Using Iterative Discrete Wavelet Transform to Improve Trace Element Analysis by XRF Spectrometer

Abstract:

X-ray fluorescence (XRF) spectrometry has certain difficulties of detecting trace amount material components accurately when measuring material samples composed of variable elements, mainly due to low Signal to Noise Ratio (SNR) issues of the characteristic spectroscopic peaks from the measurement. In this paper, a novel method called background noise reduction using Iterative Discrete Wavelet Transform (IDWT) methodology for trace element material analysis by advanced X-ray fluorescence spectrometer is proposed to improve SNR, thereby decreasing the Limit of Detection (LOD) for elemental qualitative analysis, and then achieve a more accurate quantitative analysis of trace elemental concentration. This paper utilized handheld X-ray fluorescence spectrometer to obtain the content of Sulphur in petroleum and 4 major pollution elements in soil. A total of 81 standard samples were collected and measured. The hardware parameters of the instrument were adjusted to optimize the SNR before background noise reduction. Experimental results illustrate that X-ray tube parameters have great influences on the calibration regression. Different X-ray tube voltages were tested and the optimal results were achieved at 5kV. Furthermore, IDWT algorithm was implemented and the optimal results were achieved by wavelet base “db5” and “sym4” with 7 level decomposition. The calibration regression curves were established for the Sulphur in petroleum. The regression R² values after IDWT were increased effectively when compared with original data without IDWT. Finally, the experimental results demonstrate a very good linearity between the weight contents of the target material and the XRF spectral characteristic peak intensity, and also it is found the LOD for Sulphur in petroleum can be reduced when combing with the IDWT.

• Periodical: Advances in Science and Technology (Volume 105)
• Pages: 79-89
• DOI: https://doi.org/10.4028/www.scientific.net/AST.105.79
• Online since: April 2021
• Authors: Qian Ma, Lian Liu, Fu Sheng Li*, Yan Chun Zhao
• Keywords: Iterative Discrete Wavelet Transform, Material Analysis, X-Ray Fluorescence, X-Ray Tube Voltage

References

[1] T. Radu, D. Diamond, Comparison of soil pollution concentrations determined using AAS and portable XRF techniques. Journal of Hazardous Materials, 2009,171(1-3), 1168-1171.

DOI: 10.1016/j.jhazmat.2009.06.062

[2] L. Fusheng, R.P. Gardner, Implementation of the elemental library stratified sampling technique on the GUI-based Monte Carlo library least squares (MCLLS) approach for EDXRF analysis. Applied radiation and isotopes, 70(7), 2012, 1243-1249.

DOI: 10.1016/j.apradiso.2011.09.012

[3] B. Yan, Z. Biliang, Evaluation of uncertainty in measurement of total sulfur content in petroleum products by energy dispersion X-ray fluorescence spectrometry. Chemical Engineering of Oil & Gas / Shi You Yu Tian Ran Qi Hua Gong, 2019, 48(6), pp.83-89.

[4] J.H. Liang, P.P. Liu, Z. Chen, G.X. Sun, H. Li, 2018 Rapid evaluation of arsenic contamination in paddy soils using field portable X-ray fluorescence spectrometry. Journal of Environmental ences, 64(002) 345-351.

DOI: 10.1016/j.jes.2017.11.020

[5] J.A. Stankey, C. Akbulut, J.E. Romero, et al, Evaluation of X-ray fluorescence spectroscopy as a method for the rapid and direct determination of sodium in cheese. Journal of Dairy ence, 2015, 98(8) 5040-5051.

DOI: 10.3168/jds.2014-9055

[6] A. Turner, H. Poon, A. Taylor, et al, In situ determination of trace elements in Fucus spp. by field-portable-XRF. ence of The Total Environment, 2017, 593-594 227-235.

DOI: 10.1016/j.scitotenv.2017.03.091

[7] C. Feng, G.U. Yi, G.E. Liang-Quan, Z. Jian-Kun, L.I. Meng-Ting, Z. Ning, The Research on Matrix Effect and Correction Technology of Rock Sample in In-Situ Energy Dispersive X-Ray Fluorescence Analysis. Guang pu xue yu guang pu fen xi = Guang pu, 2017, 37(3) 919-923.

[8] B. Tanc, M. Kaya, L. Gumus, M. Kumral, Spectral Interferences Manganese (Mn) - Europium (Eu) Lines in X-Ray Fluorescence Spectrometry Spectrum. Egu General Assembly Conference, EGU General Assembly Conference Abstracts. (2016).

[9] S. Rui, T. Xian-Guo, L.I. Zhe, L. Ming-Zhe, L. Min, The research of BP network quantitative analysis technology of the full X-Ray spectrum detected by SDD. Chinese Journal of Analysis Laboratory, 2013, 32(01) pp.121-124.

[10] K.H. Ghazali, M.F. Mansor, M.M. Mustafa, A. Hussain, Feature Extraction Technique using Discrete Wavelet Transform for Image Classification. Conference on Research and Development, 2008, pp.1-4.

DOI: 10.1109/scored.2007.4451366

[11] A. Zuschlag, G. Hahn, Detection Limit of XRF Measurements at Different Synchrotron Radiation Facilities. International Journal of Contemporary Hospitality Management, 2011, 23(3) 344-360.

[12] A.F. Jones, J.N. Turner, J.S. Daly, P. Francus, R.J. Edwards, Signal-to-noise ratios, instrument parameters and repeatability of Itrax XRF core scan measurements of floodplain sediments. Quaternary International, 2019, 514(APR.30) 44-54.

DOI: 10.1016/j.quaint.2018.09.006

[13] Singh, H. Kumar, Tomar, S.K., Analysis of Multispectral Image Using Discrete Wavelet Transform. Third International Conference on Advanced Computing and Communication Technologies (ACCT), 2013, pp.59-62.

DOI: 10.1109/acct.2013.46

[14] Y. Hu, T. Jiang, A. Shen, W. Li, X. Wang, J. Hu, A background elimination method based on wavelet transform for Raman spectra. Chemometrics and Intelligent Laboratory Systems, 2007, 85(1) 94-101.

DOI: 10.1016/j.chemolab.2006.05.004

[15] C.M. Galloway, E.C. Le Ru, P.G. Etchegoin, An iterative algorithm for background removal in spectroscopy by wavelet transforms. Applied spectroscopy, 2009, 63(12) pp.1370-1376.

DOI: 10.1366/000370209790108905

[16] H. Yaogai, Baseline correction and background elimination using wavelet transforms. Journal of Huazhong University of ence and Technology (Natural ence Edition), 2011, 39(6) 36-40.

[17] I.T. Okumus, A New Approach of Image Denoising Based on Discrete Wavelet Transform. 2016 World Symposium on Computer Applications and Research (WSCAR), 2016, pp.36-40.

DOI: 10.1109/wscar.2016.30

[18] L. Fang, L. Anxiang, W. Jihua, Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform. International journal of environmental research and public health, 2017, 14(10) 1163.

DOI: 10.3390/ijerph14101163