Infrared spectra neural networks

M.E. Munk, M.S. Madison and E.W. Robb, Neural network models for infrared spectrum interpretation. Microchim. Acta, 2 (1991) 505-524. 

Robb EW, Munk ME (1990) A neural network approach to infrared spectrum interpretation. Mikrochim Acta [Wien] 1990/1 131... 

We have already met one tool that can be used to investigate the links that exist among data items. When the features of a pattern, such as the infrared absorption spectrum of a sample, and information about the class to which it belongs, such as the presence in the molecule of a particular functional group, are known, feedforward neural networks can create a computational model that allows the class to be predicted from the spectrum. These networks might be effective tools to predict suitable protective glove material from a knowledge of molecular structure, but they cannot be used if the classes to which samples in the database are unknown because, in that case, a conventional neural network cannot be trained. 

Munk, M.E., Madison, M.S., and Robb, E.W., Neural Network Models for Infrared Spectrum Interpretation, Mikrochim. Acta, [Wien] 2, 505, 1991. 

Neural networks have been applied to infrared spectrum interpreting systems in many variations and applications. Anand introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90% [37]. Robb used a linear neural network model for interpreting infrared spectra in routine analysis purposes with a similar performance [38]. Ehrentreich et al. used a counterpropagation (CPG) network based on a strategy of Novic and Zupan to model the correlation of structures and infrared spectra [39]. Penchev and colleagues compared three types of spectral features derived from infrared peak tables for their ability to be used in automatic classification of infrared spectra [40]. 

Training a Kohonen neural network with a molecular descriptor and a spectrum vector models the rather complex relationship between a molecule and an infrared spectrum. This relationship is stored in the Kohonen network by assigning the weights through a competitive learning technique from a suitable training set of... 

FIGURE 6.5 The infrared spectrum of a query compound compressed by Hadamard transform for the prediction of benzene derivatives by a CPG neural networks. The spectrum exhibits some typical bands for aromatic systems and chlorine atoms. 

Database Approach is a specific method for deriving the molecular structure from an infrared spectrum by predicting a molecular descriptor from an artificial neural network and retrieving the structure with the most similar descriptor from a structure database. 

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. 

---