Service Organic Contaminants and Additives, Department of Chemical and Physical Health Risks, Sciensano, 14 rue Juliette Wytsman, 1050 Brussels, Belgium.
Service Risk and Health Impact Assessment, Department of Chemical and Physical Health Risks, Sciensano, 14 rue Juliette Wytsman, 1050 Brussels, Belgium.
Food Chem. 2023 Dec 15;429:136893. doi: 10.1016/j.foodchem.2023.136893. Epub 2023 Jul 17.
Pesticide residues in tea and herbal tea often exceed EU maximum residue limits. Consideration of the transfer of pesticides from the leaves (called transfer factors) to the brew is essential to assess the associated risk. This study identified infusion parameters influencing the transfer behaviour of 61 pesticides and elaborated a predictive model for pesticides with unknown transfer factors in black, green, herbal and flavoured teas. Tea type and the presence of flavours were the criteria that most influenced the pesticide transfer. Interestingly, infusion parameters that are individual and area dependent such as infusion time, temperature and water hardness, did not play a significant role. Beta regression models developed to characterise pesticide behaviour during infusion showed good predictions for most pesticides and revealed that log (P) was the main physico-chemical parameter to estimate the pesticide transfer. The transfer factors database and validated models are valuable tools for improving risk assessment.
茶叶和花草茶中的农药残留经常超过欧盟的最大残留限量。考虑到农药从叶片（称为转移因子）转移到冲泡液中的情况，对于评估相关风险至关重要。本研究确定了影响 61 种农药转移行为的冲泡参数，并为黑、绿、花草和调味茶中具有未知转移因子的农药制定了预测模型。茶的类型和是否有调味是影响农药转移的最重要因素。有趣的是，冲泡时间、温度和水硬度等与个体和面积相关的冲泡参数并没有起到显著作用。用于描述冲泡过程中农药行为的贝塔回归模型对大多数农药都有很好的预测效果，并且表明 log（P）是估计农药转移的主要物理化学参数。转移因子数据库和验证后的模型是改进风险评估的有用工具。
