毛细管聚焦的X射线荧光光谱拟合软件的开发及应用

基于Python语言设计了一款毛细管聚焦的X射线荧光光谱拟合软件QMXRS（quantitative analysis of micro-energy dispersive X-ray fluorescence spectra）并实现其在毛细管聚焦的X射线荧光光谱拟合方面的应用。QMXRS具有小波降噪、本底扣除、能量刻度、元素特征峰的识别、分峰和拟合、能谱的批处理和元素分布成像等功能。毛细管聚焦的X射线荧光分析技术采用毛细管X光透镜对X射线源激发出的X射线束进行聚焦导致X射线荧光光谱分布发生改变。这一变化影响了毛细管聚焦的X射线荧光光谱本底分布。因此QMXRS利用本底预估模型对毛细管聚焦的X荧光光谱本底进行本底分布的修正；同时在全谱拟合过程中，利用半高宽与能量的关系对高斯峰半高宽进行约束，减少高斯峰模型中变量，在保证全谱拟合收敛的同时提高了拟合速度。为验证上述方法的可行性，分别利用QMXRS，PyMca(python multichannel analyzer)和QXAS(quantitative X-ray analysis system)三款软件分析NIST 610标准样品的毛细管聚焦的微束X射线荧光光谱，并对比三款软件的元素特征峰拟合结果。结果表明，相较于QXAS和PyMca采用的非最小二乘法拟合，QMXRS采用约束化的非线性最小二乘法进行拟合能有效的减少能谱拟合过程中带来的误差，提高分析数据的准确度。

The Development and Its Applications of a Software Fitting Micro-X-Ray Fluorescence Spectrum Focused by Poly-Capillary Optics

The fitting software QMXRS(Quantitative analysis of Micro-Energy Dispersive X-ray fluorescence spectra)which is used for fitting micro-energy X-ray fluorescence spectrum focused by poly-capillary optics is developed in our Lab with Python language.The signal de-noising of the wavelet transform,background subtraction,energy calibration and characteristic peak fitting of spectrum is carried out by QMXRS.The ploy-capillary X-ray optics used here can improve the intensity of X-ray and change the distributions of X-ray fluorescence spectra.The background prediction model of QMXRS can effectively retain peak information and improve the accuracy of data processing of energy spectra.On the other hand,the constraint of the full width at half maximum(FWHM)of the Gaussian peak is added to the conventional nonlinear least-squares fitting.The Micro-Energy dispersive X-ray fluorescence spectra of the NIST 610 standard sample is used to demonstrate the performances of QMXRS.The results show that our QMXRS is better than PyMca and QXAS.