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Abstract

Tea () is one of the most popular nonalcoholic beverages in the world, second only to water. Six main types of teas are produced globally: green, white, black, oolong, yellow, and Pu-erh. Each type has a distinctive taste, quality, and cultural significance. The health-promoting effects of tea are attributed to the complex metabolite compositions present in tea leaves. These metabolite compositions vary in response to different factors. In addition to manufacturing processes in processed tea, the primary factors influencing variations of fresh tea leaf metabolites include genetics, cultivation management, and environmental conditions. Metabolomics approaches, coupled with high-throughput statistical analysis, offer promising tools for the comprehensive identification and characterization of tea leaf metabolites according to growing conditions, cultivation practices, manufacturing processes, seasonality, climate, cultivars, and geography. This review highlights the distinctive variations in fresh tea leaf metabolites, which change in response to various factors, using a metabolomics approach, which are also extended to various processed teas.

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2025-04-28
2025-06-24
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Literature Cited

  1. Ahmed S, Griffin T, Kraner D, Schaffner MK, Sharma D, et al. 2019.. Environmental factors variably impact tea secondary metabolites in the context of climate change: a systematic review. . Front. Plant Sci. 10::939
    [Crossref] [Google Scholar]
  2. Alcazar A, Ballesteros O, Jurado J, Pablos F, Martin M, et al. 2007.. Differentiation of green, white, black, oolong, and Pu-erh teas according to their free amino acids content. . J. Agric. Food Chem. 55::596065
    [Crossref] [Google Scholar]
  3. Alseekh S, Fernie AR. 2018.. Metabolomics 20 years on: What have we learned and what hurdles remain?. Plant J. 94::93342
    [Crossref] [Google Scholar]
  4. Anjali, Sumit K, Korra T, Thakur R, Arutselvan R, et al. 2023.. Role of plant secondary metabolites in defence and transcriptional regulation in response to biotic stress. . Plant Stress 3::100154
    [Crossref] [Google Scholar]
  5. Apel K, Hirt H. 2004.. Reactive oxygen species: metabolism, oxidative stress, and signal transduction. . Annu. Rev. Plant Biol. 55::37399
    [Crossref] [Google Scholar]
  6. Ashihara H. 2015.. Occurrence, biosynthesis and metabolism of theanine (γ-glutamyl-L-ethylamide) in plants: a comprehensive review. . Nat. Prod. Comm. 10::80310
    [Google Scholar]
  7. Boehm R, Cash SB, Anderson BT, Ahmed S, Griffin TS, et al. 2016.. Association between empirically estimated monsoon dynamics and other weather factors and historical tea yields in China: results from a yield response model. . Climate 4::20
    [Crossref] [Google Scholar]
  8. Cao H, Wang F, Lin H, Ye Y, Zheng Y, et al. 2020.. Transcriptome and metabolite analyses provide insights into zigzag-shaped stem formation in tea plants (Camellia sinensis). . BMC Plant Biol. 20::98
    [Crossref] [Google Scholar]
  9. Chang K. 2015.. World tea production and trade: current and future development. Rep. , FAO, Rome:. https://openknowledge.fao.org/server/api/core/bitstreams/c0ccb19d-1e9b-46e7-a3c3-b1917d1a7faf/content
    [Google Scholar]
  10. Chen H, Cui F, Li H, Sheng J, Lv J. 2013.. Metabolic changes during the Pu-erh tea pile-fermentation revealed by a liquid chromatography tandem mass-spectrometry-based metabolomics approach. . J. Food Sci. 78::C166572
    [Crossref] [Google Scholar]
  11. Chen S, Li M, Zheng G, Wang T, Lin J, et al. 2018a.. Metabolite profiling of 14 Wuyi Rock tea cultivars using UPLC-QTOF MS and UPLC-QqQ MS combined with chemometrics. . Molecules 23::104
    [Crossref] [Google Scholar]
  12. Chen S, Lin J, Liu H, Gong Z, Wang X, et al. 2018b.. Insights into tissue-specific specialized metabolism in Tieguanyin tea cultivar by untargeted metabolomics. . Molecules 23::1817
    [Crossref] [Google Scholar]
