{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T22:27:39Z","timestamp":1768084059937,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2011,1,5]],"date-time":"2011-01-05T00:00:00Z","timestamp":1294185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4\/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes\u2014a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR) estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process.<\/jats:p>","DOI":"10.3390\/s110100485","type":"journal-article","created":{"date-parts":[[2011,1,5]],"date-time":"2011-01-05T11:04:44Z","timestamp":1294225484000},"page":"485-505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression"],"prefix":"10.3390","volume":"11","author":[{"given":"Kai","family":"Song","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Hongquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Biochemistry Center, No.49 Institute of China Electronics Technology Group Corporation, Harbin 150001, China"}]},{"given":"Yingguo","family":"Cheng","sequence":"additional","affiliation":[{"name":"Biochemistry Center, No.49 Institute of China Electronics Technology Group Corporation, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2011,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gardner, J.W., and Bartlett, P.N. 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