[1] Pan, S. Y., Nie, Q., Tai, H. C., Song, X. L., Tong, Y. F., Zhang, L. J. F., ... & Liang, C. (2022). Tea and tea drinking: China’s outstanding contributions to the mankind.
Chin. Med.,
17(1), 27. doi:
10.1186/s13020-022-00571-1
[2] Samanta, S. (2022). Potential bioactive components and health promotional benefits of tea (Camellia sinensis).
J. Am. Nutr. Assoc., 41(1), 65-93. doi:
10.1080/07315724.2020.1827082
[3] Zhang, Z. B., Xiong, T., Chen, J. H., Ye, F., Cao, J. J., Chen, Y. R. & Luo, T. (2023). Understanding the origin and evolution of tea (Camellia sinensis [L.]): genomic advances in tea.
J. Mol. Evol.,
91(2), 156-168. doi:
10.1007/s00239-023-10099-z
[4] Kim, Y., & Je, Y. (2024). Tea consumption and risk of all-cause, cardiovascular disease, and cancer mortality: a meta-analysis of thirty-eight prospective cohort data sets.
Epidemiol. Health,
46, e2024056. doi:
10.4178/epih.e2024056
[5] Liu, J., Sun, H., & Katto, J. (2023). Learned image compression with mixed transformer-cnn architectures. In:
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 14388-14397). doi:
10.48550/arXiv.2303.14978
[6] Hursel, R., Viechtbauer, W., Dulloo, A. G., Tremblay, A., Tappy, L., Rumpler, W., & Westerterp‐Plantenga, M. S. (2011). The effects of catechin rich teas and caffeine on energy expenditure and fat oxidation: a meta‐analysis.
Obes. Rev.,
12(7), e573-e581. doi:
10.1111/j.1467-789X.2011.00862.x
[7] Kennedy, S. P., Gonzales, P., & Roungchun, J. (2021). Coffee and tea fraud. In Food fraud (pp. 139-150). Academic Press.
[9] Shalaby, H. Y. (2024). Prevention of food fraud in egypt: policy implementation challenges and the way forward (Master's thesis, The American University in Cairo (Egypt)).
[10] Priyadarshana, P. H. M. G. C., Jayasinghe, J. A. V. R., Perera, H. K. I., & Udari, A. H. G. S. (2022). Development of a herbal tea with potential antiglycation effects using Phyllanthus emblica (Indian Gooseberry), Zingiber officinale (Ginger), and Coriander sativum (Coriander).
Sri Lankan J. Agric. Ecosyst.,
4(1). doi:
10.4038/sljae.v4i1.52
[11] Gunathilaka, D. M. N. M., & Warnasooriya, W. M. R. S. K. (2021). Adulteration and quality of black tea in Sri Lankan market. In: 2nd Faculty Annual Research Session. Faculty of Applied Sciences, University of Vavuniya (pp. 47-51).
[12] Li, Y., Logan, N., Quinn, B., Hong, Y., Birse, N., Zhu, H. & Wu, D. (2024). Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification.
Food Chem.,
438, 138029. doi:
10.1016/j.foodchem.2023.138029
[13] Zhang, B., Zhang, Y., Zhang, K., Zhang, Y., Ji, Y., Zhu, B. & Ge, X. (2023). Machine learning assesses drivers of PM2. 5 air pollution trend in the Tibetan Plateau from 2015 to 2022.
Sci. Total Environ.,
878, 163189.
https://doi.org/10.1016/j.scitotenv.2023.163189
[14] Dubey, A. (2020). An analysis of the challenges faced by India in the protection and enforcement of geographical indication through the case study of Darjeeling tea.
Int. J. Law Manag. Humanities., 3(6), 819.
doi: http://doi.one/10.1732/IJLMH.25247
[15] Zhao, J., Yang, W., Cai, H., Cao, G., & Li, Z. (2025). Current progress and future trends of genomics-based techniques for food adulteration identification.
Foods,
14(7), 1116. doi:
10.3390/foods14071116
[16] Li, S., Lo, C. Y., Pan, M. H., Lai, C. S., & Ho, C. T. (2013). Black tea: chemical analysis and stability.
Food Funct.,
4(1), 10-18. doi:
10.1039/c2fo30093a
[17] Priyadarshi, A. (2024). Trustea India sustainable tea code and democratizing agricultural standards. In SDGs in the Asia and Pacific Region (pp. 839-865). Cham: Springer International Publishing. doi:
10.1007/978-3-030-91262-8_73-1
[18] Prasetya, A. T. E., Wibowo, N. A., & Rondonuwu, F. S. (2018, September). Determination of total quality of black tea fanning grade using near-infrared spectroscopy.
