Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method

Autores
Schenone, Agustina Violeta; Gomes, Adriano de Araújo; Culzoni, Maria Julia; Campiglia, Andres D.; Araújo, Mário Cesar Ugulino de; Goicoechea, Hector Casimiro
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.
Fil: Schenone, Agustina Violeta. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Gomes, Adriano de Araújo. Universidade Federal da Paraíba; Brasil
Fil: Culzoni, Maria Julia. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Campiglia, Andres D.. University Of Central Florida. Department Of Chemistry; Estados Unidos
Fil: Araújo, Mário Cesar Ugulino de. Universidade Federal da Paraíba; Brasil
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Materia
Second-Order Advantage
Synchronous Fluorescence
Residual Modeling
Ciprofloxacin
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/17630
Acceder
id CONICETDig_18436eea9eb77d07fa1bee530cbf42e6
oai_identifier_str oai:ri.conicet.gov.ar:11336/17630
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares methodSchenone, Agustina VioletaGomes, Adriano de AraújoCulzoni, Maria JuliaCampiglia, Andres D.Araújo, Mário Cesar Ugulino deGoicoechea, Hector CasimiroSecond-Order AdvantageSynchronous FluorescenceResidual ModelingCiprofloxacinhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.Fil: Schenone, Agustina Violeta. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Gomes, Adriano de Araújo. Universidade Federal da Paraíba; BrasilFil: Culzoni, Maria Julia. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Campiglia, Andres D.. University Of Central Florida. Department Of Chemistry; Estados UnidosFil: Araújo, Mário Cesar Ugulino de. Universidade Federal da Paraíba; BrasilFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaElsevier2015-01info: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/11336/17630Schenone, Agustina Violeta; Gomes, Adriano de Araújo; Culzoni, Maria Julia; Campiglia, Andres D.; Araújo, Mário Cesar Ugulino de; et al.; Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method; Elsevier; Analytica Chimica Acta; 859; 1-2015; 20-280003-2670enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.aca.2014.12.014info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0003267014014159info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:54:33Zoai:ri.conicet.gov.ar:11336/17630instacron: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 09:54:33.385CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
title Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
spellingShingle Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
Schenone, Agustina Violeta
Second-Order Advantage
Synchronous Fluorescence
Residual Modeling
Ciprofloxacin
title_short Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
title_full Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
title_fullStr Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
title_full_unstemmed Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
title_sort Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method
dc.creator.none.fl_str_mv Schenone, Agustina Violeta
Gomes, Adriano de Araújo
Culzoni, Maria Julia
Campiglia, Andres D.
Araújo, Mário Cesar Ugulino de
Goicoechea, Hector Casimiro
author Schenone, Agustina Violeta
author_facet Schenone, Agustina Violeta
Gomes, Adriano de Araújo
Culzoni, Maria Julia
Campiglia, Andres D.
Araújo, Mário Cesar Ugulino de
Goicoechea, Hector Casimiro
author_role author
author2 Gomes, Adriano de Araújo
Culzoni, Maria Julia
Campiglia, Andres D.
Araújo, Mário Cesar Ugulino de
Goicoechea, Hector Casimiro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Second-Order Advantage
Synchronous Fluorescence
Residual Modeling
Ciprofloxacin
topic Second-Order Advantage
Synchronous Fluorescence
Residual Modeling
Ciprofloxacin
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 residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.
Fil: Schenone, Agustina Violeta. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Gomes, Adriano de Araújo. Universidade Federal da Paraíba; Brasil
Fil: Culzoni, Maria Julia. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Campiglia, Andres D.. University Of Central Florida. Department Of Chemistry; Estados Unidos
Fil: Araújo, Mário Cesar Ugulino de. Universidade Federal da Paraíba; Brasil
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
description A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/17630
Schenone, Agustina Violeta; Gomes, Adriano de Araújo; Culzoni, Maria Julia; Campiglia, Andres D.; Araújo, Mário Cesar Ugulino de; et al.; Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method; Elsevier; Analytica Chimica Acta; 859; 1-2015; 20-28
0003-2670
url http://hdl.handle.net/11336/17630
identifier_str_mv Schenone, Agustina Violeta; Gomes, Adriano de Araújo; Culzoni, Maria Julia; Campiglia, Andres D.; Araújo, Mário Cesar Ugulino de; et al.; Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method; Elsevier; Analytica Chimica Acta; 859; 1-2015; 20-28
0003-2670
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.2014.12.014
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0003267014014159
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 13.069144

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