Prediction of retention times for anions in ion chromatography using Artificial Neural Networks

Abstract

An Artificial Neural Network (ANN) was investigated as a method to model retention times of anions in nonsuppressed and suppressed ion chromatography (IC) using a range of eluents and stationary phases, with the results being compared to those obtained using mathematical retention models. The optimal ANN architecture was determined for six specific IC cases of increasing complexity. Analysis of the retention times predicted using the ANN and those predicted by the mathematical models showed that the ANN approach yielded superior performance in all of the above cases. The use of a limited training data set configured in a central composite experimental design was suitable for application of the ANN to non-suppressed IC but was not applicable to suppressed IC, for which a more extensive training data set was necessary.