Multiple Linear Regression (MLR) and Neural Network (NN) calculations of some disazo dye adsorption on cellulose

Abstract Multiple Linear Regression (MLR) analysis and Neural Network (NN) calculations are applied to a series of 21 disazo anionic dyes. Three-dimensional Q SAR parameters were derived from the Cartesian coordinates of the dye molecules. Low energy conformations were obtained by molecular mechanics and quantum chemical calculations. Electronic and steric effects in the dye-cellulose binding are present. The proposed MLR models are rough approximations of nonlinear models. Good correlation with the dye affinity from the MLR calculations and a significantly improved fitting of the NN over the MLR models are observed. The model validity was checked for two proposed models derived from different sets of structural parameters by the leave-one-out cross-validation procedure. For the first model, a better validity (‘cross-validated r2’ value, of 0.622) of the NN model is noticed by leaving out one compound (found as outlier) from the training set, in comparison to that of the MLR model obtained for the same set of compounds (q2 = 0.434). The q2 value of a second MLR proposed model is better than that of the corresponding NN model.