Support vector machine classification model for color fastness to ironing of vat dyes

Investigations of quantitative relationships between structure and color fastness of dyes are crucial in seeking novel dyes. For the first time, this work reported a classification model based on a quantitative structure–property relationship to predict color fastness to ironing of vat dyes. By performing binary classification analysis based on a support vector machine (SVM) and genetic algorithm, 56 vat dyes in the training set together with seven molecular descriptors were used to develop the classification model, which was validated with 59 vat dyes in the test set. The optimal SVM model ( C = 208.465 and γ = 5.9692) possesses overall accuracy of 91.1% for the training set and 83.1% for the test set, which is more accurate than those from the binary logistic regression model (87.5% and 81.4%, respectively). Furthermore, the mechanism of molecular descriptors correlated with color fastness to ironing of vat dyes is discussed.