Trace detection of explosives with low vapor emissions by laser surface photofragmentation–fragment detection spectroscopy with an improved ionization probe

In recent years progressively increasing terrorist activities, which use homemade explosives; such as acetone peroxide and other cyclic organic peroxides have led to worldwide awareness by security and defense agencies. Then the development of methodologies for the detection of cyclic organic peroxides have become an urgent need. Until quite recently, most of the current technology in use for trace detection of explosives had been unable to detect these energetic compounds. Differences in physical properties between cyclic organic peroxides is the main barrier for the development of a general method for analysis and detection of the peroxide explosives. In this short review, the most relevant contributions related to preparation, characterization and detection of the most important cyclic organic peroxides have been presented. It also includes few recent investigations about the toxicity and metabolism of some peroxide explosives.

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Trace detection of explosives by ion mobility spectrometry

10.1016/b978-0-444-64104-5.00013-8 ◽

2022 ◽

pp. 163-234

Author(s):

Reno DeBono ◽

Richard T. Lareau

Keyword(s):

Ion Mobility Spectrometry ◽

Ion Mobility ◽

Trace Detection ◽

Detection Of Explosives

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Orthogonal Sensors for the Trace Detection of Explosives

IEEE Sensors Letters ◽

10.1109/lsens.2019.2944587 ◽

2019 ◽

Vol 3 (10) ◽

pp. 1-4 ◽

Cited By ~ 1

Author(s):

Peter P. Ricci ◽

Andrew S. Rossi ◽

Otto J. Gregory

Keyword(s):

Trace Detection ◽

Detection Of Explosives

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Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Sensors ◽

10.3390/s19010097 ◽

2018 ◽

Vol 19 (1) ◽

pp. 97 ◽

Cited By ~ 4

Author(s):

Siddharth Chaudhary ◽

Sarawut Ninsawat ◽

Tai Nakamura

Keyword(s):

Hyperspectral Imaging ◽

Hyperspectral Image ◽

Imaging System ◽

Classification Model ◽

Support Vector ◽

Trace Detection ◽

Svm Model ◽

Detection Of Explosives ◽

Non Destructive ◽

Hyperspectral Sensor

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

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