Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

Document Type : Review Paper


1 Engineering Systems Management Graduate Program, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates‎

2 Department of Mechanical Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates‎

3 Department of Industrial Engineering, American University of Sharjah, Sharjah, P.O. Box 26666, United Arab Emirates‎


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.


Main Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 

‎[1]‎ Chen, J., Gu, J., Zhang, R., Mao, Y., Tian, S., Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose, Sensors (Switzerland), ‎‎19(3), 2019, 605.‎
‎[2]‎ Matindoust, S., Baghaei-Nejad, M., Abadi, M. H. S., Zou, Z., Zheng, L. R., Food Quality and Safety Monitoring Using Gas Sensor Array in ‎Intelligent Packaging, Sensor Review, 36(2), 2016, 169–183.‎
‎[3]‎ Blanco-Novoa, O., Fernández-Caramés, T. M., Fraga-Lamas, P., Castedo, L., A Cost-Effective IoT System for Monitoring Indoor Radon Gas ‎Concentration, Sensors (Switzerland), 18(7), 2018, 2198.‎
‎[4]‎ He, J., Xu, L., Wang, P., Wang, Q., A High Precise E-Nose for Daily Indoor Air Quality Monitoring in Living Environment, Integration, the VLSI ‎Journal, 58, 2017, 286–294.‎
‎[5]‎ Oosthuizen, D. N., Motaung, D. E., Swart, H. C., Selective Detection of CO at Room Temperature with CuO Nanoplatelets Sensor for Indoor ‎Air Quality Monitoring Manifested by Crystallinity, Applied Surface Science, 466, 2019, 545–553.‎
‎[6]‎ Kao, K.-W. A., Cheng, C.-J., Gwo, S., Yeh, J. A., A Semiconductor Gas System of Healthcare for Liver Disease Detection Using Ultrathin InN-‎Based Sensor, ECS Transactions, 66(7), 2015, 151–157.‎
‎[7]‎ Sonuç Karaboga, M. N., Sezgintürk, M. K., Analysis of Tau-441 Protein in Clinical Samples Using RGO/AuNP Nanocomposite-Supported ‎Disposable Impedimetric Neuro-Biosensing Platform: Towards Alzheimer’s Disease Detection, Talanta, 219, 2020, 121257.‎
‎[8]‎ Guo, K., Yang, P., Guo, D. H., Liu, Y., Gas Leakage Monitoring with Mobile Wireless Sensor Networks, Procedia Computer Science, 154, 2018, 430–‎‎438.‎
‎[9]‎ Joshila Grace, L. K., Sai Teja, K., Sai Kishan Reddy, J. V., A Robotic Platform to Identify Gas Pipe Leakage Using IOT, IOP Conference Series: ‎Materials Science and Engineering, 590(1), 2019, 012048.‎
‎[10]‎ Ashari, I. A., Widodo, A. P., Suryono, S., The Monitoring System for Ammonia Gas (NH3) Hazard Detection in the Livestock Environment ‎Uses Inverse Distance Weight Method, Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019, 2019.‎
‎[11]‎ Hunter, G. W. et al., Editors’ Choice—Critical Review—A Critical Review of Solid State Gas Sensors, Journal of the Electrochemical Society, 167(3), ‎‎2020, 037570.‎
‎[12]‎ Dey, A., Semiconductor Metal Oxide Gas Sensors: A Review, Materials Science and Engineering B: Solid-State Materials for Advanced Technology, ‎‎229, 2018, 206–217.‎
‎[13]‎ Chen, Z., Chen, Z., Song, Z., Ye, W., Fan, Z., Smart Gas Sensor Arrays Powered by Artificial Intelligence, Journal of Semiconductors, 40(11), 2019, ‎‎111601.‎
‎[14]‎ Feng, S. et al., Review on Smart Gas Sensing Technology, Sensors (Switzerland), 19(17), 2019, 3760.‎
‎[15]‎ Gradišek, A. et al., Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning, Sensors ‎‎(Switzerland), 19(23), 2019, 5207.‎
‎[16]‎ Jasinski, G., Influence of Operation Temperature Instability on Gas Sensor Performance, European Microelectronics Packaging Conference, ‎Warsaw, Poland, 2017.‎
‎[17]‎ Song, R. et al., High Selective Gas Sensors Based on Surface Modified Polymer Transistor, Organic Electronics, 91, 2021, 106083.‎
‎[18]‎ Fonollosa, J., Fernández, L., Gutiérrez-Gálvez, A., Huerta, R., Marco, S., Calibration Transfer and Drift Counteraction in Chemical Sensor ‎Arrays Using Direct Standardization, Sensors and Actuators, B: Chemical, 236, 2016, 1044–1053.‎
‎[19]‎ Jafri, R. I., Ramaprabhu, S., Multi Walled Carbon Nanotubes Based Micro Direct Ethanol Fuel Cell Using Printed Circuit Board Technology, ‎International Journal of Hydrogen Energy, 35(3), 2010, 1339–1346.‎
‎[20]‎ Ruhland, B., Becker, T., Müller, G., Gas-Kinetic Interactions of Nitrous Oxides with SnO2 Surfaces, Sensors and Actuators B: Chemical, 50(1), ‎‎1998, 85–94.‎
‎[21]‎ Vergara, A., Vembu, S., Ayhan, T., Ryan, M. A., Homer, M. L., Huerta, R., Chemical Gas Sensor Drift Compensation Using Classifier Ensembles, ‎Sensors and Actuators, B: Chemical, 166–167, 2012, 320–329.‎
‎[22]‎ Mielle, P., Managing Dynamic Thermal Exchanges in Commercial Semiconducting Gas Sensors, Sensors and Actuators B: Chemical, 34, 1996, ‎‎533–538.‎
‎[23]‎ Pashami, S., Lilienthal, A. J., Trincavelli, M., Detecting Changes of a Distant Gas Source with an Array of MOX Gas Sensors, Sensors ‎‎(Switzerland), 12(12), 2012, 16404–16419.‎
‎[24]‎ Gomri, S., Contaret, T., Seguin, J. L., A New Gases Identifying Method with MOX Gas Sensors Using Noise Spectroscopy, IEEE Sensors Journal, ‎‎18(16), 2018, 6489–6496.‎
‎[25]‎ Hübner, M., Simion, C. E., Tomescu-Stanoiu, A., Pokhrel, S., Bârsan, N., Weimar, U., Influence of Humidity on CO Sensing with P-Type CuO ‎Thick Film Gas Sensors, Sensors and Actuators, B: Chemical, 153(2), 2011, 347–353.‎
‎[26]‎ Wu, Y., Yuan, L., Hua, Z., Zhen, D., Qiu, Z., Design and Optimization of Heating Plate for Metal Oxide Semiconductor Gas Sensor, ‎Microsystem Technologies, 25(9), 2019, 3511–3519.‎
‎[27]‎ Park, C. S., Kim, D. H., Shin, B. J., Kim, D. Y., Lee, H. K., Tae, H. S., Conductive Polymer Synthesis with Single-Crystallinity via a Novel ‎Plasma Polymerization Technique for Gas Sensor Applications, Materials, 9(10), 2016, 812.‎
‎[28]‎ Esteves, C. H. A., Iglesias, B. A., Li, R. W. C., Ogawa, T., Araki, K., Gruber, J., New Composite Porphyrin-Conductive Polymer Gas Sensors for ‎Application in Electronic Noses, Sensors and Actuators, B: Chemical, 193, 2014, 136–141.‎
‎[29]‎ Dube, I. et al., Understanding the Electrical Response and Sensing Mechanism of Carbon-Nanotube-Based Gas Sensors, Carbon, 87(C), 2015, ‎‎330–337.‎
‎[30]‎ Ueda, T., Bhuiyan, M. M. H., Norimatsu, H., Katsuki, S., Ikegami, T., Mitsugi, F., Development of Carbon Nanotube-Based Gas Sensors for NOx ‎Gas Detection Working at Low Temperature, Physica E: Low-Dimensional Systems and Nanostructures, 40(7), 2008, 2272–2277.‎
