茶叶等级量化评价方法研究进展

doi:10.13305/j.cnki.jts.2023.06.011

Abstract

Abstract: Grade is an important indicator for discerning the quality of green tea and other types of tea, and it is directly related to their selling prices. However, there is often a phenomenon of falsely labeling tea grades in the market, which seriously undermines consumer rights. At present, tea grade is mainly assessed by sensory evaluation. Sensory evaluation, after long-term development, has formed relatively unified standard, but it still has a certain subjectivity. To evaluate the tea grade more accurately and objectively, researchers have developed a series of quantitative evaluation methods for tea grade. Given the significant practical importance of evaluating tea grade, this article provided a comprehensive review of the recent research status in the qualitative and quantitative evaluation of tea grades, including bionic sensing intelligent sensory detection technology, specific chemical components detection and metabolomics techniques. Major problems and challenges on tea grade evaluation were discussed, and future development trend was also prospected in this study. It has positive guiding significance for objective, scientific and standardized evaluation and quality control of tea quality.

Key words: computer vision, electronic nose, electronic tongue, metabolomics, chemical composition

CLC Number:

S571.1

ZOU Dan, YIN Xiaoli, GU Huiwen, LONG Wanjun, FU Haiyan, SHE Yuanbin. Research Progress of Quantitative Evaluation Methods for Tea Grade[J]. Journal of Tea Science, 2023, 43(6): 733-746.

