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| Research on Identification of Danshen Origin Based on Micro-Focused
Raman Spectroscopy Technology |
| LI Qing1, 2, XU Li1, 2, PENG Shan-gui1, 2, LUO Xiao1, 2, ZHANG Rong-qin1, 2, YAN Zhu-yun3, WEN Yong-sheng1, 2* |
1. Chengdu Institute for Drug Control, Chengdu 610045, China
2. NMPA Key Laboratory for Quality Monitoring and Evaluation of Traditional (Chinese Medicine Chinese Materia Medica), Chengdu 610045, China
3. Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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Abstract The origin is an important factor affecting the quality of Chinese herbal medicine. The difference of origin leads to the uneven quality of Chinese herbal medicine. In order to maintain the market order, it is necessary to establish the method of identification of the origin of Chinese herbal medicine to identify and analyze the quality of Chinese herbal medicine more accurately. This article takes danshen, a major clinical medicinal material, from many origins as the research object, and 150 samples of danshen were collected from different origins. The surface of each root of danshen sample was scanned 1~n times randomly by micro focusing Raman spectroscopy under non-destructive conditions, and the average spectrum of each sample was calculated. By analyzing the original spectral data, it is found that the surface spectral signal of danshen contains both the Raman spectra of tanshinones and the fluorescence spectra of impurities. Mainly reflected in that danshen from different origins has their aggregation regions, and the signal intensity of the surface spectral signal of danshen is significantly weaker or stronger than that of tanshinones in the specific wavelength range. After preprocessing the spectral data of 1~n scans, the classification model of danshen origin was established by partial least squares discriminant analysis (partial least squares-discriminant analysis, PLS-DA) and random forest classification algorithm [no screening (random forest, RF) or screening of important variables (RF-VS)]. Results the training set and test set accuracy of the optimal model obtained by random 1 scanning were 88% and 87% respectively, and the samples with low quality and high quality could be distinguished with an accurate of 97%; the training set and test set accuracy of the optimal model obtained by random scanning 2 and 3 times were both 89% and 87% respectively. Combined with the operation efficiency of the model The spectrum obtained by random 1 scanning was selected, and the identification model of the origin of danshen was obtained by first derivative (1ST-D) pretreatment and RF-VS calculation. In conclusion, the micro focused Raman spectroscopy technology can establish a rapid and accurate prediction model of the origin of danshen under non-invasive conditions and provide a reference for the further application of this technology in identifying the origin and authenticity of expensive and scarce Chinese herbal medicine.
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Received: 2021-03-31
Accepted: 2021-09-19
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Corresponding Authors:
WEN Yong-sheng
E-mail: 1245551207@qq.com
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[1] Fang J, Little P J, Xu S W. Medicinal Research Reviews, 2017, 38(1): 201.
[2] DENG Ai-ping, GUO Lan-ping, ZHAN Zhi-lai, et al(邓爱平, 郭兰萍, 詹志来, 等). China Journal of Chinese Materia Medica(中国中药杂志), 2016, 41(22): 4274.
[3] Liang W Y, Chen W J, Wu L F, et al. Molecules, 2017, 22: 478.
[4] Ni J L, Zhang F F, Han M Y, et al. Journal of Pharmaceutical and Biomedical Analysis, 2019, 170: 295.
[5] Wang H, Hao N, Chen L, et al. Springerplus, 2016, 5(1): 1919.
[6] MING Jing, CHEN Long, CHEN Ke-li, et al(明 晶, 陈 龙, 陈科力, 等). Journal of Chinese Medicinal Materials(中药材), 2017, 40(1): 32.
[7] CHEN Long, MING Jing, YUAN Ming-yang, et al(陈 龙, 明 晶, 袁明洋, 等). Chinese Journal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2016, 22(21): 77.
[8] Rinnan A, Berg F V, Engelsen S B. Trends in Analytical Chemistry, 2009, 28(10): 1201.
[9] WANG Ya-xuan, TAN Feng, XIN Yuan-ming, et al(王亚轩, 谭 峰, 辛元明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(2): 565.
[10] Schulz H, Baranska M. Vibrational Spectroscopy, 2007，43(1): 13.
[11] ZHU Zi-ying, GU Ren-ao, LU Tian-hong(朱自莹, 顾仁敖，陆天虹). Application of Raman Spectroscopy in Chemistry(拉曼光谱在化学中的应用). Changchun: Northeast University Press(长春：东北大学出版社), 1998. 295.
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