  13. Chen S, Liu H, Zhao X, Li X, Shan W, et al. 2020.. Non-targeted metabolomics analysis reveals dynamic changes of volatile and non-volatile metabolites during oolong tea manufacture. . Food Res. Int. 128::108778
    [Crossref] [Google Scholar]
  14. Cheng S, Fu X, Wang X, Liao Y, Zeng L, et al. 2017.. Studies on the biochemical formation pathway of the amino acid L-theanine in tea (Camellia sinensis) and other plants. . J. Food Chem. 65::721016
    [Crossref] [Google Scholar]
  15. Cheng Y, Mao J, Wang H, Li S, Jin X, et al. 2023.. Molecular basis of flavonoid accumulation in tea leaves grafted with Camellia sinensis var. assamica cv. “Yinghong9” as rootstock based on multi-omics analysis. . Sci. Hortic. 321::112290
    [Crossref] [Google Scholar]
  16. Cornelis MC, Tordoff MG, El-Sohemy A, van Dam RM. 2017.. Recalled taste intensity, liking and habitual intake of commonly consumed foods. . Appetite 109::18289
    [Crossref] [Google Scholar]
  17. Daglia M, Antiochia R, Sobolev AP, Mannina L. 2014.. Untargeted and targeted methodologies in the study of tea (Camellia sinensis L.). . Food Res. Int. 63::27589
    [Crossref] [Google Scholar]
  18. Dai W, Qi D, Yang T, Lv H, Guo L, et al. 2015.. Nontargeted analysis using ultraperformance liquid chromatography–quadrupole time-of-flight mass spectrometry uncovers the effects of harvest season on the metabolites and taste quality of tea (Camellia sinensis L.). . J. Agric. Food Chem. 63::986978
    [Crossref] [Google Scholar]
  19. Dai W, Xie D, Lu M, Li P, Lv H, et al. 2017.. Characterization of white tea metabolome: comparison against green and black tea by a nontargeted metabolomics approach. . Food Res. Int. 96::4045
    [Crossref] [Google Scholar]
  20. De Costa W, Mohotti AJ, Wijeratne MA. 2007.. Ecophysiology of tea. . Braz. J. Plant Physiol. 19:(4):299332
    [Crossref] [Google Scholar]
  21. de Mejia EG, Ramirez-Mares MV, Puangpraphant S. 2009.. Bioactive components of tea: cancer, inflammation and behavior. . Brain Behav. Immun. 23:(6):72131
    [Crossref] [Google Scholar]
  22. Fan K, Zhang Q, Liu M, Ma L, Shi Y, Ruan J. 2019.. Metabolomic and transcriptional analyses reveal the mechanism of C, N allocation from source leaf to flower in tea plant (Camellia sinensis. L). . J. Plant Physiol. 232::2008
    [Crossref] [Google Scholar]
  23. Fang R, Redfern SP, Kirkup D, Porter EA, Kite GC, et al. 2017.. Variation of theanine, phenolic, and methylxanthine compounds in 21 cultivars of Camellia sinensis harvested in different seasons. . Food Chem. 220::51726
    [Crossref] [Google Scholar]
  24. Farag MA, Elmetwally F, Elghanam R, Kamal N, Hellall K, et al. 2023.. Metabolomics in tea products; a compile of applications for enhancing agricultural and quality control analysis of Camellia sinensis. . Food Chem. 404::134628
    [Crossref] [Google Scholar]
  25. Fiehn O. 2002.. Metabolomics—the link between genotypes and phenotypes. . In Functional Genomics, ed. C Town , pp. 15571. Berlin:: Springer Nature
    [Google Scholar]
  26. Fraser K, Lane GA, Otter DE, Harrison SJ, Quek S-Y, et al. 2014.. Non-targeted analysis by LC-MS of major metabolite changes during the oolong tea manufacturing in New Zealand. . Food Chem. 151::394403
    [Crossref] [Google Scholar]
  27. Fujimura Y, Kurihara K, Ida M, Kosaka R, Miura D, et al. 2011.. Metabolomics-driven nutraceutical evaluation of diverse green tea cultivars. . PLOS ONE 6:(8):e23426
    [Crossref] [Google Scholar]
  28. Fujimura Y, Tachibana H. 2015.. Metabolomics of green tea. . In Genomics, Proteomics and Metabolomics in Nutraceuticals and Functional Foods, ed. D Bagchi, A Swaroop, M Bagchi , pp. 397406. New York:: Wiley & Sons
    [Google Scholar]
  29. Fujiwara M, Ando I, Arifuku K. 2006.. Multivariate analysis for 1H-NMR spectra of two hundred kinds of tea in the world. . Anal. Sci. 22:(10):130714
    [Crossref] [Google Scholar]
  30. Gunathilaka RPD, Smart JCR, Fleming CM. 2018.. Adaptation to climate change in perennial cropping systems: options, barriers and policy implications. . Environ. Sci. Policy 82::10816
    [Crossref] [Google Scholar]
