Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage

Autores
Garcia Reiriz, Alejandro Gabriel; Damiani, Patricia Cecilia; Olivieri, Alejandro Cesar
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A new second-order multivariate calibration model is presented which allows one to process matrix data showing a non-linear relationship between signal and concentration, and achieving the important second-order advantage. The latter property permits analyte quantitation even in the presence of unexpected sample components, i.e., those not present in the calibration set. The model is based on a combination of residual bilinearization, which provides the second-order advantage, and kernel partial least-squares of unfolded data, a flexible non-linear version of partial least-squares. The latter one involves projection of the measured data onto a non-linear space, which in the present case consists of a set of Gaussian radial basis functions. Simulations concerning two ideal systems are analyzed: one where the signal-concentration relation is quadratic with positive deviations from linearity, and another one where it is sigmoidal. The results are favorably compared with those provided by several artificial neural network approaches. Two experimental systems are also studied, involving the analysis of: 1) the lipid degradation product malondialdehyde in olive oil samples, where the background oil provides a strong interferent signal, and 2) the antibiotic amoxicillin in the presence of the anti-inflammatory salicylate as interferent. The results for these experimental cases are also encouraging.
Fil: Garcia Reiriz, Alejandro Gabriel. 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
Fil: Damiani, Patricia Cecilia. 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
Materia
KERNEL PARTIAL LEAST-SQUARES
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER CALIBRATION
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:11336/133271
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/133271
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantageGarcia Reiriz, Alejandro GabrielDamiani, Patricia CeciliaOlivieri, Alejandro CesarKERNEL PARTIAL LEAST-SQUARESRESIDUAL BILINEARIZATIONSECOND-ORDER ADVANTAGESECOND-ORDER CALIBRATIONhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A new second-order multivariate calibration model is presented which allows one to process matrix data showing a non-linear relationship between signal and concentration, and achieving the important second-order advantage. The latter property permits analyte quantitation even in the presence of unexpected sample components, i.e., those not present in the calibration set. The model is based on a combination of residual bilinearization, which provides the second-order advantage, and kernel partial least-squares of unfolded data, a flexible non-linear version of partial least-squares. The latter one involves projection of the measured data onto a non-linear space, which in the present case consists of a set of Gaussian radial basis functions. Simulations concerning two ideal systems are analyzed: one where the signal-concentration relation is quadratic with positive deviations from linearity, and another one where it is sigmoidal. The results are favorably compared with those provided by several artificial neural network approaches. Two experimental systems are also studied, involving the analysis of: 1) the lipid degradation product malondialdehyde in olive oil samples, where the background oil provides a strong interferent signal, and 2) the antibiotic amoxicillin in the presence of the anti-inflammatory salicylate as interferent. The results for these experimental cases are also encouraging.Fil: Garcia Reiriz, Alejandro Gabriel. 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; ArgentinaFil: Damiani, Patricia Cecilia. 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; ArgentinaFil: 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; ArgentinaElsevier Science2010-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/133271Garcia Reiriz, Alejandro Gabriel; Damiani, Patricia Cecilia; Olivieri, Alejandro Cesar; Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 100; 2; 2-2010; 127-1350169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743909002093info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2009.11.009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:47Zoai:ri.conicet.gov.ar:11336/133271instacron: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 10:05:47.522CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
title Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
spellingShingle Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
Garcia Reiriz, Alejandro Gabriel
KERNEL PARTIAL LEAST-SQUARES
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER CALIBRATION
title_short Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
title_full Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
title_fullStr Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
title_full_unstemmed Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
title_sort Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage
dc.creator.none.fl_str_mv Garcia Reiriz, Alejandro Gabriel
Damiani, Patricia Cecilia
Olivieri, Alejandro Cesar
author Garcia Reiriz, Alejandro Gabriel
author_facet Garcia Reiriz, Alejandro Gabriel
Damiani, Patricia Cecilia
Olivieri, Alejandro Cesar
author_role author
author2 Damiani, Patricia Cecilia
Olivieri, Alejandro Cesar
author2_role author
author
dc.subject.none.fl_str_mv KERNEL PARTIAL LEAST-SQUARES
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER CALIBRATION
topic KERNEL PARTIAL LEAST-SQUARES
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER CALIBRATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A new second-order multivariate calibration model is presented which allows one to process matrix data showing a non-linear relationship between signal and concentration, and achieving the important second-order advantage. The latter property permits analyte quantitation even in the presence of unexpected sample components, i.e., those not present in the calibration set. The model is based on a combination of residual bilinearization, which provides the second-order advantage, and kernel partial least-squares of unfolded data, a flexible non-linear version of partial least-squares. The latter one involves projection of the measured data onto a non-linear space, which in the present case consists of a set of Gaussian radial basis functions. Simulations concerning two ideal systems are analyzed: one where the signal-concentration relation is quadratic with positive deviations from linearity, and another one where it is sigmoidal. The results are favorably compared with those provided by several artificial neural network approaches. Two experimental systems are also studied, involving the analysis of: 1) the lipid degradation product malondialdehyde in olive oil samples, where the background oil provides a strong interferent signal, and 2) the antibiotic amoxicillin in the presence of the anti-inflammatory salicylate as interferent. The results for these experimental cases are also encouraging.
Fil: Garcia Reiriz, Alejandro Gabriel. 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
Fil: Damiani, Patricia Cecilia. 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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
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. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Analítica; Argentina
description A new second-order multivariate calibration model is presented which allows one to process matrix data showing a non-linear relationship between signal and concentration, and achieving the important second-order advantage. The latter property permits analyte quantitation even in the presence of unexpected sample components, i.e., those not present in the calibration set. The model is based on a combination of residual bilinearization, which provides the second-order advantage, and kernel partial least-squares of unfolded data, a flexible non-linear version of partial least-squares. The latter one involves projection of the measured data onto a non-linear space, which in the present case consists of a set of Gaussian radial basis functions. Simulations concerning two ideal systems are analyzed: one where the signal-concentration relation is quadratic with positive deviations from linearity, and another one where it is sigmoidal. The results are favorably compared with those provided by several artificial neural network approaches. Two experimental systems are also studied, involving the analysis of: 1) the lipid degradation product malondialdehyde in olive oil samples, where the background oil provides a strong interferent signal, and 2) the antibiotic amoxicillin in the presence of the anti-inflammatory salicylate as interferent. The results for these experimental cases are also encouraging.
publishDate 2010
dc.date.none.fl_str_mv 2010-02
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/11336/133271
Garcia Reiriz, Alejandro Gabriel; Damiani, Patricia Cecilia; Olivieri, Alejandro Cesar; Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 100; 2; 2-2010; 127-135
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/133271
identifier_str_mv Garcia Reiriz, Alejandro Gabriel; Damiani, Patricia Cecilia; Olivieri, Alejandro Cesar; Residual bilinearization combined with kernel-unfolded partial least-squares: A new technique for processing non-linear second-order data achieving the second-order advantage; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 100; 2; 2-2010; 127-135
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0169743909002093
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2009.11.009
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
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 13.13397

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