Improvement of Prediction Ability of Multicomponent Regression Model by a Method Based on Data Mining in Chemometrics

Abstract

A novel method named OSCWPTPLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as preprocessed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. In this case, using trials, the kind of wavelet function, the decomposition level, the number of OSC components and the number of PLS factors for the OSCWPTPLS method were selected as Daubechies 4, 3, 2 and 3, respectively. A program (POSCWPTPLS) was designed to perform the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). The relative standard errors of prediction (RSEP) obtained for all components using OSCWPTPLS, WPTPLS and PLS were compared. Experimental results demonstrated that the OSCWPTPLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.