ENHANCING THE EFFECTIVENESS OF TOXIC GAS IDENTIFICATION USING A MOS MULTI-SENSOR AND MACHINE LEARNING MODELS

Nguyễn Ngọc Việt; Ninh Thị Như Hoa; Phan Hồng Phước; Nguyễn Văn Hiếu

doi:10.34238/tnu-jst.9592

ENHANCING THE EFFECTIVENESS OF TOXIC GAS IDENTIFICATION USING A MOS MULTI-SENSOR AND MACHINE LEARNING MODELS

About this article

Received: 15/01/24 Revised: 23/02/24 Published: 23/02/24

Authors

1. Nguyen Ngoc Viet , Phenikaa University
2. Ninh Thi Nhu Hoa, Phenikaa University
3. Phan Hong Phuoc, Phenikaa University
4. Nguyen Van Hieu, Phenikaa University

Abstract

An electronic nose is defined as a smart device for the detection and analysis of gases. It typically consists of two main components: an array of sensors (olfactory system) and an intelligent processing unit (brain). This study presents the design of a gas-sensing device utilizing a multi-sensor chip based on metal oxide semiconductors (MOS). Surveys measuring the response to various concentrations of harmful gases, such as NH₃, CO, and NO₂, were conducted. The measurement data indicate that the employed multi-sensor chip with three MOS sensors exhibits excellent selectivity for each gas. This outcome also demonstrates that using a sensor array allows for easier identification of gases compared to using a single sensor. Additionally, several typical machine learning models in artificial intelligence (AI), including PCA, LDA, SVM, DT, and RF, were employed to analyze gas response data. The performance of these models was evaluated based on the accuracy rate of gas sample identification. The results reveal that the utilization of machine learning models has enhanced the efficiency of gas classification, particularly with models such as DT and RF. This research may provide valuable contributions to the design of electronic noses for the analysis of multiple gases in various environmental settings.

Keywords

Selectivity; Machine learning; Multi-sensor; Electronic nose; Toxic gas detection

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References

[1] S. Feng et al., “Review on smart gas sensing technology,” Sensors (Switzerland), vol. 19, no. 17, pp. 1–22, 2019, doi: 10.3390/s19173760.

[2] P. Boeker, “On ‘Electronic Nose’ methodology,” Sensors Actuators, B Chem., vol. 204, pp. 2–17, 2014, doi: 10.1016/j.snb.2014.07.087.

[3] D. Karakaya, O. Ulucan, and M. Turkan, “Electronic Nose and Its Applications: A Survey,” Int. J. Autom. Comput., vol. 17, no. 2, pp. 179–209, 2020, doi: 10.1007/s11633-019-1212-9.

[4] H. Nazemi, A. Joseph, J. Park, and A. Emadi, “Advanced micro-and nano-gas sensor technology: A review,” Sensors (Switzerland), vol. 19, no. 6, 2019, doi: 10.3390/s19061285.

[5] Z. Wu, H. Wang, X. Wang, H. Zheng, Z. Chen, and C. Meng, “Development of electronic nose for qualitative and quantitative monitoring of volatile flammable liquids,” Sensors (Switzerland), vol. 20, no. 7, 2020, doi: 10.3390/s20071817.

[6] J. Zhang et al., “A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases,” Sensors Actuators, B Chem., vol. 326, 2021, Art. no. 128822, doi: 10.1016/j.snb.2020.128822.

[7] V. M. H. Ho et al., “Superior detection and classification of ethanol and acetone using 3D ultra-porous γ-Fe₂O₃ nanocubes-based sensor,” Sensors Actuators B Chem., vol. 362, 2022, Art. no. 131737, doi: 10.1016/j.snb.2022.131737.

[8] V. T. Nguyen et al., “Enhanced NH₃ and H₂ gas sensing with H₂S gas interference using multilayer SnO2/Pt/WO3 nanofilms,” J. Hazard. Mater., vol. 412, 2021, Art. no. 125181, doi: 10.1016/j.jhazmat.2021.125181.

[9] J. Oh et al., “Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature,” Sensors Actuators B Chem., vol. 364, 2022, Art. no. 131894, doi: 10.1016/j.snb.2022.131894.

[10] B. Ehret, K. Safenreiter, F. Lorenz, and J. Biermann, “A new feature extraction method for odour classification,” Sensors Actuators, B Chem., vol. 158, no. 1, pp. 75–88, 2011, doi: 10.1016/j.snb.2011.05.042.

[11] M. A. H. Khan, B. Thomson, R. Debnath, A. Motayed, and M. V. Rao, “Nanowire-Based Sensor Array for Detection of Cross-Sensitive Gases Using PCA and Machine Learning Algorithms,” IEEE Sens. J., vol. 20, no. 11, pp. 6020–6028, 2020, doi: 10.1109/JSEN.2020.2972542.

[12] X. Zhao, Z. Wen, X. Pan, W. Ye, and A. Bermak, “Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network,” IEEE Access, vol. 7, pp. 12630–12637, 2019, doi: 10.1109/ACCESS.2019.2892754.

DOI: https://doi.org/10.34238/tnu-jst.9592

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