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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/134992
Ver los metadatos del registro completo
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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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1842268977692147712 |
score |
13.13397 |