Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated inte...

Autores
Mohammadi, Ghobad; Rashidi, Khodabakhsh; Mahmoudi, Majid; Goicoechea, Hector Casimiro; Jalalvand, Ali R.
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, we are going to develop an efficient electroanalytical methodology based on generation of second-order differential pulse voltammetric (DPV) data at different pulse heights to exploit second-order advantage for simultaneous determination of levodopa (LDP), carbidopa (CDP), methyldopa (MDP), benserazide (BA), tolcapone (TOL) and entacapone (ENT) in the presence of dopamine (DPA) as uncalibrated interference. The recorded data were baseline- and potential shift-corrected by asymmetric least square spline regression (AsLSSR) and correlation optimized warping (COW) algorithms, respectively. After data pre-processing, multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2) were used to develop three-way calibration models and then, the abilities of the developed models to predict analytes’ concentrations in the absence and presence of DPA were examined in validation and test sets, respectively. MCR-ALS acted better than PARAFAC2 to predict analytes’ concentrations in the absence and presence of DPA as uncalibrated interference. Therefore, MCR-ALS was chosen to predict antiparkinson agents’ concentrations in spiked human serum samples as real cases. Fortunately, acceptable results were obtained which were comparable to those obtained by high performance liquid chromatography with UV detection (HPLC-UV) as reference method.
Fil: Mohammadi, Ghobad. Kermanshah University of Medical Sciences; Irán
Fil: Rashidi, Khodabakhsh. Kermanshah University of Medical Sciences; Irán
Fil: Mahmoudi, Majid. Kermanshah University of Medical Sciences; Irán
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Jalalvand, Ali R.. Kermanshah University of Medical Sciences; Irán
Materia
Antiparkinson agents
Simultaneous determination
Multi-way calibration
Second-order advantage
Uncalibrated interference
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:11336/89418
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/89418
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interferenceMohammadi, GhobadRashidi, KhodabakhshMahmoudi, MajidGoicoechea, Hector CasimiroJalalvand, Ali R.Antiparkinson agentsSimultaneous determinationMulti-way calibrationSecond-order advantageUncalibrated interferencehttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this work, we are going to develop an efficient electroanalytical methodology based on generation of second-order differential pulse voltammetric (DPV) data at different pulse heights to exploit second-order advantage for simultaneous determination of levodopa (LDP), carbidopa (CDP), methyldopa (MDP), benserazide (BA), tolcapone (TOL) and entacapone (ENT) in the presence of dopamine (DPA) as uncalibrated interference. The recorded data were baseline- and potential shift-corrected by asymmetric least square spline regression (AsLSSR) and correlation optimized warping (COW) algorithms, respectively. After data pre-processing, multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2) were used to develop three-way calibration models and then, the abilities of the developed models to predict analytes’ concentrations in the absence and presence of DPA were examined in validation and test sets, respectively. MCR-ALS acted better than PARAFAC2 to predict analytes’ concentrations in the absence and presence of DPA as uncalibrated interference. Therefore, MCR-ALS was chosen to predict antiparkinson agents’ concentrations in spiked human serum samples as real cases. Fortunately, acceptable results were obtained which were comparable to those obtained by high performance liquid chromatography with UV detection (HPLC-UV) as reference method.Fil: Mohammadi, Ghobad. Kermanshah University of Medical Sciences; IránFil: Rashidi, Khodabakhsh. Kermanshah University of Medical Sciences; IránFil: Mahmoudi, Majid. Kermanshah University of Medical Sciences; IránFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Jalalvand, Ali R.. Kermanshah University of Medical Sciences; IránElsevier Science2018-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/89418Mohammadi, Ghobad; Rashidi, Khodabakhsh; Mahmoudi, Majid; Goicoechea, Hector Casimiro; Jalalvand, Ali R.; Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference; Elsevier Science; Journal Of The Taiwan Institute Of Chemical Engineers; 88; 7-2018; 49-611876-1070CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S1876107018302189info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jtice.2018.04.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:42Zoai:ri.conicet.gov.ar:11336/89418instacron: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 09:38:42.335CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
title Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
spellingShingle Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
Mohammadi, Ghobad
Antiparkinson agents
Simultaneous determination
Multi-way calibration
Second-order advantage
Uncalibrated interference
title_short Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
title_full Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
title_fullStr Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
title_full_unstemmed Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
title_sort Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference
dc.creator.none.fl_str_mv Mohammadi, Ghobad
Rashidi, Khodabakhsh
Mahmoudi, Majid
Goicoechea, Hector Casimiro
Jalalvand, Ali R.