‎[31]‎ Sayago, I. et al., New Sensitive Layers for Surface Acoustic Wave Gas Sensors Based on Polymer and Carbon Nanotube Composites, Sensors ‎and Actuators, B: Chemical, 175, 2012, 67–72.‎
‎[32]‎ van Quy, N., Minh, V. A., van Luan, N., Hung, V. N., van Hieu, N., Gas Sensing Properties at Room Temperature of a Quartz Crystal ‎Microbalance Coated with ZnO Nanorods, Sensors and Actuators, B: Chemical, 153(1), 2011, 188–193.‎
‎[33]‎ Xu, L., Li, T., Gao, X., Wang, Y., A High Heating Efficiency Two-Beam Microhotplate for Catalytic Gas Sensors, 2012 7th IEEE International ‎Conference on Nano/Micro Engineered and Molecular Systems, NEMS 2012, 65–68, 2012.‎
‎[34]‎ Xu, L., Wang, Y., Zhou, H., Liu, Y., Li, T., Wang, Y., Design, Fabrication, and Characterization of a High-Heating-Efficiency 3-D Microheater for ‎Catalytic Gas Sensors, Journal of Microelectromechanical Systems, 21(6), 2012, 1402–1409.‎
‎[35]‎ Wang, L., Metal-Organic Frameworks for QCM-Based Gas Sensors: A Review, Sensors and Actuators, A: Physical, 307, 2020, 111984.‎
‎[36]‎ Drobek, M., Kim, J. H., Bechelany, M., Vallicari, C., Julbe, A., Kim, S. S., MOF-Based Membrane Encapsulated ZnO Nanowires for Enhanced ‎Gas Sensor Selectivity, ACS Applied Materials and Interfaces, 8(13), 2016, 8323–8328.‎
‎[37]‎ Pohle, R., Tawil, A., Davydovskaya, P., Fleischer, M., Metal Organic Frameworks as Promising High Surface Area Material for Work Function ‎Gas Sensors, Procedia Engineering, 25, 2011, 108–111.‎
‎[38]‎ Lochbaum, A., Fedoryshyn, Y., Dorodnyy, A., Koch, U., Hafner, C., Leuthold, J., On-Chip Narrowband Thermal Emitter for Mid-IR Optical Gas ‎Sensing, ACS Photonics, 4(6), 2017, 1371–1380.‎
‎[39]‎ Paliwal, A., Sharma, A., Tomar, M., Gupta, V., Carbon Monoxide (CO) Optical Gas Sensor Based on ZnO Thin Films, Sensors and Actuators, B: ‎Chemical, 250, 2017, 679–685.‎
‎[40]‎ Kornienko, V. v. et al., Machine Learning for Optical Gas Sensing: A Leaky-Mode Humidity Sensor as Example, IEEE Sensors Journal, 20(13), 2020, ‎‎6954–6963.‎
‎[41]‎ Bhopate, D. et al., Fluorescent Chemosensor for Quantitation of Multiple Atmospheric Gases, Journal of Nanomedicine & Nanotechnology, 8(2), ‎‎2017, 1000436.‎
‎[42]‎ Liu, Y. H., Chang, S. J., Lai, L. T., Tu, Y. P., Young, S. J., Aluminum-Doped Zinc Oxide Nanorods and Methyl Alcohol Gas Sensor Application, ‎Microsystem Technologies, 28, 2022, 377–382.‎
‎[43]‎ Choi, K. J., Jang, H. W., One-Dimensional Oxide Nanostructures as Gas-Sensing Materials: Review and Issues, Sensors, 10(4), 2010, 4083–4099.‎
‎[44]‎ Degler, D., Weimar, U., Barsan, N., Current Understanding of the Fundamental Mechanisms of Doped and Loaded Semiconducting Metal-‎Oxide-Based Gas Sensing Materials, ACS Sensors, 4(9), 2019, 2228–2249.‎
‎[45]‎ Collier-Oxandale, A. M., Thorson, J., Halliday, H., Milford, J., Hannigan, M., Understanding the Ability of Low-Cost MOx Sensors to Quantify ‎Ambient VOCs, Atmospheric Measurement Techniques, 12(3), 2019, 1441–1460.‎
‎[46]‎ Ciciotti, F., Baschirotto, A., Buffa, C., Gaggl, R., A MOX Gas Sensors Resistance-to-Digital CMOS Interface with 8-Bits Resolution and 128dB ‎Dynamic Range for Low-Power Consumer Applications, PRIME 2017 - 13th Conference on PhD Research in Microelectronics and Electronics, ‎Proceedings, 21–24, 2017.‎
‎[47]‎ Albert, K. J. et al., Cross-Reactive Chemical Sensor Arrays, Chemical Reviews, 100(7), 2000, 2595–2626.‎
‎[48]‎ Martinez, D., Burgués, J., Marco, S., Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution, Sensors ‎‎(Switzerland), 19(18), 2019, 4029.‎
‎[49]‎ Rebholz, J. et al., Selectivity Enhancement by Using Double-Layer Mox-Based Gas Sensors Prepared by Flame Spray Pyrolysis (FSP), Sensors ‎‎(Switzerland), 16(9), 2016, 1437.‎
‎[50]‎ Bierer, B., Kneer, J., Wöllenstein, J., Palzer, S., MEMS Based Metal Oxide Sensor for Simultaneous Measurement of Gas Induced Changes of ‎the Heating Power and the Sensing Resistance, Microsystem Technologies, 22(7), 2016, 1855–1863.‎
‎[51]‎ Palzer, S., Moretton, E., Ramirez, F. H., Romano-Rodriguez, A., Wöllenstein, J., Nano- and Microsized Metal Oxide Thin Film Gas Sensors, ‎Microsystem Technologies, 14(4–5), 2008, 645–651.‎
‎[52]‎ Faglia, G. et al., Micromachined Gas Sensors for Environmental Pollutants, Microsystem Technologies, 6(2), 1999, 54–59.‎
‎[53]‎ Gessner, T. et al., Metal Oxide Gas Sensor for High Temperature Application, Microsystem Technologies, 6, 2000, 169–174.‎
‎[54]‎ Prajesh, R., Goyal, V., Saini, V., Bhargava, J., Sharma, A., Agarwal, A., Development and Reliability Analysis of Micro Gas Sensor Platform on ‎Glass Substrate, Microsystem Technologies, 25(9), 2019, 3589–3597.‎
‎[55]‎ Nambiar, S., Yeow, J. T. W., Conductive Polymer-Based Sensors for Biomedical Applications, Biosensors and Bioelectronics, 26(5), 2011, 1825–‎‎1832.‎
‎[56]‎ Liu, H. et al., Electrically Conductive Polymer Composites for Smart Flexible Strain Sensors: A Critical Review, Journal of Materials Chemistry C, ‎‎6(45), 2018, 12121–12141.‎
‎[57]‎ Holze, R., Wu, Y. P., Intrinsically Conducting Polymers in Electrochemical Energy Technology: Trends and Progress, Electrochimica Acta, 122, ‎‎2014, 93–107.‎
‎[58]‎ Bai, H., Shi, G., Gas Sensors Based on Conducting Polymers, Sensors, 7, 2007, 267–307.‎
‎[59]‎ Zamiri, G., Haseeb, A. S. M. A., Recent Trends and Developments in Graphene/Conducting Polymer Nanocomposites Chemiresistive Sensors, ‎Materials, 13(15), 2020, 3311.‎
‎[60]‎ Pandhi, T., Chandnani, A., Subbaraman, H., Estrada, D., A Review of Inkjet Printed Graphene and Carbon Nanotubes Based Gas Sensors, ‎Sensors (Switzerland), 20(19), 2020, 1–20.‎
‎[61]‎ Tang, R., Shi, Y., Hou, Z., Wei, L., Carbon Nanotube-Based Chemiresistive Sensors, Sensors (Switzerland), 17(4), 2017, 882.‎
‎[62]‎ Akbari, E. et al., Analytical Calculation of Sensing Parameters on Carbon Nanotube Based Gas Sensors, Sensors (Switzerland), 14(3), 2014, 5502–‎‎5515.‎
‎[63]‎ Hoang, N. D., van Cat, V., Nam, M. H., Phan, V. N., Le, A. T., van Quy, N., Enhanced SO2 Sensing Characteristics of Multi-Wall Carbon ‎Nanotubes Based Mass-Type Sensor Using Two-Step Purification Process, Sensors and Actuators, A: Physical, 295, 2019, 696–702.‎
