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Recent Technological Advances in Tea Quality and Safety

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Natural Products in Beverages

Part of the book series: Reference Series in Phytochemistry ((RSP))

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Abstract

Tea is one of the world’s three most popular beverages, so the detection of its quality (physical, chemical, microbiological, and classified parameters), as well as its safety (inorganic and organic contaminants), is a very crucial process. Emerging technologies (spectral technology, computer vision, electronic nose, electronic tongue, and electrochemical methods) have provided a fast, nondestructive (or light damaging) and convenient method for the detection of tea products, including fresh, fermented, and processed tea leaves, and commercial tea. These technologies are beneficial for automating the tea processing process, improving the quality and production of tea, and increasing the economic benefits for the industry. Furthermore, it can also provide ideas regarding the detection and evaluation of other food products.

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References

  1. John B (2021) The Chinese art of tea. Taylor and Francis

    Google Scholar 

  2. Besky S (2020) Tasting qualities: the past and future of tea. University of California Press

    Book  Google Scholar 

  3. Yu XL, Sun DW, He Y (2020) Emerging techniques for determining the quality and safety of tea products: a review. Compr Rev Food Sci Food Saf 19(5):2613–2638

    Article  PubMed  Google Scholar 

  4. ISO 1573:1980. (R2015) (2015) Tea – determination of loss in mass at 103 °C. International Organization for Standardization, pp 1–4

    Google Scholar 

  5. GB 5009–2016 (2016) Tea-determination of moisture content. Standard of the People’s Republic of China, pp 2–5

    Google Scholar 

  6. GB/T 18798.2–2018 (2018) Instant tea in solid state – determination of ash content. Standard of the People’s Republic of China, pp 5–10

    Google Scholar 

  7. ISO 15598:1999 (R2016) (2016) Tea -determination of crude fibre content. International Organization for Standardization, pp 1–10

    Google Scholar 

  8. GB/T 8310–2002 (2002) Tea-determination of crude fiber. Standard of the People’s Republic of China, pp 5–10

    Google Scholar 

  9. ISO 10727:2002 (R2018) (2018) Tea and instant tea in solid form – determination of caffeine content – method using highperformance liquid chromatography. International Organization for Standardization, pp 1–14

    Google Scholar 

  10. GB/T 31740.2–2015 (2015) Tea products – determination of caffeine. Standard of the People’s Republic of China, pp 1–5

    Google Scholar 

  11. GB/T 8312–2013 (2013) Tea – determination of caffeine. Standard of the People’s Republic of China, pp 1–5

    Google Scholar 

  12. ISO 14502-1:2005 (R2015) (2015) Determination of substances characteristic of green and black tea- Part 1: content of total polyphenols in tea – colorimetric method using Folin–Ciocalteu reagent. International Organization for Standardization, pp 1–16

    Google Scholar 

  13. GB/T 31740.2–2015 (2015) Tea products – determination of total polyphenols. Standard of the People’s Republic of China, pp 1–5

    Google Scholar 

  14. ISO 14502-2:2005 (R2015) (2015) Determination of substances characteristic of green and black tea – part 2: content of catechins in green tea – method using high-performance liquid chromatography. International Organization for Standardization, pp 1–28

    Google Scholar 

  15. GB/T 31740.2–2015 (2015) Tea products – determination of catechins. Standard of the People’s Republic of China, pp 6–10

    Google Scholar 

  16. GB/T 8314–2013 (2013) Tea – determination of free amino acids. Standard of the People’s Republic of China, pp 1–10

    Google Scholar 

  17. ISO 19563:2017 (2017) Determination of theanine in tea and instant tea in solid form using high-performance liquid chromatography. International Organization for Standardization, pp 1–20

    Google Scholar 

  18. GB/T 23193–2017 (2017) Tea – determination of theanine. Standard of the People’s Republic of China, pp 1–10

    Google Scholar 

  19. GB 5009.22–2016 (2016) Food safety – determination of aflatoxin B1. Standard of the People’s Republic of China, pp 2–5

    Google Scholar 

  20. GB 5009.12–2016 (2016) Food safety – determination of lead. Standard of the People’s Republic of China, pp 6–10

    Google Scholar 

  21. BJS 201910 (2019) Determination of lead chromate in tea. P. R. China: State Administration for Market Regulation, pp 1–14

