[1] 张颖彬, 刘栩, 鲁成银. 中国茶叶感官审评术语的形成与发展现状[J]. 茶叶科学, 2019, 39(2): 123-130.
Zhang Y B, Liu X, Lu C Y.Formation and development of Chinese tea sensory terminology[J]. Journal of Tea Science, 2019, 39(2): 123-130.
[2] 张颖彬, 刘栩, 鲁成银. 中国茶叶感官审评术语基元语素研究与风味轮构建[J]. 茶叶科学, 2019, 39(4): 474-483.
Zhang Y B, Liu X, Lu C Y.Study on primitive morpheme in sensory terminology and flavor wheel construction of Chinese tea[J]. Journal of Tea Science, 2019, 39(4): 474-483.
[3] Li H H, Luo L Y, Wang J, et al.Lexicon development and quantitative descriptive analysis of Hunan fuzhuan brick tea infusion[J]. Food Research International, 2019, 120: 275-284.
[4] 戴前颖, 叶颖君, 安琪, 等. 黄大茶感官特征定量描述与风味轮构建[J]. 茶叶科学, 2021, 41(4): 535-544.
Dai Q Y, Ye Y J, An Q, et al.Sensory characteristics of yellow large leaf tea by quantitative descriptive analysis and construction of flavor wheel[J]. Journal of Tea Science, 2021, 41(4): 535-544.
[5] 安会敏, 熊一帆, 张杨波, 等. 临沧工夫红茶季节判定模型及风味轮的构建[J]. 食品工业科技, 2019, 40(23): 199-206.
An H M, Xiong Y F, Zhang Y B, et al.Construction of seasonal decision model and flavor wheel for Lincang gongfu black tea[J]. Science and Technology of Food Industry, 2019, 40(23): 199-206.
[6] 高涛, 罗黄洋, 吴韧, 等. 主客观组合权重法在食品感官评价中的应用[J]. 食品工业科技, 2021, 42(18): 300-307.
Gao T, Luo H Y, Wu R, et al.Application of subjective and objective combination weighting method in food sensory evaluation[J]. Science and Technology of Food Industry, 2021, 42(18): 300-307.
[7] Galmarini M V.The role of sensory science in the evaluation of food pairing[J]. Current Opinion in Food Science, 2020, 33: 149-155.
[8] Li H, Luo L, Ma M, et al.Characterization of volatile compounds and sensory analysis of jasmine scented black tea produced by different scenting processes[J]. Journal of Food Science, 2018, 83(11): 2718-2732.
[9] Ni H, Jiang Q X, Lin Q, et al.Enzymatic hydrolysis and auto-isomerization during β-glucosidase treatment improve the aroma of instant white tea infusion[J]. Food Chemistry, 2021, 342: 128565. doi: 10.1016/j.foodchem.2020.128565.
[10] Qi D D, Miao A Q, Cao J X, et al.Study on the effects of rapid aging technology on the aroma quality of white tea using GC-MS combined with chemometrics: in comparison with natural aged and fresh white tea[J]. Food Chemistry, 2018, 265: 189-199.
[11] Wang Y, Yang X, Li K, et al.Simultaneous determination of theanine, gallic acid, purine alkaloids, catechins, and theaflavins in black tea using HPLC[J]. International Journal of Food Science & Technology, 2010, 45(6): 1263-1269.
[12] Pan H B, Zhang D, Li B, et al.A rapid UPLC method for simultaneous analysis of caffeine and 13 index polyphenols in black tea[J]. Journal of Chromatographic Science, 2017, 55(5): 495-496.
[13] Tan J, Dai W, Lu M, et al.Study of the dynamic changes in the non-volatile chemical constituents of black tea during fermentation processing by a non-targeted metabolomics approach[J]. Food Research International, 2016, 79: 106-113.
[14] Yun J, Cui C J, Zhang S H, et al.Use of headspace GC/MS combined with chemometric analysis to identify the geographic origins of black tea[J]. Food Chemistry, 2021, 360: 130033. doi: 10.1016/j.foodchem.2021.130033.
