Cross-column prediction of gas-chromatographic retention indices of saturated esters

We combine computational molecular descriptors and variables related with the gas-chromatographicstationary phase into a comprehensive model able to predict the retention of solutes in external columns.To explore the quality of various approaches based on alternative column descriptors, we analyse theKováts retention indices (RIs) of 90 saturated esters collected with seven columns of different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP). Cross-column retention prediction is evaluatedon an internal validation set consisting of data of 40 selected esters collected with each of the sevencolumns, sequentially excluded from calibration. The molecular descriptors are identified by a geneticalgorithm variable selection method applied to a large set of non-empirical structural quantities aimed atfinding the best multi-linear quantitative structure–retention relationship (QSRR) for the column OV-25having intermediate polarity. To describe the columns, we consider the sum of the first five McReynoldsphase constants and, alternatively, the coefficients of the corresponding QSRRs. Moreover, the mean RIvalue for the subset of esters used in QSRR calibration or RIs of a few selected compounds are usedas column descriptors. For each combination of solute and column descriptors, the retention model isgenerated both by multi-linear regression and artificial neural network regression.