Component analysis of gas mixtures by THz spectra based on machine learning with training on mixtures or individual components data
Author:
Funder
National Research Center “Kurchatov Institute”
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12596-024-02182-5.pdf
Reference12 articles.
1. Y. Jiang, G. Li, H. Ge, F. Wang, L. Li, X. Chen, Y. Zhang, Machine learning and application in terahertz technology: a review on achievements and future challenges. IEEE Access. 10, 53761 (2022). https://doi.org/10.1109/ACCESS.2022.3174595
2. H. Yan, W.H. Fan, C. Qin, X.Q. Jiang, Y.M. Zhang, Fingerprint terahertz spectroscopy combined with machine learning for multicomponent mixture analysis. Vib. Spectrosc. 128, 103581 (2023). https://doi.org/10.1016/j.vibspec.2023.103581
3. X. Zhang, S. Lu, Y. Liao, Z. Zhang, Simultaneous determination of amino acid mixtures in cereal by using terahertz time domain spectroscopy and chemometrics. Chemometr. Intell. Lab. Syst. 164, 8–15 (2017). https://doi.org/10.1016/j.chemolab.2017.03.001
4. D. Suhandy, T. Suzuki, Y. Ogawa, N. Kondo, H. Naito, T. Ishihara, W. Liu, A quantitative study for determination of glucose concentration using attenuated total reflectance terahertz (ATR-THz) spectroscopy. Eng. Agric. Environ. Food. 5(3), 90–95 (2012). https://doi.org/10.1016/S1881-8366(12)80020-6
5. Y. Li, X.A. Shen, R.L. Ewing, J. Li, Terahertz spectroscopic material identification using approximate entropy and deep neural network. In 2017 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 52–56). IEEE (2017)., https://doi.org/10.1109/NAECON.2017.8268744
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