Interpretation of Raman Spectra of Nitro-Containing Explosive Materials. Part II: The Implementation of Neural, Fuzzy, and Statistical Models for Unsupervised Pattern Recognition

The implementation of neural, fuzzy, and statistical models for the unsupervised pattern recognition and clustering of Fourier transform (FT)-Raman spectra of explosive materials is reported. In this work a statistical pattern recognition technique based on the concept of nearest-neighbors classification is described. Also the first application of both fuzzy clustering and a fuzzified Kohonen clustering network for the analysis of vibrational spectra is presented. Fuzzified Kohonen networks were found to perform as well as or better than the traditional fuzzy clustering technique. The unsupervised pattern recognition techniques, without the need for a prioristructural information, yielded results which were comparable with those obtained by using a combination of a prioristructural information and manual group-frequency analysis. This work demonstrates, via the use of a nitro-containing explosive data set, the utility of unsupervised pattern recognition techniques for the clustering, novelty detection, prototyping, and feature mapping of Raman spectra. The results of this work are directly applicable to the characterization of Raman spectra of explosives recorded with fiber-optic sampling.