A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination

Autores
Novaes, Cleber G.; Ferreira, Sergio L.C.; Neto, João H. S.; de Santana, Fernanda A.; Portugal, Lindomar A.; Goicoechea, Hector Casimiro
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a comparison between a multiple response function (MR) proposed for optimization of analyticalstrategies involving multi-element determinations with the desirability function D, which was proposed by Derringerand Suich in 1980. The MR function is established by the average of the sum of the normalized responses for eachanalyte considering the highest value of these. This comparison was performed during the optimization of an spectrometerfor quantification of six elements using inductively coupled plasma optical emission spectrometry (ICP OES). Four instrumentalfactors were studied (auxiliary gas flow rate, plasma gas flow rate, nebulizer gas flow rate and radio frequencypower). A (24) two-level full factorial design and a Box Behnken matrix were developed to evaluate the performance ofthe two multiple response functions. The results found demonstrated great similarity in the interpretations obtained consideringthe effect values of the factors calculated using the two-level full factorial design employing the two multiple responses.Also a Box Behnken design was performed to compare the applicability of the two multiple response functions inquadratic models. The results achieved demonstrated high correlation (0.9998) between the regression coefficients of thetwo models. Also the response surfaces obtained showed great similarity in terms of formats and experimental conditionsfound for the studied factors. Thus, the multiple response (MR) is presented as a simple tool, easy to manipulate, efficientand very helpful for application in analytical procedures involving multi-response. An overview of applications of thisfunction in several multivariate optimization tools as well as in various analytical techniques is presented.
Fil: Novaes, Cleber G.. Universidade Estadual do Sudoeste da Bahia; Brasil. Universidade Federal da Bahia; Brasil
Fil: Ferreira, Sergio L.C.. Universidade Federal da Bahia; Brasil
Fil: Neto, João H. S.. Universidade Estadual do Sudoeste da Bahia; Brasil
Fil: de Santana, Fernanda A.. Universidade Federal da Bahia; Brasil
Fil: Portugal, Lindomar A.. Universidad de las Islas Baleares; España
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Materia
Multiple Response Function
Experimental Design
Desirability Function D
Icp Oes
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:11336/56003
Acceder
id CONICETDig_e3937c8adfb965026c8bec0c3b8a9d34
oai_identifier_str oai:ri.conicet.gov.ar:11336/56003
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental DeterminationNovaes, Cleber G.Ferreira, Sergio L.C.Neto, João H. S.de Santana, Fernanda A.Portugal, Lindomar A.Goicoechea, Hector CasimiroMultiple Response FunctionExperimental DesignDesirability Function DIcp Oeshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This paper presents a comparison between a multiple response function (MR) proposed for optimization of analyticalstrategies involving multi-element determinations with the desirability function D, which was proposed by Derringerand Suich in 1980. The MR function is established by the average of the sum of the normalized responses for eachanalyte considering the highest value of these. This comparison was performed during the optimization of an spectrometerfor quantification of six elements using inductively coupled plasma optical emission spectrometry (ICP OES). Four instrumentalfactors were studied (auxiliary gas flow rate, plasma gas flow rate, nebulizer gas flow rate and radio frequencypower). A (24) two-level full factorial design and a Box Behnken matrix were developed to evaluate the performance ofthe two multiple response functions. The results found demonstrated great similarity in the interpretations obtained consideringthe effect values of the factors calculated using the two-level full factorial design employing the two multiple responses.Also a Box Behnken design was performed to compare the applicability of the two multiple response functions inquadratic models. The results achieved demonstrated high correlation (0.9998) between the regression coefficients of thetwo models. Also the response surfaces obtained showed great similarity in terms of formats and experimental conditionsfound for the studied factors. Thus, the multiple response (MR) is presented as a simple tool, easy to manipulate, efficientand very helpful for application in analytical procedures involving multi-response. An overview of applications of thisfunction in several multivariate optimization tools as well as in various analytical techniques is presented.Fil: Novaes, Cleber G.. Universidade Estadual do Sudoeste da Bahia; Brasil. Universidade Federal da Bahia; BrasilFil: Ferreira, Sergio L.C.. Universidade Federal da Bahia; BrasilFil: Neto, João H. S.. Universidade Estadual do Sudoeste da Bahia; BrasilFil: de Santana, Fernanda A.. Universidade Federal da Bahia; BrasilFil: Portugal, Lindomar A.. Universidad de las Islas Baleares; EspañaFil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; ArgentinaBentham Science Publishers2016-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/56003Novaes, Cleber G.; Ferreira, Sergio L.C.; Neto, João H. S.; de Santana, Fernanda A.; Portugal, Lindomar A.; et al.; A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination; Bentham Science Publishers; Current Analytical Chemistry; 12; 2; 3-2016; 94-1011573-4110CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.2174/1573411011666150722220335info:eu-repo/semantics/altIdentifier/url/http://www.eurekaselect.com/133390/articleinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:19:08Zoai:ri.conicet.gov.ar:11336/56003instacron: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:34982025-10-22 11:19:08.38CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
title A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
spellingShingle A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
Novaes, Cleber G.
