راضیه رضوی پاریزی

Micellization phenomenon occurs in natural and technical processes, necessitating the need to develop predictive models capable of predicting self-assembly behavior of surfactants. A least squares support vector machine (LSSVM) based quantitative structure property relationships (QSPR) model is developed in order to predict critical micelle concentration (CMC) for sugar-based surfactants. Model development is based on training and validating a predictive LSSVM strategy using a comprehensive data base consisting of 83 sugar-based surfactants. Model’s reliability and robustness has been evaluated using different visual and statistical parameters, revealing its great predictive capabilities. Results are also compared to previously reported best multi-linear regression (BMLR) based QSPR and group contribution based models, showing better performance of the proposed LSSVMbased QSPR model regarding lower RMSE value of 0.023 compared to the group contribution based and the best results from BMLR-based QSPR.