A quantitative structure–property relationship (QSPR) study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques is carried out to investigate the retention time behavior of some pesticides on the DB-5ms fused-silica column in gas chromatography. Five descriptors selected in the MLR model are: first component WHIM index (E1v), highest eigenvalue n.7 of burden matrix / weighted by atomic van der waals volume (BEHv7); average connectivity index Chi-2 (X2a), 3D-MoRSE signal 23 weighted by atomic Sanderson electronegativity (MoR23m); and principal moments of inertia B (PMIB). A 5-5-1 ANN is also generated to investigate the retention behavior of described pesticides using the same descriptors MLR model as inputs. The statistical parameters derived from MLR and ANN for all molecules are: correlation coefficient (R)MLR = 0.929, standard errors (SE)MLR = 3.452, RANN = 0.943, and SEANN = 3.112. The mean of relative errors between the MLR and ANN calculated and the experimental values of the retention times for the prediction set are 13.8% and 9.04%, respectively. The correlation coefficient and standard error of ANN model compared with MLR models showed the superiority of ANNs over regression models. This is partly due to the fact that ANN considers the interaction between different parameters as well as nonlinear relation
