摘要
建立了食品中常见的黄曲霉毒素B1(AFB1)、黄曲霉毒素B2(AFB2)、黄曲霉毒素G1(AFG1)、黄曲霉毒素G2(AFG2)、赭曲霉毒素A(OTA)、赭曲霉毒素B(OTB)和赭曲霉毒素C(OTC) 7种真菌毒素的QuEChERS前处理净化结合液相色谱-串联质谱(LC-MS/MS)检测方法。样品用甲酸-乙腈(10∶90)进行酸化稀释,离心后取上清液经吸附净化剂(1. 2 g MgSO4+0. 25 g C18+0. 4 g PSA+0. 25 g Al-N)富集净化,过滤后采用LC-MS/MS在多反应监测(MRM)模式下测定。7种真菌毒素在各自范围内线性良好,相关系数(r)均不小于0. 999。在最佳条件下,方法的定量下限(LOQ)为0. 25~5. 0μg/kg,7种毒素的相对标准偏差(RSD,n=6)为1. 1%~7. 7%,平均回收率为71. 5%~119%。该方法操作方便、灵敏度高、重现性好,能满足大批量食品中上述7种真菌毒素残留的检测要求。
A method of liquid chromatograph-tandem mass spectrometry(LC-MS/MS)with QuEChERS pretreatment was developed for the determination of seven kinds of mycotoxins,including aflatoxin B 1(AFB 1),aflatoxin B 2(AFB 2),aflatoxin G 1(AFG 1),aflatoxin G 2(AFG 2),ochratoxin A(OTA),ochratoxin B(OTB)and ochratoxin C(OTC)in foods.The samples were diluted with formic acid-acetonitrile(10∶90,by volume),and then purified by QuEChERS using a scavenging agent(1.2 g MgSO 4+0.25 g C 18+0.4 g PSA+0.25 g Al-N)after centrifugation.The detection on the analytes was performed by LC-MS/MS in positive ionization under multiple-reaction monitoring(MRM)mode.There existed good linearities for seven mycotoxins in their respective concentraction ranges with correlation coeffieionts not less than 0.999.The limits of quantitation(LOQs)were in the range of 0.25-5.0μg/kg,and the average recoveries for seven mycotoxins were between 71.5%and 119%with the relative standard deviations(RSDs,n=6)of 1.1%-7.7%.The developed method was simple,sensitive and reproducible,and was suitable for the determination of these mycotoxins in foods in batch production.
作者
熊欣
刘青
张广文
庞世琦
曾广丰
陈文锐
XIONG Xin;LIU Qing;ZHANG Guang-wen;PANG Shi-qi;ZENG Guang-feng;CHEN Wen-rui(College of Science and Engineering,Jinan University,Guangzhou 510632,China;Inspection and Quarantine Technology Center,Guangdong Entry-Exit Inspection and Quarantine Bureau,Guangzhou 510623,China;Guangdong Key Laboratory of Import and Export Technical Measures of Animal,Plant and Food,Guangzhou 510623,China)
出处
《分析测试学报》
CAS
CSCD
北大核心
2018年第9期1008-1013,共6页
Journal of Instrumental Analysis
基金
国家质检总局科技计划项目(2016IK050)