Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/3929 |
Resumo: | Near infrared reflectance spectroscopy (NIRS) has been successfully applied in the quantitative determination of the main constituents of beef but it has been presenting inconsistent results in determining characteristics relating to tenderness. In addition, the various aspects related to data processing (mathematical pre-treatments, spectral bands, sample presentation, regression method), should be constantly evaluated, since they affect the prediction cap acity of NIRS. In this context, the present study was developed to determine which spectral data-processing methods make it possible, using the PLS regression method, to obtain robust calibration models that determine the chemical composition and tenderness characteristics of beef. The accuracy of the models was determined by external validation, which has been little used in previously published studies. To develop the calibration models, three spectra were collected from each sample of the Longissimus dorsi muscle of 25 mixed-breed castrated dairy calves, divided into five treatments (five repetitions in each) based on supplying diets containing millet and including babassu mesocarp bran at proportions of 0; 12; 24; 36 and 48% in the dry matter of the total diet, comprising 75 spectra. For the external validation set, samples were used from five mixedbreed castrated dairy calves fed on a diet based on maize and soybean, totalling 15 spectra. To determine the chemical composition (fat content, protein, ash content and moisture) and the tenderness properties (water holding capacity – WHC -, total and soluble collagen, shear force, FMI and pH), 135 calibration models were developed with mathematical pre-treatments available on VISION software, version 3.1, using PLS regression, from which 37 (27.41% of the total) presented coefficients of determination considered good or excellent in their predictive capacity. The pre-treatment with “first derivatives” made it possible to develop more robust models for the chemical composition properties, except for RMF, in which “Savitzky-Golay” and “second derivatives” were more efficient, obtaining R 2 and RPD values above those available in the literature. For determining the tenderness properties in beef, the models develope d with “first and second derivatives” pre-treatments, in isolation or with “Savitzky -Golay” or “multiplicative scatter correction” smoothing methods, presented the highest values of RPD, demonstrating that themselves are efficient chemometric tools for obtaining robust calibration models. Models were obtained with limited predictive capacity only in the determination of total fats and total collagen quantification. This was probably due to the low variability presented in the samples used a nd to the low sensitivity of NIRS for total collagen. It was concluded that NIRS can be used to replace conventional methods, being a fast and precise technique, as well as allowing simultaneous analysis of beef quality characteristics. |
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Lage, Moacir Evandrohttp://lattes.cnpq.br/2053334263459111Rezende, Cíntia Silva Minafra ePrado, Cristiano SalesLage, Moacir EvandroAmoril, José GabrielBueno, Cláudia PeixotoOliveira, Jaison Pereira deNicolau, Edmar Soareshttp://lattes.cnpq.br/6869560815938822Oliveira, Raphael Rocha de2015-01-21T18:47:50Z2014-06-28OLIVEIRA, Raphael Rocha de. Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina. 2014. 133 f. Tese (Doutorado em Ciência Animal) - Universidade Federal de Goiás, Goiânia, 2014.http://repositorio.bc.ufg.br/tede/handle/tede/3929Near infrared reflectance spectroscopy (NIRS) has been successfully applied in the quantitative determination of the main constituents of beef but it has been presenting inconsistent results in determining characteristics relating to tenderness. In addition, the various aspects related to data processing (mathematical pre-treatments, spectral bands, sample presentation, regression method), should be constantly evaluated, since they affect the prediction cap acity of NIRS. In this context, the present study was developed to determine which spectral data-processing methods make it possible, using the PLS regression method, to obtain robust calibration models that determine the chemical composition and tenderness characteristics of beef. The accuracy of the models was determined by external validation, which has been little used in previously published studies. To develop the calibration models, three spectra were collected from each sample of the Longissimus dorsi muscle of 25 mixed-breed castrated dairy calves, divided into five treatments (five repetitions in each) based on supplying diets containing millet and including babassu mesocarp bran at proportions of 0; 12; 24; 36 and 48% in the dry matter of the total diet, comprising 75 spectra. For the external validation set, samples were used from five mixedbreed castrated dairy calves fed on a diet based on maize and soybean, totalling 15 spectra. To determine the chemical composition (fat content, protein, ash content and moisture) and the tenderness properties (water holding capacity – WHC -, total and soluble collagen, shear force, FMI and pH), 135 calibration models were developed with mathematical pre-treatments available on VISION software, version 3.1, using PLS regression, from which 37 (27.41% of the total) presented coefficients of determination considered good or excellent in their predictive capacity. The pre-treatment with “first derivatives” made it possible to develop more robust models for the chemical composition properties, except