Predicting retention data by target factor analysis and multiple regression analysis

Target factor analysis is used to predict gas-chromatographic retention indices from a training-set data matrix for 13 solutes and 15 stationary phases. In the target-combination approach, sets of data vectors are target-tested in combination and the resulting coefficients for the best model are used for prediction. Retention indices for 42 solutes and 24 stationary phases are predicted to better than 1% even with a three-factor model. In the target free-float approach, values for missing retention indices on target test vectors are predicted. Predictions from sets of target-test data selected by chemical intuition are compared to those obtained from sets of target-test data selected by using models from the combination step. The target-combination approach and multiple-regression approach are overall of similar utility for predicting new data.