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Non-negative matrix analysis in x-ray spectromicroscopy: choosing regularizers.

Rachel Mak1, Stefan M Wild2, Chris Jacobsen3

  • 1Dept. Physics & Astronomy, Northwestern University, Evanston, Illinois, USA.

AIP Conference Proceedings
|April 5, 2016
PubMed
Summary

This study introduces a method for optimizing non-negative matrix approximation in x-ray spectromicroscopy. This improves chemical speciation analysis from absorption edge imaging data.

Keywords:
multivariate statistical analysisnon-negative matrix analysisoptimizationx-ray microscopyx-ray spectromicroscopy

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Data Analysis

Background:

  • X-ray spectromicroscopy generates datasets across absorption edges for chemical speciation.
  • Non-negative matrix approximation (NMA) methods enhance classification and analysis of this data.
  • Optimization of NMA requires appropriate regularization parameter selection.

Purpose of the Study:

  • To present an approach for determining optimal regularization parameters for NMA in x-ray spectromicroscopy.
  • To improve the accuracy and reliability of chemical speciation analysis using this technique.

Main Methods:

  • Development of an optimization approach to find regularization parameter values.
  • Application of NMA to x-ray spectromicroscopy datasets.
  • Evaluation of the proposed method for parameter selection.

Main Results:

  • An effective approach for selecting regularization parameters was developed.
  • The method facilitates improved chemical speciation analysis.
  • Enhanced data interpretation in x-ray spectromicroscopy is achieved.

Conclusions:

  • The presented approach successfully identifies appropriate regularization parameters for NMA.
  • This optimization enhances the utility of NMA for chemical speciation in x-ray spectromicroscopy.
  • The findings contribute to more robust analysis of complex spectroscopic datasets.