Lubna Mahmood, Zied Bahroun, Mehdi Ghommem, Hussam Alshraideh

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E-noses that combine machine learning and gas sensor arrays (GSAs) are widely used for the detection and identification of various gases. GSAs produce signals that provide vital information about the exposed gases for the machine learning algorithms, rendering them indispensable within the smart-gas sensing arena. In this work, we present a detailed assessment of several machine learning techniques employed for the detection of gases and estimation of their concentrations. The modeling and predictive analysis conducted in this paper are based on kNN, ANN, Decision Trees, Random Forests, SVM and other ensembling-based techniques. Predictive models are implemented and tested on three different MoX gas sensor-based experimental datasets as reported in the literature. The assessment includes a delineated analysis of the different models’ performance followed by a detailed comparison against results found in the literature. It highlights factors that play a pivotal role in machine learning for gas sensing and sheds light on the predictive capability of different machine learning approaches applied on experimental GSA datasets.


Gas sensor arrays, E-nose, Machine learning, Feature extraction, Feature selection, Classification, Regression

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Miller, D.R., Akbar, S.A., Morris, P.A., 2014, Nanoscale metal oxide-based heterojunctions for gas sensing: A review, Sensors and Actuators B: Chemical, 204, pp. 250-272.

Chen, X., Wong, C.K.Y., Yuan, C.A., Zhang, G., 2013, Nanowire-based gas sensors, Sensors and Actuators B: Chemical, 177, pp. 178–195.

Park, H.J., Kim, W., Lee, H., Lee, D., Shin, J., Jun, Y., Yun, Y., 2018, Highly flexible, mechanically stable, and sensitive NO2 gas sensors based on reduced graphene oxide nanofibrous mesh fabric for flexible electronics, Sensors and Actuators B: Chemical, 257, pp. 846-852.

Fois, M., Cox, T., Ratcliffe, N., Costello, B., 2021, Rare earth doped metal oxide sensor for the multimodal detection of volatile organic compounds (VOCs), Sensors and Actuators B: Chemical, 330, 129264.

Kazemi, E., Zadeh, D.S., Moshiri, B., 2021, Metal-oxide-semiconductor Sensors Modeling Using Ordered Weighted Averaging (OWA) Operators in Electronic Nose, Measurement, 184, 109932.

El-Shamy, A.G., 2021, New nano-composite based on carbon dots (CDots) decorated magnesium oxide (MgO) nano-particles (CDots@MgO) sensor for high H2S gas sensitivity performance, Sensors and Actuators B: Chemical, 329, 129154.

Yoo, R., Kim, J., Song, M.J., Lee, W., Noh, J.S., 2015, Nano-composite sensors composed of single- walled carbon nanotubes and polyaniline for the detection of a nerve agent simulant gas, Sensors and Actuators B: Chemical, 209, pp. 444-448.

Matindoust, S., Farzi, G., Nejad, M.B., Shahrokhabadi, M.H., 2021, Polymer-based gas sensors to detect meat spoilage: A review, Reactive and Functional Polymers, 165, 104962.

Zhou, Z., Xu, Y., Qiao, C., Liu, L., Jia, Y., 2021, A novel low-cost gas sensor for CO2 detection using polymer-coated fiber Bragg grating, Sensors and Actuators B: Chemical, 332, 129482.

Jakubik, W.P., 2011, Surface acoustic wave-based gas sensors, Thin Solid Films, 520, pp. 986-993.

Nitzsche, L., Goldschmidt, J., Lambrecht, A., Wöllenstein, J., 2021, Two-component gas sensing with MIR dual comb spectroscopy, tm - Technisches Messen, 89, pp. 50-59.

Blanco-Novoa, O., Fernández-Caramés, T.M., Fraga-Lamas, P., Castedo, L., 2018, A cost-effective IoT system for monitoring indoor radon gas concentration, Sensors (Switzerland), 18, 2198.

Chen, J., Gu, J., Zhang, R., Mao, Y., Tian, S., 2019, Freshness evaluation of three kinds of meats based on the electronic nose, Sensors (Switzerland), 19, 605.

Ashari, I.A., Widodo, A.P., Suryono, S., 2019, The Monitoring System for Ammonia Gas (NH3) Hazard Detection in the Livestock Environment uses Inverse Distance Weight Method, 2019 Fourth International Conference on Informatics and Computing (ICIC), pp. 1-6.

Kao, K.A., Cheng, C., Gwo, S., Yeh, J.A., 2015, A Semiconductor Gas System of Healthcare for Liver Disease Detection Using Ultrathin InN-Based Sensor, ECS Transactions, 66, pp. 151-157.

