Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections a...

Autores
de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Hector Casimiro; Galvão, Roberto Kawakami Harrop; Araújo, Mario Cesar Ugulino
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.
Fil: de Almeida, Valber Elias. Universidade Federal da Paraíba; Brasil
Fil: de Araújo Gomes, Adriano. Universidade Federal do Sul e Sudoeste do Pará; Brasil
Fil: de Sousa Fernandes, David Douglas. Universidade Federal da Paraíba; Brasil
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Galvão, Roberto Kawakami Harrop. Instituto Tecnológico de Aeronáutica; Brasil
Fil: Araújo, Mario Cesar Ugulino. Universidade Federal da Paraíba; Brasil
Materia
Kernel Partial Least Squares
Near Infrared Spectrometry
Nonlinear Multivariate Calibration
Successive Projections Algorithm
Sugar
Variable Selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/58681
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/58681
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithmde Almeida, Valber Eliasde Araújo Gomes, Adrianode Sousa Fernandes, David DouglasGoicoechea, Hector CasimiroGalvão, Roberto Kawakami HarropAraújo, Mario Cesar UgulinoKernel Partial Least SquaresNear Infrared SpectrometryNonlinear Multivariate CalibrationSuccessive Projections AlgorithmSugarVariable Selectionhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.Fil: de Almeida, Valber Elias. Universidade Federal da Paraíba; BrasilFil: de Araújo Gomes, Adriano. Universidade Federal do Sul e Sudoeste do Pará; BrasilFil: de Sousa Fernandes, David Douglas. Universidade Federal da Paraíba; BrasilFil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; ArgentinaFil: Galvão, Roberto Kawakami Harrop. Instituto Tecnológico de Aeronáutica; BrasilFil: Araújo, Mario Cesar Ugulino. Universidade Federal da Paraíba; BrasilElsevier Science2018-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/58681de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Hector Casimiro; Galvão, Roberto Kawakami Harrop; et al.; Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm; Elsevier Science; Talanta; 181; 5-2018; 38-430039-9140CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.talanta.2017.12.064info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0039914017312699info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:43:27Zoai:ri.conicet.gov.ar:11336/58681instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:43:27.889CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
title Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
spellingShingle Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
de Almeida, Valber Elias
Kernel Partial Least Squares
Near Infrared Spectrometry
Nonlinear Multivariate Calibration
Successive Projections Algorithm
Sugar
Variable Selection
title_short Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
title_full Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
title_fullStr Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
title_full_unstemmed Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
title_sort Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm
dc.creator.none.fl_str_mv de Almeida, Valber Elias
de Araújo Gomes, Adriano
de Sousa Fernandes, David Douglas
Goicoechea, Hector Casimiro
Galvão, Roberto Kawakami Harrop
Araújo, Mario Cesar Ugulino
author de Almeida, Valber Elias
author_facet de Almeida, Valber Elias
de Araújo Gomes, Adriano
de Sousa Fernandes, David Douglas
Goicoechea, Hector Casimiro
Galvão, Roberto Kawakami Harrop
Araújo, Mario Cesar Ugulino
author_role author
author2 de Araújo Gomes, Adriano
de Sousa Fernandes, David Douglas
Goicoechea, Hector Casimiro
Galvão, Roberto Kawakami Harrop
Araújo, Mario Cesar Ugulino
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Kernel Partial Least Squares
Near Infrared Spectrometry
Nonlinear Multivariate Calibration
Successive Projections Algorithm
Sugar
Variable Selection
topic Kernel Partial Least Squares
Near Infrared Spectrometry
Nonlinear Multivariate Calibration
Successive Projections Algorithm
Sugar
Variable Selection
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.
Fil: de Almeida, Valber Elias. Universidade Federal da Paraíba; Brasil
Fil: de Araújo Gomes, Adriano. Universidade Federal do Sul e Sudoeste do Pará; Brasil
Fil: de Sousa Fernandes, David Douglas. Universidade Federal da Paraíba; Brasil
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Galvão, Roberto Kawakami Harrop. Instituto Tecnológico de Aeronáutica; Brasil
Fil: Araújo, Mario Cesar Ugulino. Universidade Federal da Paraíba; Brasil
description This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.
publishDate 2018
dc.date.none.fl_str_mv 2018-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/58681
de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Hector Casimiro; Galvão, Roberto Kawakami Harrop; et al.; Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm; Elsevier Science; Talanta; 181; 5-2018; 38-43
0039-9140
CONICET Digital
CONICET
url http://hdl.handle.net/11336/58681
identifier_str_mv de Almeida, Valber Elias; de Araújo Gomes, Adriano; de Sousa Fernandes, David Douglas; Goicoechea, Hector Casimiro; Galvão, Roberto Kawakami Harrop; et al.; Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm; Elsevier Science; Talanta; 181; 5-2018; 38-43
0039-9140
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.talanta.2017.12.064
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0039914017312699
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268603227832320
score 13.13397

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