‎[64]‎ Casanova-Cháfer, J., Navarrete, E., Noirfalise, X., Umek, P., Bittencourt, C., Llobet, E., Gas Sensing with Iridium Oxide Nanoparticle ‎Decorated Carbon Nanotubes, Sensors (Switzerland), 19(1), 2019, 113.‎
‎[65]‎ Selvakumar, V. S., Sujatha, L., Fast Response and Recovery of Nano-Porous Silicon Based Gas Sensor, Microsystem Technologies, 26(3), 2020, ‎‎823–834.‎
‎[66]‎ Seals, L., Gole, J. L., Tse, L. A., Hesketh, P. J., Rapid, Reversible, Sensitive Porous Silicon Gas Sensor, Journal of Applied Physics, 91(4), 2002, ‎‎2519–2523.‎
‎[67]‎ Baratto, C. et al., A Novel Porous Silicon Sensor for Detection of Sub-Ppm NO2 Concentrations, Sensors and Actuators, B: Chemical, 77(1–2), ‎‎2001, 62–66.‎
‎[68]‎ Sabdo Yuwono, A., Schulze Lammers, P., Odor Pollution in the Environment and the Detection Instrumentation, Agricultural Engineering ‎International: The CIGR Journal of Scientific Research and Development, 1, 2004, 552.‎
‎[69]‎ Yang, M., He, J., Hu, X., Yan, C., Cheng, Z., CuO Nanostructures as Quartz Crystal Microbalance Sensing Layers for Detection of Trace ‎Hydrogen Cyanide Gas, Environmental Science and Technology, 45(14), 2011, 6088–6094.‎
‎[70]‎ Alev, O., Sarıca, N., Özdemir, O., Arslan, L. Ç., Büyükköse, S., Öztürk, Z. Z., Cu-Doped ZnO Nanorods Based QCM Sensor for Hazardous ‎Gases, Journal of Alloys and Compounds, 826, 2020, 154177.‎
‎[71]‎ Liu, X., Wang, W., Zhang, Y., Pan, Y., Liang, Y., Li, J., Enhanced Sensitivity of a Hydrogen Sulfide Sensor Based on Surface Acoustic Waves at ‎Room Temperature, Sensors (Switzerland), 18(11), 2018, 3796.‎
‎[72]‎ Mariani, E. A., Mariani, E. A., US Patent No. 5325704, 1994.‎
‎[73]‎ Jakubik, W. P., Surface Acoustic Wave-Based Gas Sensors, Thin Solid Films, 520(3), 2011, 986–993.‎
‎[74]‎ Fan, L., Ge, H., Zhang, S. Y., Zhang, H., Zhu, J., Optimization of Sensitivity Induced by Surface Conductivity and Sorbed Mass in Surface ‎Acoustic Wave Gas Sensors, Sensors and Actuators, B: Chemical, 161(1), 2012, 114–123.‎
‎[75]‎ Bhasker Raj, V., Singh, H., Nimal, A. T., Tomar, M., Sharma, M. U., Gupta, V., Effect of Metal Oxide Sensing Layers on the Distinct Detection of ‎Ammonia Using Surface Acoustic Wave (SAW) Sensors, Sensors and Actuators, B: Chemical, 187, 2013, 563–573.‎
‎[76]‎ Lukman Hekiem, N. L. et al., Advanced Vapour Sensing Materials: Existing and Latent to Acoustic Wave Sensors for VOCs Detection as the ‎Potential Exhaled Breath Biomarkers for Lung Cancer, Sensors and Actuators, A: Physical, 329, 2021, 112792.‎
‎[77]‎ Panneerselvam, G., Thirumal, V., Pandya, H. M., Review of Surface Acoustic Wave Sensors for the Detection and Identification of Toxic ‎Environmental Gases/Vapours, Archives of Acoustics, 43(3), 2018, 357–367.‎
‎[78]‎ Karelin, A., Baranov, A. M., Akbari, S., Mironov, S., Karpova, E., Measurement Algorithm for Determining Unknown Flammable Gas ‎Concentration Based on Temperature Sensitivity of Catalytic Sensor, IEEE Sensors Journal, 19(11), 2019, 4173–4180.‎
‎[79]‎ Lee, E. B. et al., Micromachined Catalytic Combustible Hydrogen Gas Sensor, Sensors and Actuators, B: Chemical, 153(2), 2011, 392–397.‎
‎[80]‎ Brauns, E., Morsbach, E., Kunz, S., Bäumer, M., Lang, W., A Fast and Sensitive Catalytic Gas Sensors for Hydrogen Detection Based on ‎Stabilized Nanoparticles as Catalytic Layer, Sensors and Actuators, B: Chemical, 193, 2014, 895–903.‎
‎[81]‎ Hu, Y., Tian, Y., Zhuang, Y., Zhao, C., Wang, F., Rapid Gas Sensing Based on Pulse Heating and Deep Learning, 2021 IEEE 34th International ‎Conference on Micro Electro Mechanical Systems (MEMS), 2021-January, 438–441, 2021.‎
‎[82]‎ Sturm, H., Brauns, E., Seemann, T., Zoellmer, V., Lang, W., A Highly Sensitive Catalytic Gas Sensor for Hydrogen Detection Based on ‎Sputtered Nanoporous Platinum, Procedia Engineering, 5, 2010, 123–126.‎
‎[83]‎ Jaber, N., Ilyas, S., Shekhah, O., Eddaoudi, M., Younis, M. I., Resonant Gas Sensor and Switch Operating in Air with Metal-Organic ‎Frameworks Coating, Journal of Microelectromechanical Systems, 27(2), 2018, 156–163.‎
‎[84]‎ Jaber, N., Ilyas, S., Shekhah, O., Eddaoudi, M., Younis, M. I., Multimode Excitation of a Metal Organics Frameworks Coated Microbeam for ‎Smart Gas Sensing and Actuation, Sensors and Actuators, A: Physical, 283, 2018, 254–262.‎
‎[85]‎ Jaber, N., Ilyas, S., Shekhah, O., Eddaoudi, M., Younis, M. I., Smart Gas Sensing and Actuation Using Multimode of a MOFs Coated Microbeam, ‎Proceedings of IEEE Sensors, 2018-October, 2018.‎
‎[86]‎ Jaber, N., Ilyas, S., Shekhah, O., Eddaoudi, M., Younis, M. I., Simultaneous Sensing of Vapor Concentration and Temperature Utilizing ‎Multimode of a MEMS Resonator, Proceedings of IEEE Sensors, 2018-October, 2018.‎
‎[87]‎ Ghommem, M., Puzyrev, V., Sabouni, R., Najar, F., Deep Learning for Gas Sensing Using MOFs Coated Weakly-Coupled Microbeams, Applied ‎Mathematical Modelling, 105, 2022, 711-728.‎
‎[88]‎ Sepulveda, N., Aslam, D., Sullivan, J. P., Polycrystalline Diamond MEMS Resonator Technology for Sensor Applications, Diamond and Related ‎Materials, 15(2–3), 2006, 398–403.‎
‎[89]‎ Massie, C., Stewart, G., McGregor, G., Gilchrist, J. R., Design of a Portable Optical Sensor for Methane Gas Detection, Sensors and Actuators, B: ‎Chemical, 113(2), 2006, 830–836.‎
‎[90]‎ Jin, W., Ho, H. L., Cao, Y. C., Ju, J., Qi, L. F., Gas Detection with Micro- and Nano-Engineered Optical Fibers, Optical Fiber Technology, 19(6 PART ‎B), 2013, 741–759.‎
‎[91]‎ Garcia-Romeo, D., Fuentes, H., Medrano, N., Calvo, B., Martínez, P. A., Azcona, C., A NDIR-Based CO2 Monitor System for Wireless Sensor ‎Networks, 2012 IEEE 3rd Latin American Symposium on Circuits and Systems, LASCAS 2012 - Conference Proceedings, 2012.‎
‎[92]‎ Tan, Q. et al., Three-Gas Detection System with IR Optical Sensor Based on NDIR Technology, Optics and Lasers in Engineering, 74, 2015, 103–‎‎108.‎
‎[93]‎ Kudo, H. et al., Fiber-Optic Biochemical Gas Sensor (Bio-Sniffer) for Sub-Ppb Monitoring of Formaldehyde Vapor, Sensors and Actuators, B: ‎Chemical, 161(1), 2012, 486–492.‎
‎[94]‎ Zhu, Y., Shi, J., Zhang, Z., Zhang, C., Zhang, X., Development of a Gas Sensor Utilizing Chemiluminescence on Nanosized Titanium Dioxide, ‎Analytical Chemistry, 74(1), 2002, 120–124.‎