  31. Harrigan GG, Martino-Catt S, Glenn KC. 2007.. Metabolomics, metabolic diversity and genetic variation in crops. . Metabolomics 3:(3):25972
    [Crossref] [Google Scholar]
  32. Huang H, Yao Q, Xia E, Gao L. 2018.. Metabolomics and transcriptomics analyses reveal nitrogen influences on the accumulation of flavonoids and amino acids in young shoots of tea plant (Camellia sinensis L.) associated with tea flavor. . J. Agric. Food Chem. 66:(37):982838
    [Crossref] [Google Scholar]
  33. Ikeda T, Altaf-Ul-Amin M, Parvin AK, Kanaya S, Yonetani T, Fukusaki E. 2008.. Predicting rank of Japanese green teas by derivative profiles of spectra obtained from Fourier transform near-infrared reflectance spectroscopy. . J. Comput. Aided Chem. 9::3746
    [Crossref] [Google Scholar]
  34. Ikeda T, Kanaya S, Yonetani T, Kobayashi A, Fukusaki E. 2007.. Prediction of Japanese green tea ranking by Fourier transform near-infrared reflectance spectroscopy. . J. Agric. Food Chem. 55:(24):990812
    [Crossref] [Google Scholar]
  35. Ji HG, Lee YR, Lee MS, Hwang KH, Kim EH, et al. 2017.. Metabolic phenotyping of various tea (Camellia sinensis L.) cultivars and understanding of their intrinsic metabolism. . Food Chem. 233::32130
    [Crossref] [Google Scholar]
  36. Ji HG, Lee YR, Lee MS, Hwang KH, Park CY, et al. 2018.. Diverse metabolite variations in tea (Camellia sinensis L.) leaves grown under various shade conditions revisited: a metabolomics study. . J. Agric. Food Chem. 66:(8):188997
    [Crossref] [Google Scholar]
  37. Jiang CK, Ma JQ, Apostolides Z, Chen L. 2019.. Metabolomics for a millenniums-old crop: tea plant (Camellia sinensis). . J. Agric. Food Chem. 67:(23):644557
    [Crossref] [Google Scholar]
  38. Jiao W, Zhang P, Cui C, Yan M, Li QX, et al. 2023.. Metabolic responses of tea (Camellia sinensis L.) to the insecticide thiamethoxam. . Pest Manag. Sci. 79::357080
    [Crossref] [Google Scholar]
  39. Johnson CH, Ivanisevic J, Siuzdak G. 2016.. Metabolomics: beyond biomarkers and towards mechanisms. . Nat. Rev. Mol. Cell Biol. 17:(7):45159
    [Crossref] [Google Scholar]
  40. Jumtee K, Bamba T, Fukusaki E. 2009.. Fast GC-FID based metabolic fingerprinting of Japanese green tea leaf for its quality ranking prediction. . J. Sep. Sci. 32:(13):2296304
    [Crossref] [Google Scholar]
  41. Jumtee K, Komura H, Bamba T, Fukusaki E. 2011.. Predication of Japanese green tea (sen-cha) ranking by volatile profiling using gas chromatography mass spectrometry and multivariate analysis. . J. Biosci. Bioeng. 112:(3):25255
    [Crossref] [Google Scholar]
  42. Kamau D. 2007.. Confounding factors affecting the growth and production of ageing tea agro-ecosystems: a review. . Tea 28:(1/2):2640
    [Google Scholar]
  43. Kamau DM, Spiertz JH, Oenema O, Owuor PO. 2008.. Productivity and nitrogen use of tea plantations in relation to age and genotype. . Field Crops Res. 108:(1):6070
    [Crossref] [Google Scholar]
  44. Kaneko S, Kumazawa K, Masuda H, Henze A, Hofmann T. 2006.. Molecular and sensory studies on the umami taste of Japanese green tea. . J. Agric. Food Chem. 54:(7):268894
    [Crossref] [Google Scholar]
  45. Kell DB. 2004.. Metabolomics and systems biology: making sense of the soup. . Curr. Opin. Microbiol. 7:(3):296307
    [Crossref] [Google Scholar]
  46. Keller AC, Weir TL, Broeckling CD, Ryan EP. 2013.. Antibacterial activity and phytochemical profile of fermented Camellia sinensis (Fuzhuan tea). . Food Res. Int. 53:(2):94549
    [Crossref] [Google Scholar]
  47. Kfoury N, Morimoto J, Kern A, Scott ER, Orians CM, et al. 2018.. Striking changes in tea metabolites due to elevational effects. . Food Chem. 264::33441
    [Crossref] [Google Scholar]
  48. Kfoury N, Scott ER, Orians CM, Ahmed S, Cash S, et al. 2019.. Plant-climate interaction effects: changes in the relative distribution and concentration of the volatile tea leaf metabolome in 2014–2016. . Front. Plant Sci. 10::1518
    [Crossref] [Google Scholar]
  49. Kim MJ, John KMM, Choi JN, Lee S, Kim AJ, et al. 2013.. Changes in secondary metabolites of green tea during fermentation by Aspergillus oryzae and its effect on antioxidant potential. . Food Res. Int. 53:(2):67077
