Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

Abstract

In this study, a quantitative structure-property relationship technique has been used for the prediction of retention factors for some organic compounds in supercritical fluid chromatography using an artificial neural network. The best descriptors that appear in this model are the number of single bonds, the number of double bonds, and the hydrophilic factor. These descriptors were used as inputs for a generated artificial neural network. This network has a 3 : 3 : 1 topology that was trained using a back-propagation algorithm. The crossvalidation method was used to evaluate the predictive power of the generated network. The results obtained by artificial neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression technique. Comparison of these results shows the ability of the artificial neural network model to predict retention factors.

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

“…The model is used to derive solubility (d) values for new compounds and is extended to retention prediction for disubstituted aromatics. Also, we have used QSRR techniques for prediction of retention factors of some organic compounds in SFC from their molecular structures [21]. In the obtained QSRR model, the number of single bonds, number of double bonds, and the hydrophilic factor were used as molecular descriptors.…”