  A recently proposed mutual information based algorithm for decomposing data
into least dependent components (MILCA) is applied to spectral analysis, namely
to blind recovery of concentrations and pure spectra from their linear
mixtures. The algorithm is based on precise estimates of mutual information
between measured spectra, which allows to assess and make use of actual
statistical dependencies between them. We show that linear filtering performed
by taking second derivatives effectively reduces the dependencies caused by
overlapping spectral bands and, thereby, assists resolving pure spectra. In
combination with second derivative preprocessing and alternating least squares
postprocessing, MILCA shows decomposition performance comparable with or
superior to specialized chemometrics algorithms. The results are illustrated on
a number of simulated and experimental (infrared and Raman) mixture problems,
including spectroscopy of complex biological materials.
  MILCA is available online at http://www.fz-juelich.de/nic/cs/software