    [Crossref] [Google Scholar]
  50. Kim S, Kim J, Yun EJ, Kim KH. 2016.. Food metabolomics: from farm to human. . Curr. Opin. Biotechnol. 37::1623
    [Crossref] [Google Scholar]
  51. Kito M, Kokura H, Izaki J, Sasaoka K. 1968.. Theanine, a precursor of the phloroglucinol nucleus of catechins in tea plants. . Phytochemistry 7:(4):599603
    [Crossref] [Google Scholar]
  52. Ku KM, Choi JN, Kim J, Kim JK, Yoo LG, et al. 2009.. Metabolomics analysis reveals the compositional differences of shade grown tea (Camellia sinensis L.). . J. Agric. Food Chem. 58:(1):41826
    [Crossref] [Google Scholar]
  53. Ku KM, Kim J, Park HJ, Liu KH, Lee CH. 2010.. Application of metabolomics in the analysis of manufacturing type of Pu-erh tea and composition changes with different postfermentation year. . J. Agric. Food Chem. 58:(1):34552
    [Crossref] [Google Scholar]
  54. Kumari M, Thakur S, Kumar A, Joshi R, Kumar P, et al. 2020.. Regulation of color transition in purple tea (Camellia sinensis). . Planta 251:(1):35
    [Crossref] [Google Scholar]
  55. Le Gall G, Colquhoun IJ, Defernez M. 2004.. Metabolite profiling using 1H NMR spectroscopy for quality assessment of green tea, Camellia sinensis (L.). . J. Agric. Food Chem. 52:(4):692700
    [Crossref] [Google Scholar]
  56. Lee JE, Lee BJ, Chung JO, Hwang JA, Lee SJ, et al. 2010.. Geographical and climatic dependencies of green tea (Camellia sinensis) metabolites: a 1H NMR-based metabolomics study. . J. Agric. Food Chem. 58:(19):1058289
    [Crossref] [Google Scholar]
  57. Lee JE, Lee BJ, Chung JO, Kim HN, Kim EH, et al. 2015.. Metabolomic unveiling of a diverse range of green tea (Camellia sinensis) metabolites dependent on geography. . Food Chem. 174::45259
    [Crossref] [Google Scholar]
  58. Lee JE, Lee BJ, Chung JO, Shin HJ, Lee SJ, et al. 2011a.. 1H NMR-based metabolomic characterization during green tea (Camellia sinensis) fermentation. . Food Res. Int. 44:(2):597604
    [Crossref] [Google Scholar]
  59. Lee JE, Lee BJ, Hwang JA, Ko KS, Chung JO, et al. 2011b.. Metabolic dependence of green tea on plucking positions revisited: a metabolomic study. . J. Agric. Food Chem. 59:(19):1057985
    [Crossref] [Google Scholar]
  60. Lee LS, Choi JH, Son N, Kim SH, Park JD, et al. 2013.. Metabolomic analysis of the effect of shade treatment on the nutritional and sensory qualities of green tea. . J. Agric. Food Chem. 61:(2):33238
    [Crossref] [Google Scholar]
  61. Li CF, Ma JQ, Huang DJ, Ma CL, Jin JQ, et al. 2018.. Comprehensive dissection of metabolic changes in albino and green tea cultivars. . J. Agric. Food Chem. 66:(8):204048
    [Crossref] [Google Scholar]
  62. Li J, Wu S, Yu Q, Wang J, Deng Y, et al. 2022.. Chemical profile of a novel ripened Pu-erh tea and its metabolic conversion during pile fermentation. . Food Chem. 378::132126
    [Crossref] [Google Scholar]
  63. Li J, Yuan H, Rong Y, Qian MC, Liu F, et al. 2023.. Lipid metabolic characteristics and marker compounds of ripened Pu-erh tea during pile fermentation revealed by LC-MS-based metabolomics. . Food Chem. 404::134665
    [Crossref] [Google Scholar]
  64. Lindon JC, Nicholson JK, Holmes E, Everett JR. 2000.. Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids. . Concepts Magn. Reason. Educ. J. 12:(5):289320
    [Crossref] [Google Scholar]
  65. Liu JW, Zhang QF, Liu MY, Ma LF, Shi YZ, Ruan JY. 2016.. Metabolomic analyses reveal distinct change of metabolites and quality of green tea during the short duration of a single spring season. . J. Agric. Food Chem. 64:(16):33029
    [Crossref] [Google Scholar]
  66. Liu ZW, Wu ZJ, Li H, Wang YX, Zhuang J. 2017.. L-theanine content and related gene expression: novel insights into theanine biosynthesis and hydrolysis among different tea plant (Camellia sinensis L.) tissues and cultivars. . Front. Plant Sci. 8::498
    [Google Scholar]
  67. Long P, Wen M, Granato D, Zhou J, Wu Y, et al. 2020.. Untargeted and targeted metabolomics reveal the chemical characteristic of Pu-erh tea (Camellia assamica) during pile-fermentation. . Food Chem. 311::125895
    [Crossref] [Google Scholar]