References 90

[1]	Ge G Q, Gao W J, Yan M, et al.Comparation study on the metabolism destination of neonicotinoid and organophosphate insecticides in tea plant (Camellia sinensis L.)[J]. Food Chemistry, 2021, 344: 128579. doi: 10.1016/ j.foodchem.2020.128579.
[2]	刘仲华, 黄建安, 龚雨顺, 等. 茶叶功能成分的健康作用研究新进展[J]. 中国茶叶, 2021, 43(9): 1-11.Liu Z H, Huang J A, Gong Y S, et al.New progress in health benefits of functional components of tea[J]. China Tea, 2021, 43(9): 1-11.
[3]	Schroeder A C, Xiao H P, Zhu Z W, et al.A potential role for green tea as a radiation sensitizer for prostate cancer[J]. Pathology & Oncology Research, 2019, 25(1): 263-268.
[4]	Ambigaipalan P, Oh W Y, Shahidi F.Epigallocatechin (EGC) esters as potential sources of antioxidants[J]. Food Chemistry, 2020, 309: 125609. doi: 10.1016/j.foodchem.2019.125609.
[5]	Pervin M, Unno K, Ohishi T, et al.Beneficial effects of green tea catechins on neurodegenerative diseases[J]. Molecules, 2018, 23(6): 1297. doi: 10.3390/molecules23061297.
[6]	Huang S, Li J J, Wu Y T, et al.Tea consumption and longitudinal change in high-density lipoprotein cholesterol concentration in Chinese adults[J]. Journal of the American Heart Association, 2018, 7(13): e008814. doi: 10.1161/JAHA.118.008814.
[7]	Huang J B, Feng S M, Liu A N, et al. Green tea polyphenol EGCG alleviates metabolic abnormality and fatty liver by decreasing bile acid and lipid absorption in mice [J]. Molecular Nutrition & Food Research, 2018, 62(4): 10.1002/mnfr.201700696. doi: 10.1002/mnfr.201700696.
[8]	陈裕炜, 高琳, 陈晓斌. 茶提取物在口腔护理领域的应用和研究[J]. 口腔护理用品工业, 2019, 29(6): 18-21.Chen Y W, Gao L, Chen X B.Tea extracts in oral care applications and research[J]. Oral Care Industry, 2019, 29(6): 18-21.
[9]	翁昆, 张亚丽. GB/T 30766—2014《茶叶分类》简介[J]. 中国标准导报, 2015(1): 34-35.Weng K, Zhang Y L.GB/T 30766-2014 Classification of tea summary[J]. China Standards Review, 2015(1): 34-35.
[10]	吴双, 张骏, 江仕龙, 等. 茶叶斑病病原菌Epicoccum mackenziei致病性与生长特性研究[J]. 山地农业生物学报, 2023, 42(5): 9-14.Wu S, Zhang J, Jiang S L.et al.Studies on the pathogenicity and growth characteristics of the tea leaf spot pathogen, Epicoccum macjenziei[J]. Journal of Mountain Agriculture and Biology, 2023, 42(5): 9-14.
[11]	Ahmed S, Griffin T S, Kraner D, et al.Environmental factors variably impact tea secondary metabolites in the context of climate change[J]. Frontiers in Plant Science, 2019, 10: 939. doi: 10.3389/fpls.2019.00939.
[12]	Qin Z H, Pang X L, Chen D, et al.Evaluation of Chinese tea by the electronic nose and gas chromatography-mass spectrometry: correlation with sensory properties and classification according to grade level[J]. Food Research International, 2013, 53(2): 864-874.
[13]	李春霖. 基于化学计量学和近红外光谱技术的龙井茶感官及化学品质评价研究[D]. 杭州: 浙江大学, 2019.Li C L.Sensory and chemical quality evaluation of Longjing tea using chemometrics and near-infrared spectroscopy technique [D]. Hangzhou: Zhejiang University, 2019.
[14]	Wang H J, Gu J N, Wang M N.A review on the application of computer vision and machine learning in the tea industry[J]. Frontiers in Sustainable Food Systems, 2023, 7: 1172543. doi: 10.3389/fsufs.2023.1172543.
[15]	常睿, 张莹, 杨娟, 等. 计算机视觉技术在茶叶加工领域应用研究进展[J]. 南方农业, 2021, 15(28): 137-140.Chang R, Zhang Y, Yang J, et al.Study progress in the application of computer visual technology in tea processing[J]. South China Agriculture, 2021, 15(28): 137-140.
[16]	刘鹏, 吴瑞梅, 杨普香, 等. 基于计算机视觉技术的茶叶品质随机森林感官评价方法研究[J]. 光谱学与光谱分析, 2019, 39(1): 193-198.Liu P, Wu R M, Yang P X, et al.Study of sensory quality evaluation of tea using computer vision technology and forest random method[J]. Spectroscopy and Spectral Analysis, 2019, 39(1): 193-198.
[17]	高达睿. 基于颜色和形状特征的茶叶分选研究[D]. 合肥: 中国科学技术大学, 2016.Gao D R.Research on the tea sorting based on characteristic of color and shape [D]. Hefei: University of Science and Technology of China, 2016.
