General interaction properties function (GIPF) approach can apply for systems that their interaction are non-covalent [1]. Non-covalent interactions are responsible for retention of molecule in column so we can use this approach for prediction of equivalent chain lengths (ECL) of C4-C23 monomethyl-branched fatty acid methyl esters (FAMEs) [2]. Each molecule optimized at HF/STO-3G. Then electrostatic potentials calculated at HF/6-31G* level of theory. We show GIPF theory can predict chromatographic behaviors of monomethyl-branched FAMEs on methylsilicone OV-1 stationary phase truly. By means of the final variable selection method, which is elimination selection stepwise regression (ES-SWR) algorithm, two optimal GIPF descriptors were selected to develop a QSRR model to predict the ECL of FMAEs derivatives. Furthermore, the accuracy of model was confirmed using procedures of Y-randomization, external validation through an odd-even number and division of the entire dataset into training and test sets [3]. A successful interpretation of the complex relationship between GC ECLs of FAMEs and the chemical structures was achieved by QSRR. Both GIPF descriptors in the models are also rationally interpreted, which indicated that all FAMEs derivative's ECL was precisely represented by GIPF descriptors.