‎[95]‎ Lv, Y. Y., Wu, J., Xu, Z. K., Colorimetric and Fluorescent Sensor Constructing from the Nanofibrous Membrane of Porphyrinated Polyimide for ‎the Detection of Hydrogen Chloride Gas, Sensors and Actuators, B: Chemical, 148(1), 2010, 233–239.‎
‎[96]‎ Wang, X. D., Wolfbeis, O. S., Optical Methods for Sensing and Imaging Oxygen: Materials, Spectroscopies and Applications, Chemical Society ‎Reviews, 43(10), 2014, 3666–3761.‎
‎[97]‎ Vafaei, M., Amini, A., Siadatan, A., Breakthrough in CO2 Measurement with a Chamberless NDIR Optical Gas Sensor, IEEE Transactions on ‎Instrumentation and Measurement, 69(5), 2020, 2258–2268.‎
‎[98]‎ Zhang, W., Tian, F., Song, A., Hu, Y., Research on Electronic Nose System Based on Continuous Wide Spectral Gas Sensing, Microchemical ‎Journal, 140, 2018, 1–7.‎
‎[99]‎ Ma, D., Gao, J., Zhang, Z., Zhao, H., Gas Recognition Method Based on the Deep Learning Model of Sensor Array Response Map, Sensors and ‎Actuators, B: Chemical, 330, 2021, 129349.‎
‎[100]‎ Storcheus, D., Rostamizadeh, A., Kumar, S., The 1st International Workshop “Feature Extraction: Modern Questions and Challenges” A ‎Survey of Modern Questions and Challenges in Feature Extraction Google Research Google Research, 2015.‎
‎[101]‎ Patgiri, R., Katari, H., Kumar, R., Sharma, D., Empirical Study on Malicious URL Detection Using Machine Learning, Lecture Notes in Computer ‎Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11319, 2019, 380–388.‎
‎[102]‎ Li, W., Leung, H., Kwan, C., Linnell, B. R., E-Nose Vapor Identification Based on Dempster-Shafer Fusion of Multiple Classifiers, IEEE ‎Transactions on Instrumentation and Measurement, 57(10), 2008, 2273–2282.‎
‎[103]‎ Khalaf, W., Pace, C., Gaudioso, M., Gas Detection via Machine Learning, International Journal Electrical and Computer Engineering, 3(5), 2008, 1–5.‎
‎[104]‎ Yu, H., Wang, J., Yao, C., Zhang, H., Yu, Y., Quality Grade Identification of Green Tea Using E-Nose by CA and ANN, LWT - Food Science and ‎Technology, 41(7), 2008, 1268–1273.‎
‎[105]‎ Li, Q., Gu, Y., Wang, N. F., Application of Random Forest Classifier by Means of a QCM-Based E-Nose in the Identification of Chinese Liquor ‎Flavors, IEEE Sensors Journal, 17(6), 2017, 1788–1794.‎
‎[106]‎ Liu, H., Li, Q., Yan, B., Zhang, L., Gu, Y., Bionic Electronic Nose Based on Mos Sensors Array and Machine Learning Algorithms Used for ‎Wine Properties Detection, Sensors (Switzerland), 19(1), 2019, 45.‎
‎[107]‎ Wakhid, S., Sarno, R., Sabilla, S. I., Maghfira, D. B., Detection and Classification of Indonesian Civet and Non-Civet Coffee Based on ‎Statistical Analysis Comparison Using E-Nose, International Journal of Intelligent Engineering and Systems, 13(4), 2020, 56–65.‎
‎[108]‎ Xue, B., Zhang, M., Browne, W. N., Yao, X., A Survey on Evolutionary Computation Approaches to Feature Selection, IEEE Transactions on ‎Evolutionary Computation, 20(4), 2016, 606–626.‎
‎[109]‎ Khalid, S., Khalil, T., Nasreen, S., A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning, Proceedings of 2014 ‎Science and Information Conference, SAI 2014, 372–378, 2014.‎
‎[110]‎ Postma, E., Van, H. J., Herik, D., van der Maaten, L. J. P., Postma, E. O., van den Herik, H. J., Dimensionality Reduction: A Comparative ‎Review, Journal of Machine Learning Research, 10(1), 2008, 1-35.‎
‎[111]‎ Hira, Z. M., Gillies, D. F., A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data, Advances in ‎Bioinformatics, 2015, 2015, 198363.‎
‎[112]‎ Pashami, S., Lilienthal, A. J., Schaffernicht, E., Trincavelli, M., TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas ‎Sensors, Sensors (Switzerland), 13(6), 2013, 7323–7344.‎
‎[113]‎ Zhang, S., Xie, C., Hu, M., Li, H., Bai, Z., Zeng, D., An Entire Feature Extraction Method of Metal Oxide Gas Sensors, Sensors and Actuators, B: ‎Chemical, 132(1), 2008, 81–89.‎
‎[114]‎ Faleh, R., Othman, M., Gomri, S., Aguir, K., Kachouri, A., A Transient Signal Extraction Method of WO3 Gas Sensors Array to Identify ‎Polluant Gases, IEEE Sensors Journal, 16(9), 2016, 3123–3130.‎
‎[115]‎ Carmel, L., Levy, S., Lancet, D., Harel, D., A Feature Extraction Method for Chemical Sensors in Electronic Noses, Sensors and Actuators, B: ‎Chemical, 93(1–3), 67–76, 2003.‎
‎[116]‎ Ziyatdinov, A., Fonollosa, J., Fernández, L., Gutiérrez-Gálvez, A., Marco, S., Perera, A., Data Set from Gas Sensor Array under Flow ‎Modulation, Data in Brief, 3, 2015, 131–136.‎
‎[117]‎ Effrosynidis, D., Arampatzis, A., An Evaluation of Feature Selection Methods for Environmental Data, Ecological Informatics, 61, 2021, 101224.‎
‎[118]‎ Lee, L. C., Jemain, A. A., On Overview of PCA Application Strategy in Processing High Dimensionality Forensic Data, Microchemical Journal, ‎‎169, 2021, 106608.‎
‎[119]‎ Li, G., Hu, Y., An Enhanced PCA-Based Chiller Sensor Fault Detection Method Using Ensemble Empirical Mode Decomposition Based ‎Denoising, Energy and Buildings, 183, 2019, 311–324.‎
‎[120]‎ Wang, Y., Wu, D., Yuan, X., LDA-Based Deep Transfer Learning for Fault Diagnosis in Industrial Chemical Processes, Computers and Chemical ‎Engineering, 140, 2020, 106964.‎
‎[121]‎ Leng, Y. et al., LDA-Based Data Augmentation Algorithm for Acoustic Scene Classification, Knowledge-Based Systems, 195, 2020, 105600.‎
‎[122]‎ Dimigen, O., Optimizing the ICA-Based Removal of Ocular EEG Artifacts from Free Viewing Experiments, NeuroImage, 207, 2020, 116117.‎
‎[123]‎ Khan, M. A. H., Thomson, B., Debnath, R., Motayed, A., Rao, M. v., Nanowire-Based Sensor Array for Detection of Cross-Sensitive Gases ‎Using PCA and Machine Learning Algorithms, IEEE Sensors Journal, 20(11), 2020, 6020–6028.‎
‎[124]‎ Brems, M., A One-Stop Shop for Principal Component Analysis, Towards Data Science, 2017.‎shop-for-principal-component-analysis-5582fb7e0a9c (accessed Dec. 03, 2022).‎
‎[125]‎ Delgado, D. B., Understanding Principal Component Analysis Once And For All, blukiri, 2018.‎principal-component-analysis-once-and-for-all-9f75e7b33635 (accessed Dec. 03, 2022).‎
‎[126]‎ López, M. M. et al., SVM-Based CAD System for Early Detection of the Alzheimer’s Disease Using Kernel PCA and LDA, Neuroscience Letters, ‎‎464(3), 2009, 233–238.‎
‎[127]‎ Aliyari Ghassabeh, Y., Rudzicz, F., Moghaddam, H. A., Fast Incremental LDA Feature Extraction, Pattern Recognition, 48(6), 2015, 1999–2012.‎
