{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:16:24Z","timestamp":1762341384022,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["J7-8272"],"award-info":[{"award-number":["J7-8272"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19235207","type":"journal-article","created":{"date-parts":[[2019,11,28]],"date-time":"2019-11-28T08:14:04Z","timestamp":1574928844000},"page":"5207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6480-9587","authenticated-orcid":false,"given":"Anton","family":"Gradi\u0161ek","sequence":"first","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"}]},{"given":"Marion","family":"van Midden","sequence":"additional","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1544-2595","authenticated-orcid":false,"given":"Matija","family":"Koterle","sequence":"additional","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"}]},{"given":"Vid","family":"Prezelj","sequence":"additional","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3277-6503","authenticated-orcid":false,"given":"Drago","family":"Strle","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, EE dep., Tr\u017ea\u0161ka 25, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8709-9853","authenticated-orcid":false,"given":"Bogdan","family":"\u0160tefane","sequence":"additional","affiliation":[{"name":"Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ve\u010dna pot 113, 1000 Ljubljana, Slovenia"}]},{"given":"Helena","family":"Brodnik","sequence":"additional","affiliation":[{"name":"Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ve\u010dna pot 113, 1000 Ljubljana, Slovenia"}]},{"given":"Mario","family":"Trifkovi\u010d","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, EE dep., Tr\u017ea\u0161ka 25, 1000 Ljubljana, Slovenia"}]},{"given":"Ivan","family":"Kvasi\u0107","sequence":"additional","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5737-1150","authenticated-orcid":false,"given":"Erik","family":"Zupani\u010d","sequence":"additional","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"}]},{"given":"Igor","family":"Mu\u0161evi\u010d","sequence":"additional","affiliation":[{"name":"Jo\u017eef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia"},{"name":"Faculty of Mathematics and Physics, University of Ljubljana, Jadranska 19, 1000 Ljubljana, Slovenia"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"093001","DOI":"10.1088\/1361-6439\/aac849","article-title":"Intelligent gas-sensing systems and their applications","volume":"28","author":"Hsieh","year":"2018","journal-title":"J. Micromechanics Microengineering"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Strle, D., \u0160tefane, B., Trifkovi\u010d, M., Van Miden, M., Kvasi\u0107, I., Zupani\u010d, E., and Mu\u0161evi\u010d, I. (2017). Chemical selectivity and sensitivity of a 16-channel electronic nose for trace vapour detection. Sensors, 17.","DOI":"10.3390\/s17122845"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jfoodeng.2014.07.019","article-title":"Electronic noses for food quality: A review","volume":"144","author":"Loutfi","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1021\/cr068121q","article-title":"Electronic nose: Current status and future trends","volume":"108","author":"Barsan","year":"2008","journal-title":"Chem. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jfoodeng.2015.06.007","article-title":"Comparison of ELM, RF, and SVM on E-nose and E-tongue to trace the quality status of mandarin (Citrus unshiu Marc.)","volume":"166","author":"Qiu","year":"2015","journal-title":"J. Food Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Adak, M.F., and Yumusak, N. (2016). Classification of E-nose aroma data of four fruit types by ABC-based neural network. Sensors, 16.","DOI":"10.3390\/s16030304"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"245831","DOI":"10.1155\/2014\/245831","article-title":"Detection of adulteration in argan oil by using an electronic nose and a voltammetric electronic tongue","volume":"2014","author":"Bougrini","year":"2014","journal-title":"J. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.foodchem.2017.11.013","article-title":"Electronic noses in classification and quality control of edible oils: A review","volume":"246","author":"Majchrzak","year":"2018","journal-title":"Food Chem."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8055","DOI":"10.3390\/s120608055","article-title":"Electronic nose based on independent component analysis combined with partial least squares and artificial neural networks for wine prediction","volume":"12","author":"Aguilera","year":"2012","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.snb.2013.10.065","article-title":"Classification of tea specimens using novel hybrid artificial intelligence methods","volume":"192","author":"Maziarz","year":"2014","journal-title":"Sens. Actuators B Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"715763","DOI":"10.1155\/2012\/715763","article-title":"Electronic nose for microbiological quality control of food products","volume":"2012","author":"Falasconi","year":"2012","journal-title":"Int. J. Electrochem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"046002","DOI":"10.1088\/1752-7155\/9\/4\/046002","article-title":"Comparison of classification methods in breath analysis by electronic nose","volume":"9","author":"Leopold","year":"2015","journal-title":"J. Breath Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7350","DOI":"10.1109\/TIE.2017.2694353","article-title":"Sensor array optimization of electronic nose for detection of bacteria in wound infection","volume":"64","author":"Sun","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"27804","DOI":"10.3390\/s151127804","article-title":"Electronic nose feature extraction methods: A review","volume":"15","author":"Yan","year":"2015","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2472","DOI":"10.1016\/j.snb.2017.09.040","article-title":"A review of algorithms for SAW sensors e-nose based volatile compound identification","volume":"255","author":"Hotel","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1108\/SR-07-2015-0104","article-title":"Comparison of machine learning algorithms for concentration detection and prediction of formaldehyde based on electronic nose","volume":"36","author":"Xu","year":"2016","journal-title":"Sens. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7173","DOI":"10.1109\/JSEN.2018.2853674","article-title":"Drift-Insensitive Features for Learning Artificial Olfaction in E-Nose System","volume":"18","author":"Rehman","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/TIM.2018.2863438","article-title":"Deep Nearest Class Mean Model for Incremental Odor Classification","volume":"68","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1109\/TIM.2017.2669818","article-title":"Odor recognition in multiple E-nose systems with cross-domain discriminative subspace learning","volume":"66","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/JSEN.2011.2168203","article-title":"Surface-functionalized COMB capacitive sensors and CMOS electronics for vapor trace detection of explosives","volume":"12","author":"Strle","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11467","DOI":"10.3390\/s140711467","article-title":"Sensitivity comparison of vapor trace detection of explosives based on chemo-mechanical sensing with optical detection and capacitive sensing with electronic detection","volume":"14","author":"Strle","year":"2014","journal-title":"Sensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.trac.2012.09.010","article-title":"The vapor pressures of explosives","volume":"42","author":"Ewing","year":"2013","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_23","first-page":"32","article-title":"Spring research presentation: A theoretical foundation for inductive transfer","volume":"1","author":"West","year":"2007","journal-title":"Brigh. Young Univ. Coll. Phys. Math. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:38:08Z","timestamp":1760189888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,27]]},"references-count":23,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235207"],"URL":"https:\/\/doi.org\/10.3390\/s19235207","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,27]]}}}