摘要
在正交试验设计基础上,采用GANN模型及Matlab遗传算法工具箱对超声辅助萃取(ultrasonic-assisted extraction,UAE)-上浮溶剂固化(solidification of floating organic drop,SFO)-分散液液微萃取(dispersive liquid-liquid microextraction,DLLME)的萃取条件进行优化,建立了沉积物中十溴联苯醚的液相色谱测定方法.结果表明:所建方法线性范围为2～9 595 ng/g,相关系数R2=0.999 4,检出限(S/N=3)及定量限(S/N=10)分别为0.6 ng/g及2.0 ng/g;在434.4 ng/g质量比下,方法加标回收率为98.20%(RSD=5.2%,n=3).
A method for the determination of decabrominated diphenyl ether（BDE-209） in surficial sediments was developed based on solidification of floating organic drop coupled with ultrasonic-assisted dispersive liquid-liquid microextraction（UAE-DLLME-SFO） and genetic algorithm neural network（GANN） model.The established UAE-DLLME-SFO-HPLC method has a wide linear range（2— 9 595 ng/g） with R2=0.999 4.The limit of detection（LOD,S/N=3） and limit of quantitation（LOQ,S/N=10） of the proposed method were 0.6 ng/g and 2.0 ng/g,respectively.The recovery of added BDE-209 in the real samples at BDE-209 level of 434.4 ng/g was 98.20%（RSD=5.2%,n=3）.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2011年第3期554-558,共5页
Journal of Jilin University:Science Edition
基金
国家"十一五"科技支撑项目(批准号:2008BAC43B01)
关键词
十溴联苯醚
遗传神经网络
上浮溶剂固化
分散液液微萃取
高效液相色谱
decabrominated diphenyl ether
genetic algorithm neural network
solidification of floating organic drop
dispersive liquid-liquid microextraction
high performance liquid chromatography