[15] Rubel Mozumder N H M, Lee Y R, Hwang K H, et al. Characterization of tea leaf metabolites dependent on tea (Camellia sinensis) plant age through 1H NMR-based metabolomics[J]. Applied Biological Chemistry, 2020, 63: 10. doi: 10.1186/s13765-020-0492-7.
[16] Wang W N, Zhang L, Wang S, et al.8-C N-ethyl-2-pyrrolidinone substituted flavan-3-ols as the marker compounds of Chinese dark teas formed in the post-fermentation process provide significant antioxidative activity[J]. Food Chemistry, 2014, 152: 539-545.
[17] Dai W D, Tan J F, Lu M L, et al.Metabolomics investigation reveals that 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols are potential marker compounds of stored white teas[J]. Journal of Agricultural and Food Chemistry, 2018, 66(27): 7209-7218.
[18] 王家勤, 姚月凤, 袁海波, 等. 基于色差系统的工夫红茶茶汤亮度的量化评价方法研究[J]. 茶叶科学, 2020, 40(2): 259-268.
Wang J Q, Yao Y F, Yuan H B, et al.A quantitative method for brightness evaluation of Congou black tea infusions based on color difference analysis[J]. Journal of Tea Science, 2020, 40(2): 259-268.
[19] Huang D Y, Qiu Q L, Wang Y M, et al.Rapid identification of different grades of Huangshan Maofeng tea using ultraviolet spectrum and color difference[J]. Molecules, 2020, 25(20): 4665. doi: 10.3390/molecules25204665.
[20] 宛晓春. 茶叶生物化学[M]. 3版. 北京: 中国农业出版社, 2003.
Wan X C.Tea Biochemistry [M]. 3rd ed. Beijing: China Agriculture Press, 2003.
[21] 张雪, 张志强. 学科交叉研究系统综述[J]. 图书情报工作, 2020, 64(14): 112-125.
Zhang X, Zhang Z Q.Review on interdisciplinary research[J]. Library and Information Service, 2020, 64(14): 112-125.
[22] 宛晓春, 李大祥, 张正竹, 等. 茶叶生物化学研究进展[J]. 茶叶科学, 2015, 35(1): 1-10.
Wan X C, Li D X, Zhang Z Z, et al.Research advance on tea biochemistry[J]. Journal of Tea Science, 2015, 35(1): 1-10.
[23] Huang T S.Computer vision: evolution and promise[J]. 19th CERN School of Computing, 1996: 21-25.
[24] Chen Q, Zhao J, Cai J.Identification of tea varieties using computer vision[J]. Transactions of the ASABE, 2008, 51(2): 623-628.
[25] Chen Y T, Chen S F.Localizing plucking points of tea leaves using deep convolutional neural networks[J]. Computers and Electronics in Agriculture, 2020, 171: 105298. doi: 10.1016/j.compag.2020.105298.
[26] Yang H L, Chen L, Ma Z B, et al.Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator[J]. Computers and Electronics in Agriculture, 2021, 181(7): 105946. doi: 10.1016/j.compag.2020.105946.
[27] Gill G S, Kumar A, Agarwal R.Nondestructive grading of black tea based on physical parameters by texture analysis[J]. Biosystems Engineering, 2013, 116(2): 198-204.
[28] Zhu H K, Ye Y, He H F, et al.Evaluation of green tea sensory quality via process characteristics and image information[J]. Food and Bioproducts Processing, 2017, 102: 116-122.
[29] 伍洵, 刘飞, 陈之威, 等. 基于计算机视觉的绿茶炒干中在制品理化变化研究[J]. 茶叶科学, 2020, 40(2): 194-204.
Wu X, Liu F, Chen Z W, et al.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.
[30] 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.
[31] Zou Y, Ma W J, Tang Q, et al.A high-precision method evaluating color quality of Sichuan Dark Tea based on colorimeter combined with multi-layer perceptron[J]. Journal of Food Process Engineering, 2020, 43(8): e13444. doi: 10.1111/jfpe.13444.
[32] Bakhshipour A, Sanaeifar A, Payman S H, et al.Evaluation of data mining strategies for classification of black tea based on image-based features[J]. Food Analytical Methods, 2018, 11(4): 1041-1050.
