Application of Tandem Dispersive Liquid-Liquid Microextraction as an Efficient Method for Preconcentration of Two Antidepressant Drugs in Real Samples Combined with High Performance Liquid Chromatography.
Autor: | Arabkhani B; Faculty of Chemistry, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran., Goudarzi N; Faculty of Chemistry, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran., Bagherian G; Faculty of Chemistry, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran., Chamjangali MA; Faculty of Chemistry, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran. |
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Jazyk: | angličtina |
Zdroj: | Journal of chromatographic science [J Chromatogr Sci] 2022 Jan 01; Vol. 60 (1), pp. 96-103. |
DOI: | 10.1093/chromsci/bmab038 |
Abstrakt: | In the present work, tandem dispersive liquid-liquid microextraction (TDLLME) coupled with liquid chromatography was used for the determination of the two antidepressant drugs citalopram (CIT) and sertraline (SER) in complicated matrices. Indeed, the present approach was used to improve the suitability and appropriateness of the dispersive liquid-liquid microextraction (DLLME) method in complicated matrices. Firstly, 10 mL of the sample solution containing the two understudied drugs was extracted into an organic solvent (200 μL of 1,2-dichloromethane) using the DLLME method. Then the extracted analytes were back-extracted into 100 μL of an aqueous acceptor phase. The total extraction time of this method is about 6 min. To achieve the best efficiency for this method, efficient parameters like the kind and volume of the organic solvent and the effect of the ionic strength on the effectiveness of extraction were reviewed and improved. Under the optimized experimental conditions, TDLLME showed good linearity in the range of 100.0-7000.0 ng mL-1. The limits of detection were found to be 10.0 ng mL-1 for CIT and 2.0 ng mL-1 for SER. The relative standard deviation (RSD) is obtained in the range of 0.7-4.1%. (© Crown copyright 2021.) |
Databáze: | MEDLINE |
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