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Development of an effervescent reaction-enhanced microextraction method for preconcentration/extraction of trace estrogens in milk using a reduced graphene oxide-assisted ionic liquid-based nanofluidElectronic supplementary information (ESI) available. See DOI: 10.1039/c9ay00803a
- Source :
- Analytical Methods; 2019, Vol. 11 Issue: 28 p3608-3618, 11p
- Publication Year :
- 2019
-
Abstract
- Herein, we developed a novel effervescent reaction-enhanced microextraction method for preconcentration/extraction of trace estrogens in milk samples. This method utilized a reduced graphene oxide (rGO)-assisted ionic liquid (IL)-based nanofluid as an extractant in the microextraction procedure, and was referred to as rGO/ILN-ETD. This newly developed method has distinct advantages: (1) the dispersive and extraction processes are integrated into one synchronous step, thereby avoiding utilization of additional toxic organic solvents as dispersants, and (2) addition of rGO to ILs increases dispersive/fluid properties of an extractant, leading to enhanced extraction efficiency. An effervescent tablet (8 mm diameter × 2.5 mm thickness) was prepared using three precursors: 0.31 g of tartaric acid, 0.21 g of Na2CO3and 55 μL of nanofluid (rGO/ILs, 2/1000 by w/w). A series of operational parameters were first screened using Plackett–Burman design, and the significant variables were then optimized using central composite design. Under optimized conditions, the rGO/ILN-ETD-HPLC-DAD method gave limits of detection of 0.09–0.30 μg L−1, relative recoveries of 85.6–114.6% and high intra-day and inter-day precisions of 1.7–6.3% for three estrogens (diethylstilbestrol, hexestrol and estrone) in fresh, whole-fat, low-fat and fat-skimmed milk samples. Moreover, estrone was detected at 0.96 μg L−1in fresh milk samples by the present method. Compared to the previously reported methods, this proposed method is simple, rapid, robust, highly efficient and eco-friendly, and thus has great potential in trace determination of estrogens in liquid food matrices.
Details
- Language :
- English
- ISSN :
- 17599660 and 17599679
- Volume :
- 11
- Issue :
- 28
- Database :
- Supplemental Index
- Journal :
- Analytical Methods
- Publication Type :
- Periodical
- Accession number :
- ejs50621102
- Full Text :
- https://doi.org/10.1039/c9ay00803a