Kohonen and Counterpropagation Neural Networks Applied for Mapping and Interpretation of IR Spectra

  • Marjana NovicEmail author
Part of the Methods in Molecular Biology™ book series (MIMB, volume 458)


The principles of learning strategy of Kohonen and counterpropagation neural networks are introduced. The advantages of unsupervised learning are discussed. The self-organizing maps produced in both methods are suitable for a wide range of applications. Here, we present an example of Kohonen and counterpropagation neural networks used for mapping, interpretation, and simulation of infrared (IR) spectra. The artificial neural network models were trained for prediction of structural fragments of an unknown compound from its infrared spectrum. The training set contained over 3,200 IR spectra of diverse compounds of known chemical structure. The structure-spectra relationship was encompassed by the counterpropagation neural network, which assigned structural fragments to individual compounds within certain probability limits, assessed from the predictions of test compounds. The counterpropagation neural network model for prediction of fragments of chemical structure is reversible, which means that, for a given structural domain, limited to the training data set in the study, it can be used to simulate the IR spectrum of a chemical defined with a set of structural fragments.


Counterpropagation neural network infrared spectra Kohonen neural network mapping predictive ability reliability of predictions spectra interpretation spectra simulation structural fragments supervised learning unsupervised learning 


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Copyright information

© Humana Press, a part of Springer Science + Business Media, LLC 2008

Authors and Affiliations

  1. 1.National Institute of ChemistryLjubljanaSlovenia

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