Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais

Detalhes bibliográficos
Autor(a) principal: Penoni, Nayara
Data de Publicação: 2006
Tipo de documento: Dissertação
Idioma: por
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: http://locus.ufv.br/handle/123456789/2057
Resumo: A potentiometric system was mounted. It was composed of 6 electrodes, ion selective (ISE) of bromide, iodide, nitrate, nitrite, sulfate and cyanide. These electrodes had been placed in line, allowing it to do potentiometric determination in continuous flow. The variables, flow control, and the stabilization of readouts had been verified through a developed computational program in Delphi language. With the use of this system, instrumental techniques of calibration had been developed, through chemometric, capable to separate answers of different ions. The techniques of multivariate analysis used had been the principal components analysis (PCA) and the artificial neural networks. With the use of the cubical experimental planning of net simplex lattice , it was possible to get 63 mixtures with the six described anions with concentrations that had varied between 10-2 mol L-1 to 1,33x10-3 mol L-1, others 3 mixtures had been used for validation. Also, in different environments, 8 samples of water had been collected. These samples had been separated in a group in the graph of PCA, which seems to indicate the presence of different ions from the six that had been analyzed. The other mixtures had been separate by the PCA according to the concentration and the interferings. The PC1 explained 79.09% of the variance and the PC2 explained 10.28% of the variance. The architecture of the artificial neural networks was optimized, and the minors mid square-errors of forecast (RMSEP) had been obtained. The used architecture has three layers. The entrance layer has 6 neurons, the intermediate layer has 13 neurons and the exit layer has 6 neurons. As a post of transferences, the sigmoidal tangent was used for the intermediate layer, and the linear tangent was used for the exit layer. The estimate error by the neural networks was in a order of 10-3, which shows a considerable interference of anions, causing an inadequate forecast by the net. The samples of collected water could not have been submitted to the forecast by the artificial neural networks because the concentrations of anions may have values that surpass the interval of training of the artificial neural networks.
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spelling Penoni, Nayarahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4759408Y5Reis, Efraim Lázarohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788214H7Reis, Césarhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4785327P6Queiroz, Maria Eliana Lopes Ribeiro dehttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4781671U3Fidencio, Paulo Henriquehttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4728400E4Milagres, Benjamin Gonçalveshttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4767834T12015-03-26T13:00:08Z2006-12-122015-03-26T13:00:08Z2006-02-20PENONI, Nayara. Simultaneous potentiometric characterization in flow, with anionic species, using principal components analysis and artificial neural networks. 2006. 100 f. Dissertação (Mestrado em Agroquímica analítica; Agroquímica inorgânica e Físico-química; Agroquímica orgânica) - Universidade Federal de Viçosa, Viçosa, 2006.http://locus.ufv.br/handle/123456789/2057A potentiometric system was mounted. It was composed of 6 electrodes, ion selective (ISE) of bromide, iodide, nitrate, nitrite, sulfate and cyanide. These electrodes had been placed in line, allowing it to do potentiometric determination in continuous flow. The variables, flow control, and the stabilization of readouts had been verified through a developed computational program in Delphi language. With the use of this system, instrumental techniques of calibration had been developed, through chemometric, capable to separate answers of different ions. The techniques of multivariate analysis used had been the principal components analysis (PCA) and the artificial neural networks. With the use of the cubical experimental planning of net simplex lattice , it was possible to get 63 mixtures with the six described anions with concentrations that had varied between 10-2 mol L-1 to 1,33x10-3 mol L-1, others 3 mixtures had been used for validation. Also, in different environments, 8 samples of water had been collected. These samples had been separated in a group in the graph of PCA, which seems to indicate the presence of different ions from the six that had been analyzed. The other mixtures had been separate by the PCA according to the concentration and the interferings. The PC1 explained 79.09% of the variance and the PC2 explained 10.28% of the variance. The architecture of the artificial neural networks was optimized, and the minors mid square-errors of forecast (RMSEP) had been obtained. The used architecture has three layers. The entrance layer has 6 neurons, the intermediate layer has 13 neurons and the exit layer has 6 neurons. As a post of transferences, the sigmoidal tangent