Predicting gas chromatography relative retention times for polychlorinated biphenyls using chlorine substitution pattern contribution method

Abstract

Various quantitative structure retention relationships have been published in an effort to understand and predict chromatographic retention times. This work presents a chlorine substitution pattern contribution (Cl-SPC) model for relative retention times (RRT) of polychlorinated biphenyls (PCBs), using 27 sets of previously published gas chromatography RRT data. The Cl-SPC model calculates the contribution factors (βk) for each of 19 chlorine substitution “patterns” (such as 2-, 2,4-, 2,3,6-, 2,3,4,5,6-, etc.) using multiple linear regression (MLR). The 27 separate MLRs had R2 values ranging from 0.961 to 1.000; the average absolute errors were 0.55% for the training sets and 0.95% for the test sets. Cross-validation of the model was carried out by splitting each data set into training and test sets for groupings based on nine PCB congener mixes commercialized by AccuStandard. No weakening of the model performance was observed when the size of data set used to develop the model was decreased from 209 to 39 congeners. In addition to the separate models, a single mixed model was fit combining all 27 data sets. The estimated random effects, which reflect the impact of GC configuration and operational conditions on RRTs, are minor compared with the fixed effects estimated for the βk values. The major advantages of the Cl-SPC model are its unmatched simplicity and equally excellent robustness when compared with other quantitative structure retention relationship models.