由于元素间特征峰的相互干扰, 受实验仪器能量分辨率的影响, 当多个元素的特征峰峰位相近且展宽较宽时就会形成重叠峰。 以分离度低且分解需求精度高的重叠峰为研究对象, 提出一种基于多峰协同和纯元素特征峰面积归一化的重叠峰快速解析算法, 并结合实际的X射线荧光光谱进行了新方法的验证。 选取镝铁合金的X射线荧光光谱图作为实例, 在该实验条件下镝 L_α特征峰和铁 K_α特征峰形成的重叠峰分离度约为0.273 5, 同时还存在分离度较大但荧光产额较低的镝 L_β特征峰和铁 K_β特征峰。 首先, 配置浓度范围(7.8~8.2 mg·mL^-1)的镝标液和浓度范围(1.8~2.2 mg·mL^-1)的铁标液进行测量获取到纯元素谱图, 分别进行面积归一化处理并取平均得到镝 L_α峰和铁 K_α峰的归一化特征峰。 然后, 使用镝、 铁标液混兑出铁元素质量百分比范围在19.1%~21%, 梯度为0.1%的20组样品液进行测量。 由于重叠峰部分仅由镝 L_α峰和铁 K_α峰组成, 因此设定重叠峰中镝 L_α峰权重加上铁 K_α峰权重等于1, 使用镝 L_α峰和铁 K_α峰的归一化特征峰进行重叠峰的拟合。 通过镝 L_β特征峰和铁 K_β特征峰确定铁元素大概权重区间, 结合粒子群寻优算法进行权重值寻优完成重叠峰的分解。 通过寻优解出的铁元素权重与实际样品液的铁元素质量百分比进行拟合, 得到一条镝铁样品液中铁元素权重到实际铁元素质量百分比的回归线。 最后进行实际样品实验, 对比该方法检测的铁元素含量值与国标重铬酸钾容量法测定的铁元素含量参考值, 得出两方法测定结果偏差绝对值小于0.2%, 低于国标允许差。 结果证明: 基于多峰协同和纯元素特征峰面积归一化的重叠峰快速解析算法能够解析分离度低且分解需求精度高的重叠峰。

Due to the mutual interference of characteristic peaks between elements and affected by the energy resolution of the experimental instrument, overlapping peaks will be formed when the characteristic peaks of multiple elements have similar peak positions and widen broadening. Taking the overlapping peaks with low resolution and high-resolution requirements as the research object, a fast resolution algorithm for overlapping peaks based on multi-peak synergy and pure element characteristic peak area was proposed, and combined with the actual X-ray fluorescence spectrum to verify the new method. Taking the X-ray fluorescence spectrum of dysprosium-ferrum alloy as an example, under the experimental conditions of this paper, the separation degree of the overlapping peaks formed by the characteristic peaks of dysprosium L_α and iron K_α is about 0.273 5. The lower L_β characteristic peaks of dysprosium and iron K_β characteristic peaks. First, configure the dysprosium standard solution in the concentration range (7.8~8.2 mg·mL^-1) and the iron standard solution in the concentration range (1.8~2.2 mg·mL^-1) to measure and obtain pure element spectra and calculate the area respectively. Normalized and averaged to obtain normalized characteristic peaks of dysprosium L_α and iron K_α peak. Then, use dysprosium and iron standard solutions to mix 20 groups of sample solutions with a mass percentage of iron elements ranging from 19.1% to 21% and a step of 0.1% for measurement. Since the overlapping peak part is only composed of the dysprosium L_α peak and the iron K_α peak, the weight of the dysprosium L_α peak plus the iron K_α peak weight in the overlapping peak is set to 1, using the dysprosium L_α peak and the iron peak. The normalized characteristic peaks of the K_α peaks are fitted for overlapping peaks. The approximate weight range of iron element is determined by the characteristic peaks of dysprosium L_β and iron K_β, and the weight value optimization is carried out with the particle swarm optimization algorithm to complete the decomposition of overlapping peaks. By fitting the iron element weight obtained by the optimization solution with the iron element mass percentage of the actual sample solution, a regression line from the iron element weight in the dysprosium iron sample solution to the actual iron element mass percentage is obtained. Finally, the actual sample experiment was carried out, and the iron content detected by the method in this paper was compared with the reference value of iron content determined by the national standard potassium dichromate volumetric method. The results show that the rapid resolution algorithm of overlapping peaks based on multi-peak synergy and normalization of characteristic peak areas of pure elements can resolve overlapping peaks with low resolution and high resolution requirements.