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Multivariate Discrimination Model for TNT and Gunpowder Using an Electronic Nose Prototype: A Proof of Concept

  • Ana V. GuamanEmail author
  • Patricio Lopez
  • Julio Torres-Tello
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)

Abstract

In this work a proof of concept for discriminating explosive substances is presented, where a discrimination model for the classification of TNT and gunpowder is developed. An electronic nose was designed for sensing volatile organic compounds present in TNT and gunpowder, and a model that combines Principal Component Analysis and Fisher Discriminant Analysis was built for enhancing class discrimination. The model was tested in two scenarios: discriminating among the two explosive substances and one non-explosive, and discriminating between explosives and non-explosives, obtaining better results in the second case. In order to test model confidence a permutation test was used proving an accuracy of 67% with a p-value <0.01 for the first scenario, and an accuracy of 86.6% for the second one. These results make us think that by enhancing the prototype characteristics in both hardware and software, we would be able to achieve better results.

Keywords

Electronic nose Explosive discrimination model Classification model Permutation test 

Notes

Acknowledgments

This work was founded by Universidad de las Fuerzas Armadas ESPE under the project 2016-pic-009.

References

  1. 1.
    Gui, Y., Xie, C., Xu, J., Wang, G.: Detection and discrimination of low concentration explosives using MOS nanoparticle sensors. J. Hazard. Mater. 164, 1030–1035 (2009)CrossRefGoogle Scholar
  2. 2.
    Redacción el Comercio: Gobierno confirma secuestro de equipo periodístico de EL COMERCIO en Mataje (2018). http://www.elcomercio.com/actualidad/mataje-secuestro-equipoperiodistas-elcomercio-ecuador.html
  3. 3.
    Castillo Egüez, J.L.: Armas de fuego y políticas públicas (Ecuador 2.009–2.015) (Opinión) (2015)Google Scholar
  4. 4.
    Nebbia, G., Pesente, S., Lunardon, M., Moretto, S., Viesti, G., Cinausero, M., Barbui, M., Fioretto, E., Filippini, V., Sudac, D., Nađ, K., Blagus, S., Valković, V.: Detection of hidden explosives in different scenarios with the use of nuclear probes. Nucl. Phys. A 752, 649–658 (2005)CrossRefGoogle Scholar
  5. 5.
    Aleksandrov, V.D., Bogolubov, E.P., Bochkarev, O.V., Korytko, L.A., Nazarov, V.I., Polkanov, Y.G., Ryzhkov, V.I., Khasaev, T.O.: Application of neutron generators for high explosives, toxic agents and fissile material detection. Appl. Radiat. Isot. 63, 537–543 (2005)CrossRefGoogle Scholar
  6. 6.
    Barthet, C., Montméat, P., Eloy, N., Prené, P.: Detection of explosives vapours using a multi-quartz crystal microbalance system. Procedia Eng. 5, 472–475 (2010)CrossRefGoogle Scholar
  7. 7.
    Brudzewski, K., Osowski, S., Pawlowski, W.: Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose. Sens. Actuators B: Chem. 161, 528–533 (2012)CrossRefGoogle Scholar
  8. 8.
    Guillemot, M., Dayber, F., Montméat, P., Barthet, C., Prené, P.: Detection of explosives vapours on quartz crystal microbalances: generation of very low-concentrated vapours for sensors calibration. Procedia Chem. 1, 967–970 (2009)CrossRefGoogle Scholar
  9. 9.
    Rousier, R., Bouat, S., Bordy, T., Grateau, H., Darboux, M., Hue, J., Gaillard, G., Besnard, S., Veignal, F., Montméat, P., Lebrun, G., Larue, A.: T-REX: a portable device to detect and identify explosives vapors. Procedia Eng. 47, 390–393 (2012)CrossRefGoogle Scholar
  10. 10.
    Stetter, J.R., Strathmann, S., McEntegart, C., Decastro, M., Penrose, W.R.: New sensor arrays and sampling systems for a modular electronic nose. Sens. Actuators B: Chem. 69, 410–419 (2000)CrossRefGoogle Scholar
  11. 11.
    Röck, F., Barsan, N., Weimar, U.: Electronic nose: current status and future trends. Chem. Rev. 108, 705–725 (2008)CrossRefGoogle Scholar
  12. 12.
    Lopez, P., Trivino, R., Calderon, D., Arcentales, A., Guaman, A.V.: Electronic nose prototype for explosive detection. Presented at the 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Chile, October 2017Google Scholar
  13. 13.
    Banerjee, R., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N.: A review on combined odor and taste sensor systems. J. Food Eng. 190, 10–21 (2016)CrossRefGoogle Scholar
  14. 14.
    Di Carlo, S., Falasconi, M.: Drift correction methods for gas chemical sensors in artificial olfaction systems: techniques and challenges. In: Advances in Chemical Sensors, p. 326. InTech, Rijeka (2012)Google Scholar
  15. 15.
    Jollife, I.T.: Principal Component Analysis. Springer, New York (2002)Google Scholar
  16. 16.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification (Pt. 1). Wiley, New York (2001)zbMATHGoogle Scholar
  17. 17.
    Wang, M., Perera, A., Gutierrez-Osuna, R.: Principal discriminants analysis for small-sample-size problems: application to chemical sensing. 2004 Presented at the Sensors. IEEE (2004)Google Scholar
  18. 18.
    Luo, D., Ding, C., Huang, H.: Linear discriminant analysis: new formulation and overfit analysis. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pp. 417–422 (2011)Google Scholar
  19. 19.
    Gupta, V., Mittal, M.: KNN and PCA classifier with Autoregressive modelling during different ECG signal interpretation. Procedia Comput. Sci. 125, 18–24 (2018)CrossRefGoogle Scholar
  20. 20.
    Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Good, P.I.: Permutation, Parametric, and Bootstrap Tests of Hypotheses. Springer-Verlag, New York (2005)zbMATHGoogle Scholar
  22. 22.
    Mukherjee, S., Golland, P., Panchenko, D.: Permutation Tests for Classification. Massachusetts Institute of Technology, Cambridge, #2003-019 (2003)Google Scholar
  23. 23.
    Ojala, M.: Permutation tests for studying classifier performance. J. Mach. Learn. Res. 11, 1833–1863 (2010)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ana V. Guaman
    • 1
    Email author
  • Patricio Lopez
    • 1
  • Julio Torres-Tello
    • 1
  1. 1.Universidad de las Fuerzas Armadas – ESPESangolquíEcuador

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