[18]	Ren G X, Gan N, Song Y, et al.Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics[J]. Microchemical Journal, 2021, 160: 105600. doi: 10.1016/j.microc.2020.105600.
[19]	Chen Z W, He L Y, Ye Y, et al.Automatic sorting of fresh tea leaves using vision-based recognition method[J]. Journal of Food Process Engineering, 2020, 43(9): e13474. doi: 10.1111/jfpe.1347.
[20]	张程, 王进, 鲁晓卉, 等. 基于图像颜色和纹理特征的成品茶种类与等级识别[J]. 中国茶叶加工, 2020(2): 5-11.Zhang C, Wang J, Lu X H, et al.Recognition of types and grades of tea products based on image color and texture features[J]. China Tea Processing, 2020(2): 5-11.
[21]	张金炎, 曹成茂, 李文宝, 等. 基于多特征融合的茶叶鲜叶等级识别的方法研究[J]. 安徽农业大学学报, 2021, 48(3): 480-487.Zhang J Y, Cao C M, Li W B, et al.Study on the method of recognition of fresh leaf grade of tea based on multi-feature fusion[J]. Journal of Anhui Agricultural University, 2021, 48(3): 480-487.
[22]	Song Y, Wang X Z, Xie H L, et al.Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 252: 119522. doi: 10.1016/j.saa.2021.119522.
[23]	Jia W S, Liang G, Jiang Z J, et al.Advances in electronic nose development for application to agricultural products[J]. Food Analytical Methods, 2019, 12(10): 2226-2240.
[24]	罗影, 王舒婷, 曲凤凤, 等. 电子鼻技术与感官审评联用评价山东抹茶香气品质[J]. 山东农业科学, 2022, 54(11): 54-61.Luo Y, Wang S T, Qu F F, et al.Evaluation on aroma quality of Shangdong Matcha by combining electronic nose and sensory evaluation technology[J]. Shandong Agricultural Sciences, 2022, 54(11): 54-61.
[25]	Xu M, Wang J, Zhu L Y.Tea quality evaluation by applying E-nose combined with chemometrics methods[J]. Journal of Food Science and Technology, 2021, 58(4): 1549-1561.
[26]	Yuan H B, Chen X Q, Shao Y D, et al.Quality evaluation of green and dark tea grade using electronic nose and multivariate statistical analysis[J]. Journal of Food Science, 2019, 84(12): 3411-3417.
[27]	刘建波, 张君才, 王晓玲, 等. 电子舌及其在饮料区分辨识方面研究进展[J]. 光谱实验室, 2013, 30(5): 2600-2603.Liu J B, Zhang J C, Wang X L, et al.Research progress of electronic tongue and its drinks distinguish identification[J]. Chinese Journal of Spectroscopy Laboratory, 2013, 30(5): 2600-2603.
[28]	Bhondekar A P, Dhiman M, Sharma A, et al.A novel iTongue for Indian black tea discrimination[J]. Sensors and Actuators B: Chemical, 2010, 148(2): 601-609.
[29]	范培珍, 郑雨婷, 王梦馨, 等. 不同等级霍山黄芽茶滋味的电子舌评价及呈味氨基酸组成[J]. 贵州农业科学, 2017, 45(5): 105-109.Fan P Z, Zheng Y T, Wang M X, et al.Taste and delicious amino acid composition of Huoshanhuangya tea with different grades determined by an electronic tongue[J]. Guizhou Agricultural Sciences, 2017, 45(5): 105-109.
[30]	Zhang S F, Zhu D H, Chen X J.Analysis of E-tongue data for tea classification based on semi-supervised learning of generative adversarial network[J]. Chinese Journal of Analytical Chemistry, 2022, 50(2): 77-85.
[31]	Chen X J, Xu Y L, Meng L W, et al.Non-parametric partial least squares-discriminant analysis model based on sum of ranking difference algorithm for tea grade identification using electronic tongue data[J]. Sensors and Actuators B: Chemical, 2020, 311: 127924. doi: 10.1016/j.snb.2020.127924.
[32]	Xu M, Wang J, Gu S.Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy[J]. Journal of Food Engineering, 2019, 241: 10-17.
[33]	Zhi R C, Zhao L, Zhang D Z.A framework for the multi-level fusion of electronic nose and electronic tongue for tea quality assessment[J]. Sensors, 2017, 17(5): 1007. doi: 10.3390/s17051007.
[34]	Banerjee M B, Roy R B, Tudu B, et al.Black tea classification employing feature fusion of E-nose and E-tongue responses[J]. Journal of Food Engineering, 2019, 244: 55-63.
[35]	Xu M, Wang J, Zhu L Y.The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics[J]. Food Chemistry, 2019, 289: 482-489.
[36]	郑瑛珠. 茶叶的主要化学成分及其营养价值[J]. 福建茶叶, 2020, 42(11): 21-22.Zheng Y Z.The main chemical composition of tea and its nutritional value[J]. Tea in Fujian, 2020, 42(11): 21-22.
