Improved Background Removal Method Using Principal Components Analysis for Spatially Resolved Electron Energy Loss Spectroscopy
Improved Background Removal Method Using Principal Components Analysis for Spatially Resolved Electron Energy Loss Spectroscopy
复制标题
使用主成分分析改进空间分辨电子能量损失光谱的背景去除方法
DOI：
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发表时间：
2005
影响因子：
2.8
通讯作者：
S. Csillag
中科院分区：
文献类型：
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作者：
N. Borglund;P;S. Csillag
采用主成分分析（PCA）因子滤波法对含噪光谱进行背景去除。当PCA被用作在电子能量损失谱元素图中的背景去除之前用于过滤的方法时，实现了具有非常短的拟合间隔的背景拟合的准确性的改善，从而导致从噪声谱中改善元素图的质量。这使得使用更短的曝光时间进行元素映射成为可能，从而导致更少的问题，例如漂移和光束损坏。
Principal components analysis (PCA) factor filtering is implemented for the improvement of background removal in noisy spectra. When PCA is used as a method for filtering before background removal in electron energy loss spectroscopy elemental maps, an improvement in the accuracy of the background fit with very short fitting intervals is achieved, leading to improved quality of elemental maps from noisy spectra. This opens the possibility to use shorter exposure times for elemental mapping, leading to fewer problems with, for example, drift and beam damage.