Prediction of the basic density of tropical woods by near-infrared spectroscopy
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cerne (Online) |
Texto Completo: | https://cerne.ufla.br/site/index.php/CERNE/article/view/3262 |
Resumo: | Background: Determining the basic density of the wood is usually defined as a simple process, but it requires caution and the operator’s skill to avoid errors in the analysis. In addition, it involves sample preparation and time to saturate the wood until obtaining the dry sample mass. The development of alternative measurement techniques could reduce the time to obtain the results and provide reliable values. Therefore, this study aimed to develop multivariate models to estimate the basic density of native woods using near-infrared spectra (NIR). Basic densities were determined by the water immersion method, and the values were associated with NIR signatures. Spectra were directly collected on the wood transversal and radial faces with an integrating sphere. Partial least squares regression (PLS-R) was calibrated and validated to estimate basic density based on spectral signatures.Results: In the cross-validation and prediction of the models, the results were promising. The coefficients of determination varied from 0.87 to 0.93 with a standard error of 0.01 %. The partial least squares discriminant analysis (PLS-DA) efficiently classified the wood species. The ratio of performance to deviation obtained satisfactory values, a minimum of 2.81 and a maximum of 4.20.Conclusion: The statistical parameters of the models based on NIR spectra showed potential for density measurements in floors, furniture, and solid wood products. |
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Prediction of the basic density of tropical woods by near-infrared spectroscopyMultivariate statisticsNon-destructive analysisAmazonian specieswood identificationclassificationBackground: Determining the basic density of the wood is usually defined as a simple process, but it requires caution and the operator’s skill to avoid errors in the analysis. In addition, it involves sample preparation and time to saturate the wood until obtaining the dry sample mass. The development of alternative measurement techniques could reduce the time to obtain the results and provide reliable values. Therefore, this study aimed to develop multivariate models to estimate the basic density of native woods using near-infrared spectra (NIR). Basic densities were determined by the water immersion method, and the values were associated with NIR signatures. Spectra were directly collected on the wood transversal and radial faces with an integrating sphere. Partial least squares regression (PLS-R) was calibrated and validated to estimate basic density based on spectral signatures.Results: In the cross-validation and prediction of the models, the results were promising. The coefficients of determination varied from 0.87 to 0.93 with a standard error of 0.01 %. The partial least squares discriminant analysis (PLS-DA) efficiently classified the wood species. The ratio of performance to deviation obtained satisfactory values, a minimum of 2.81 and a maximum of 4.20.Conclusion: The statistical parameters of the models based on NIR spectra showed potential for density measurements in floors, furniture, and solid wood products.CERNECERNE2023-10-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/3262CERNE; Vol. 29 No. 1 (2023); e-103262CERNE; v. 29 n. 1 (2023); e-1032622317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/3262/1357http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMedeiros, Dayane TarginoMelo, Rafael Rodolfo deCademartori, Pedro Henrique GonçalezBatista, Felipe GomesMascarenhas, Adriano Reis PrazeresScatolino, Mario VanoliHein, Paulo Ricardo Gherardi 2023-10-30T18:53:46Zoai:cerne.ufla.br:article/3262Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:51.775793Cerne (Online) - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
title |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
spellingShingle |
Prediction of the basic density of tropical woods by near-infrared spectroscopy Medeiros, Dayane Targino Multivariate statistics Non-destructive analysis Amazonian species wood identification classification |
title_short |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
title_full |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
title_fullStr |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
title_full_unstemmed |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
title_sort |
Prediction of the basic density of tropical woods by near-infrared spectroscopy |
author |
Medeiros, Dayane Targino |
author_facet |
Medeiros, Dayane Targino Melo, Rafael Rodolfo de Cademartori, Pedro Henrique Gonçalez Batista, Felipe Gomes Mascarenhas, Adriano Reis Prazeres Scatolino, Mario Vanoli Hein, Paulo Ricardo Gherardi |
author_role |
author |
author2 |
Melo, Rafael Rodolfo de Cademartori, Pedro Henrique Gonçalez Batista, Felipe Gomes Mascarenhas, Adriano Reis Prazeres Scatolino, Mario Vanoli Hein, Paulo Ricardo Gherardi |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Medeiros, Dayane Targino Melo, Rafael Rodolfo de Cademartori, Pedro Henrique Gonçalez Batista, Felipe Gomes Mascarenhas, Adriano Reis Prazeres Scatolino, Mario Vanoli Hein, Paulo Ricardo Gherardi |
dc.subject.por.fl_str_mv |
Multivariate statistics Non-destructive analysis Amazonian species wood identification classification |
topic |
Multivariate statistics Non-destructive analysis Amazonian species wood identification classification |
description |
Background: Determining the basic density of the wood is usually defined as a simple process, but it requires caution and the operator’s skill to avoid errors in the analysis. In addition, it involves sample preparation and time to saturate the wood until obtaining the dry sample mass. The development of alternative measurement techniques could reduce the time to obtain the results and provide reliable values. Therefore, this study aimed to develop multivariate models to estimate the basic density of native woods using near-infrared spectra (NIR). Basic densities were determined by the water immersion method, and the values were associated with NIR signatures. Spectra were directly collected on the wood transversal and radial faces with an integrating sphere. Partial least squares regression (PLS-R) was calibrated and validated to estimate basic density based on spectral signatures.Results: In the cross-validation and prediction of the models, the results were promising. The coefficients of determination varied from 0.87 to 0.93 with a standard error of 0.01 %. The partial least squares discriminant analysis (PLS-DA) efficiently classified the wood species. The ratio of performance to deviation obtained satisfactory values, a minimum of 2.81 and a maximum of 4.20.Conclusion: The statistical parameters of the models based on NIR spectra showed potential for density measurements in floors, furniture, and solid wood products. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-30 |
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://cerne.ufla.br/site/index.php/CERNE/article/view/3262 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/3262 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/3262/1357 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
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 |
CERNE CERNE |
publisher.none.fl_str_mv |
CERNE CERNE |
dc.source.none.fl_str_mv |
CERNE; Vol. 29 No. 1 (2023); e-103262 CERNE; v. 29 n. 1 (2023); e-103262 2317-6342 0104-7760 reponame:Cerne (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Cerne (Online) |
collection |
Cerne (Online) |
repository.name.fl_str_mv |
Cerne (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
cerne@dcf.ufla.br||cerne@dcf.ufla.br |
_version_ |
1799874944589365248 |