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Cloud Point-Dispersive Liquid–Liquid Microextraction for Extraction of Organic Acids from Biological Samples

  • Original Article
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Journal of Surfactants and Detergents

Abstract

A new method combining cloud point extraction (CPE) with dispersive liquid–liquid microextraction (DLLME) named “cloud point-dispersive liquid–liquid microextraction (CP-DLLME)” was presented in this work for the first time for the determination of organic acids as model compounds in biological samples using high performance liquid chromatography and UV detection (HPLC–UV). Water (disperser solvent) containing tert-octylphenol ethoxylate (7.5EO) as extraction solvent, was rapidly injected into a warmed sample solution and cloudy state formed immediately. After that, phase separation was performed by centrifugation. The surfactant-rich phase was diluted with acetonitrile and injected into a HPLC–UV apparatus. The influence of several important parameters on the extraction efficiencies was evaluated. Under optimized experimental conditions, the calibration graphs were linear in the range of 0.06–1,500 µg L−1 for target analytes. Coefficient of determination (r 2) ranged from 0.9985 to 0.9994. The limits of detection were in the range of 0.05–17.1 µg L−1. The new method was successfully applied with satisfactory results for analysis of target analytes in spiked samples. The relative mean recoveries of the spiked samples ranged from 91.8 to 107.1 % with relative standard deviations in the range of 2.1 to 4.3 %.

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Acknowledgments

The authors thank the research council of Ilam University for financial support.

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Authors and Affiliations

  1. Department of Chemistry, Faculty of Science, Ilam University, 69315-516, Ilam, Iran

    Ali Daneshfar & Tahere Khezeli

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  1. Ali Daneshfar
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  2. Tahere Khezeli
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Correspondence to Ali Daneshfar.

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Daneshfar, A., Khezeli, T. Cloud Point-Dispersive Liquid–Liquid Microextraction for Extraction of Organic Acids from Biological Samples. J Surfact Deterg 17, 1259–1267 (2014). https://doi.org/10.1007/s11743-014-1625-y

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  • DOI: https://doi.org/10.1007/s11743-014-1625-y

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