摘要
基于溶剂提取-固相微萃取-气质联用技术建立了新鲜肝脏中26种常见投毒农药的快速筛查与定量方法。通过考察有机溶剂、萃取头、萃取温度等影响因素优化萃取条件;新鲜肝脏经丙酮水溶液提取,离心分离、氮吹浓缩后补充超纯水,经SPME萃取后采用GC-MS进行定性筛查与定量分析。实验结果表明,以丙酮水溶液(4:1,V/V)提取,选择PDMS/DVB/CAR萃取头,在pH 7.4缓冲溶液体系下、室温下(20℃)萃取30 min,搅拌速度为200 r/min时萃取效率最高;新鲜肝脏中26种农药在50~200 ng/g范围内具有良好的线性,相关系数均≥0.992,方法检出限(LOD)为0.15~16 ng/g(S/N=3),定量限(LOQ)为0.48~48 ng/g(S/N=10);在加入量为50,100,200 ng/g时,农药混合标准溶液的回收率为66.7%~108.2%,相对标准偏差为1.4%~16%。方法适用于对投毒案件受害者肝脏中常见投毒农药进行快速筛查与定量分析。
A method for rapid screening and quantification of 26 common poisoning pesticides in liver samples by solid-phase microextraction and GC-MS was established. Influencing factors including solvent, SPME fiber and extraction time were studied in order to optimize the condition of extraction. The pesticides were extracted by acetone/water solution, separated by the centrifuge, condensed by the nitrogen flow with addition of ultrapure water afterwards, extracted in aqueous solution by SPME and analyzed qualitatively and quantitatively by GC/MS. As the results shown, the optimized extraction efficiency is achieved on the condition of extraction with acetone/water (4:1, V/V), extraction for 30 min in buffer solution of pH7.4 by PDMS/DVB/CAR SPME fiber at room temperature (20℃) and stirring at the rate of 200 r/min. Under this circumstance, high linearity of 26 pesticides is followed in the range of 50 - 200 ng/g, with the correlation coefficients ( R2 ) higher than 0. 992 in liver samples. The limit of detections ( LODs ) are between 0. 15 and 16 ng/g ( S/N= 3 ) and the limit of quantifications (LOQs) are between 0. 48 and 48 ng/g (S/N = 10). At the spiked levels ranging from 50 to 200 ng/g, the recoveries (n = 6) are between 66. 7% and 108.2% and the relative standard deviations (RSDs) are between 1.4% and 16%. This method can be applied to rapid screening and quantification of common poisoning pesticides in liver samples of victims in poisoning cases, in order to provide investigative leads and evidences in courts timely and precisely.
出处
《分析试验室》
CAS
CSCD
北大核心
2017年第11期1259-1263,共5页
Chinese Journal of Analysis Laboratory
基金
"十二五"国家科技支撑计划课题(2012BAK02B02)资助