Extracting different spectral components and their corresponding concentrations from spectrum images is one of the key challenges for electron energy-loss spectroscopy analysis due to the large amount of data, differing spectral features and low signal-to-noise ratio. Here, an open-source software framework of hyperspectral unmixing for energy-loss near-edge fine structure analysis is proposed. This software determines the number of independent spectral components, the signature of each spectral component and the abundance of each spectral component in each pixel, without reference spectrum or prior knowledge of the datasets. This approach should be suitable for automated materials and chemical analysis.

中文翻译：

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用于能量损失近边缘结构分析的高光谱解混框架

由于数据量大、光谱特征不同、信噪比低，从光谱图像中提取不同的光谱成分及其对应的浓度是电子能量损失光谱分析的关键挑战之一。在这里，提出了一种用于能量损失近边缘精细结构分析的高光谱解混开源软件框架。该软件确定独立光谱分量的数量、每个光谱分量的特征以及每个像素中每个光谱分量的丰度，无需参考光谱或数据集的先验知识。这种方法应该适用于自动化材料和化学分析。