Clustering of infrared spectra with Kohonen networks

The design of systems for spectral data interpretation requires clustering of chemical compounds based on their spectral characteristics. Kohonen netw orks have been shown to be efficient tools to achieve this clustering. Thes e auto-organising systems perform a mapping between a high-dimensional vari able space and a two-dimensional one. An application to infrared spectra of organic compounds is presented here. The non-supervised learning algorithm used allows classification of compounds by spectral characteristics withou t a priori knowledge. An analysis of the distribution of spectra on the res ulting maps is used to build models for predicting the presence or absence of specific structural features. The performance of the models in recognisi ng structural features is discussed and compared with the prediction of a m ultilayered feed forward network (MLFFN). Localisation of compounds wrongly classified by the MLFFN on the Kohonen maps allows to establish a link bet ween the supervised and the unsupervised approaches.