Application of Fuzzy Support Vector Regression to Predict the Kovats Retention Indices of Flavors and Fragrances

Abstract

The Kovats retention index is the most robust retention parameter used to identify analytes in gas chromatography. The Kovats retention index’s value in similar compounds tends to have a similar pattern. This pattern allows a close relationship between chemical structure and the retention index value. The Fuzzy Support Vector Regression (FSVR) method is a robust algorithm that has been used to solve various regression problems. This study aimed to evaluate the FSVR algorithm performance to predict the Kovats retention index of flavor and fragrance compounds. The model was built using the Python programming language. The retention index value calculation begins with inputting the molecular descriptor data into the sigmoid fuzzy membership function. The dataset consists of 51 compounds then partitioned into two sets, namely 80% in the training set and 20% in the testing set. The best parameters to build the model were obtained using hyperparameter tuning. The kernels used are linear kernel and Radial Basis Function (RBF) kernel. The model with a linear kernel with parameter values of C = 1000 and epsilon = 10 managed to get the best results compared to the model with an RBF kernel, with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.977 and Root Mean Square Error (RMSE) = 37.21 on the testing set. This model also can predict the Kovats retention index on the testing set with an average difference of 2.8%. These results showed that the linear kernel is better than the RBF kernel in predicting the value of the Kovats retention index. It can be concluded that the FSVR method can be used to predict the retention index of flavor and fragrance compounds.