Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Animal Sciences (Online) |
Texto Completo: | https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/64555 |
Resumo: | The purpose of this study was to establish a multivariate model using two complementary multivariate statistical techniques: Factor Analysis and Stepwise Multiple Regression, to predict tissue composition through carcass characteristics of Santa Inês sheep. The data was obtained from 82 Santa Inês sheep under confinement. The predictor variables were carcass characteristics related to weight, yield, morphometric measures and meat cuts. The use of latent variables from factor analysis in multiple regression models eliminates the problem of multicollinearity of the explanatory variables, improving the accuracy of interpretation of results by proposing a better fit of the mathematical model. However, the coefficient of determination (R²) values were moderate for muscle proportion and total fat, and low for bone proportion, indicating that more appropriate independent variables should be used to better predict the proportion of tissues in Santa Inês sheep. |
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Acta Scientiarum. Animal Sciences (Online) |
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Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheepMultivariate modeling to estimate the composition of carcass tissues of Santa Inês sheepmultivariate analysis; carcass dissection; factor scores; morphometric measures.multivariate analysis; carcass dissection; factor scores; morphometric measures.The purpose of this study was to establish a multivariate model using two complementary multivariate statistical techniques: Factor Analysis and Stepwise Multiple Regression, to predict tissue composition through carcass characteristics of Santa Inês sheep. The data was obtained from 82 Santa Inês sheep under confinement. The predictor variables were carcass characteristics related to weight, yield, morphometric measures and meat cuts. The use of latent variables from factor analysis in multiple regression models eliminates the problem of multicollinearity of the explanatory variables, improving the accuracy of interpretation of results by proposing a better fit of the mathematical model. However, the coefficient of determination (R²) values were moderate for muscle proportion and total fat, and low for bone proportion, indicating that more appropriate independent variables should be used to better predict the proportion of tissues in Santa Inês sheep.The purpose of this study was to establish a multivariate model using two complementary multivariate statistical techniques: Factor Analysis and Stepwise Multiple Regression, to predict tissue composition through carcass characteristics of Santa Inês sheep. The data was obtained from 82 Santa Inês sheep under confinement. The predictor variables were carcass characteristics related to weight, yield, morphometric measures and meat cuts. The use of latent variables from factor analysis in multiple regression models eliminates the problem of multicollinearity of the explanatory variables, improving the accuracy of interpretation of results by proposing a better fit of the mathematical model. However, the coefficient of determination (R²) values were moderate for muscle proportion and total fat, and low for bone proportion, indicating that more appropriate independent variables should be used to better predict the proportion of tissues in Santa Inês sheep.Editora da Universidade Estadual de Maringá2023-12-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/6455510.4025/actascianimsci.v46i1.64555Acta Scientiarum. Animal Sciences; Vol 46 (2024): Publicação contínua; e64555Acta Scientiarum. Animal Sciences; v. 46 (2024): Publicação contínua; e645551807-86721806-2636reponame:Acta Scientiarum. Animal Sciences (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttps://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/64555/751375156938Copyright (c) 2024 Acta Scientiarum. Animal Scienceshttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Maciel, Marilene dos SantosArandas, Janaína Kelli Gomes Carvalho, Francisco Fernando Ramos de Cruz, George Rodrigo Beltrão da Costa, Roberto GermanoRibeiro, Neila Lidiany Ribeiro, Maria Norma2024-02-08T19:30:49Zoai:periodicos.uem.br/ojs:article/64555Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSciPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAnimSci/oaiactaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com1807-86721806-2636opendoar:2024-02-08T19:30:49Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
title |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
spellingShingle |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep Maciel, Marilene dos Santos multivariate analysis; carcass dissection; factor scores; morphometric measures. multivariate analysis; carcass dissection; factor scores; morphometric measures. |
title_short |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
title_full |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
title_fullStr |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
title_full_unstemmed |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
title_sort |
Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep |
author |
Maciel, Marilene dos Santos |
author_facet |
Maciel, Marilene dos Santos Arandas, Janaína Kelli Gomes Carvalho, Francisco Fernando Ramos de Cruz, George Rodrigo Beltrão da Costa, Roberto Germano Ribeiro, Neila Lidiany Ribeiro, Maria Norma |
author_role |
author |
author2 |
Arandas, Janaína Kelli Gomes Carvalho, Francisco Fernando Ramos de Cruz, George Rodrigo Beltrão da Costa, Roberto Germano Ribeiro, Neila Lidiany Ribeiro, Maria Norma |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Maciel, Marilene dos Santos Arandas, Janaína Kelli Gomes Carvalho, Francisco Fernando Ramos de Cruz, George Rodrigo Beltrão da Costa, Roberto Germano Ribeiro, Neila Lidiany Ribeiro, Maria Norma |
dc.subject.por.fl_str_mv |
multivariate analysis; carcass dissection; factor scores; morphometric measures. multivariate analysis; carcass dissection; factor scores; morphometric measures. |
topic |
multivariate analysis; carcass dissection; factor scores; morphometric measures. multivariate analysis; carcass dissection; factor scores; morphometric measures. |
description |
The purpose of this study was to establish a multivariate model using two complementary multivariate statistical techniques: Factor Analysis and Stepwise Multiple Regression, to predict tissue composition through carcass characteristics of Santa Inês sheep. The data was obtained from 82 Santa Inês sheep under confinement. The predictor variables were carcass characteristics related to weight, yield, morphometric measures and meat cuts. The use of latent variables from factor analysis in multiple regression models eliminates the problem of multicollinearity of the explanatory variables, improving the accuracy of interpretation of results by proposing a better fit of the mathematical model. However, the coefficient of determination (R²) values were moderate for muscle proportion and total fat, and low for bone proportion, indicating that more appropriate independent variables should be used to better predict the proportion of tissues in Santa Inês sheep. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-13 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/64555 10.4025/actascianimsci.v46i1.64555 |
url |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/64555 |
identifier_str_mv |
10.4025/actascianimsci.v46i1.64555 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/64555/751375156938 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2024 Acta Scientiarum. Animal Sciences http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2024 Acta Scientiarum. Animal Sciences http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Editora da Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Animal Sciences; Vol 46 (2024): Publicação contínua; e64555 Acta Scientiarum. Animal Sciences; v. 46 (2024): Publicação contínua; e64555 1807-8672 1806-2636 reponame:Acta Scientiarum. Animal Sciences (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Animal Sciences (Online) |
collection |
Acta Scientiarum. Animal Sciences (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Animal Sciences (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaanim@uem.br||actaanim@uem.br|| rev.acta@gmail.com |
_version_ |
1799315364382769152 |