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Infrared Fingerprint and Multivariate Statistical Analysis of Rehmannia Glutinosa |
ZHANG Wei-fang1, 2, FAN Ke-feng3, LEI Jing-wei1, 2*, JI Liang1, 2 |
1. School of Pharmary, Henan University of Traditional Chinese Medicine, Zhengzhou 450046, China
2. Henan Engineering Research Center for Quality Control and Evaluation of Traditional Chinese Medicine, Zhengzhou 450046, China
3. School of Animal Medicine, Henan College of Animal Husbandry Economics, Zhengzhou 450046, China |
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Abstract The place of origin of Chinese medicine is an important factor affecting the quality of medicinal materials. The growth environment of different places of production directly impacts the growth of Chinese medicine and the accumulation of metabolites. Chinese medicinal materials are known for the difference between authentic and non-dao regions, and they have a long history in China. The change of its production area and the increase of modern main production areas have resulted in slight discrepancies between the main production areas of current medicinal materials and historical records. Fourier transform infrared spectroscopy technology has the advantages of being fast and non-destructive. Fourier Transform Infrared spectroscopy is characterized for its high speed and non-destruction. Infrared spectroscopy can completely express the information on different origins of Rehmannia glutinosa. Combined with chemometrics, FTIS can also express the digitization of information embodied in infrared spectroscopy. It can collect different Infrared spectroscopy of Rehmannia glutinosa by using Fourier transform infrared spectrometer. The original spectral data can be preprocessed like baseline correction of the original spectrum, 6 smoothing points, selection of 900~1 200 cm-1 band for highest peak normalization and so on. Moreover, FTIS can calculate the relative peak height of the main characteristic peaks of the infrared spectrum of each origin. FTIS is trying to put up quality differences with normal distribution, clustering (CA) and principal component analysis (PCA). In addition, the identification of the origin of Rehmannia glutinosa has scientific significance for the rational application of Chinese medicine. The results showed that the infrared spectra of 73 batches of Rehmannia glutinosa from different origins were collected by Fourier transform infrared spectroscopy. The peak shape, peak position and height of the fingerprints of 73 batches of Rehmannia glutinosa from different origins were basically similar, and the same chemical components were contained in different origins. The characteristic peaks and shapes are basically the same. Rehmannia glutinosa produced in Henan has prominent heights of individual characteristic peaks, and there are certain differences in fingerprint areas. The main contribution bands for the differences are: 1 639, 1 424, 1 354 and 1 260 cm-1. Four bands, a total of 13 common peaks are calibrated. Cluster analysis can divide 73 batches of Rehmannia glutinosa samples into two types, namely Huai Rehmannia glutinosa produced in Henan and other Rehmannia glutinosa, which indicates that there are internal quality differences in different origins of Rehmannia glutinosa. The normal distribution is consistent with the cluster analysis results. It showed that at the peak of 1 639 cm-1, the normal distribution curves of Huai Rehmannia glutinosa produced in Henan and other provinces are in order as follows: Shandong, Shanxi, Hebei. Therefore, this method can distinguish authentic medicinal materials from non-authentic medicinal materials well. It can reduce the dimension of the relative peak height of the resulting common peaks. And it can calculate the principal component composite scores of different origins of Rehmannia glutinosa. The results showed that the comprehensive scores of Rehmannia glutinosa produced in Henan were higher than those of other origins, indicating that the quality of Rehmannia glutinosa produced in Henan was the best. Fourier transform infrared spectroscopy combined with multivariate statistical analysis methods can non-destructively, effectively and quickly identify different origins of Rehmannia glutinosa.
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Received: 2020-10-13
Accepted: 2021-02-25
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Corresponding Authors:
LEI Jing-wei
E-mail: 925390812@qq.com
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[1] ZHANG Bo-yong, JIANG Zhen-zuo, WANG Yue-fei, et al(张波泳, 江振作, 王跃飞, 等). Chinese Patent Medicine(中成药), 2016, 38(5): 1104.
[2] YE Ding-jiang, ZHANG Shi-chen(叶定江, 张世臣). Traditional Chinese Medicine Processing(中药炮制). Beijing: People’s Medical Publishing House(北京:人民卫生出版社), 1999.
[3] Chinese Pharmacopoeia Commission(中华人民共和国药典委员会) . Pharmacopoeia of the People’s Republic of China, Part One (中华人民共和国药典一部) . Beijing: China Medical Science Press(北京: 中国医药科技出版社), 2015.
[4] GENG Xiao-tong, WANG Feng-qing, SU Xiu-hong, et al(耿晓桐, 王丰青, 苏秀红, 等). China Pharmacy(中国药房), 2019, 30 (2): 225.
[5] ZHANG Hui-wen, SONG Xiao-ling, SHI Song-li, et al(张慧文, 宋晓玲, 石松利, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(1): 174.
[6] LI Xiao-qiang, ZHANG De-li, WANG Ting, et al(李晓强, 张德莉, 王 婷, 等). Chinese Journal of Hospital Pharmacy(中国医院药学杂志), 2015, 35(5): 421.
[7] Sasaki H, Nishimura H, Chin M, et al. Phytochemistry, 1989, 28(30): 875.
[8] Jia X H, Wang C Q, Liu J H, et al. Journal of Natural Medicines, 2013, 67(2): 339.
[9] TANG Wei-wei, LIANG Xian-kui, MA Chi-hong, et al(唐维维, 梁献葵, 马驰虹, 等). Chinese Medicinal Materials(中药材), 2019, 42(9): 2079. |
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