Prediction of thermal conductivity detection response factors using an artificial neural network

Abstract

The main aim of the present work was the development of a quantitative structure–activity relationship method using an artificial neural network (ANN) for predicting the thermal conductivity detector response factor. As a first step a multiple linear regression (MLR) model was developed and the descriptors appearing in this model were considered as inputs for the ANN. The descriptors of molecular mass, number of vibrational modes of the molecule, molecular surface area and Balaban index appeared in the MLR model. In agreement with the molecular diameter approach, molecular mass and molecular surface area play a major role in estimating the thermal conductivity detector response factor (TCD-RF). A 4-7-1 neural network was generated for the prediction of the TCD-RFs of a collection of 110 organic compounds including hydrocarbons, benzene derivatives, esters, alcohols, aldehydes, ketones and heterocyclics. The mean absolute error between the ANN calculated and the experimental values of the response factors was 0.02 for the prediction set.