  68. Lv HP, Zhang YJ, Lin Z, Liang YR. 2013.. Processing and chemical constituents of Pu-erh tea: a review. . Food Res. Int. 53:(2):60818
    [Crossref] [Google Scholar]
  69. Mozumder NR, Hwang KH, Lee MS, Kim EH, Hong YS. 2021.. Metabolomic understanding of the difference between unpruning and pruning cultivation of tea (Camellia sinensis) plants. . Food Res. Int. 140::109978
    [Crossref] [Google Scholar]
  70. Mozumder NR, Hwang KH, Lee MS, Kim EH, Hong YS. 2023.. Metabolic evidence on vintage effect in tea (Camellia sinensis L.) plant. . Appl. Biol. Chem. 66::86
    [Crossref] [Google Scholar]
  71. Mozumder NR, Lee YR, Hwang KH, Lee MS, Kim EH, Hong YS. 2020.. Characterization of tea leaf metabolites dependent on tea (Camellia sinensis) plant age through 1H NMR-based metabolomics. . Appl. Biol. Chem. 63::10
    [Crossref] [Google Scholar]
  72. Nicholson JK, Lindon JC. 2008.. Metabonomics. . Nature 455::105456
    [Crossref] [Google Scholar]
  73. Norbe A, Rao A, Owen G. 2008.. L-theanine, a natural constituent in tea, and its effect on mental state. Asia Pac. . J. Clin. Nutr. 17::16768
    [Google Scholar]
  74. Ohno A, Oka K, Sakuma C, Okuda H, Fukuhara K. 2011.. Characterization of tea cultivated at four different altitudes using 1H NMR analysis coupled with multivariate statistics. . J. Agric. Food Chem. 59:(10):518187
    [Crossref] [Google Scholar]
  75. Pongsuwan W, Bamba T, Harada K, Yonetani T, Kobayashi A, Fukusaki E. 2008a.. High-throughput technique for comprehensive analysis of Japanese green tea quality assessment using ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS). . J. Agric. Food Chem. 56:(22):107058
    [Crossref] [Google Scholar]
  76. Pongsuwan W, Bamba T, Yonetani T, Kobayashi A, Fukusaki E. 2008b.. Quality prediction of Japanese green tea using pyrolyzer coupled GC/MS based metabolic fingerprinting. . J. Agric. Food Chem. 56:(3):74450
    [Crossref] [Google Scholar]
  77. Pongsuwan W, Fukusaki E, Bamba T, Yonetani T, Yamahara T, Kobayashi A. 2007.. Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting. . J. Agric. Food Chem. 55:(2):23136
    [Crossref] [Google Scholar]
  78. Qi D, Li J, Qiao X, Lu M, Chen W, et al. 2019.. Non-targeted metabolomic analysis based on ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry reveals the effects of grafting on non-volatile metabolites in fresh tea leaves (Camellia sinensis L.). . J. Agric. Food Chem. 67:(23):667282
    [Crossref] [Google Scholar]
  79. Ravichandran R. 2004.. The impact of pruning and time from pruning on quality and aroma constituents of black tea. . Food Chem. 84:(1):711
    [Crossref] [Google Scholar]
  80. Roessner U, Bowne J. 2009.. What is metabolomics all about?. Biotechniques 46:(5):36365
    [Crossref] [Google Scholar]
  81. Sang S, Lambert JD, Ho CT, Yang CS. 2011.. The chemistry and biotransformation of tea constituents. . Pharmacol. Res. 64:(2):8799
    [Crossref] [Google Scholar]
  82. Sasaki T, Tanase Y, Yonezawa T, Michihata T, Kawamura-Konishi Y. 2018.. Metabolomics profiling of roasted stem tea using gas chromatography-mass spectrometry and a sensory test. . Food Sci. Technol. Res. 24:(6):105967
    [Crossref] [Google Scholar]
  83. Scharbert S, Hofmann T. 2005.. Molecular definition of black tea taste by means of quantitative studies, taste reconstitution, and omission experiments. . J. Agric. Food Chem. 53:(13):537784
    [Crossref] [Google Scholar]
  84. Shen J, Wang Y, Chen C, Ding Z, Hu J, et al. 2015.. Metabolite profiling of tea (Camellia sinensis L.) leaves in winter. . Sci. Hortic. 192::19
    [Crossref] [Google Scholar]
  85. Shi J, Zhu Y, Zhang Y, Lin Z, Lv HP. 2019.. Volatile composition of Fu-brick tea and Pu-erh tea analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. . LWT 103::2733
    [Crossref] [Google Scholar]
  86. Shi YL, Zhu Y, Ma WJ, Shi J, Peng QH, et al. 2022.. Comprehensive investigation on non-volatile and volatile metabolites in four types of green teas obtained from the same tea cultivar of Longjing 43 (Camellia sinensis var. sinensis) using the widely targeted metabolomics. . Food Chem. 394::133501
