Neural networks for interpretation of infrared spectra using extremely reduced spectral data

✍ Scribed by M. Meyer; K. Meyer; H. Hobert

• Publisher: Elsevier Science
• Year: 1993
• Tongue: English
• Weight: 682 KB
• Volume: 282
• Category: Article
• ISSN: 0003-2670
• DOI: 10.1016/0003-2670(93)80227-c

✦ Synopsis

In order to classify infrared (IR) spectra into structural groups the application of artificial neural networks is a powerful tool. The network architecture used for this work was a back propagation model with one hidden layer. For the minimization of the network the number of input elements has been reduced extremely. Instead of the whole infrared spectrum (512 measuring points) the score values calculated by principal component analysis (PCA) involving the spectra of the training set (700) and the prediction set (348) were used as input elements for the network The number of principal components or scores per spectrum as network input, respectively, has been varied (4,6, 8 and 10) to find out the minimal spectral information needed for a sufficient prediction ability of the network. 13 IR sensitive structural patterns have been defined as output elements of the network. Results obtained for the training and prediction step are discussed. The prediction performance of the neural network operating with such extremely reduced spectral data is surprisingly good.