Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography

In this work multiple linear regression (MLR) and artificial neural network (ANN) were used to predict the gradient retention times of diverse sets of organic compounds in four separate data sets. Descriptors which were used as inputs of these models are five linear free energy relationship (LFER) solute parameters including E, S, A, B and V. In the first step eight separate multiple linear regression and artificial neural network models were used to predict the gradient retention time for each gradient condition separately. Results obtained in this step reveal that there are significant relations between LFER parameters and gradient retention times of solutes in liquid chromatography. Then MLR and ANN were applied to develop more general models in which several different gradient elution conditions were used. The performances of these models are compared in terms of their standard errors and also correlation analysis. The results obtained reveal that although there are no significant differences between ANN and MLR in separate modeling of the gradient retention times, ANN has a significant superiority over MLR models in developing the general models for various gradient elution conditions. The results of sensitivity analysis on ANN models indicate that the order of importance for input terms in separate ANN models is Vx > B > S > E > A and in the case of combined ANN model is Vx > B > tg > S > E > A, which are in agreement with the order of percentage of significance terms that obtained from the MLR models.