|
|
|
|
|
|
| Decomposition of X-Ray Fluorescence Overlapping Peaks Based on Quantum Genetic Algorithm With Multi-Fitness Function |
| WANG Xue-yuan1, 2, 3, HE Jian-feng1, 2, 3*, NIE Feng-jun2, YUAN Zhao-lin1, 2, 3, LIU Lin1, 2, 3 |
1. Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology，East China University of Technology，Nanchang 330013，China
2. Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System，East China University of Technology，Nanchang 330013，China
3. Software College, East China University of Technology, Nanchang 330013，China |
|
|
|
|
Abstract When the intelligent algorithm is used to analyze the complex geological samples with serious overlapping spectral peaks, there are some problems such as big calculation, large error of weak peaks, convergence to local minimum or non-convergence. Because of its good convergence, the quantum genetic algorithm can decompose overlapping peaks in X-ray fluorescence spectra. A method of overlapping peak decomposition based on the GMM-ER model and quantum genetic algorithm with multi-fitness function is proposed. The overlapping peak model (GMM-ER) is first introduced based on K-series and L-Series of element characteristic X-ray. Then, based on the physical characteristics of the X-ray fluorescence spectrum, a multi-fitness function is introduced into the traditional quantum genetic algorithm. The simulated spectra are generated by the characteristic X-rays of Mn, Fe, Co and Ni. Then, based on the GMM-ER model, the simulated spectra are analyzed 10 times by traditional quantum genetic algorithm and improved multi-fitness quantum genetic algorithm, respectively. The experimental results show that the average decomposition accuracy of overlapping peaks is improved by 32.1%, and the optimal decomposition accuracy is improved by 73.9%. Using the improved algorithm, the decomposition accuracy of elements with a low content ratio is greatly improved, and the relative error range of element decomposition is reduced by 64.5% under the optimal decomposition accuracy. Moreover, the convergence speed of the improved algorithm is faster than that of the traditional algorithm. This method is suitable for the decomposition of seriously overlapped peaks and has a high resolution for weak peaks.
|
|
Received: 2020-11-19
Accepted: 2021-02-25
|
|
|
|
Corresponding Authors:
HE Jian-feng
E-mail: hjf_10@yeah.net
|
|
[1] YANG Xi, HUANG Hong-quan, JIANG Kai-ming, et al(杨 熙，黄洪全，蒋开明，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2017，37(8): 2376.
[2] HUANG Fan, ZHANG Xu-kun, SUN Lu, et al(黄 凡，张旭坤，孙 陆，等). Laser Optoelectronics Progress(激光与光电子学进展)，2020，57(9): 093001.
[3] LI Ying-na, XU Zhi-bin(李颖娜，徐志彬). Metallurgical Analysis(冶金分析)，2019，39(1): 35.
[4] XU Xi-rong, PAN Zi-qi, LI Xing-hua, et al(徐喜荣，潘子琦，李兴华，等). Journal of Dalian University of Technology(大连理工大学学报)，2019，59(1): 97.
[5] Du Hua, Chen Wuhui, Zhu Qingjun, et al. Applied Radiation and Isotopes, 2018, 141: 149.
[6] LI Qiang, LIU Jian, LI Xiao-sui, et al(李 强，刘 坚，李小穗，等). Rock and Mineral Analysis(岩矿测试)，2016，35(5): 488.
[7] HUANG Hong-quan, HE Zi-shu, FANG Fang, et al(黄洪全，何子述，方 方，等). Atomic Energy Science and Technology(原子能科学技术)，2010，44(9): 1114.
[8] CAO Bang-xing(曹邦兴). Journal of Gannan Normal University(赣南师范大学学报)，2019，(3): 14.
[9] MA Ying, WANG Huai-xiao, LIU He, et al(马 莹，王怀晓，刘 贺，等). Computer Engineering and Applications(计算机工程与应用)，2018，54(20): 99.
[10] HAO Huan, CHEN Liang, WU Feng-ge, et al(郝 欢，陈 亮，武凤阁，等). Journal of Military Communications Technology(军事通信技术)，2017，38(2): 17. |
| [1] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
| [2] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [3] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
| [4] |
ZHOU Qing-qing1, LI Dong-ling1, 2, JIANG Li-wu1, 3*, WAN Wei-hao1, ZENG Qiang4, XUE Xin4, WANG Hai-zhou1, 2*. Quantitative Statistical Study on Dendritic Component Distribution of Single Crystal Blade Based on Microbeam X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2112-2118. |
| [5] |
DU Zhi-heng1, 2, 3, HE Jian-feng1, 2, 3*, LI Wei-dong1, 2, 3, WANG Xue-yuan1, 2, 3, YE Zhi-xiang1, 2, 3, WANG Wen1, 2, 3. A New EDXRF Spectral Decomposition Method for Sharpening Error Wavelets[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1719-1724. |
| [6] |
LIN Jing-tao, XIN Chen-xing, LI Yan*. Spectral Characteristics of “Trapiche-Like Sapphire” From ChangLe, Shandong Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1199-1204. |
| [7] |
WU Lei1, LI Ling-yun2, PENG Yong-zhen1*. Rapid Determination of Trace Elements in Water by Total Reflection
X-Ray Fluorescence Spectrometry Using Direct Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 990-996. |
| [8] |
XU Wei-xuan1, CHEN Wen-bin2, 3*. Determination of Barium in Purple Clay Products for Food Contact by
Energy Dispersive X-Ray Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 475-483. |
| [9] |
CHEN Ji-wen, YANG Zhen, ZHANG Shuai, CUI En-di, LI Ming*. Fast Resolution Algorithm for Overlapping Peaks Based on Multi-Peak Synergy and Pure Element Characteristic Peak Area Normalization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 151-155. |
| [10] |
JIA Wen-bao1, LI Jun1, ZHANG Xin-lei1, YANG Xiao-yan2, SHAO Jin-fa3, CHEN Qi-yan1, SHAN Qing1*LING Yong-sheng1, HEI Da-qian4. Study on Sample Preparation Method of Plant Powder Samples for Total Reflection X-Ray Fluorescence Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 169-174. |
| [11] |
TANG Ju1, 2, DAI Zi-yun2*, LI Xin-yu2, SUN Zheng-hai1*. Investigation and Research on the Characteristics of Heavy Metal Pollution in Children’s Sandpits Based on XRF Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3879-3882. |
| [12] |
GUO Xiao-hua1, ZHAO Peng1, WU Ya-qing1, TANG Xue-ping3, GENG Di2*, WENG Lian-jin2*. Application of XRF and ICP-MS in Elements Content Determinations of Tieguanyin of Anxi and Hua’an County, Fujian Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3124-3129. |
| [13] |
WANG Yi-ya1, WANG Yi-min1*, GAO Xin-hua2. The Evaluation of Literature and Its Metrological Statistics of X-Ray Fluorescence Spectrometry Analysis in China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1329-1338. |
| [14] |
JIANG Xiao-yu1, 2, LI Fu-sheng2*, WANG Qing-ya1, 2, LUO Jie3, HAO Jun1, 2, XU Mu-qiang1, 2. Determination of Lead and Arsenic in Soil Samples by X Fluorescence Spectrum Combined With CARS Variables Screening Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1535-1540. |
| [15] |
NI Zi-yue1, CHENG Da-wei2, LIU Ming-bo2, YUE Yuan-bo2, HU Xue-qiang2, CHEN Yu2, LI Xiao-jia1, 2*. The Detection of Mercury in Solutions After Thermal Desorption-
Enrichment by Energy Dispersive X-Ray Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1117-1121. |
|
|
|
|
