Application of XGBoost in Gas Infrared Spectral Recognition

Mengqi Tao; Jiaxiang Liu; Yue Wu; Zhiqiang Ning; Yonghua Fang

doi:10.3788/AOS202040.0730002

Acta Optica Sinica, Volume. 40, Issue 7, 0730002(2020)

Application of XGBoost in Gas Infrared Spectral Recognition

Mengqi Tao^1,2, Jiaxiang Liu¹, Yue Wu^1,2, Zhiqiang Ning^1,2, and Yonghua Fang^1,2、*

¹Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, Anhui 230031, China

²University of Science and Technology of China, Hefei, Anhui 230026, China

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Figures & Tables(7)

Fig. 1. Flow chart of XGBoost algorithm

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Fig. 2. Schematic of XGBoost algorithm^[13]

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Fig. 3. Comparison before and after spectral pretreatment. (a)(b) Trichloromethane; (c)(d) paraxylene; (e)(f) tetrachloroethylene

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Fig. 4. Flow chart of XGBoost model training

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Table 1. Features for gas spectral data classification

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Table 1. Features for gas spectral data classification

Feature	Meaning
Width	Full width at half maximum of characteristic peak
Kurtosis	Sharpness of characteristic peak
Skewness	Symmetry of characteristic peak
Correlation	Correlation coefficient with standard spectrum on NIST
SNR	Signal to noise ratio of characteristic peak

Table 2. Classification error matrix for three kinds of gases
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Table 2. Classification error matrix for three kinds of gases
Gas name Trichloromethane Paraxylene Tetrachloroethylene
Trichloromethane 887 15 5
Paraxylene 31 814 11
Tetrachloroethylene 9 13 799

Table 3. Classification accuracy for five models
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Table 3. Classification accuracy for five models
Model Accuracy /%
RF 96.35
SVM 79.48
CNN 80.37
FNN 95.61
XGBoost 96.75

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Mengqi Tao, Jiaxiang Liu, Yue Wu, Zhiqiang Ning, Yonghua Fang. Application of XGBoost in Gas Infrared Spectral Recognition[J]. Acta Optica Sinica, 2020, 40(7): 0730002

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