Improved Calibration for Inductively Coupled Plasma-Atomic Emission Spectrometry Using Generalized Regression Neural Networks

Abstract

Artificial neural networks have been recently used in different fields of science in applications ranging from pattern recognition to semi-quantitative analysis. In this work, two types of neural networks were applied to the problems of spectral interferences, matrix effects, and the measurement drift in ICP-AES. Their performance was compared to that of the more conventional technique of multiple linear regressions (MLR). The two types of neural networks examined were “traditional” multilayer perceptron neural networks and generalized regression neural networks (GRNNs). The GRNN is comparable to, or better than, MLR for modeling spectral interferences and matrix effects covering several orders of magnitude. In the case of an Fe spectral interference on Zn, the GRNN reduced the error from 81% to 24%, while MLR reduced the average error to only 49%. For matrix effects caused by large backgrounds of Mg (0–10,000 ppm) on Zn, average error was reduced to 55% from 67%. In the case of combinations of spectral overlaps and matrix effects, the GRNN reduced average error by approximately 10%. MLR performed poorly on systems involving matrix effects. GRNN is also a very promising tool for the correction of drift caused by fluctuations in power levels, reducing drift over a two-hour period from 2.3% to 0.6%. GRNNs, both by themselves and in multinetwork combinations, seem to be highly promising for the correction of nonlinear matrix effects and long-term signal drift in ICP-AES.