Multiple Response Function
Experimental Design
Desirability Function D
Icp Oes
title_short A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
title_full A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
title_fullStr A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
title_full_unstemmed A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
title_sort A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination
dc.creator.none.fl_str_mv Novaes, Cleber G.
Ferreira, Sergio L.C.
Neto, João H. S.
de Santana, Fernanda A.
Portugal, Lindomar A.
Goicoechea, Hector Casimiro
author Novaes, Cleber G.
author_facet Novaes, Cleber G.
Ferreira, Sergio L.C.
Neto, João H. S.
de Santana, Fernanda A.
Portugal, Lindomar A.
Goicoechea, Hector Casimiro
author_role author
author2 Ferreira, Sergio L.C.
Neto, João H. S.
de Santana, Fernanda A.
Portugal, Lindomar A.
Goicoechea, Hector Casimiro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Multiple Response Function
Experimental Design
Desirability Function D
Icp Oes
topic Multiple Response Function
Experimental Design
Desirability Function D
Icp Oes
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper presents a comparison between a multiple response function (MR) proposed for optimization of analyticalstrategies involving multi-element determinations with the desirability function D, which was proposed by Derringerand Suich in 1980. The MR function is established by the average of the sum of the normalized responses for eachanalyte considering the highest value of these. This comparison was performed during the optimization of an spectrometerfor quantification of six elements using inductively coupled plasma optical emission spectrometry (ICP OES). Four instrumentalfactors were studied (auxiliary gas flow rate, plasma gas flow rate, nebulizer gas flow rate and radio frequencypower). A (24) two-level full factorial design and a Box Behnken matrix were developed to evaluate the performance ofthe two multiple response functions. The results found demonstrated great similarity in the interpretations obtained consideringthe effect values of the factors calculated using the two-level full factorial design employing the two multiple responses.Also a Box Behnken design was performed to compare the applicability of the two multiple response functions inquadratic models. The results achieved demonstrated high correlation (0.9998) between the regression coefficients of thetwo models. Also the response surfaces obtained showed great similarity in terms of formats and experimental conditionsfound for the studied factors. Thus, the multiple response (MR) is presented as a simple tool, easy to manipulate, efficientand very helpful for application in analytical procedures involving multi-response. An overview of applications of thisfunction in several multivariate optimization tools as well as in various analytical techniques is presented.
Fil: Novaes, Cleber G.. Universidade Estadual do Sudoeste da Bahia; Brasil. Universidade Federal da Bahia; Brasil
Fil: Ferreira, Sergio L.C.. Universidade Federal da Bahia; Brasil
Fil: Neto, João H. S.. Universidade Estadual do Sudoeste da Bahia; Brasil
Fil: de Santana, Fernanda A.. Universidade Federal da Bahia; Brasil
Fil: Portugal, Lindomar A.. Universidad de las Islas Baleares; España
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
description This paper presents a comparison between a multiple response function (MR) proposed for optimization of analyticalstrategies involving multi-element determinations with the desirability function D, which was proposed by Derringerand Suich in 1980. The MR function is established by the average of the sum of the normalized responses for eachanalyte considering the highest value of these. This comparison was performed during the optimization of an spectrometerfor quantification of six elements using inductively coupled plasma optical emission spectrometry (ICP OES). Four instrumentalfactors were studied (auxiliary gas flow rate, plasma gas flow rate, nebulizer gas flow rate and radio frequencypower). A (24) two-level full factorial design and a Box Behnken matrix were developed to evaluate the performance ofthe two multiple response functions. The results found demonstrated great similarity in the interpretations obtained consideringthe effect values of the factors calculated using the two-level full factorial design employing the two multiple responses.Also a Box Behnken design was performed to compare the applicability of the two multiple response functions inquadratic models. The results achieved demonstrated high correlation (0.9998) between the regression coefficients of thetwo models. Also the response surfaces obtained showed great similarity in terms of formats and experimental conditionsfound for the studied factors. Thus, the multiple response (MR) is presented as a simple tool, easy to manipulate, efficientand very helpful for application in analytical procedures involving multi-response. An overview of applications of thisfunction in several multivariate optimization tools as well as in various analytical techniques is presented.
publishDate 2016
dc.date.none.fl_str_mv 2016-03
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/11336/56003
Novaes, Cleber G.; Ferreira, Sergio L.C.; Neto, João H. S.; de Santana, Fernanda A.; Portugal, Lindomar A.; et al.; A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination; Bentham Science Publishers; Current Analytical Chemistry; 12; 2; 3-2016; 94-101
1573-4110
CONICET Digital
CONICET
url http://hdl.handle.net/11336/56003
identifier_str_mv Novaes, Cleber G.; Ferreira, Sergio L.C.; Neto, João H. S.; de Santana, Fernanda A.; Portugal, Lindomar A.; et al.; A Multiple Response Function for Optimization of Analytical Strategies Involving Multi-elemental Determination; Bentham Science Publishers; Current Analytical Chemistry; 12; 2; 3-2016; 94-101
1573-4110
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.2174/1573411011666150722220335
info:eu-repo/semantics/altIdentifier/url/http://www.eurekaselect.com/133390/article
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 Bentham Science Publishers
publisher.none.fl_str_mv Bentham Science Publishers
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
_version_ 1846781664331563008
score 12.982451

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