| Popis: |
The theme of this paper is to foresee relative retention time of 122 volatile organic compounds. QSRR analysis was accomplished on a serial of 122 VOCs. Multiple Linear Regression (MLR) and support vector machine (SVM) methods were used to build linear and nonlinear (QSRR) models, respectively, which correlate the (RTT) values of these chemical substance to their structural descriptors. At first, the data set was separated using Kennard and Stone algorithm into a training set (92 chemicals) and a test set (30 chemicals) for statistical external validation. The five-dimensional models were developed using as independent variables the theoretical descriptors derived from the DRAGON software during the application of the procedure GA (genetic algorithm) - VSS (Variable Subset Selection). The robustness and the predictive performance of the MLR model have been demonstrated by inside and outer statistical validation. Non-linear technique leads to the best QSRR model with good internal and external predictive abilities. It is based on support vector machines using the RBF function for the optimal parameters values. The values of and were 17, 0.2, 0.2, respectively. |
