Abstract
An application of Artificial Neural Networks (ANN) to the substructure detection, from infrared spectra, of organic compounds is described. Several ANNs have already been implemented for this purpose, and show promising initial results; however, many problems remain to be resolved. We wished to train ANNs to assist in a decision support system using several spectroscopic methods to elucidate the structure of unknown molecules. To optimise the ANN with respect to spectral feature extraction, network architecture, training regime and threshold determination, we have investigated several indices for use in the evaluation of network performance. Since much published work on ANN application in this field present performance indices that are poorly defined or of limited use, we recommend that the basic results be reported so that readers may calculate indices to suit their own particular needs. These basic quantities are identified and a set of derived indices recommended.
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