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Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index

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This study aims to compare linear and nonlinear prediction methods in predicting the Kovats retention index of 126 compounds extracted from the Lippia origanoides plant. The retention index of each compound has been predicted based on its molecular descriptors. There have been 189 molecular descriptors for each compound in this study, and the best descriptors have been selected using the Genetic Algorithm (GA). It has succeeded in selecting the best five descriptors used to build the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) model. MLR has obtained R2 of 0.959, 0.946, 0.955, and a Root Mean Square Error (RMSE) of 48.00, 50.84, 47.19 on training, validation, and testing, respectively. Meanwhile, ANN has obtained R2 of 0.963, 0.947, 0.962, and an RMSE of 45.45, 50.59, 43.20, respectively. Compared to MLR, there has been an increase in the ANN model's performance, with an increase in R2 of 0.004, 0.001, and 0.007 and a decrease in RMSE of 2.55, 0.25, and 3.99. Based on the prediction results obtained, it is known that in this case, the ANN method can provide better predictive results than MLR.
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Kovats Retention Index; Lippia Origanoides; Essential Oil; Molecular Descriptor; Genetic Algorithm; Multiple Linear Regression; Artificial Neural Network

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