XRF-ROI-Finder: Machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

Abstract

<p>The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli (E. coli), also serve as label-free biological fingerprints to identify differently treated samples via fuzzy clustering. The key limitations of achieving good identification performance is the extraction of cells from raw XRF measurement via binary conversion, definition of features based on domain knowledge, dwell time and proportion of differently treated cells in the measurement.</p>