Open AccessAbridged spectral matrix inversion: parametric fitting of X‐ray fluorescence spectra following integrative data reduction
Author(s) -
Crawford Andrew M.,
Huntsman Ben,
Weng Monica Y.,
Ponomarenko Olena,
Kiani Cheyenne D.,
George Simon J.,
George Graham N.,
Pickering Ingrid J.
Publication year - 2021
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s1600577521008419
Subject(s) - spectral line , x ray fluorescence , parametric statistics , synchrotron radiation , synchrotron , inversion (geology) , fluorescence , data reduction , curve fitting , physics , matrix (chemical analysis) , algorithm , biological system , chemistry , computational physics , optics , computer science , mathematics , statistics , geology , data mining , biology , quantum mechanics , paleontology , chromatography , structural basin
Recent improvements in both X‐ray detectors and readout speeds have led to a substantial increase in the volume of X‐ray fluorescence data being produced at synchrotron facilities. This in turn results in increased challenges associated with processing and fitting such data, both temporally and computationally. Herein an abridging approach is described that both reduces and partially integrates X‐ray fluorescence (XRF) data sets to obtain a fivefold total improvement in processing time with negligible decrease in quality of fitting. The approach is demonstrated using linear least‐squares matrix inversion on XRF data with strongly overlapping fluorescent peaks. This approach is applicable to any type of linear algebra based fitting algorithm to fit spectra containing overlapping signals wherein the spectra also contain unimportant (non‐characteristic) regions which add little (or no) weight to fitted values, e.g. energy regions in XRF spectra that contain little or no peak information.