20.500.12556/RUL-133128 Improving the chemical selectivity of an electronic nose to TNT, DNT and RDX using machine learning 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. artificial nose e-nose electronic nose detection of explosives chemical selectivity of e-nose arrays of sensors machine learning and sensor arrays true false true Angleški jezik Ni določen Članek v reviji 2021-11-12 09:13:34 2021-11-12 09:13:35 2022-09-12 04:14:59 0000-00-00 00:00:00 2019 0 0 15 str. iss. 23, art. 5207 Vol. 19 2019 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 53 1424-8220 10.3390/s19235207 32908327 RAZ_Gradisek_Anton_2019.pdf RAZ_Gradisek_Anton_2019.pdf 1 54CC6496CCF411D96B77288FF0591092 9a50ff379296f13cd41c7396b00d457a9e434aedc08d0cb74e54723311064585 d8ca53a2-4390-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150942 https://www.mdpi.com/1424-8220/19/23/5207 0 ca301e0d-4390-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150941 Fakulteta za elektrotehniko Fakulteta za kemijo in kemijsko tehnologijo Fakulteta za matematiko in fiziko 0 0 0