Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm
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...
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| Published in: | Analytical sciences Vol. 39; no. 2; pp. 241 - 248 |
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01-02-2023
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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.
Graphical Abstract |
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| AbstractList | 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.
Graphical 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. |
| Author | Jin, Zhu Han, Siqingaowa Zhang, Yulan Han, Tana Deji, Dema Feng, Meiling Hasi, Wuliji |
| Author_xml | – sequence: 1 givenname: Siqingaowa surname: Han fullname: Han, Siqingaowa organization: Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities – sequence: 2 givenname: Zhu surname: Jin fullname: Jin, Zhu organization: Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities – sequence: 3 givenname: Dema surname: Deji fullname: Deji, Dema organization: Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities – sequence: 4 givenname: Tana surname: Han fullname: Han, Tana organization: Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities – sequence: 5 givenname: Yulan surname: Zhang fullname: Zhang, Yulan email: zhangyl1974@126.com organization: Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities – sequence: 6 givenname: Meiling surname: Feng fullname: Feng, Meiling email: fengmeiling2022@126.com organization: Department of Combination of Mongolian Medicine and Western Medicine Stomatology, Affiliated Hospital of Inner Mongolia University for the Nationalities – sequence: 7 givenname: Wuliji surname: Hasi fullname: Hasi, Wuliji email: hasiwuliji@126.com organization: National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology |
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| Cites_doi | 10.1002/jrs.6167 10.1016/j.apsb.2014.04.001 10.1039/D0CS01019G 10.1039/D0AY00483A 10.1007/s00216-021-03381-w 10.1016/j.vibspec.2018.05.004 10.1016/j.microc.2021.106913 10.1016/j.foodchem.2021.130191 10.3389/fphar.2022.891273 10.1016/j.jcma.2016.11.009 10.1002/jrs.5481 10.1109/TSMC.2019.2958647 10.1080/00032719.2021.1982959 10.1016/j.vibspec.2022.103430 10.1186/s13020-021-00554-8 10.1016/j.cej.2021.131344 10.1016/j.foodhyd.2017.11.020 10.1016/j.sab.2012.09.003 10.1186/s13020-022-00577-9 10.2138/am.2014.4632 10.1039/D0RA09992A 10.1016/j.resconrec.2022.106217 10.1038/s41536-017-0014-3 10.1039/C9CP00472F 10.1039/c3ay41887a 10.1021/acs.jpclett.9b02517 10.1039/D0AY00741B 10.1039/D1AY01377G 10.1177/0003702817727016 10.1366/0003702011953405 10.1080/00387010.2011.610421 10.1038/s41467-018-07601-3 10.1016/j.ijleo.2019.02.126 10.1155/2022/4404749 10.1145/3548775 10.1155/2019/6967984 |
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| Keywords | Support vector machine (SVM) Raman spectroscopy Carbonate mineral medicines Sulfate mineral medicines Principal component analysis (PCA) Classification and identification |
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| Snippet | The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is... |
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| SubjectTerms | Algorithms Analytical Chemistry Chemistry Chemistry and Materials Science Original Paper Principal Component Analysis Spectrum Analysis, Raman - methods Sulfates Support Vector Machine |
| Title | 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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