    Google Scholar 

  22. GB 5009.256–2016 (2016) Food safety – determination of PAHs. Standard of the People’s Republic of China, pp 11–15

    Google Scholar 

  23. GB 23200–2016 (2016) Food safety – determination of pesticides. Standard of the People’s Republic of China, pp 11–15

    Google Scholar 

  24. GB/T 23776–2018. Tea – determination of sensory evaluation. Standard of the People’s Republic of China, pp 1–15

    Google Scholar 

  25. ISO 3103:2019 (2019) Preparation of liquor for use in sensory tests. International Organization for Standardization, pp 1–14

    Google Scholar 

  26. ISO 8589:2007. (R2017) (2017) Sensory analysis – general guidance for the design of test rooms. International Organization for Standardization, pp 1–22

    Google Scholar 

  27. Tom V (2020) Infrared spectra of pesticides. CRC Press

    Google Scholar 

  28. Robert W (2020) Chromatography/Fourier transform infrared spectroscopy and its applications. CRC Press

    Google Scholar 

  29. Smith E, Dent G (2019) Modern Raman spectroscopy: a practical approach. John Wiley & Sons

    Book  Google Scholar 

  30. Palleschi V (2022) Chemometrics and numerical methods in LIBS. John Wiley & Sons

    Book  Google Scholar 

  31. Carlo C, Fedir S (2014) THz and security applications. Springer, Dordrecht

    Google Scholar 

  32. Arabnia HR, Deligiannidis L, Tinetti FG (2020) Image processing, computer vision, and pattern recognition. CSREA (MLI)

    Google Scholar 

  33. Sheila A (2020) A guide for machine vision in quality control. CRC Press

    Google Scholar 

  34. Hui G (2016) Electronic noses and tongues in food science. Elsevier

    Google Scholar 

  35. Bagheri S (2016) Nanocomposites in electrochemical sensors. CRC Press

    Book  Google Scholar 

  36. Zhang Y, Gao W, Cui C, Zhang Z, He L, Zheng J, Hou R (2020) Development of a method to evaluate the tenderness of fresh tea leaves based on rapid, in-situ Raman spectroscopy scanning for carotenoids. Food Chem 308:125648

    Article  CAS  PubMed  Google Scholar 

  37. Chen B, Yan J (2020) Fresh tea shoot maturity estimation via multispectral imaging and deep label distribution learning. IEICE Trans Inf Syst 103(9):2019–2022

    Article  Google Scholar 

  38. **n W, Song F, Pei Y et al (2021) Study on the tea quality changes and predictions during the microwave fixation process by machine vision. J Tea Sci 41(6):854–864

    Google Scholar 

  39. Wei Y, He Y, Li X (2021) Tea moisture content detection with multispectral and depth images. Comput Electron Agric 183:106082

    Article  Google Scholar 

  40. Wang M, Zhang Z, Ning J, Wei L, Li L (2014) Study on quality analysis and class rapid evaluation of tea leaf materials based on near infrared technology. Sci Technol Food Ind 35(22):57–60

    Google Scholar 

  41. Li X, ** J, Sun C, Ye D, Liu Y (2019) Simultaneous determination of six main types of lipid-soluble pigments in green tea by visible and near-infrared spectroscopy. Food Chem 270:236–242

    Article  CAS  PubMed  Google Scholar 

  42. Sanaeifar A, Huang X, Chen M, Zhao Z, Ji Y, Li X, Yu X (2020) Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy. Food Sci Nutr 8(11):5860–5874

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Huang Y, Dong W, Sanaeifar A, Wang X, Luo W, Zhan B, Li X (2020) Development of simple identification models for four main catechins and caffeine in fresh green tea leaf based on visible and near-infrared spectroscopy. Comput Electron Agric 173:105388

    Article  Google Scholar 

  44. Li X, Zhou R, Xu K, Xu J, ** J, Fang H, He Y (2018) Rapid determination of chlorophyll and pheophytin in green tea using Fourier transform infrared spectroscopy. Molecules 23(5):1010

    Article  PubMed  PubMed Central  Google Scholar 

  45. He Y, Zhao Y, Zhang C, Sun C, Li X (2019) Determination of ß-carotene and lutein in green tea using Fourier transform infrared spectroscopy. Trans ASABE 62(1):75–81

    Article  CAS  Google Scholar 

  46. Zhou RQ, Li XL, He Y, ** JJ (2018) Determination of catechins and caffeine content in tea (camellia Sinensis L.) leaves at different positions by Fourier-transform infrared spectroscopy. Trans ASABE 61(4):1221–1230

    Article  CAS  Google Scholar 

  47. Li XL, Yuzhen W, Jie X et al (2018) EGCG distribution visualization in tea leaves based on hyperspectral imaging technology. Trans Chin Soc Agric Eng 34(07):180–186

