SELF-MODELING MIXTURE ANALYSIS OF CATEGORIZED PYROLYSIS MASS-SPECTRAL DATA WITH THE SIMPLISMA APPROACH

A new self-modeling approach to the resolution of spectral data of mixtures has recently been described. Since the interactive method is not based on principal component analysis and since it expresses all intermediate steps in the form of spectral plots, it is relatively easy to use. The new method is a pure variable method and is called SIMPLISMA (SIMPLe-to-use Interactive Mixture Analysis). This paper demonstrates the use of SIMPLISMA on data obtained from pyrolysis mass spectrometry of biomass feedstocks. Mass spectral data are more difficult to evaluate since noise in discrete spectra is not so easy to detect as in continuous spectra. Furthermore, the extension of SIMPLISMA towards the use of category information (in this case replicate analysis of the samples) in the algorithm is described.