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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/56003
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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 Técnicas2025-09-29T09:49:46Zoai: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:34982025-09-29 09:49:47.126CONICET 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 |
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1844613538356658176 |
score |
13.070432 |