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
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario: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.

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