for RMF, in which “Savitzky-Golay” and “second derivatives” were more efficient, obtaining R 2 and RPD values above those available in the literature. For determining the tenderness properties in beef, the models develope d with “first and second derivatives” pre-treatments, in isolation or with “Savitzky -Golay” or “multiplicative scatter correction” smoothing methods, presented the highest values of RPD, demonstrating that themselves are efficient chemometric tools for obtaining robust calibration models. Models were obtained with limited predictive capacity only in the determination of total fats and total collagen quantification. This was probably due to the low variability presented in the samples used a nd to the low sensitivity of NIRS for total collagen. It was concluded that NIRS can be used to replace conventional methods, being a fast and precise technique, as well as allowing simultaneous analysis of beef quality characteristics.A espectroscopia de reflectância no infravermelho próximo (NIRS) tem sido aplicada com êxito na determinação quantitativa dos principais constituintes da carne bovina, mas tem apresentado resultados inconsistentes na determinação das características relacionadas à maciez. Além disso, os diferentes aspectos relacionados ao processamento dos dados (pré-tratamentos matemáticos, faixas espectrais, apresentação das amostras, método de regressão), devem ser avaliados constantemente, já que afetam a capacidade de predição do NIRS. Assim sendo, o presente estudo foi desenvolvido para determinar quais métodos de processamento de dados espectrais possibilitam, com o método de regressão PLS, a obtenção de modelos de calibração robustos para a determinação d a composição química e das características de maciez da carne bovina, sendo a acurácia dos modelos determinada por validação externa. Para o desenvolvimento dos modelos de calibração, foram coletados três espectros de cada amostra do músculo Longissimus dorsi de 25 novilhos mestiços leiteiros castrados, divididos em cinco tratamentos, cinco repetições em cada, com base no fornecimento de dietas contendo milheto e inclusão de farelo do mesocarpo do babaçu nas proporções de 0; 12; 24; 36 e 48% na matéria seca da dieta total, totalizando 75 espectros. Para o conjunto de validação externa, foram utilizadas amostras de cinco novilhos mestiços leiteiros castrados submetidos à dieta à base de milho e soja, totalizando 15 espectros. Para a determinação da composição química (lipídios totais, proteína, resíduo mineral fixo e umidade ) e de propriedades de maciez (capacidade de retenção de água, colágeno total e solúvel, força de cisalhamento, IFM e pH), foram desenvolvidos 135 modelos de calibração com os pré-tratamentos matemáticos disponíveis no software VISION, versão 3.1, utilizando a regressão PLS, dos quais 37 (27,41% do total) apresentaram valores de coeficientes de determinação considerados como boa ou excelente capacidade preditiva. O pré-tratamento com “primeira derivada” possibilitou o desenvolvimento de modelos mais robustos para as propriedades de composição química, exceto para RMF, em que “Savitzky-Golay” e “segunda derivada” foram mais eficientes, obtendo valores de R 2 e RPD superiores aos disponíveis na literatura. Para a determinação das propriedades de maciez em carne bovina, os modelos desenvolvidos com os pré-tratamentos com “primeira e segunda derivadas”, isoladamente ou com a utilização dos métodos de suavização “Savitzky-Golay” ou “correção multiplicativa de sinal”, apresentaram os maiores valores de RPD, demonstrando ser ferramentas quimiométricas eficientes para a obtenção de modelos de calibração robustos. Foram obtidos modelos com capacidade preditiva limitada apenas para a determinação de lipídios totais e quantificação do colágeno total, provavelmente, devido à baixa variabilidade apresentada nas amostras utilizadas e à baixa sensibilidade do NIRS para o colágeno total. Conclui-se, que a espectroscopia de reflectância no infravermelho próximo pode s er utilizada em substituição aos métodos convencionais, por ser uma técnica rápida, precisa, sensível e que permite a análise simultânea das características de qualidade da carne bovina.Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-01-21T18:47:40Z No. of bitstreams: 2 Tese - Raphael Rocha de Oliveira - 2014.pdf: 1885225 bytes, checksum: 5adb0d9c490f337d13e5335be96b08f2 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2015-01-21T18:47:50Z (GMT) No. of bitstreams: 2 Tese - Raphael Rocha de Oliveira - 2014.pdf: 1885225 bytes, checksum: 5adb0d9c490f337d13e5335be96b08f2 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Made available in DSpace on 2015-01-21T18:47:50Z (GMT). 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dc.title.por.fl_str_mv |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
dc.title.alternative.eng.fl_str_mv |
Multivariate calibration models for NIRS to predict beef quality characteristics |
title |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
spellingShingle |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina Oliveira, Raphael Rocha de Força de cisalhamento Infravermelho próximo Maciez Proteína Quimiometria Regressão PLS Chemometric Near infrared PLS regression Protein Shear force Tenderness CIENCIA E TECNOLOGIA DE ALIMENTOS::TECNOLOGIA DE ALIMENTOS |
title_short |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
title_full |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
title_fullStr |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
title_full_unstemmed |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
title_sort |
Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina |
