Prediction of relative response factors of electron-capture detection for some polychlorinated biphenyls using...

The relative response factor (RRF) of an electron-capture detection (ECD) system is predicted for a set of 118 polychlorinated biphenyls (PCBs). Due to the wide range of relative retention times of PCB congeners, the RRFs of these compounds were calculated based on two different internal standards. Therefore, the compounds were divided into two molecular subsets. As a first step, multiple linear regression (MLR) was employed to find informative descriptors that can predict the RRFs of these compounds. Two descriptors of molecular ion ionization potential (MIIP) and ionization potential of the molecule (IP) that are related to affinity of the compounds for the electrons show the highest mean effects in subsets 1 and 2. respectively. The descriptors appearing in the MLR models were considered as inputs for developing the back-propagation artificial neural networks (BP-ANN). Two networks with the architectures of 5-5-1 and 7-6-1 were generated for the prediction of RRFs of molecules of subsets 1 and 2, respectively. Comparison of the results indicates the superiority of neural networks over that of the MLR method indicating the nonlinear behaviors of the ECD system. Inspection of the models reveals that the surface of the molecules play different roles in response factors of two subsets due to rotation of one phenyl group with respect to the other for the subset consisting of larger number of chlorine atoms.