author Mohammadi, Ghobad
author_facet Mohammadi, Ghobad
Rashidi, Khodabakhsh
Mahmoudi, Majid
Goicoechea, Hector Casimiro
Jalalvand, Ali R.
author_role author
author2 Rashidi, Khodabakhsh
Mahmoudi, Majid
Goicoechea, Hector Casimiro
Jalalvand, Ali R.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Antiparkinson agents
Simultaneous determination
Multi-way calibration
Second-order advantage
Uncalibrated interference
topic Antiparkinson agents
Simultaneous determination
Multi-way calibration
Second-order advantage
Uncalibrated interference
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work, we are going to develop an efficient electroanalytical methodology based on generation of second-order differential pulse voltammetric (DPV) data at different pulse heights to exploit second-order advantage for simultaneous determination of levodopa (LDP), carbidopa (CDP), methyldopa (MDP), benserazide (BA), tolcapone (TOL) and entacapone (ENT) in the presence of dopamine (DPA) as uncalibrated interference. The recorded data were baseline- and potential shift-corrected by asymmetric least square spline regression (AsLSSR) and correlation optimized warping (COW) algorithms, respectively. After data pre-processing, multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2) were used to develop three-way calibration models and then, the abilities of the developed models to predict analytes’ concentrations in the absence and presence of DPA were examined in validation and test sets, respectively. MCR-ALS acted better than PARAFAC2 to predict analytes’ concentrations in the absence and presence of DPA as uncalibrated interference. Therefore, MCR-ALS was chosen to predict antiparkinson agents’ concentrations in spiked human serum samples as real cases. Fortunately, acceptable results were obtained which were comparable to those obtained by high performance liquid chromatography with UV detection (HPLC-UV) as reference method.
Fil: Mohammadi, Ghobad. Kermanshah University of Medical Sciences; Irán
Fil: Rashidi, Khodabakhsh. Kermanshah University of Medical Sciences; Irán
Fil: Mahmoudi, Majid. Kermanshah University of Medical Sciences; Irán
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Jalalvand, Ali R.. Kermanshah University of Medical Sciences; Irán
description In this work, we are going to develop an efficient electroanalytical methodology based on generation of second-order differential pulse voltammetric (DPV) data at different pulse heights to exploit second-order advantage for simultaneous determination of levodopa (LDP), carbidopa (CDP), methyldopa (MDP), benserazide (BA), tolcapone (TOL) and entacapone (ENT) in the presence of dopamine (DPA) as uncalibrated interference. The recorded data were baseline- and potential shift-corrected by asymmetric least square spline regression (AsLSSR) and correlation optimized warping (COW) algorithms, respectively. After data pre-processing, multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis 2 (PARAFAC2) were used to develop three-way calibration models and then, the abilities of the developed models to predict analytes’ concentrations in the absence and presence of DPA were examined in validation and test sets, respectively. MCR-ALS acted better than PARAFAC2 to predict analytes’ concentrations in the absence and presence of DPA as uncalibrated interference. Therefore, MCR-ALS was chosen to predict antiparkinson agents’ concentrations in spiked human serum samples as real cases. Fortunately, acceptable results were obtained which were comparable to those obtained by high performance liquid chromatography with UV detection (HPLC-UV) as reference method.
publishDate 2018
dc.date.none.fl_str_mv 2018-07
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/11336/89418
Mohammadi, Ghobad; Rashidi, Khodabakhsh; Mahmoudi, Majid; Goicoechea, Hector Casimiro; Jalalvand, Ali R.; Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference; Elsevier Science; Journal Of The Taiwan Institute Of Chemical Engineers; 88; 7-2018; 49-61
1876-1070
CONICET Digital
CONICET
url http://hdl.handle.net/11336/89418
identifier_str_mv Mohammadi, Ghobad; Rashidi, Khodabakhsh; Mahmoudi, Majid; Goicoechea, Hector Casimiro; Jalalvand, Ali R.; Exploiting second-order advantage from mathematically modeled voltammetric data for simultaneous determination of multiple antiparkinson agents in the presence of uncalibrated interference; Elsevier Science; Journal Of The Taiwan Institute Of Chemical Engineers; 88; 7-2018; 49-61
1876-1070
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://linkinghub.elsevier.com/retrieve/pii/S1876107018302189
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jtice.2018.04.007
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 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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