光谱学与光谱分析 |
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Distinguish and Quality Estimation of the Leaves of Alstonia scholaris (L.) R. Br. from Different Harvest Time Based on the UV-Vis·FP and HPLC·FP |
YANG Ni-na1, 2, ZHANG Ji3, ZHAO Yan-li3, WANG Yuan-zhong3*, ZHAO Ying-hong1* |
1. Dai Hospital of Xishuangbanna Dai Autonomous Prefecture, Xishuangbanna 666100, China 2. College of TCM, Yunnan University of TCM, Kunming 650500, China 3. Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kuming 650200, China |
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Abstract UV-Vis and HPLC fingerprint of different harvest time of the leaves of Alstonia scholaris (L.) R. Br. were establish the for identification and quality evaluation to promote the development of Dai Medicine modernization. The optimal extraction condition was used to obtain UV - vis data of different harvest time which were deducted background and eight spot smooth, were collected to make the principal component analysis in SIMCA-P+11.5, identifying the samples quickly with the first three principal component three-dimensional diagram. The HPLC fingerprint were obtained with Agilent ZORBAX Eclipse XDB C18 (250×4.6 mm, 5 μm) chromatographic column with the mobile phase of acetonitrile (B) - water (contain 0.1% formic acid) (A) for gradient elution (0~5 min, 5% B; 5~35 min, 5% B→26% B; 35~40 min, 26% B→56% B). The wavelength was set at 287 nm and the column temperature was maintained at 30 ℃. The flow rate was 1.0 mL·min-1 and the injection volume was 7 μL. The HPLC fingerprint of different harvest time of the leaves of Alstonia scholaris (L.) R. Br. was analysised by cluster analysis to quality evaluation. Research findings showing: (1) The UV-Vis spectrogram of different harvest time of the leaves of Alstonia scholaris (L.) R. Br. were divided into three parts according to the absorption peak position and amplitude of variation. The first was 235 to 400 nm, the second was 400 to 500 nm, and the third was 500 to 800 nm. In the first part, absorption peak were focused on 270, 287 and 325 nm, which can reflect the fingerprint character for the high absorbance and amplitude of variation. Absorption peak were distributed in 410 and 464 nm in the second part, absorbance and amplitude of variation were lower than the first part. There was a bigger absorption peak at 665 nm in the third part, but the absorbance had no difference. The UV-Vis data of different harvest time were gathered to make the principal component analysis, the result was that the samples of same month were concentrated distribution, but different month samples were dispersed distribution. (2) HPLC fingerprint were divided into three categories through hierarchical cluster analysis, 3, 4, 5 and 7 month were the first category, 6, 8, 9 month samples were second category, the others were third category. Chemical composition and content of the same category samples were similar, but the different category samples had a obvious difference, more important is that the third category samples content was the highest. Combining UV-Vis FP and HPLC FP can identify and evaluate quickly the samples of different harvest time of the leaves of Alstonia scholaris (L.) R. Br. The optimal harvest time of Alstonia scholaris (L.) R. Br. was from October to next February, which was the coldest season in the Dai calendar.
