[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119709-en":3,"doc-seo-119709-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119709,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",6,"Technology","Smart Gas Sensors - Materials, Technologies, Practical Applications, and Use of Machine Learning","The electronic nose (E-nose) integrates gas sensor arrays with machine learning to detect and identify volatile compounds through distinct signal fingerprints and pattern recognition. Its portable, non-invasive operation supports broad use across food quality control, health management and disease diagnosis, water and air quality monitoring, and toxic gas leakage detection. The review surveys gas sensor array fabrication technologies and the E-nose operational framework, emphasizing signal pre-processing, feature extraction and selection, and machine learning methods (SVM, kNN, ANN, Random Forests) for gas type and concentration estimation, while also outlining applications and future directions for tackling key challenges.","J. Appl. Comput. Mech., 9(3) (2023) 775-803 DOI: 10.22055/jacm.2023.41985.3851  \nISSN: 2383-4536 [jacm.scu.ac.ir](jacm.scu.ac.ir)  \nShahid Chamran University ofAhvaz  \nJournal of  \nApplied and Computational Mechanics  \n􀀁􀀂􀀃􀀄􀀂􀀅 􀀇􀀈􀀉􀀂􀀊  \n􀀂􀀃􀀄􀀅􀀆 􀀇􀀄􀀈 􀀂􀀉􀀊􀀈􀀋􀀅􀀈􀀌 􀀍􀀄􀀆􀀉􀀅􀀎􀀄􀀏􀀈􀀐 􀀑􀀉􀀒􀀓􀀊􀀋􀀏􀀋􀀔􀀎􀀉􀀈􀀐 􀀕􀀅􀀄􀀒􀀆􀀎􀀒􀀄􀀏􀀖􀀗􀀗􀀏􀀎􀀒􀀄􀀆􀀎􀀋􀀊􀀈􀀐 􀀄􀀊􀀘 􀀙􀀈􀀉 􀀋􀀚 􀀍􀀄􀀒􀀓􀀎􀀊􀀉 􀀛􀀉􀀄􀀅􀀊􀀎􀀊􀀔 􀀜 􀀖 􀀝􀀉􀀞􀀎􀀉  \n􀀋􀀌􀀍􀀎􀀈 􀀏􀀈􀀐􀀑􀀒􀀒􀀓􀀔􀀕 􀀏􀀂􀀐􀀓􀀄 􀀖􀀐􀀒􀀑􀀑􀀂􀀑􀀗􀀕 􀀘􀀄􀀂􀀓 􀀙􀀈􀀐􀀊􀀒􀀌􀀎􀀚  \n1 Engineering Systems Management Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates, Email: [lubnasmahmood@gmail.com](lubnasmahmood@gmail.com)  \n2 Department of Mechanical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates, [Email: mghommem@aus.edu](Email: mghommem@aus.edu)  \n3 Department of Industrial Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates, Email: [zbahroun@aus.edu](zbahroun@aus.edu)  \nReceived September 26 2022; Revised December 13 2022; Accepted for publication January 10 2023. Corresponding author: M. Ghommem ([mghommem@aus.edu](mghommem@aus.edu))  \n© 2023 Published by Shahid Chamran University ofAhvaz  \n􀀛􀀍􀀜􀀝􀀊􀀈􀀞􀀝 The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses.  \nK􀀂y􀀅􀀒􀀊􀀓􀀜: Gas sensor arrays; E-nose; Disease diagnosis; Leakage detection; Machine learning; Volatile organic compounds.  \n􀀔 I􀀎􀀝􀀊􀀒􀀓􀀌􀀞􀀝􀀄􀀒􀀎  \nOver the last few years, gas sensing technology has garnered immense popularity for its wide-range applications in several industries. Gas sensors help in identifying and detecting various chemical compounds and are used for several applications. These include:  \n􀀁 Quality control in the food industry such as evaluating the freshness of meat [1] and detecting toxins in packaged food [2] .  \n􀀁 Air quality control such as detecting radon concentration [3] and indoor air quality monitoring [4, 5] .  \n􀀁 Disease diagnosis such as liver disease detection [6] and Alzheimer’s detection [7] .  \n􀀁 Gas leakage detection [8, 9] .  \n􀀁 Hazard detection such as in livestock environment [10] .  \nThese sensors typically operate to generate an outcome usually in the form of an electrical signal that provides relevant information about the target gas. Owing to their contribution in numerous industries, gas sensors are required to be small and costeffective so that they can be easily integrated with other systems. This allows them to properly identi","cbCaimTdeIRxi5YC","https://ap.wps.com/l/cbCaimTdeIRxi5YC","pdf",5035049,1,29,"English","en",105,"# Introduction\n## Applications of gas sensors and E-nose\n## Performance metrics for gas sensing\n## Challenges in gas sensing\n# E-nose system overview\n## Overall operation using a gas sensor array\n# Sensor array fabrication and operational framework\n# Signal pre-processing and machine learning methods\n## Feature extraction and feature selection\n## Algorithms for classification and concentration estimation\n# Applications of E-nose\n## Disease diagnosis and health management\n## Air and water quality monitoring\n## Food quality assessment and VOC concentration estimation\n# Challenges, mitigation strategies, and future work","[{\"question\":\"What is the electronic nose (E-nose) and how does it work?\",\"answer\":\"An E-nose combines gas sensor arrays with machine learning. Each volatile compound produces a characteristic signal fingerprint, and algorithms use these patterns to detect and identify the target gas.\"},{\"question\":\"Which key metrics are used to evaluate gas sensor performance?\",\"answer\":\"Gas sensor performance is assessed using selectivity, sensitivity, stability, and response time. These metrics determine how specifically the sensor responds, how strongly it reacts, how reliably it repeats over time, and how quickly it produces a usable signal.\"},{\"question\":\"What challenges do gas sensors and E-noses face in real environments?\",\"answer\":\"Key challenges include cross selectivity, where interfering gases affect detection, and low sensitivity influenced by factors like temperature and humidity. The review also discusses additional difficulties and proposes approaches for future improvements.\"}]","Smart Gas Sensors - Materials, Technologies, Practical Applications, and Use of Machine Learning | PDF",1785725905,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"smart-gas-sensors-materials-technologies-practical-applications-and-use-of-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/smart-gas-sensors-materials-technologies-practical-applications-and-use-of-machine-learning/119709/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the electronic nose (E-nose) and how does it work?","Question",{"text":75,"@type":76},"An E-nose combines gas sensor arrays with machine learning. Each volatile compound produces a characteristic signal fingerprint, and algorithms use these patterns to detect and identify the target gas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which key metrics are used to evaluate gas sensor performance?",{"text":80,"@type":76},"Gas sensor performance is assessed using selectivity, sensitivity, stability, and response time. These metrics determine how specifically the sensor responds, how strongly it reacts, how reliably it repeats over time, and how quickly it produces a usable signal.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges do gas sensors and E-noses face in real environments?",{"text":84,"@type":76},"Key challenges include cross selectivity, where interfering gases affect detection, and low sensitivity influenced by factors like temperature and humidity. 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