首页|A simple and fast sample preparation method based on ionic liquid treatment for determination of Cd and Pb in dried solid agricultural products by graphite furnace atomic absorption spectrometry
A simple and fast sample preparation method based on ionic liquid treatment for determination of Cd and Pb in dried solid agricultural products by graphite furnace atomic absorption spectrometry
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
The Cd and Pb are two typical heavy metal contaminants of agricultural products which have posed great threats to human health. The precise determination of Cd and Pb in agricultural products is extremely important for human health. In this paper, a simple and fast sample preparation method based on ionic liquid treatment was proposed to determine Cd and Pb in dried solid agricultural products by graphite furnace atomic absorption spectrometry (GFAAS). The samples were prepared by dissolving the tested agricultural products into the ionic liquid 1-butyl-3-methylimidazolium chloride. The temperature program and matrix modifiers for GFFAS analysis of these samples were optimized. The reliability of sample preparation was verified by comparing the between- and within-batch precision, which showed no significant differences at 95% confidence level. Detection limits were 0.019 mu g L-1 Cd and 0.042 mu g L-1 Pb for these samples. Results for determining Cd and Pb in these samples were in good agreement with those obtained from the conventional wet digested samples. Both the certified reference materials validation experiments and the spiked and recovery tests indicated this sample preparation for determining Cd and Pb had satisfactory accuracy and could be an alternative to traditional sample preparation.
Ionic liquidSample preparationDried solid agricultural productsGFAASCadmium and lead
Ling, Yuetao、Luo, Fang、Zhu, Shengdong
展开 >
Wuhan Inst Technol, Sch Chem Engn & Pharm, Adv Fine Chem Res Engn Ctr Hubei Prov, Key Lab Green Chem Proc,Minist Educ,Hubei Key Lab, Wuhan 430205, Peoples R China
NSTL
Elsevier
