The Future is Smelling: Exploring the Potential of e-Nose

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Year : 2025 | Volume :12 | Issue : 01 | Page : –
    By

    Dr. Kazi Kutubuddin Sayyad Liyakat,

  1. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

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The human sense of smell, a complex and often underestimated faculty, allows us to perceive the world through volatile organic compounds (VOCs). This ability to detect and differentiate between aromas, flavors, and even potential hazards has inspired the development of the electronic nose, or e-nose. More than just a novelty, e-noses are rapidly becoming sophisticated analytical tools with applications that span across numerous industries. By mimicking the biological processes of olfaction, these devices are opening up new avenues for quality control, environmental monitoring, and even disease diagnosis. At their core, e-noses are devices that use an array of chemical sensors to detect and analyze the “smellprint” of a sample. Unlike human noses which rely on olfactory receptors in the nasal cavity, e-noses employ a variety of sensors that react to different chemical compounds. These sensors, often made of materials like conducting polymers, metal oxides, or quartz crystal microbalances, generate unique electrical signals upon interacting with VOCs. This collective response forms a distinct pattern, which is then analyzed by sophisticated software using pattern recognition algorithms. This pattern is the e-nose’s way of “smelling.” The brilliance of e-noses doesn’t just stem from their ability to detect VOCs; it lies in their capacity to learn and differentiate between complex mixtures of these compounds. Through training with known samples, the e-nose can learn to identify specific odors and their corresponding chemical profiles. This capability makes them incredibly versatile, applicable across a spectrum of fields.

Keywords: Sensors, e-Nose, senses of smell, Sensor array, pattern recognition

[This article belongs to Journal of Semiconductor Devices and Circuits (josdc)]

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Dr. Kazi Kutubuddin Sayyad Liyakat. The Future is Smelling: Exploring the Potential of e-Nose. Journal of Semiconductor Devices and Circuits. 2025; 12(01):-.
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Dr. Kazi Kutubuddin Sayyad Liyakat. The Future is Smelling: Exploring the Potential of e-Nose. Journal of Semiconductor Devices and Circuits. 2025; 12(01):-. Available from: https://journals.stmjournals.com/josdc/article=2025/view=0

