Resolve of overlapping voltammetric signals in using a wavelet packet transform based Elman recurrent neural network

A novel method named wavelet packet transform based Elman recurrent neural network (WPTERNN) was applied to differential pulse voltammetric techniques for simultaneous determination of Ni(II), Zn(II) and Co(II) by combining wavelet packet denoising with Elman recurrent neural network (ERNN). The performances of the WPT methods were compared with seven other filtering techniques in terms of root mean square deviations between reconstructed and original mean voltammogram. The visual inspection of filtering effects was supplied by figure. Wavelet packet representations of signals provided a local time–frequency description, thus in the wavelet domain, the quality of the noise removal can be improved. Elman recurrent network was applied for non-linear multivariate calibration and together with the whole voltammogram to improve predictive ability. In this case, by trials wavelet function, decomposition level and numbers of hidden nodes for WPTERNN method were selected as Daubechies 4, 6 and 8, respectively. A program PWPTERNN was designed to perform simultaneous determination of Ni(II), Zn(II) and Co(II). The relative standard errors of prediction (RSEP) for all components with WPTERNN, ERNN, PLS, PCR, TTFA and MLR were 9.53, 9.82, 12.3, 17.0, 16.7 and 1.46 × 105%, respectively. Experimental results demonstrated that the WPTERRN method had the best performance among the six methods and the two ANN methods had the clear superiority over the three factor-based method.