Newbury Dale E
National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
Scanning. 2009 May-Jun;31(3):91-101. doi: 10.1002/sca.20151.
Automated peak identification in electron beam-excited X-ray microanalysis with energy dispersive X-ray spectrometry has been shown to be subject to occasional mistakes even on well-separated, high-intensity peaks arising from major constituents (arbitrarily defined as a concentration, C, which exceeds a mass fraction of 0.1). The peak identification problem becomes even more problematic for constituents present at minor (0.01< or =C< or =0.1) and trace (C<0.01) levels. "Problem elements" subject to misidentification as major constituents are even more vulnerable to misidentification when present at low concentrations in the minor and trace ranges. Additional misidentifications attributed to trace elements include minor X-ray family members associated with major constituents but not assigned properly, escape and coincidence peaks associated with major constituents, and false peaks owing to chance groupings of counts in spectra with poor counting statistics. A strategy for robust identification of minor and trace elements can be based on application of automatic peak identification with careful inspection of the results followed by multiple linear least-squares peak fitting with complete peak references to systematically remove each identified major element from the spectrum before attempting to assign remaining peaks to minor and trace constituents.
在采用能量色散X射线光谱法的电子束激发X射线微分析中，自动峰识别已被证明即使对于由主要成分产生的分离良好、高强度的峰（任意定义为浓度C超过质量分数0.1）也偶尔会出现错误。对于以微量（0.01≤C≤0.1）和痕量（C＜0.01）水平存在的成分，峰识别问题变得更加棘手。容易被误识别为主要成分的“问题元素”，当以低浓度存在于微量和痕量范围内时，更容易被误识别。归因于痕量元素的其他误识别包括与主要成分相关但未正确分配的次要X射线家族成员、与主要成分相关的逃逸峰和符合峰，以及由于计数统计不佳的光谱中计数的偶然分组而产生的假峰。一种用于可靠识别微量和痕量元素的策略可以基于应用自动峰识别，仔细检查结果，然后进行多重线性最小二乘峰拟合，并使用完整的峰参考，在尝试将剩余峰分配给微量和痕量成分之前，系统地从光谱中去除每个识别出的主要元素。
