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.

[1]  Marjana Novic,et al.  Investigation of Infrared Spectra-Structure Correlation Using Kohonen and Counterpropagation Neural Network , 1995, J. Chem. Inf. Comput. Sci..

[2]  H. Lohninger,et al.  Comparing the performance of neural networks to well-established methods of multivariate data analysis: the classification of mass spectral data , 1992 .

[3]  Daniel Cabrol-Bass,et al.  Neural network approach to structural feature recognition from infrared spectra , 1993, J. Chem. Inf. Comput. Sci..

[4]  Bradley D. Christie,et al.  Structure generation by reduction: a new strategy for computer-assisted structure elucidation , 1988, J. Chem. Inf. Comput. Sci..

[5]  Morton E. Munk,et al.  A neural network approach to infrared spectrum interpretation , 1990 .

[6]  David E. Rumelhart,et al.  MSnet: A Neural Network which Classifies Mass Spectra , 1990 .

[7]  M. Meyer,et al.  Neural networks for interpretation of infrared spectra using extremely reduced spectral data , 1993 .

[8]  Rainer Herges,et al.  Automatic interpretation of infrared spectra: recognition of aromatic substitution patterns using neural networks , 1992, J. Chem. Inf. Comput. Sci..

[9]  M. Meyer,et al.  Interpretation of infrared spectra by artificial neural networks , 1992 .

[10]  M. Munk,et al.  Neural network models for infrared spectrum interpretation , 1991 .

[11]  Charles L. Wilkins,et al.  A novel algorithm for local minimum escape in back-propagation neural networks: application to the interpretation of matrix isolation infrared spectra , 1994, J. Chem. Inf. Comput. Sci..