Qualitative and quantitative analysis of mixtures using terahertz spectroscopy combined with machine learning

Ying Wang; Huifang Ma; Hao Ren; Wenyue Guo

doi:10.1117/12.2690216

17 August 2023 Qualitative and quantitative analysis of mixtures using terahertz spectroscopy combined with machine learning

Ying Wang, Huifang Ma, Hao Ren, Wenyue Guo

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Proceedings Volume 12757, 3rd International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2023); 1275703 (2023) https://doi.org/10.1117/12.2690216
Event: 3rd International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2023), 2023, Kunming, China

Abstract

Terahertz spectroscopy has many excellent characteristics. This paper uses terahertz spectroscopy to test glucose, fructose, mannose, three kinds of monosaccharides and three binary mixtures according to different mixing ratios. The results show that terahertz spectroscopy is effective in characterizing in the field of similar substances. Based on the experimental data of fructose and mannose binary mixture components, machine learning methods are used to predict the content of each component in the mixture. The prediction accuracy reaches 99.91%, which proves that our model is reasonable. It further proves that the machine learning method combined with the terahertz spectrum of the mixture has an important application in predicting the content of each component of the mixture.

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Ying Wang, Huifang Ma, Hao Ren, and Wenyue Guo "Qualitative and quantitative analysis of mixtures using terahertz spectroscopy combined with machine learning", Proc. SPIE 12757, 3rd International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2023), 1275703 (17 August 2023); https://doi.org/10.1117/12.2690216

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KEYWORDS

Terahertz radiation

Mixtures

Machine learning

Binary data

Glucose

Absorption

Terahertz spectroscopy

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