Combining orthogonal signal correction and wavelet packet transform with radial basis function neural networks for multicomponent determination

This paper presented a novel method named OSCWPTRBFN based on the concept of data mining in chemometrics for resolving overlapping spectra. The method combines orthogonal signal correction, wavelet packet transform and radial basis function neural network for enhancing the ability of removing noise and eliminating unrelated information as well as improving the quality of the regression method. OSC was applied to remove structured noise that is unrelated to the concentration variables. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet packet domain, the quality of noise removal can be improved. Radial basis function network was applied for overcoming the convergence problem met in back propagation training and for facilitating nonlinear calculation. In this spectrophotometric case, through optimization, the number of OSC components, wavelet function, decomposition level, the number of hidden nodes and the width (σ) of RBFN for OSCWPTRBFN method were selected as 1, Coif 2, 4, 15 and 0.7 respectively. The relative standard errors of prediction (RSEP) for all components with OSCWPTRBFN, WPTRBFN, RBFN, partial least squares (PLS), OSCWPTPLS, principal component regression (PCR), Fourier transform based PCR (FTPCR) and multivariate linear regression (MLR) methods were 6.85, 7.74, 22.0, 10.1, 8.93, 13.5, 13.1, and 2.38 × 103% respectively. Experimental results showed that the OSCWPTRBFN method was successful and had advantages over the other approaches. The results obtained from an additional test case, simultaneous differential pulse stripping voltammetric determination of Pb(II), Cd(II) and Ni(II), also demonstrated that the OSCWPTRBFN method performed very well.