    Google Scholar 

  48. Zeng J, ** W, Sanaeifar A, Xu X, Luo W, Sha J, Li X (2021) Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer. Plant Methods 17(1):1–13

    Article  Google Scholar 

  49. Kilmartin PA, Hsu CF (2003) Characterisation of polyphenols in green, oolong, and black teas, and in coffee, using cyclic voltammetry. Food Chem 82(4):501–512

    Article  CAS  Google Scholar 

  50. Goodwin A, Banks CE, Compton RG (2006) Electroanalytical sensing of green tea anticarcinogenic catechin compounds: epigallocatechin gallate and epigallocatechin. Electroanalysis 18(9):849–853

    Article  CAS  Google Scholar 

  51. Novak I, Šeruga M, Komorsky-Lovrić Š (2010) Characterisation of catechins in green and black teas using square-wave voltammetry and RP-HPLC-ECD. Food Chem 122(4):1283–1289

    Article  CAS  Google Scholar 

  52. Guo D, Zheng D, Mo G, Ye J (2009) Adsorptive strip** voltammetric detection of tea polyphenols at multiwalled carbon nanotubes-chitosan composite electrode. Electroanalysis 21(6):762–766

    Article  CAS  Google Scholar 

  53. Roginsky V, Barsukova T, Hsu CF, Kilmartin PA (2003) Chain-breaking antioxidant activity and cyclic voltammetry characterization of polyphenols in a range of green, oolong, and black teas. J Agric Food Chem 51(19):5798–5802

    Article  CAS  PubMed  Google Scholar 

  54. Lei B, He XD, Ji H et al (2022) Research and application of intelligent identification of Empoasca onukii based on machine vision. J Tea Sci 42(3):376–386

    Google Scholar 

  55. Mukhopadhyay S, Paul M, Pal R, De D (2021) Tea leaf disease detection using multi-objective image segmentation. Multimed Tools Appl 80(1):753–771

    Article  Google Scholar 

  56. Jianxiong L, Li M, Tao D et al (2013) Tea geometrid damage based on hyperspectral technology. J Hunan Agric Univ 39(3):315–318

    Google Scholar 

  57. Yuan L, Yan P, Han W, Huang Y, Wang B, Zhang J, Bao Z (2019) Detection of anthracnose in tea plants based on hyperspectral imaging. Comput Electron Agric 167:105039

    Article  Google Scholar 

  58. Li XL, Luo LB, Hu XQ, Lou BG, He Y (2014) Revealing the chemical changes of tea cell wall induced by anthracnose with confocal Raman microscopy. Spectrosc Spectr Anal 34(6):1571–1576

    CAS  Google Scholar 

  59. **aolan Y, Ji-yu P, Fei L, Yong H (2017) Fast identification of matcha and green tea powder with laser-induced breakdown spectroscopy. Spectrosc Spectr Anal 37(6):1908–1911

    Google Scholar 

  60. Chen Z, He L, Ye Y, Chen J, Sun L, Wu C, Wang R (2020) Automatic sorting of fresh tea leaves using vision-based recognition method. J Food Process Eng 43(9):e13474

    Article  Google Scholar 

  61. Sun X, Cui D, Shen Y, Li W, Wang J (2022) Non-destructive detection for foreign bodies of tea stalks in finished tea products using terahertz spectroscopy and imaging. Infrared Phys Technol 121:104018

    Article  Google Scholar 

  62. Liu L, Hu P, Yang F, Song M (2020) Application of terahertz time-domain spectroscopy combined with support vector machine to determine tea and pesticide samples. Mater Express 10(10):1646–1653

    Article  CAS  Google Scholar 

  63. He Y, Deng X, Li X (2006) Discrimination of varieties of tea using near infrared spectroscopy. In: Fourth international conference on photonics and imaging in biology and medicine, vol 6047. SPIE, pp 477–483

    Google Scholar 

  64. Buyukgoz GG, Soforoglu M, Basaran-Akgul N, Boyaci IH (2016) Spectroscopic fingerprint of tea varieties by surface-enhanced Raman spectroscopy. J Food Sci Technol 53(3):1709–1716

    Article  CAS  PubMed  Google Scholar 

  65. Li M, Dai G, Chang T, Shi C, Wei D, Du C, Cui HL (2017) Accurate determination of geographical origin of tea based on terahertz spectroscopy. Appl Sci 7(2):172

    Article  Google Scholar 

  66. Liu C, Lu W, Gao B, Kimura H, Li Y, Wang J (2020) Rapid identification of chrysanthemum teas by computer vision and deep learning. Food Sci Nutr 8(4):1968–1977

