Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Química Nova (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000600012 |
Resumo: | The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way. |
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Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersivachemometricsmultivariate calibrationX-ray fluorescenceThe objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way.Sociedade Brasileira de Química2002-11-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000600012Química Nova v.25 n.6a 2002reponame:Química Nova (Online)instname:Sociedade Brasileira de Química (SBQ)instacron:SBQ10.1590/S0100-40422002000600012info:eu-repo/semantics/openAccessSchimidt,FernandoBueno,Maria Izabel M. S.Poppi,Ronei J.por2002-11-20T00:00:00Zoai:scielo:S0100-40422002000600012Revistahttps://www.scielo.br/j/qn/ONGhttps://old.scielo.br/oai/scielo-oai.phpquimicanova@sbq.org.br1678-70640100-4042opendoar:2002-11-20T00:00Química Nova (Online) - Sociedade Brasileira de Química (SBQ)false |
dc.title.none.fl_str_mv |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
title |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
spellingShingle |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva Schimidt,Fernando chemometrics multivariate calibration X-ray fluorescence |
title_short |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
title_full |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
title_fullStr |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
title_full_unstemmed |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
title_sort |
Aplicação de alguns modelos quimiométricos à espectroscopia de fluorescência de raios-X de energia dispersiva |
author |
Schimidt,Fernando |
author_facet |
Schimidt,Fernando Bueno,Maria Izabel M. S. Poppi,Ronei J. |
author_role |
author |
author2 |
Bueno,Maria Izabel M. S. Poppi,Ronei J. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Schimidt,Fernando Bueno,Maria Izabel M. S. Poppi,Ronei J. |
dc.subject.por.fl_str_mv |
chemometrics multivariate calibration X-ray fluorescence |
topic |
chemometrics multivariate calibration X-ray fluorescence |
description |
The objective of this work was to accomplish the simultaneous determination of some chemical elements by Energy Dispersive X-ray Fluorescence (EDXRF) Spectroscopy through multivariate calibration in several sample types. The multivariate calibration models were: Back Propagation neural network, Levemberg-Marquardt neural network and Radial Basis Function neural network, fuzzy modeling and Partial Least Squares Regression. The samples were soil standards, plant standards, and mixtures of lead and sulfur salts diluted in silica. The smallest Root Mean Square errors (RMS) were obtained with Back Propagation neural networks, which solved main EDXRF problems in a better way. |
publishDate |
2002 |
dc.date.none.fl_str_mv |
2002-11-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000600012 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422002000600012 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
10.1590/S0100-40422002000600012 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Química |
publisher.none.fl_str_mv |
Sociedade Brasileira de Química |
dc.source.none.fl_str_mv |
Química Nova v.25 n.6a 2002 reponame:Química Nova (Online) instname:Sociedade Brasileira de Química (SBQ) instacron:SBQ |
instname_str |
Sociedade Brasileira de Química (SBQ) |
instacron_str |
SBQ |
institution |
SBQ |
reponame_str |
Química Nova (Online) |
collection |
Química Nova (Online) |
repository.name.fl_str_mv |
Química Nova (Online) - Sociedade Brasileira de Química (SBQ) |
repository.mail.fl_str_mv |
quimicanova@sbq.org.br |
_version_ |
1750318102764060672 |