Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy

Detalhes bibliográficos
Autor(a) principal: Magalhães, Ana Fabrícia Braga [UNESP]
Data de Publicação: 2018
Outros Autores: Teixeira, Gustavo Henrique de Almeida [UNESP], Ríos, Ana Cristina Herrera [UNESP], Silva, Danielly Beraldo dos Santos [UNESP], Mota, Lúcio Flávio Macedo [UNESP], Muniz, Maria Malane Magalhães [UNESP], de Morais, Camilo de Lelis Medeiros, de Lima, Kássio Michell Gomes, Júnior, Luis Carlos Cunha [UNESP], Baldi, Fernando [UNESP], Carvalheiro, Roberto [UNESP], de Oliveira, Henrique Nunes [UNESP], Chardulo, Luis Artur Loyola [UNESP], de Albuquerque, Lucia Galvão [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1093/jas/sky284
http://hdl.handle.net/11449/189799
Resumo: The main definition for meat quality should include factors that affect consumer appreciation of the product. Physical laboratory analyses are necessary to identify factors that affect meat quality and specific equipment is used for this purpose, which is expensive and destructive, and the analyses are usually time consuming. An alternative method to performing several beef analyses is near-infrared reflectance spectroscopy (NIRS), which permits to reduce costs and to obtain faster, simpler, and nondestructive measurements. The objective of this study was to evaluate the feasibility of NIRS to predict shear force [Warner-Bratzler shear force (WBSF)], marbling, and color (*a = redness; b* = yellowness; and L* = lightness) in meat samples of uncastrated male Nelore cattle, that were approximately 2-yr-old. Samples of longissimus thoracis (n = 644) were collected and spectra were obtained prior to meat quality analysis. Multivariate calibration was performed by partial least squares regression. Several preprocessing techniques were evaluated alone and in combination: raw data, reduction of spectral range, multiplicative scatter correction, and 1st derivative. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), coefficient of determination in the calibration (R2C), and prediction (R2P) groups. Among the different preprocessing techniques, the reduction of spectral range provided the best prediction accuracy for all traits. The NIRS showed a better performance to predict WBSF (RMSEP = 1.42 kg, R2P = 0.40) and b* color (RMSEP = 1.21, R2P = 0.44), while its ability to accurately predict L* (RMSEP = 1.98, R2P = 0.16) and a* (RMSEP = 1.42, R2P = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle.
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spelling Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopyMarblingMeat colorPreprocessing techniquesShear forceThe main definition for meat quality should include factors that affect consumer appreciation of the product. Physical laboratory analyses are necessary to identify factors that affect meat quality and specific equipment is used for this purpose, which is expensive and destructive, and the analyses are usually time consuming. An alternative method to performing several beef analyses is near-infrared reflectance spectroscopy (NIRS), which permits to reduce costs and to obtain faster, simpler, and nondestructive measurements. The objective of this study was to evaluate the feasibility of NIRS to predict shear force [Warner-Bratzler shear force (WBSF)], marbling, and color (*a = redness; b* = yellowness; and L* = lightness) in meat samples of uncastrated male Nelore cattle, that were approximately 2-yr-old. Samples of longissimus thoracis (n = 644) were collected and spectra were obtained prior to meat quality analysis. Multivariate calibration was performed by partial least squares regression. Several preprocessing techniques were evaluated alone and in combination: raw data, reduction of spectral range, multiplicative scatter correction, and 1st derivative. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), coefficient of determination in the calibration (R2C), and prediction (R2P) groups. Among the different preprocessing techniques, the reduction of spectral range provided the best prediction accuracy for all traits. The NIRS showed a better performance to predict WBSF (RMSEP = 1.42 kg, R2P = 0.40) and b* color (RMSEP = 1.21, R2P = 0.44), while its ability to accurately predict L* (RMSEP = 1.98, R2P = 0.16) and a* (RMSEP = 1.42, R2P = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Department of Animal Science School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)Institute of Chemistry Biological Chemistry and Chemometric Federal University of Rio Grande do NorteSchool of Pharmacy and Biomedical Sciences University of Central LancashireDepartment of Animal Nutrition and Improvement College of Veterinary and Animal Science São Paulo State University (Unesp)Department of Animal Science School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)Department of Animal Nutrition and Improvement College of Veterinary and Animal Science São Paulo State University (Unesp)Universidade Estadual Paulista (Unesp)Federal University of Rio Grande do NorteUniversity of Central LancashireMagalhães, Ana Fabrícia Braga [UNESP]Teixeira, Gustavo Henrique de Almeida [UNESP]Ríos, Ana Cristina Herrera [UNESP]Silva, Danielly Beraldo dos Santos [UNESP]Mota, Lúcio Flávio Macedo [UNESP]Muniz, Maria Malane Magalhães [UNESP]de Morais, Camilo de Lelis Medeirosde Lima, Kássio Michell GomesJúnior, Luis Carlos Cunha [UNESP]Baldi, Fernando [UNESP]Carvalheiro, Roberto [UNESP]de Oliveira, Henrique Nunes [UNESP]Chardulo, Luis Artur Loyola [UNESP]de Albuquerque, Lucia Galvão [UNESP]2019-10-06T16:52:33Z2019-10-06T16:52:33Z2018-09-29info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article4229-4237http://dx.doi.org/10.1093/jas/sky284Journal of Animal Science, v. 96, n. 10, p. 4229-4237, 2018.1525-31630021-8812http://hdl.handle.net/11449/18979910.1093/jas/sky2842-s2.0-850547340889820754011277263Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Animal Scienceinfo:eu-repo/semantics/openAccess2024-06-07T18:41:04Zoai:repositorio.unesp.br:11449/189799Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:54:58.843503Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
title Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
spellingShingle Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
Magalhães, Ana Fabrícia Braga [UNESP]
Marbling
Meat color
Preprocessing techniques
Shear force
title_short Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
title_full Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
title_fullStr Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
title_full_unstemmed Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
title_sort Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
author Magalhães, Ana Fabrícia Braga [UNESP]
author_facet Magalhães, Ana Fabrícia Braga [UNESP]
Teixeira, Gustavo Henrique de Almeida [UNESP]
Ríos, Ana Cristina Herrera [UNESP]
Silva, Danielly Beraldo dos Santos [UNESP]
Mota, Lúcio Flávio Macedo [UNESP]
Muniz, Maria Malane Magalhães [UNESP]
de Morais, Camilo de Lelis Medeiros
de Lima, Kássio Michell Gomes
Júnior, Luis Carlos Cunha [UNESP]
Baldi, Fernando [UNESP]
Carvalheiro, Roberto [UNESP]
de Oliveira, Henrique Nunes [UNESP]
Chardulo, Luis Artur Loyola [UNESP]
de Albuquerque, Lucia Galvão [UNESP]
author_role author
author2 Teixeira, Gustavo Henrique de Almeida [UNESP]
Ríos, Ana Cristina Herrera [UNESP]
Silva, Danielly Beraldo dos Santos [UNESP]
Mota, Lúcio Flávio Macedo [UNESP]
Muniz, Maria Malane Magalhães [UNESP]
de Morais, Camilo de Lelis Medeiros
de Lima, Kássio Michell Gomes
Júnior, Luis Carlos Cunha [UNESP]
Baldi, Fernando [UNESP]
Carvalheiro, Roberto [UNESP]
de Oliveira, Henrique Nunes [UNESP]
Chardulo, Luis Artur Loyola [UNESP]
de Albuquerque, Lucia Galvão [UNESP]
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Federal University of Rio Grande do Norte
University of Central Lancashire
dc.contributor.author.fl_str_mv Magalhães, Ana Fabrícia Braga [UNESP]
Teixeira, Gustavo Henrique de Almeida [UNESP]
Ríos, Ana Cristina Herrera [UNESP]
Silva, Danielly Beraldo dos Santos [UNESP]
Mota, Lúcio Flávio Macedo [UNESP]
Muniz, Maria Malane Magalhães [UNESP]
de Morais, Camilo de Lelis Medeiros
de Lima, Kássio Michell Gomes
Júnior, Luis Carlos Cunha [UNESP]
Baldi, Fernando [UNESP]
Carvalheiro, Roberto [UNESP]
de Oliveira, Henrique Nunes [UNESP]
Chardulo, Luis Artur Loyola [UNESP]
de Albuquerque, Lucia Galvão [UNESP]
dc.subject.por.fl_str_mv Marbling
Meat color
Preprocessing techniques
Shear force
topic Marbling
Meat color
Preprocessing techniques
Shear force
description The main definition for meat quality should include factors that affect consumer appreciation of the product. Physical laboratory analyses are necessary to identify factors that affect meat quality and specific equipment is used for this purpose, which is expensive and destructive, and the analyses are usually time consuming. An alternative method to performing several beef analyses is near-infrared reflectance spectroscopy (NIRS), which permits to reduce costs and to obtain faster, simpler, and nondestructive measurements. The objective of this study was to evaluate the feasibility of NIRS to predict shear force [Warner-Bratzler shear force (WBSF)], marbling, and color (*a = redness; b* = yellowness; and L* = lightness) in meat samples of uncastrated male Nelore cattle, that were approximately 2-yr-old. Samples of longissimus thoracis (n = 644) were collected and spectra were obtained prior to meat quality analysis. Multivariate calibration was performed by partial least squares regression. Several preprocessing techniques were evaluated alone and in combination: raw data, reduction of spectral range, multiplicative scatter correction, and 1st derivative. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), coefficient of determination in the calibration (R2C), and prediction (R2P) groups. Among the different preprocessing techniques, the reduction of spectral range provided the best prediction accuracy for all traits. The NIRS showed a better performance to predict WBSF (RMSEP = 1.42 kg, R2P = 0.40) and b* color (RMSEP = 1.21, R2P = 0.44), while its ability to accurately predict L* (RMSEP = 1.98, R2P = 0.16) and a* (RMSEP = 1.42, R2P = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle.
publishDate 2018
dc.date.none.fl_str_mv 2018-09-29
2019-10-06T16:52:33Z
2019-10-06T16:52:33Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1093/jas/sky284
Journal of Animal Science, v. 96, n. 10, p. 4229-4237, 2018.
1525-3163
0021-8812
http://hdl.handle.net/11449/189799
10.1093/jas/sky284
2-s2.0-85054734088
9820754011277263
url http://dx.doi.org/10.1093/jas/sky284
http://hdl.handle.net/11449/189799
identifier_str_mv Journal of Animal Science, v. 96, n. 10, p. 4229-4237, 2018.
1525-3163
0021-8812
10.1093/jas/sky284
2-s2.0-85054734088
9820754011277263
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Animal Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 4229-4237
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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