[33] Bakhshipour A, Zareiforoush H, Bagheri I.Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features[J]. Journal of Food Measurement and Characterization, 2020, 14(3): 1402-1416.
[34] Wu X Y, Yang J Q, Wang S H.Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm[J]. Multimedia Tools and Applications, 2018, 77(3): 3745-3759.
[35] Zhang Y D, Muhammad K, Tang C.Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform[J]. Multimedia Tools and Applications, 2018, 77(17): 22821-22839.
[36] Chen J, Liu Q, Gao L W.Visual tea leaf disease recognition using a convolutional neural network model[J]. Symmetry, 2019, 11(3): 343. doi: 10.3390/sym11030343.
[37] Chen J F, He Q.Tea disease spot recognition based on image feature points extraction and matching[J]. Global NEST Journal, 2020, 22(4): 492-501.
[38] Yang N, Yuan M, Wang P, et al.Tea diseases detection based on fast infrared thermal image processing technology[J]. Journal of the Science of Food and Agriculture, 2019, 99(7): 3459-3466.
[39] 吴正敏, 曹成茂, 谢承健, 等. 基于图像处理技术和神经网络实现机采茶分级[J]. 茶叶科学, 2017, 37(2): 182-190.
Wu Z M, Cao C M, Xie C J, et al.Grading of machine picked tea based on image processing technology and neural network[J]. Journal of Tea Science, 2017, 37(2): 182-190.
[40] Zhang L, Zhang H, Chen Y, et al.Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision[J]. International Journal of Agricultural and Biological Engineering, 2019, 12(1): 6-9.
[41] 张怡, 赵珠蒙, 王校常, 等. 基于ResNet卷积神经网络的绿茶种类识别模型构建[J]. 茶叶科学, 2021, 41(2): 261-271.
Zhang Y, Zhao Z M, Wang X C, et al.Construction of green tea recognition model based on ResNet convolutional neural network[J]. Journal of Tea Science, 2021, 41(2): 261-271.
[42] Li L Q, Wang Y J, Jin S S, et al.Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 246: 118991. doi: 10.1016/j.saa.2020.118991.
[43] Wang S H, Phillips P, Liu A J, et al.Tea category identification using computer vision and generalized eigenvalue proximal SVM[J]. Fundamenta Informaticae, 2017, 151(1/4): 325-339.
[44] Liu C L, Lu W Y, Gao B Y, et al.Rapid identification of chrysanthemum teas by computer vision and deep learning[J]. Food Science & Nutrition, 2020, 8(4): 1968-1977.
[45] Xu M, Wang J, Jia P F, et al.Identification of Longjing teas with different geographic origins based on e-nose and computer vision system combined with data fusion strategies[J]. Transactions of the ASABE, 2021, 64(1): 327-340.
[46] Lin X, Sun D W.Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses[J]. Trends in Food Science & Technology, 2020, 104: 163-176.
[47] 任广鑫, 金珊珊, 李露青, 等. 近红外光谱技术在茶叶品控与装备创制领域的研究进展[J]. 茶叶科学, 2020, 40(6): 707-714.
Ren G X, Jin S S, Li L Q, et al.Research progress of near-infrared spectroscopy in tea quality control and equipment development[J]. Journal of Tea Science, 2020, 40(6): 707-714.
[48] Huang Y F, Dong W T, Sanaeifar A, et al.Development of simple identification models for four main catechins and caffeine in fresh green tea leaf based on visible and near-infrared spectroscopy[J]. Computers and Electronics in Agriculture, 2020, 173: 105388. doi: 10.1016/j.compag.2020.105388.
[49] Hazarika A K, Chanda S, Sabhapondit S, et al.Quality assessment of fresh tea leaves by estimating total polyphenols using near infrared spectroscopy[J]. Journal of Food Science and Technology, 2018, 55(12): 4867-4876.
[50] Sanaeifar A, Huang X, Chen M, et al.Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy[J]. Food Science & Nutrition, 2020, 8(11): 5860-5874.
[51] Chen Q S, Chen M, Liu Y, et al.Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea[J]. Journal of Food Science & Technology, 2018, 55(10): 4363-4368.
