The scanning electron microscope/X-ray energy dispersive spectrometer (SEM/EDS) system is widely applied to rare earth minerals (REMs) to qualitatively describe their mineralogy and quantitatively determine their composition. The performance of multivariate statistical analysis on the EDS raw dataset can enhance the efficiency and the accuracy of phase identification. In this work, the principal component analysis (PCA) and the blind source separation (BSS) algorithms were performed on an EDS map of a REM sample, assisting to achieve an efficient phase map analysis. The PCA significantly denoised the phase map and was used as a preprocessing step for the following BSS. The BSS separated the mixed EDS signals into a set of physically interpretable components, bringing convenience to the phase separation and identification. Through the comparison between the independent component analysis (ICA) and the nonnegative matrix factorization (NMF) algorithms, the NMF was confirmed to be more suitable for the EDS mapping analysis.

中文翻译：

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稀土矿物 SEM/EDS 相图的多元统计分析

扫描电子显微镜/X 射线能谱仪 (SEM/EDS) 系统广泛应用于稀土矿物 (REM) 以定性描述其矿物学并定量确定其成分。对 EDS 原始数据集进行多变量统计分析可以提高相识别的效率和准确性。在这项工作中，主成分分析 (PCA) 和盲源分离 (BSS) 算法在 REM 样品的 EDS 图上执行，有助于实现有效的相位图分析。PCA 对相位图进行了显着去噪，并用作以下 BSS 的预处理步骤。BSS 将混合后的 EDS 信号分离成一组物理上可解释的分量，为相分离和识别带来方便。