‎[128]‎ Raschka, S., Linear Discriminant Analysis – Bit by Bit, Sebastian Raschka, 2014. ‎‎(accessed Dec. 03, 2022).‎
‎[129]‎ Pinto, L. S. et al., Compression Method of Power Quality Disturbances Based on Independent Component Analysis and Fast Fourier ‎Transform, Electric Power Systems Research, 187, 2020, 106428.‎
‎[130]‎ Sun, L., Liu, Y., Beadle, P. J., Independent Component Analysis of EEG Signals, Proceedings of the 2005 IEEE International Workshop on VLSI Design ‎and Video Technology, IWVDVT 2005, 293–296, 2005.‎
‎[131]‎ Huang, S. H., Supervised Feature Selection: A Tutorial, Artificial Intelligence Research, 4(2), 2015, 22-37.‎
‎[132]‎ Cai, J., Luo, J., Wang, S., Yang, S., Feature Selection in Machine Learning: A New Perspective, Neurocomputing, 300, 2018, 70–79.‎
‎[133]‎ Smialowski, P., Frishman, D., Kramer, S., Pitfalls of Supervised Feature Selection, Bioinformatics, 26(3), 2009, 440–443.‎
‎[134]‎ Song, L., Smola, A., Gretton, A., Borgwardt, K. M., Bedo, J., Supervised Feature Selection via Dependence Estimation, Proceedings of the 24th ‎international conference on Machine learning - ICML ’07, 823–830, 2007.‎
‎[135]‎ Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C. M., Sun, J., Anomaly Detection for a Water Treatment System Using Unsupervised Machine ‎Learning, IEEE International Conference on Data Mining Workshops, ICDMW, 2017-November, 1058–1065, 2017.‎
‎[136]‎ Wang, S., Tang, J., Liu, H., Embedded Unsupervised Feature Selection, AAAI’15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial ‎Intelligence, 470–476, 2015.‎
‎[137]‎ Sheikhpour, R., Sarram, M. A., Gharaghani, S., Chahooki, M. A. Z., A Survey on Semi-Supervised Feature Selection Methods, Pattern ‎Recognition, 64, 2017, 141–158.‎
‎[138]‎ Ang, J. C., Mirzal, A., Haron, H., Hamed, H. N. A., Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene ‎Selection, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(5), 2016, 971–989.‎
‎[139]‎ Tang, J., Alelyani, S., Liu, H., Feature Selection for Classification: A Review, in Data Classification: Algorithms and Applications, 2014, 37–64.‎
‎[140]‎ Kashef, S., Nezamabadi-pour, H., Nikpour, B., Multilabel Feature Selection: A Comprehensive Review and Guiding Experiments, Wiley ‎Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), 2018, e1240.‎
‎[141]‎ Odhiambo Omuya, E., Onyango Okeyo, G., Waema Kimwele, M., Feature Selection for Classification Using Principal Component Analysis and ‎Information Gain, Expert Systems with Applications, 174, 2021, 114765.‎
‎[142]‎ Deng, C. et al., Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework, ‎Sensors (Switzerland), 18(6), 2018, 1909.‎
‎[143]‎ Han, J. S., Lee, S. W., Bien, Z., Feature Subset Selection Using Separability Index Matrix, Information Sciences, 223, 2013, 102–118.‎
‎[144]‎ Saeys, Y., Inza, I., Larrañaga, P., A Review of Feature Selection Techniques in Bioinformatics, Bioinformatics, 23(19), 2007, 2507–2517.‎
‎[145]‎ Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K., KNN Model-Based Approach in Classification, Lecture Notes in Computer Science (including subseries ‎Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2888, 2003, 986–996.‎
‎[146]‎ Begum, S., Chakraborty, D., Sarkar, R., Data Classification Using Feature Selection and KNN Machine Learning Approach, Proceedings - 2015 ‎International Conference on Computational Intelligence and Communication Networks, CICN 2015, 811–814, 2016.‎
‎[147]‎ Chauhan, V. K., Dahiya, K., Sharma, A., Problem Formulations and Solvers in Linear SVM: A Review, Artificial Intelligence Review, 52(2), 2019, ‎‎803–855.‎
‎[148]‎ Marjanovic, M., Kovacevic, M., Bajat, B., Voženílek, V., Landslide Susceptibility Assessment Using SVM Machine Learning Algorithm, ‎Engineering Geology, 123(3), 2011, 225–234.‎
‎[149]‎ Maxwell, A. E., Warner, T. A., Fang, F., Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review, ‎International Journal of Remote Sensing, 39(9), 2018, 2784–2817.‎
‎[150]‎ Sá, J. A. S., Almeida, A. C., Rocha, B. R. P., Mota, M. A. S., Souza, J. R. S., Dentel, L. M., Lightning Forecast Using Data Mining Techniques On ‎Hourly Evolution Of The Convective Available Potential Energy, Congresso Brasileiro de Inteligência Computacional, Fortaleza, Ceará Brazil, 1–5, ‎‎2016.‎
‎[151]‎ Thorson, J., Collier-Oxandale, A., Hannigan, M., Using a Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex ‎Pollutant Mixtures and Identify Likely Sources, Sensors (Switzerland), 19(17), 2019, 3723.‎
‎[152]‎ Tan, J., Xu, J., Applications of Electronic Nose (e-Nose) and Electronic Tongue (e-Tongue) in Food Quality-Related Properties Determination: ‎A Review, Artificial Intelligence in Agriculture, 4, 2020, 104–115.‎
‎[153]‎ Rubesam, A., Machine Learning Portfolios with Equal Risk Contributions: evidence from the Brazilian market, SSRN Electronic Journal, 2019, ‎‎1-55.‎
‎[154]‎ Voyant, C. et al., Machine Learning Methods for Solar Radiation Forecasting: A Review, Renewable Energy, 105, 2017, 569–582.‎
‎[155]‎ Al-Hadidi, M. R., Alarabeyyat, A., Alhanahnah, M., Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm, ‎Proceedings - 2016 9th International Conference on Developments in eSystems Engineering, DeSE 2016, 35–39, 2017.‎
‎[156]‎ Polikar, R., Ensemble Based Systems in Decision Making, IEEE Circuits and Systems Magazine, 6(3), 2006, 21–44.‎
‎[157]‎ Cordón, O., Kazienko, P., Trawiński, B., Special Issue on Hybrid and Ensemble Methods in Machine Learning, New Generation Computing, 29(3), ‎‎2011, 241–244.‎
‎[158]‎ Ardabili, S., Mosavi, A., Varkonyi-Koczy, A. R., Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods, In: ‎Várkonyi-Kóczy, A. (eds) Engineering for Sustainable Future. INTER-ACADEMIA 2019. Lecture Notes in Networks and Systems, Springer, Cham, 2020.‎
‎[159]‎ González, S., García, S., del Ser, J., Rokach, L., Herrera, F., A Practical Tutorial on Bagging and Boosting Based Ensembles for Machine ‎Learning: Algorithms, Software Tools, Performance Study, Practical Perspectives and Opportunities, Information Fusion, 64, 2020, 205–237.‎
