光谱学与光谱分析 |
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Quantitative Prediction of Active Constituents in Rhubarb by Near Infrared Spectroscopy and Radial Basis Function Neural Networks |
YU Xiao-hui1,ZHANG Zhuo-yong1*,MA Qun2,FAN Guo-qiang2 |
1. Department of Chemistry, Resources Environment and GIS Key Lab of Beijing, Capital Normal University, Beijing 100037,China 2. Research Institute, Tongrentang Group Co. Ltd., Beijing 100011, China |
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Abstract Near infrared spectroscopy (NIRS) and artificial neural networks were used for the quantitative prediction of four active constituents in rhubarb: anthraquinones, anthraquinone glucosides, stilbene glucosides, Tannins and related compounds. The near infrared spectra of the samples were acquired in 1 100-2 500 nm from powdered rhubarb samples. Four calibration models using radial basis function neural networks (RBFNN) were set up to correlate the spectra with the values determined by HPLC. RMSECVs of the models for the constituents studied were 2.572, 0.442, 2.794 and 9.438, respectively. RMSEPs for the were 4.598, 8.657, 0.458 6, and 5.106, respectively. The method is fast, and satisfactory results were obtained. The proposed method can be used for determining the active constituents in Chinese herbal medicine.
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Received: 2005-12-30
Accepted: 2006-03-28
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Corresponding Authors:
ZHANG Zhuo-yong
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Cite this article: |
YU Xiao-hui,ZHANG Zhuo-yong,MA Qun, et al. Quantitative Prediction of Active Constituents in Rhubarb by Near Infrared Spectroscopy and Radial Basis Function Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(03): 481-485.
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URL: |
https://www.gpxygpfx.com/EN/Y2007/V27/I03/481 |
[1] LOU Zhi-cen(楼之岑). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):1. [2] ZHAO Jun, CHANG Jun-min, DU Nian-sheng(赵 军, 常军民, 堵年生). China Journal of Chinese Materia Medica(中国中药杂志),2002,27(4):281. [3] Liu C L, Zhu P L, Liu M C. Journal of Chromatography A,1999,857:167. [4] ZHENG Wen-jie,CHEN Xing-guo,JIA Wei(郑文捷, 陈兴国, 贾 伟). China Journal of Chinese Materia Medica(中国中药杂志),2004,29(9):870. [5] Shang Xiaoyu,Yuan Zhuo-bin. Analytica Chimica Acta,2002,456:183. [6] TANG Yan-feng,ZHANG Zhuo-yong,FAN Guo-qiang(汤彦丰,张卓勇,范国强). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2005,25(4):521. [7] MA Shu-min,LIU Si-dong,ZHANG Zhuo-yong,et al (马书民,刘思东,张卓勇,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2005,25(6):874. [8] Woo YA,Kim HJ,Cho J, et al. J. Pharmaceut. Biomed.,1999,21:407. [9] LIU Guo-lin,CHEN Guo-guang,XIANG Bing-ren(刘国林,陈国广,相秉仁). Chinese Journal of Modern Applied Pharmacy(中国现代应用药学),2000,17(5):383. [10] YUAN Hong-fu,LU Wan-zhen (袁洪福, 陆婉珍). Petroleum Processing and Petrochemicals(石油炼制与化工),1998,29(9):47. [11] LIU Ling,SHANG An-ming,ZHENG Jun-hua(刘 凌, 尚安明, 郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):40. [12] ZUO Jun,ZHANG Zhi-guo,ZHENG Jun-hua(左 君, 张治国, 郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):82. [13] ZUO Jun,SHI Lu-wen,ZHENG Jun-hua(左 君, 史录文,郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):99. [14] WANG Ying-fen,ZHENG Jun-hua,WANG Jing-xia(王映芬, 郑俊华, 王京霞). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):93. [15] ZUO Jun,AN Gen-lu,ZHENG Jun-hua(左 君, 安根录, 郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):101. [16] SHANG An-ming,WANG Jing-xia,ZHENG Jun-hua(尚安明, 王京霞,郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):122. [17] WANG Ying-fen,ZHENG Jun-hua,SHANG An-ming(王映芬, 郑俊华, 尚安明). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):126. [18] XIE Xiao-hui,ZHANG Guo-qi,ZHENG Jun-hua(谢晓慧, 张国奇, 郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):120. [19] WANG Jing-xia,AN Gen-lu,ZHENG Jun-hua(王京霞, 安根录, 郑俊华). Journal of Beijing Medical University(北京医科大学学报),1993,25(5):129. [20] Zhang Zhuoyong,Tang Yanfeng,Fan Guoqiang. Spectroscopy Letters,2005,38:447.
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