[37]	Zhai X T, Zhang L, Granvogl M, et al.Flavor of tea (Camellia sinensis): a review on odorants and analytical techniques[J]. Comprehensive Reviews in Food Science and Food Safety, 2022, 21(5): 3867-3909.
[38]	Horne J, Hayes J, Lawless H T.Turbidity as a measure of salivary protein reactions with astringent substances[J]. Chemical Senses, 2002, 27(7): 653-659.
[39]	张英娜, 嵇伟彬, 许勇泉, 等. 儿茶素呈味特性及其感官分析方法研究进展[J]. 茶叶科学, 2017, 37(1): 1-9.Zhang Y N, Ji W B, Xu Y Q, et al.Rewiew on taste characteristic of catechins and its sensory analysis method[J]. Journal of Tea Science, 2017, 37(1): 1-9.
[40]	Ning J M, Ding D, Song Y S, et al.Chemical constituents analysis of white tea of different qualities and different storage times[J]. European Food Research and Technology, 2016, 242(12): 2093-2104.
[41]	Tan J F, Engelhardt U H, Lin Z, et al.Flavonoids, phenolic acids, alkaloids and theanine in different types of authentic Chinese white tea samples[J]. Journal of Food Composition and Analysis, 2017, 57: 8-15.
[42]	Gu H W, Yin X L, Ma Y X, et al.Differentiating grades of Xihu Longjing teas according to the contents of ten major components based on HPLC-DAD in combination with chemometrics[J]. LWT, 2020, 130: 109688. doi: 10.1016/j.lwt.2020.109688.
[43]	He G Y, Hou X, Han M, et al.Discrimination and polyphenol compositions of green teas with seasonal variations based on UPLC-QTOF/MS combined with chemometrics[J]. Journal of Food Composition and Analysis, 2022, 105: 104267. doi: 10.1016/j.jfca.2021.104267.
[44]	Selim D A, Shawky E, Abu El-Khair R M. Identification of the discriminatory chemical markers of different grades of Sri Lankan white, green and black tea (Camellia sinenesis L.) via metabolomics combined to chemometrics[J]. Journal of Food Composition and Analysis, 2022, 109: 104473. doi: 10.1016/j.jfca.2022.104473.
[45]	Glisan S L, Grove K A, Yennawar N H, et al.Inhibition of pancreatic lipase by black tea theaflavins: comparative enzymology and in silico modeling studies[J]. Food Chemistry, 2017, 216: 296-300.
[46]	刘昌伟, 张梓莹, 王俊懿, 等. 茶黄素生物学活性研究进展[J]. 食品科学, 2022, 43(19): 318-329.Liu C W, Zhang Z Y, Wang J Y, et al.Progress in research on the bioactivity of theaflavins[J]. Food Science, 2022, 43(19): 318-329.
[47]	Li Y C, He C, Yu X L, et al.Effects of red-light withering on the taste of black tea as revealed by non-targeted metabolomics and transcriptomics analysis[J]. LWT, 2021, 147: 111620. doi: 10.1016/j.lwt.2021.111620.
[48]	郭雪梅. 不同等级红茶样品主要成分分析和代谢组学研究[D]. 合肥: 安徽农业大学, 2018.Guo X M.The discrimination of polyphenols and metabolomics analysis different grades of black tea [D]. Hefei: Anhui Agricultural University, 2018.
[49]	席佳丽, 张广辉, 李国荣, 等. 云南临沧不同级别红茶化学成分比较研究[J]. 西南农业学报, 2014, 27(4): 1447-1452.Xi J L, Zhang G H, Li G R, et al.Comparative analysis of chemical compositions in different grades black tea from Lincang City, Yunnan Province[J]. Southwest China Journal of Agricultural Sciences, 2014, 27(4): 1447-1452.
[50]	Stodt U W, Blauth N, Niemann S, et al.Investigation of processes in black tea manufacture through model fermentation (oxidation) experiments[J]. Journal of Agricultural and Food Chemistry, 2014, 62(31): 7854-7861.
[51]	Satoh E, Ishii T, Shimizu Y, et al.The mechanism underlying the protective effect of the thearubigin fraction of black tea (Camellia sinensis) extract against the neuromuscular blocking action of botulinum neurotoxins[J]. Pharmacology & Toxicology, 2002, 90(4): 199-202.
[52]	Lorenz M, Urban J, Engelhardt U, et al.Green and black tea are equally potent stimuli of NO production and vasodilation: new insights into tea ingredients involved[J]. Basic Research in Cardiology, 2009, 104(1): 100-110.
[53]	Imran A, Butt M S, Xiao H, et al.Inhibitory effect of black tea (Camellia sinensis) theaflavins and thearubigins against HCT 116 colon cancer cells and HT 460 lung cancer cells[J]. Journal of Food Biochemistry, 2019, 43(5): e12822. doi: 10.1111/jfbc.12822.
