| 光谱学与光谱分析 |
|
|
|
|
|
| Identification and Analysis of Genuine and False Flos Rosae Rugosae by FTIR and 2D Correlation IR Spectroscopy |
| CAI Fang1,SUN Su-qin2,YAN Wen-rong1,NIU Shi-jie1,LI Xian-en1* |
1. Institute of Medicinal Plant; Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 1000193, China
2. Department of Chemistry, Tsinghua University, Beijing 100084, China |
|
|
|
|
Abstract The genuine and false Flos Rosae Rugosae (Flos Rosae Chinensis and Flos Rosa multiflora) were examined in terms of their differences by using Fourier transforrn infrared spectroscopy (FTIR) combined with two-dimensional (2D) correlation IR spectroscopy. The three species were shown very similar in FTIR spectra. The peak of 1 318 cm-1 of genuine Flos Rosae Rugosae is not obvious but this peak could be found sharp in Flos Rosae Chinensis and Flos Rosa multiflora. Generally, the second derivative IR spectrum can clearly enhance the spectral resolution. Flos Rosae Rugosae and Flos rosae Chinensis have aromatic compounds distinct fingerprint characteristics at 1 617 and 1 618 cm-1, respectively. Nevertheless, Flos Rosa multiflora has the peak at 1 612 cm-1. There is a discrepancy of 5 to 6 cm-1. Flos Rosa multiflora has glucide’s distinct fingerprint characteristics at 1 044 cm-1,but Flos Rosae Rugosae and Flos Rosae Chinensis don’t. The second derivative infrared spectra indicated different fingerprint characteristics. Three of them showed aromatic compounds with autopeaks at 1 620, 1 560 and 1 460 cm-1. Flos Rosae Chinensis and Flos Rosa multiflora have the shoulder peak at 1 660 cm-1. In the range of 850-1 250 cm-1, three of them are distinct different, Flos Rosae Rugosae has the strongest autopeak, Flos Rosae Chinensis has the feeble autopeak and Flos Rosa multiflora has no autopeak at 1 050 cm-1. In third-step identification, the different contents of aromatic compounds and glucide in Flos Rosae Rugosae, Flos Rosae Chinensis and Flos Rosa multiflora were revealed. It is proved that the method is fast and effective for distinguishing and analyzing genuine Flos Rosae Rugosae and false Flos Rosae Rugosae (Flos Rosae Chinensis and Flos Rosa multiflora).
|
|
Received: 2008-05-26
Accepted: 2008-08-28
|
|
|
|
Corresponding Authors:
LI Xian-en
E-mail: xianenli@sina.com
|
|
[1] Jiangsu New Medical University(江苏新医学院编). The Big Chinese Drug Dictionary(中药大辞典). Shanghai: Shanghai Science and Technology Press(上海: 上海科学技术出版社), 1997. 1223.
[2] Committee of State Administration of Traditional Chinese Medicine of the People’s Republic of China(中国中医药管理局编). Chinese Herb(中华本草, 第6卷). Shanghai: Shanghai Science and Technology Press(上海：上海科技出版社), 1999. 238.
[3] Pharmacopoeia Committe of Ministry of Health, the People’s Republic of China(中华人民共和国药典委员会). Pharmacopeia of the People’s Republic of China(中华人民共和国药典, 第1卷) . Beijing: Chemical Industry Press(北京: 化学工业出版社)，2005. 139.
[4] JIANG Ju-xian, LI Yong-fu(江菊仙，李永福). Chinese Traditional and Herbal Drugs(中草药), 2003, 34(7): 667.
[5] LU Wen-sheng(卢文生). China Pharmaceuticals(中国药业), 2003, 12(5): 62.
[6] YAO Yan-song(姚延松). Lishizhen Medicine and Materia Medica Research(李时珍国医国药), 2001, 12(7): 609.
[7] Hua Rui, Sun Suqin, Zhou Qun. Journal of Pharmaceutical and Biomedical Analysis, 2003, 33: 199.
[8] Yu Lu, Sun Suqin, Fan Kefeng, et al. Spectrochimica Acta Part A, 2005, 62: 22.
[9] Zhou Q, Sun S Q, Yu L, et al. Journal of Molecular Structure, 2006, 799: 77.
[10] Liu H X, Sun S Q, Lü G H, et al. Spectrochimica Acta Part A, 2006, 64: 321.
[11] Li Y M, Sun S Q, Zhou Q, et al. Spectrochimica Acta Part A, 2006, 63: 565.
[12] Yu L, Sun S Q, Fan K F, et al. Spectrochimica Acta Part A, 2005, 62: 22.
[13] Zhou Q, Sun S Q, Zuo L, Vibrational Spectroscopy, 2004, 36: 207.
[14] Noda I. Appl. Spectrosc., 1990, 44: 550.
[15] Noda I. Appl. Spectrosc., 1993, 47: 1329.
[16] Noda I, Dowrey A E, Marcott C, et al. Appl. Spectrosc., 2000, 54: 236. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [3] |
ZHANG Yu-hui1, 2, DING Yong-kang3, PEI Jing-cheng1, 2*, GU Yi-lu1, 2, YU Min-da1, 2. Chemical Constituents and Spectra Characterization of Monocrystal
Rhodonite From Brazil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3504-3508. |
| [4] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [5] |
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. |
| [6] |
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. |
| [7] |
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*. Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2928-2934. |
| [8] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
| [9] |
JIN Cheng-liang1, WANG Yong-jun2*, HUANG He2, LIU Jun-min3. Application of High-Dimensional Infrared Spectral Data Preprocessing in the Origin Identification of Traditional Chinese Medicinal Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2238-2245. |
| [10] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
| [11] |
LI Yuan-jing1, 2, CHEN Cai-yun-fei1, 2, LI Li-ping1, 2*. Spectroscopy Study of γ-Ray Irradiated Gray Akoya Pearls[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1056-1062. |
| [12] |
WU Mu-lan1, SONG Xiao-xiao1*, CUI Wu-wei1, 2, YIN Jun-yi1. The Identification of Peas (Pisum sativum L.) From Nanyang Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1095-1102. |
| [13] |
KONG De-ming1, CUI Yao-yao2, 3, ZHONG Mei-yu2, MA Qin-yong2, KONG Ling-fu2. Study on Identification Seawater Submersible Oil Based on Total
Synchronous Fluorescence Spectroscopy Combined With
High-Order Tensor Feature Extraction Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 62-69. |
| [14] |
WANG Zhi-xin, WANG Hui-hui, ZHANG Wen-bo, WANG Zhong, LI Yue-e*. Classification and Recognition of Lilies Based on Raman Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 183-189. |
| [15] |
YAN Wen-hao1, YANG Xiao-ying1, GENG Xin1, WANG Le-shan1, LÜ Liang1, TIAN Ye1*, LI Ying1, LIN Hong2. Rapid Identification of Fish Products Using Handheld Laser Induced Breakdown Spectroscopy Combined With Random Forest[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3714-3718. |
|
|
|
|
