ISSN:
1432-1130
Source:
Springer Online Journal Archives 1860-2000
Topics:
Chemistry and Pharmacology
Notes:
Abstract The possibility of using one- and two-dimensional Kohonen self-organizing maps (SOMs) to recognize similarities in low-resolution vapor-phase infrared spectra without any additional information, i.e., in an unsupervised mode, has been investigated. Full-range vapor-phase FT-IR reference spectra were first used to train the networks and the trained networks were then used to classify the reference spectra into several groups. The feasibility of reducing the spectral range to be consistent with the atmospheric windows used in open-path FT-IR spectrometry was also studied. Kohonen networks are shown to be relatively immune to the presence of noise. An example of using a trained Kohonen map to recognize the presence of selected compounds in field-measured open-path FT-IR spectra is given.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/s002160051031
Permalink