Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks

Autores
Tesio, Alvaro Yamil; Robledo, Sebastian Noel; Granero, Adrian Marcelo; Fernandez, Hector; Zon, María Alicia
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this study, we propose an electroanalytical method to quantify simultaneously luteolin and rutin, two flavonoids which are present in a pharmaceutical formulation. The methodology is based on square wave voltammetry at glassy carbon electrodes modified with multiwalled carbon nanotubes dispersed in polyethylenimine. Both flavonoids show quasi-reversible surface redox couples in 10% ethanol + 1 mol L−1 HClO4 aqueous solutions, which are defined in potential regions very close to each other. The adsorption process of flavonoids on the modified electrode surface was carried out using an accumulation potential of 0.55 V (vs. Ag/AgCl, 3 mol L−1 KCl), and an accumulation time of 20 min. Considering that luteolin and rutin electrochemical responses show a high degree of overlapping, we processed the electrochemical signals using artificial neural networks. We used a supervised network, feed-forward network with Levenberg-Marquardt back propagation training. Values of 92.6 ± 0.4 and 92 ± 1 mg per tablet were determined by the artificial neural networks methodology for luteolin and rutin, respectively. According to values declared by the manufacturer, differences of 7.4 and 8.0% were calculated for luteolin and rutin, respectively. Results obtained with electroanalytical methodologies were in very good agreement with those obtained by HPLC.
Fil: Tesio, Alvaro Yamil. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Robledo, Sebastian Noel. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Granero, Adrian Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fernandez, Hector. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zon, María Alicia. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Luteolin
Artificial
Neural
Network
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:11336/31815
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networksTesio, Alvaro YamilRobledo, Sebastian NoelGranero, Adrian MarceloFernandez, HectorZon, María AliciaLuteolinArtificialNeuralNetworkhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this study, we propose an electroanalytical method to quantify simultaneously luteolin and rutin, two flavonoids which are present in a pharmaceutical formulation. The methodology is based on square wave voltammetry at glassy carbon electrodes modified with multiwalled carbon nanotubes dispersed in polyethylenimine. Both flavonoids show quasi-reversible surface redox couples in 10% ethanol + 1 mol L−1 HClO4 aqueous solutions, which are defined in potential regions very close to each other. The adsorption process of flavonoids on the modified electrode surface was carried out using an accumulation potential of 0.55 V (vs. Ag/AgCl, 3 mol L−1 KCl), and an accumulation time of 20 min. Considering that luteolin and rutin electrochemical responses show a high degree of overlapping, we processed the electrochemical signals using artificial neural networks. We used a supervised network, feed-forward network with Levenberg-Marquardt back propagation training. Values of 92.6 ± 0.4 and 92 ± 1 mg per tablet were determined by the artificial neural networks methodology for luteolin and rutin, respectively. According to values declared by the manufacturer, differences of 7.4 and 8.0% were calculated for luteolin and rutin, respectively. Results obtained with electroanalytical methodologies were in very good agreement with those obtained by HPLC.Fil: Tesio, Alvaro Yamil. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Robledo, Sebastian Noel. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Granero, Adrian Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez, Hector. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Zon, María Alicia. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2014-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/31815Zon, María Alicia; Fernandez, Hector; Granero, Adrian Marcelo; Robledo, Sebastian Noel; Tesio, Alvaro Yamil; Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks; Elsevier Science; Sensors and Actuators B: Chemical; 203; 7-2014; 655-6620925-4005CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.snb.2014.07.005info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0925400514008338info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:01:08Zoai:ri.conicet.gov.ar:11336/31815instacron: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 10:01:08.709CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
title Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
spellingShingle Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
Tesio, Alvaro Yamil
Luteolin
Artificial
Neural
Network
title_short Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