was used for the intermediate layer, and the linear tangent was used for the exit layer. The estimate error by the neural networks was in a order of 10-3, which shows a considerable interference of anions, causing an inadequate forecast by the net. The samples of collected water could not have been submitted to the forecast by the artificial neural networks because the concentrations of anions may have values that surpass the interval of training of the artificial neural networks.Foi montado um sistema potenciométrico composto por 6 eletrodos seletivos à íons (ISE) de brometo, iodeto, nitrato, nitrito, sulfeto e cianeto. Os seis eletrodos foram colocados em linha, permitindo fazer determinações potenciométricas em fluxo contínuo. As variáveis, o controle de fluxo e a estabilização das leituras foram verificados através de um programa computacional desenvolvido em linguagem Delphi. Com a utilização deste sistema, foram desenvolvidas técnicas instrumentais de calibração, através da quimiometria, capazes de separar as respostas dos diferentes íons. As técnicas de análise multivariada utilizadas foram a análise das componentes principais (PCA) e as redes neurais artificiais. Com o uso do planejamento experimental cúbico de rede simplex lattice foi possível obter 63 misturas contendo os seis ânions descritos com concentrações que variaram entre 10-2 mol L-1 a 1,33x10-3 mol L-1, outras 3 misturas foram utilizadas para validação. Foram ainda coletadas 8 amostras de águas em diferentes ambientes. Estas ficaram separadas em um grupo no gráfico da PCA, o que parece indicar a presença de íons diferentes dos seis que foram analisados. As demais misturas foram separadas pela PCA de acordo com a concentração e com os interferentes, sendo que a PC1 explicou 79,09% da variância e a PC2 explicou 10,28% da variância. A arquitetura das redes neurais artificiais foi otimizada, tendo sido obtidos os menores erros quadrado médio de previsão (RMSEP). A arquitetura utilizada foi composta por três camadas, sendo a camada de entrada com 6 neurônios, a intermediária com 13 neurônios e a de saída com 6 neurônios. Foram utilizadas como função de transferências para a camada intermediária a tangente sigmoidal e na camada de saída a linear. O erro estimado pela rede neural foi da ordem de 10-3, o que mostra uma considerável interferência dos ânions, ocasionando uma previsão inadequada pela rede. As amostras de água coletadas não puderam ser submetidas à previsão pela rede neural artificial, pois as concentrações dos ânions podem ter valores que extrapolam o intervalo de treinamento da rede neural artificial.Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorapplication/pdfporUniversidade Federal de ViçosaMestrado em AgroquímicaUFVBRAgroquímica analítica; Agroquímica inorgânica e Físico-química; Agroquímica orgânicaPotenciometriaEletrodos seletivos a íonsRedes neuraisAnálise de componentes principaisAnálise por injeção de fluxoPotentiometryAnions selective eletrodesNeural networksPrincipal components analysisFlow injection analysisCNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICACaracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiaisSimultaneous potentiometric characterization in flow, with anionic species, using principal components analysis and artificial neural networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALtexto completo.pdfapplication/pdf939643https://locus.ufv.br//bitstream/123456789/2057/1/texto%20completo.pdf881a4948cb961ba741af1dbe5744ac54MD51TEXTtexto completo.pdf.txttexto completo.pdf.txtExtracted texttext/plain145200https://locus.ufv.br//bitstream/123456789/2057/2/texto%20completo.pdf.txt552eaeffb7799112f4e0e3c41fc08e5dMD52THUMBNAILtexto completo.pdf.jpgtexto completo.pdf.jpgIM Thumbnailimage/jpeg3731https://locus.ufv.br//bitstream/123456789/2057/3/texto%20completo.pdf.jpg86fe7e5e2b4c3d720fced5c46eefe702MD53123456789/20572016-04-07 23:17:42.861oai:locus.ufv.br:123456789/2057Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452016-04-08T02:17:42LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.por.fl_str_mv Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
dc.title.alternative.eng.fl_str_mv Simultaneous potentiometric characterization in flow, with anionic species, using principal components analysis and artificial neural networks
title Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
spellingShingle Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
Penoni, Nayara
Potenciometria
Eletrodos seletivos a íons
Redes neurais
Análise de componentes principais
Análise por injeção de fluxo
Potentiometry
Anions selective eletrodes
Neural networks
Principal components analysis
Flow injection analysis
CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA
title_short Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
title_full Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
title_fullStr Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
title_full_unstemmed Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
title_sort Caracterização potenciométrica simultânea em fluxo, de espécies aniônicas, empregando análise das componentes principais e redes neurais artificiais
