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

摘要

In the presented study, orthogonal projection to latent structures (OPLS) is introduced asa data preprocessing method that handles nonlinear data prior to modelling with two well established nonlinear multivariate models; namely support vector regression (SVR) and artificial neural networks (ANN). The proposed preprocessing proved to significantly improve prediction abilities through removal of uncorrelated data. The study was established based on a case study nonlinear spectrofluorimetric data of agomelatine (AGM) and its hydrolysis degradation products (Deg I and Deg II), where a 3 factor 4 level experimental design was used to provide a training set of 16 mixtures with different proportions of studied components. An independent test set which consisted of 9 mixtures was established to confirm the prediction ability of the introduced models. Excitation wavelength was 227nm, and working range for emission spectra was 320–440nm. 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 asa major comparison parameter, where RMSEP results for OPLS-SVR and OPLS-ANN are 2.19 and 1.50 respectively.