[52] Chanda S, Hazarika A K, Choudhury N, et al.Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy[J]. Journal of Chemometrics, 2019, 33(10): e3172. doi: 10.1002/cem.3172.
[53] 卢莉, 程曦, 张渤, 等. 小种红茶茶多酚和咖啡碱近红外定量分析模型的建立[J]. 茶叶科学, 2020, 40(5): 689-695.
Lu L, Cheng X, Zhang B, et al.Establishment of predictive model for quantitative analysis of tea polyphenols and caffeine of souchong by near infrared spectroscopy[J]. Journal of Tea Science, 2020, 40(5): 689-695.
[54] Zareef M, Chen Q, Ouyang Q, et al.Prediction of amino acids, caffeine, theaflavins and water extract in black tea using FT-NIR spectroscopy coupled chemometrics algorithms[J]. Analytical Methods, 2018, 10(25): 3023-3031.
[55] Lee M S, Hwang Y S, Lee J, et al.The characterization of caffeine and nine individual catechins in the leaves of green tea (Camellia sinensis L.) by near-infrared reflectance spectroscopy[J]. Food Chemistry, 2014, 158: 351-357.
[56] Guo Z M, Barimah A O, Shujat A, et al.Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm[J]. LWT, 2020, 129: 109510. doi: 10.1016/j.lwt.2020.109510.
[57] Liu Z, Xie H L, Chen L, et al.An improved weighted partial least squares method coupled with near infrared spectroscopy for rapid determination of multiple components and anti-oxidant activity of pu-erh tea[J]. Molecules, 2018, 23(5): 1058. doi: 10.3390/molecules23051058.
[58] Li X L, Jin J J, Sun C J, et al.Simultaneous determination of six main types of lipid-soluble pigments in green tea by visible and near-infrared spectroscopy[J]. Food Chemistry, 2019, 270: 236-242.
[59] Wang J H, Wang Y F, Cheng J J, et al.Enhanced cross-category models for predicting the total polyphenols, caffeine and free amino acids contents in Chinese tea using NIR spectroscopy[J]. LWT, 2018, 96: 90-97.
[60] Huang Z X, Sanaeifar A, Tian Y, et al.Improved generalization of spectral models associated with Vis-NIR spectroscopy for determining the moisture content of different tea leaves[J]. Journal of Food Engineering, 2021, 293: 110374. doi: 10.1016/j.jfoodeng.2020.110374.
[61] Liu P, Wen Y P, Huang J S, et al.A novel strategy of near-infrared spectroscopy dimensionality reduction for discrimination of grades, varieties and origins of green tea[J]. Vibrational Spectroscopy, 2019, 105: 102984. doi: 10.1016/j.vibspec.2019.102984.
[62] Ren G X, Wang Y J, Ning J M, et al.Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 230: 118079. doi: 10.1016/j.saa.2020.118079.
[63] Firmani P, Luca S D, Bucci R, et al.Near infrared (NIR) spectroscopy-based classification for the authentication of Darjeeling black tea[J]. Food Control, 2019, 100: 292-299.
[64] Meng W, Xu X, Cheng K K, et al.Geographical origin discrimination of oolong tea (Tieguanyin, Camellia sinensis (L.) O. Kuntze) using proton nuclear magnetic resonance spectroscopy and near-infrared spectroscopy[J]. Food Analytical Methods, 2017, 10(11): 3508-3522.
[65] Guo Z M, Barimah A O, Yin L M, et al.Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy[J]. Food Chemistry, 2021, 353: 129372. doi: 10.1016/j.foodchem.2021.129372.
[66] Ning J M, Sun J J, Li S H, et al.Classification of five Chinese tea categories with different fermentation degrees using visible and near-infrared hyperspectral imaging[J]. International Journal of Food Properties, 2017, 20(s2): 1515-1522.
[67] Yan L, Pang L, Wang H, et al.Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics[J]. Journal of Food Process Engineering, 2020, 43(4): e13378. doi: 10.1111/jfpe.13378.
[68] Sun J, Zhou X, Hu Y G, et al.Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2019, 160: 153-159.
