Quantitative Structure-Retention Index Relationship (QSRIR) Study of Monomethylalkanes on the Methylsilicone OV-1 Stationary Phase
Nasser Goudarzi, Payam Kalhor · Analytical Chemistry Letters · 2012
A back-propagation artificial neural network (ANN) is used to create a model of gas chromatography retention indices for a data set of 196 monomethylalkanes on the methylsilicone OV-1 stationary phase. Multlinear regression (MLR) model of the same data is developed for comparison. The quantitative structure-property relationship (QSPR) models are validated by cross-validation as well as application of the models for prediction of retention indices of external set of compounds which do not have contribution to model development steps. The models are also validated by statistical parameters and Y-randomization. Both linear and non-linear methods provide accurate predictions. The mean-squared errors (MSE) for validation and test sets for MLR are 18.44 and 28.96, and those for ANN are 2.24 and 4.21, respectively.