XRFast a new software package for processing of MA-XRF datasets using machine learning

Abstract

X-ray fluorescence imaging is a common method of analysis in the field of heritage science. However, data processing and data interpretation remains challenging as it often requires a-priori knowledge of...

2023 atomic spectrometry update – a review of advances in X-ray fluorescence spectrometry and its special applications

“…Machine learning, artificial intelligence (AI) and, more specifically, machine and deep learning have been applied to cultural heritage data sets to identify patterns automatically. Vermeulen et al 80 used the Julia programming language in an open-access, machine-learning approach. This gave faster data processing than when Python and R were used.…”

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

“…A new software package for the processing of macro (MA)-XRF datasets using machine learning called XRFast was described by Vermeulen et al 326 It is an open-source, open access unsupervised dictionary learning algorithm that reduces the complexity of large datasets containing tens of thousands of spectra and identifies patterns. The methodology through which it achieves this was given in the text.…”

Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach

“…Building on its proven track record in data analysis, image processing, and materials synthesis [21][22][23][24][25][26][27][28][29][30][31][32], machine learning offers promising ways to overcome obstacles in the GE-XANES data collection process. This integration of machine learning represents a new approach, as its potential benefits in facilitating the data collection process are yet to be fully explored.…”