CRCU

Chemical Research in Chinese Universities ›› 2010, Vol. 26 ›› Issue (6): 899-904.

• Articles • Previous Articles     Next Articles

Application of Back-propagation Artificial Neural Network in Speciation of Cadmium

WANG Lin-lin1, ZHANG Jie2, LIU Hai-yan1, ZHANG Hai-tao1, WANG Hong-yan1, YANG Xiu-rong2 and WANG Ying-hua1*   

  1. 1. College of Chemistry, Jilin University, Changchun 130012, P. R. China;
    2. State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China
  • Received:2010-02-01 Revised:2010-04-02 Online:2010-11-25 Published:2010-12-01
  • Contact: WANG Ying-hua E-mail:wangyh@jlu.edu.cn
  • About author:WANG Ying-hua. E-mail: wangyh@jlu.edu.cn
  • Supported by:

    Supported by the Fund of the State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences.

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Abstract: A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry(BP-ANN-GF-AAS). Based on the strong learning function and the features of the information distributed storage of artificial neural network(ANN), a single ANN was constituted in which only one determination point of every sample was required. The exchangeable, carbonated, Fe-Mn oxidable, organic and residual species of cadmium for 20 kinds of soil samples from the two sections of Changchun(China) were determined by BP-ANN-GF-AAS. The detection limit of the method is 0.024 ?g/L and the limit of quantification is 0.080 ?g/L. t-Test indicates that there is not any systemic error of the results obtained by the Tessier sequential extraction graphite furnace atomic absorption spectrometry method(Tessier -GF-AAS) and BP-ANN-GF-AAS. Compared with those of the Tessier-GF-AAS, the prediction errors of BP-ANN-GF-AAS are less than 10%. The proposed method is fast, convenient, sensitive, and can eliminate the interference among various species.

Key words: Artificial neural network(ANN), Speciation, Graphite furnace atomic absorption spectrometry(GF-AAS), Cadmium