‎[160]‎ Abadi, M., Isard, M., Murray, D. G., A Computational Model for TensorFlow an Introduction, MAPL 2017 - Proceedings of the 1st ACM SIGPLAN ‎International Workshop on Machine Learning and Programming Languages, co-located with PLDI 2017, 1–7, 2017.‎
‎[161]‎ Karamchandani, S., Sekhani, B., Nair, K., Shah, K., E-Nose for Shelf-Life Prediction of Climacteric Fruits, 2021 IEEE 4th International Conference ‎on Computing, Power and Communication Technologies, GUCON 2021, 2021.‎
‎[162]‎ Wei, H., Gu, Y., A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a Mos-Based e-‎Nose, Sensors (Switzerland), 20(16), 2020, 1–15.‎
‎[163]‎ Liu, H., Yu, D., Gu, Y., Classification and Evaluation of Quality Grades of Organic Green Teas Using an Electronic Nose Based on Machine ‎Learning Algorithms, IEEE Access, 7, 2019, 172965–172973.‎
‎[164]‎ Wang, Q., Qi, H., Liu, F., Time Series Prediction of E-Nose Sensor Drift Based on Deep Recurrent Neural Network, 2019 Chinese Control ‎Conference (CCC), 3479–3484, 2019.‎
‎[165]‎ Mo, Z., Luo, D., Wen, T., Cheng, Y., Li, X., FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural ‎Network, Sensors (Switzerland), 21(3), 2021, 1–19.‎
‎[166]‎ Paszke, A. et al., Automatic Differentiation in PyTorch, 31st Conference on Neural Information Processing Systems (NIPS), 2017.‎
‎[167]‎ Pedregosa, F. et al., Scikit-Learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2012, 2825–2830.‎
‎[168]‎ Banerjee, M. B., Roy, R. B., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N., Black Tea Classification Employing Feature Fusion of E-Nose and ‎E-Tongue Responses, Journal of Food Engineering, 244, 2019, 55–63.‎
‎[169]‎ Gharpure, D., Kukade, M., Moshayedi, A. J., Kukade, M. V., Gharpure, D. C., Electronic-Nose (E-Nose) for Recognition of Cardamom, Nutmeg ‎and Clove Oil Odor, Electronics and its Interdisciplinary Applications (NC AE IA - 2014) at: Fergusson College, Pune, Maharashtra, 2014.‎
‎[170]‎ Galang, M. G. K., Zarra, T., Naddeo, V., Belgiorno, V., Ballesteros, F., Artificial Neural Network in the Measurement of Environmental Odours ‎by E-Nose, Chemical Engineering Transactions, 68, 2018, 247–252.‎
‎[171]‎ Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H., The WEKA Data Mining Software, ACM SIGKDD Explorations ‎Newsletter, 11(1), 2009, 10–18.‎
‎[172]‎ Han, J., Rodriguze, J. C., Beheshti, M., Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner, Proceedings of the 2008 2nd ‎International Conference on Future Generation Communication and Networking, FGCN 2008 and BSBT 2008: 2008 International Conference on Bio-Science ‎and Bio-Technology, 3, 96–99, 2008.‎
‎[173]‎ Dietz, C., Berthold, M. R., KNIME for Open-Source BioImage Analysis-A Tutorial, In: De Vos, W., Munck, S., Timmermans, JP. (eds) Focus on Bio-‎Image Informatics. Advances in Anatomy, Embryology and Cell Biology, Springer, 2016.‎
‎[174]‎ Sonnenburg, S. et al., The SHOGUN Machine Learning Toolbox, Journal of Machine Learning Research, 11, 2010, 1799–1802.‎
‎[175]‎ Adnan, K. N. A. K. et al., Water Quality Classification and Monitoring Using E-Nose and e-Tongue in Aquaculture Farming, 2014 2nd ‎International Conference on Electronic Design, ICED 2014, 343–346, 2014.‎
‎[176]‎ Jasinski, G., Wozniak, L., Kalinowski, P., Jasinski, P., Evaluation of the Electronic Nose Used for Monitoring Environmental Pollution, 2018 15th ‎International Scientific Conference on Optoelectronic and Electronic Sensors, COE 2018, 2018.‎
‎[177]‎ Jha, S. K., Hayashi, K., A Novel Odor Filtering and Sensing System Combined with Regression Analysis for Chemical Vapor Quantification, ‎Sensors and Actuators, B: Chemical, 200, 2014, 269–287.‎
‎[178]‎ Rodríguez-Aguilar, M. et al., Identification of Breath-Prints for the COPD Detection Associated with Smoking and Household Air Pollution by ‎Electronic Nose, Respiratory Medicine, 163, 2020, 105901.‎
‎[179]‎ Conti, P. P., Andre, R. S., Mercante, L. A., Fugikawa-Santos, L., Correa, D. S., Discriminative Detection of Volatile Organic Compounds Using ‎an Electronic Nose Based on TiO2 Hybrid Nanostructures, Sensors and Actuators, B: Chemical, 344, 2021, 130124.‎
‎[180]‎ Wang, Q. et al., Discrimination of Mutton from Different Sources (Regions, Feeding Patterns and Species) by Mineral Elements in Inner ‎Mongolia, China, Meat Science, 174, 2021, 108415.‎
‎[181]‎ Mahmodi, K., Mostafaei, M., Mirzaee-Ghaleh, E., Detection and Classification of Diesel-Biodiesel Blends by LDA, QDA and SVM Approaches ‎Using an Electronic Nose, Fuel, 258, 2019, 116114.‎
‎[182]‎ Özsandikcioǧlu, Ü., Atasoy, A., Yapici, Ş., Hybrid Sensor Based E-Nose for Lung Cancer Diagnosis, MeMeA 2018 - 2018 IEEE International ‎Symposium on Medical Measurements and Applications, Proceedings, 2018.‎
‎[183]‎ Liu, B. et al., Lung Cancer Detection via Breath by Electronic Nose Enhanced with a Sparse Group Feature Selection Approach, Sensors and ‎Actuators, B: Chemical, 339, 2021, 129896.‎
‎[184]‎ Mohamed, E. I., Khalil, G. I., Abdel-Mageed, S. M., Bayoumi, A. M., Ramadan, H. S., Kotb, M. A., Electronic Noses for Monitoring Benzene ‎Occupational Exposure in Biological Samples of Egyptian Workers, International Journal of Occupational Medicine and Environmental Health, 26(1), ‎‎2013, 165–172.‎
‎[185]‎ Durán Acevedo, C. M., Carrillo Gómez, J. K., Albarracín Rojas, C. A., Academic Stress Detection on University Students during COVID-19 ‎Outbreak by Using an Electronic Nose and the Galvanic Skin Response, Biomedical Signal Processing and Control, 68, 2021, 102756.‎
‎[186]‎ Dragonieri, S. et al., An Electronic Nose in the Discrimination of Patients with Asthma and Controls, Journal of Allergy and Clinical ‎Immunology, 120(4), 2007, 856–862.‎
‎[187]‎ Marom, O., Nakhoul, F., Tisch, U., Shiban, A., Abassi, Z., Haick, H., Gold Nanoparticle Sensors for Detecting Chronic Kidney Disease and ‎Disease Progression, Nanomedicine, 7(5), 2012, 639–650.‎
‎[188]‎ Ezhilan, M., Nesakumar, N., Babu, K. J., Srinandan, C. S., Rayappan, J. B. B., Freshness Assessment of Broccoli Using Electronic Nose, ‎Measurement: Journal of the International Measurement Confederation, 145, 2019, 735–743.‎
‎[189]‎ Sanaeifar, A., Li, X., He, Y., Huang, Z., Zhan, Z., A Data Fusion Approach on Confocal Raman Microspectroscopy and Electronic Nose for ‎Quantitative Evaluation of Pesticide Residue in Tea, Biosystems Engineering, 210, 2021, 206–222.‎