author |
Oliveira, Raphael Rocha de |
author_facet |
Oliveira, Raphael Rocha de |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Lage, Moacir Evandro |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2053334263459111 |
dc.contributor.advisor-co1.fl_str_mv |
Rezende, Cíntia Silva Minafra e |
dc.contributor.advisor-co2.fl_str_mv |
Prado, Cristiano Sales |
dc.contributor.referee1.fl_str_mv |
Lage, Moacir Evandro |
dc.contributor.referee2.fl_str_mv |
Amoril, José Gabriel |
dc.contributor.referee3.fl_str_mv |
Bueno, Cláudia Peixoto |
dc.contributor.referee4.fl_str_mv |
Oliveira, Jaison Pereira de |
dc.contributor.referee5.fl_str_mv |
Nicolau, Edmar Soares |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6869560815938822 |
dc.contributor.author.fl_str_mv |
Oliveira, Raphael Rocha de |
contributor_str_mv |
Lage, Moacir Evandro Rezende, Cíntia Silva Minafra e Prado, Cristiano Sales Lage, Moacir Evandro Amoril, José Gabriel Bueno, Cláudia Peixoto Oliveira, Jaison Pereira de Nicolau, Edmar Soares |
dc.subject.por.fl_str_mv |
Força de cisalhamento Infravermelho próximo Maciez Proteína Quimiometria Regressão PLS |
topic |
Força de cisalhamento Infravermelho próximo Maciez Proteína Quimiometria Regressão PLS Chemometric Near infrared PLS regression Protein Shear force Tenderness CIENCIA E TECNOLOGIA DE ALIMENTOS::TECNOLOGIA DE ALIMENTOS |
dc.subject.eng.fl_str_mv |
Chemometric Near infrared PLS regression Protein Shear force Tenderness |
dc.subject.cnpq.fl_str_mv |
CIENCIA E TECNOLOGIA DE ALIMENTOS::TECNOLOGIA DE ALIMENTOS |
description |
Near infrared reflectance spectroscopy (NIRS) has been successfully applied in the quantitative determination of the main constituents of beef but it has been presenting inconsistent results in determining characteristics relating to tenderness. In addition, the various aspects related to data processing (mathematical pre-treatments, spectral bands, sample presentation, regression method), should be constantly evaluated, since they affect the prediction cap acity of NIRS. In this context, the present study was developed to determine which spectral data-processing methods make it possible, using the PLS regression method, to obtain robust calibration models that determine the chemical composition and tenderness characteristics of beef. The accuracy of the models was determined by external validation, which has been little used in previously published studies. To develop the calibration models, three spectra were collected from each sample of the Longissimus dorsi muscle of 25 mixed-breed castrated dairy calves, divided into five treatments (five repetitions in each) based on supplying diets containing millet and including babassu mesocarp bran at proportions of 0; 12; 24; 36 and 48% in the dry matter of the total diet, comprising 75 spectra. For the external validation set, samples were used from five mixedbreed castrated dairy calves fed on a diet based on maize and soybean, totalling 15 spectra. To determine the chemical composition (fat content, protein, ash content and moisture) and the tenderness properties (water holding capacity – WHC -, total and soluble collagen, shear force, FMI and pH), 135 calibration models were developed with mathematical pre-treatments available on VISION software, version 3.1, using PLS regression, from which 37 (27.41% of the total) presented coefficients of determination considered good or excellent in their predictive capacity. The pre-treatment with “first derivatives” made it possible to develop more robust models for the chemical composition properties, except for RMF, in which “Savitzky-Golay” and “second derivatives” were more efficient, obtaining R 2 and RPD values above those available in the literature. For determining the tenderness properties in beef, the models develope d with “first and second derivatives” pre-treatments, in isolation or with “Savitzky -Golay” or “multiplicative scatter correction” smoothing methods, presented the highest values of RPD, demonstrating that themselves are efficient chemometric tools for obtaining robust calibration models. Models were obtained with limited predictive capacity only in the determination of total fats and total collagen quantification. This was probably due to the low variability presented in the samples used a nd to the low sensitivity of NIRS for total collagen. It was concluded that NIRS can be used to replace conventional methods, being a fast and precise technique, as well as allowing simultaneous analysis of beef quality characteristics. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-06-28 |
dc.date.accessioned.fl_str_mv |
2015-01-21T18:47:50Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
OLIVEIRA, Raphael Rocha de. Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina. 2014. 133 f. Tese (Doutorado em Ciência Animal) - Universidade Federal de Goiás, Goiânia, 2014. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/3929 |
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OLIVEIRA, Raphael Rocha de. Modelos de calibração multivariada por NIRS para a predição de características de qualidade da carne bovina. 2014. 133 f. Tese (Doutorado em Ciência Animal) - Universidade Federal de Goiás, Goiânia, 2014. |
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http://repositorio.bc.ufg.br/tede/handle/tede/3929 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Universidade Federal de Goiás |
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Escola de Veterinária e Zootecnia - EVZ (RG) |
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Universidade Federal de Goiás |
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