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

A Comparative study of Artificial Neural Networks Using Reinforcement  learning and Multidimensional Bayesian Classification Using Parzen Density  Estimation for Identification of GC-EIMS Spectra of Partially Methylated  Alditol Acetates