Plasmonic enhanced terahertz time-domain spectroscopy system for identification of common explosives

In this study, we present a classification algorithm for terahertz time-domain spectroscopy systems (THz-TDS) that can be trained to identify most commonly used explosives (C4, HMX, RDX, PETN, TNT, composition-B and blackpowder) and some non-explosive samples (lactose, sucrose, PABA). Our procedure can be used in any THz-TDS system that detects either transmission or reflection spectra at room conditions. After preprocessing the signal in low THz regime (0.1 3 THz), our algorithm takes advantages of a latent space transformation based on principle component analysis in order to classify explosives with low false alarm rate.