title: Optimization and empirical modeling of HG-ICP-AES analytical technique through artificial neural networks.
abstract: 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.
authors: Unidad de Actividad Química, Centro Atómico Constituyentes, Comisión Nacional de Energía Atómica, Av. del Libertador 8250, 1429 Buenos Aires, Argentina. magallan@cnea.gov.ar
authors: J F
authors: JF
authors: Magallanes
authors: P
authors: P
authors: Smichowski
authors: J
authors: J
authors: Marrero