{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:59:10Z","timestamp":1772773150832,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,6]],"date-time":"2018-05-06T00:00:00Z","timestamp":1525564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH4) and carbon monoxide (CO), using an array of SnO2 gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH4 and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.<\/jats:p>","DOI":"10.3390\/s18051446","type":"journal-article","created":{"date-parts":[[2018,5,7]],"date-time":"2018-05-07T03:12:21Z","timestamp":1525662741000},"page":"1446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Least Squares Neural Network-Based Wireless E-Nose System Using an SnO2 Sensor Array"],"prefix":"10.3390","volume":"18","author":[{"given":"Areej","family":"Shahid","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jong-Hyeok","family":"Choi","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abu ul Hassan Sarwar","family":"Rana","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1127-5766","authenticated-orcid":false,"given":"Hyun-Seok","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1088\/0957-0233\/10\/6\/320","article-title":"Non-destructive banana ripeness determination using a neural network-based electronic nose","volume":"10","author":"Llobet","year":"1999","journal-title":"Meas. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.3390\/s6111428","article-title":"Application of Electronic Noses for Disease Diagnosis and Food Spoilage Detection","volume":"6","author":"Casalinuovo","year":"2006","journal-title":"Sensors"},{"key":"ref_3","unstructured":"Hanson, C.W. (2003). Method and System of Diagnosing Intrapulmonary Infection using an Electronic Nose. (6,620,109), U.S. Patent."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1038\/nrmicro823","article-title":"Electronic noses and disease diagnostics","volume":"2","author":"Turner","year":"2004","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/0925-4005(96)01892-8","article-title":"Gas identification using micro gas sensor array and neural-network pattern recognition","volume":"33","author":"Hong","year":"1996","journal-title":"Sens. Actuators B Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0040-6090(01)00987-7","article-title":"Mixed oxides as gas sensors","volume":"391","author":"Zakrzewska","year":"2001","journal-title":"Thin Solid Films"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4497","DOI":"10.1039\/ft9969204497","article-title":"Resolving combustible gas mixtures using gas sensitive resistors with arrays of electrodes","volume":"92","author":"Keith","year":"1996","journal-title":"J. Chem. Soc. Faraday Trans."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24870","DOI":"10.1038\/srep24870","article-title":"Microwave-assisted facile and ultrafast growth of ZnO nanostructures and proposition of alternative microwave-assisted methods to address growth stoppage","volume":"6","author":"Rana","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1038\/179668a0","article-title":"Electric conductivity and catalytic activity of semiconducting oxide catalysts","volume":"179","author":"Bielanski","year":"1957","journal-title":"Nature"},{"key":"ref_10","unstructured":"(2018, January 29). Nissha FIS, Inc.. Available online: http:\/\/www.fisinc.co.jp."},{"key":"ref_11","unstructured":"City Technology Ltd. (2018, January 29). Global Leaders in Gas Sensor Technology. Available online: http:\/\/www.citytech.com."},{"key":"ref_12","unstructured":"(2018, January 29). MicroChem: Innovative Chemical Solutions for MEMS and Microelectronics. Available online: http:\/\/www.microchem.com."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/332337a0","article-title":"The mechanism of operation of tin (IV) oxide carbon monoxide sensors","volume":"332","author":"Harrison","year":"1988","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s002160051490","article-title":"Fundamental and practical aspects in the design of nanoscaled SnO2 gas sensors: A status report","volume":"365","author":"Barsan","year":"1999","journal-title":"Fresenius J. Anal. Chem."},{"key":"ref_15","unstructured":"Ihokura, K., and Watson, J. (1994). The Stannic Oxide Gas Sensor: Principles and Applications, CRC Press."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.progsurf.2005.09.002","article-title":"The surface and materials science of tin oxide","volume":"79","author":"Batzill","year":"2005","journal-title":"Prog. Surf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.3390\/s100302088","article-title":"Metal oxide gas sensors: Sensitivity and influencing factors","volume":"10","author":"Wang","year":"2010","journal-title":"Sensors"},{"key":"ref_18","unstructured":"SparkFun Electronics (2018, January 29). (Model:MQ-4). Available online: https:\/\/cdn.sparkfun.com\/datasheets\/Sensors\/Biometric\/MQ-4%20Ver1.3%20-%20Manual.pdf."},{"key":"ref_19","unstructured":"SparkFun Electronics (2018, January 29). (Model:MQ-7). Available online: https:\/\/cdn.sparkfun.com\/datasheets\/Sensors\/Biometric\/MQ-7%20Ver1.3%20-%20Manual.pdf."},{"key":"ref_20","unstructured":"(2018, January 29). Pololu Robotics and Electronics. Available online: https:\/\/www.pololu.com\/file\/0J309\/MQ2.pdf."},{"key":"ref_21","unstructured":"(2018, January 29). MQ-9 Semiconductor Sensor for CO\/Combustible GAS. Available online: http:\/\/www.haoyuelectronics.com\/Attachment\/MQ-9\/MQ9.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Suematsu, K., Ma, N., Watanabe, K., Yuasa, M., Kida, T., and Shimanoe, K. (2018). Effect of Humid Aging on the Oxygen Adsorption in SnO2 Gas Sensors. Sensors, 18.","DOI":"10.3390\/s18010254"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0039-6028(79)90411-4","article-title":"Interactions of tin oxide surface with O2, H2O and H2","volume":"86","author":"Yamazoe","year":"1979","journal-title":"Surf. Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0039-6028(89)90574-8","article-title":"Adsorption behavior of CO and interfering gases on SnO2","volume":"221","author":"Tamaki","year":"1989","journal-title":"Surf. Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TNN.2002.1000127","article-title":"The multisynapse neural network and its application to fuzzy clustering","volume":"13","author":"Wei","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/JSEN.2005.846186","article-title":"Discrimination between different samples of olive oil using variable selection techniques and modified fuzzy artmap neural networks","volume":"5","author":"Brezmes","year":"2005","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1109\/TIM.2014.2298691","article-title":"Performance study of multilayer perceptrons in a low-cost electronic nose","volume":"63","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/JSEN.2004.827207","article-title":"Remarks on the use of multilayer perceptrons for the analysis of chemical sensor array data","volume":"4","author":"Pardo","year":"2004","journal-title":"IEEE Sens. J."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.aca.2004.07.062","article-title":"Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue","volume":"525","author":"Buratti","year":"2004","journal-title":"Anal. Chim. Acta"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.foodres.2013.01.053","article-title":"Geographical origin identification of propolis using GC\u2013MS and electronic nose combined with principal component analysis","volume":"51","author":"Cheng","year":"2013","journal-title":"Food Res. Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.snb.2004.12.005","article-title":"Classification of electronic nose data with support vector machines","volume":"107","author":"Pardo","year":"2005","journal-title":"Sens. Actuators B Chem."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/S0014-5793(03)01275-4","article-title":"Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines","volume":"555","author":"Peng","year":"2003","journal-title":"FEBS Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1164\/rccm.200409-1184OC","article-title":"Detection of lung cancer by sensor array analyses of exhaled breath","volume":"171","author":"Machado","year":"2005","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ojha, V.K., Dutta, P., Chaudhuri, A., and Saha, H. (2013, January 24\u201326). Study of various conjugate gradient based ann training methods for designing intelligent manhole gas detection system. Proceedings of the IEEE 2013 International Symposium on Computational and Business Intelligence (ISCBI), New Delhi, India.","DOI":"10.1109\/ISCBI.2013.24"},{"key":"ref_37","unstructured":"Srivastava, A.K., Srivastava, S.K., and Shukla, K.K. (2000, January 19\u201322). On the design issue of intelligent electronic nose system. Proceedings of the IEEE International Conference on Industrial Technology 2000, Goa, India."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/S0925-4005(03)00477-5","article-title":"Detection of Volatile Organic Compounds (VOCs) Using SnO2 Gas-Sensor Array and Artificial Neural Network","volume":"96","author":"Srivastava","year":"2003","journal-title":"Sens. Actuators B Chem."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"649","DOI":"10.2478\/mms-2014-0053","article-title":"Differential electronic nose in on-line dynamic measurements","volume":"21","author":"Osowski","year":"2014","journal-title":"Metrol. Meas. Syst."},{"key":"ref_40","first-page":"457","article-title":"Noise measurement set-ups for fluctuations-enhanced gas sensing","volume":"16","author":"Kotarski","year":"2009","journal-title":"Metrol. Meas. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.snb.2013.07.101","article-title":"Engineering approaches for the improvement of conductometric gas sensor parameters. Part 1. Improvement of sensor sensitivity and selectivity (short survey)","volume":"188","author":"Korotcenkov","year":"2013","journal-title":"Sens. Actuators B Chem."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"501","DOI":"10.2478\/mms-2013-0043","article-title":"Efficiency of linear and nonlinear classifiers for gas identification from electrocatalytic gas sensor","volume":"20","author":"Kalinowski","year":"2013","journal-title":"Metrol. Meas. Syst."},{"key":"ref_43","unstructured":"(2018, March 12). Micropik. Available online: http:\/\/www.micropik.com\/PDF\/dht11.pdf."},{"key":"ref_44","unstructured":"Hagan, M.T., Demuth, H.B., and Beale, M.H. (1996). Neural Network Design, PWS Publishing Co."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"16262","DOI":"10.3390\/s121216262","article-title":"Pattern recognition for selective odor detection with gas sensor arrays","volume":"12","author":"Kim","year":"2012","journal-title":"Sensors"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.sigpro.2012.07.001","article-title":"Surface recognition improvement in 3D medical laser scanner using Levenberg\u2013Marquardt method","volume":"93","author":"Sergiyenko","year":"2013","journal-title":"Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jclepro.2015.12.082","article-title":"The optimized artificial neural network model with Levenberg\u2013Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey","volume":"116","author":"Teke","year":"2016","journal-title":"J. Clean Prod."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1016\/j.eswa.2011.08.087","article-title":"Comparing the performance of neural networks developed by using Levenberg\u2013Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process","volume":"39","author":"Mukherjee","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_49","unstructured":"(2018, January 29). Arduino. Available online: https:\/\/www.arduino.cc\/en\/Main\/ArduinoBoardMega2560?setlang=en."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1446\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:29Z","timestamp":1760195009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1446"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,6]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051446"],"URL":"https:\/\/doi.org\/10.3390\/s18051446","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,6]]}}}