Abstract

This paper describes the simultaneous determination of Pr, Nd and Sm by EDXRF spectrometry using mixtures of oxides of these metals in a silica matrix. The data were treated by distinct neural network algorithms: back-propagation (BP), Levenberg-Marquardt (LM) and two variations of back-propagation (called BP-SC, single component, and BP-MC, multiple component), using results from the PLS model (partial least square regression) for comparison. The best applied model was the BP-SC neural network, which produced relative standard errors of prediction of 17.5% for Pr, 12.5% for Nd and 12.6% for Sm. Copyright © 2003 John Wiley & Sons, Ltd.