J. Phys. IV France
Data analysis for X-ray fluorescence imagingS. Vogt1, J. Maser1 and C. Jacobsen2
Experimental Facilities Division, Argonne National Laboratory, 9700 South Cass Avenue,
Argonne, IL 60439, U.S.A.2
Department of Physics and Astronomy, state University of New York
at stony Brook,
Stony Brook, U. S. A.
X-ray-microprobe-based X-ray fluorescence (XRF) scanning microscopy is a powerful technique to
map and quantify element distributions in biological specimens, such as cells and bacteria.
Principal component analysis (PCA)
provides a method to correlate an XRF data set with full spectra
at each scan point and to weigh each component of the spectrum, and its corresponding eigenimage, according to its respective
significance in the data set. In particular, photon noise is not correlated among pixels and therefore does not contribute
to the principal components. We show that, by fitting the eigenspectra of the principal components, one can then generate
maps of fitted elemental components with high accuracy, without the need to fit the spectra of single pixels. Additionally,
the correlation of elemental distributions can be used to reveal information about the number and composition of the différent
major constituents of a cell. We also demonstrate that cluster analysis can be used to classify the sample into spatially
separate régions of characteristic elemental compositions, for example nucleus, cytoplasm, and vesicles.
© EDP Sciences 2003