The neural network as a tool for multispectral interpretation.
A neural network which utilized data from the infrared spectra, carbon-13 NMR spectra, and molecular formulas of organic compounds was developed. The network, which had one layer of hidden units, was trained by backpropagation; network parameters were determined by a simplex optimization procedure. A database of 1560 compounds was used for training and testing. The trained network was able to identify with high accuracy the presence of a broad range of substructural features present in the compounds. The number of features identified and the accuracy were significantly greater as compared with networks using data from a single form of spectroscopy. The results have significance for the SESAMI computer-enhanced structure elucidation system.
Munk
M E
ME
Department of Chemistry and Biochemistry, Arizona State University, Tempe 85287-1604, USA.
Madison
M S
MS
Robb
E W
EW
D003201:Computers; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D015394:Molecular Structure; D016571:Neural Networks, Computer; D013055:Spectrophotometry, Infrared / Q000379:methods / Q000706:statistics & numerical data; D013057:Spectrum Analysis / Q000379:methods* / Q000706:statistics & numerical data