基于PIEspline的土壤XRF光谱背景扣除方法研究  

Background Subtraction Method for Soil XRF Spectrum Based on PIEspline

作　　者：李唐虎 甘婷婷 赵南京 殷高方[2,4,5] 叶紫琪 汪颖 盛若愚 LI Tang-hu;GAN Ting-ting;ZHAO Nan-jing;YIN Gao-fang;YE Zi-qi;WANG Ying;SHENG Ruo-yu(Institute of Physical Science and Information Technology,Anhui University,Hefei 230601,China;Key Laboratory of Environmental Optics and Technology,Anhui Institute of Optics and Fine Mechanics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China;University of Science and Technology of China,Hefei 230026,China;Key Laboratory of Optical Monitoring Technology for Environment of Anhui Province,Hefei 230031,China;Institute of Environment Hefei Comprehensive National Science Center,Hefei 230031,China)

机构地区：[1]安徽大学物质科学与信息技术研究院,安徽合肥230601 [2]中国科学院合肥物质科学研究院安徽光学精密机械研究所,中国科学院环境光学与技术重点实验室,安徽合肥230031 [3]中国科学技术大学,安徽合肥230026 [4]安徽省环境光学监测技术重点实验室,安徽合肥230031 [5]合肥综合性国家科学中心环境研究院,安徽合肥230031

出　　处：《光谱学与光谱分析》2025年第5期1364-1372,共9页Spectroscopy and Spectral Analysis

基　　金：国家重点研发计划项目(2022YFC3700804);国家自然科学基金项目(61805254);合肥综合性国家科学中心环境研究院科研团队建设项目(HYKYTD2024004)资助。

摘　　要：XRF光谱法作为重金属现场快速检测的重要技术手段,当其用于土壤重金属检测时,受土壤基质影响XRF光谱中存在强度较高且复杂的背景光谱,严重影响重金属特征谱峰信息的准确获取及定量分析准确性。针对该问题,提出一种极值法峰谷识别与惩罚项修正的三次平滑样条曲线拟合相结合(PIEspline)的土壤XRF光谱背景扣除方法,该方法首先通过极值法对土壤完整XRF光谱中的峰谷点进行识别,获取光谱中对背景具有代表性的数据点,再对一系列峰谷点进行惩罚项修正的三次平滑样条曲线拟合形成背景基线,从而实现土壤XRF光谱中复杂背景的扣除;并通过与自适应迭代重加权惩罚最小二乘法(airPLS)、迭代小波变换法(IWT)、统计敏感的非线性迭代剥峰算法(SNIP)三种传统光谱背景扣除方法对比,进一步验证了PIEspline方法的性能。结果表明:对于模拟的土壤XRF光谱,PIEspline方法所获取的背景谱线与光谱真实背景谱线间的均方根误差(RMSE)分别为0.4258和0.6441,均低于其他三种方法,并且具有最快的背景扣除运行效率;对于栗钙土、盐碱土和黄土三种不同类型土壤及农用、工业、建筑三种不同用途土壤,PIEspline方法背景拟合所获得的XRF光谱中10个特征谷点处荧光强度的平均相对误差为10.87%,与三种传统方法相比分别降低了84.88%、76.30%和16.51%;且PIEspline方法用于上述6种土壤中Cr、Pb、Cd定量分析的平均相对误差分别为4.01%、2.50%和5.20%,与airPLS、IWT、SNIP三种方法相比分别降低了22.39%~84.07%、60.15%~71.92%和79.18%~84.07%,且当土壤类型和用途发生变化时,PIEspline方法的相对误差波动最小,展现出了最好的稳定性,表明PIEspline方法在不同类型及不同用途土壤多种重金属同时XRF定量分析中具有最好的普适性。因此该研究所提出的PIEspline方法能够实现不同类型与不同用途土壤XRF光谱背景的精准扣除,有利�X-ray fluorescence(XRF)spectroscopy is a crucial technique for the rapid on-site detection of heavy metals.However,when applied to soil heavy metal analysis,the presence of high-intensity and complex background spectra due to soil matrix effects significantly hinders the accurate acquisition of characteristic spectral peaks and the precision of quantitative analyses.To address this issue,this paper proposes a background subtraction method for soil XRF spectra,combining peak-valley recognition using an extremum method with penalized correction for cubic smoothing spline fitting,termed PIEspline.Initially,the extremum method identifies peak and valley points in the complete soil XRF spectra to extract data points representative of the background.These points are then used to fit a cubic smoothing spline curve with penalized corrections,forming the background baseline and thus enabling the subtraction of complex backgrounds from soil XRF spectra.The performance of the PIEspline method is further validated by comparing it with three traditional spectral background subtraction methods:adaptive iteratively reweighted penalized least squares(airPLS),iterative wavelet transform(IWT),and statistical sensitive nonlinear iterative peak-clipping(SNIP).The results indicate that,for simulated soil XRF spectra,the root mean square errors(RMSE)between the background spectra obtained by the PIEspline method and the true background spectra are 0.4258 and 0.6441,respectively,which are lower than those of the other three methods.Additionally,the PIEspline demonstrates the fastest background subtraction efficiency.For three different soil types cinnamon soil,saline-alkali soil,and loess and three different soil uses agricultural,industrial,and construction the average relative error of fluorescence intensity at 10 characteristic valley points in the XRF spectra fitted by PIEspline is 10.87%.Compared to the traditional methods,this error is reduced by 84.88%,76.30%,and 16.51%,respectively.Furthermore,for quantitative analysis of Cr,Pb

关 键 词：X射线荧光 背景扣除 重金属检测 光谱解析 土壤 

分 类 号：O657.34[理学—分析化学]