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

Abstract

In the presented study, orthogonal projection to latent structures (OPLS) is introduced as a 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 227 nm, and working range for emission spectra was 320–440 nm.

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.