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Yuan X, Chen X, Chai C, Feng M, Hu Y, Yi Z, Gu Y, Ruan L, Yi L. Identifying key contributors to the sweet aftertaste of raw Pu-erh tea through analytical and sensory methods. Food Chem 2025; 481:144067. [PMID: 40179506 DOI: 10.1016/j.foodchem.2025.144067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Revised: 03/12/2025] [Accepted: 03/25/2025] [Indexed: 04/05/2025]
Abstract
In this study, the key components contributing to raw Pu-erh tea (RAPT) sweet aftertaste were identified. Six RAPTs were investigated through sensory evaluation, mass spectrometry, and taste addition experiments, and 96 taste components of tea infusion were annotated and analyzed. Saliva analysis after drinking tea revealed that 27 components present in tea remained in the mouth. On the basis of the results of the multivariate statistical analyses, we hypothesized that alkaloids and flavonoids might influence the sweet aftertaste strength of RAPT. Finally, the results of the taste addition experiments revealed that theophylline and rutin are key components that significantly influence the sweet aftertaste intensity of the RAPT. This strategy can be used as a methodology for analyzing the taste of tea, and the results can provide an evaluation index for evaluating the quality of RAPT.
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Affiliation(s)
- Xiaoping Yuan
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Xian Chen
- Kunming Institute for Food and Drug Control, Kunming, 650032, China
| | - Chunrong Chai
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Min Feng
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Yongdan Hu
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Zhibiao Yi
- Kunming Huzhimeng Pharmaceutical Co., LTD., Kunming 652201, China
| | - Ying Gu
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China.
| | - Linguang Ruan
- Yunnan State Farms Group CO., LTD., Kunming 650233, China.
| | - Lunzhao Yi
- Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650500, China; Yunnan State Farms Group CO., LTD., Kunming 650233, China.
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Yang Z, Lu X, Chen L. Discriminating the adulteration of varieties and misrepresentation of vintages of Pu'er tea based on Fourier transform near infrared diffuse reflectance spectroscopy. Front Chem 2025; 13:1546702. [PMID: 39974614 PMCID: PMC11835838 DOI: 10.3389/fchem.2025.1546702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2024] [Accepted: 01/17/2025] [Indexed: 02/21/2025] Open
Abstract
In the Pu'er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu'er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu'er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu'er tea quality inspection, and offering a robust method for combating the pervasive issues within the market.
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Affiliation(s)
- Zhenfa Yang
- State Key Laboratory of Massive Personalized Customization System and Technology, Qingdao, China
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Xiaoping Lu
- State Key Laboratory of Massive Personalized Customization System and Technology, Qingdao, China
| | - Lucheng Chen
- State Key Laboratory of Massive Personalized Customization System and Technology, Qingdao, China
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Wang Z, Han Y, Zhang L, Ye Y, Wei L, Li L. The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes. Food Chem X 2024; 22:101447. [PMID: 38779497 PMCID: PMC11108843 DOI: 10.1016/j.fochx.2024.101447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/21/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
Dark tea refers to a kind of post-fermented product, and its quality and price vary owing to the distinct altitudes at which it grows. In this study, a novel method based on high performance liquid chromatography with a diode-array detector (HPLC-DAD) and an evaporative light scattering detector (HPLC-ELSD) was proposed for the classification of dark teas from distinct altitudes in China. Through implementing a strategy fusing feature-level data to construct a combined dataset, the classification performance of dark teas from distinct altitudes in China was evaluated after preprocessing. The results suggested that, through the feature fusion strategy, the identification accuracy rate increased from <70% of a single detector to 76.923%. After the implementation of preprocessing, the identification accuracy rate was further improved. Typically, the model identification accuracy rate after short-time Fourier Transform (STFT) treatment reached 92.85%, and the AUROC value was higher than 0.84, exhibiting a favorable generalization ability. This study provides a new thinking for the identification technology of dark teas from different altitudes in China.