[69] Wang Y J, Liu Y, Chen Y Y, et al.Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging[J]. LWT, 2021, 148: 111737. doi: 10.1016/j.lwt.2021.111737.
[70] 徐敏. 基于电子鼻、电子舌和电子眼的多源信息融合技术对龙井茶品质的检测[D]. 杭州: 浙江大学, 2020.
Xu M.Quality detection of Longjing tea based on the multi-source information fusion of E-nose, E-tongue and E-eye [D]. Hangzhou: Zhejiang University, 2020.
[71] 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.
[72] 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.
[73] Hidayat S N, Triyana K, Fauzan I, et al.The electronic nose coupled with chemometric tools for discriminating the quality of black tea samples in situ[J]. Chemosensors, 2019, 7(3): 29. doi: 10.3390/chemosensors7030029.
[74] Liu H X, Yu D B, Gu Y.Classification and evaluation of quality grades of organic green teas using an electronic nose based on machine learning algorithms[J]. IEEE Access, 2019, 7: 172965-172973.
[75] 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.
[76] Wang S Y, Zhao F, Wu W X, et al.Comparison of volatiles in different jasmine tea grade samples using electronic nose and automatic thermal desorption-gas chromatography-mass spectrometry followed by multivariate statistical analysis[J]. Molecules, 2020, 25(2): 380. doi: 10.3390/molecules25020380.
[77] 王淑燕, 赵峰, 饶耿慧, 等. 基于电子鼻和ATD-GC-MS技术分析茉莉花茶香气成分的产地差异[J]. 食品工业科技, 2021, 42(15): 234-239.
Wang S Y, Zhao F, Rao G H, et al.Origin difference analysis of aroma components in jasmine tea based on electronic nose and ATD-GC-MS[J]. Science and Technology of Food Industry, 2021, 42(15): 234-239.
[78] Zhu J C, Chen F, Wang L Y, et al.Evaluation of the synergism among volatile compounds in Oolong tea infusion by odour threshold with sensory analysis and E-nose[J]. Food Chemistry, 2017, 221: 1484-1490.
[79] 王宝怡, 王培强, 李晓晗, 等. 基于电子鼻技术对不同季节山东绿茶香气的分析[J]. 现代食品科技, 2020, 36(10): 284-289, 259.
Wang B Y, Wang P Q, Li X H, et al.Analysis of aroma of Shandong green tea in different seasons based on electronic nose technology[J]. Modern Food Science & Technology, 2020, 36(10): 284-289, 259.
[80] 王佳音, 王凤玲, 王福东, 等. 电子鼻对不同种类抹茶香气成分的辨别与分析[J]. 食品与发酵工业, 2019, 45(19): 270-276.
Wang J Y, Wang F L, Wang F D, et al.Identification and analysis of the aroma components of different species of Matcha by electronic nose[J]. Food And Fermentation Industries, 2019, 45(19): 270-276.
[81] 孙玉冰. 基于电子鼻技术的茶树虫害信息检测[D]. 杭州: 浙江大学, 2018.
Sun Y B.Study of pest information for tea plant based on electroinc nose [D]. Hangzhou: Zhejiang University, 2018.
[82] Sun Y, Wang J, Cheng S, et al.Detection of pest species with different ratios in tea plant based on electronic nose[J]. Annals of Applied Biology, 2019, 174(2): 209-218.
[83] Sun Y, Wang J, Sun L, et al.Evaluation of E-nose data analyses for discrimination of tea plants with different damage types[J]. Journal of Plant Diseases and Protection, 2019, 126(1): 29-38.
[84] Sun Y, Wang J, Cheng S.Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method[J]. Computers and Electronics in Agriculture, 2017, 143: 293-301.
[85] Wang X R, Gu Y, Liu H X.A transfer learning method for the protection of geographical indication in china using an electronic nose for the identification of Xihu Longjing tea[J]. IEEE Sensors Journal, 2021, 21(6): 8065-8077.
[86] Loutfi A, Coradeschi S, Mani G K, et al.Electronic noses for food quality: a review[J]. Journal of Food Engineering, 2015, 144: 103-111.
