Optimization of functional group prediction from infrared spectra using neural networks

In a large-scale effort, numerous parameters influencing the neural network interpretation of gas phase infrared spectra have been investigated. Predictions of the presence or absence of 26 different substructural entities were-optimized by systematically observing the impact on functional group prediction accuracy for the following parameters: training duration, learning rate, momentum, sigmoidal discrimination and bias, spectral data reduction with four different methods, number of hidden nodes, individual instead of multioutput networks, size of the training set, noise level, and 12 different spectral preprocessing functions. The most promising approaches included constant monitoring of training progress with a 500 spectra cross-validation set, increasing the number of spectral examples in the training set from 511 to 2588, employing variance scaling, and using specialized instead of multioutput networks. An overall recognition accuracy of 93.8% for the presence and 95.7% for the absence of functionalities was achieved, while perfect prediction was reached for several present functional groups.