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Multivariate analysis applied to particle‐induced X‐ray emission mapping
Author(s) -
F. Silva Tiago,
F. Trindade Gustavo,
A. Rizzutto Marcia
Publication year - 2018
Publication title -
x‐ray spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
eISSN - 1097-4539
pISSN - 0049-8246
DOI - 10.1002/xrs.2953
Subject(s) - pixel , multivariate statistics , non negative matrix factorization , computer science , raster graphics , instrumentation (computer programming) , matrix (chemical analysis) , process (computing) , principal component analysis , detector , elemental analysis , data mining , pattern recognition (psychology) , algorithm , artificial intelligence , matrix decomposition , materials science , physics , chemistry , machine learning , telecommunications , eigenvalues and eigenvectors , organic chemistry , composite material , quantum mechanics , operating system
The use of particle‐induced X‐ray emission (PIXE) for elemental speciation and quantification has gained new attention thanks to mapping capabilities. Microprobes are able to raster a proton beam and produce elemental maps on the micrometre scale. Moreover, recent developments of in‐air PIXE instrumentation have enabled the acquisition of large area elemental maps. However, the amount of data produced is very large, and the data processing is not trivial. In this paper, we propose the use of multivariate analysis to process data of PIXE mapping. First, we apply the non‐negative matrix factorization (NMF), which is a nonsupervised machine‐learning algorithm, to decompose the data into a smaller number of components; then, we use the k ‐means algorithm to divide the pixels into categories regarding similarities in the NMF results; finally, we sum the spectra of all pixels in the same category so that the results can be analyzed by standard procedures for PIXE quantification. This last step is important to enable the quantification of the elements found in each component by correctly accounting for matrix self‐absorptions. With the procedure described in this paper, not just, we reduced the number of variables, facilitating the reasoning process on the data by employing the multivariate analysis, but we also increased the counting statistics by summing similar pixels leading to better results concerning the quantification of trace elements. We also propose methods for both, the automatic determination of the optimal number of components to describe the dataset, and for the combined analysis of multiple detectors.