Automated phase maps are an important tool for characterizing samples but the data quality must be evaluated. Common options include the overlay of phases on backscattered electron (BSE) images and phase composition averages and standard deviations. Both these methods have major limitations. We propose two methods of evaluation involving principal component analysis. First, a red-green-blue composite image of the first three principal components, which comprise the majority of the chemical variation, which provides a good reference against which phase maps can be compared. Advantages over a BSE image include discriminating between similar mean atomic number phases and sensitivity across the entire range of mean atomic numbers present in a sample. Second, principal component maps for identified phases, to examine for chemical variation within phases. This ensures the identification of unclassified phases and provides the analyst with information regarding the chemical heterogeneity of phases (e.g., chemical zoning within a mineral or mineral chemistry changing across an alteration zone). Spatial information permits a good understanding of heterogeneity within a phase and allows analytical artifacts to be easily identified. These methods of evaluation were tested on a complex geological sample. K-means clustering and K-nearest neighbor algorithms were used for phase classification, with the evaluation methods demonstrating their limitations.
Keywords: K-means clustering; electron probe microanalysis; elemental maps; phase mapping; scanning electron microscopy.