an improved structure models to explain retention behavior of atmospheric nanoparticles

the quantitative structure-retention relationship (qsrr) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. the genetic algorithm (ga) was employed to select the variables that resulted in the best-fitted models. after the variables were selected, the linear multivariate regressions [e.g. the partial least squares (pls)] as well as the nonlinear regressions [e.g. the kernel pls (kpls) and levenberg- marquardt artificial neural network (l-m ann)] were utilized to construct the linear and nonlinear qsrr models. the correlation coefficient cross validation (q2) and relative error for test set l-m ann model are 0.939 and 4.89, respectively. the resulting data indicated that l-m ann could be used as a powerful modeling tool for the qspr studies.