Artificial Neural Network Modelling of the Retention of Acidic Analytes in Strong Anion-Exchange HPLC: Elucidation of Structure-Retention Relationships

Abstract

Computational models can be used to increase understanding of physical processes within chromatographic systems, leading to more efficient method development and optimisation strategies. In ion-exchange chromatography, various models have been derived to predict retention time; however, there remains a gap in understanding regarding the elucidation of fundamental processes contributing to retention. Here, artificial neural networks have been used to model retention of simple acidic analytes by strong anion-exchange HPLC in an attempt to understand what other factors aside from simple electrostatic interactions between ionised analyte, stationary phase and counter-ion contribute to the differential elution order of such compounds. The weights assigned by each neuron to the inputs in trained networks were used to infer the influence of a number of physicochemical analyte properties to retention under various conditions. These showed that several retention mechanisms were operating simultaneously, and that the contribution of each varied as eluent ionic strength and composition were altered at constant apparent pH. Analyte pKa had most influence on retention under most conditions, but analyte volume, LogP, and steric and electronic effects were also prominent, especially in eluents containing water.