Column liquid chromatography
Multiple linear regressions
Springer Online Journal Archives 1860-2000
Chemistry and Pharmacology
Summary The use of theoretically calculated molecular properties as predictors for retention in reversed-phase HPLC has been explored. HPLC retention times have been measured for a series of 47 substituted aromatic molecules in three solvent mixtures and steric and electronic properties of these compounds have been derived using semi-empirical molecular orbital and empirical theoretical methods. A subset of the experimental data (a training set) was used to derive property-retention time relationships and the remaining data were then used to test the predictive capability of the methods. Good retention time prediction was possible using derived regression equations for individual solvents and after including solvent parameters it was possible to predict retention for all solvents using a single equation. This method showed that the most useful properties were calculated log P and the calculated dipole moment of the solutes, and the calculated solvent polarisability. In addition, 90% of the data were used to train an artificial neural network and the remaining 10% of the data used to test the network; excellent prediction was obtained, the neural network approach being as successful as the regression analysis.
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