适合模型的加权最小平方作为主要组件分析的替代方法,用于分析能量分散式X射线光谱光谱图像
1Centre for Analysis and Synthesis, and NanoLund, Lund University, Box 124, 22100 Lund, Sweden.
概括
权重最小方形 (WLS) 在能量分散式X射线光谱 (EDS) 频谱成像中优于主要成分分析 (PCA). 与WLS (mfWLS) 方法相适应的模型准确地识别了组成梯度和包含,避免了PCA工件.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 能量分散式X射线光谱 (EDS) 是材料表征的一个关键技术.
- 诸如主要组件分析 (PCA) 等多变量统计方法通常用于EDS数据分析.
- PCA可以引入人工物,特别是在较低的信号噪声比率下,并且缺乏与替代方法的公平比较.
研究的目的:
- 直接比较PCA与基于权重最小平方 (WLS) 的策略用于EDS频谱图像分析.
- 为了评估与PCA相比的WLS (mfWLS) 之后的模型装配的性能.
- 为了证明mfWLS在处理构成梯度,包含和工件方面的优势.
主要方法:
- 直接比较PCA与加权最小平方 (WLS) 方法.
- 应用模型拟合的总谱,其次是WLS (mfWLS).
- 在EDS频谱图像的四个代表性案例研究中进行评估.
主要成果:
- 在识别和描述组合梯度和包含方面,mfWLS始终优于PCA.
- 与PCA相比,mfWLS作为集群的预处理步骤被证明是优越的.
- 使用mfWLS方法可以避免与PCA相关的常见伪造和偏见.
结论:
- mfWLS为分析EDS频谱图像提供了PCA的优质替代方案.
- mfWLS方法为EDS数据提供了简单而准确的信号建模.
- 这种方法提高了使用电子显微镜进行材料表征的可靠性和准确性.
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