The predicting study for chromatographic retention index of saturated alcohols by MLR and ANN

A QSRR method was followed to relate the observed Kovats retention indexes of saturated alcohol compounds with their molecular connectivity indices by means of multilinear regression analysis and artificial neural networks technique. The alcohols included linear, branched with hydroxyl group on a primary, secondary, or tertiary carbon atom. At first, models were generated for six OV (Ohio Valley) series columns separately, with high value of R and F statistics. Then a combined model, added a polarity term of stationary phase (M), was also developed for all these columns, and the result was satisfactory. For comparison, the neural network of BP algorithm was applied, and it was found that the neural network could exceed the level of the multiple regression method. The stability and validity of both models were tested by cross-validation technique and by prediction response values for the prediction set.