Data analysis for X-ray fluorescence imaging

Abstract
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.