面向XRF的竞争性自适应重加权算法和粒子群优化的支持向量机定量分析研究

doi:10.3964/j.issn.1000-0593(2023)12-3742-05

Abstract
References
Related Citation (15)

Download: PDF (3719 KB)
Export: BibTeX | EndNote (RIS)

Abstract Research on efficient, accurate and convenient soil heavy metal detection methods is of great significance for understanding soil pollution and carrying out pollution prevention and control. Because X-ray fluorescence spectrometry (XRF) technology has the advantages of being fast, accurate and non-destructive, it has been widely used in detecting element content. The XRF method obtains the concentration of the sample to be tested by measuring the fluorescence intensity of the sample to be tested, and establishing a corresponding relationship using the fluorescence intensity of the standard sample of the calibration curve and the corresponding concentration. However, due to the existence of matrix effect and spectral line overlapping interference, the element spectral line intensity obtained in the actual XRF analysis test and its corresponding concentration do not show a relatively perfect linear relationship. In order to solve the above problems, this paper uses wavelet transform and asymmetric weighted penalized least squares (arPLS) to denoise the spectrum and correct the baseline, which improves the determination coefficient of the calibration curve to a certain extent. The characteristic energy spectral line selection model of different heavy metal elements was constructed by the Competitive Adaptive Reweighting Algorithm (CARS) algorithm to explore the aggregation performance of the characteristic spectral lines. Further, based on the selected features, the particle swarm optimization (PSO) optimized support vector machine regression (SVR) model is used to predict the element content, and the generalization ability of the quantitative analysis model is improved. Partial least squares regression (PLSR) and SVR models are used to compare. The results show that: after pretreatment, the coefficients of determination of the calibration curves of Cr, Cu, Zn, As, Pb are improved from 0.965, 0.979, 0.971, 0.794, 0.915 to 0.979, 0.987, 0.981, 0.828, 0.953; the characteristic lines selected by CARS In addition to the elements to be analyzed, some also correspond to the soil matrix effect elements and the corresponding spectral line interference elements, which shows the effectiveness of the CARS algorithm in feature selection, and the number of variables has changed from 2 048 to 9~29, which is 0.43%~1.42% of the original number of variables, which makes the variables of feature selection more statistical and intelligent; the content prediction using the PSO-optimized SVR model is higher than the accuracy of SVR and PLSR, training set and test set. The coefficients of determination are above 0.99 and 0.89, respectively.

Key words：X-ray fluorescence spectroscopy;Soil heavy metals;Competitive adaptive reweighted sampling;Particle swarm optimization;Support vector regression

Received: 2022-04-23 Accepted: 2022-10-20

ZTFLH:

O434.13

Corresponding Authors: LI Fu-sheng E-mail: lifusheng@uestc.edu.cn

Cite this article:

CHENG Hui-zhu,YANG Wan-qi,LI Fu-sheng, et al. 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.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)12-3742-05 OR https://www.gpxygpfx.com/EN/Y2023/V43/I12/3742

[1] FENG Yong-jie（冯永杰）. Environmental and Development（环境与发展），2020，32(4)：77.
[2] Zhou Y，Aamir M，Liu K，et al. Environmental Pollution，2018，240：116.
[3] Khan S，Naushad M，Lima E C，et al. Journal of Hazardous Materials，2021，417: 126039.
[4] YU Tao，JIANG Tian-yu，LIU Xu，et al（余涛，蒋天宇，刘旭，等）. Geology in China（中国地质），2021，48(2)：460.
[5] AN Qi-qi（安琪琪）. Modern Agricultural Science and Technology（现代农业科技），2020，17：166.
[6] ZHANG Lian-xiang，FU Bin（章连香，符斌）. Chinese Journal of Inorganic Analytical Chemistry（中国无机分析化学），2013，3：1.
[7] Gardner R P，Li F S. X-Ray Spectrometry，2011，40(6)：405.
[8] Li F S, Yang W Q，Ma Q，et al. Measurement Science & Technology，2021，32(10)：105501.
[9] REN Shun，ZHANG Xiong，REN Dong，et al（任顺，张雄，任东，等）. Journal of Instrumental Analysis（分析测试学报），2020，39(7)：829.
[10] CHEN Ying，LIU Zheng-ying，XIAO Chun-yan，et al（陈颖，刘峥莹，肖春艳，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2021，41(7)：2175.
[11] TANG Hai-tao，MENG Xiang-tian，SU Xun-xin，et al（唐海涛，孟祥添，苏循新，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2021，37(2)：105.
[12] CHEN Yu，QIU Zhi-jun，ZHANG Bin（陈煜，邱智军，张彬）. Journal of Instrumental Analysis（分析测试学报），2021，40(12)：1004.
[13] LIU Jin，XU Wen-li，SUN Tong，et al（刘津，许文丽，孙通，等）. Chinese Journal of Analysis Laboratory（分析试验室），2018，37(1)：1.
[14] Li Hongdong，Liang Yizeng，Xu Qingsong，et al. Analytica Chimica Acta，2009，648(1)，77.
[15] Da Silva D J，Wiebeck H. Journal of Polymer Research，2018，25(5): 112.
[16] HONG Qian，ZHAO Jin-hui，YUAN Hai-chao，et al（洪茜，赵进辉，袁海超，等）. Chinese Journal of Analysis Laboratory（分析试验室），2013，32(12)：6.
[17] GUO Yang，GUO Jun-xian，SHI-Yong，et al（郭阳，郭俊先，史勇，等）. Food & Machinery（食品与机械），2021，37(6)：81.