Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination.
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.
Ren
Shouxin
S
Department of Chemistry, Inner Mongolian University, 010021, Huhhot, Inner Mongolia, China. cersx@mail.imu.edu.cn
Gao
Ling
L
D003300:Copper / Q000032:analysis; D007501:Iron / Q000032:analysis; D007700:Kinetics; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D009532:Nickel / Q000032:analysis; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted
D003300:Copper; D009532:Nickel; D007501:Iron