    [Crossref] [Google Scholar]
  87. Skelton D, Ekman D, Martinovic-Weigelt D, Ankley G, Villeneuve D, et al. 2014.. Metabolomics for in situ environmental monitoring of surface waters impacted by contaminants from both point and nonpoint sources. . Environ. Sci. Technol. 48:(4):2395403
    [Google Scholar]
  88. Sumi RS, Kabir G. 2018.. Factors affecting the buying intention of organic tea consumers of Bangladesh. . J. Open Innov. Technol. Mark. Complex. 4:(3):24
    [Crossref] [Google Scholar]
  89. Sun M, Zhang C, Lu M, Gan N, Chen Z, et al. 2018.. Metabolic flux enhancement and transcriptomic analysis displayed the changes of catechins following long-term pruning in tea trees (Camellia sinensis). . J. Agric. Food Chem. 66:(32):856673
    [Crossref] [Google Scholar]
  90. Tarachiwin L, Ute K, Kobayashi A, Fukusaki E. 2007.. 1H NMR based metabolic profiling in the evaluation of Japanese green tea quality. . J. Agric. Food Chem. 55:(23):933036
    [Crossref] [Google Scholar]
  91. Trethewey RN. 2001.. Gene discovery via metabolic profiling. . Curr. Opin. Biotechnol. 12:(2):13538
    [Crossref] [Google Scholar]
  92. Trivedi DK, Hollywood KA, Goodacre R. 2017.. Metabolomics for the masses: the future of metabolomics in a personalized world. . New Horiz. Transl. Med. 3:(6):294305
    [Google Scholar]
  93. Wang C, Zhang C, Kong Y, Peng X, Li C, et al. 2017.. A comparative study of volatile components in Dianhong teas from fresh leaves of four tea cultivars by using chromatography-mass spectrometry, multivariate data analysis, and descriptive sensory analysis. . Food Res. Int. 100::26775
    [Crossref] [Google Scholar]
  94. Wang H, Cao X, Yuan Z, Guo G. 2021.. Untargeted metabolomics coupled with chemometrics approach for Xinyang Maojian green tea with cultivar, elevation and processing variations. . Food Chem. 352::129359
    [Crossref] [Google Scholar]
  95. Wang L, Wei K, Jiang Y, Cheng H, Zhou J, et al. 2011a.. Seasonal climate effects on flavanols and purine alkaloids of tea (Camellia sinensis L.). . Eur. Food Res. Technol. 233:(6):104955
    [Crossref] [Google Scholar]
  96. Wang P, Zheng Y, Guo Y, Liu B, Jin S, et al. 2020.. Widely targeted metabolomic and transcriptomic analyses of a novel albino tea mutant of “Rougui. .” Forests 11:(2):229
    [Crossref] [Google Scholar]
  97. Wang QS, Qin DD, Huang GZ, Jiang XH, Fang KX, et al. 2022.. Identification and characterization of the key volatile flavor compounds in black teas from distinct regions worldwide. . J. Food Sci. 87::343346
    [Crossref] [Google Scholar]
  98. Wang TW, Larson MG, Vasaan RS, Cheng S, Rhee EP, et al. 2011b.. Metabolite profiles and the risk of developing diabetes. . Nat. Med. 17::44853
    [Crossref] [Google Scholar]
  99. Wang WW, Zhang C, Hao WJ, Ma CL, Ma JQ, et al. 2018a.. Transcriptome and metabolome analysis reveal candidate genes and biochemicals involved in tea geometrid defense in Camellia sinensis. . PLOS ONE 13::e0201670
    [Crossref] [Google Scholar]
  100. Wang X, Xiang Y, Sun M, Xiong Y, Li C, et al. 2023.. Transcriptomic and metabolomic analyses reveals keys genes and metabolic pathways in tea (Camellia sinensis) against six-spotted spider mite (Eotetranychus sexmaculatus). . BMC Plant Biol. 23::638
    [Crossref] [Google Scholar]
  101. Wang Y, Kan Z, Thompson HJ, Ling T, Ho CT, et al. 2019.. Impact of six typical processing methods on the chemical composition of tea leaves using a single Camellia sinensis cultivar, Longjing 43. . J. Agric. Food Chem. 67:(19):542336
    [Crossref] [Google Scholar]
  102. Wang Y, Kan Z, Wang D, Zhang L, Wan X, et al. 2018b.. Differences in chemical composition among commercially important cultivars of genus Camellia. . J. Agric. Food Chem. 67:(19):545764
    [Crossref] [Google Scholar]
  103. Weckwerth W. 2003.. Metabolomics in systems biology. . Annu. Rev. Plant Biol. 54::66989
    [Crossref] [Google Scholar]
  104. Wei Y, Yin X, Zhao M, Zhang J, Li T, et al. 2023.. Metabolomics analysis reveals the mechanism underlying the improvement in the color and taste of yellow tea after optimized yellowing. . Food Chem. 428::136785
