Combining direct orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping voltammograms of nitroaniline isomers

Abstract

A novel method named DOSCWPTPLS approach based on partial least squares (PLS) regression with direct orthogonal signal correction (DOSC) and wavelet packet transform (WPT) as pre-processed tools was proposed for the simultaneous differential pulse voltammetric determination of o-nitroaniline, m-nitroaniline and p-nitroaniline with overlapping peaks. The method combines the ideas of DOSC and WPT with PLS regression for enhancing the ability in the extraction of characteristic information and the quality of regression. Data reduction was performed using DOSC, WPT and PLS algorithm. DOSC is used to remove information in the response matrix D by subtracting the structured noise that is orthogonal to the concentration matrix C. Wavelet packet representations of signals provide a local time–frequency description, thus in the wavelet packet domain, the quality of the noise removal, data compression, and relevant information extraction can be improved. PLS was applied for multivariate calibration and reduced noise by eliminating the less important latent variables. In this case, by optimization, the kind of wavelet function, the decomposition level, and the number of DOSC components, tolerance factor and the number of PLS factors for the DOSCWPTPLS method were selected as Coiflet 2, 3, 3, 0.001 and 3, respectively. A program (PDOSCWPTPLS) was designed to perform the simultaneous voltammetric determination of o-nitroaniline, m-nitroaniline and p-nitroaniline. The relative standard errors of prediction (RSEP) obtained for all components using DOSCWPTPLS, DOSCPLS, WPTPLS and PLS were compared. Experimental results demonstrated that the DOSCWPTPLS method had the best result among the four methods and was successful even when there was severe overlap of voltammograms.