Cluster analysis in soft X-ray spectromicroscopy: Finding the patterns in complex specimens

Abstract

Soft X-ray spectromicroscopy provides spectral data on the chemical speciation of light elements at sub-100 nanometer spatial resolution. If 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 situations such as in biology or environmental science, this approach may not be possible. We have previously described [M. Lerotic, C. Jacobsen, T. Schäfer, S. Vogt, Ultramicroscopy 100 (1–2) (2004) 35] the use of principle component analysis (PCA) to orthogonalize and noise-filter spectromicroscopy data, and cluster analysis (CA) to classify the analyzed data and obtain thickness maps of representative spectra. We describe here an extension of that work employing an angle distance measure; this measure provides better classification based on spectral signatures alone in specimens with significant thickness variations. The method is illustrated using simulated data, and also to examine sporulation in the bacterium Clostridium sp.