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| Advances in the Application of Machine Learning to the Spectrum
Detection of Gases |
| ZHANG Ying1, 2, ZHANG Chi1, 2, SHI Jian-bo1, 2, LIU Si-si3* |
1. Electric Power Research Institute, State Grid Hubei Electric Power Co., Ltd., Wuhan 430077, China
2. Hubei Key Laboratory of Electric Power Digital and Intelligent Carbon Monitoring and Collaborative Emission Reduction (State Grid Hubei Electric Power Co., Ltd.), Wuhan 430077, China
3. School of Environment/Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China
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Abstract Artificial intelligence (AI) technology has garnered significant attention in spectral detection recently, establishing a data-driven scientific research paradigm. Machine learning (ML) exhibits advantages in exploring correlations within high-dimensional and complex spectral data. It enhances detection efficiency and accuracy, thereby effectively addressing challenges in the spectral detection of gas mixtures. This paper introduces the fundamental principles of ML, common algorithms, and the general workflow for modeling of spectral data. It briefly reviews various spectroscopy-based gas detection techniques with typical applications, and provides an up-to-date overview (since 2020) of ML advancements in mixed-gas spectral detection. ML-driven spectroscopy technologies for gas show great potential across diverse fields, including industrial process control, early cancer diagnosis, environmental monitoring, and earth observation systems. Specifically, this work explores ML applications in qualitative identification and quantitative analysis of gas mixtures, as well as hyperspectral imaging. Finally, the paper discusses critical challenges and prospects for the practical implementation of ML-based gas spectral detection technologies. This review aims to provide novel insights and recommendations for researchers in gas spectroscopy detection and sensing, thereby advancing and expanding ML applications in this field.
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Received: 2025-05-21
Accepted: 2025-08-11
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Corresponding Authors:
LIU Si-si
E-mail: sisi.liu@m.scnu.edu.cn
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