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.
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This work was founded by Universidad de las Fuerzas Armadas ESPE under the project 2016-pic-009.
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Guaman, A.V., Lopez, P., Torres-Tello, J. (2019). Multivariate Discrimination Model for TNT and Gunpowder Using an Electronic Nose Prototype: A Proof of Concept. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_28
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DOI: https://doi.org/10.1007/978-3-030-11890-7_28
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