author Penoni, Nayara
author_facet Penoni, Nayara
author_role author
dc.contributor.authorLattes.por.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4759408Y5
dc.contributor.author.fl_str_mv Penoni, Nayara
dc.contributor.advisor1.fl_str_mv Reis, Efraim Lázaro
dc.contributor.advisor1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788214H7
dc.contributor.referee1.fl_str_mv Reis, César
dc.contributor.referee1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4785327P6
dc.contributor.referee2.fl_str_mv Queiroz, Maria Eliana Lopes Ribeiro de
dc.contributor.referee2Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4781671U3
dc.contributor.referee3.fl_str_mv Fidencio, Paulo Henrique
dc.contributor.referee3Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4728400E4
dc.contributor.referee4.fl_str_mv Milagres, Benjamin Gonçalves
dc.contributor.referee4Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4767834T1
contributor_str_mv Reis, Efraim Lázaro
Reis, César
Queiroz, Maria Eliana Lopes Ribeiro de
Fidencio, Paulo Henrique
Milagres, Benjamin Gonçalves
dc.subject.por.fl_str_mv Potenciometria
Eletrodos seletivos a íons
Redes neurais
Análise de componentes principais
Análise por injeção de fluxo
topic Potenciometria
Eletrodos seletivos a íons
Redes neurais
Análise de componentes principais
Análise por injeção de fluxo
Potentiometry
Anions selective eletrodes
Neural networks
Principal components analysis
Flow injection analysis
CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA
dc.subject.eng.fl_str_mv Potentiometry
Anions selective eletrodes
Neural networks
Principal components analysis
Flow injection analysis
dc.subject.cnpq.fl_str_mv CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA
description A potentiometric system was mounted. It was composed of 6 electrodes, ion selective (ISE) of bromide, iodide, nitrate, nitrite, sulfate and cyanide. These electrodes had been placed in line, allowing it to do potentiometric determination in continuous flow. The variables, flow control, and the stabilization of readouts had been verified through a developed computational program in Delphi language. With the use of this system, instrumental techniques of calibration had been developed, through chemometric, capable to separate answers of different ions. The techniques of multivariate analysis used had been the principal components analysis (PCA) and the artificial neural networks. With the use of the cubical experimental planning of net simplex lattice , it was possible to get 63 mixtures with the six described anions with concentrations that had varied between 10-2 mol L-1 to 1,33x10-3 mol L-1, others 3 mixtures had been used for validation. Also, in different environments, 8 samples of water had been collected. These samples had been separated in a group in the graph of PCA, which seems to indicate the presence of different ions from the six that had been analyzed. The other mixtures had been separate by the PCA according to the concentration and the interferings. The PC1 explained 79.09% of the variance and the PC2 explained 10.28% of the variance. The architecture of the artificial neural networks was optimized, and the minors mid square-errors of forecast (RMSEP) had been obtained. The used architecture has three layers. The entrance layer has 6 neurons, the intermediate layer has 13 neurons and the exit layer has 6 neurons. As a post of transferences, the sigmoidal tangent was used for the intermediate layer, and the linear tangent was used for the exit layer. The estimate error by the neural networks was in a order of 10-3, which shows a considerable interference of anions, causing an inadequate forecast by the net. The samples of collected water could not have been submitted to the forecast by the artificial neural networks because the concentrations of anions may have values that surpass the interval of training of the artificial neural networks.
publishDate 2006
dc.date.available.fl_str_mv 2006-12-12
2015-03-26T13:00:08Z
dc.date.issued.fl_str_mv 2006-02-20
dc.date.accessioned.fl_str_mv 2015-03-26T13:00:08Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.citation.fl_str_mv PENONI, Nayara. Simultaneous potentiometric characterization in flow, with anionic species, using principal components analysis and artificial neural networks. 2006. 100 f. Dissertação (Mestrado em Agroquímica analítica; Agroquímica inorgânica e Físico-química; Agroquímica orgânica) - Universidade Federal de Viçosa, Viçosa, 2006.
dc.identifier.uri.fl_str_mv http://locus.ufv.br/handle/123456789/2057
identifier_str_mv PENONI, Nayara. Simultaneous potentiometric characterization in flow, with anionic species, using principal components analysis and artificial neural networks. 2006. 100 f. Dissertação (Mestrado em Agroquímica analítica; Agroquímica inorgânica e Físico-química; Agroquímica orgânica) - Universidade Federal de Viçosa, Viçosa, 2006.
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dc.publisher.department.fl_str_mv Agroquímica analítica; Agroquímica inorgânica e Físico-química; Agroquímica orgânica
publisher.none.fl_str_mv Universidade Federal de Viçosa
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