Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , , , |
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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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 |
|
_version_ |
1808128720407363584 |