温倩茹,梁 婷,王 晨,陈红平,梁思辰,马桂岑.超高效液相色谱-串联质谱法分析枸杞中二氧化硫残留物及膳食暴露风险评估[J].食品安全质量检测学报,2025,16(17):299-305
超高效液相色谱-串联质谱法分析枸杞中二氧化硫残留物及膳食暴露风险评估
Determination of sulfur dioxide residues in Lycium barbarum L. by ultra performance liquid chromatography-tandem mass spectrometry and dietary exposure risk assessment
投稿时间:2025-03-11  修订日期:2025-07-19
DOI:
中文关键词:  二氧化硫  检测方法  枸杞  健康风险评估
英文关键词:sulfur dioxide  detection method  Lycium barbarum L.  dietary exposure risk assessment
基金项目:中国农业科学院创新工程项目(CAAS-ASTIP-2016-TRICAAS)、国家现代农业产业技术体系(CARS-19)
作者单位
温倩茹 1. 中国农业科学院茶叶研究所, 2. 中国农业科学院研究生院 
梁 婷 1. 中国农业科学院茶叶研究所, 2. 中国农业科学院研究生院 
王 晨 1. 中国农业科学院茶叶研究所, 3. 农业农村部茶叶质量安全控制重点实验室, 4. 农业农村部茶叶质量安全风险评估实验室(杭州) 
陈红平 1. 中国农业科学院茶叶研究所, 3. 农业农村部茶叶质量安全控制重点实验室, 4. 农业农村部茶叶质量安全风险评估实验室(杭州) 
梁思辰 5. 上海康识食品科技有限公司 
马桂岑 1. 中国农业科学院茶叶研究所, 3. 农业农村部茶叶质量安全控制重点实验室, 4. 农业农村部茶叶质量安全风险评估实验室(杭州) 
AuthorInstitution
WEN Qian-Ru 1. Tea Research Institute Chinese Academy of Agricultural Sciences, 2. Graduate School of Chinese Academy of Agricultural Sciences 
LIANG Ting 1. Tea Research Institute Chinese Academy of Agricultural Sciences, 2. Graduate School of Chinese Academy of Agricultural Sciences 
WANG Chen 1. Tea Research Institute Chinese Academy of Agricultural Sciences, 3. Key Laboratory of Tea Quality and Safety Control Ministry of Agriculture and Rural Affairs, 4. Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou) Ministry of Agriculture and Rural Affairs 
CHEN Hong-Ping 1. Tea Research Institute Chinese Academy of Agricultural Sciences, 3. Key Laboratory of Tea Quality and Safety Control Ministry of Agriculture and Rural Affairs, 4. Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou) Ministry of Agriculture and Rural Affairs 
LIANG Si-Chen 5. Shanghai Kangshi Food Technology Co., Ltd. 
MA Gui-Cen 1. Tea Research Institute Chinese Academy of Agricultural Sciences, 3. Key Laboratory of Tea Quality and Safety Control Ministry of Agriculture and Rural Affairs, 4. Laboratory of Quality and Safety Risk Assessment for Tea Products (Hangzhou) Ministry of Agriculture and Rural Affairs 
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中文摘要:
      目的 建立超高效液相色谱-串联质谱法(ultra performance liquid chromatography-tandem mass spectrometry, UPLC-MS/MS)检测枸杞中二氧化硫残留量的分析方法, 并评估其膳食暴露风险。方法 以亚硫酸盐能与甲醛反应转化为更稳定的衍生物为原理, 采用0.2%甲醛溶液为提取液、石墨化炭黑(graphitized carbon black, GCB)为吸附剂, 并结合UPLC-MS/MS检测枸杞中的二氧化硫残留量; 采用危害熵值法计算枸杞实际样品中二氧化硫残留量可能产生的风险系数(hazard quotient, HQ)。结果 目标物在0.001~1.000 mg/L范围内线性关系良好, 检出限为0.28 μg/L, 定量限为0.93 μg/L, 加标回收率为77.6%~101.7%, 相对标准偏差为2.3%~10.1%。采用该方法对枸杞实际样品中二氧化硫进行检测, 检出率为100%; 根据GH/T 1091—2014《代用茶》中规定的二氧化硫最大残留量为100 mg/kg, 其合格率为83.3%; 膳食暴露风险评估结果显示其HQ均远小于1, 说明膳食摄入风险较低。结论 该方法前处理简便, 检测灵敏度高, 精密度高且可批量检测样品, 能够有效弥补目前报道检测方法的局限性。
英文摘要:
      Objective To establish a method for the determination of sulfur dioxide residues in Lycium barbarum L. by ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and evaluate the dietary exposure risk. Methods Based on the principle that sulfites could react with formaldehyde to convert into more stable derivatives, 0.2% formaldehyde solution was used as the extraction solution, graphitized carbon black (GCB) was used as the adsorbent, and UPLC-MS/MS to detect sulfur dioxide residue in Lycium barbarum L.. The health risks of sulfur dioxide residues in actual Lycium barbarum L. samples were calculated using the hazard quotient (HQ) method. Results The target analyte showed good linearity in the range of 0.001–1.000 mg/L, the limit of detection was 0.28 μg/L, the limit of quantitation was 0.93 μg/L, the spiked recovery rates were 77.6%–101.7%, and the relative standard deviations were 2.3%–10.1%. This method was used to detect sulfur dioxide in actual samples of Lycium barbarum L., and the detection rate was 100%. According to the maximum residue limit (100 mg/kg) specified in GH/T 1091—2014 Substitute tea, the qualified rate was 83.3%. Exposure risk assessment indicated that the HQ for all the samples was far less than 1, suggesting the very low intake risk. Conclusion This method features simple pretreatment, high sensitivity, excellent precision and batch sample detection capability, effectively overcoming the limitations of existing reported methods.
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