Teuku Rizky Noviandy, Aga Maulana, Novi Reandy Sasmita, Rivansyah Suhendra, Irvanizam Irvanizam, Muslem Muslem, Ghazi Mauer Idroes, Muhammad Yusuf, Hizir Sofyan, Taufik Fuadi Abidin, Rinaldi Idroes
The Kovats retention indices of 340 essential oil compounds have been successfully predicted based on their molecular descriptor using the Multiple Linear Regression (MLR) and Support Vector Regression (SVR). The genetic algorithm (GA) was used to select the best molecular descriptors, resulting in the selection of the five best molecular descriptors to construct the Kovats retention index prediction model. As the results, MLR had R2 training = 0.970, R2 testing = 0.970, RMSE training = 56.55, and RMSE testing = 56.99. Meanwhile SVR model produced R2 training = 0.981, R2 testing = 0.973, RMSE training = 44.62 and RMSE testing = 53.60. The MLR model obtained the average difference of the predicted values as 3.8% for the training set and 3.4% for the testing set. Meanwhile, SVR yielded a 2.4% difference for the training set and 3.4% for the testing set. Compared to MLR, the SVR model gave higher R2, lower RMSE, and a lower average difference between the predicted and observed values. In conclusion, our results indicate that the SVR is a more accurate predictor of the Kovats retention index than the MLR. © School of Engineering, Taylor’s University
Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Indonesia; Computational and Applied Statistics Research Group, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Indonesia; Department of Chemistry, Faculty of Science and Technology, Universitas Islam Negeri Ar-Raniry, Banda Aceh, 23111, Indonesia; Department of Chemical Engineering, Faculty of Engineering, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Indonesia; Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Indonesia; Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Indonesia; Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Kopelma Darussalam, Banda Aceh, 23111, Indonesia
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