This Insight Note follows a series of three previous insight notes on X-ray photoelectron spectroscopy image analysis that focused on the importance of analyzing the raw data, the use of summary statistics, and principal component analysis (PCA). The same X-ray photoelectron spectroscopy image data set was analyzed in all three notes. We now show an analysis of this same data set using multivariate curve resolution (MCR). MCR is a widely used exploratory data analysis method. Because of MCR's nonnegativity constraints, it has the important advantage of producing factors that look like real spectra. That is, both its scores and loadings are positive, so its results are often more interpretable than those from PCA. The requirements for preprocessing data are also, in general, lower for MCR compared with PCA. To help determine the number of factors that best describe the data set, a series of MCR models with different numbers of factors was created. Based on the chemical reasonableness of its factors, a two-factor model was selected. Scores plots/images show the regions of the image that correspond to these two factors.

中文翻译：

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表面分析洞察笔记：X 射线光电子能谱图像的多元曲线分辨率

本见解笔记是继之前关于 X 射线光电子能谱图像分析的一系列三篇见解笔记之后，重点关注分析原始数据、汇总统计数据的使用和主成分分析 (PCA) 的重要性。在所有三个笔记中分析了相同的 X 射线光电子能谱图像数据集。现在，我们使用多元曲线分辨率 (MCR) 展示对同一数据集的分析。MCR是一种广泛使用的探索性数据分析方法。由于 MCR 的非负性约束，它具有产生看起来像真实光谱的因子的重要优势。也就是说，它的分数和载荷都是正的，因此它的结果通常比 PCA 的结果更容易解释。一般来说，与 PCA 相比，MCR 对数据预处理的要求也较低。为了帮助确定最能描述数据集的因素数量，创建了一系列具有不同数量因素的 MCR 模型。基于其因素的化学合理性，选择了双因素模型。分数图/图像显示与这两个因素相对应的图像区域。