Cluster analysis of soft X-ray spectromicroscopy data

Soft X-ray spectromicroscopy provides spectral data on the chemical speciation of light elements at sub- 100 nm spatial resolution. When all chemical species in a specimen are known and separately characterized, existing approaches can be used to measure the concentration of each component at each pixel. In other cases (such as often occur in biology or environmental science), some spectral signatures may not be known in advance so other approaches must be used. We describe here an approach that uses principal component analysis to orthogonalize and noise-filter spectromicroscopy data. We then use cluster analysis (a form of unsupervised pattern matching) to classify pixels according to spectral similarity, to extract representative, cluster-averaged spectra with good signal-to-noise ratio, and to obtain gradations of concentration of these representative spectra at each pixel. The method is illustrated with a simulated data set of organic compounds, and a mixture of lutetium in hematite used to understand colloidal transport properties of radionuclides.