Zhang Siyuan, Scheu Christina
Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237 Düsseldorf, Germany.
Materials Analytics, RWTH Aachen University, Kopernikusstraße 10, 52074 Aachen, Germany.
Microscopy (Oxf). 2018 Mar 1;67(suppl_1):i133-i141. doi: 10.1093/jmicro/dfx091.
Multivariate analysis is a powerful tool to process spectrum imaging datasets of electron energy loss spectroscopy. Most spatial variance of the datasets can be explained by a limited numbers of components. We explore such dimension reduction to facilitate quantitative analyses of spectrum imaging data, supervising the spectral components instead of spectra at individual pixels. In this study, we use non-negative matrix factorization to decompose datasets from Fe2O3 thin films with different Sn doping profiles on SnO2 and Si substrates. Case studies are presented to analyse spectral features including background models, signal integrals, peak positions and widths. Matlab codes are written to guide microscopists to perform these data analyses.
多变量分析是处理电子能量损失谱的光谱成像数据集的强大工具。数据集中的大多数空间方差可以由有限数量的成分来解释。我们探索这种降维方法以促进光谱成像数据的定量分析，对光谱成分进行监督而不是对单个像素处的光谱进行监督。在本研究中，我们使用非负矩阵分解来分解在SnO₂和Si衬底上具有不同Sn掺杂分布的Fe₂O₃薄膜的数据集。通过案例研究来分析光谱特征，包括背景模型、信号积分、峰位置和宽度。编写了Matlab代码以指导显微镜学家进行这些数据分析。
