Classification and recognition of compounds in low-resolution open-path FT-IR spectrometry by Kohonen self-organizing maps

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.