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Mohammad Reza Hadjmohammadi

Mohammad Reza Hadjmohammadi

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Faculty of Chemistry
Address: Babolsar
Phone: 01135302350

Research

Title
Determination of selective serotonin reuptake inhibitors in biological samples via magnetic stirring-assisted dispersive liquid-liquid microextraction followed by high performance liquid chromatography
Type
JournalPaper
Keywords
magnetic stirring-assisted dispersive liquid–liquid microextraction high performance liquid chromatography biological samples
Year
2016
Journal RSC Advances
DOI
Researchers Mohammad Reza Hadjmohammadi ، Elias ranjbar ، Ali golbabanezhad ، Hassan Daneshinejad

Abstract

This work is the first report of an efficient procedure for the simultaneous determination of five important selective serotonin reuptake inhibitors (SSRIs) in low concentration levels in biological fluids (urine and plasma samples), which is expedient, quick and of low cost. Despite the wide usage of citalopram, paroxetine, fluvoxamine, fluoxetine, and sertraline in the treatment of depression, and in spite of several advantages and developments of dispersive liquid–liquid microextraction (DLLME), there is not any report about the simultaneous preconcentration of these SSRIs using the DLLME technique. A developed mode of the DLLME technique, i.e., magnetic stirring-assisted dispersive liquid–liquid microextraction (MSA-DLLME), was employed and the parameters affecting the extraction process were optimized using a response surface methodology. The extraction method is based on the fast injection of a mixture of 1-octanol (extraction solvent) and methanol (disperser solvent) into the aqueous solution being stirred by a magnetic stirrer to form a cloudy ternary component solvent (aqueous solution, extracting solvent, disperser solvent) system. The potential variables affecting the extraction recovery such as the volume of the extraction and disperser solvents, pH of sample solution, salt addition, vortex time, and stirring rate were considered in the optimization process. A methodology according to a fractional factorial design (262) was performed to choose the significant variables for optimization. The significant factors including the volume of the extraction solvent and the pH of the sample solution were then optimized using a central composite design (CCD). A quadratic model between dependent and independent variables was built and the optimum conditions were obtained. Under the optimum conditions, the proposed method was successfully applied for the determination of SSRIs in urine and plasma samples. Linearity (R2 > 0.999) was obtained in the range of 2–1000 an