Simulation of polysaccharide carbon-13 nuclear magnetic resonance spectra using regression analysis and neural networks

A set of 13 polysaccharides was used as a training set to generate regression equations and to train back-propagation and quasi-Newton networks. These 13 polymers contained glucose, mannose, and xylose monomers with α and β anomeric configurations linked at the (1→3), (1→4), and (1→6) positions. An additional polysaccharide was utilized as a cross-validation set compound to test the external predictive ability of the regression equation and trained neural networks

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