[54]	Wang Q P, Gong J S, Chisti Y, et al.Fungal isolates from a pu-erh type tea fermentation and their ability to convert tea polyphenols to theabrownins[J]. Journal of Food Science, 2015, 80(4): M809-M817.
[55]	Wang Y, Zhao A Q, Du H P, et al.Theabrownin from Fu brick tea exhibits the thermogenic function of adipocytes in high-fat-diet-induced obesity[J]. Journal of Agricultural and Food Chemistry, 2021, 69(40): 11900-11911.
[56]	Sun Y, Wang Y W, Song P P, et al.Anti-obesity effects of instant fermented teas in vitro and in mice with high-fat-diet-induced obesity[J]. Food & Function, 2019, 10(6): 3502-3513.
[57]	郭丽, 彭群华, 赵锋, 等. 不同等级新九曲红梅茶的风味化学特征[J]. 食品科学, 2021, 42(4): 215-220.Guo L, Peng Q H, Zhao F, et al.Flavor chemistry characteristics of new Jiuqu Hongmei tea of different grade levels[J]. Food Science, 2021, 42(4): 215-220.
[58]	何龙, 李红, 李招, 等. 茶叶中生物碱的提取及抑菌作用的探究[J]. 山东化工, 2016, 45(9): 6-8.He L, Li H, Li Z, et al.The extraction of alkaloids in tea and the bacteriostatic action of inquiry[J]. Shandong Chemical Industry, 2016, 45(9): 6-8.
[59]	Scharbert S, Hofmann T.Molecular definition of black tea taste by means of quantitative studies, taste reconstitution, and omission experiments[J]. Journal of Agricultural and Food Chemistry, 2005, 53(13): 5377-5384.
[60]	Denoeud F, Carretero-Paulet L, Dereeper A, et al.The coffee genome provides insight into the convergent evolution of caffeine biosynthesis[J]. Science, 2014, 345(6201): 1181-1184.
[61]	Pang X M, Chen F Y, Liu G Y, et al.Comparative analysis on the quality of Wuyi Rougui (Camellia sinensis) tea with different grades[J]. Food Science and Technology, 2022, 42: 115321. doi: 10.1590/fst.115321.
[62]	Li R, Peng S, Ye H, et al.Metabolite differentiation and antiobesity effects between different grades of Yuexi Cuilan green tea[J]. Journal of Functional Foods, 2021, 87: 104794. doi: 10.1016/j.jff.2021.104794.
[63]	Ho C T, Zheng X, Li S M.Tea aroma formation[J]. Food Science and Human Wellness, 2015, 4(1): 9-27.
[64]	Jiang C K, Ma J Q, Apostolides Z, et al.Metabolomics for a millenniums-old crop: tea plant (Camellia sinensis)[J]. Journal of Agricultural and Food Chemistry, 2019, 67(23): 6445-6457.
[65]	Feng L, Gao M J, Hou R Y, et al.Determination of quality constituents in the young leaves of albino tea cultivars[J]. Food Chemistry, 2014, 155: 98-104.
[66]	Du Y Y, Liang Y R, Wang H, et al.A study on the chemical composition of albino tea cultivars[J]. The Journal of Horticultural Science and Biotechnology, 2015, 81(5): 809-812.
[67]	Zeng C Z, Lin H Y, Liu Z X, et al.Metabolomics analysis of Camellia sinensis with respect to harvesting time[J]. Food Research International, 2020, 128: 108814. doi: 10.1016/j.foodres.2019.108814.
[68]	Zeng L T, Watanabe N, Yang Z Y.Understanding the biosyntheses and stress response mechanisms of aroma compounds in tea (Camellia sinensis) to safely and effectively improve tea aroma[J]. Critical Rreviews in Food Science and Nutrition, 2019, 59(14): 2321-2334.
[69]	查旻昱, 吴悠, 张梁. 茶叶中挥发性香气物质研究进展[J]. 食品安全质量检测学报, 2020, 11(13): 4298-4303.Zha M Y, Wu Y, Zhang L.Research advances on volatile aroma compounds of tea[J]. Journal of Food Safety & Quality, 2020, 11(13): 4298-4303.
[70]	Hu C J, Li D, Ma Y X, et al.Formation mechanism of the oolong tea characteristic aroma during bruising and withering treatment[J]. Food Chemistry, 2018, 269: 202-211.
[71]	Feng Z H, Li M, Li Y F, et al.Characterization of the orchid-like aroma contributors in selected premium tea leaves[J]. Food Research International, 2020, 129: 108841. doi: 10.1016/j.foodres.2019.108841.
[72]	Zou Y S, Tang C, Yang X Y, et al.Discovery and flavor characterization of high-grade markers in baked green tea[J]. Molecules, 2023, 28(6): 2462. doi: 10.3390/molecules28062462.
