Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination

Ren, Shouxin; Gao, Ling
March 2004
Analytical & Bioanalytical Chemistry;Mar2004, Vol. 378 Issue 5, p1392
Academic Journal
This paper presents a novel method, named wavelet packet transform based multilayer feedforward neural network with Levenberg–Marquardt and back propagation algorithm (WPTLMBP), developed for simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). 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. The artificial neural network was applied for non-linear multivariate calibration. In this study, by optimization, wavelet packet function, decomposition level and number of hidden nodes for WPTLMBP method were selected as Db2, 2, and 4 respectively. A program PWPTLMBP was designed to perform simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). The relative standard error of prediction (RSEP) for all components with WPTLMBP, LM-BP-MLFN, and PLS methods were 6.39, 10.4, and 8.30%, respectively. Experimental results showed the proposed method to be successful and better than the others.


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