数字农科院2.0

Aroma analysis and biomarker screening of 27 tea cultivars based on four leaf color types

文献类型: 外文期刊

作者: Feiquan Wang;Hua Feng;Yucheng Zheng;Ruihua Liu;Jiahao Dong;Yao Wu;Shuai Chen;Bo Zhang;Pengjie Wang;Jiawei Yan

作者机构:

关键词: Aroma metabolomics;Biomarker;GC–MS;Tea plant

期刊名称: Food Research International

ISSN: 0963-9969

年卷期: 2025 年 201 卷

页码:

收录情况: SCIE(2025版) ; ; EI(2025版)

摘要: Green is no longer the only color used to describe tea leaves. As tea plants with different leaf colors—white, yellow, and purple—yield significant economic benefits, scholars are growing increasingly curious about whether these differently colored leaves possess unique aromatic characteristics. Headspace solid-phase microextraction (HS-SPME) combined with GC–MS was used to analyze the volatile metabolites of buds and leaves from 7 white-leaf tea plants, 9 yellow-leaf tea plants, 4 purple-leaf tea plants, and 7 normal (green) tea plants. A total of 125 aroma metabolites were identified. The aroma compounds of heterochromatic tea leaves and green-leaf tea were compared separately. It was found that white-leaf tea had the most upregulated compounds (63 up), mainly floral and fruity aromas, including nerol, Z-isogeraniol, and E-3-hexen-1-yl acetate. Purple-leaf tea had the most downregulated compounds (31 down), including β-myrcene, benzyl alcohol, and methyl salicylate, which are related to fresh and fruity aromas. According to variable importance in projection (VIP > 1) and a p-value < 0.05, a total of 40 differential compounds were detected, among which Z-3-hexenol, 1-nonanol, 2,4-di-tert-butylphenol, and 2,6,10,15-tetramethyl-heptadecane were common in all heterochromatic tea. The random forest model constructed using differential metabolites screened out five aroma metabolites, including Z-3-hexenyl isobutyrate, E-3-hexen-1-yl acetate, 2,4-di-tert-butylphenol, Z-jasmone, and Z-isogeraniol. These metabolites demonstrated high accuracy in the model (AUC = 1) and have the potential to serve as characteristic aroma compounds for distinguishing tea leaf colors.

分类号:

  • 相关文献

[1]设施葡萄不同新梢间距处理对冠层光环境及果实品质的影响. 史祥宾,刘凤之,程存刚,王孝娣,冀晓昊,王宝亮,郑晓翠,王海波. 2018

[2]Dynamic lipid profile of hyperlipidemia mice. Chen, Yu-Lian,Hu, Zhi-Xiong,Liu, Xiao-Shan,Liu, Zhiguo,Zhao, Xiu-Ju,Zhao, Xiu-Ju,Zhang, Wei-Nong,Xiao, Chuan-Hao.

[3]Biological control of gray mold of tomato by Bacillus altitudinis B1-15. Jia Song,Ling Ling,Xi Xu,Mengqi Jiang,Lifeng Guo,Qiuying Pang,Wen Sheng Xiang,Junwei Zhao,Xiangjing Wang. 2023

[4]Volatolomics-assisted characterization of the key odorants in green off-flavor black tea and their dynamic changes during processing. Yanqin Yang,Jialing Xie,Qiwei Wang,Lilei Wang,Yan Shang,Yongwen Jiang,Haibo Yuan. 2024

[5]Unraveling the dynamic changes of volatile compounds during the rolling process of Congou black tea via GC-E-nose and GC–MS. Qiwei Wang,Daliang Shi,Jiajing Hu,Jiahao Tang,Xianxiu Zhou,Lilei Wang,Jialing Xie,Yongwen Jiang,Haibo Yuan,Yanqin Yang. 2024

[6]Comprehensive investigation on the dynamic changes of volatile metabolites in fresh scent green tea during processing by GC-E-Nose, GC–MS, and GC × GC-TOFMS. Qiwei Wang,Jialing Xie,Lilei Wang,Yongwen Jiang,Yuliang Deng,Jiayi Zhu,Haibo Yuan,Yanqin Yang. 2024

[7]Multivariate and multi-interface insights into carbon and energy recovery and conversion characteristics of hydrothermal carbonization of biomass waste from duck farm. Ting Yan,Tao Zhang,Shunli Wang,Kruse Andrea,Hua Peng,Haihang Yuan,Zhiping Zhu. 2023