Chen, Z., Chen, Z., Song, Z., Ye, W., Fan, Z., 2019, Smart gas sensor arrays powered by artificial intelligence, Journal of Semiconductors, 40, 111601.

Hunter, G.W., Akbar, S., Bhansali, S., Daniele, M., Erb, P.D., Johnson, K., Liu, C., Miller, D., Oralkan, O., Hesketh, P.J., 2020, Editors’ Choice—Critical Review—A Critical Review of Solid State Gas Sensors, Journal of The Electrochemical Society, 167, 037570.

Fonollosa, J., Fernández, L., Gutiérrez-Gálvez, A., Huerta, R., Marco, S., 2016, Calibration transfer and drift counteraction in chemical sensor arrays using Direct Standardization, Sensors and Actuators B: Chemical, 236, pp. 1044-1053.

Vergara, A., Vembu, S., Ayhan, T., Ryan, M.A., Homer, M.L., Huerta, R., 2012, Chemical gas sensor drift compensation using classifier ensembles, Sensors and Actuators B: Chemical, 166-167, pp. 320-329.

Deng, C., Lv, K., Shi, D., Yang, B., Yu, S., He, Z., Yan, J., 2018, Enhancing the discrimination ability of a gas sensor array based on a novel feature selection and fusion framework, Sensors (Switzerland), 18, 1909.

Hira, Z.M., Gillies, D.F., 2015, A review of feature selection and feature extraction methods applied on microarray data, Advances in Bioinformatics, 2015, 198363.

Geng, A., Moghiseh, A., Redenbach, C., Schladitz, K., 2021, Comparing optimization methods for deep learning in image processing applications, tm - Technisches Messen, 88, pp. 443-453.

Hoffmann, L., Fortmeier, I., Elster, C., 2021, Deep learning for tilted-wave interferometry, tm - Technisches Messen, 89, pp. 33-42.

Adhikari, S., Saha, S., 2014, Multiple classifier combination technique for sensor drift compensation using ANN & KNN, 2014 IEEE International Advance Computing Conference (IACC), pp. 1184-1189.

Rehman, A.U., Bermak, A., 2019, Heuristic random forests (HRF) for drift compensation in electronic nose applications, IEEE Sensors Journal, 19, pp. 1443–1453.

Ma, D., Gao, J., Zhang, Z., Zhao, H., 2021, Gas recognition method based on the deep learning model of sensor array response map, Sensors and Actuators B: Chemical, 330, 129349.

Fu, X., Wang, L., 2003, Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 33, pp. 399–409.

Xue, X., Zhang, M., Browne, W.N., Yao, X., 2016, A Survey on Evolutionary Computation Approaches to Feature Selection, IEEE Transactions on Evolutionary Computation, 20, pp. 606–626.

Sánchez-Maroño, N., Alonso-Betanzos, A., Tombilla-Sanrománm M., 2007, Filter methods for feature selection - A comparative study, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4881, pp. 178–187.

Borowik, P., Adamowicz, L., Tarakowski, R., Siwek, K., Grzywacz, T., 2020, Odor detection using an e-nose with a reduced sensor array, Sensors (Switzerland), 20, 3542.

Vito, S.D., Massera, E., Piga, M., Martinotto, L., Francia, G.D., 2008, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, 129, pp. 750-757.

Pashami, S., Lilienthal, A.J., Schaffernicht, E., Trincavelli, M., 2013, TREFEX: Trend estimation and change detection in the response of MOX gas sensors, Sensors (Switzerland), 13, pp. 7323-7344.

Zhang, S., Xie, C., Hu, M., Li, H., Bai, Z., Zeng, D., 2008, An entire feature extraction method of metal oxide gas sensors, Sensors and Actuators B: Chemical, 132, pp. 81–89.

Fonollosa, J., Rodríguez-Luján, I., Huerta, R., 2015, Chemical gas sensor array dataset, Data in Brief, 3, pp. 85–89.

Destro, R., Matakas, L., Komatsu, W., Ama, N.R.N., 2013, Implementation aspects of adaptive window moving average filter applied to PLLs - Comparative study, 2013 Brazilian Power Electronics Conference, COBEP 2013 – Proceedings, pp. 730–736.

Zhao, Y., He, X., Pecht, M.G., Zhang, J., Zhou, D., 2020, Detection and detectability of intermittent faults based on moving average T2 control charts with multiple window lengths, Journal of Process Control, 92, pp. 296–309.

Burgués, J., Jiménez-Soto, J.M., Marco, S., 2018, Estimation of the limit of detection in semiconductor gas sensors through linearized calibration models, Analytica Chimica Acta, 1013, pp. 13–25.



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