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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2-methyl-1,3,5-trinitrobenzene (TNT). A classification model was developed using the Random Forest machine-learning algorithm and trained the models on a set of signals, where the concentration and flow of a selected single vapour were varied independently. It is demonstrated that our classification models are successful in recognizing the signal pattern of different sets of substances. An excellent accuracy of 96% was achieved for identifying the explosives from among the other substances. These experiments clearly demonstrate that the silane monolayers used in our sensors as receptor layers are particularly well suited to selecting and recognizing TNT and similar types of explosives from among other substances.

Details

Title
Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning
Author
Gradišek, Anton 1   VIAFID ORCID Logo  ; Marion van Midden 1 ; Koterle, Matija 1   VIAFID ORCID Logo  ; Vid Prezelj 1 ; Drago Strle 2   VIAFID ORCID Logo  ; Štefane, Bogdan 3   VIAFID ORCID Logo  ; Brodnik, Helena 3 ; Trifkovič, Mario 2 ; Kvasić, Ivan 1 ; Zupanič, Erik 1   VIAFID ORCID Logo  ; Muševič, Igor 4 

 Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia; [email protected] (M.v.M.); [email protected] (M.K.); [email protected] (V.P.); [email protected] (I.K.); [email protected] (E.Z.); [email protected] (I.M.) 
 Faculty of Electrical Engineering, University of Ljubljana, EE dep., Tržaška 25, 1000 Ljubljana, Slovenia; [email protected] (D.S.); [email protected] (M.T.) 
 Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia; [email protected] (B.Š.); [email protected] (H.B.) 
 Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia; [email protected] (M.v.M.); [email protected] (M.K.); [email protected] (V.P.); [email protected] (I.K.); [email protected] (E.Z.); [email protected] (I.M.); Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000 Ljubljana, Slovenia 
First page
5207
Publication year
2019
Publication date
2019
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2535578427
Copyright
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.