光谱学与光谱分析 |
|
|
|
|
|
Classification of Licorice Based on Inorganic Elements Characteristics |
ZHOU Ying-qun1, 2, YU Hua1, LI Ying1, SUN Su-qin3*, ZHAO Run-huai2, CHEN Shi-lin1 |
1. Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100193, China 2. China National Group Corp. of Traditional & Herbal Medicine, Beijing 100195, China 3. Department of Chemistry, Tsinghua University, Beijing 100084, China |
|
|
Abstract The present study focused on the ecological characteristics of medicinal plants Glycyrrhiza uralensis from two typical ecological environments with two different growth patterns respectively. The authors detected the contents of 16 kinds of inorganic elements in 24 licorice samples with two different producing areas (i.e. Gansu and Inner-Mongolia) and growth patterns (i.e., wild species and the cultivated), using the methods of ICP-MS and ICP-AES after microwave-assisted digestion. With the systematic analytic methods, including the analysis of total element distribution, Q-type cluster analysis of the characteristic elements, and the comparison of element and the ratio of two elements among the samples one by one, the authors constructed the inorganic element fingerprint chromatogram of G. uralensis based on the contents of the 16 inorganic elements (K, Ca, Na, Mg, P, Ca, Cr, Mn, Fe, S, Ni, Cu, Zn, Sr, Mo and Sn). Based on the characteristic elements selected by principal component analysis, the result of Q-type cluster analysis showed consistency with the growth pattern of licorice. By comparing the differences of the inorganic elements in different samples, the authors discovered that the combination of elements Mo and Sr not only provides the bases for the growth pattern of licorice, but also can be used as a diagnostic criteria for the division of its producing area. This study also indicated that the content ratios of Na∶P and K∶Ca can also provide reliable references for the assessment of different production patterns. It gives insight into the differences in the inorganic element of licorice with different producing area and production pattern treatments. In conclusion, the method we founded here turned out to be intuitive, informative, and highly accurate, and can be used to reveal the characteristic of inoganic elements in medicinal plant.
|
Received: 2009-06-29
Accepted: 2009-09-30
|
|
Corresponding Authors:
SUN Su-qin
E-mail: sunsq@mail.tsinghua.edu.cn
|
|
[1] LIN Shou-quan,LIN Lin(林寿全,林 琳). Chinese Journal of Ecology(生态学杂志),l992,11(6):17. [2] WANG Yue-fei,WEN Hong-mei,GUO Li-wei,et al(王跃飞,文红梅,郭立玮,等). Chinese Traditional and Herbal Drugs(中草药),2006,37(3):435. [3] LU Shou-ping,SUN Qun,WANG Jian-hua,et al(鲁守平,孙 群,王建华,等). Chinese Traditional and Herbal Drugs(中草药),2005,36(8):1261. [4] ZHAO Ze-hai,YANG Feng-jian,CAO Jian-guo,et al(赵则海,杨逢建,曹建国,等). Bulletin of Botanical Research(植物研究),2005,25(4):444. [5] Giancarlo S,Rosa T,Gianni S,et al. Fitoterapia,2004,75:371. [6] MI Mu-zhen,ZHANG Li-xia(米慕真,张莅峡). Journal of Shengyang Pharmaceutical University(沈阳药科大学学报),1995,12(3):214. [7] LI Qian,REN Qian,WANG Dang-ping,et al(李 强,任 茜,王党平,等). Territory & Natural Resources Study(国土与自然资源研究),1998,2:66. [8] SHENG Ji-ping,LIU Can,SHEN Lin(生吉萍, 刘 灿, 申 琳). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009,29(1):247. [9] JIN Hang,CUI Xiu-ming,XU Luo-shan(金 航,崔秀明,徐珞珊). Journal of Yunnan University(云南大学学报), 2006,28(2):144. [10] GUO Lan-ping,HUANG Lu-qi,YAN Yu-ning(郭兰萍,黄璐琦,阎玉凝). China Journal of Chinese Materia Medica(中国中药杂志),2002,27(4):245. [11] Yoshiki M, Yasuyuki T, Nagayo O. Chem. Pharm. Bull., 1984, 32(2): 571. [12] LI Zhen-hua,ZHANG Li,LI Hong-gang(李振华,张 莉,李洪刚). Gansu Medical Journal(甘肃医药),1995,14(1):37. [13] China National Group Corp. of Traditional &Herbal Medicine(中国药材公司编). Regionalization of Traditional Chinese Medicine(中国中药区划). Beijing: Science Press(北京: 科学出版社),1995. 63. [14] LI Ming,WANG Gen-xuan,WEI Xiao-ping(李 明,王根轩,魏小平). Acta Botanica Borealis-Occidenttalia Sin.(西北植物学报),2006,26(2):368. [15] Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences(中国医学科学院药用植物研究所). Cultivation of Medicinal Plants in China(中国药用植物栽培学). Beijing: Agricultural Press(北京: 农业出版社),1991. 102.