    Article  PubMed  PubMed Central  Google Scholar 

  67. Chen XJ, Di W, He Y, Li XL, Liu S (2008) Study on discrimination of tea based on color of multispectral image. Spectrosc Spectr Anal 28(11):2527–2530

    CAS  Google Scholar 

  68. Yu H, Wang J, Yao C, Zhang H, Yu Y (2008) Quality grade identification of green tea using E-nose by CA and ANN. LWT-Food Sci Technol 41(7):1268–1273

    Article  CAS  Google Scholar 

  69. Yu H, Wang J (2007) Discrimination of Long**g green-tea grade by electronic nose. Sensors Actuators B Chem 122(1):134–140

    Article  CAS  Google Scholar 

  70. Dai Y, Zhi R, Zhao L, Gao H, Shi B, Wang H (2015) Long**g tea quality classification by fusion of features collected from E-nose. Chemom Intell Lab Syst 144:63–70

    Article  CAS  Google Scholar 

  71. Borah S, Hines EL, Leeson MS, Iliescu DD, Bhuyan M, Gardner JW (2008) Neural network based electronic nose for classification of tea aroma. Sens & Instrumen Food Qual 2(1):7–14

    Article  Google Scholar 

  72. He W, Hu X, Zhao L, Liao X, Zhang Y, Zhang M, Wu J (2009) Evaluation of Chinese tea by the electronic tongue: correlation with sensory properties and classification according to geographical origin and grade level. Food Res Int 42(10):1462–1467

    Article  Google Scholar 

  73. Tian SY, Deng SP, Chen ZX (2007) Multifrequency large amplitude pulse voltammetry: a novel electrochemical method for electronic tongue. Sensors Actuators B Chem 123(2):1049–1056

    Article  CAS  Google Scholar 

  74. Bhuyan A, Tudu B, Bandyopadhyay R, Ghosh A, Kumar S (2019) ARMAX modeling and impedance analysis of voltammetric E-tongue for evaluation of infused tea. IEEE Sensors J 19(11):4098–4105

    Article  CAS  Google Scholar 

  75. Fu XS, Xu L, Yu XP, Ye ZH, Cu HF (2013) Robust and automated internal quality grading of a Chinese green tea (Long**g) by near-infrared spectroscopy and chemometrics. J Spectrosc 62(4):12–30

    Google Scholar 

  76. Zhao J, Chen Q, Cai J, Ouyang Q (2009) Automated tea quality classification by hyperspectral imaging. Appl Opt 48(19):3557–3564

    Article  CAS  PubMed  Google Scholar 

  77. Dutta R, Hines EL, Gardner JW, Kashwan KR, Bhuyan M (2003) Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach. Sensors Actuators B Chem 94(2):228–237

    Article  CAS  Google Scholar 

  78. Qin Z, Pang X, Chen D, Cheng H, Hu X, Wu J (2013) Evaluation of Chinese tea by the electronic nose and gas chromatography–mass spectrometry: correlation with sensory properties and classification according to grade level. Food Res Int 53(2):864–874

    Article  CAS  Google Scholar 

  79. Bhattacharyya N, Seth S, Tudu B, Tamuly P, Jana A, Ghosh D, Sabhapandit S (2007) Detection of optimum fermentation time for black tea manufacturing using electronic nose. Sensors Actuators B Chem 122(2):627–634

    Article  CAS  Google Scholar 

  80. Bhattacharyya N, Seth S, Tudu B, Tamuly P, Jana A, Ghosh D, Bhuyan M (2007) Monitoring of black tea fermentation process using electronic nose. J Food Eng 80(4):1146–1156

    Article  CAS  Google Scholar 

  81. Kaur R, Kumar R, Gulati A, Ghanshyam C, Kapur P, Bhondekar AP (2012) Enhancing electronic nose performance: a novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) O. Kuntze). Sensors Actuators B Chem 166:309–319

    Article  Google Scholar 

  82. Xu M, Wang J, Zhu L (2019) The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chem 289:482–489

    Article  CAS  PubMed  Google Scholar 

  83. Huo D, Wu Y, Yang M, Fa H, Luo X, Hou C (2014) Discrimination of Chinese green tea according to varieties and grade levels using artificial nose and tongue based on colorimetric sensor arrays. Food Chem 145:639–645

    Article  CAS  PubMed  Google Scholar 

  84. Sanaeifar A, Zhu F, Sha J, Li X, He Y, Zhan Z (2022) Rapid quantitative characterization of tea seedlings under lead-containing aerosol particles stress using Vis-NIR spectra. Sci Total Environ 802:149824