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References

[1]. Liyakat, K.K.S. (2024). Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore.https://doi.org/10.1007/978-981-99-3932-9_12 available at: https://link.springer.com/chapter/10.1007/978-981-99-3932-9_12
[2]. M Pradeepa, et al. (2022). Student Health Detection using a Machine Learning Approach and IoT, 2022 IEEE 2nd Mysore sub section International Conference (MysuruCon), 2022. Available at: https://ieeexplore.ieee.org/document/9972445
[3]. K. K. S. Liyakat. (2023).Detecting Malicious Nodes in IoT Networks Using Machine Learning and Artificial Neural Networks, 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1-5, doi:10.1109/ESCI56872.2023.10099544. Available at: https://ieeexplore.ieee.org/document/10099544/
[4]. K. Kasat, N. Shaikh, V. K. Rayabharapu, M. Nayak. (2023). Implementation and Recognition of Waste Management System with Mobility Solution in Smart Cities using Internet of Things, 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2023, pp. 1661-1665, doi: 10.1109/ICAISS58487.2023.10250690 . Available at: https://ieeexplore.ieee.org/document/10250690/
[5]. Liyakat, K.K.S. (2023). Machine Learning Approach Using Artificial Neural Networks to Detect Malicious Nodes in IoT Networks. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_3
[6]. Kazi, K. (2024a). AI-Driven IoT (AIIoT) in Healthcare Monitoring. In T. Nguyen & N. Vo (Eds.), Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 77-101). IGI Global. https://doi.org/10.4018/979-8-3693-0639-0.ch003 available at: https://www.igi-global.com/chapter/ai-driven-iot-aiiot-in-healthcare-monitoring/336693
[7]. Kazi, K. (2024b). Modelling and Simulation of Electric Vehicle for Performance Analysis: BEV and HEV Electrical Vehicle Implementation Using Simulink for E-Mobility Ecosystems. In L. D., N. Nagpal, N. Kassarwani, V. Varthanan G., & P. Siano (Eds.), E-Mobility in Electrical Energy Systems for Sustainability (pp. 295-320). IGI Global.https://doi.org/10.4018/979-8-3693-2611-4.ch014 Available at: https://www.igi-global.com/gateway/chapter/full-text-pdf/341172
[8]. Kazi, K. S. (2024a). Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications. In M. Garcia & R. de Almeida (Eds.), Transformative Approaches to Patient Literacy and Healthcare Innovation (pp. 112-135). IGI Global. https://doi.org/10.4018/979-8-3693-3661-8.ch006 Available at: https://www.igi-global.com/chapter/computer-aided-diagnosis-in-ophthalmology/342823
[9]. Prashant K Magadum (2024). Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems, Grenze International Journal of Engineering and Technology, Jan Issue, Vol 10, Issue 1, pp. 2074-2080. Grenze ID: 01.GIJET.10.1.4_1 Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8
[10]. Priya Mangesh Nerkar, BhagyarekhaUjjwalganeshDhaware. (2023). Predictive Data Analytics Framework Based on Heart Healthcare System (HHS) Using Machine Learning, Journal of Advanced Zoology, 2023, Volume 44, Special Issue -2, Page 3673:3686. Available at: https://jazindia.com/index.php/jaz/article/view/1695
[11]. P. Neeraja, R. G. Kumar, M. S. Kumar, K. K. S. Liyakat and M. S. Vani. (2024), DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring. 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), Greater Noida, India, 2024, pp. 589-594, doi: 10.1109/IC2PCT60090.2024.10486714. Available at: https://ieeexplore.ieee.org/document/10486714
[12]. Kazi Kutubuddin Sayyad Liyakat, (2024). Explainable AI in Healthcare. In: Explainable Artificial Intelligence in healthcare System, editors: A. Anitha Kamaraj, Debi Prasanna Acharjya. ISBN: 979-8-89113-598-7. DOI: https://doi.org/10.52305/GOMR8163
[13]. Liyakat Kazi, K. S. (2024). ChatGPT: An Automated Teacher’s Guide to Learning. In R. Bansal, A. Chakir, A. Hafaz Ngah, F. Rabby, & A. Jain (Eds.), AI Algorithms and ChatGPT for Student Engagement in Online Learning (pp. 1-20). IGI Global. https://doi.org/10.4018/979-8-3693-4268-8.ch001
[14]. C. Veena, M. Sridevi, K. K. S. Liyakat, B. Saha, S. R. Reddy and N. Shirisha,(2023). HEECCNB: An Efficient IoT-Cloud Architecture for Secure Patient Data Transmission and Accurate Disease Prediction in Healthcare Systems, 2023 Seventh International Conference on Image Information Processing (ICIIP), Solan, India, 2023, pp. 407-410, doi: 10.1109/ICIIP61524.2023.10537627. Available at: https://ieeexplore.ieee.org/document/10537627
[15]. K. Rajendra Prasad, Santoshachandra Rao Karanam (2024). AI in public-private partnership for IT infrastructure development, Journal of High Technology Management Research, Volume 35, Issue 1, May 2024, 100496. https://doi.org/10.1016/j.hitech.2024.100496
[16]. Kazi, K. S. (2024b). IoT Driven by Machine Learning (MLIoT) for the Retail Apparel Sector. In T. Tarnanidis, E. Papachristou, M. Karypidis, & V. Ismyrlis (Eds.), Driving Green Marketing in Fashion and Retail (pp. 63-81). IGI Global. https://doi.org/10.4018/979-8-3693-3049-4.ch004
