Genotypic variation of beta-carotene and lutein contents in tea germplasms, Camellia sinensis (L.) O. Kuntze
文献类型: 外文期刊
第一作者: Wang, Xin-Chao
作者: Wang, Xin-Chao;Chen, Liang;Ma, Chun-Lei;Yao, Ming-Zhe;Yang, Ya-Jun
作者机构:
关键词: Tea plant;Germplasm;Camellia sinensis;Beta-carotene;Lutein;Food analysis;Food composition
期刊名称:JOURNAL OF FOOD COMPOSITION AND ANALYSIS ( 影响因子:4.556; 五年影响因子:4.89 )
ISSN: 0889-1575
年卷期: 2010 年 23 卷 1 期
页码:
收录情况: SCI
摘要: Tea is the most popular non-alcoholic healthy beverage in the world, and its water-soluble components such as polyphenols hold important benefits for human health. The lipid-soluble components such as beta-carotene and lutein, however, have not yet been utilized. In order to assess the possibility in using tea leaves as a new source of natural carotenoids, beta-carotene and lutein contents were estimated in new spring shoots of different genotypes and leaf positions of tea plant (Camellia sinensis) using a reverse-phase HPLC approach. There was no significant difference among the three assessed tea varieties (C. sinensis var. sinensis, var. assamica and var. pubilimba) from four different countries (p = 0.549 and 0.092, respectively). But significant differences were found among different leaf positions (maturity). The highest levels of beta-carotene (343.2 mg/kg) and lutein (232.0 mg/kg) were found in C. sinensis var. assamica and, highest coefficient of variation was present in C. sinensis var. sinensis (44.3%). The highest level of beta-carotene content and lutein contents were 42.1 and 16.3 times higher than the lowest among the studied 119 germplasms, respectively. Furthermore, contents of beta-carotene and lutein in mature leaves were higher than young leaves and stems. The results showed that tea may have potential as a new source of carotenoids. (C) 2009 Elsevier Inc. All rights reserved.
分类号:
- 相关文献
作者其他论文 更多>>
-
Mechanisms of Baicalin Alleviates Intestinal Inflammation: Role of M1 Macrophage Polarization and Lactobacillus amylovorus
作者:Zhang, Shunfen;Zhong, Ruqing;Li, Kai;Wang, Huixin;Xu, Ye;Liu, Dadan;Chen, Liang;Zhang, Hongfu;Zhang, Shunfen;Lv, Huiyuan;Ma, Qiugang;Zhou, Miao
关键词:baicalin;
E. coli ; intestinal inflammation;Lactobacillus amylovorus ; macrophages polarization; TLR4 -
Evaluation and Analysis of Traditional Chinese Medicine Treatment of Bovine Viral Diarrhea/Mucosal Disease Based on Network Pharmacology and In Vitro Studies
作者:Chen, Liang;Lan, Shijie;Shen, Sisi;Wang, Jiahui;Liu, Xuesong;Jiang, Botao;Zhong, Peng;Liu, Bochao;Yao, Shuang;Qin, Pingwei;Feng, Wanyu
关键词:Bovine viral diarrhea/mucosal disease; traditional Chinese medicine; target antiviral; web-based drug screening
-
Integration of digital phenotyping, GWAS, and transcriptomic analysis revealed a key gene for bud size in tea plant (Camellia sinensis)
作者:Zhang, Shuran;Chen, Si;Fu, Zhilu;Li, Fang;Chen, Qiyu;Ma, Jianqiang;Chen, Liang;Chen, Jiedan;Chen, Yuanquan
关键词:
-
A cellulose-degrading Bacillus altitudinis from Tibetan pigs improved the in vitro fermentation characteristics of wheat bran
作者:Wang, Junhong;Ma, Teng;Xie, Yining;Li, Kai;Luo, Chengzeng;Teng, Chunran;Yi, Bao;Chen, Liang;Zhang, Hongfu
关键词:Cellulose-degrading bacteria; Wheat bran; Tibetan pigs; Fermentation
-
Discovery of novel thiazole-pleuromutilin derivatives with potent antibacterial activity
作者:Qi, Xian-Long;Zhang, He-Chao;Xu, Xiao;Liu, Xi-Wang;Yang, Ya-Jun;Li, Zhun;Li, Jian-Yong
关键词:Pleuromutilin derivatives; Thiazole; Synthesis; Antibacterial activity; Toxicity
-
Synthesis, Antimicrobial Activities, and Model of Action of Indolyl Derivatives Containing Amino-Guanidinium Moieties
作者:Li, Yu-Xi;Geng, Xiang;Tao, Qi;Hao, Ruo-Chen;Yang, Ya-Jun;Liu, Xi-Wang;Li, Jian-Yong;Geng, Xiang
关键词:aminoguanidine; indole; synthesis; antibacterial activity;
K. pneumoniae -
KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction
作者:Li, Mianyan;Wang, Lixian;Zhao, Fuping;Li, Mianyan;Hall, Thomas;Machugh, David E.;Machugh, David E.;Machugh, David E.;Chen, Liang;Garrick, Dorian
关键词:KPRR; polynomial kernel; machine learning; genomic prediction; nonadditive effects