摘要
建立了基于自动QuEChERS方法的花生中297种农药的气相色谱-串联质谱(GC-MS/MS)快速检测技术,并对提取剂种类及用量、缓冲盐用量、净化剂种类及用量进行了优化。花生样品加水浸润后,采用1%(体积分数)醋酸乙腈提取,结合自动QuEChERS前处理设备,以N-丙基乙二胺(PSA)、十八烷基硅烷键合硅胶(C_(18))、碳十八键合锆胶(Z-Sep^(+))和无水硫酸镁为填料进行净化。净化液经1 mL乙酸乙酯复溶后,过0.22μm有机微孔滤膜,采用GC-MS/MS在多重反应监测(MRM)模式下进行测定,基质匹配外标法进行定量。结果表明,所有农药的相关系数(r^(2))均大于0.995,定量下限为2~10μg/kg;在10、20、50、100μg/kg 4个加标水平下的平均回收率分别为72.7%~116%、71.9%~117%、73.2%~112%和71.5%~120%,相对标准偏差(RSDs)分别为0.90%~15%、0.70%~15%、0.60%~14%和0.40%~15%。应用所建立的方法对市售8批次花生样品进行检测,结果表明,8批次样品中共有6批次检出农药残留,共检出17种农药,其中一批样品中百治磷检出浓度最高,达到34.67μg/kg。该方法简便、快速、灵敏度高且自动化程度高,适用于花生中数百种农药多残留的快速检测分析。
Peanut is an important oil and economic crop in China,which has been well accepted by consumers as it could be eaten fresh,used to extract oil,and also be processed into peanut products.In recent years,as a result of abuse of pesticide,the quality and safety risks of peanuts have become an increasingly important issue.Gas chromatography-tandem mass spectrometry(GC-MS/MS)combined with automatic QuEChERS pre-treatment equipment is an effective method for the detection of multi-pesticide residues.Thus,a rapid method based on GC-MS/MS was established for the simultaneous determination of 297 pesticides in peanut.The pretreatment parameters was optimized by comparing the spiked recoveries obtained from different extract solvents(acetonitrile,acetonitrile containing 1% acetic acid and acetonitrile containing 2% acetic acid),different volumes of extract solvents(10,15 and 20 mL),different buffer salts(anhydrous magnesium sulfate,sodium acetate)and different purification combinations(primary secondary amine(PSA)+octadecylsilane(C_(18)),PSA+C_(18)-bonded zirconium rubber(Z-Sep^(+)),PSA+C_(18)+Z-Sep^(+),EMR-lipid).Under the optimized conditions,2 g samples were soaked with 2 mL ultrapure water,the pesticides in the peanuts were extracted using acetonitrile containing 1% acetic acid,salted out using 4 g anhydrous magnesium sulfate and 1 g sodium acetate.Then the solution was purified using 100 mg PSA+200 mg C_(18)+100 mg ZSep+automatic QuEChERS pre-treatment device combined with centrifuge device by vortex vibration through a purification tube which included an inner tube,an outer tube and a filtration membrane.Pesticides were separated on an HP-5MS UI capillary column using temperature programming,determined by GC-MS/MS in positive ion multiple mode and quantified by external standard method.The matrix effects were evaluated with the established method.Results showed that 25.25% of the pesticides exhibited enhancing matrix effects,while 2.02% of them exhibited inhibition matrix effects and 72.73% of them exhibited weak matrix effects.Matrix-matched standard curve was used to reduce the matrix effects on detection of the pesticide residues.Methodological verifications were evaluated for the established method,and the results showed that there were good linear relationships for all the pesticides in the corresponding range,with their correlation coefficients(r^(2))greater than 0.995.The limits of quantitation(LOQs)were 2-10μg/kg.The recoveries for 297 pesticides at four spiked levels of 10,20,50 and 100μg/kg were in the range of 72.7%-116%,71.9%-117%,73.2%-112% and 71.5%-120%,with relative standard deviations(RSDs)of 0.90%-15%,0.70%-15%,0.60%-14% and 0.40%-15%,respectively.The proposed method was applied to the detection of 8 batches of peanut samples purchased from local supermarket.The results demonstrated that 6 batches of the peanut samples were found containing pesticide residues,in which 17 pesticides were detected in total.The concentration of dicrotophos was the highest among all pesticides,reaching 34.67μg/kg.Therefore,the developed method was simple,rapid,sensitive and automatic,and was suitable for the rapid detection of pesticide residues in peanuts.
作者
蒋康丽
扈斌
吴兴强
谢瑜杰
李铁梅
范春林
王明林
王雯雯
陈辉
JIANG Kang-li;HU Bin;WU Xing-qiang;XIE Yu-jie;LI Tie-mei;FAN Chun-lin;WANG Ming-lin;WANG Wen-wen;CHEN Hui(Chinese Academy of Inspection and Quarantine,Beijing 100176,China;College of Food Science and Engineering,Shandong Agricultural University,Taian 271018,China;Agilent Technologies(China)Limited,Beijing 100102,China)
出处
《分析测试学报》
CAS
CSCD
北大核心
2021年第9期1257-1270,共14页
Journal of Instrumental Analysis
基金
中国检科院基本科研业务费项目(2020JK009)
国家重点研发计划项目(2017YFF0211304)。
