Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning.

Clicks: 197
ID: 71421
2019
Article Quality & Performance Metrics
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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.
Reference Key
gradiek2019improvingsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gradišek, Anton;van Midden, Marion;Koterle, Matija;Prezelj, Vid;Strle, Drago;Štefane, Bogdan;Brodnik, Helena;Trifkovič, Mario;Kvasić, Ivan;Zupanič, Erik;Muševič, Igor;
Journal sensors
Year 2019
DOI
E5207
URL
Keywords
electronic nose e-nose arrays of sensors artificial nose chemical selectivity of e-nose detection of explosives machine learning and sensor arrays

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