  This study reports the development of a pattern recognition search engine for
a World Wide Web-based database of gas chromatography-electron impact mass
spectra (GC-EIMS) of partially methylated Alditol Acetates (PMAAs). Here, we
also report comparative results for two pattern recognition techniques that
were employed for this study. The first technique is a statistical technique
using Bayesian classifiers and Parzen density estimators. The second technique
involves an artificial neural network module trained with reinforcement
learning. We demonstrate here that both systems perform well in identifying
spectra with small amounts of noise. Both system's performance degrades with
degrading signal-to-noise ratio (SNR). When dealing with partial spectra
(missing data), the artificial neural network system performs better. The
developed system is implemented on the world wide web, and is intended to
identify PMAAs using submitted spectra of these molecules recorded on any
GC-EIMS instrument. The system, therefore, is insensitive to instrument and
column dependent variations in GC-EIMS spectra.
