Establishment and Improvement of Portable X-Ray Fluorescence Spectrometer Detection Model Based on Wavelet Transform
LI Fang1, 2, WANG Ji-hua1, 2*, LU An-xiang2, HAN Ping2
1. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China 2. Beijing Research Center for Agricultural Standards and Testing, Beijing 100097, China
Abstract：The concentration of Cr, Cu, Zn, As and Pb in soil was tested by portable X-ray fluorescence spectrometer. Each sample was tested for 3 times, then after using wavelet threshold noise filtering method for denoising and smoothing the spectra, a standard curve for each heavy metal was established according to the standard values of heavy metals in soil and the corresponding counts which was the average of the 3 processed spectra. The signal to noise ratio (SNR), mean square error (MSE) and information entropy (H) were taken to assess the effects of denoising when using wavelet threshold noise filtering method for determining the best wavelet basis and wavelet decomposition level. Some samples with different concentrations and H3BO3 (blank) were chosen to retest this instrument to verify its stability. The results show that: the best denoising result was obtained with the coif3 wavelet basis at the decomposition level of 3 when using the wavelet transform method. The determination coefficient (R2) range of the instrument is 0.990~0.996, indicating that a high degree of linearity was found between the contents of heavy metals in soil and each X-ray fluorescence spectral characteristic peak intensity with the instrument measurement within the range(0~1 500 mg·kg-1). After retesting and calculating, the results indicate that all the detection limits of the instrument are below the soil standards at national level. The accuracy of the model has been effectively improved, and the instrument also shows good precision with the practical application of wavelet transform to the establishment and improvement of X-ray fluorescence spectrometer detection model. Thus the instrument can be applied in on-site rapid screening of heavy metal in contaminated soil.
李 芳1, 2, 王纪华1, 2*, 陆安祥2, 韩 平2 . 基于小波变换的便携式X射线荧光光谱仪检测模型的建立与改进 [J]. 光谱学与光谱分析, 2015, 35(04): 1111-1115.
LI Fang1, 2, WANG Ji-hua1, 2*, LU An-xiang2, HAN Ping2 . Establishment and Improvement of Portable X-Ray Fluorescence Spectrometer Detection Model Based on Wavelet Transform. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(04): 1111-1115.
[1] Mahmoud M E, Kenawy I M M, Hafez M M, et al. Desalination, 2010, 250(1): 62. [2] Cai Q, Long M L, Zhu M, et al. Environmental Pollution, 2009, 157(11): 3078. [3] DONG Yue, LIU Xiao-qun, LI Cui-lan, et al(董 悦, 刘晓群, 李翠兰, 等). Modern Agricultural Science and Technology(现代农业科技), 2009, 4: 143. [4] Cindric I J, Krizman I. Food Chemistry, 2012, 135(4): 2675. [5] Ocak H. Expert Systems with Applications, 2009, 36(2): 2027. [6] Arzhantsev S, Li X, Kauffman J F. Analytical Chemistry, 2011, 83(3): 1061. [7] Paiva H M, Soares S F C, Galvo R K H, et al. Chemometrics and Intelligent Laboratory Systems, 2012, 118: 260. [8] Facchinetti A, Sparacino G, Cobelli C. Biomedical Engineering, IEEE Transactions on, 2010, 57(3): 634. [9] Newland D E. An Introduction to Random Vibrations, Spectral & Wavelet Analysis. Courier Dover Publications, UK(London). 2012. 64. [10] LIU Tao, ZENG Xiang-li, ZENG Jun(刘 涛, 曾祥利, 曾 军). Getting Practical Wavelet Analysis(实用小波分析入门). Beijing: Defense Industry Press(北京: 国防工业出版社), 2006. 101. [11] Mallat S. A Wavelet Tour of Signal Processing: the Sparse Way. Academic Press, US(Burlinglon MA) 2008. 103. [12] Addison P S. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance. Institute of Physics Publishing, UK(Edinburgh), 2010. 152. [13] Wang Y, He Z, Zi Y. Mechanical Systems and Signal Processing, 2010, 24(1): 119.
