Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.vibspec.2019.05.001 http://hdl.handle.net/11449/189091 |
Resumo: | Near infrared spectroscopy (NIR)is a technique capable of rapidly generating rich chemical information. However, many chemical problems are limited to the low sensitivity and selectivity due to the spectral similarity of the components in the sample. Therefore, this study aimed to evaluate the use of multivariate filters combined with variable selection to optimize analytical parameters of partial least square (PLS)models developed with NIR data. This strategy was applied to 64 spectra of solutions containing ethanol, acetic acid, and lactic acid in 1-octanol. The multivariate filters evaluated were orthogonal signal correction (OSC), generalized least squares weighting (GLSW)and external parameter orthogonalization (EPO). Firstly, the multivariate filters were evaluated using the complete spectra and then with the variables selected by the interval partial least square (iPLS)algorithm. The figures of merit, such as accuracy, precision, linearity, sensitivity, and selectivity were used to evaluate the performance of the models. The PLS models for ethanol, acetic acid and lactic acid prediction showed a reduction of, respectively, 46%, 32% and 74% of RMSEP values after the use of multivariate filters combined with iPLS. The proposed strategy increased the analytical sensitivity and selectivity by up to 25 and 17 times, respectively. Therefore, the use of multivariate filters combined with the selection of variables by iPLS can make PLS models more sensitive, selective, and accurate for NIR data of multicomponent organic solutions. |
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Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutionsFigures of meritInterval partial least squareMulticomponent solutionsMultivariate filtersNear infrared spectroscopyNear infrared spectroscopy (NIR)is a technique capable of rapidly generating rich chemical information. However, many chemical problems are limited to the low sensitivity and selectivity due to the spectral similarity of the components in the sample. Therefore, this study aimed to evaluate the use of multivariate filters combined with variable selection to optimize analytical parameters of partial least square (PLS)models developed with NIR data. This strategy was applied to 64 spectra of solutions containing ethanol, acetic acid, and lactic acid in 1-octanol. The multivariate filters evaluated were orthogonal signal correction (OSC), generalized least squares weighting (GLSW)and external parameter orthogonalization (EPO). Firstly, the multivariate filters were evaluated using the complete spectra and then with the variables selected by the interval partial least square (iPLS)algorithm. The figures of merit, such as accuracy, precision, linearity, sensitivity, and selectivity were used to evaluate the performance of the models. The PLS models for ethanol, acetic acid and lactic acid prediction showed a reduction of, respectively, 46%, 32% and 74% of RMSEP values after the use of multivariate filters combined with iPLS. The proposed strategy increased the analytical sensitivity and selectivity by up to 25 and 17 times, respectively. Therefore, the use of multivariate filters combined with the selection of variables by iPLS can make PLS models more sensitive, selective, and accurate for NIR data of multicomponent organic solutions.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Analytical Chemistry Department Institute of Chemistry São Paulo State University (UNESP), Prof. Francisco Degni 55Center for Monitoring and Research of the Quality of Fuels Biofuels Crude Oil and Derivatives (Cempeqc) Institute of Chemistry São Paulo State University (UNESP), Prof. Francisco Degni 55Analytical Chemistry Department Institute of Chemistry São Paulo State University (UNESP), Prof. Francisco Degni 55Center for Monitoring and Research of the Quality of Fuels Biofuels Crude Oil and Derivatives (Cempeqc) Institute of Chemistry São Paulo State University (UNESP), Prof. Francisco Degni 55Universidade Estadual Paulista (Unesp)Nespeca, Maurílio Gustavo [UNESP]Pavini, Weslei Diego [UNESP]de Oliveira, José Eduardo [UNESP]2019-10-06T16:29:29Z2019-10-06T16:29:29Z2019-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article97-102http://dx.doi.org/10.1016/j.vibspec.2019.05.001Vibrational Spectroscopy, v. 102, p. 97-102.0924-2031http://hdl.handle.net/11449/18909110.1016/j.vibspec.2019.05.0012-s2.0-85065408607Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengVibrational Spectroscopyinfo:eu-repo/semantics/openAccess2021-10-22T21:54:10Zoai:repositorio.unesp.br:11449/189091Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:52:19.126152Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
title |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
spellingShingle |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions Nespeca, Maurílio Gustavo [UNESP] Figures of merit Interval partial least square Multicomponent solutions Multivariate filters Near infrared spectroscopy |
title_short |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
title_full |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
title_fullStr |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
title_full_unstemmed |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
title_sort |
Multivariate filters combined with interval partial least square method: A strategy for optimizing PLS models developed with near infrared data of multicomponent solutions |
author |
Nespeca, Maurílio Gustavo [UNESP] |
author_facet |
Nespeca, Maurílio Gustavo [UNESP] Pavini, Weslei Diego [UNESP] de Oliveira, José Eduardo [UNESP] |
author_role |
author |
author2 |
Pavini, Weslei Diego [UNESP] de Oliveira, José Eduardo [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Nespeca, Maurílio Gustavo [UNESP] Pavini, Weslei Diego [UNESP] de Oliveira, José Eduardo [UNESP] |
dc.subject.por.fl_str_mv |
Figures of merit Interval partial least square Multicomponent solutions Multivariate filters Near infrared spectroscopy |
topic |
Figures of merit Interval partial least square Multicomponent solutions Multivariate filters Near infrared spectroscopy |
description |
Near infrared spectroscopy (NIR)is a technique capable of rapidly generating rich chemical information. However, many chemical problems are limited to the low sensitivity and selectivity due to the spectral similarity of the components in the sample. Therefore, this study aimed to evaluate the use of multivariate filters combined with variable selection to optimize analytical parameters of partial least square (PLS)models developed with NIR data. This strategy was applied to 64 spectra of solutions containing ethanol, acetic acid, and lactic acid in 1-octanol. The multivariate filters evaluated were orthogonal signal correction (OSC), generalized least squares weighting (GLSW)and external parameter orthogonalization (EPO). Firstly, the multivariate filters were evaluated using the complete spectra and then with the variables selected by the interval partial least square (iPLS)algorithm. The figures of merit, such as accuracy, precision, linearity, sensitivity, and selectivity were used to evaluate the performance of the models. The PLS models for ethanol, acetic acid and lactic acid prediction showed a reduction of, respectively, 46%, 32% and 74% of RMSEP values after the use of multivariate filters combined with iPLS. The proposed strategy increased the analytical sensitivity and selectivity by up to 25 and 17 times, respectively. Therefore, the use of multivariate filters combined with the selection of variables by iPLS can make PLS models more sensitive, selective, and accurate for NIR data of multicomponent organic solutions. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T16:29:29Z 2019-10-06T16:29:29Z 2019-05-01 |
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/10.1016/j.vibspec.2019.05.001 Vibrational Spectroscopy, v. 102, p. 97-102. 0924-2031 http://hdl.handle.net/11449/189091 10.1016/j.vibspec.2019.05.001 2-s2.0-85065408607 |
url |
http://dx.doi.org/10.1016/j.vibspec.2019.05.001 http://hdl.handle.net/11449/189091 |
identifier_str_mv |
Vibrational Spectroscopy, v. 102, p. 97-102. 0924-2031 10.1016/j.vibspec.2019.05.001 2-s2.0-85065408607 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Vibrational Spectroscopy |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
97-102 |
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_ |
1808128285999104000 |