Chinese Journal of Quantum Electronics, Volume. 40, Issue 3, 376(2023)

Application of relevance vector machine combined with principal component analysis in quantitative analysis of LIBS

ZHANG Ranran... YING Luna and ZHOU Weidong* |Show fewer author(s)
Author Affiliations
  • Key Laboratory of Researching Optical Information Detecting and Display Technology in Zhejiang Province,Zhejiang Normal University, Jinhua 321004, China
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    A quantitative analysis model for detecting Cr in soil with laser induced breakdown spectroscopy (LIBS) was established by using correlation vector machine (RVM) combined with principal component analysis (PCA). Fourteen soil samples with different Cr concentrations were prepared, of which ten were selected as training samples for model construction, and the other four as test samples for model performance evaluation. The results show that the prediction accuracy of PCA-RVM model is significantly better than that of RVM model for the measurement of Cr content in soil. The root mean square error (RMSE) of the whole prediction is reduced from 8.00% of RVM model to 3.21% of PCA-RVM model, and the prediction accuracy is improved by 59.9%. Compared with RVM model, the relative standard deviation of repeated prediction results of PCA-RVM model for all four samples in the test sample set is significantly reduced and is less than 1.89%, indicating that the prediction results of PCA-RVM model have better stability.

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    Ranran ZHANG, Luna YING, Weidong ZHOU. Application of relevance vector machine combined with principal component analysis in quantitative analysis of LIBS[J]. Chinese Journal of Quantum Electronics, 2023, 40(3): 376

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    Paper Information

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    Received: Apr. 29, 2021

    Accepted: --

    Published Online: Jun. 30, 2023

    The Author Email: ZHOU Weidong (wdzhou@zjnu.cn)

    DOI:10.3969/j.issn.1007-5461.2023.03.009

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