J. Phys.: Conf. Ser., 1097(1):012008. doi:
10.1088/1742-6596/1097/1/012008
[19] Yao, J., Lin, X., Qiu, Z., Meng, X., Chen, J., Li, A., ... & Kong, H. (2025). Enhancement of flavor components of oolong tea and dark tea based on graphene heating film.
Food Chem., X,
27, 102433.
https://doi.org/10.1016/j.fochx.2025.102433
[20] Chen, K., Zhurbenko, P., Danilov, L., Matveeva, T., & Otten, L. (2022). Conservation of an Agrobacterium cT-DNA insert in Camellia section Thea reveals the ancient origin of tea plants from a genetically modified ancestor.
Front. Plant Sci., 13, 997762.
https://doi.org/10.3389/fpls.2022.997762
[21] Stoeckle, M. Y., Gamble, C. C., Kirpekar, R., Young, G., Ahmed, S., & Little, D. P. (2011). Commercial teas highlight plant DNA barcode identification successes and obstacles.
Sci. Rep.,
1(1), 42. doi:
10.1038/srep00042
[22] Osathanunkul, M., Ounjai, S., Osathanunkul, R., & Madesis, P. (2017). Evaluation of a DNA-based method for spice/herb authentication, so you do not have to worry about what is in your curry, buon appetito!.
PLoS One,
12(10), e0186283. doi:
10.1371/journal.pone.0186283
[23] Hu, O., Chen, J., Gao, P., Li, G., Du, S., Fu, H. & Xu, L. (2019). Fusion of near‐infrared and fluorescence spectroscopy for untargeted fraud detection of Chinese tea seed oil using chemometric methods.
J. Sci. Food Agric.,
99(5), 2285-2291. doi:
10.1002/jsfa.9424
[24] Tang, T., Luo, Q., Yang, L., Gao, C., Ling, C., & Wu, W. (2023). Research review on quality detection of fresh tea leaves based on spectral technology.
Foods,
13(1), 25. doi:
10.3390/foods13010025
[25] Campmajó, G., Rodríguez-Javier, L. R., Saurina, J., & Núñez, O. (2021). Assessment of paprika geographical origin fraud by high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprinting.
Food Chem.,
352, 129397. doi:
10.1016/j.foodchem.2021.129397
[26] Zou, J., Zhao, M., Chan, S. A., Song, Y., Yan, S., & Song, W. (2024). Rapid and simultaneous determination of ultrashort-, short-and long-chain perfluoroalkyl substances by a novel liquid chromatography mass spectrometry method.
J. Chromatogr. A,
1734(19), 465324. doi:
10.1016/j.chroma.2024.465324
[27] Zhang, P., Cheng, J., Chen, Q., Zheng, Z., Wei, C., Zou, T. & Huang, Y. (2025). Near‐infrared spectroscopy coupled with Gramian angular field two‐dimensional convolutional neural network for white tea adulteration detection.
J. Sci. Food Agric., 105(11), 6269-6279 doi:
10.1002/jsfa.14353
[28] Alhichri, H., Alswayed, A. S., Bazi, Y., Ammour, N., & Alajlan, N. A. (2021). Classification of remote sensing images using EfficientNet-B3 CNN model with attention.
IEEE access,
9, 14078-14094. doi:
10.1109/ACCESS.2021.3051085
[29] Liu, W., Luo, Y., Zhu, X., Dong, D., Wang, M., Ma, J. & Liu, D. (2025). Optimizing tea plantation productivity: Magnesium-modified tea pruning litter biochar enhances soil quality and tea aroma profiles. Environ. Technol. Inn, 104375.
[30] Pourdarbani, R., & Sabzi, S. (2023). Diagnosis of common cauliflower diseases using image processing and deep learning. J. Environ. Sci. Stud., 8(3), 7087-7092. doi: 10.22034/JESS.2023.391624.1995
[31] Mhaskar, A. M., Mangrulkar, P. P., Rane, R. E., & Sairise, R. M. (2022). Computer vision in food quality control: applications and innovations. IJFANS Int. J. Food Nutr. Sci., 11(8).
[32] Patel KK, Kar A, Jha SN, Khan MA. (2011). Machine vision system: a tool for quality inspection of food and agricultural products. J. Food Sci. Technol., 49(2):123-41. doi: 10.1007/s13197-011-0321-4.