Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://www.cerne.ufla.br/site/index.php/CERNE/article/view/106 http://repositorio.ufla.br/jspui/handle/1/14652 |
Resumo: | Near infrared (NIR) spectroscopy is a fast and efficient technique to predict a range of wood traits; however, methods for extracting useful information from the NIR spectra could be improved. Thus, the aim of this study was to evaluate the statistic performance of two regression methods for estimating the basic density in Eucalyptus urophylla x grandis wood from near infrared spectroscopic data. The predictive models calibrated by principal component regression (PCR) or partial least square regression (PLSR) method provided fine correlations. The coefficients of determination (R²cv) of the PCR models ranged from 0.78 to 0.85 with standard error of cross-validation (SECV) and the ratio of performance to deviation (RPD) varying from 32.8 to 41.2 kg/m³ and from 1.6 to 1.9, respectively. The PLSR models presented R²cv with relatively lower magnitude (from 0.65 to 0.78); but also lower SECV (from 29.8 to 38.9 kg/m³) and higher RPD values (from 1.6 to 2.1). In short, PCR method provides higher R² between Lab-measured and NIR-predicted values while PLSR produces lower standard errors of cross-validations. For both regression methods, the pre-treatments on NIR spectra, and the wavelength selection improved the calibration statistics, reducing the SECV and increasing the R²cv and the RPD values. Thus, PCR and PLS regression can be applied successfully for predicting basic density in Eucalyptus urophylla x grandis wood from the near infrared spectroscopic data. |
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Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic dataMétodos de regressão multivariada para estimativa da densidade básica da madeira de Eucalyptus por espectroscopia no infravemelho próximoChemometricsEucalyptusWood - Basic densityNear infrared spectroscopyQuimiometriaEspectroscopia no infravermelho próximoMadeira - Densidade básicaNear infrared (NIR) spectroscopy is a fast and efficient technique to predict a range of wood traits; however, methods for extracting useful information from the NIR spectra could be improved. Thus, the aim of this study was to evaluate the statistic performance of two regression methods for estimating the basic density in Eucalyptus urophylla x grandis wood from near infrared spectroscopic data. The predictive models calibrated by principal component regression (PCR) or partial least square regression (PLSR) method provided fine correlations. The coefficients of determination (R²cv) of the PCR models ranged from 0.78 to 0.85 with standard error of cross-validation (SECV) and the ratio of performance to deviation (RPD) varying from 32.8 to 41.2 kg/m³ and from 1.6 to 1.9, respectively. The PLSR models presented R²cv with relatively lower magnitude (from 0.65 to 0.78); but also lower SECV (from 29.8 to 38.9 kg/m³) and higher RPD values (from 1.6 to 2.1). In short, PCR method provides higher R² between Lab-measured and NIR-predicted values while PLSR produces lower standard errors of cross-validations. For both regression methods, the pre-treatments on NIR spectra, and the wavelength selection improved the calibration statistics, reducing the SECV and increasing the R²cv and the RPD values. Thus, PCR and PLS regression can be applied successfully for predicting basic density in Eucalyptus urophylla x grandis wood from the near infrared spectroscopic data.A espectroscopia no infravermelho próximo (NIR) é uma técnica rápida e eficiente para estimar características da madeira, no entanto, métodos para extrair as informações úteis a partir do NIRS podem ser melhorados. Assim, este estudo foi realizado com o objetivo de avaliar o desempenho estatístico de dois métodos de regressão para estimar a densidade básica da madeira de Eucalyptus urophylla x grandis a partir da espectroscopia no NIR. Os modelos preditivos calibrados pelos métodos da regressão de componentes principais (PCR) ou regressão de mínimos quadrados parciais (PLSR) apresentaram boas correlações. Os coeficientes de determinação (R²cv) dos modelos PCR variaram de 0,78 a 0,85, com erro padrão na validação cruzada (SECV) e a relação do desempenho de desvio (RPD) variando de 32,8-41,2 kg/m³ e 1,6-1,9 kg/m³, respectivamente. Os modelos PLSR apresentaram R²cv com magnitude relativamente menor (de 0,65 a 0,78), mas também menores valores de SECV (29,8-38,9 kg/m³) e maiores de RPD (de 1,6 a 2,1 kg/m³). De maneira geral, o método de PCR oferece maior R² entre os valores medidos e estimados, enquanto a PLSR produz menor erro padrão de validação cruzada. Para ambos os métodos de regressão, o pré-tratamento dos espectros NIR e a seleção de comprimento de onda melhoraram as estatísticas de calibração, reduzindo o SECV e aumentando os valores de R²cv e de RPD. Assim, as regressões PCR e PLS-R podem ser aplicadas com sucesso para a estimativa da densidade básica da madeira de Eucalyptus por espectroscopia no infravermelho próximo.CERNE2015-05-122017-08-01T20:15:29Z2017-08-01T20:15:29Z2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://www.cerne.ufla.br/site/index.php/CERNE/article/view/106HEIN, P. R. G. Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data. Cerne, Lavras, v. 16, p. 90-96, jul. 2010. Suplemento.http://repositorio.ufla.br/jspui/handle/1/14652CERNE; VOL 16, No 5 (2010) - SUPLEMENTO EBRAMEM; 090–096CERNE; VOL 16, No 5 (2010) - SUPLEMENTO EBRAMEM; 090–0962317-63420104-7760reponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAporhttp://www.cerne.ufla.br/site/index.php/CERNE/article/view/106/80Copyright (c) 2015 Paulo Ricardo Gherardi HeinAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessHein, Paulo Ricardo Gherardi2021-04-06T18:01:28Zoai:localhost:1/14652Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-04-06T18:01:28Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data Métodos de regressão multivariada para estimativa da densidade básica da madeira de Eucalyptus por espectroscopia no infravemelho próximo |
title |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data |
spellingShingle |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data Hein, Paulo Ricardo Gherardi Chemometrics Eucalyptus Wood - Basic density Near infrared spectroscopy Quimiometria Espectroscopia no infravermelho próximo Madeira - Densidade básica |
title_short |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data |
title_full |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data |
title_fullStr |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data |
title_full_unstemmed |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data |
title_sort |
Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data |
author |
Hein, Paulo Ricardo Gherardi |
author_facet |
Hein, Paulo Ricardo Gherardi |
author_role |
author |
dc.contributor.author.fl_str_mv |
Hein, Paulo Ricardo Gherardi |
dc.subject.por.fl_str_mv |
Chemometrics Eucalyptus Wood - Basic density Near infrared spectroscopy Quimiometria Espectroscopia no infravermelho próximo Madeira - Densidade básica |
topic |
Chemometrics Eucalyptus Wood - Basic density Near infrared spectroscopy Quimiometria Espectroscopia no infravermelho próximo Madeira - Densidade básica |
description |
Near infrared (NIR) spectroscopy is a fast and efficient technique to predict a range of wood traits; however, methods for extracting useful information from the NIR spectra could be improved. Thus, the aim of this study was to evaluate the statistic performance of two regression methods for estimating the basic density in Eucalyptus urophylla x grandis wood from near infrared spectroscopic data. The predictive models calibrated by principal component regression (PCR) or partial least square regression (PLSR) method provided fine correlations. The coefficients of determination (R²cv) of the PCR models ranged from 0.78 to 0.85 with standard error of cross-validation (SECV) and the ratio of performance to deviation (RPD) varying from 32.8 to 41.2 kg/m³ and from 1.6 to 1.9, respectively. The PLSR models presented R²cv with relatively lower magnitude (from 0.65 to 0.78); but also lower SECV (from 29.8 to 38.9 kg/m³) and higher RPD values (from 1.6 to 2.1). In short, PCR method provides higher R² between Lab-measured and NIR-predicted values while PLSR produces lower standard errors of cross-validations. For both regression methods, the pre-treatments on NIR spectra, and the wavelength selection improved the calibration statistics, reducing the SECV and increasing the R²cv and the RPD values. Thus, PCR and PLS regression can be applied successfully for predicting basic density in Eucalyptus urophylla x grandis wood from the near infrared spectroscopic data. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-05-12 2017-08-01T20:15:29Z 2017-08-01T20:15:29Z 2017-08-01 |
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 |
http://www.cerne.ufla.br/site/index.php/CERNE/article/view/106 HEIN, P. R. G. Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data. Cerne, Lavras, v. 16, p. 90-96, jul. 2010. Suplemento. http://repositorio.ufla.br/jspui/handle/1/14652 |
url |
http://www.cerne.ufla.br/site/index.php/CERNE/article/view/106 http://repositorio.ufla.br/jspui/handle/1/14652 |
identifier_str_mv |
HEIN, P. R. G. Multivariate regression methods for estimating basic density in Eucalyptus wood from near infrared spectroscopic data. Cerne, Lavras, v. 16, p. 90-96, jul. 2010. Suplemento. |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
http://www.cerne.ufla.br/site/index.php/CERNE/article/view/106/80 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2015 Paulo Ricardo Gherardi Hein Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2015 Paulo Ricardo Gherardi Hein Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
CERNE |
publisher.none.fl_str_mv |
CERNE |
dc.source.none.fl_str_mv |
CERNE; VOL 16, No 5 (2010) - SUPLEMENTO EBRAMEM; 090–096 CERNE; VOL 16, No 5 (2010) - SUPLEMENTO EBRAMEM; 090–096 2317-6342 0104-7760 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
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1815439001825312768 |