes, Cristina
dc.contributor.authorTeixeira, Alfredo
dc.contributor.authorSilva, Severiano
dc.contributor.authorSantos, Virgínia
dc.date.accessioned2024-07-22T09:29:32Z
dc.date.available2024-07-22T09:29:32Z
dc.date.issued2024
dc.description.abstractOver the years, numerous techniques have been explored to assess the composition and quality of sheep carcasses. This study focuses on the utilization of video image analysis (VIA) to evaluate the composition of light lamb carcasses (4.52 ± 1.34 kg, mean cold carcass weight ± SD). Photographic images capturing the lateral and dorsal sides of fifty-five light lamb carcasses were subjected to analysis. A comprehensive set of measurements was recorded, encompassing dimensions such as lengths, widths, angles, areas, and perimeters, totaling 21 measurements for the lateral view images and 29 for the dorsal view images. K-Folds stepwise multiple regression analyses were employed to construct prediction models for carcass tissue weights (including muscle, subcutaneous fat, intermuscular fat, and bone) and their respective percentages. The most effective prediction equations were established using data from cold carcass weight (CCW) and measurements from both dorsal and lateral views. These models accounted for a substantial portion of the observed variation in the weights of all carcass tissues (with K-fold-R2 ranging from 0.83 to 0.98). In terms of carcass tissue percentages, although the degree of variation explained was slightly lower (with K-fold-R2 ranging from 0.41 to 0.78), the VIA measurements remained integral to the predictive models. These findings underscore the efficacy of VIA as an objective tool for assessing the composition of light lamb carcasses, which are carcasses weighing ≈ 4–8 kg.pt_PT
dc.description.sponsorshipThe authors acknowledge the financial support of the research unit CECAV, which was financed by the National Funds from FCT, the Portuguese Foundation for Science and Technology (FCT), project number UIDB/00772/2020 (Doi:10.54499/UIDB/00772/2020). na Catharina Batista also acknowledges the financial support from the Brazilian agency CAPES—Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (Process 1052/13-6).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAfonso, João J.; Almeida, Mariana; Batista, Ana Catharina; Guedes, Cristina; Teixeira, Alfredo; Silva, Severiano; Santos, Virgínia (2024). Using Image Analysis Technique for Predicting Light Lamb Carcass Composition. Animals. ISSN 2076-2615. 14:11, p. 1-13pt_PT
dc.identifier.doi10.3390/ani14111593pt_PT
dc.identifier.issn2076-2615
dc.identifier.urihttp://hdl.handle.net/10198/30042
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationVeterinary and Animal Science Research Centre
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCarcass compositionpt_PT
dc.subjectLight lambspt_PT
dc.subjectVideo image analysispt_PT
dc.titleUsing Image Analysis Technique for Predicting Light Lamb Carcass Compositionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleVeterinary and Animal Science Research Centre
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00772%2F2020/PT
oaire.citation.endPage13pt_PT
oaire.citation.issue11pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleAnimalspt_PT
oaire.citation.volume14pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameTeixeira
person.givenNameAlfredo
person.identifier958487
person.identifier.ciencia-id2A1A-FF0C-185B
person.identifier.orcid0000-0003-4607-4796
person.identifier.ridG-4118-2011
person.identifier.scopus-author-id56195849200
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication27cc89a2-6661-4d8d-a727-21109c04a74e
relation.isAuthorOfPublication.latestForDiscovery27cc89a2-6661-4d8d-a727-21109c04a74e
relation.isProjectOfPublicationf4910f6b-8c6d-4416-bdcb-f1c208046de2
relation.isProjectOfPublication.latestForDiscoveryf4910f6b-8c6d-4416-bdcb-f1c208046de2

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