A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data

Autores
Olivieri, Alejandro Cesar
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available multivariate methodologies for obtaining this advantage are all based on linear models, and hence they are not applicable to spectral information behaving in a non-linear manner with respect to target analyte concentrations. This work describes the combination of a back-propagation artificial neural network model with a technique known as residual bilinearization, applicable to second-order spectral information. The joint model allows one to efficiently extract analyte concentrations from intrinsically non-linear data, even in the presence of unsuspected constituents. Simulations have been performed by mimicking deviations from linearity brought about by: (1) exponential relationship between fluorescence and concentration, (2) kinetic evolution of responsive reaction products and (3) analytes acting as reaction catalysts. In all of these cases, successful prediction of the analyte concentrations was achieved on large test sample sets, which included the presence of overlapping components not included in the training step. The new method not only obtains the second-order advantage, but also correctly retrieves the contribution of the unsuspected components to the total test sample signals. The comparison with a multivariate methodology based on partial least-squares regression with second-order advantage shows that the presently described method displays better predictive ability.
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
ARTIFICIAL NEURAL NETWORKS
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER SPECTROSCOPIC DATA
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:11336/134992

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network_name_str CONICET Digital (CONICET)
spelling A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear dataOlivieri, Alejandro CesarARTIFICIAL NEURAL NETWORKSRESIDUAL BILINEARIZATIONSECOND-ORDER ADVANTAGESECOND-ORDER SPECTROSCOPIC DATAhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available multivariate methodologies for obtaining this advantage are all based on linear models, and hence they are not applicable to spectral information behaving in a non-linear manner with respect to target analyte concentrations. This work describes the combination of a back-propagation artificial neural network model with a technique known as residual bilinearization, applicable to second-order spectral information. The joint model allows one to efficiently extract analyte concentrations from intrinsically non-linear data, even in the presence of unsuspected constituents. Simulations have been performed by mimicking deviations from linearity brought about by: (1) exponential relationship between fluorescence and concentration, (2) kinetic evolution of responsive reaction products and (3) analytes acting as reaction catalysts. In all of these cases, successful prediction of the analyte concentrations was achieved on large test sample sets, which included the presence of overlapping components not included in the training step. The new method not only obtains the second-order advantage, but also correctly retrieves the contribution of the unsuspected components to the total test sample signals. The comparison with a multivariate methodology based on partial least-squares regression with second-order advantage shows that the presently described method displays better predictive ability.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; ArgentinaJohn Wiley & Sons Ltd2005-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/134992Olivieri, Alejandro Cesar; A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data; John Wiley & Sons Ltd; Journal of Chemometrics; 19; 11-12; 11-2005; 615-6240886-9383CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/cem.967info:eu-repo/semantics/altIdentifier/doi/10.1002/cem.967info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:31Zoai:ri.conicet.gov.ar:11336/134992instacron: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 09:49:31.569CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
title A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
spellingShingle A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
Olivieri, Alejandro Cesar
ARTIFICIAL NEURAL NETWORKS
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER SPECTROSCOPIC DATA
title_short A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
title_full A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
title_fullStr A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
title_full_unstemmed A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
title_sort A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data
dc.creator.none.fl_str_mv Olivieri, Alejandro Cesar
author Olivieri, Alejandro Cesar
author_facet Olivieri, Alejandro Cesar
author_role author
dc.subject.none.fl_str_mv ARTIFICIAL NEURAL NETWORKS
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER SPECTROSCOPIC DATA
topic ARTIFICIAL NEURAL NETWORKS
RESIDUAL BILINEARIZATION
SECOND-ORDER ADVANTAGE
SECOND-ORDER SPECTROSCOPIC DATA
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available multivariate methodologies for obtaining this advantage are all based on linear models, and hence they are not applicable to spectral information behaving in a non-linear manner with respect to target analyte concentrations. This work describes the combination of a back-propagation artificial neural network model with a technique known as residual bilinearization, applicable to second-order spectral information. The joint model allows one to efficiently extract analyte concentrations from intrinsically non-linear data, even in the presence of unsuspected constituents. Simulations have been performed by mimicking deviations from linearity brought about by: (1) exponential relationship between fluorescence and concentration, (2) kinetic evolution of responsive reaction products and (3) analytes acting as reaction catalysts. In all of these cases, successful prediction of the analyte concentrations was achieved on large test sample sets, which included the presence of overlapping components not included in the training step. The new method not only obtains the second-order advantage, but also correctly retrieves the contribution of the unsuspected components to the total test sample signals. The comparison with a multivariate methodology based on partial least-squares regression with second-order advantage shows that the presently described method displays better predictive ability.
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 Three-way instrumental data offer the second-order advantage to analysts, a property of great utility in the field of complex sample analysis in the presence of unsuspected components as potential interferents. The available multivariate methodologies for obtaining this advantage are all based on linear models, and hence they are not applicable to spectral information behaving in a non-linear manner with respect to target analyte concentrations. This work describes the combination of a back-propagation artificial neural network model with a technique known as residual bilinearization, applicable to second-order spectral information. The joint model allows one to efficiently extract analyte concentrations from intrinsically non-linear data, even in the presence of unsuspected constituents. Simulations have been performed by mimicking deviations from linearity brought about by: (1) exponential relationship between fluorescence and concentration, (2) kinetic evolution of responsive reaction products and (3) analytes acting as reaction catalysts. In all of these cases, successful prediction of the analyte concentrations was achieved on large test sample sets, which included the presence of overlapping components not included in the training step. The new method not only obtains the second-order advantage, but also correctly retrieves the contribution of the unsuspected components to the total test sample signals. The comparison with a multivariate methodology based on partial least-squares regression with second-order advantage shows that the presently described method displays better predictive ability.
publishDate 2005
dc.date.none.fl_str_mv 2005-11
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/11336/134992
Olivieri, Alejandro Cesar; A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data; John Wiley & Sons Ltd; Journal of Chemometrics; 19; 11-12; 11-2005; 615-624
0886-9383
CONICET Digital
CONICET
url http://hdl.handle.net/11336/134992
identifier_str_mv Olivieri, Alejandro Cesar; A combined artificial neural network/residual bilinearization approach for obtaining the second-order advantage from three-way non-linear data; John Wiley & Sons Ltd; Journal of Chemometrics; 19; 11-12; 11-2005; 615-624
0886-9383
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://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/cem.967
info:eu-repo/semantics/altIdentifier/doi/10.1002/cem.967
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 John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
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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