Regression models based on new local strategies for near infrared spectroscopic data

Autores
Allegrini, Franco; Fernández Pierna, J. A.; Fragoso, W. D.; Olivieri, Alejandro Cesar; Baeten, V.; Dardenne, P.
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.
Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Fernández Pierna, J. A.. Walloon Agricultural Research Centre; Bélgica
Fil: Fragoso, W. D.. Universidade Federal da Paraíba; Brasil
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Baeten, V.. Walloon Agricultural Research Centre; Bélgica
Fil: Dardenne, P.. Walloon Agricultural Research Centre; Bélgica
Materia
Local Regression Models
Near Infrared Spectroscopy
Partial Least Squares Regression
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:11336/52648
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Regression models based on new local strategies for near infrared spectroscopic dataAllegrini, FrancoFernández Pierna, J. A.Fragoso, W. D.Olivieri, Alejandro CesarBaeten, V.Dardenne, P.Local Regression ModelsNear Infrared SpectroscopyPartial Least Squares Regressionhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Fernández Pierna, J. A.. Walloon Agricultural Research Centre; BélgicaFil: Fragoso, W. D.. Universidade Federal da Paraíba; BrasilFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Baeten, V.. Walloon Agricultural Research Centre; BélgicaFil: Dardenne, P.. Walloon Agricultural Research Centre; BélgicaElsevier Science2016-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/52648Allegrini, Franco; Fernández Pierna, J. A.; Fragoso, W. D.; Olivieri, Alejandro Cesar; Baeten, V.; et al.; Regression models based on new local strategies for near infrared spectroscopic data; Elsevier Science; Analytica Chimica Acta; 933; 8-2016; 50-580003-2670CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2016.07.006info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0003267016308273info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:13:09Zoai:ri.conicet.gov.ar:11336/52648instacron: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:34982025-09-29 10:13:09.861CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Regression models based on new local strategies for near infrared spectroscopic data
title Regression models based on new local strategies for near infrared spectroscopic data
spellingShingle Regression models based on new local strategies for near infrared spectroscopic data
Allegrini, Franco
Local Regression Models
Near Infrared Spectroscopy
Partial Least Squares Regression
title_short Regression models based on new local strategies for near infrared spectroscopic data
title_full Regression models based on new local strategies for near infrared spectroscopic data
title_fullStr Regression models based on new local strategies for near infrared spectroscopic data
title_full_unstemmed Regression models based on new local strategies for near infrared spectroscopic data
title_sort Regression models based on new local strategies for near infrared spectroscopic data
dc.creator.none.fl_str_mv Allegrini, Franco
Fernández Pierna, J. A.
Fragoso, W. D.
Olivieri, Alejandro Cesar
Baeten, V.
Dardenne, P.
author Allegrini, Franco
author_facet Allegrini, Franco
Fernández Pierna, J. A.
Fragoso, W. D.
Olivieri, Alejandro Cesar
Baeten, V.
Dardenne, P.
author_role author
author2 Fernández Pierna, J. A.
Fragoso, W. D.
Olivieri, Alejandro Cesar
Baeten, V.
Dardenne, P.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Local Regression Models
Near Infrared Spectroscopy
Partial Least Squares Regression
topic Local Regression Models
Near Infrared Spectroscopy
Partial Least Squares Regression
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.
Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Fernández Pierna, J. A.. Walloon Agricultural Research Centre; Bélgica
Fil: Fragoso, W. D.. Universidade Federal da Paraíba; Brasil
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Fil: Baeten, V.. Walloon Agricultural Research Centre; Bélgica
Fil: Dardenne, P.. Walloon Agricultural Research Centre; Bélgica
description In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.
publishDate 2016
dc.date.none.fl_str_mv 2016-08
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/11336/52648
Allegrini, Franco; Fernández Pierna, J. A.; Fragoso, W. D.; Olivieri, Alejandro Cesar; Baeten, V.; et al.; Regression models based on new local strategies for near infrared spectroscopic data; Elsevier Science; Analytica Chimica Acta; 933; 8-2016; 50-58
0003-2670
CONICET Digital
CONICET
url http://hdl.handle.net/11336/52648
identifier_str_mv Allegrini, Franco; Fernández Pierna, J. A.; Fragoso, W. D.; Olivieri, Alejandro Cesar; Baeten, V.; et al.; Regression models based on new local strategies for near infrared spectroscopic data; Elsevier Science; Analytica Chimica Acta; 933; 8-2016; 50-58
0003-2670
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.aca.2016.07.006
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0003267016308273
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
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
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score 13.070432

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