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Proton nuclear magnetic resonance (1H-NMR) is a widely used tool for chemicalstructural analysis. However, 1H-NMR spectra suffer from natural aberrationsthat render computer-assisted automated identification of these spectradifficult, and at times impossible. Previous efforts have successfullyimplemented instrument dependent or conditional identification of thesespectra. In this paper, we report the first instrument independentcomputer-assisted automated identification system for a group of complexcarbohydrates known as the xyloglucan oligosaccharides. The developed system isalso implemented on the world wide web (http://www.ccrc.uga.edu) as part of anidentification package called the CCRC-Net and is intended to recognize anysubmitted 1H-NMR spectrum of these structures with reasonable signal-to-noiseratio, recorded on any 500 MHz NMR instrument. The system uses ArtificialNeural Networks (ANNs) technology and is insensitive to the instrument andenvironment-dependent variations in 1H-NMR spectroscopy. In this paper,comparative results of the ANN engine versus a multidimensional Bayes'classifier is also presented.

Identification of 1H-NMR Spectra of Xyloglucan Oligosaccharides: A  Comparative Study of Artificial Neural Networks and Bayesian Classification  Using Nonparametric Density Estimation