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高等学校化学研究 ›› 2012, Vol. 28 ›› Issue (5): 802-806 .

• Articles • 上一篇    下一篇

Simultaneous Forecast for Three Speciations of Heavy Metal Elements Using Fuzzy Cluster-Artificial Neural Network

ZHAO Tian-qi, MENG Fan-yu, WANG Hong-yan, GAO Yan   

  1. College of Chemistry, Jilin University, Changchun 130012, P. R. China
  • 收稿日期:2012-03-02 修回日期:2012-04-20 出版日期:2012-09-25 发布日期:2012-09-07
  • 通讯作者: GAO Yan E-mail:gaoy@jlu.edu.cn
  • 基金资助:
    Supported by the National Natural Science Foundation of China(No.29975004).

Simultaneous Forecast for Three Speciations of Heavy Metal Elements Using Fuzzy Cluster-Artificial Neural Network

ZHAO Tian-qi, MENG Fan-yu, WANG Hong-yan, GAO Yan   

  1. College of Chemistry, Jilin University, Changchun 130012, P. R. China
  • Received:2012-03-02 Revised:2012-04-20 Online:2012-09-25 Published:2012-09-07
  • Supported by:
    Supported by the National Natural Science Foundation of China(No.29975004).

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摘要： The three speciations(water extract, adsorption and organic speciations) of Cu, Zn, Fe and Mn in geo-chemical samples were determined by fuzzy cluster-artificial neural network(FC-ANN) method coupled with atomic absorption spectrometry. A back-propagation artificial neural network with one input node and three export nodes was constructed, which could forecaste three speciations of heavy metals simultaneously. In the learning sample set, the three speciations of each element were allowed to change in a wide concentration range and the accuracy of the analysis was apparently increased via the learning sample set optimized with the help of the fuzzy cluster analysis. The average relative errors of the three speciations of Cu, Zn, Fe or Mn from 100 geo-chemical samples were less than 5%. The relative standard deviations of the three speciations of each of four heavy metals were 0.008%―4.43%.

关键词: Fuzzy cluster, Artificial neural network, Speciation

Abstract: The three speciations(water extract, adsorption and organic speciations) of Cu, Zn, Fe and Mn in geo-chemical samples were determined by fuzzy cluster-artificial neural network(FC-ANN) method coupled with atomic absorption spectrometry. A back-propagation artificial neural network with one input node and three export nodes was constructed, which could forecaste three speciations of heavy metals simultaneously. In the learning sample set, the three speciations of each element were allowed to change in a wide concentration range and the accuracy of the analysis was apparently increased via the learning sample set optimized with the help of the fuzzy cluster analysis. The average relative errors of the three speciations of Cu, Zn, Fe or Mn from 100 geo-chemical samples were less than 5%. The relative standard deviations of the three speciations of each of four heavy metals were 0.008%―4.43%.

Key words: Fuzzy cluster, Artificial neural network, Speciation