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

Abstract

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 (200–3500cm−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.