Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform
  Mass Spectrometry Data
  A fuzzy logic based classification engine has been developed for classifying
mass spectra obtained with an imaging internal source Fourier transform mass
spectrometer (I^2LD-FTMS). Traditionally, an operator uses the relative
abundance of ions with specific mass-to-charge (m/z) ratios to categorize
spectra. An operator does this by comparing the spectrum of m/z versus
abundance of an unknown sample against a library of spectra from known samples.
Automated positioning and acquisition allow I^2LD-FTMS to acquire data from
very large grids, this would require classification of up to 3600 spectrum per
hour to keep pace with the acquisition. The tedious job of classifying numerous
spectra generated in an I^2LD-FTMS imaging application can be replaced by a
fuzzy rule base if the cues an operator uses can be encapsulated. We present
the translation of linguistic rules to a fuzzy classifier for mineral phases in
basalt. This paper also describes a method for gathering statistics on ions,
which are not currently used in the rule base, but which may be candidates for
making the rule base more accurate and complete or to form new rule bases based
on data obtained from known samples. A spatial method for classifying spectra
with low membership values, based on neighboring sample classifications, is
also presented.