[8]Comparison of Volatile Compounds in Jingshan Green Tea Scented with Different Flowers Using GC-IMS and GC-MS Analyses. Zhiwei Hou,Ziyue Chen,Le Li,Hongping Chen,Huiyuan Zhang,Sitong Liu,Ran Zhang,Qiyue Song,Yuxuan Chen,Zhucheng Su,Liying Xu. 2024

[9]Quality Assessment of Loquat under Different Preservation Methods Based on Physicochemical Indicators, GC–MS and Intelligent Senses. Mingfeng Qiao,Siyue Luo,Zherenyongzhong Z,Xuemei Cai,Xinxin Zhao,Yuqin Jiang,Baohe Miao. 2024

[10]Optimization of the determination of volatile organic compounds in plant tissue and soil samples: Untargeted metabolomics of main active compounds. Kanjana N.,Ahmed M.A.,Shen Z.,Li Y.,Zhang L.. 2024

[11]Optimized extraction methodology for phenolic compounds in soil and plant tissues: Their implications in plant growth and gall formation. Nipapan Kanjana,Yuyan Li,Muhammad Afaq Ahmed,Zhongjian Shen,Lisheng Zhang. 2024

[12]Multidimensional analysis of the flavor characteristics of yellow peach at different ripening stages: Chemical composition profiling and sensory evaluation. Huayu Liu,Minghao Zhang,Mingshen Su,Wenfang Zeng,Shouchuang Wang,Jihong Du,Huijuan Zhou,Xiaofeng Yang,Xianan Zhang,Xiongwei Li,Zhengwen Ye. 2025

[13]Exploring the Effects of Nitrogen and Potassium on the Aromatic Characteristics of Ginseng Roots Using Non-Targeted Metabolomics Based on GC-MS and Multivariate Analysis. Weiyu Cao,Hai Sun,Cai Shao,Hongjie Long,Yanmei Cui,Changwei Sun,Yayu Zhang. 2025

[14]Characterization of the microbial community in different types of Daqu samples as revealed by 16S rRNA and 26S rRNA gene clone libraries. Zheng, Xiao-Wei,Yan, Zheng,Han, Bei-Zhong,Zheng, Xiao-Wei,Nout, M. J. Robert,Zwietering, Marcel H.,Smid, Eddy J.,Yan, Zheng,Boekhout, Teun.

[15]Nonylphenol Toxicity Evaluation and Discovery of Biomarkers in Rat Urine by a Metabolomics Strategy through HPLC-QTOF-MS. Zhang, Yan-Xin,Yang, Xin,Zou, Pan,Du, Peng-Fei,Wang, Jing,Jin, Fen,Jin, Mao-Jun,She, Yong-Xin,Du, Peng-Fei,Wang, Jing,Jin, Fen,Jin, Mao-Jun,She, Yong-Xin. 2016

[16]Measuring the damage of heavy metal cadmium in rice seedlings by SRAP analysis combined with physiological and biochemical parameters. Zhang, Xiaoqin,Chen, Huinan,Lu, Wenyi,Pan, Jiangjie,Qian, Qian,Xue, Dawei,Jiang, Hua,Qian, Qian.

[17]The application of proteomics in different aspects of hepatocellular carcinoma research. Xing, Xiaohua,Liang, Dong,Huang, Yao,Zeng, Yongyi,Liu, Xiaolong,Liu, Jingfeng,Liang, Dong,Xing, Xiaohua,Huang, Yao,Zeng, Yongyi,Liu, Xiaolong,Liu, Jingfeng,Huang, Yao,Zeng, Yongyi,Liu, Jingfeng,Han, Xiao.

[18]Metabolomic signatures for liver tissue and cecum contents in high-fat diet-induced obese mice based on UHPLC-Q-TOF/MS. Hongying Cai,Zhiguo Wen,Kun Meng,Peilong Yang. 2021

[19]Metabolite changes of apple Penicillium expansum infection based on a UPLC-Q-TOF metabonomics approach. Youming Shen,Mingyu Liu,Jiyun Nie,Ning Ma,Guofeng Xu,Jianyi Zhang,Yinping Li,Haifei Li,Lixue Kuang,Zhiyuan Li. 2021

[20]Characterization and difference of lipids and metabolites from Jianhe White Xiang and Large White pork by high-performance liquid chromatography–tandem mass spectrometry. Run Zhang,Man Yang,Xinhua Hou,Renda Hou,Ligang Wang,Lijun Shi,Fuping Zhao,Xin Liu,Qingshi Meng,Lixian Wang,Longchao Zhang. 2022

作者其他论文 更多>>