Improved predictions of nonlinear support vector regression and artificial neural network models via preprocessing of data with orthogonal projection to latent structures: A case study

The couplings of OPLS-SVR and OPLS-ANN provided better accuracy for prediction of independent nonlinear test set. The root mean square error of prediction RMSEP for the test set mixtures was used as a major comparison parameter, where RMSEP results for OPLS-SVR and OPLS-ANN are 2.19 and 1.50 respectively.