Fast and sensitive recognition of various explosive compounds using Raman spectroscopy and principal component analysis

Abstract

Recently, the development of methods for the identification of explosive materials that are faster, more sensitive, easier to use, and more cost-effective has become a very important issue for homeland security and counter-terrorism applications. However, limited applicability of several analytical methods such as, the incapability of detecting explosives in a sealed container, the limited portability of instruments, and false alarms due to the inherent lack of selectivity, have motivated the increased interest in the application of Raman spectroscopy for the rapid detection and identification of explosive materials. Raman spectroscopy has received a growing interest due to its stand-off capacity, which allows samples to be analyzed at distance from the instrument. In addition, Raman spectroscopy has the capability to detect explosives in sealed containers such as glass or plastic bottles. 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.