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Received: 2015-05-19
Accepted: 2015-10-02
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Corresponding Authors:
WANG Yuan-zhong, ZHAO Ying-hong
E-mail: yzwang1981@126.com; 15887797260@163.com
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[1] Yue C, Xiang L. 2011 6th IEEE Joint International. IEEE, 2011, 2: 51. [2] ZHENG Jin(郑 近). The history of Traditional Dai Medicine (傣医药学史). Beijing: China Press of Traditional Chinese Medicine(北京: 中国中医药出版社), 2007. 35. [3] ZHENG Jin, LIN Yan-fang, ZHANG Chao(郑 进, 林艳芳, 张 超). Basic Theories of Dai Medicine(傣医基础理论). Beijing: China Press of Traditional Chinese Medicine(北京: 中国中医药出版社), 2008. 15. [4] ZHU Cheng-lan, ZHAO Ying-hong, MA Wei-guang(朱成兰, 赵应红, 马伟光). Traditional Dai Medicine(傣药学). Beijing: China Press of Traditional Chinese Medicine(北京: 中国中医药出版社), 2007. 35. [5] Gong A G, Li N, Lau K, et al. Journal of Ethnopharmacology, 2015, 168: 150. [6] Wan L, Cheng Y, Luo Z, et al. Journal of Ethnopharmacology, 2015, 165: 118. [7] Li J, He X, Li M, et al. Food Chemistry, 2015, 176: 7. [8] Jing J, Parekh H S, Wei M, et al. TrAC Trends in Analytical Chemistry, 2013, 44: 39. [9] Wu H, Guo J, Chen S, et al. Journal of Pharmaceutical and Biomedical Analysis, 2013, 72: 267. [10] LI Fa-mei(李发美). Analytical Chemistry(分析化学). Beijing: People’s Medical Publishing House(北京: 人民卫生出版社), 2011. [11] LIU Yan, CAI Wen-sheng, SHAO Xue-guang(刘 言, 蔡文生, 邵学广). Science China Press(科学通报), 2015, 60(8): 704. [12] Valderrama L, Gonalves R P, Maro P H, et al. Revista Brasileira de Pesquisa em Alimentos, 2014, 5(2): 32. [13] ZHANG Jin-yu, WANG Yuan-zhong, ZHAO Yan-li, et al(张金渝, 王元忠, 赵艳丽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(8): 2176. [14] Tistaert C, Dejaegher B, Vander Heyden Y. Analytica Chimica Acta, 2011, 690: 148. [15] Chinese Academy of Sciences Editorial Board of Flora of China(中科院植物志编委会). Flora of China(中国植物志). Beijing: Science Press(北京: 科学出版社), 1977, 63: 90. [16] KANG Lang-la(康朗腊). Dang Ha Ya Long(档哈雅龙). Kunming: The Nationalities Publishing House of Yunnan(昆明: 云南民族出版社), 2003. 20. [17] Khyade M S, Kasote D M, Vaikos N P. Journal of Ethnopharmacology, 2014, 153(1): 1. [18] Wang C M, Chen H T, Li T C, et al. Journal of Chemical Ecology, 2014, 40(1): 90. [19] Arulmozhi S, Mazumder P M, Sathiyanarayanan L, et al. European Journal of Integrative Medicine, 2011, 3(2): e83. [20] Arulmozhi S, Mazumder P M, Lohidasan S, et al. European Journal of Integrative Medicine, 2010, 2(1): 23. [21] Feng L, Chen Y, Yuan L, et al. Molecules, 2013, 18(11): 13920. [22] Seifert B, Pflanz M, Zude M. Food and Bioprocess Technology, 2014, 7(7): 2050. [23] Nakajima H, Hara K, Yamamoto Y, et al. Ecotoxicology and Environmental safety, 2015, 113: 477. [24] Erten-Ela S, Vakuliuk O, Tarnowska A, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015, 135: 676. [25] Szakiel A, P?czkowski C, Henry M. Phytochemistry Reviews, 2011, 10(4): 471. [26] Hassiotis C N, Ntana F, Lazari D M, et al. Industrial Crops and Products, 2014, 62: 359. [27] Liu W, Liu J, Yin D, et al. PloS One, 2015, 10(4). [28] Chauhan A, Verma R S, Padalia R C. Climate Change Effect on Crop Productivity, 2014: 251. [29] Moghaddam M, Farhadi N. Journal of Applied Research on Medicinal and Aromatic Plants, 2015. [30] Pacifico S, Galasso S, Piccolella S, et al. Food Research International, 2015, 69: 121. [31] Kindlovits S, Radácsi P, Sárosi S, et al. European Journal of Horticultural Science, 2014, 79(2): 76. [32] JIANG Yan-juan(姜艳娟). Acta Bot. Boreal. -Occident. Sin. (西北植物学报), 2008, 28(8): 1675. |
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