{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T19:11:11Z","timestamp":1792869071103,"version":"4.5.0"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T00:00:00Z","timestamp":1515369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61504087"],"award-info":[{"award-number":["61504087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"fundamental Research Foundation of Shenzhen","award":["JCYJ20160308094919279"],"award-info":[{"award-number":["JCYJ20160308094919279"]}]},{"name":"fundamental Research Foundation of Shenzhen","award":["JCYJ20170302151123005"],"award-info":[{"award-number":["JCYJ20170302151123005"]}]},{"name":"of ucation of Guangdong Province","award":["2015KQNCX142"],"award-info":[{"award-number":["2015KQNCX142"]}]},{"name":"Natural Science Foundation of Shenzhen University","award":["2016020"],"award-info":[{"award-number":["2016020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).<\/jats:p>","DOI":"10.3390\/s18010157","type":"journal-article","created":{"date-parts":[[2018,1,8]],"date-time":"2018-01-08T12:26:02Z","timestamp":1515414362000},"page":"157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":217,"title":["Gas Classification Using Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Pai","family":"Peng","sequence":"first","affiliation":[{"name":"School of Electronic Science and Technology, Shenzhen University, Shenzhen 518060, China"},{"name":"Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9965-3516","authenticated-orcid":false,"given":"Xiaojin","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Technology, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Technology, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,8]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review","volume":"3","author":"Kiani","year":"2016","journal-title":"J. Appl. Res. Med. Aromat. Plants"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.snb.2009.12.027","article-title":"Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview","volume":"146","author":"Romain","year":"2010","journal-title":"Sens. Actuators B Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19979","DOI":"10.3390\/s141119979","article-title":"Electronic noses for environmental monitoring applications","volume":"14","author":"Capelli","year":"2014","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0925-4005(03)00248-X","article-title":"Transient response analysis for temperature-modulated chemoresistors","volume":"93","author":"Powar","year":"2003","journal-title":"Sens. Actuators B Chem."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, J., Sun, Z., and Chen, Y. (2016). Fault detection using the clustering-kNN rule for gas sensor arrays. Sensors, 28.","DOI":"10.3390\/s16122069"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1109\/JSEN.2005.858926","article-title":"Fast and robust gas identification system using an integrated gas sensor technology and Gaussian mixture models","volume":"5","author":"Belhouari","year":"2005","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/S0003-2670(97)00202-X","article-title":"Neural network based electronic nose for the classification of aromatic species","volume":"348","author":"Brezmes","year":"1997","journal-title":"Anal. Chim. Acta"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Omatul, S., and Yano, M. (2015, January 24\u201326). Mixed Odors Classification by Neural Networks. Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Warsaw, Poland.","DOI":"10.1109\/IDAACS.2015.7340723"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8138","DOI":"10.1109\/ACCESS.2016.2619181","article-title":"MLP neural network based gas classification system on Zynq SoC","volume":"4","author":"Zhai","year":"2016","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.snb.2006.05.033","article-title":"Probabilistic support vector machines for multi-class alcohol identification","volume":"122","author":"Acevedo","year":"2007","journal-title":"Sens. Actuators B Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1515\/mms-2015-0039","article-title":"Determination of gas mixture components using fluctuation enhanced sensing and the LS-SVM regression algorithm","volume":"3","author":"Lentka","year":"2015","journal-title":"Metrol. Meas. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.snb.2009.04.030","article-title":"Classification of data from electronic nose using relevance vector machines","volume":"140","author":"Wang","year":"2009","journal-title":"Sens. Actuators B Chem."},{"key":"ref_14","unstructured":"Langkvist, M., and Loutfi, A. (2011, January 12\u201317). Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood. Proceedings of the NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1002\/int.21731","article-title":"Gas recognition under sensor drift by using deep learning","volume":"30","author":"Liu","year":"2015","journal-title":"Int. J. Intell. Syst."},{"key":"ref_16","unstructured":"(2017, September 13). Available online: http:\/\/www.image-net.org\/."},{"key":"ref_17","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"1","author":"Krizhevsky","year":"2012","journal-title":"NIPS"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zeiler, D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks, in: Computer Vision. Proceedings of the 13th European Conference in Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, ICML, Lille, France."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.snb.2012.01.074","article-title":"Chemical gas sensor drift compensation using classifier ensembles","volume":"9","author":"Vergara","year":"2012","journal-title":"Sens. Actuators B Chem."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/157\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:50:30Z","timestamp":1760194230000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,8]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["s18010157"],"URL":"https:\/\/doi.org\/10.3390\/s18010157","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,8]]}}}