Evaluation of Chinese tea by the electronic nose and gas chromatography-mass spectrometry: Correlation with sensory properties and classification according to grade level
Evaluation of Chinese tea by the electronic nose and gas chromatography-mass spectrometry: Correlation with sensory properties and classification according to grade level
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DOI：
10.1016/j.foodres.2013.02.005
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发表时间：
2013-10-01
影响因子：
8.1
通讯作者：
Wu, Jihong
中科院分区：
文献类型：
--
作者：
Qin, Zihan;Pang, Xueli;Wu, Jihong
采用电子鼻、气相色谱-质谱联用技术和定量描述分析技术对中国高、中、低档绿茶和红茶的风味特征进行了比较分析。感官分析显示，不同品级的茶叶之间存在显著差异，表明其与茶叶品级分类中的市场价格吻合较好。高品级的茶叶样品在令人愉悦的香气特性（纯净持久、麦芽味、甜味、柑橘味、果味、花香、清爽和海藻味）上得分最高。在3个品级的绿茶和红茶中共鉴定出37种挥发性化合物，其中己醛、A -松皮醇、水杨酸甲酯、香叶醇、芳樟醇及其氧化物在两种中国茶中含量最多。观察了三个等级的挥发性成分组成的差异。一般来说，优质茶比劣质茶有更多的挥发物。结果表明，电子鼻能快速、清晰地分辨出不同等级茶叶的差异。采用偏最小二乘回归（PLSR）建立了不同品级绿茶和红茶感官特征、GC-MS数据和电子鼻响应之间的关系模型。(C) 2013 Elsevier Ltd.版权所有。
Flavor profiles of high, middle and low grades of Chinese green and black teas were comparatively analyzed by electronic nose, gas chromatography-mass spectrometry (GC-MS) and quantitative descriptive analysis (QDA) in this study. Sensory analysis showed marked differences among different grades of tea, demonstrating it agreed well with the market price in tea grade classification. The high grade of tea samples got the highest score of pleasant aroma properties (pure and persistent, malty, sweet, citrus, fruity, floral flavor, refreshing, and seaweed). A total of 37 volatile compounds in three grades of green and black teas were identified and hexanal, a-terpinol, methyl salicylate, geraniol, linalool and its oxides were the most abundant compounds in both varieties of Chinese tea. Differences in the composition of volatile components from three grades were observed. In general, high grade tea had more volatiles than low grade tea. The results of electronic nose indicated that it could clearly and rapidly distinguish the difference among different grade teas. The relationship between sensory profiles, the GC-MS data and electronic nose responses of different grades of Chinese green and black teas was modeled by partial least squares regression (PLSR). (C) 2013 Elsevier Ltd. All rights reserved.