title_full Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
title_fullStr Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
title_full_unstemmed Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
title_sort Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks
dc.creator.none.fl_str_mv Tesio, Alvaro Yamil
Robledo, Sebastian Noel
Granero, Adrian Marcelo
Fernandez, Hector
Zon, María Alicia
author Tesio, Alvaro Yamil
author_facet Tesio, Alvaro Yamil
Robledo, Sebastian Noel
Granero, Adrian Marcelo
Fernandez, Hector
Zon, María Alicia
author_role author
author2 Robledo, Sebastian Noel
Granero, Adrian Marcelo
Fernandez, Hector
Zon, María Alicia
author2_role author
author
author
author
dc.subject.none.fl_str_mv Luteolin
Artificial
Neural
Network
topic Luteolin
Artificial
Neural
Network
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 study, we propose an electroanalytical method to quantify simultaneously luteolin and rutin, two flavonoids which are present in a pharmaceutical formulation. The methodology is based on square wave voltammetry at glassy carbon electrodes modified with multiwalled carbon nanotubes dispersed in polyethylenimine. Both flavonoids show quasi-reversible surface redox couples in 10% ethanol + 1 mol L−1 HClO4 aqueous solutions, which are defined in potential regions very close to each other. The adsorption process of flavonoids on the modified electrode surface was carried out using an accumulation potential of 0.55 V (vs. Ag/AgCl, 3 mol L−1 KCl), and an accumulation time of 20 min. Considering that luteolin and rutin electrochemical responses show a high degree of overlapping, we processed the electrochemical signals using artificial neural networks. We used a supervised network, feed-forward network with Levenberg-Marquardt back propagation training. Values of 92.6 ± 0.4 and 92 ± 1 mg per tablet were determined by the artificial neural networks methodology for luteolin and rutin, respectively. According to values declared by the manufacturer, differences of 7.4 and 8.0% were calculated for luteolin and rutin, respectively. Results obtained with electroanalytical methodologies were in very good agreement with those obtained by HPLC.
Fil: Tesio, Alvaro Yamil. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Robledo, Sebastian Noel. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Granero, Adrian Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fernandez, Hector. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Zon, María Alicia. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In this study, we propose an electroanalytical method to quantify simultaneously luteolin and rutin, two flavonoids which are present in a pharmaceutical formulation. The methodology is based on square wave voltammetry at glassy carbon electrodes modified with multiwalled carbon nanotubes dispersed in polyethylenimine. Both flavonoids show quasi-reversible surface redox couples in 10% ethanol + 1 mol L−1 HClO4 aqueous solutions, which are defined in potential regions very close to each other. The adsorption process of flavonoids on the modified electrode surface was carried out using an accumulation potential of 0.55 V (vs. Ag/AgCl, 3 mol L−1 KCl), and an accumulation time of 20 min. Considering that luteolin and rutin electrochemical responses show a high degree of overlapping, we processed the electrochemical signals using artificial neural networks. We used a supervised network, feed-forward network with Levenberg-Marquardt back propagation training. Values of 92.6 ± 0.4 and 92 ± 1 mg per tablet were determined by the artificial neural networks methodology for luteolin and rutin, respectively. According to values declared by the manufacturer, differences of 7.4 and 8.0% were calculated for luteolin and rutin, respectively. Results obtained with electroanalytical methodologies were in very good agreement with those obtained by HPLC.
publishDate 2014
dc.date.none.fl_str_mv 2014-07
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/11336/31815
Zon, María Alicia; Fernandez, Hector; Granero, Adrian Marcelo; Robledo, Sebastian Noel; Tesio, Alvaro Yamil; Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks; Elsevier Science; Sensors and Actuators B: Chemical; 203; 7-2014; 655-662
0925-4005
CONICET Digital
CONICET
url http://hdl.handle.net/11336/31815
identifier_str_mv Zon, María Alicia; Fernandez, Hector; Granero, Adrian Marcelo; Robledo, Sebastian Noel; Tesio, Alvaro Yamil; Simultaneous electroanalytical determination of luteolin and rutin using artificial neural networks; Elsevier Science; Sensors and Actuators B: Chemical; 203; 7-2014; 655-662
0925-4005
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.snb.2014.07.005
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0925400514008338
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
application/pdf
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 13.13397

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