光谱学与光谱分析 |
|
|
|
|
|
Analysis and Identification of H. rhamnoides subsp. sinensis Harvested From Different Producing Areas by FTIR Spectroscopy |
LIU Yue1, LI Jing-yi2, FAN Gang1, LIU Chuan1, SUN Su-qin3, SU Yong-wen1, JIANG Dao-feng1, ZHANG Yi1*, TU Ya4* |
1. College of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China 2. Department of Traditional Chinese Pharmacy, Harbin University of Commerce, Harbin 150001, China 3. Department of Chemistry, Tsinghua University, Beijing 100084, China 4. China Academy of Chinese Medical Sciences, Beijing 100700, China |
|
|
Abstract The accurate identification of traditional Chinese medicine (TCM) which collected from different producing areas is important for its quality control and clinical effects. In the present study, Fourier transform infrared spectroscopy (FTIR) combined with second derivative spectra were used to identify and analyze H. rhamnoides subsp. sinensis from different producing areas. The characteristic absorption peaks, including 2 925, 2 854, 1 743, 1 541 and 1 173 cm-1 belonging to fatty acids, flavonoids and saccharides appear in all 20 samples. But the absorption peak intensities and locations varied due to the different geographical regions. The results also showed that the absorption peaks at the range of 3 429~3 336 and 1 744 cm-1 were important characteristic absorption peaks which can identify H. rhamnoides subsp. sinensis from different producing areas. Also, absorption peaks at 1 030 and 1 516 cm-1 further confirmed the existence of flavonoids in all samples by comparing the second derivative infrared spectra in the range of 1 800~1 000 cm-1. However, the samples’ differences can be intuitively found around peaks 1 711, 1 476 cm-1 and ranges from 1 689~1 515 and 1 400~1 175 cm-1. The results demonstrated that FTIR was a simple, convenient, fast and intuitive approach to identify and analyze H. rhamnoides subsp. sinensis from different producing areas. This method provides foundations for the analysis of chemical compositions and quality control for the TCM.
|
Received: 2015-03-27
Accepted: 2015-07-15
|
|
Corresponding Authors:
ZHANG Yi, TU Ya
E-mail: 1175332408@qq.com; tuya126@126.com
|
|
[1] Yutuoningma·Yuandangongbu(允妥宁玛·云丹贡布). Four-Volume Medical Code(四部医典). Beijing: People’s Medical Publishing House(北京:人民卫生出版社), 1983. [2] MA Shi-lin, MAO Ji-zu(马世林, 毛继祖,译注). Yue Wang Yao Zhen(月王药诊). Shanghai: Shanghai Science and Technology Press(上海: 上海科技出版社), 2012. [3] Dimaer·Danzengpengcuo(帝玛尔·丹增彭措). Jing Zhu Materia Medica(晶珠本草). Beijing: Nationalities Publishing House(北京: 民族出版社), 2005. 65. [4] Zhanbula·Daoerji (占布拉·道尔吉). Classic Canon of Mongolian Materia Medica(蒙药正典). Beijing: Nationalities Publishing House(北京: 民族出版社), 2006. [5] Chinese Pharmacopoeia Commission(国家药典委员会): The Pharmacopoeia of the People’s Republic of China(中华人民共和国药典). Beijing: China Medical Science Press(北京: 中国医药科技书版社), 2010. 171. [6] CHEN Chu, ZHANG Hao, XIAO Wei(陈 雏, 张 浩, 肖 蔚). The Global Seabuckthorn Research and Development(国际沙棘研究与开发), 2005, 3(4): 25. [7] Adiana Mohamed Adib, Fadzureena Jamaludin, Ling Sui Kion, et al. Journal of Pharmaceutical and Biomedical Analysis, 2014, 96: 104. [8] Dugeer, MA Chun-jie, CAI Qiu-jie, et al(都格尔, 麻春杰, 蔡秋杰, 等). World Science and Technology-Modernization of Traditional Chinese Medicine and Materia Medica(世界科学技术-中医药现代化), 2014, 16(3): 661. [9] Li Jianrui, Sun Suqin, Wang Xiaoxiao, et al. Journal of Molecular Structure, 2014, 1069: 229. [10] ZHANG Hai-hong, ZHANG Shu-juan, WANG Feng-hua, et al(张海红, 张淑娟, 王凤花, 等). Acta Optica Sinica(光学学报), 2010, 30(2): 574. [11] Wu Xianxue, Xu Changhua, Li Ming, et al. Journal of Molecular Structure, 2014, 1069: 133. [12] LEI Yu, TU Ya, ZHANG Yan-ling, et al(雷 雨, 图 雅, 张艳玲, 等). Analytical Instrumentation(分析仪器), 2010, 3: 24. [13] Li Mingyu, Cheng Shichao, Li Dan, et al. Chinese Chemical Letters, 2015, 26: 221. [14] Alam Zeb. Important Therapeutic Uses of Sea Buckthorn (<i>Hippophae</i>): A Review. Journal of Biological Sciences, 2004, 4(5): 687.
|
[1] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
[2] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[3] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[4] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[5] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[6] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[7] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
[8] |
ZHANG Yue1, 3, ZHOU Jun-hui1, WANG Si-man1, WANG You-you1, ZHANG Yun-hao2, ZHAO Shuai2, LIU Shu-yang2*, YANG Jian1*. Identification of Xinhui Citri Reticulatae Pericarpium of Different Aging Years Based on Visible-Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3286-3292. |
[9] |
TIAN Ze-qi1, WANG Zhi-yong1, YAO Jian-guo1, GUO Xu1, LI Hong-dou1, GUO Wen-mu1, SHI Zhi-xiang2, ZHAO Cun-liang1, LIU Bang-jun1*. Quantitative FTIR Characterization of Chemical Structures of Highly Metamorphic Coals in a Magma Contact Zone[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2747-2754. |
[10] |
LUAN Xin-xin1, ZHAI Chen2, AN Huan-jiong3, QIAN Cheng-jing2, SHI Xiao-mei2, WANG Wen-xiu3, HU Li-ming1*. Applications of Molecular Spectral Information Fusion to Distinguish the Rice From Different Growing Regions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2818-2824. |
[11] |
ZHANG Xiao-xu1, LIN Xiao-xian3, ZHANG Dan2, ZHANG Qi1, YIN Xue-feng2, YIN Jia-lu3, 4, ZHANG Wei-yue4, LI Yi-xuan1, WANG Dong-liang3, 4*, SUN Ya-nan1*. Study on the Analysis of the Relationship Between Functional Factors and Intestinal Flora in Freshly Stewed Bird's Nest Based on Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2452-2457. |
[12] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
[13] |
LI Shu-fei1, LI Kai-yu1, QIAO Yan2, ZHANG Ling-xian1*. Cucumber Disease Detection Method Based on Visible Light Spectrum and Improved YOLOv5 in Natural Scenes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2596-2600. |
[14] |
LENG Jun-qiang, LAN Xin-yu, JIANG Wen-shuo, XIAO Jia-yue, LIU Tian-xin, LIU Zhen-bo*. Molecular Fluorescent Probe for Detection of Metal Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2002-2011. |
[15] |
LI Wen-xia1, DU Yu-jun2, WANG Yue1, LIU Zheng-dong3*, ZHENG Jia-hui1, DU Wen-qian1, WANG Hua-ping4. Research on On-Line Efficient Near-Infrared Spectral Recognition and Automatic Sorting Technology of Waste Textiles Based on Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2139-2145. |
|
|
|
|