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

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

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.

• Language: English
• Keywords: artificial nose, e-nose, electronic nose, detection of explosives, chemical selectivity of e-nose, arrays of sensors, machine learning and sensor arrays
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering; FKKT - Faculty of Chemistry and Chemical Technology; FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2019
• Number of pages: 15 str.
• Numbering: Vol. 19, iss. 23, art. 5207
• PID: 20.500.12556/RUL-133128
• UDC: 53
• ISSN on article: 1424-8220
• DOI: 10.3390/s19235207
• COBISS.SI-ID: 32908327
• Publication date in RUL: 12.11.2021

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 01.12.2019

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: J7-8272
• Name: Integrirani večkanalni umetni nos za zaznavanje sledov molekul v parni fazi