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Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm

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Abstract

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines.

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Data available on request from the authors.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 82160806 and 31871873) and Inner Mongolia Autonomous Region Natural Science Foundation of China (Grant No. 2022MS08064).

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Authors and Affiliations

  1. Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities, Tongliao, 028043, China

    Siqingaowa Han, Zhu Jin, Dema Deji, Tana Han, Yulan Zhang & Meiling Feng

  2. National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin, 150080, China

    Wuliji Hasi

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  1. Siqingaowa Han
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  2. Zhu Jin
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Correspondence to Yulan Zhang, Meiling Feng or Wuliji Hasi.

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Han, S., Jin, Z., Deji, D. et al. Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm. ANAL. SCI. 39, 241–248 (2023). https://doi.org/10.1007/s44211-022-00224-1

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  • DOI: https://doi.org/10.1007/s44211-022-00224-1

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