| 光谱学与光谱分析 |
|
|
|
|
|
| Determination of Ginsenosides Amount and Geographical Origins of Ginseng by NIR Spectroscopy |
| WANG Jing-jing1, 2, 4, YAN Shu-mo3, 4, YANG Bin2, 4* |
1. Anhui University of Chinese Medicine, Hefei 230031, China
2. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
3. National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
4. State Key Laboratory Breeding Base of Dao-di Herbs, China Academy of Chinese Medical Sciences, Beijing 100700, China |
|
|
|
|
Abstract In the study, 74 samples of ginseng were harvested from three provinces located in the northeast of China. Method for the quantification of total amount of ginsenosides Rg1, Rb1, and Re in samples with near infrared (NIR) spectroscopy was developed with the application of partial least squares regression (PLSR). The reference analysis was performed using a UPLC method. Different pretreatments like multiplicative scatter correction (MSC), Norris-Derivative were applied on the spectra to optimize the calibration and the spectral regions from 6 001 to 4 007 cm-1 and from 10 000 to 8 786 cm-1 were selected for the calculation of the PLSR model. The root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) were 0.115 and 0.167, respectively, and the correlation coefficients were 0.947 7 and 0.915 3, respectively. At the same time, the spectral region from 8 531 to 7 559 cm-1 was chosen to establish a model for identification the geographical origins of ginseng samples. MSC and Savitzky-Golay smoothing were utilized on the spectral preprocessing. According to the result, 74 samples were separated into three clusters, corresponding to the three geographical origins, i.e., Jilin, Liaoning and Heilongjiang Province. The cross-validation ability and the prediction ability were 96% and 90%, respectively. In Chinese Pharmacopeia, the total amount of ginsenosides Rg1, Rb1, and Re is used as one of the indexes to evaluate the quality of ginseng, the established quantification method herein is rapid and accurate, it can be applied as an alternative method for quality control of ginseng sample.
|
|
Received: 2014-06-05
Accepted: 2014-09-21
|
|
|
|
Corresponding Authors:
YANG Bin
E-mail: ybinmm@hotmail.com
|
|
[1] Chinese Pharmacopoeia Commission(国家药典委员会). Pharmacopoeia of the People’s Republic of China, Volume I (中华人民共和国药典，一部). Beijing: China Medical Science and Technology Press(北京：中国医药科技出版社), 2010.
[2] CHENG Hai-tao, XU Yong-hua, GUO Shuang(程海涛，许永华，郭 爽). Ginseng Research(人参研究), 2010, 22(3): 27.
[3] MA Wei, WANG Zhen-yue, ZHANG Lian-xue(马 伟，王振月，张连学). Modern Chinese Medicine(中国现代中药), 2012, 14(9): 44.
[4] ZENG Yan, GUO Lan-ping, YANG Guang(曾 燕，郭兰萍，杨 光). ChineseJournal of Experimental Traditional Medical Formulae(中国实验方剂学杂志), 2012, 18(17): 313.
[5] Xie Hongping, Jiang Jianhui, Chen Zeqin. Analytical Sciences, 2006, 22(8): 1111.
[6] WANG Ling-ling, HUANG Ya-wei, QI Shu-ye(王灵灵，黄亚伟，戚淑叶). Spectral Instrument and Analysis(光谱仪器与分析), 2012, 32(4): 925.
[7] HUANG Ya-wei, WANG Jia-hua, SHAN Jacqueline J(黄亚伟，王加华，SHAN Jacqueline J). Analytical Chemistry(分析化学), 2011, 39(3): 377.
[8] WOO Young-ah, Cho Changhee, KIM Hyo-jin. Microchemical Journal, 2002, 73(3): 299.
[9] Xu Xinfang, Nie Lixing, Pan Lilian. Journal of Analytical Methods in Chemistry, 2014: 1. |
| [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] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|
