Journal Article

DESY-2014-02565

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Non-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF

Alfeld, M. (Corresponding Author)DESY* ; Wahabzada, M.Extern* ; Bauckhage, C. ; Kersting, K. ; Wellenreuther, G.Extern*XFEL* ; Falkenberg, G.DESY*

2014
IOP Publ. Bristol

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Please use a persistent id in citations: doi:10.1088/1742-6596/499/1/012013

Abstract: Stacks of elemental distribution images acquired by XRF can be difficult to interpret, if they contain high degrees of redundancy and components differing in their quantitative but not qualitative elemental composition. Factor analysis, mainly in the form of Principal Component Analysis (PCA), has been used to reduce the level of redundancy and highlight correlations. PCA, however, does not yield physically meaningful representations as they often contain negative values. This limitation can be overcome, by employing factor analysis that is restricted to non-negativity. In this paper we present the first application of the Python Matrix Factorization Module (pymf) on XRF data. This is done in a case study on the painting Saul and David from the studio of Rembrandt van Rijn. We show how the discrimination between two different Co containing compounds with minimum user intervention and a priori knowledge is supported by Non-Negative Matrix Factorization (NMF).

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Contributing Institute(s):

1. Experimentebetreuung PETRA III (FS-PE)
2. DOOR-User (DOOR)
3. European XFEL (Eur.XFEL)

Research Program(s):

1. PETRA Beamline P06 (POF2-54G14) (POF2-54G14)

Experiment(s):

1. PETRA Beamline P06 (PETRA III)

Appears in the scientific report 2014

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Database coverage:
; ; ; ; Nationallizenz ; SCOPUS

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