2-DIMENSIONAL MAPPING OF IR-SPECTRA USING A PARALLEL IMPLEMENTED SELF-ORGANIZING FEATURE MAP

A large data base containing 3284 infrared (IR) spectra (1327 wavelengths) of various molecules was investigated with a self-organising feature map (Kohonen network). In order to reduce the time required to train the network, a parallel implementation of the algorithm was developed. Application of the Kohonen network appears to be a powerful technique in mapping a high dimensional data space onto a two-dimensional one. Fragment coding was used to indicate the presence or absence of chemical functional groups in a molecule. Two-dimensional maps have been constructed for several fragments. Some preliminary results are presented in this paper. It appeared that some of the fragments were mapped onto relatively small regions (clusters) in the map. Mostly, these fragments were characterised by a high separability index, indicating that these functional groups were easily recognised by the network. Next, it was shown that, for some of the fragments which formed clusters in the map, a further differentiation into sub-fragments appeared to be possible. We conclude that the analysis of Kohonen maps yields valuable information which may be used for the practical design of a modular tree-like system of dedicated multi-layer feed-forward neural networks for the automated interpretation of infrared spectra.