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| Identification of Metal Components Characteristic Peak Position of Energy Dispersive X-Ray Fluorescence Spectra Based on the Wavelet Transformation |
| ZHANG Wei1, XU Hua1, DUAN Lian-fei3, MA Ming-jun2, GAN Ting-ting2, LIU Jing4, WANG Liu-jun1, ZHANG Yu-jun2, ZHAO Nan-jing2，LIU Wen-qing2 |
1. Army Officer Academy of PLA, Hefei 230031，China
2. Key Laboratory of Environment Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
3. Anhui KeLi Information Industry Co., Ltd., Hefei 230088, China
4. Key Laboratory of Ion Beam Bioengineering，Institute of Technical Biology and Agriculture Engineering, Chinese Academy of Sciences, Hefei 230031, China |
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Abstract In this paper, the accurate identification problem of energy dispersive X-ray fluorescence (EDXRF) characteristic peak position was studied. Based on the characteristic spectra character of the different metal components, the choosing rule of the characteristic spectra was analyzed. According to the theories of singular value analysis and modulus maxima, the extraction method of modulus maxima was analyzed which based on the wavelet decomposition coefficients of characteristic spectra. Moreover, the feature of the characteristic spectra wavelet decomposition coefficients and their propagation were analyzed in detail. The method of the interval characteristic peak selection was put forward based on the propagation of modulus maxima. And this method was applied to the actual measurement spectra. The result showed that the wavelet transform of four levels was applied to full energy spectra data using the basis function of bior4.4 wavelet. For the full energy spectra, the phase step influence of the some superimposed noise could be eliminated using the propagation of modulus maxima. In order to increase the identification probability of characteristic spectra, the decomposition coefficients were compressed which were less than the threshold value. In addition, 667 peak positions were identified for the fourth level wavelet decomposition coefficients of EDXRF spectra which were not processed. 186 peak positions were identified when they were compressed. Then the method of interval characteristic peak selection using modulus maxima propagation feature was applied and the initial value of the screening interval was set 600 eV. The identified result of the characteristic peak position was 27. The experimental result showed that the accurate rate of peak location identification was enhanced effectively.
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Received: 2017-06-21
Accepted: 2017-11-06
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