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Development of an in-syringe dispersive liquid–liquid micro-extraction process for the measurement of malachite green in aqueous solutions

Monireh Gorgich and Mashaallah Rahmani

Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, Zahedan, Iran

 

E-mail: rahmani341@hamoon.usb.ac.ir

Received: 14 July 2023  Accepted: 30 November 2023

Abstract:

This work aimed to develop pre-concentration of malachite green in aqueous solutions through an efficient, fast, and easy-to-use model based on the dispersive liquid–liquid micro-extraction process in a suitable glass syringe (IS-DLLME). To implement this model, the important parameters of the process in this micro-extraction model such as pH, microliter volumes of extraction solvent, time of extraction, and salt concentration were investigated. Performance optimization of the IS-DLLME model was assayed through multivariate data analysis. Among the advantages of this method are the absence of dispersive solvent and centrifugation step. The data obtained from the extraction tests in optimal values indicated that the process has a good extraction percentage. The best extraction efficiency was obtained by multivariate methods under the conditions equivalent to pH of 3.4, extraction time of 7.8 min, salt amount of 1.3%W/V, and 298 µl of extraction solvent. At optimal values, a linear response was collected in the range of 40–500 µg L−1 malachite green with a LOD of 9 µg L−1. The within-day and between-day precision at a concentration of 100 µg L−1 of MG (n = 5) was 2.5 and 3.1%, respectively. The IS-DLLME model enabled us to develop a simple approach and tool for monitoring malachite green within water and fish samples with good recoveries of 97.28–99.86%.

Keywords: Artificial neural network; Response surface method; Micro-extraction; Malachite green; Taguchi method

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-023-03254-9

 

Chemical Papers 78 (4) 2479–2491 (2024)

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