Chemical map classification in XMapTools

Chemical mapping using electron beam or laser instruments is an important analytical technique that allows the study of the compositional variability of materials in two dimensions. While quantitative compositional mapping of minerals has received considerable attention over the last two decades, pixel misclassification in commonly used software solutions remains a fundamental limitation affecting several applications. Calibration of intensity maps to fully quantitative compositional maps requires accurate classification, for example when a calibration curve is applied to a group of pixels that are assumed to have the same matrix behavior under the electron beam or the laser. This paper compares seven automated supervised machine learning classification algorithms implemented in the open source XMapTools software along with various tools for manual classification, for selecting training data and assessing the quality of a classification result. This new implementation aims to provide the research and industry communities with a free software tool for fast and robust classification of chemical maps. A standardized color scheme with reference colors for minerals and mineral groups is proposed to improve the readability of the classified maps in petrological studies. The performance of each algorithm varies depending on the data set, especially when minerals exhibit strong compositional zoning or when different minerals have similar compositions for a given element. The random forest algorithm based on bootstrap aggregation provides satisfactory results in most situations and is recommended for general use in XMapTools. • Rapid classification of multi-dimensional chemical dataset • Implementation and test of multiple machine learning algorithms • Training sets are crucial for accurate classification • Random forest is combined with principal component analysis • Classification algorithms are implemented in XMapTools