Surface analysis insight note. Principal component analysis (PCA) of an X‐ray photoelectron spectroscopy image. The importance of preprocessing

Abstract

This Insight Note follows two previous Insight Notes on X‐ray photoelectron spectroscopy (XPS) image analysis that dealt with the importance of analyzing the raw data and the use of summary statistics. As a next step in the exploratory data analysis (EDA) of XPS images, we now show principal component analysis (PCA) of an XPS image. PCA is appropriate when the spectra in a data set are correlated to some degree and the noise in the spectra is unimportant. In these cases, PCA can significantly reduce the dimensionality and complexity of data sets. Preprocessing is an important part of many PCAs. Its usefulness is illustrated with a small, mock data set, where the potential pitfalls of not preprocessing are shown. PCAs of XPS image data that were not preprocessed and preprocessed by mean centering are illustrated. Scree plots, which are used to determine the number of abstract factors (principal components, PCs) that describe a data set, are shown. The spectra in our XPS image are quite noisy, which is consistent with the moderate, but still significant, amount of variance that is captured by the first two PCs in our PCA. With both preprocessing methods, the loadings on PC1 and PC2 are remarkably smooth. The loadings on the next six PCs also appear to contain some chemical information. Scores images generated using both no preprocessing and preprocessing by mean centering reveal many of the same general features in the data set that were found in our two previous Insight Notes.