食品科学 ›› 2008, Vol. 29 ›› Issue (6): 342-345.

• 分析检测 • 上一篇    下一篇

基于小波包神经网络的食品中锌、铁、锰元素电化学同时检测方法研究

 殷勇, 陈朝魁, 易军鹏   

  1. 河南科技大学; 洛阳师范学院; 河南科技大学 河南洛阳471003; 河南洛阳471022; 河南洛阳471003;
  • 出版日期:2008-06-15 发布日期:2011-08-26

Electroanalytical Chemistry Method for Determination of Trace Elements in Food Based on Assistant Wavelet Neural Networks

 YIN  Yong, CHEN  Chao-Kui, YI  Jun-Peng   

  1. 1. Henan University of Science and Technology, Luoyang 471003, China; 2. Luoyang Normal University, Luoyang 471022, China
  • Online:2008-06-15 Published:2011-08-26

摘要: 为了实现食品中锌、铁、锰微量元素的同时检测,深入研究了差分脉冲阴极溶出伏安(DPCSV)法的测试条件。运用小波包对测试数据分解,提取了不同信号频率带内的能量值作为测试数据的特征信息。将这种特征信息训练BP神经网络,建立了3种微量元素同时测量的检测模型。实际样品检测结果表明,该检测方法抗干扰能力强、测试准确、快捷,具有实际应用价值。

关键词: 小波包分析, 人工神经网络, 电化学, 检测, 微量元素

Abstract: A multivariate calibration method was used for the data analysis of trace elements determination, which is based on the combination of wavelet packet decomposition and neural network. Firstly,the differential pulse cathodic stripping voltammetry (DPCSV) signal was decomposed by wavelet packet, and the eigenvector was extracted based on the power features which are distributed in different frequency bands. Then a three layers BP network was used for training the features. The forecast results and calculated results of samples showed that the method has strong anti-jamming capability, high measure accuracy and practical values.

Key words: wavelet packet analysis, BP neural network, electrochemistry, simultaneous determination, trace element