{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:33:24Z","timestamp":1776699204089,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T00:00:00Z","timestamp":1678320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Special Science and Technology project of Anhui Province","award":["202103a07020007"],"award-info":[{"award-number":["202103a07020007"]}]},{"name":"Major Special Science and Technology project of Anhui Province","award":["202104a05020057"],"award-info":[{"award-number":["202104a05020057"]}]},{"name":"Key Research and Development Program of Anhui Province","award":["202103a07020007"],"award-info":[{"award-number":["202103a07020007"]}]},{"name":"Key Research and Development Program of Anhui Province","award":["202104a05020057"],"award-info":[{"award-number":["202104a05020057"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.<\/jats:p>","DOI":"10.3390\/s23062975","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T02:05:54Z","timestamp":1678413954000},"page":"2975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Rapid Identification Method for CH4\/CO\/CH4-CO Gas Mixtures Based on Electronic Nose"],"prefix":"10.3390","volume":"23","author":[{"given":"Jianxin","family":"Yin","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0538-7211","authenticated-orcid":false,"given":"Yongli","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"given":"Zhi","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"given":"Fushuai","family":"Ba","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"given":"Peng","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"given":"Xiaolong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Materials, Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Qian","family":"Rong","sequence":"additional","affiliation":[{"name":"School of Materials, Sun Yat-sen University, Shenzhen 518107, China"}]},{"given":"Youmin","family":"Guo","sequence":"additional","affiliation":[{"name":"School and Materials Science and Technology, Anhui University, Hefei 230601, China"}]},{"given":"Yafei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112106","DOI":"10.1016\/j.combustflame.2022.112106","article-title":"Cationic structure of premixed near-stoichiometric CH4\/O2\/Ar flames at atmospheric pressure: New insights from mass spectrometry, quantum chemistry, and kinetic modeling","volume":"241","author":"Knyazkov","year":"2022","journal-title":"Combust. Flame"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107533","DOI":"10.1016\/j.jvolgeores.2022.107533","article-title":"A new multi-GAS system for continuous monitoring of CO2\/CH4 ratios at active volcanoes","volume":"426","author":"Stix","year":"2022","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126750","DOI":"10.1016\/j.fuel.2022.126750","article-title":"Smoldering peat fire detection by time-resolved measurements of transient CO2 and CH4 emissions using a novel dual-gas optical sensor","volume":"334","author":"Raza","year":"2023","journal-title":"Fuel"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5338","DOI":"10.1016\/S1452-3981(23)17258-0","article-title":"Novel anode-supported tubular solid-oxide electrolytic cell for direct NO decomposition in N2 environment","volume":"10","author":"Tong","year":"2015","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.mseb.2017.12.036","article-title":"Semiconductor metal oxide gas sensors: A review","volume":"229","author":"Dey","year":"2018","journal-title":"Mater. Sci. Eng. B-Adv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113578","DOI":"10.1016\/j.sna.2022.113578","article-title":"Nanostructured metal oxide semiconductor-based gas sensors: A comprehensive review","volume":"341","author":"Krishna","year":"2022","journal-title":"Sens. Actuat. A-Phys."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Peng, Z., Zhao, Y., Yin, J., Peng, P., Ba, F., Liu, X., Guo, Y., Rong, Q., and Zhang, Y. (2023). A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose. Appl. Sci., 4.","DOI":"10.3390\/app13042338"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"213234","DOI":"10.1016\/j.bioadv.2022.213234","article-title":"Innovations in the synthesis of graphene nanostructures for bio and gas sensors","volume":"145","author":"Ikram","year":"2023","journal-title":"Biomater. Adv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106897","DOI":"10.1016\/j.mssp.2022.106897","article-title":"Detection of breath acetone by semiconductor metal oxide nanostructures-based gas sensors: A review","volume":"149","author":"Ahmadipour","year":"2022","journal-title":"Mat. Sci. Semicon. Proc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2422","DOI":"10.1007\/s11694-022-01351-z","article-title":"Rapid odor recognition based on reliefF algorithm using electronic nose and its application in fruit identification and classification","volume":"16","author":"Wen","year":"2022","journal-title":"J. Food Meas. Charact."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.egypro.2017.03.1095","article-title":"Indoor air quality evaluation in intelligent building","volume":"112","author":"Cociorva","year":"2017","journal-title":"Energy Proced."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shahid, A., Choi, J.H., Rana, A.U.S., and Kim, H.S. (2018). Least squares neural network-based wireless E-nose system using an SnO2 sensor array. Sensors, 18.","DOI":"10.3390\/s18051446"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ghosh, U., Maleh, Y., Alazab, M., and Pathan, A.-S.K. (2021). Machine Intelligence and Data Analytics for Sustainable Future Smart Cities, Springer International Publishing.","DOI":"10.1007\/978-3-030-72065-0"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xu, Y.H., Zhao, X., Chen, Y.S., and Yang, Z.X. (2019). Research on a mixed gas classification algorithm based on extreme random rree. Appl. Sci., 9.","DOI":"10.3390\/app9091728"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, G.F., Li, G., Zhao, J., and He, A.X. (2019). Development of a LeNet-5 gas identification CNN structure for electronic noses. Sensors, 19.","DOI":"10.3390\/s19010217"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.procs.2017.12.145","article-title":"Estimating gas concentration using artificial neural network for electronic nose","volume":"124","author":"Sabilla","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20886","DOI":"10.1109\/JSEN.2021.3100390","article-title":"Prediction of pulmonary diseases with electronic nose using SVM and XGBoost","volume":"21","author":"Binson","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s11666-019-00966-x","article-title":"Parametric Analysis and Modeling for the Porosity Prediction in Suspension Plasma-Sprayed Coatings","volume":"29","author":"Zhao","year":"2020","journal-title":"J. Therm. Spray Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Z.P., Zhou, K., and Liu, X. (2020). Broken rail prediction with machine learning-based approach. ASME Jt. Rail C., V001T08A014.","DOI":"10.1115\/JRC2020-8102"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1111\/1750-3841.14701","article-title":"Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal","volume":"84","author":"Gancarz","year":"2019","journal-title":"J. Food Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.aca.2019.05.024","article-title":"Critical review of electronic nose and tongue instruments prospects in pharmaceutical analysis","volume":"1077","author":"Wasilewski","year":"2019","journal-title":"Anal. Chim. Acta"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2975\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:51Z","timestamp":1760122311000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":22,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23062975"],"URL":"https:\/\/doi.org\/10.3390\/s23062975","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]}}}