    [Crossref] [Google Scholar]
  105. Wen M, Zhu M, Han Z, Ho C, Granato D, Zhang L. 2023.. Comprehensive applications of metabolomics on tea science and technology: opportunities, hurdles, and perspective. . Compr. Rev. Food Sci. Food Saf. 22::4890924
    [Crossref] [Google Scholar]
  106. Wishart DS. 2008.. Metabolomics: applications to food science and nutrition research. . Trends Food Sci. Technol. 19:(9):48293
    [Crossref] [Google Scholar]
  107. Wishart DS. 2016.. Emerging applications of metabolomics in drug discovery and precision medicine. . Nat. Rev. Drug Discov. 15:(7):47384
    [Crossref] [Google Scholar]
  108. Worley B, Powers R. 2013.. Multivariate analysis in metabolomics. . Curr. Metab. 1:(1):92107
    [Google Scholar]
  109. Wu H, Huang W, Chen Z, Chen Z, Shi J, et al. 2019.. GC–MS-based metabolomic study reveals dynamic changes of chemical compositions during black tea processing. . Food Res. Int. 120::33038
    [Crossref] [Google Scholar]
  110. Wu LY, Wang YH, Liu SH, Sun Y, Li CX, et al. 2022.. The stress-induced metabolites changes in the flavor formation of oolong tea during enzymatic-catalyzed process: a case study of Zhangping Shuixian tea. . Food Chem. 391::133192
    [Crossref] [Google Scholar]
  111. Xie G, Ye M, Wang Y, Ni Y, Su M, et al. 2009.. Characterization of Pu-erh tea using chemical and metabolic profiling approaches. . J. Agric. Food Chem. 57:(8):304654
    [Crossref] [Google Scholar]
  112. Xin F, Liu Y, Xiao C, Huang Y. 2023.. GC-MS and LC-MS/MS metabolomics revealed dynamic changes of volatile and non-volatile compounds during withering process of black tea. . Food Chem. 410::135396
    [Crossref] [Google Scholar]
  113. Xu J, Wang M, Zhao J, Wang YH, Tang Q, Khaan IA. 2018b.. Yellow tea (Camellia sinensis L.) a promising Chinese tea: processing, chemical constituents and health benefits. . Food Res. Int. 107::56777
    [Crossref] [Google Scholar]
  114. Xu L, Deng DH, Cai CB. 2011.. Predicting the age and type of Tuocha tea by Fourier transform infrared spectroscopy and chemometric data analysis. . J. Agric. Food Chem. 59:(19):1046169
    [Crossref] [Google Scholar]
  115. Xu Q, He Y, Yan X, Zhao S, Zhu J, Wei C. 2018a.. Unraveling a crosstalk regulatory network of temporal aroma accumulation in tea plant (Camellia sinensis) leaves by integration of metabolomics and transcriptomics. . Environ. Exp. Bot. 149::8194
    [Crossref] [Google Scholar]
  116. Xu S, Wang JJ, Wei Y, Deng WW, et al. 2019.. Metabolomics based on UHPLC-Orbitrap-MS and global natural product social molecular networking reveals effects of time scale and environment of storage on the metabolites and taste quality of raw Pu-erh Tea. . J. Agric. Food Chem. 67:(43):1208493
    [Crossref] [Google Scholar]
  117. Xu W, Song Q, Li D, Wan X. 2012.. Discrimination of the production season of Chinese green tea by chemical analysis in combination with supervised pattern recognition. . J. Agric. Food Chem. 60:(28):706470
    [Crossref] [Google Scholar]
  118. Yang C, Hu Z, Lu M, Li P, Tan J, et al. 2018.. Application of metabolomics profiling in the analysis of metabolites and taste quality in different subtypes of white tea. . Food Res. Int. 106::90919
    [Crossref] [Google Scholar]
  119. Yao L, Caffin N, D'Arcy B, Jiang Y, Shi J, et al. 2005.. Seasonal variations of phenolic compounds in Australia-grown tea (Camellia sinensis). . J. Agric. Food Chem. 53:(16):647783
    [Crossref] [Google Scholar]
  120. Yu P, Low MY, Zhou W. 2018.. Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages. . Food Res. Int. 103::6875
    [Crossref] [Google Scholar]
  121. Yu P, Yeo ASL, Low MY, Zhou W. 2014.. Identifying key non-volatile compounds in ready-to-drink green tea and their impact on taste profile. . Food Chem. 155::916
    [Crossref] [Google Scholar]
  122. Yu X, Xiao J, Chen S, Yu Y, Ma J, et al. 2020.. Metabolite signatures of diverse Camellia sinensis tea populations. . Nat. Commun. 11::5586
    [Crossref] [Google Scholar]
  123. Yue W, Sun W, Rao RSP, Ye N, Yang Z, Chen M. 2019.. Non-targeted metabolomics reveals distinct chemical compositions among different grades of Bai Mudan white tea. . Food Chem. 277::28997
