Differential Reflection Spectroscopy: A Novel Method for Explosive Detection

• Authors: S. Yuksel , T. Dubroca , R. Hummel , P. Gader
• Languages of publication: EN

Abstracts

EN

In the aftermath of the recent terrorist attacks, there has been an increasing need for automated, high-speed detection technologies that can detect trace amounts of explosives without human intervention. Our group at the University of Florida has developed differential reflection spectroscopy which can detect explosive residue on surfaces such as parcel, cargo and luggage. In this differential reflection device, explosives show spectral finger-prints at specific wavelengths, for example, the spectrum of 2,4,6, trinitrotoluene shows an absorption edge at 420 nm. Additionally, we have developed a support vector machine based computer software to classify the explosives and non-explosive materials. In this study we will (i) describe this system and give an insight into the operation of our prototype, (ii) demonstrate our software for the detection of the spectral finger-prints, and (iii) discuss the normalization of the data which significantly increases classification rates and decreases the number of parameters.

Keywords

EN

74.25.Gz; 78.90.+t

Discipline

• 74.25.Gz: Optical properties
• 78.90.+t: Other topics in optical properties, condensed matter spectroscopy and other interactions of particles and radiation with condensed matter (restricted to new topics in section 78)

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2013
• Volume: 123
• Issue: 2
• Pages: 263-264

Dates

published

2013-02

Contributors

author

S. Yuksel

• Department of Materials Science and Engineering, University of Florida, Gainesville FL, USA
• Department of Computer and Information Science and Engineering, University of Florida, Gainesville, USA

author

T. Dubroca

• Department of Materials Science and Engineering, University of Florida, Gainesville FL, USA

author

R. Hummel

• Department of Materials Science and Engineering, University of Florida, Gainesville FL, USA

author

P. Gader

• Department of Computer and Information Science and Engineering, University of Florida, Gainesville, USA

References

• [1] J. Janni, B. Gilbert, R. Field, J. Steinfeld, Spectrochim. Acta A 53, 1375 (1997)
• [2] G. Muralidharan, A. Wig, L.A. Pinnaduwage, D. Hedden, T. Thundat, R.T. Lareau, Ultramicroscopy 97, 433 (2003)
• [3] F C. De-Lucia, R.S. Harmon, K.L. McNesby, R.J. Winkel, A.W. Miziolek, Appl. Opt. 42, 6148 (2003)
• [4] N.R. Council, Existing and Potential Standoff Explosives Detection Techniques, The National Academies Press, Washington, D.C. 2004
• [5] C.A. Munson, J.L. Gottfried, J.F.C. de Lucia, K.L. McNesby, A.W. Miziolek, Laser-Based Detection Methods for Explosives, Tech. Rep. ARL-TR-4279, Army Research Laboratory, Aberdeen Proving Ground, MD 2007
• [6] Counterterrorist Detection Techniques of Explosives, Ed. J. Yinon, Elsevier 2007
• [7] D. Manolakis, D. Marden, G.A. Shaw, Lincoln Lab. J. 14, 79 (2003)
• [8] D. Moore, Sens. Imag. Int. J. 8, 9 (2007)
• [9] D. Moore, J. Goodpaster, Anal. Bioanal. Chem. 395, 245 (2009)
• [10] R.E. Hummel, A.M. Fuller, C. Schoellhorn, P H. Holloway, Appl. Phys. Lett. 88, 898 (2006)
• [11] R. Hummel, A. Fuller, C. Schoellhorn, P. Holloway, Remote Sensing of Explosive Materials using Differential Reflection Spectroscopy, Wiley, New York 2007, Ch. 15, p. 301
• [12] C. Schoellhorn, A.M. Fuller, J. Gratier, R.E. Hummel, Appl. Opt. 46, 6232 (2007)
• [13] A.M. Fuller-Tedeschi, R. Hummel, J. Appl. Phys. 107, 1 (2010)
• [14] S. Yuksel, T. Dubroca, R. Hummel, P. Gader, Spectral Analysis for the Detection of Explosives with Differential Reflectometry, in: Grace Hopper Conf. (GHC) 2011
• [15] A. Zare, P. Gader, J. Bolton, S. Yuksel, T. Dubroca, R. Close, R. Hummel, in: IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon (Portugal), 2011
• [16] S.E. Yuksel, T. Dubroca, R. Hummel, P. Gader, in: SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 2012
• [17] V N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY 1995
• [18] C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer-Verlag New York, Secaucus, NJ 2006

Identifiers

DOI

10.12693/APhysPolA.123.263