Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks

An artificial neural network technique has been applied to the optimization of a hydride generation-inductively coupled plasma-atomic emission spectrometry (HG-ICP-AES) coupling for the determination of Ge at trace levels. The back propagation of errors net architecture was used. Experimental parame...

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Detalles Bibliográficos
Autores principales: Magallanes, J.F., Smichowski, P., Marrero, J.
Formato: JOUR
Materias:
article
Backpropagation
Computer software
Neural networks
Plasmas
Spectrometry
Flow rate
Chemical analysis
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00952338_v41_n3_p824_Magallanes
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00952338_v41_n3_p824_Magallanes
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spelling todo:paper_00952338_v41_n3_p824_Magallanes2023-10-03T14:56:37Z Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks Magallanes, J.F. Smichowski, P. Marrero, J. article Backpropagation Computer software Neural networks Plasmas Spectrometry Flow rate Chemical analysis An artificial neural network technique has been applied to the optimization of a hydride generation-inductively coupled plasma-atomic emission spectrometry (HG-ICP-AES) coupling for the determination of Ge at trace levels. The back propagation of errors net architecture was used. Experimental parameters and their relationship have been studied, obtaining a surface response of the system. The results and optimization aspects achieved with the neural network approach have been compared to the "one variable at time" and SIMPLEX methods. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00952338_v41_n3_p824_Magallanes
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic article
Backpropagation
Computer software
Neural networks
Plasmas
Spectrometry
Flow rate
Chemical analysis
spellingShingle article
Backpropagation
Computer software
Neural networks
Plasmas
Spectrometry
Flow rate
Chemical analysis
Magallanes, J.F.
Smichowski, P.
Marrero, J.
Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
topic_facet article
Backpropagation
Computer software
Neural networks
Plasmas
Spectrometry
Flow rate
Chemical analysis
description An artificial neural network technique has been applied to the optimization of a hydride generation-inductively coupled plasma-atomic emission spectrometry (HG-ICP-AES) coupling for the determination of Ge at trace levels. The back propagation of errors net architecture was used. Experimental parameters and their relationship have been studied, obtaining a surface response of the system. The results and optimization aspects achieved with the neural network approach have been compared to the "one variable at time" and SIMPLEX methods.
format JOUR
author Magallanes, J.F.
Smichowski, P.
Marrero, J.
author_facet Magallanes, J.F.
Smichowski, P.
Marrero, J.
author_sort Magallanes, J.F.
title Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
title_short Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
title_full Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
title_fullStr Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
title_full_unstemmed Optimization and Empirical Modeling of HG-ICP-AES Analytical Technique through Artificial Neural Networks
title_sort optimization and empirical modeling of hg-icp-aes analytical technique through artificial neural networks
url http://hdl.handle.net/20.500.12110/paper_00952338_v41_n3_p824_Magallanes
work_keys_str_mv AT magallanesjf optimizationandempiricalmodelingofhgicpaesanalyticaltechniquethroughartificialneuralnetworks
AT smichowskip optimizationandempiricalmodelingofhgicpaesanalyticaltechniquethroughartificialneuralnetworks
AT marreroj optimizationandempiricalmodelingofhgicpaesanalyticaltechniquethroughartificialneuralnetworks
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