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A comparative study of classification models for laser-induced breakdown spectroscopy of Astragalus origin

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Abstract

In this paper, laser-induced breakdown spectroscopy (LIBS) combined with machine learning algorithm was used to study the classification of Astragalus from 10 different origins. First, the spectrum of Astragalus from 10 different origins was collected based on LIBS technology, and support vector machine (SVM) and extreme learning machine (ELM) models were established, respectively. Their accuracy rates were 45% and 30%, respectively. After that, the feature variables were selected as the input vectors of SVM and ELM models using random forest (RF) feature importance ranking, and random forest-support vector machine (RF-SVM) and random forest-extreme learning machine (RF-ELM) models were established, respectively. Their accuracy rates were 83.33% and 58.33%, respectively. To improve classification accuracy, gray wolf optimizer (GWO) algorithm was used to optimize RF-SVM and RF-ELM, respectively, and two classification models of gray wolf optimizer-random forest-support vector machine (GWO-RF-SVM) and gray wolf optimizer-random forest-extreme learning machine (GWO-RF-ELM) were constructed. The accuracy rates of GWO-RF-SVM and GWO-RF-ELM models were 85% and 92%, respectively. The results show that the classification effect of GWO-RF-ELM is the best, and its macro-precision, macro-recall and macro-F1 score were 92%, 100%, 92.04% and 95.86%, respectively. It provides an effective method for the identification of Astragalus.

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Data underlying the results presented in this paper are available upon request.

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Acknowledgements

The research is funded by National Natural Science Foundation of China (No. 62105160).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Big Data Analysis Technology, School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yu Ding, Ao Hu, Jing Chen, Meiling Zhao, Yan Shu, Wenjie Chen, Yufeng Wang & Linyu Yang

  2. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yu Ding, Ao Hu, Jing Chen, Meiling Zhao, Yan Shu, Wenjie Chen, Yufeng Wang & Linyu Yang

Authors
  1. Yu Ding
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  2. Ao Hu
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  3. Jing Chen
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  4. Meiling Zhao
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  5. Yan Shu
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  6. Wenjie Chen
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  7. Yufeng Wang
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  8. Linyu Yang
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Contributions

YD contributed to conceptualization, methodology, investigation, writing—original draft, review, and editing; AH contributed to methodology, data analysis, review, and editing; JC contributed to conceptualization; MZ contributed to methodology; YS contributed to investigation; WC contributed to review; YW contributed to editing, and LY contributed to data analysis.

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Correspondence to Yu Ding.

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Ding, Y., Hu, A., Chen, J. et al. A comparative study of classification models for laser-induced breakdown spectroscopy of Astragalus origin. Appl. Phys. B 129, 125 (2023). https://doi.org/10.1007/s00340-023-08074-z

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