[73]	Jumtee K, Komura H, Bamba T, et al.Predication of Japanese green tea (Sen-cha) ranking by volatile profiling using gas chromatography mass spectrometry and multivariate analysis[J]. Journal of Bioscience and Bioengineering, 2011, 112(3): 252-255.
[74]	Li Z B, Zhao L J, Xie F, et al.Study of assessment of green tea’ grades in GC-MS determination of aromatic components based on principal component analysis (PCA)[J]. Journal of Food Processing and Preservation, 2020, 45(1): e15047. doi: 10.1111/jfpp.15047.
[75]	Yang Y Q, Zhu H K, Chen J Y, et al.Characterization of the key aroma compounds in black teas with different aroma types by using gas chromatography electronic nose, gas chromatography-ion mobility spectrometry, and odor activity value analysis[J]. LWT, 2022, 163: 113492. doi: 10.1016/j.lwt.2022.113492.
[76]	Zhang L, Cao Q Q, Granato D, et al.Association between chemistry and taste of tea: a review[J]. Trends in Food Science & Technology, 2020, 101: 139-149.
[77]	Li Y C, Ran W, He C, et al.Effects of different tea tree varieties on the color, aroma, and taste of Chinese Enshi green tea[J]. Food Chemistry X, 2022, 14: 100289. doi: 10.1016/j.fochx.2022.100289.
[78]	Du Y, Yang W R, Yang C C, et al.A comprehensive review on microbiome, aromas and flavors, chemical composition, nutrition and future prospects of Fuzhuan brick tea[J]. Trends in Food Science & Technology, 2022, 119: 452-466.
[79]	Merkx D W H, Westphal Y, Van Velzen E J J, et al. Quantification of food polysaccharide mixtures by 1H NMR[J]. Carbohydrate Polymers, 2018, 179: 379-385.
[80]	Yue W J, Sun W J, Rao R S P, et al. Non-targeted metabolomics reveals distinct chemical compositions among different grades of Bai Mudan white tea[J]. Food Chemistry, 2019, 277: 289-297.
[81]	Zhang Q, Zhang Y Y, Liu Z, et al.Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics[J]. Journal of Separation Science, 2020, 43(14): 2794-2803.
[82]	Jing J, Shi Y Z, Zhang Q F, et al.Prediction of Chinese green tea ranking by metabolite profiling using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)[J]. Food Chemistry, 2017, 221: 311-316.
[83]	Zou D, Yin X L, Gu H W, et al.Insight into the effect of cultivar and altitude on the identification of EnshiYulu tea grade in untargeted metabolomics analysis[J]. Food Chemistry, 2023, 436: 137768. doi: 10.1016/j.foodchem.2023.137768.
[84]	Cui H N, Gu H W, Li Z Q, et al.Integration of lipidomics and metabolomics approaches for the discrimination of harvest time of green tea in spring season by using UPLC-Triple-TOF/MS coupled with chemometrics[J]. Frontiers in Sustainable Food Systems, 2023, 7: 1119314. doi: 10.3389/fsufs.2023.1119314.
[85]	Chen Y H, Liu Y H, Wu Y J, et al.Metabolomic studies using high performance liquid chromatography and tandem mass spectrometry to discover quality markers for oriental beauty (Dongfang Meiren) tea[J]. Food and Nutrition Sciences, 2014, 5(9): 823-834.
[86]	Wang W, Jin S, Guo Y L.Exploration of a method of distinguishing different nongxiang Tieguanyin tea grades based on aroma determined by GC-MS combined with chemometrics[J]. Molecules, 2019, 24(9): 1707. doi: 10.3390/molecules24091707.
[87]	Yin X L, Fu W J, Chen Y, et al.GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea[J]. LWT, 2022, 171: 114148. doi: 10.1016/j.lwt.2022.114148.
[88]	Dai G W, Fan J C, Dewi C.ITF-WPI: image and text based cross-modal feature fusion model for wolfberry pest recognition[J]. Computers and Electronics in Agriculture, 2023, 212: 108129. doi: 10.1016/j.compag.2023.108129.
[89]	Esfahani S, Tiele A, Agbroko S O, et al.Development of a tuneable NDIR optical electronic nose[J]. Sensors, 2020, 20(23): 6875. doi: 10.3390/s20236875.
[90]	Lee B K, Mayhew E J, Sanchez-Lengeling B, et al.A principal odor map unifies diverse tasks in olfactory perception[J]. Science, 2023, 381(6661): 999-1006.

