Automatic Classification of NMR Spectra by Ensembles of Local Experts
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Selective Ensemble under Regularization Framework
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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A neural network was developed for the purposes of automating identification of skeletal structures of chemical compounds using H-1 Nuclear Magnetic Resonance (NMR) spectroscopy signals. The neural net developed was a three-layer, feed forward network using 21 hidden layer neurons. Back propagation of error was used to train the network with a database of 93 chemical compounds. The inputs to the neural network were relative peak integral and chemical shift (PPM) for the 31 largest peaks in each spectrum. Testing was performed using the same database. The trained network was able to identify the presence or lack of presence of several structural features correctly in 97% of the database. The results show great potential for further study of the application of neural networks to NMR spectroscopy classification.