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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928873/ https://www.ncbi.nlm.nih.gov/pubmed/31783711 http://dx.doi.org/10.3390/s19235207 |
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author | Gradišek, Anton van Midden, Marion Koterle, Matija Prezelj, Vid Strle, Drago Štefane, Bogdan Brodnik, Helena Trifkovič, Mario Kvasić, Ivan Zupanič, Erik Muševič, Igor |
author_facet | Gradišek, Anton van Midden, Marion Koterle, Matija Prezelj, Vid Strle, Drago Štefane, Bogdan Brodnik, Helena Trifkovič, Mario Kvasić, Ivan Zupanič, Erik Muševič, Igor |
author_sort | Gradišek, Anton |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6928873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69288732019-12-26 Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning Gradišek, Anton van Midden, Marion Koterle, Matija Prezelj, Vid Strle, Drago Štefane, Bogdan Brodnik, Helena Trifkovič, Mario Kvasić, Ivan Zupanič, Erik Muševič, Igor Sensors (Basel) Article 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. MDPI 2019-11-27 /pmc/articles/PMC6928873/ /pubmed/31783711 http://dx.doi.org/10.3390/s19235207 Text en © 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/). |
spellingShingle | Article Gradišek, Anton van Midden, Marion Koterle, Matija Prezelj, Vid Strle, Drago Štefane, Bogdan Brodnik, Helena Trifkovič, Mario Kvasić, Ivan Zupanič, Erik Muševič, Igor Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning |
title | Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning |
title_full | Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning |
title_fullStr | Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning |
title_full_unstemmed | Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning |
title_short | Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning |
title_sort | improving the chemical selectivity of an electronic nose to tnt, dnt and rdx using machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928873/ https://www.ncbi.nlm.nih.gov/pubmed/31783711 http://dx.doi.org/10.3390/s19235207 |
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