[17]. Kutubuddin Kazi, (2024a). Machine Learning (ML)-Based Braille Lippi Characters and Numbers Detection and Announcement System for Blind Children in Learning, In Gamze Sart (Eds.), Social Reflections of Human-Computer Interaction in Education, Management, and Economics, IGI Global. https://doi.org/10.4018/979-8-3693-3033-3.ch002
[18]. Kazi, K. S. (2024). Artificial Intelligence (AI)-Driven IoT (AIIoT)-Based Agriculture Automation. In S. Satapathy & K. Muduli (Eds.), Advanced Computational Methods for Agri-Business Sustainability (pp. 72-94). IGI Global. https://doi.org/10.4018/979-8-3693-3583-3.ch005
[19]. Kazi Kutubuddin, (2024c). Vehicle Health Monitoring System (VHMS) by Employing IoT and Sensors, Grenze International Journal of Engineering and Technology, Vol 10, Issue 2, pp- 5367-5374.Grenze ID: 01.GIJET.10.2.429. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3371&id=8
[20]. Kazi Kutubuddin, (2024d). A Novel Approach on ML based Palmistry, Grenze International Journal of Engineering and Technology, Vol 10, Issue 2, pp- 5186-5193. Grenze ID: 01.GIJET.10.2.393. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3344&id=8
[21]. Kazi Kutubuddin, (2024e). IoT based Boiler Health Monitoring for Sugar Industries, Grenze International Journal of Engineering and Technology, Vol 10, Issue 2, pp. 5178 -5185. Grenze ID: 01.GIJET.10.2.392. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3343&id=8
[22]. Kazi, K. S. (2024). Machine Learning-Based Pomegranate Disease Detection and Treatment. In M. Zia Ul Haq & I. Ali (Eds.), Revolutionizing Pest Management for Sustainable Agriculture (pp. 469-498). IGI Global. https://doi.org/10.4018/979-8-3693-3061-6.ch019
[23]. Liyakat. (2025). IoT Technologies for the Intelligent Dairy Industry: A New Challenge. In S. Thandekkattu& N. Vajjhala (Eds.), Designing Sustainable Internet of Things Solutions for Smart Industries (pp. 321-350). IGI Global. https://doi.org/10.4018/979-8-3693-5498-8.ch012
[24]. Liyakat, K. K. (2025). Heart Health Monitoring Using IoT and Machine Learning Methods. In A. Shaik (Ed.), AI-Powered Advances in Pharmacology (pp. 257-282). IGI Global. https://doi.org/10.4018/979-8-3693-3212-2.ch010
[25]. Sayyad. (2025f). AI-Powered-IoT (AIIoT)-Based Decision-Making System for BP Patient’s Healthcare Monitoring: KSK Approach for BP Patient Healthcare Monitoring. In S. Aouadni& I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 205-238). IGI Global.https://doi.org/10.4018/979-8-3693-6502-1.ch008
[26]. Kazi, K. S. (2025c). AI-Driven-IoT (AIIoT)-Based Decision Making in Drones for Climate Change: KSK Approach. In S. Aouadni& I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 311-340). IGI Global. https://doi.org/10.4018/979-8-3693-6502-1.ch011
[27]. Liyakat. (2025d). AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. In L. Özgür Polat & O. Polat (Eds.), AI-Driven Innovation in Healthcare Data Analytics (pp. 277-306). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7277-7.ch009
[28]. Mahant, M. A. (2025). Machine Learning-Driven Internet of Things (MLIoT)-Based Healthcare Monitoring System. In N. Wickramasinghe (Ed.), Digitalization and the Transformation of the Healthcare Sector (pp. 205-236). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9641-4.ch007
[29]. Priya Nerkar and Kazi Sultanabanu, (2024). IoT-Based Skin Health Monitoring System, International Journal of Biology, Pharmacy and Allied Sciences (IJBPAS). 2024, 13(11): 5937-5950. https://doi.org/10.31032/IJBPAS/2024/13.11.8488
[30]. Sayyad (2025e). AI-Powered IoT (AI IoT) for Decision-Making in Smart Agriculture: KSK Approach for Smart Agriculture. In S. Hai-Jew (Ed.), Enhancing Automated Decision-Making Through AI (pp. 67-96). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6230-3.ch003
[31]. Sayyad (2025f). KK Approach to Increase Resilience in Internet of Things: A T-Cell Security Concept. In D. Darwish & K. Charan (Eds.), Analyzing Privacy and Security Difficulties in Social Media: New Challenges and Solutions (pp. 87-120). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9491-5.ch005
[32]. Kazi, K. S. (2025). Machine Learning-Driven Internet of Medical Things (ML-IoMT)-Based Healthcare Monitoring System. In B. Soufiene & C. Chakraborty (Eds.), Responsible AI for Digital Health and Medical Analytics (pp. 49-86). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6294-5.ch003
[33]. Kazi Kutubuddin, (2024c). Vehicle Health Monitoring System (VHMS) by Employing IoT and Sensors, Grenze International Journal of Engineering and Technology,Vol 10, Issue 2, pp- 5367-5374. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3371&id=8
[34]. Kazi Kutubuddin, (2024e). A Novel Approach on ML based Palmistry, Grenze International Journal of Engineering and Technology,Vol 10, Issue 2, pp- 5186-5193. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3344&id=8
