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Critical factors determining fluoride concentration in tea leaves produced from Anhui province, China

Cai, Huimei, Zhu, Xiaohui, Peng, Chuanyi, Xu, Wei, Li, Daxiang, Wang, Yijun, Fang, Shihui, Li, Yeyun, Hu, Shaode, Wan, Xiaochun
Ecotoxicology and environmental safety 2016 v.131 pp. 14-21
Camellia sinensis, Monte Carlo method, fermentation, fluorides, fluorosis, leaching, leaves, manufacturing, markets, oolong tea, risk, rolling, soil, spring, summer, tea (beverage), white tea, China
This study investigated the fluoride present in tea plants (Camellia sinensis (L.) O. Kuntze) and its relationship to soils, varieties, seasons and tea leaf maturity. The study also explored how different manufacturing processes affect the leaching of fluoride into tea beverages. The fluoride concentration in the tea leaves was significantly correlate to the concentration of water-soluble fluoride in the soil. Different tea varieties accumulated different levels of fluoride, with varieties, Anji baicha having the highest and Nongkang zao having the lowest fluoride concentration. In eight different varieties of tea plant harvested over three tea seasons, fluoride concentration were highest in the summer and lowest in the spring in china. The fluoride concentration in tea leaves was directly related to the maturity of the tea leaves at harvest. Importantly, the tea manufacturing process did not introduced fluoride contamination. The leaching of fluoride was 6.8% and 14.1% higher in black and white tea, respectively, than in fresh tea leaves. The manufacturing step most affecting the leaching of fluoride into tea beverage was withering used in white, black and oolong tea rather than rolling or fermentation. The exposure and associated health risks for fluoride concentration in infusions of 115 commercially available teas from Chinese tea markets was determined. The fluoride concentration ranged from 5.0 to 306.0mgkg−1, with an average of 81.7mgkg−1. The hazard quotient (HQ) of these teas indicated that there was no risk of fluorosis from drinking tea, based on statistical analysis by Monte Carlo simulation.