Neural network approach to structural feature recognition from infrared spectra

Neural networks, with and without hidden nodes, have been trained to recognize structural features of compounds from their infrared spectra. The training of the networks was evaluated by a variety of statistical indices using threshold values obtained by simplex optimization and by evaluation of synthetic spectra of structural groups obtained from the connection weights of the single-layer networks. Results indicate that all of the networks can be trained to recognize the structural groups in the compounds used to train the network. The network with a hidden layer, and dedicated to a single structural group, was better able to recognize structural groups in compounds that had not been used in training the network. Although not as efficient, the single-layer networks are particularly useful in that information may be extracted for use in writing more effective rules for an expert system-based infrared interpreter.

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