摘要
目的:探索建立一种在八角茴香复杂基质中利用科瓦茨保留指数辅助定性的12个拟除虫菊酯类农药化合物GC-QQQ-MS/MS定量方法。方法:采用三重四极杆气质联用仪,SHIMADZU SH-Rxi-5Sil MS毛细管柱(30 m×0.25 mm×0.25μm);进样口温度250℃,进样量1.0μL,不分流进样,高压进样压力为250 kPa;载气为高纯氦气,载气控制方式为恒线速度模式;色谱柱流量1.69 mL·min^-1,线速度47.2 cm·s^-1,吹扫流量为5 mL·min^-1;程序升温:初始温度50℃,保持1 min,先以25℃·min^-1升温至℃,再以10℃·min^-1升温至300℃,保持15 min;柱平衡时间为2min^-1。离子源为电子轰击源,离子化能量70 eV,离子源温度230℃,质谱传输接口温度250℃,碰撞气为氩气;质谱监测模式为多反应监测,溶剂延迟时间为6 min。利用正构烷烃C9~C33对照品的保留时间结合SmartDatabase_Pesticides数据库,计算出12个目标农药化合物的预测保留时间,用于快速筛查样品中存在的农药和辅助定性。以氚代倍硫磷为内标物质,制备12个目标农药化合物的标准曲线,以内标标准曲线法计算样品中目标农药化合物的含量。结果:目标农药化合物(包括同分异构体)的预测保留时间和实测保留时间非常接近,时间差均小于0.02 min,证明了保留指数在中药材复杂基质溶液中辅助定性的准确性和可行性。测定结果显示,20批次八角茴香样品中有2个批次检测到了微量的氯氰菊酯,1个批次检测到了微量的氯菊酯。结论:基于保留指数辅助定性的八角茴香中12个拟除虫菊酯类农药化合物的GC-QQQ-MS/MS定量方法具有较好的适用性,对其他中药材多农药残留检测方法的建立具有参考价值。
Objective:To establish a GC-QQQ-MS/MS quantitative method for the determination of 12 pyrethroid pesticides in complex basis of Anisi Stellati Fructus based on Kovats retention index. Methods:This study was performed on a GC-QQQ-MS/MS spectrometer,equipped with a SHIMADZU SH-Rxi-5 Sil MS column(30 m×0.25 mm×0.25 μm). The temperature of the injection port was 250 ℃,the injection volume was 1.0 μL,the injection mode was splitless,the injection pressure was 250 kPa;the carrier gas was high purity helium and the carrier gas control mode was constant linear velocity mode;the column flow rate was 1.69 mL·min^-1,the line speed was 47.2 cm· s^-1,the purge flow rate was 5 mL·min^-1. The temperature rise method was the gradient program:the initial temperature was 50 ℃,kept for 1 minute,first raised the temperature to 125 ℃ at a speed of 25 ℃ per minute,and then raised the temperature to 300 ℃ at a speed of 10 ℃ per minute,kept for 15 minutes;the balance time was 2 minutes. The ion source was an electron bombardment source,the ionization energy was 70 e V,the ion source temperature was 230 ℃,the mass spectrometry transmission interface temperature was 250 ℃,the collision gas was argon gas,and the mass spectrometry monitoring mode was multiple reaction monitoring,the solvent delay time was 6 min. Using retention times of n-alkanes C9-C33 combined with the smartDatabase_Pesticides database,the predicted retention times of the 12 target pesticides were calculated. The predicted retention times could be used to quickly screen out the pesticides existed in the sample and to help identify the target pesticideds. Using fenthion-d6 as the internal reference substance,the standard curves of 12 targeted pesticides were prepared. The contents of the targeted pesticides in the sample were calculated by the internal standard curve method. Results:The predicted retention times of 25 targeted pesticides(including isomers)were very close to the actual measured retention times with deviations below 0.02 min,which proved the accuracy and feasibility of the retention index in qualitative analysis for complex basis of traditional Chinese medicine. Quantitative determination of 12 pesticides in 20 batches of Anisi Stellati Fructus samples were carried out. The results showed that trace cypermethrin was detected in 2 batches of samples with contents ranging from 15.22 μg·kg^-1(YP201718)to 38.19 μg·kg^-1(YP201703). Permethrin was detected in 1 batch of sample,and the content was 24.05 μg·kg^-1. Conclusion:The newly developed GC-QQQ-MS/MS method based on retention index-assisted qualitative for quantitative analysis of 12 pyrethroid pesticides in Anisi Stellati Fructus is feasible,and this method has reference value for establishment of multi-pesticides residue assay method for other Chinese medicinal materials.
作者
谭鹏
张海珠
张定堃
文永盛
包晓明
TAN Peng;ZHANG Hai-zhu;ZHANG Ding-kun;WEN Yong-sheng;BAO Xiao-ming(Chengdu Institute for Food and Drug Control,Chengdu 610045,China;Department of Pharmacy and Chemistry,Dali University,Dali 671000,China;College of Pharmacy,Chengdu University of Traditional Chinese Medicine,Chengdu 611137,China;Shimadzu Enterprise Management(China)Co.LTD,Chengdu 610023,China)
出处
《药物分析杂志》
CAS
CSCD
北大核心
2019年第7期1256-1266,共11页
Chinese Journal of Pharmaceutical Analysis
基金
四川省科技厅项目(2019YJ0640)
成都市科技局项目(2016-HM01-00312-SF,2018-YF05-01277-SN)
