Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-ord...

Autores
Gholivand, Mohammad Bagher; Jalalvand, Alí R.; Goicoechea, Hector Casimiro; Skov, Thomas
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLRSPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky–Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples.
Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry; Irán
Fil: Jalalvand, Alí R.. Razi University. Faculty of Chemistry; Irán. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina
Fil: Skov, Thomas. University of Copenhagen. Faculty of Life Sciences. Department of Food Science. Quality and Technology group; Dinamarca
Materia
Ascorbic Acid
Uric Acid
Dopamine
Nitrite
Simultaneous Determination
Linear And Non-Linear Determination
Calibration Models
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:11336/15435
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/15435
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantageGholivand, Mohammad BagherJalalvand, Alí R.Goicoechea, Hector CasimiroSkov, ThomasAscorbic AcidUric AcidDopamineNitriteSimultaneous DeterminationLinear And Non-Linear DeterminationCalibration Modelshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLRSPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky–Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples.Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry; IránFil: Jalalvand, Alí R.. Razi University. Faculty of Chemistry; Irán. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; ArgentinaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; ArgentinaFil: Skov, Thomas. University of Copenhagen. Faculty of Life Sciences. Department of Food Science. Quality and Technology group; DinamarcaElsevier Science2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15435Gholivand, Mohammad Bagher; Jalalvand, Alí R.; Goicoechea, Hector Casimiro; Skov, Thomas; Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage; Elsevier Science; Talanta; 119; 1-2014; 553-5630039-9140enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.talanta.2013.11.028info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0039914013009053info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:29:48Zoai:ri.conicet.gov.ar:11336/15435instacron: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 10:29:48.986CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
title Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
spellingShingle Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
Gholivand, Mohammad Bagher
Ascorbic Acid
Uric Acid
Dopamine
Nitrite
Simultaneous Determination
Linear And Non-Linear Determination
Calibration Models
title_short Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
title_full Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
title_fullStr Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
title_full_unstemmed Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
title_sort Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage
dc.creator.none.fl_str_mv Gholivand, Mohammad Bagher
Jalalvand, Alí R.
Goicoechea, Hector Casimiro
Skov, Thomas
author Gholivand, Mohammad Bagher
author_facet Gholivand, Mohammad Bagher
Jalalvand, Alí R.
Goicoechea, Hector Casimiro
Skov, Thomas
author_role author
author2 Jalalvand, Alí R.
Goicoechea, Hector Casimiro
Skov, Thomas
author2_role author
author
author
dc.subject.none.fl_str_mv Ascorbic Acid
Uric Acid
Dopamine
Nitrite
Simultaneous Determination
Linear And Non-Linear Determination
Calibration Models
topic Ascorbic Acid
Uric Acid
Dopamine
Nitrite
Simultaneous Determination
Linear And Non-Linear Determination
Calibration Models
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLRSPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky–Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples.
Fil: Gholivand, Mohammad Bagher. Razi University. Faculty of Chemistry; Irán
Fil: Jalalvand, Alí R.. Razi University. Faculty of Chemistry; Irán. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Cátedra de Química Analítica I. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe; Argentina
Fil: Skov, Thomas. University of Copenhagen. Faculty of Life Sciences. Department of Food Science. Quality and Technology group; Dinamarca
description For the first time, several multivariate calibration (MVC) models including partial least squares-1 (PLS-1), continuum power regression (CPR), multiple linear regression-successive projections algorithm (MLRSPA), robust continuum regression (RCR), partial robust M-regression (PRM), polynomial-PLS (PLY-PLS), spline-PLS (SPL-PLS), radial basis function-PLS (RBF-PLS), least squares-support vector machines (LS-SVM), wavelet transform-artificial neural network (WT-ANN), discrete wavelet transform-ANN (DWT-ANN), and back propagation-ANN (BP-ANN) have been constructed on the basis of non-bilinear first order square wave voltammetric (SWV) data for the simultaneous determination of ascorbic acid (AA), uric acid (UA), dopamine (DP) and nitrite (NT) at a glassy carbon electrode (GCE) to identify which technique offers the best predictions. The compositions of the calibration mixtures were selected according to a simplex lattice design (SLD) and validated with an external set of analytes' mixtures. An asymmetric least squares splines regression (AsLSSR) algorithm was applied for correcting the baselines. A correlation optimized warping (COW) algorithm was used to data alignment and lack of bilinearity was tackled by potential shift correction. The effects of several pre-processing techniques such as genetic algorithm (GA), orthogonal signal correction (OSC), mean centering (MC), robust median centering (RMC), wavelet denoising (WD), and Savitsky–Golay smoothing (SGS) on the predictive ability of the mentioned MVC models were examined. The best preprocessing technique was found for each model. According to the results obtained, the RBF-PLS was recommended to simultaneously assay the concentrations of AA, UA, DP and NT in human serum samples.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
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/11336/15435
Gholivand, Mohammad Bagher; Jalalvand, Alí R.; Goicoechea, Hector Casimiro; Skov, Thomas; Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage; Elsevier Science; Talanta; 119; 1-2014; 553-563
0039-9140
url http://hdl.handle.net/11336/15435
identifier_str_mv Gholivand, Mohammad Bagher; Jalalvand, Alí R.; Goicoechea, Hector Casimiro; Skov, Thomas; Chemometrics-assisted simultaneous voltammetric determination of ascorbic acid,uricacid,dopamine and nitrite: Application of non-bilinear voltammetric data for exploiting first-order advantage; Elsevier Science; Talanta; 119; 1-2014; 553-563
0039-9140
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.talanta.2013.11.028
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0039914013009053
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
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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