    Article  CAS  PubMed  Google Scholar 

  85. Rehan I, Gondal MA, Aldakheel RK, Almessiere MA, Rehan K, Khan S, Khan MZ (2022) Determination of nutritional and toxic metals in black tea leaves using calibration free LIBS and ICP: AES technique. Arab J Sci Eng 47(6):7531–7539

    Article  CAS  Google Scholar 

  86. Gondal MA, Habibullah YB, Baig U, Oloore LE (2016) Direct spectral analysis of tea samples using 266 nm UV pulsed laser-induced breakdown spectroscopy and cross validation of LIBS results with ICP-MS. Talanta 152:341–352

    Article  CAS  PubMed  Google Scholar 

  87. Li XL, Zhang YY, He Y (2017) Study on the detection of talc illegally added in tea based on mid infrared spectroscopy. Spectrosc Spectr Anal 04:1081–1085

    Google Scholar 

  88. Li XL, Zhou RQ, Sun CJ, He Y (2017) Detection of lead chrome green illegally added in tea based on confocal Raman spectroscopy. Spectrosc Spectr Anal 37(2):461–466

    CAS  Google Scholar 

  89. Limei X, Lai GY, Kang HZ (2020) Determination and quantitative analysis of pesticide residues in tea leachate by surface-enhanced Raman spectroscopy. Chin J Anal Lab 39(2):148–153

    Google Scholar 

  90. Zhu J, Sharma AS, Xu J, Xu Y, Jiao T, Ouyang Q, Chen Q (2021) Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering. Spectrochim Acta A Mol Biomol Spectrosc 246:118994

    Article  CAS  PubMed  Google Scholar 

  91. Liang X, Li L, Han C, Dong Y, Xu F, Lv Z, Sun Y (2022) Rapid limit test of seven pesticide residues in tea based on the combination of TLC and Raman imaging microscopy. Molecules 27(16):5151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lei L, Wu RM, Guo P (2015) Rapid detection of thiabendazole pesticide residue in fresh tea leaves using surface-enhanced Raman spectroscopy. Mod Food Sci Technol 31(05):291–296

    Google Scholar 

  93. Herrador MÁ, González AG (2001) Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry. Talanta 53:1249–1257

    Article  CAS  PubMed  Google Scholar 

  94. Xu M, Wang J, Gu S (2019) Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. J Food Eng 241:10–17

    Article  CAS  Google Scholar 

  95. Fareed S, Siraj K, Ahmad Z, Ahsen R, Awan RA, Rahim MSA, Younas Q (2020) Identification of chemical elements in tea leaves and calculation of plasma parameters using laser-induced breakdown spectroscopy (LIBS). Instrum Exp Tech 63(5):744–749

    Article  Google Scholar 

  96. Yu H, Wang J, **ao H, Liu M (2009) Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals. Sensors Actuators B Chem 140(2):378–382

    Article  CAS  Google Scholar 

  97. Chen Q, Zhao J, Vittayapadung S (2008) Identification of the green tea grade level using electronic tongue and pattern recognition. Food Res Int 41(5):500–504

    Article  CAS  Google Scholar 

  98. Lvova L, Legin A, Vlasov Y, Cha GS, Nam H (2003) Multicomponent analysis of Korean green tea by means of disposable all-solid-state potentiometric electronic tongue microsystem. Sensors Actuators B Chem 95(1–3):391–399

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China

    **aoli Li, Alireza Sanaeifar, Shuai Zhang, Zhihao Zhan & Yong He

  2. Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN, USA

    Alireza Sanaeifar

Authors
  1. **aoli Li
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  2. Alireza Sanaeifar
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  3. Shuai Zhang
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  4. Zhihao Zhan
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  5. Yong He
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Correspondence to **aoli Li or Yong He .

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Editors and Affiliations

  1. Faculty of Pharmaceutical Sciences, University of Bordeaux, Bordeaux, France

    Jean-Michel Mérillon

  2. UMRt BioEcoAgro, University of Lille, Lille, France

    Céline Riviere

  3. UMRt BioEcoAgro, University of Lille, Lille, France

    Gabriel Lefèvre

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Li, X., Sanaeifar, A., Zhang, S., Zhan, Z., He, Y. (2023). Recent Technological Advances in Tea Quality and Safety. In: Mérillon, JM., Riviere, C., Lefèvre, G. (eds) Natural Products in Beverages. Reference Series in Phytochemistry. Springer, Cham. https://doi.org/10.1007/978-3-031-04195-2_35-1

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  • DOI: https://doi.org/10.1007/978-3-031-04195-2_35-1

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  • Print ISBN: 978-3-031-04195-2

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