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| Rapid Analysis of the Quality of Ginkgo Biloba Leaf Based on UV, Near Infrared and Multi-Source Composite Spectral Information |
| ZHANG Li-guo, CHENG Jia-jia, NI Li-jun*, LUAN Shao-rong |
| School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China |
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Abstract In order to study the adaptability of using different kinds of spectra to analyze the quality of Ginkgo biloba leaves quickly, 58 samples of Ginkgo biloba leaves were collected. The contents of the active components of flavonoid glycosides and terpene lactones were determined as dependent variables (y) by high performance liquid chromatography (HPLC), and the independent variables (x) included ultraviolet (UV), visible and near infrared spectra signals. Quantitative analysis models of flavonoids and lactones in Ginkgo biloba leaves were established by partial least square regression (PLSR) and an innovative method of keeping a same relationship between X and Y space (KNN-KSR method for short). The method predicted dependent variables based on the object’s independent variables and the relationship between the object and its K nearest neighbors in independent variable space. Correlation coefficient R between the measured values and the model values, root mean square error of prediction (RMSEP), and the average relative error of the prediction (MRE) were applied to evaluate the models. All evaluated indicators of PLSR models based on three kinds of spectral information were inferior to those of KNN-KSR method, and the results of PLSR models based on UV spectra were very poor; However, when KNN-KSR method was used to predict the flavonoids and lactones in Ginkgo biloba leaves based on three kinds of spectral information, R was higher than 0.8; RMSEP of flavonoids and lactones were less than 0.05 and 0.025, respectively; MRE of flavonoids and lactones content were below 8%. UV, NIR and multi-source composite spectral information combing KNN-KSR method could achieve rapid analysis of four kinds of flavonoid glycosides and three kinds of terpene lactones in Ginkgo biloba leaves. The present work broke through the limitation of existing work that only analyzed total flavonoids in Ginkgo biloba leaves by PLSR method based on NIR; The proposed new ideas to rapidly determine flavonoids and lactones in Ginkgo biloba leaves using UV and multi-spectral information by KNN-KSR method provided more available methods and choices for the quality analysis of ginkgo biloba leaves. The multi-source composite spectrometer, which can provide spectral information of various types, is portable, of small volume and low cost. It is very suitable for the rapid detection of on-the-spot Ginkgo biloba leaves acquisition and follow-up product quality analysis and monitoring.
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Received: 2016-09-06
Accepted: 2017-01-12
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Corresponding Authors:
NI Li-jun
E-mail: nljfyt@163.com
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[1] Liu Xinguang, Wu Siqi, Li Ping, et al. Journal of Pharmaceutical and Biomedical Analysis，2015，113：212.
[2] Chinese Pharmacopoeia Commission(国家药典委员会). Pharmacopoeia of the People’s Republic of China. Beijing: Chinese Medical science and Technology Press(北京: 中国医药科技出版社), 2015. 316.
[3] Wang Wenping, Kang Qian, Liu Na, et al. Fitoterapia，2015, 102: 189.
[4] Singh P, Singh I N, Mondal S C, et al. Fitoterapia，2013，84: 180.
[5] Guan Hanliang, Qian Dawei, Ren Hao, et al. Journal of Ethnopharmacology, 2014, 155(1)：758.
[6] Shi Jiyong，Zou Xiaobo，Zhao Jiewen, et al. Spectrochimica Acta Part A，2012，94：271.
[7] Coppa M, Revello-Chion A，Giaccone, et al. Food Chemistry, 2014, 150: 49.
[8] NI Li-jun，ZHAO Qun，ZHANG Li-guo(倪力军，赵 群, 张立国). Chinese Patent(中国专利), 10214067, 2016.
[9] Galvo R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67: 736.
[10] LIU Wei，ZHAO Zhong，YUAN Hong-fu，et al(刘 伟, 赵 众, 袁洪福, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014，34(4): 947.
[11] NI Li-jun，ZENG Xiao-hong，ZHANG Li-guo(倪力军, 曾晓虹, 张立国). Journal of East China University of Science and Technology·Natural Science Edition(华东理工大学学报·自然科学版)，2008, 34(4): 547.
[12] NI Li-jun，LUAN Shao-rong，ZHANG Li-guo(倪力军，栾绍嵘，张立国). China Journal of Chinese Materia Medica(中国中药杂志)，2016, 41(19)：3520. |
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