光谱学与光谱分析 |
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Rapid Determination of Pinoresinol Diglucoside and Geniposidic Acid in Eucommia ulmoides with Near Infrared Spectroscopy Combined with Chemometrics Methods |
LI Fang-fei1, 2, PENG Ying-zhi1, 2, XU Xiong-bo1, 2, ZI Wen1, 2, LIU Rui1, 2, LIU Shao1, 2* |
1. Xiangya Hospital, Central South University, Changsha 410008, China2. School of Pharmaceutical Sciences, Central South University, Changsha 410013, China |
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Abstract To develop a quantitative models for simultaneous determination of pinoresinol diglucoside (PDG) and geniposidic acid (GPA) in Eucommia ulmoides with near-infrared (NIR) spectroscopy combined with chemometrics. The NIR spectra were collected in diffuse reflection mode and pretreated with various spectra preprocessing methods including first-order differentiator, multiplicative scatter correction and so on. The optimal wavelength variables were screened out by competition adaptive weighted sampling method. The quantitative models for the simultaneous determination of PDG and GPA in Eucommia ulmoides were established with partial least squares (PLS) algorithm and cross validation methods. The quantitative prediction models for simultaneous determination of PDG and GPA in Eucommia ulmoides showed good predictive ability. The correlation coefficients (R2) of the two calibration models were 0.961 5, 0.958 3 while the roots mean square of cross-validation (RMSECV) were 0.001 5, 0.006 4, respectively. The quantitative prediction models proved that near infrared spectra method used for the quantitative analysis of PDG and GPA in Eucommia ulmoides owned high prediction accuracy and can meet the precision need of rapid determinations of PDG and GPA in Eucommia ulmoides in reality so t it provides a new method to realize the real time on line of quality control of Eucommia ulmoides.
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Received: 2015-04-26
Accepted: 2015-08-08
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Corresponding Authors:
LIU Shao
E-mail: liushao999@hotmail.com
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[1] GUANG Shu-yu, SU Wei-wei(管淑玉, 苏薇薇). Journal of Chinese Medicinal Materials(中药材), 2003, 26(2): 124. [2] WANG Li-nan, YANG Mei-hua(王丽楠, 杨美华). Natural Product Research and Development(天然产物研究与开发), 2009, 1: 108. [3] WANG Zhi-hong, PENG Sheng, LEI Ming-sheng, et al(王志宏, 彭 胜, 雷明盛, 等). Natural Product Research and Development(天然产物研究与开发), 2013, 25: 1302. [4] Chinese Pharmacopoeia 2015. VolⅠ(中国药典2015年版. 一部). 2015. 165. [5] LI Wei,SUN Su-qin,QIN Jie-ping,et al(李 伟, 孙素琴,覃洁萍,等). China Journal of Chinese Materia Medica(中国中药杂志), 2010, 35 (24): 3318. [6] Kwon Y K, Jie E Y, Sartie A, et al. Food Chemistry, 2015, 166: 389. [7] TANG Qi-kun, WANG Jue, WU Yue-jin, et al(汤其坤,王 钰,吴跃进,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(10): 2719. [8] Ixasonen M, Pulkkinen T H, Simard C, et al. Anal. Chem., 2003, 75(4): 754. [9] WU Li-min, YANG Qiong, ZHOU Shang, et al(吴利敏,杨 琼,周 尚,等). Chinese Journal of Pharmaceutical Analysis(药物分析杂志),2012,32(9):1583. [10] WANG Xiao-mei,JIAO Long,LIU Xiao-li, et al(王小梅,焦 龙,刘小丽,等). Chin. J. Pharm. Anal.(药物分析杂志), 2011, 6: 1016. [11] Zhang C, Yun Y, Fan W, et al. International Journal of Biological Macromolecules, 2015, 79: 983. [12] CHANG Jing, TANG Yan-lin, XU Jin, et al(常 静,唐延林,徐 锦,等). Computers and Applied Chemistry(计算机与应用化学), 2011, 28 (3): 288. [13] Cao Dong-sheng, Liang Yi-zeng, Xu Qing-song, et al. Journal of Computional Chemistry,2010, 31: 592. [14] Li Wen-long, Cheng Zhi-wei, Wang Yue-fei, et al. Journal of Pharmaceutical and Biomedical Analysis, 2013, (72): 33. [15] Li Xiao-li, He Yong, Wu Cang-qing, et al. Journal of Food Engineering, 2007, 82(3): 316. [16] Li Hong-dong, Liang Yi-zeng, Xu Qing-song, et al. Analytica Chimica Acta, 2009, 648: 77. |
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