Quantitative structure-critical micelle concentration modeling of anionic gemini surfactants, comparison of MLR, PLS, WNN, and ANFIS models with eigenvalue and correlation ranking methods

Abstract

Unique properties of gemini surfactants have attracted the attention of the cosmetics, paints, textile, and detergents industries. Therefore, design of new gemini surfactants is considered important. In this work, the critical micelle concentration of 85 anionic gemini surfactants was predicted by structural descriptors for the first time. Genetic algorithm-multiple linear regression (GA-MLR), genetic algorithm-partial least squares (GA-PLS), principal component-adaptive neuro-fuzzy inference systems (PC-ANFIS), and principal component-wavelet neural network (PC-WNN) were used as linear and nonlinear models. Between linear models, the PLS method had better root mean square errors (RMSE) and mean relative error (MRE) values than MLR model. The eigenvalue and correlation ranking methods were used to select the best PCs in nonlinear models such as PC-ANFIS and PC-WNN. The correlation ranking selection method has better results in both the ANFIS and WNN models. WNN has better results with respect to other models. The RMSE of the training, test, and validation sets for the best PC-WNN model with eight inputs were 0.250, 0.359, and 0.310, respectively.