[87] Liu T, Chen Y B, Li D Q, et al.Electronic tongue recognition with feature specificity enhancement[J]. Sensors, 2020, 20(3): 772. doi: 10.3390/s20030772.
[88] Ouyang Q, Yang Y C, Wu J Z, et al.Measurement of total free amino acids content in black tea using electronic tongue technology coupled with chemometrics[J]. LWT, 2020, 118: 108768. doi: 10.1016/j.lwt.2019.108768.
[89] 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.
[90] Ren G X, Li T H, Wei Y M, et al.Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis[J]. Microchemical Journal, 2021, 163: 105899. doi: 10.1016/j.microc.2020.105899.
[91] Huang D Y, Bian Z, Qiu Q L, et al.Identification of similar Chinese Congou black teas using an electronic tongue combined with pattern recognition[J]. Molecules, 2019, 24(24): 4549. doi: 10.3390/molecules24244549.
[92] Zou G Y, Xiao Y Z, Wang M S, et al.Detection of bitterness and astringency of green tea with different taste by electronic nose and tongue[J]. Plos One, 2018, 13(12): e0206517. doi: 10.1371/journal.pone.0206517.
[93] Jin G, Wang Y J, Li L Q, et al.Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy[J]. LWT, 2020, 125: 109216. doi: 10.1016/j.lwt.2020.109216.
[94] 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.
[95] Li L Q, Jin S S, Wang Y J, et al.Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 247: 119096. doi: 10.1016/j.saa.2020.119096.
[96] Wang J, Zareef M, He P, et al.Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms[J]. Journal of the Science of Food and Agriculture, 2019, 99(11): 5019-5027.
[97] Wang Y J, Li T H, Li L Q, et al.Evaluating taste-related attributes of black tea by micro-NIRS[J]. Journal of Food Engineering, 2020, 290: 110181. doi: 10.1016/j.jfoodeng.2020.110181.
[98] Dutta L, Talukdar C, Hazarika A, et al.A novel low-cost hand-held tea flavor estimation system[J]. IEEE Transactions on Industrial Electronics, 2018, 65(6): 4983-4990.
[99] Zhang Y Y, Gao W J, Cui C J, et al.Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids[J]. Food chemistry, 2020, 308: 125648. doi: 10.1016/j.foodchem.2019.125648.
[100] Ghosh S, Tudu B, Bhattacharyya N, et al.A recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea[J]. Neural Computing and Applications, 2019, 31(2): 1165-1171.
[101] Sharma P, Ghosh A, Tudu B, et al.Monitoring the fermentation process of black tea using QCM sensor based electronic nose[J]. Sensors and Actuators B: Chemical, 2015, 219: 146-157.
[102] Ghosh A, Bag A K, Sharma P, et al.Monitoring the fermentation process and detection of optimum fermentation time of black tea using an electronic tongue[J]. IEEE Sensors Journal, 2015, 15(11): 6255-6262.
[103] Dong C W, Liang G Z, Hu B, et al.Prediction of Congou black tea fermentation quality indices from color features using non-linear regression methods[J]. Scientific Reports, 2018, 8(1): 10535. doi: 10.1038/s41598-018-28767-2.
[104] Dong C W, Li J, Wang J J, et al.Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 205: 227-234.
[105] Dong C W, Ye Y L, Yang C S, et al.Rapid detection of catechins during black tea fermentation based on electrical properties and chemometrics[J]. Food Bioscience, 2021, 40: 100855. doi: 10.1016/j.fbio.2020.100855.
[106] Yang C S, Zhao Y, An T, et al.Quantitative prediction and visualization of key physical and chemical components in black tea fermentation using hyperspectral imaging[J]. LWT, 2021, 141: 110975. doi: 10.1016/j.lwt.2021.110975.
[107] Jin G, Wang Y J, Li M H, et al.Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system[J]. Food Chemistry, 2021, 358: 129815. doi: 10.1016/j.foodchem.2021.129815.
[108] Tseng T S, Hsiao M H, Chen P A, et al.Utilization of a gas-sensing system to discriminate smell and to monitor fermentation during the manufacture of Oolong tea leaves[J]. Micromachines, 2021, 12(1): 93. doi: 10.3390/mi12010093.
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