Use of kernel orthogonal projection to latent structure in modeling of retention indices of pesticides

摘要

The gas chromatography retention indices of 168 pesticides were used to construct a robust quantitative structure - retention relationship (QSRR) model. After outlier detection by Cook's influence measurement, the remaining compounds were subjected to two different modeling strategies. The first one was stepwise multiple linear regression (stepwise-MLR). Results of this method revealed that 81.7 percent of variances of the response could be explained by the model. The other strategy was kernel orthogonal projection to latent structure (KOPLS). R2 and RMSE values for the prediction set established by Monte Carlo cross validation of the KOPLS were 0.906 and 0.093, respectively. Y-randomization technique was used to assess the chance correlation in the developed models. This technique indicated that there was no chance correlation in the KOPLS constructed model. From the results of this work one may conclude that KOPLS is superior over the stepwise-MLR in term of predictability and can be used as an alternative method in quantitative structure-activity/-retention relationship (QSAR/QSRR) studies.