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Affiliation(s)
- Zhenhong Wang
- Resources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
| | - Yuanxi Han
- Food Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China
| | - Liyou Zhang
- Resources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
| | - Yongxiang Ye
- Food Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China
| | - Liping Wei
- Resources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
| | - Liang Li
- Food Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China
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Ma C, Gao C, Li Y, Zhou X, Fan G, Tian D, Huang Y, Li Y, Zhou H. The Characteristic Aroma Compounds of GABA Sun-Dried Green Tea and Raw Pu-Erh Tea Determined by Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry and Relative Odor Activity Value. Foods 2023; 12:4512. [PMID: 38137315 PMCID: PMC10742727 DOI: 10.3390/foods12244512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 11/26/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
We aim to improve the product quality of GABA raw Pu-erh tea during development and processing. In this study, headspace solid-phase microextraction gas chromatography-mass spectrometry technology combined with relative odor activity evaluations was used to compare the volatile compounds of GABA sun-dried green tea and GABA raw Pu-erh tea. Sensory evaluation showed a higher aroma score of GABA raw Pu-erh tea than that of GABA sun-dried green tea, with significant differences in aroma type and purity. A total of 147 volatile compounds of 13 categories were detected, which differed in composition and quantity between the two teas. 2-Buten-1-one,1-(2,6,6-trimethyl-1,3-cyclohexadien-1-yl)-,(E)- and beta.-myrcene largely contributed to the aroma formation of both teas. Five volatile compounds were screened as potential markers for tea aroma. Metabolic pathway analysis showed that monoterpenoid biosynthesis may be beneficial to the formation of flowery and fruity aromas in the teas. We suggest that the findings of this study may provide important guidance for the processing and optimization of GABA tea.
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Affiliation(s)
- Chenyang Ma
- College of Tea Science, Yunnan Agricultural University, Kunming 650500, China; (C.M.); (C.G.); (X.Z.); (G.F.)
| | - Chang Gao
- College of Tea Science, Yunnan Agricultural University, Kunming 650500, China; (C.M.); (C.G.); (X.Z.); (G.F.)
| | - Yuanda Li
- Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha 410128, China;
| | - Xiaohui Zhou
- College of Tea Science, Yunnan Agricultural University, Kunming 650500, China; (C.M.); (C.G.); (X.Z.); (G.F.)
| | - Guofu Fan
- College of Tea Science, Yunnan Agricultural University, Kunming 650500, China; (C.M.); (C.G.); (X.Z.); (G.F.)
| | - Di Tian
- College of Food Science and Technology, Yunnan Agricultural University, Kunming 650500, China;
| | - Yuan Huang
- College of Pu-Erh Tea, West Yunnan University of Applied Sciences, Puer 671000, China;
| | - Yali Li
- College of Tea Science, Yunnan Agricultural University, Kunming 650500, China; (C.M.); (C.G.); (X.Z.); (G.F.)
| | - Hongjie Zhou
- College of Food Science and Technology, Yunnan Agricultural University, Kunming 650500, China;
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Chien HJ, Zheng YF, Wang WC, Kuo CY, Hsu YM, Lai CC. Determination of adulteration, geographical origins, and species of food by mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:2273-2323. [PMID: 35652168 DOI: 10.1002/mas.21780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.
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Affiliation(s)
- Han-Ju Chien
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Yi-Feng Zheng
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Wei-Chen Wang
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Cheng-Yu Kuo
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Yu-Ming Hsu
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
| | - Chien-Chen Lai
- Institute of Molecular Biology, National Chung Hsing University, Taichung, Taiwan
- Graduate Institute of Chinese Medical Science, China Medical University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan
- Rong Hsing Research Center For Translational Medicine, National Chung Hsing University, Taichung, Taiwan
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Machine learning applications for identify the geographical origin, variety and processing of black tea using 1H NMR chemical fingerprinting. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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Farag MA, Elmetwally F, Elghanam R, Kamal N, Hellal K, Hamezah HS, Zhao C, Mediani A. Metabolomics in tea products; a compile of applications for enhancing agricultural traits and quality control analysis of Camellia sinensis. Food Chem 2023; 404:134628. [DOI: 10.1016/j.foodchem.2022.134628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/06/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
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Yin XL, Fu WJ, Chen Y, Zhou RF, Sun W, Ding B, Peng XT, Gu HW. GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.114148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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9
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Metabolomic navigated Citrus waste repurposing to restore amino acids disorder in neural lesion. Food Chem 2022; 387:132933. [DOI: 10.1016/j.foodchem.2022.132933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/01/2022] [Accepted: 04/07/2022] [Indexed: 12/23/2022]
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Excitation-emission matrix fluorescence spectroscopy coupled with chemometric methods for characterization and authentication of Anhua brick tea. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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11
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Pu-erh tea: A review of a healthful brew. JOURNAL OF TRADITIONAL CHINESE MEDICAL SCIENCES 2022. [DOI: 10.1016/j.jtcms.2022.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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12
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Wang S, Qiu Y, Gan RY, Zhu F. Chemical constituents and biological properties of Pu-erh tea. Food Res Int 2022; 154:110899. [DOI: 10.1016/j.foodres.2021.110899] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 12/21/2022]
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13
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Discrimination and Recognition of Bentong Ginger Based on Multi-elemental Fingerprints and Chemometrics. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02167-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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