Improving the chemical selectivity of an electronic nose to TNT, DNT and RDX using machine learning

Gradišek, Anton (avtor), Midden, Marion van (avtor), Koterle, Matija (avtor), Prezelj, Vid (avtor), Strle, Drago (avtor), Štefane, Bogdan (avtor), Brodnik, Helena (avtor), Trifković, Mario (avtor), Kvasić, Ivan (avtor), Zupanič, Erik (avtor), Muševič, Igor (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: artificial nose, e-nose, electronic nose, detection of explosives, chemical selectivity of e-nose, arrays of sensors, machine learning and sensor arrays
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko; FKKT - Fakulteta za kemijo in kemijsko tehnologijo ; FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2019
• Št. strani: 15 str.
• Številčenje: Vol. 19, iss. 23, art. 5207
• PID: 20.500.12556/RUL-133128
• UDK: 53
• ISSN pri članku: 1424-8220
• DOI: 10.3390/s19235207
• COBISS.SI-ID: 32908327
• Datum objave v RUL: 12.11.2021

Gradivo je del revije

• Naslov: Sensors
• Skrajšan naslov: Sensors
• Založnik: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 01.12.2019

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: J7-8272
• Naslov: Integrirani večkanalni umetni nos za zaznavanje sledov molekul v parni fazi