Research Progress of Quantitative Evaluation Methods for Tea Grade

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References 90

Related Articles 15

Metrics

Comments

Recommended 10

[1]	WU Zongjie, OU Xiaoxi, LIN Hongzheng, YU Xinru, CHEN Shouyue, WU Qingyang, LI Xinlei, SUN Yun. Study on the Glycosidically Bound Volatiles and Aroma Constituents in the Processing of Wuyi Rougui [J]. Journal of Tea Science, 2024, 44(1): 84-100.
[2]	MAO Chun, HE Ji, WEN Xuefeng, WU Chuanmei, YI Chengxi, LIAN Jianhong, GUO Wenmin. Advances in the Application of Metabolomics in the Study of Physiological and Biochemical Metabolism of Tea Plants [Camellia sinensis (L.) O. Kuntze] [J]. Journal of Tea Science, 2023, 43(5): 607-620.
[3]	GAO Jianjian, CHEN Dan, PENG Jiakun, WU Wenliang, CAI Liangsui, CAI Yawei, TIAN Jun, WAN Yunlong, SUN Weijiang, HUANG Yan, WANG Zhe, LIN Zhi, DAI Weidong. Comparison on Chemical Components of Yunnan and Fuding White Tea Based on Metabolomics Approach [J]. Journal of Tea Science, 2022, 42(5): 623-637.
[4]	LIU Qi, OUYANG Jian, LIU Changwei, CHEN Hongyu, LI Juan, XIONG Ligui, LIU Zhonghua, HUANG Jian'an. Research Progress of Tea Quality Evaluation Technology [J]. Journal of Tea Science, 2022, 42(3): 316-330.
[5]	CHEN Jiajia, ZHU Chensong, ZHU Wenwei, SHANG Hu, LIN Lin, LUO Yuqin, SUN Weijiang. Analysis of the Metabolism of Amino Acids during the Withering of White Tea [J]. Journal of Tea Science, 2021, 41(4): 471-481.
[6]	YUE Wenjie, JIN Xinyi, CHEN Mingjie, YE Naixing, GUO Li, ZHAO Feng. Analysis of Metabolite Changes in the Natural Withering Process of Fu′an White Tea Based on Non-targeted Metabolomics Approach [J]. Journal of Tea Science, 2021, 41(3): 379-392.
[7]	MAO Yalin, WANG Fang, YIN Junfeng, XU Yongquan. Quality Analysis of Tencha Made from Different Tea Cultivars [J]. Journal of Tea Science, 2020, 40(6): 782-794.
[8]	ZHAO Miaomiao, HE Lunan, LI Guo, YI Chao, LIU Xueyan, LUO Chaoguang, LYU Caiyou. Study on Quality of Mechanical Production and Numerical Control Fermentation of Puer Tea [J]. Journal of Tea Science, 2020, 40(5): 676-688.
[9]	MA Wanjun, MA Shicheng, LIU Chunmei, LONG Zhirong, TANG Baojun, LIN Zhi, LYU Haipeng. Research Progress on Chemical Composition and Biological Activity of Liupao Tea [J]. Journal of Tea Science, 2020, 40(3): 289-304.
[10]	WU Xun, LIU Fei, CHEN Zhiwei, WANG Yuwan, CHEN Lin, TU Zheng, ZHOU Xiaofen, YANG Yunfei, YE Yang, TONG Huarong. Study on the Changes of Physical and Chemical Components during the Frying Process of Green Tea by Computer Vision [J]. Journal of Tea Science, 2020, 40(2): 194-204.
[11]	GONG Zhiping, YIN Junfeng, CHEN Gensheng. Effects of Different Types of Water Quality on the Sensory Properties and Main Chemcial Compositions of Longjing Tea Infusions [J]. Journal of Tea Science, 2020, 40(2): 215-224.
[12]	HUANG Fan, LIU Fei, WANG Yun, LUO Fan. Research Progress and Prospect on Computer Vision Technology Application in Tea Production [J]. Journal of Tea Science, 2019, 39(1): 81-87.
[13]	CAO Qiong, SU Huan, WAN Xiaochun, NING Jingming. Identification of the Geographical Origins of Oolong Tea Based on EGCG, ECG and Caffeine Contents [J]. Journal of Tea Science, 2018, 38(3): 237-243.
[14]	YU Xinlei, AI Yujie, QU Fengfeng, AI Zeyi, LIU Shuyuan, CHEN Yuqiong, NI Dejiang. Metabolomics Application in the Study of Tea Quality Formation [J]. Journal of Tea Science, 2018, 38(1): 20-32.
[15]	YANG Chen, DAI Weidong, LYU Meiling, LI Pengliang, LIU Xu, TIAN Jun, WAN Yunlong, LI Ji, LIN Zhi. Study on the Chemical Constituents of Pu-erh Teas from Different Areas by UHPLC-Q-TOF/MS [J]. Journal of Tea Science, 2017, 37(6): 605-615.