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Artificial neural networks in chemotype analysis of Cryptococcus neoformans

Peak fitting is one of the popular conventional methods for quantitative analysis of /sup 1/H-NMR (proton-nuclear magnetic resonance) spectra in order to establish the serotype or chemotype composition of an antigen. This method of analysis requires human supervision to interpret and manipulate the collected data. Often, due to human error and other factors the results of this analysis are incorrect and unreliable, not mentioning time consuming. A new artificial neural network is developed to automate the same quantitative analysis which previously required human interaction with better precision. ID proton NMR spectra of nearly 100 different strains of Cryptococcus neoformans were used to train and test the network. The results obtained from this network were very comparable and often better than the results of the conventional peak fitting method. The results of neural network however, were produced quickly, without human supervision and thus free of human error.