[35]. Kazi Kutubuddin, (2024e).IoT based Boiler Health Monitoring for Sugar Industries,Grenze International Journal of Engineering and Technology,Vol 10, Issue 2, pp. 5178 -5185. Available at: https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3343&id=8
[36]. Prashant K Magadum (2024). Machine Learning for Predicting Wind Turbine Output Power in Wind Energy Conversion Systems, Grenze International Journal of Engineering and Technology, Jan Issue, Vol 10, Issue 1, pp. 2074-2080. Grenze ID: 01.GIJET.10.1.4_1 Available at:https://thegrenze.com/index.php?display=page&view=journalabstract&absid=2514&id=8
[37]. Altaf O. Mulani, Arti Vasant Bang, Ganesh B. Birajadar, Amar B. Deshmukh, Hemlata Makarand Jadhav, (2024). IoT Based Air, Water, and Soil Monitoring System for Pomegranate Farming, Annals of Agri-Bio Research. 29 (2): 71-86, 2024.
[38]. Kazi, K. S. (2025a). Transformation of Agriculture Effectuated by Artificial Intelligence-Driven Internet of Things (AIIoT). In J. Garwi, M. Dzingirai, & R. Masengu (Eds.), Integrating Agriculture, Green Marketing Strategies, and Artificial Intelligence (pp. 449-484). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6468-0.ch015
[39]. Sunil B. Mishra (2024e). AI-Driven-IoT (AIIoT) Based Decision-Making in Molten Metal Processing, Journal of Industrial Mechanics, 9(2), 45-56.
[40]. Kazi Kutubuddin Sayyad Liyakat, Impact of Nanotechnology on Battlefield Welfare: A Study. International journal of Nanobiotechnology. 2024; 10(02): 19-32p.
[41]. Kazi Sultanabanu Sayyad Liyakat and Kazi Kutubuddin Sayyad Liyakat, Nanosensors in Agriculture Field: A Study. International Journal of Applied Nanotechnology. 2024; 10(02): 12-22p. Available from:https://journalspub.com/publication/ijan-v10i02-11625/
[42]. Kazi Kutubuddin Sayyad Liyakat, Nanotechnology in Space Study. International Journal of Applied Nanotechnology. 2024; 10(02): 39-46p. Available from:https://journalspub.com/publication/ijan-v10i02-11616/
[43]. Dr. Kazi Kutubuddin Sayyad Liyakat. (2024). KSK Approach to Smart Agriculture: Utilizing AI-Driven Internet of Things (AI IoT). Journal of Microcontroller Engineering and Applications. 2024; 11(03):21-32.
[44]. Kazi Kutubuddin Sayyad Liyakat. (2024). Microwave Communication in the Internet of Things: A Study. Journal of RF and Microwave Communication Technologies, 38–49. Retrieved from https://matjournals.net/engineering/index.php/JoRFMCT/article/view/1276
[45]. Kazi Kutubuddin Sayyad Liyakat, (2023). Nanorobotics: A Review, International Journal of Applied Nanotechnology (IJAN), 9(2), pp. 36 – 43. DOI: https://doi.org/10.37628/ijan.v9i2.1019
[46]. Kazi Kutubuddin Sayyad Liyakat. (2024). Internet of Battlefield Things: An IoBT-inspired Battlefield of Tomorrow. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(3): 11–19p.
[47]. Kazi Kutubuddin Sayyad Liyakat (2024). Nanotechnology in Medical Applications: A Study. Nano Trends: A Journal of Nanotechnology and Its Applications. 2024; 26(2): 1–11p.
[48]. Kazi Kutubuddin Sayyad Liyakat. (2024). Nanotechnology in BattleField: A Study. Journal of Nanoscience, Nanoengineering & Applications. 2024; 14(2): 18–30p.
[49]. Sultananbanu Sayyad Liyakat Kazi, (2024). Polymer Applications in Energy Generation and Storage: A Forward Path. Journal of Nanoscience, Nanoengineering & Applications. 2024; 14(2): 31–39p.
[50]. Kazi Kutubuddin Sayyad Liyakat, (2024). Review of Biopolymers in Agriculture Application: An Eco-Friendly Alternative. International Journal of Composite and Constituent Materials. 2024; 10(1): 50–62p.
[51]. K K Sayyad Liyakat. (2024). Impact of Nanotechnology on Battlefield Welfare: A Study. International Journal of Nanobiotechnology. 2024; 10(2): 19– 32p.
[52]. Sultanabanu Sayyad Liyakat, (2024q). Nanotechnology in Healthcare Applications: A Study. International Journal of Nanobiotechnology. 2024; 10(2): 48–58p.
[53]. Upadhyaya, Ajay N. and Surekha, Chintolla and Malathi, P. and Suresh, G. and Suriyan, Kannadhasan, Pioneering Cognitive Computing for Transformative Healthcare Innovations (November 15, 2024). Available at SSRN: https://ssrn.com/abstract=5086894 or http://dx.doi.org/10.2139/ssrn.5086894
[54]. Shinde, S. S., Nerkar, P. M., Kazi, S. S., & Kazi, V. S. (2025). Machine Learning for Brand Protection: A Review of a Proactive Defense Mechanism. In M. Khan & M. Amin Ul Haq (Eds.), Avoiding Ad Fraud and Supporting Brand Safety: Programmatic Advertising Solutions (pp. 175-220). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7041-4.ch007
[55]. Mulani AO, Liyakat KKS, Warade NS, et al. ML-powered Internet of Medical Things Structure for Heart Disease Prediction. Journal of Pharmacology and Pharmacotherapeutics. 2025;0(0). doi:10.1177/0976500X241306184

Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 20/01/2025
Accepted 30/01/2025
Published 08/02/2025
88