期刊
JOURNAL OF MOLECULAR STRUCTURE
卷 1039, 期 -, 页码 130-136出版社
ELSEVIER
DOI: 10.1016/j.molstruc.2013.01.079
关键词
Raman spectroscopy; Explosives detection; Principal component analysis; Feature extraction; Spectral database
资金
- ADD in South Korea
- Ministry of Knowledge Economy (MKE)
- Korea Industrial Technology Foundation (KOTEF) through the Human Resource Training Project for Strategic Technology
We report a rapid and sensitive recognition technique for explosive compounds using Raman spectroscopy and principal component analysis (PCA). Seven hundreds of Raman spectra (50 measurements per sample) for 14 selected explosives were collected, and were pretreated with noise suppression and baseline elimination methods. PCA, a well-known multivariate statistical method, was applied for the proper evaluation, feature extraction, and identification of measured spectra. Here, a broad wavenumber range (200-3500 cm(-1)) on the collected spectra set was used for the classification of the explosive samples into separate classes. It was found that three principal components achieved 99.3% classification rates in the sample set. The results show that Raman spectroscopy in combination with PCA is well suited for the identification and differentiation of explosives in the field. (C) 2013 Elsevier B.V. All rights reserved.
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