Development of Gradient Retention Model in Ion Chromatography. Part II: Artificial Intelligence QSRR Approach

Abstract

Methodology that integrates traditional QSRR modeling with transfer of information from isocratic to gradient environment was presented in the previous paper as an efficient new chromatographic approach. The previous research included application of conventional regression techniques that resulted in relatively high prediction errors. Also, it was shown that prediction error of the integrated model was mostly caused by prediction of the QSRR models. Therefore, artificial intelligence was applied in this work in order to improve the prediction ability of QSRR-based model: Artificial neural networks were selected for QSRR modeling, while genetic algorithm was used for the selection of optimal descriptors. Both artificial neural networks and genetic algorithm were optimized in order to build accurate and reliable QSRR models. Selection function, crossover function, and percentage of genes’ mutations were varied in case of genetic algorithm, while artificial neural networks were optimized by means of different network type, training algorithm and number of neurons in hidden layer. During retention modeling, basic QSRR models developed for specific eluent strength were upgraded to isocratic, and thereafter to gradient retention model. None of the three developed models showed systematic errors, and the obtained predictions (RMSEP 11.66, 10.67, and 7.10 %, respectively) indicated significant improvement from the results presented in previous paper.