Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks

The relative response factors (RRFs) of a flame ionization detection (FID) system and two pulsed discharge photoionization detection (PID) systems with different discharge gases are predicted for a set of organic compounds containing various functional groups. As a first step, numerical descriptors were calculated based on the molecular structures of compounds. Then, multiple linear regression (MLR) was employed to find informative subsets of descriptors that can predict the RRFs of these compounds. The selected MLR model for the FID system includes seven descriptors and two selected MLR models for the PID systems with argon- and krypton-doped helium as the discharge gases, respectively, include six and five descriptors. The descriptors appearing in the MLR models were considered as inputs for the self-training artificial neural networks (STANNs). A 7-7-1 STANN was generated for prediction of RRFs of the FID system, and two STANNs with the topologies of 6-7-1 and 5-6-1 were generated for the two PID systems. Comparison of the results indicates the superiority of neural networks over that of the MLR method. This is due to the nonlinear behaviors of relative response factors for all type of detectors studied in this work.