|
[1] |
ZHU Zhao-zhou1*, YANG Xin-xin1, LI Jun1, HE Hui-jun2, ZHANG Zi-jing1, YAN Wen-rui1. Determination of Rare Earth Elements in High-Salt Water by ICP-MS
After Pre-Concentration Using a Chelating Resin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1862-1866. |
[2] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
[3] |
WANG Bin1, 2, ZHENG Shao-feng2, LI Wei-cai2, ZHONG Kang-hua2, GAN Jiu-lin1, YANG Zhong-min1, SONG Wu-yuan3*. Determination of Rare Earth Elements in Imported Copper Concentrate by Inductively Coupled Plasma Mass Spectrometry With High Matrix
Injection System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1822-1826. |
[4] |
MIAO Shu-guang1, SHAO Dan1*, LIU Zhong-yu2, 3, FAN Qiang1, LI Su-wen1, DING En-jie2, 3. Study on Coal-Rock Identification Method Based on Terahertz
Time-Domain Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1755-1760. |
[5] |
TIAN Xue1, CHE Qian1, YAN Wei-min1, OU Quan-hong1, SHI You-ming2, LIU Gang1*. Discrimination of Millet Varieties and Producing Areas Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1841-1847. |
[6] |
WANG Ling-ling1, 2, 3, WANG Bo1, 2, 3, XIONG Feng1, 2, 3, YANG Lu-cun1, 2, LI Jing-jing4, XIAO Yuan-ming1, 2, 3, ZHOU Guo-ying1, 2*. A Comparative Study of Inorganic Elements in Cultivativing Astragalus Membranaceus From Different Habitats[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1407-1412. |
[7] |
TAN Yang1, WU Xiao-hong2, 3*, WU Bin4, SHEN Yan-jun1, LIU Jin-mao1. Qualitative Analysis of Pesticide Residues on Chinese Cabbage Based on GK Improved Possibilistic C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1465-1470. |
[8] |
YANG Yu-qing1, CAI Jiang-hui1, 2*, YANG Hai-feng1*, ZHAO Xu-jun1, YIN Xiao-na1. LAMOST Unknown Spectral Analysis Based on Influence Space and Data Field[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1186-1191. |
[9] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
[10] |
YAO Shan1, ZHANG Xuan-ling1, CAI Yu-xin1, HE Lian-qiong1, LI Jia-tong1, WANG Xiao-long1, LIU Ying1, 2*. Study on Distribution Characteristics of Different Nitrogen and
Phosphorus Fractions by Spectrophotometry in Baiyangdian
Lake and Source Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1306-1312. |
[11] |
CAO Qiu-hong, LIN Hong-mei, ZHOU Wei, LI Zhao-xin, ZHANG Tong-jun, HUANG Hai-qing, LI Xue-min, LI De-hua*. Water Quality Analysis Based on Terahertz Attenuated Total Reflection Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 31-37. |
[12] |
ZHANG Xin-xin1, LI Shang-ke1, LI Pao1, 2*, SHAN Yang2, JIANG Li-wen1, LIU Xia1. A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3695-3700. |
[13] |
WU Ye-lan1, CHEN Yi-yu1, LIAN Xiao-qin1, LIAO Yu2, GAO Chao1, GUAN Hui-ning1, YU Chong-chong1. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3837-3843. |
[14] |
OUYANG Ai-guo, WAN Qi-ming, LI Xiong, XIONG Zhi-yi, WANG Shun, LIAO Qi-cheng. Research on Rich Borer Detection Methods Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3844-3850. |
[15] |
LIN Hong-mei1, CAO Qiu-hong1, ZHANG Tong-jun1, LI Zhao-xin1, HUANG Hai-qing1, LI Xue-min1, WU Bin2, ZHANG Qing-jian3, LÜ Xin-min4, LI De-hua1*. Identification of Nephrite and Imitations Based on Terahertz Time-Domain Spectroscopy and Pattern Recognition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3352-3356. |
|
|
|
|