Chinese Journal of Lasers, Volume. 51, Issue 23, 2311003(2024)

Quantitative Prediction of Heavy Metal Elements in white peony root Using Laser‐Induced Breakdown Spectroscopy and Semi‐Supervised Sequential Learning

Fudong Nian1, Yujie Hu1, Fuqiang Chen2, Zhao Cheng3, and Yanhong Gu1、*
Author Affiliations
  • 1Institute of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, Anhui , China
  • 2College of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, Anhui , China
  • 3Chinese Academy of Environmental Planning, Beijing 100043, China
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    References(30)

    [7] Zhou J J, Li M G, Zhang T L et al. Quantitative analysis of Sc in rare-earth ores via laser-induced breakdown spectroscopy combined with random forest[J]. Chinese Journal of Lasers, 51, 0211001(2024).

    [18] Sun C Y, Jiao L, Yan N Y et al. Identification of Salvia miltiorrhiza from different origins by laser induced breakdown spectroscopy combined with artificial neural network[J]. Spectroscopy and Spectral Analysis, 43, 3098-3104(2023).

    [19] Wen D P. Qualitative and quantitative analysis of Chinese medicinal materials based on machine learning and LIBS technology[D], 42-58(2021).

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    Fudong Nian, Yujie Hu, Fuqiang Chen, Zhao Cheng, Yanhong Gu. Quantitative Prediction of Heavy Metal Elements in white peony root Using Laser‐Induced Breakdown Spectroscopy and Semi‐Supervised Sequential Learning[J]. Chinese Journal of Lasers, 2024, 51(23): 2311003

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    Paper Information

    Category: spectroscopy

    Received: Apr. 19, 2024

    Accepted: May. 27, 2024

    Published Online: Dec. 10, 2024

    The Author Email: Gu Yanhong (guyh@hfuu.edu.cn)

    DOI:10.3788/CJL240790

    CSTR:32183.14.CJL240790

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