In this paper, based on the general interaction properties function (GIPF) family descriptors computed at the B3LYP/6-31G* level in Gaussian98 software, a significant quantitative structure–retention relationship (QSRR) models for the high resolution gas chromatographic relative retention time (HRGC-RRT) of all PCB congeners on 18 different HRGC capillary columns were constructed by using multiple linear regression (MLR) analysis, following the guidelines for development and validation of QSRR models. By means of the elimination selection stepwise regression algorithms, the molecular surface average local ionization energy was selected as one-parameter univariate linear regression to develop a QSRR model for prediction of GC-RRT of PCBs on each stationary phase. The accuracy of all developed models was confirmed using different types of internal and external procedures. A successful interpretation of the complex relationship between HRGC-RRTs of PCBs and the chemical structures was achieved by QSRR.
