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Mixed Gas Concentration Inversion Based on the Ultraviolet Absorption Spectrum by a Hierarchical Convolutional Neural Network

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Journal of Applied Spectroscopy Aims and scope

A hierarchical convolutional neural network (CNN) model for mixed gas concentration inversion is proposed. In our experiment, mixtures of SO2, NO2, and NH3 were analyzed. SO2 and NO2 were the detected gases, while NH3 was the interfering gas. For the simulation samples, the average absolute errors were 0.5 and 0.9 ppm for SO2 and NO2, respectively. For the experimental samples, the model performed well when the absorption intensities of components differed by no more than one order of magnitude. Compared with the single-module CNN model without a hierarchical structure, the results demonstrate that the hierarchical structure reduces cross-interference and improves the prediction accuracy to a great extent. We believe that our model will have a promising application in the field of gas detection.

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Authors and Affiliations

  1. Advanced Photonics Center, School of Electronic Science & Engineering at Southeast University, Nanjing, Jiangsu, China

    C. Lu, Y. Bian, X. Hu, S. Jin, Y. Huang & Y. Cui

Authors
  1. C. Lu
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  2. Y. Bian
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  3. X. Hu
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  4. S. Jin
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  5. Y. Huang
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  6. Y. Cui
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Correspondence to C. Lu.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 89, No. 4, p. 591, July–August, 2022.

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Lu, C., Bian, Y., Hu, X. et al. Mixed Gas Concentration Inversion Based on the Ultraviolet Absorption Spectrum by a Hierarchical Convolutional Neural Network. J Appl Spectrosc 89, 751–760 (2022). https://doi.org/10.1007/s10812-022-01421-6

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  • DOI: https://doi.org/10.1007/s10812-022-01421-6

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