Prediction of retention times for anions in linear gradient elution ion chromatography with hydroxide eluents using artificial neural networks

The feasibility of using an artificial neural network (ANN) to predict the retention times of anions when eluted from a Dionex AS11 column with linear hydroxide gradients of varying slope was investigated. The purpose of this study was to determine whether an ANN could be used as the basis of a comp uter-assisted optimisation method for the selection of optimal gradient con ditions for anion separations. Using an ANN with a (1, 10, 19) architecture and a training set comprising retention data obtained with three gradient slopes (1.67, 2.50 and 4.00 mM/min) between starting and finishing conditio ns of 0.5 and 40.0 mM hydroxide, respectively, retention times for 19 analy te anions were predicted for four different gradient slopes. Predicted and experimental retention times for 133 data points agreed to within 0.08 min and percentage normalised differences between the predicted and experimenta l data averaged 0.29% with a standard deviation of 0.29%. ANNs appear to be a rapid and accurate method for predicting retention times in ion chromato graphy using linear hydroxide gradients. (C) 2001 Elsevier Science B.V. All rights reserved.