‎[190]‎ Ghasemi-Varnamkhasti, M., Mohammad-Razdari, A., Yoosefian, S. H., Izadi, Z., Siadat, M., Aging Discrimination of French Cheese Types ‎Based on the Optimization of an Electronic Nose Using Multivariate Computational Approaches Combined with Response Surface Method ‎‎(RSM), LWT, 111, 2019, 85–98.‎
‎[191]‎ Li, P., Niu, Z., Shao, K., Wu, Z., Quantitative Analysis of Fish Meal Freshness Using an Electronic Nose Combined with Chemometric ‎Methods, Measurement: Journal of the International Measurement Confederation, 179, 2021, 109484.‎
‎[192]‎ Yin, X., Zhang, L., Tian, F., Zhang, D., Temperature Modulated Gas Sensing E-Nose System for Low-Cost and Fast Detection, IEEE Sensors ‎Journal, 16(2), 2016, 464–474.‎
‎[193]‎ Salhi, L., Silverston, T., Yamazaki, T., Miyoshi, T., Early Detection System for Gas Leakage and Fire in Smart Home Using Machine Learning, ‎‎2019 IEEE International Conference on Consumer Electronics (ICCE), 1–6, 2019.‎
‎[194]‎ Binson, V. A., Subramoniam, M., Design and Development of an E-Nose System for the Diagnosis of Pulmonary Diseases, Acta of ‎Bioengineering and Biomechanics, 23(1), 2021, 35-44.‎
‎[195]‎ le Maout, P. et al., Polyaniline Nanocomposites Based Sensor Array for Breath Ammonia Analysis. Portable e-Nose Approach to Non-Invasive ‎Diagnosis of Chronic Kidney Disease, Sensors and Actuators, B: Chemical, 274, 2018, 616–626.‎
‎[196]‎ Gardner, J. W., Shin, H. W., Hines, E. L., Dow, C. S., An Electronic Nose System for Monitoring the Quality of Potable Water, Sensors and ‎Actuators B: Chemical, 69(3), 2000, 336–341.‎
‎[197]‎ Capone, S., Epifani, M., Quaranta, F., Siciliano, P., Taurino, A., Vasanelli, L., Monitoring of Rancidity of Milk by Means of an Electronic Nose ‎and a Dynamic PCA Analysis, Sensors and Actuators B: Chemical, 78(1–3), 2001, 174-179.‎
‎[198]‎ Zhang, H., Wang, J., Ye, S., Chang, M., Application of Electronic Nose and Statistical Analysis to Predict Quality Indices of Peach, Food and ‎Bioprocess Technology, 5(1), 2012, 65–72.‎
‎[199]‎ Baskar, C., Nesakumar, N., Balaguru Rayappan, J. B., Doraipandian, M., A Framework for Analysing E-Nose Data Based on Fuzzy Set Multiple ‎Linear Regression: Paddy Quality Assessment, Sensors and Actuators, A: Physical, 267, 2017, 200–209.‎
‎[200]‎ Javed, U. et al., Quantification of Gas Concentrations in NO/NO2/C3H8/NH3 Mixtures Using Machine Learning, Sensors and Actuators B: ‎Chemical, 359, 2022, 131589.‎
‎[201]‎ Javed, U., Ramaiyan, K. P., Kreller, C. R., Brosha, E. L., Mukundan, R., Morozov, A. V., Using Sensor Arrays to Decode NOx/NH3/C3H8 Gas ‎Mixtures for Automotive Exhaust Monitoring, Sensors and Actuators, B: Chemical, 264, 2018, 110–118.‎
‎[202]‎ Tsitron, J. et al., Bayesian Decoding of the Ammonia Response of a Zirconia-Based Mixed-Potential Sensor in the Presence of Hydrocarbon ‎Interference, Sensors and Actuators, B: Chemical, 192, 2014, 283–293.‎
‎[203]‎ Wilson, A. D., Review of Electronic-Nose Technologies and Algorithms to Detect Hazardous Chemicals in the Environment, Procedia ‎Technology, 1, 2012, 453–463.‎
‎[204]‎ Sironi, S., Eusebio, L., Capelli, L., Remondini, M., del Rosso, R., Use of an Electronic Nose for Indoor Air Quality Monitoring, Chemical ‎Engineering Transactions, 40(Special Issue), 2014, 73–78.‎
‎[205]‎ Moufid, M., Bouchikhi, B., Tiebe, C., Bartholmai, M., el Bari, N., Assessment of Outdoor Odor Emissions from Polluted Sites Using ‎Simultaneous Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS), Electronic Nose in Conjunction with Advanced ‎Multivariate Statistical Approaches, Atmospheric Environment, 256, 2021, 118449.‎
‎[206]‎ Tsakanikas, P., Karnavas, A., Panagou, E. Z., Nychas, G. J., A Machine Learning Workflow for Raw Food Spectroscopic Classification in a ‎Future Industry, Scientific Reports, 10(1), 2020, 11212.‎
‎[207]‎ Lei, J. et al., Detection of Ammonia Based on a Novel Fluorescent Artificial Nose and Pattern Recognition, Atmospheric Pollution Research, 7(3), ‎‎2016, 431–437.‎
‎[208]‎ Pyatt, F. B., Potential Effects on Human Health of an Ammonia Rich Atmospheric Environment in an Archaeologically Important Cave in ‎Southeast Asia, Occupational and Environmental Medicine, 60(12), 2003, 986–988.‎
‎[209]‎ Liao, Y. H., Hsiao, Y. J., Nagarjuna, Y., Shieh, J. S., The Micro-Electro-Mechanical Systems (MEMS) Gas Sensor with Bilayer SnO2/WO3 Films for ‎Ammonia Detection, Microsystem Technologies, 28, 2022, 287–293.‎
‎[210]‎ Guo, L. et al., Design and Fabrication of Micro-Nano Fusion Gas Sensor Based on Two-Beam Micro-Hotplatform, Microsystem Technologies, 23(7), ‎‎2017, 2699–2705.‎
‎[211]‎ Prajesh, R., Jain, N., Agarwal, A., Low Power Highly Sensitive Platform for Gas Sensing Application, Microsystem Technologies, 22(9), 2016, ‎‎2185–2192.‎
‎[212]‎ Kumar, R., Kumar, P., Kumar, Y., Time Series Data Prediction Using IoT and Machine Learning Technique, Procedia Computer Science, 167, 2020, ‎‎373–381.‎
‎[213]‎ Pace, C., Khalaf, W., Latino, M., Donato, N., Neri, G., E-Nose Development for Safety Monitoring Applications in Refinery Environment, ‎Procedia Engineering, 47, 2012, 1267–1270.‎
‎[214]‎ Nath, S., Dey, A., Pachal, P., Sing, J. K., Sarkar, S. K., Performance Analysis of Gas Sensing Device and Corresponding IoT Framework in Mines, ‎Microsystem Technologies, 27(11), 2021, 3977–3985.‎
‎[215]‎ Rea, H., McAuley, S., Stewart, A., Lamont, C., Roseman, P., Didsbury, P., A Chronic Disease Management Programme Can Reduce Days in ‎Hospital for Patients with Chronic Obstructive Pulmonary Disease, Internal Medicine Journal, 34(11), 2004, 608–614.‎
‎[216]‎ Menke, A., Orchard, T. J., Imperatore, G., Bullard, K. M. K., Mayer-Davis, E., Cowie, C. C., The Prevalence of Type 1 Diabetes in the United ‎States, Epidemiology, 24(5), 2013, 773–774.‎
‎[217]‎ Hegde, P. S., Chen, D. S., Top 10 Challenges in Cancer Immunotherapy, Immunity, 52(1), 2020, 17–35.‎
‎[218]‎ Brinkman, P. et al., Exhaled Volatile Organic Compounds as Markers for Medication Use in Asthma, European Respiratory Journal, 55(2), 2020, ‎‎1900544.‎
‎[219]‎ Chang, J. E. et al., Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Diagnosis Using a Sensor System, Sensors and ‎Actuators, B: Chemical, 255, 2018, 800–807.‎
