School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA.
Microsc Microanal. 2012 Aug;18(4):667-75. doi: 10.1017/S1431927612000244. Epub 2012 Jun 15.
The high beam current and subangstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopy (EELS) mapping with atomic resolution. These spectral maps are often dose limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map, one can improve background estimation and increase chemical sensitivity. We consider two such approaches--linear combination of power laws and local background averaging--that reduce background error and improve signal extraction. Principal component analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data are PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis.
具有高束流和亚埃分辨率的像差校正扫描透射电子显微镜使得电子能量损失谱（EELS）映射能够达到原子分辨率。这些光谱图谱通常受到剂量限制和空间过采样的影响，导致每个通道的计数较低，因此对背景估计的误差非常敏感。然而，通过利用数据集图谱中的冗余性，可以改进背景估计并提高化学灵敏度。我们考虑了两种方法——幂律的线性组合和局部背景平均化——它们可以降低背景误差并提高信号提取能力。主成分分析（PCA）也可用于分析谱图，但在 EELS 中，由于谱峰与背景的比值较差，如果对原始 EELS 数据进行 PCA 滤波，可能会导致严重的伪影。我们识别了常见的伪影，并讨论了替代方法。这些算法在康奈尔光谱成像仪（Cornell Spectrum Imager）中实现，这是一个用于光谱分析的开源软件包。
