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 LSSVM-based QSPR model regarding lower RMSE value of 0.023 compared to the group contribution based and the best results from BMLR-based QSPR.

胶束化现象发生在自然和技术过程中，因此有必要开发能够预测表面活性剂自组装行为的预测模型。为了预测糖基表面活性剂的临界胶束浓度（CMC），开发了基于最小二乘支持向量机（LSSVM）的定量结构性质关系（QSPR）模型。模型的开发基于使用包含83种糖基表面活性剂的综合数据库，训练和验证了预测性LSSVM策略。已使用不同的视觉和统计参数对模型的可靠性和鲁棒性进行了评估，从而揭示了其强大的预测能力。还将结果与先前报告的基于最佳多元线性回归（BMLR）的QSPR和基于小组贡献的模型进行比较，