Discrimination of easily confused tea leaves with similar appearance (Gougu tea vs. Gonglao tea) via an integrated method of electronic tongue, HPLC-QTOF-MS-VirtualTaste, electronic nose, electrochemical fingerprinting and machine learning

IF 4.6 2区 农林科学 Q2 CHEMISTRY, APPLIED
Rui-Bo Sun , Yue-Hua Chen , Xin-Ru Zhang , Fang-Tong Liu , Wen-Yu Wang , Jia-Nuo Zhang , Yi-Fan Wang , Hui Zhang , Ming Xie , Gui-Zhong Xin , Hui-Peng Song
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Abstract

Gougu tea (GG) and Gonglao tea (GL) were historically misclassified in tea markets for centuries due to their highly similar appearance. To resolve this long-standing challenge, our study focused on two objectives: elucidating the necessity for differentiating them, and constructing an efficient method for their discrimination. For the first time, E-tongue, HPLC-QTOF-MS-VirtualTaste, E-nose, electrochemical fingerprinting, and machine learning were integrated to comprehensively analyze their differences in flavor and composition. E-tongue analysis confirmed bitterness as a shared sensory attribute in GG and GL, while HPLC-QTOF-MS-VirtualTaste revealed their distinct bitter components. Organic acids and triterpenes predominated among the 85 taste components in GG, while alkaloids predominated among the 60 taste components in GL. Quantitative analysis showed that the average chlorogenic acid content (GG's primary bitter component) was 6.4787 mg/g, whereas berberine (GL's main bitter component) reached 17.0383 mg/g. E-nose analysis detected 51 and 38 volatile components in GG and GL, respectively. Eleven common components primarily exhibited fruity and sweet sensory characteristics. Furthermore, electrochemical fingerprinting combined with the random forest algorithm was established, achieving 99.85 % discrimination accuracy. Moreover, this approach possessed the advantages of low cost and simplicity. Our research contributes to addressing the centuries-old challenge of market confusion between GG and GL.
利用电子舌、HPLC-QTOF-MS-VirtualTaste、电子鼻、电化学指纹和机器学习等综合方法对外观相似的易混淆茶叶(沟沟茶和公老茶)进行鉴别
由于两种茶的外观非常相似,几个世纪以来,它们在茶叶市场上被错误地分类。为了解决这一长期存在的挑战,我们的研究集中在两个目标上:阐明区分它们的必要性,构建一种有效的区分它们的方法。首次将电子舌、HPLC-QTOF-MS-VirtualTaste、电子鼻、电化学指纹和机器学习等技术相结合,综合分析了它们在风味和成分上的差异。电子舌分析证实了GG和GL的苦味是共同的感官属性,而HPLC-QTOF-MS-VirtualTaste显示了它们不同的苦味成分。85种苦味成分以有机酸和三萜为主,60种苦味成分以生物碱为主。定量分析表明,绿原酸(GG的主要苦味成分)的平均含量为6.4787 mg/g,小檗碱(GL的主要苦味成分)的平均含量为17.0383 mg/g。电子鼻分析分别检测出51种和38种挥发性成分。11种常见成分主要表现出果味和甜味的感官特征。建立了结合随机森林算法的电化学指纹识别方法,识别准确率达到99.85 %。此外,该方法具有成本低、简单等优点。我们的研究有助于解决几个世纪以来GG和GL之间的市场混淆的挑战。
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来源期刊
Journal of Food Composition and Analysis
Journal of Food Composition and Analysis 工程技术-食品科技
CiteScore
6.20
自引率
11.60%
发文量
601
审稿时长
53 days
期刊介绍: The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects. The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.
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