Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)

Assis, Camila; Ramos, Rachel S.; Silva, Lidiane A.; Kist, Volmir; Barbosa, Márcio H.P.; Teofilo, Reinaldo F.

Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)

Detalhes bibliográficos
Autor(a) principal:	Assis, Camila
Data de Publicação:	2017
Outros Autores:	Ramos, Rachel S., Silva, Lidiane A., Kist, Volmir, Barbosa, Márcio H.P., Teofilo, Reinaldo F.
Tipo de documento:	Artigo
Idioma:	eng
Título da fonte:	LOCUS Repositório Institucional da UFV
Texto Completo:	https://doi.org/10.1177/0003702817704147 http://www.locus.ufv.br/handle/123456789/18678
Resumo:	O artigo não contém resumo em português

Metadados do item

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repository_id_str	2145
spelling	Assis, CamilaRamos, Rachel S.Silva, Lidiane A.Kist, VolmirBarbosa, Márcio H.P.Teofilo, Reinaldo F.2018-04-06T10:36:23Z2018-04-06T10:36:23Z2017-04-2819433530https://doi.org/10.1177/0003702817704147http://www.locus.ufv.br/handle/123456789/18678O artigo não contém resumo em portuguêsThe building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000–4000 cmÀ1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction (RP), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.engApplied Spectroscopyv. 71, n. 8, p. 2001-2012, Abril 2017SugarcaneLigninStalkPartial least squares regressionPLSVariable selectionPrediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdftexto completoapplication/pdf1594925https://locus.ufv.br//bitstream/123456789/18678/1/artigo.pdf71b21260557ab3d64bb2f9d94986f10eMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/18678/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILartigo.pdf.jpgartigo.pdf.jpgIM Thumbnailimage/jpeg6125https://locus.ufv.br//bitstream/123456789/18678/3/artigo.pdf.jpg72cab960d79741447d72be351df9d245MD53123456789/186782018-04-06 23:00:45.407oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452018-04-07T02:00:45LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
title	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
spellingShingle	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS) Assis, Camila Sugarcane Lignin Stalk Partial least squares regression PLS Variable selection
title_short	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
title_full	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
title_fullStr	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
title_full_unstemmed	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
title_sort	Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)
author	Assis, Camila
author_facet	Assis, Camila Ramos, Rachel S. Silva, Lidiane A. Kist, Volmir Barbosa, Márcio H.P. Teofilo, Reinaldo F.
author_role	author
author2	Ramos, Rachel S. Silva, Lidiane A. Kist, Volmir Barbosa, Márcio H.P. Teofilo, Reinaldo F.
author2_role	author author author author author
dc.contributor.author.fl_str_mv	Assis, Camila Ramos, Rachel S. Silva, Lidiane A. Kist, Volmir Barbosa, Márcio H.P. Teofilo, Reinaldo F.
dc.subject.pt-BR.fl_str_mv	Sugarcane Lignin Stalk Partial least squares regression PLS Variable selection
topic	Sugarcane Lignin Stalk Partial least squares regression PLS Variable selection
description	O artigo não contém resumo em português
publishDate	2017
dc.date.issued.fl_str_mv	2017-04-28
dc.date.accessioned.fl_str_mv	2018-04-06T10:36:23Z
dc.date.available.fl_str_mv	2018-04-06T10:36:23Z
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	https://doi.org/10.1177/0003702817704147 http://www.locus.ufv.br/handle/123456789/18678
dc.identifier.issn.none.fl_str_mv	19433530
identifier_str_mv	19433530
url	https://doi.org/10.1177/0003702817704147 http://www.locus.ufv.br/handle/123456789/18678
dc.language.iso.fl_str_mv	eng
language	eng
dc.relation.ispartofseries.pt-BR.fl_str_mv	v. 71, n. 8, p. 2001-2012, Abril 2017
dc.rights.driver.fl_str_mv	info:eu-repo/semantics/openAccess
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Applied Spectroscopy
publisher.none.fl_str_mv	Applied Spectroscopy
dc.source.none.fl_str_mv	reponame:LOCUS Repositório Institucional da UFV instname:Universidade Federal de Viçosa (UFV) instacron:UFV
instname_str	Universidade Federal de Viçosa (UFV)
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institution	UFV
reponame_str	LOCUS Repositório Institucional da UFV
collection	LOCUS Repositório Institucional da UFV
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Prediction of lignin content in different parts of sugarcane using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS)

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