‎[220]‎ Vajhadin, F., Mazloum-Ardakani, M., Amini, A., Metal Oxide-Based Gas Sensors for the Detection of Exhaled Breath Markers, Medical Devices ‎& Sensors, 4(1), 2020, e10161.‎
‎[221]‎ Li, W., Jia, Z., Xie, D., Chen, K., Cui, J., Liu, H., Recognizing Lung Cancer Using a Homemade E-Nose: A Comprehensive Study, Computers in ‎Biology and Medicine, 120, 2020, 103706.‎
‎[222]‎ Chen, K. et al., Recognizing Lung Cancer and Stages Using a Self-Developed Electronic Nose System, Computers in Biology and Medicine, 131, ‎‎2021, 104294.‎
‎[223]‎ Nawaz, M. S., Shoaib, B., Ashraf, M. A., Intelligent Cardiovascular Disease Prediction Empowered with Gradient Descent Optimization, ‎Heliyon, 7(5), 2021, e06948.‎
‎[224]‎ Tozlu, B. H., Şimşek, C., Aydemir, O., Karavelioglu, Y., A High Performance Electronic Nose System for the Recognition of Myocardial ‎Infarction and Coronary Artery Diseases, Biomedical Signal Processing and Control, 64, 2021, 102247.‎
‎[225]‎ Hong, J. et al., Retinopathy and Risk of Kidney Disease in Persons with Diabetes, Kidney Medicine, 3(5), 2021, 808–815.‎
‎[226]‎ Voss, A. et al., Smelling Renal Dysfunction via Electronic Nose, Annals of Biomedical Engineering, 33(5), 2005, 656–660.‎
‎[227]‎ Saidi, T., Zaim, O., Moufid, M., el Bari, N., Ionescu, R., Bouchikhi, B., Exhaled Breath Analysis Using Electronic Nose and Gas ‎Chromatography–Mass Spectrometry for Non-Invasive Diagnosis of Chronic Kidney Disease, Diabetes Mellitus and Healthy Subjects, Sensors ‎and Actuators, B: Chemical, 257, 2018, 178–188.‎
‎[228]‎ Benedek, P., Lázár, Z., Bikov, A., Kunos, L., Katona, G., Horváth, I., Exhaled Biomarker Pattern Is Altered in Children with Obstructive Sleep ‎Apnoea Syndrome, International Journal of Pediatric Otorhinolaryngology, 77(8), 2013, 1244–1247.‎
‎[229]‎ Liao, Y. H., Shih, C. H., Abbod, M. F., Shieh, J. S., Hsiao, Y. J., Development of an E-Nose System Using Machine Learning Methods to Predict ‎Ventilator-Associated Pneumonia, Microsystem Technologies, 28, 2022, 341–351.‎
‎[230]‎ Kanaparthi, S., Singh, S. G., Discrimination of Gases with a Single Chemiresistive Multi-Gas Sensor Using Temperature Sweeping and ‎Machine Learning, Sensors and Actuators B: Chemical, 348, 2021, 130725.‎
‎[231]‎ Mohd Ali, M., Hashim, N., Abd Aziz, S., Lasekan, O., Principles and Recent Advances in Electronic Nose for Quality Inspection of ‎Agricultural and Food Products, Trends in Food Science and Technology, 99, 2020, 1–10.‎
‎[232]‎ Wojnowski, W., Majchrzak, T., Dymerski, T., Gębicki, J., Namieśnik, J., Portable Electronic Nose Based on Electrochemical Sensors for Food ‎Quality Assessment, Sensors (Switzerland), 17(12), 2017, 2715.‎
‎[233]‎ Kodogiannis, V. S., Application of an Electronic Nose Coupled with Fuzzy-Wavelet Network for the Detection of Meat Spoilage, Food and ‎Bioprocess Technology, 10(4), 2017, 730–749.‎
‎[234]‎ Papadopoulou, O. S., Panagou, E. Z., Mohareb, F. R., Nychas, G. J. E., Sensory and Microbiological Quality Assessment of Beef Fillets Using a ‎Portable Electronic Nose in Tandem with Support Vector Machine Analysis, Food Research International, 50(1), 2013, 241–249.‎
‎[235]‎ Wijaya, D. R., Sarno, R., Zulaika, E., DWTLSTM for Electronic Nose Signal Processing in Beef Quality Monitoring, Sensors and Actuators, B: ‎Chemical, 326, 2021, 128931.‎
‎[236]‎ Qiu, S., Wang, J., The Prediction of Food Additives in the Fruit Juice Based on Electronic Nose with Chemometrics, Food Chemistry, 230, 2017, ‎‎208–214.‎
‎[237]‎ Srivastava, S., Mishra, G., Mishra, H. N., Fuzzy Controller Based E-Nose Classification of Sitophilus Oryzae Infestation in Stored Rice Grain, ‎Food Chemistry, 283, 2019, 604–610.‎
‎[238]‎ Tanaka, F., Magariyama, Y., Miyanoshita, A., Volatile Biomarkers for Early-Stage Detection of Insect-Infested Brown Rice: Isopentenols and ‎Polysulfides, Food Chemistry, 303, 2020, 125381.‎
‎[239]‎ Kumari, L., Jaiswal, P., Tripathy, S. S., Various Techniques Useful for Determination of Adulterants in Valuable Saffron: A Review, Trends in ‎Food Science and Technology, 111, 2021, 301–321.‎
‎[240]‎ Mobasseri, M. et al., Effects of Saffron Supplementation on Glycemia and Inflammation in Patients with Type 2 Diabetes Mellitus: A ‎Randomized Double-Blind, Placebo-Controlled Clinical Trial Study, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 2020, 527–‎‎534.‎
‎[241]‎ Kiani, S., Minaei, S., Ghasemi-Varnamkhasti, M., Integration of Computer Vision and Electronic Nose as Non-Destructive Systems for Saffron ‎Adulteration Detection, Computers and Electronics in Agriculture, 141, 2017, 46–53.‎
‎[242]‎ Kiani, S., Minaei, S., Ghasemi-Varnamkhasti, M., A Portable Electronic Nose as an Expert System for Aroma-Based Classification of Saffron, ‎Chemometrics and Intelligent Laboratory Systems, 156, 2016, 148–156.‎
‎[243]‎ Gradišek, A. et al., Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning, Sensors ‎‎(Switzerland), 19(23), 2019, 5207.‎
‎[244]‎ Wang, Y., Xing, J., Qian, S., Selectivity Enhancement in Electronic Nose Based on an Optimized DQN, Sensors (Switzerland), 17(10), 2017, 2356.‎
‎[245]‎ Zhang, L., Liu, Y., He, Z., Liu, J., Deng, P., Zhou, X., Anti-Drift in E-Nose: A Subspace Projection Approach with Drift Reduction, Sensors and ‎Actuators, B: Chemical, 253, 2017, 407–417.‎
‎[246]‎ Rehman, A. U., Bermak, A., Drift-Insensitive Features for Learning Artificial Olfaction in E-Nose System, IEEE Sensors Journal, 18(17), 2018, ‎‎7173–7182.‎
‎[247]‎ Latha, R. S., Lakshmi, P. K., Electronic Tongue: An Analytical Gustatory Tool, Journal of Advanced Pharmaceutical Technology and Research, 3(1), ‎‎2012, 3–8.‎
‎[248]‎ Apetrei, I. M., Apetrei, C., Voltammetric E-Tongue for the Quantification of Total Polyphenol Content in Olive Oils, Food Research International, ‎‎54(2), 2013, 2075–2082.‎
‎[249]‎ Rodríguez-Méndez, M. L. et al., Electronic Noses and Tongues in Wine Industry, Frontiers in Bioengineering and Biotechnology, 4, 2016, 81.‎
‎[250]‎ Buratti, S., Malegori, C., Benedetti, S., Oliveri, P., Giovanelli, G., E-Nose, e-Tongue and e-Eye for Edible Olive Oil Characterization and Shelf ‎Life Assessment: A Powerful Data Fusion Approach, Talanta, 182, 2018, 131–141.‎