    [Crossref] [Google Scholar]
  124. Zeng C, Lin H, Liu Z, Liu Z. 2019.. Analysis of young shoots of ‘Anji Baicha’ (Camellia sinensis) at three developmental stages using nontargeted LC-MS-based metabolomics. . J. Food Sci. 84:(7):174657
    [Crossref] [Google Scholar]
  125. Zhang L, Deng WW, Wan XC. 2014a.. Advantage of LC-MS metabolomics to identify marker compounds in two types of Chinese dark tea after different post-fermentation processes. . Food Sci. Biotechnol. 23:(2):35560
    [Crossref] [Google Scholar]
  126. Zhang L, Ho CT, Zhou J, Santos JS, Armstrong L, Granato D. 2019.. Chemistry and biological activities of processed Camellia sinensis teas: a comprehensive review. . Compr. Rev. Food Sci. Food Saf. 18::147495
    [Crossref] [Google Scholar]
  127. Zhang L, Li N, Ma ZZ, Tu PF. 2011.. Comparison of the chemical constituents of aged Pu-erh tea, ripened Pu-erh tea, and other teas using HPLC-DAD-ESI-MSn. . J. Agric. Food Chem. 59:(16):875460
    [Crossref] [Google Scholar]
  128. Zhang Q, Hu J, Liu M, Shi Y, De Vos RC, Ruan J. 2020.. Stimulated biosynthesis of delphinidin-related anthocyanins in tea shoots reducing the quality of green tea in summer. . J. Sci. Food Agric. 100::150514
    [Crossref] [Google Scholar]
  129. Zhang Q, Liu M, Ruan J. 2017.. Integrated transcriptome and metabolic analyses reveals novel insights into free amino acid metabolism in Huangjinya tea cultivar. . Front. Plant Sci. 8::291
    [Google Scholar]
  130. Zhang QF, Shi YZ, Ma LF, Yi XY, Ruan JY. 2014b.. Metabolomic analysis using ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF MS) uncovers the effects of light intensity and temperature under shading treatments on the metabolites in tea. . PLOS ONE 9:(11):e112572
    [Crossref] [Google Scholar]
  131. Zhang Y, Chen G, Liu Y, Xu Y, Wang F, et al. 2015.. Analysis of the bitter and astringent taste of baked green tea and their chemical contributors. . J. Tea Sci. 4::37783
    [Google Scholar]
  132. Zhao X, He Y, Zhang H, Ding Z, Zhou C, Zhang K. 2024.. A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model. . Sci. Rep. 14::4166
    [Crossref] [Google Scholar]
  133. Zhou B, Ma B, Ma C, Xu C, Wang J, et al. 2022a.. Classification of Pu-erh ripened teas and their differences in chemical constituents and antioxidant capacity. . LWT 153::112370
    [Crossref] [Google Scholar]
  134. Zhou B, Wang Z, Yin R, Ma B, Ma C, et al. 2022b.. Impact of prolonged withering on phenolic compounds and antioxidant capability in white tea using LC-MS-based metabolomics and HPLC analysis: comparison with green tea. . Food Chem. 368::130855
    [Crossref] [Google Scholar]
  135. Zhou P, Hu O, Fu H, Ouyang L, Gong X, et al. 2019.. UPLC-Q-TOF/MS-based untargeted metabolomics coupled with chemometrics approach for Tieguanyin tea with seasonal and year variations. . Food Chem. 283::7382
    [Crossref] [Google Scholar]
  136. Zhou XL, Hoang NH, Tao F, Fu TT, Guo SJ, et al. 2024.. Transcriptomics and phytohormone metabolomics provide comprehensive insights into the response mechanism of tea against blister blight disease. . Sci. Hortic. 324::112611
    [Crossref] [Google Scholar]
  137. Zhu M, Li N, Zhao M, Yu W, Wu JL. 2017.. Metabolomic profiling delineate taste qualities of tea leaf pubescence. . Food Res. Int. 94::3644
    [Crossref] [Google Scholar]
  138. Zhu M, Li N, Zhou F, Ouyang J, Lu DM, et al. 2020.. Microbial bioconversion of the chemical components in dark tea. . Food Chem. 312::126043
    [Crossref] [Google Scholar]
  139. Zhu M, Zhou F, Ran L, Li Y, Tan B, et al. 2021.. Metabolic profiling and gene expression analyses of purple-leaf formation in tea cultivars (Camellia sinensis var. sinensis and var. assamica). . Front. Plant Sci. 12::606962
    [Crossref] [Google Scholar]
  140. Zhuang J, Dai X, Zhu M, Zhang S, Dai Q, et al. 2020.. Evaluation of astringent taste of green tea through mass spectrometry-based targeted metabolic profiling of polyphenols. . Food Chem. 305::125507
    [Crossref] [Google Scholar]
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