Infrared spectra characteristic frequency approach

The problem of the interpretation of vibrational spectra is to calculate all possible combinations of substructures that may be present in a molecule consistent with the characteristic frequencies of a given infrared spectrum. Elyashberg and his team showed with an example that the infrared spectrum-structure correlation, as simply expressed by the characteristic frequency approach, does not allow one to establish the structure unambiguously due to a lack of information in characteristic frequencies [32]. They pointed out that the use of ANNs appears to be particularly promising. 

Algorithms Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry Molecular Models Visualization Neural Networks in Chemistry NMR Data Correlation with Chemical Structure Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Shape Analysis Spectroscopic Databases Spectroscopy Computational Methods Structure Determination by Computer-based Spectrum Interpretation Zeolites Applications of Computational Methods. 

See Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach NMR Data Correlation with Chemical Structure Spectroscopic Databases and Structure Determination by Computer-based Spectrum Interpretation. 

The following summary provides a recommended approach to the interpretation of an unknown spectrum which may be adopted until experience has developed an intuitive appreciation of the characteristics of infrared spectra. It should be used in association with the more detailed notes which follow, describing the way in which characteristic group frequencies arise and the variations in frequency position which accompany environmental changes. 

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