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Response surface optimization of a vortex-assisted dispersive liquid–liquid microextraction method for highly sensitive determination of repaglinide in environmental water by HPLC/UV


A vortex-assisted dispersive liquid–liquid microextraction (DLLME) method, mated to chemometrics and combined with HPLC/UV detection was optimized and validated for enrichment and determination of repaglinide in environmental samples using nateglinide as an internal standard (IS). A phosphate buffer (10 mM, pH 2.5): acetonitrile (45:55, v/v) was used as a mobile phase with a flow rate of 1 mL/min in an isocratic elution mode. Chemometrics-assisted optimization was performed using a quadratic integrated d-optimal design. The developed model assessed the statistical significance of the independent variables and their interactions to attain the optimum conditions revealing that extractant type, extractant volume and pH are the most influential factors. Optimization of the extraction procedures was performed with the aid of Design Expert 8® software, which suggested 58 different experiments. The optimal conditions were 30 µL of 1-octanol as extractant, 100 µL of acetonitrile as a disperser at pH 8. Under the optimized conditions, the method showed linearity over the range of 1–100 ng/mL with a limit of detection of 0.4 ng/mL. The accuracy, the intra- and inter-day precision were assessed, the %recoveries were found to be between 98.48 and 100.81% with %RSD lower than 1.3. Using chemometrics in method optimization helped achieve the maximum possible enrichment with the least effort, time, and reagents while considering all possible interactions between variables.

Peer Review reports


Non-insulin-dependent diabetes mellitus (NIDDM) is a chronic disease characterized by a defect in insulin secretion or insulin resistance. The Meglitinide family, including repaglinide and nateglinide (Fig. 1), can stimulate insulin release from the β-cells of the pancreas by inhibiting potassium ion-dependent ATP channels [1, 2]. Repaglinide is rapidly absorbed after oral administration of a 2 mg tablet achieving a Cmax of 28 ng/mL within half an hour [1]. It is eliminated via biliary-faecal and urinary excretion within 96 h after administration [3].

Fig. 1
figure 1

Chemical structure of repaglinide and nateglinide (IS)

Great attention has been paid to the life and behaviour of pharmaceuticals in water cycles. Many pharmaceuticals are taken up easily by the human body to exert their activity. Based on the pharmacology of the drug, it will be excreted unchanged or as a mixture of metabolites in urine or faeces reaching the wastewater treatment plants [4]. These drugs may retain in the sludge or may be metabolized to a more hydrophilic form passing through the wastewater treatment plant and reaching the receiving waters. Levels of many drugs are hardly reduced and so, they can be detected in wastewater-treatment plant effluents. Pharmaceuticals may have adverse effects on aquatic organisms even if they are present at low concentrations [5].

Because antidiabetic drugs such as repaglinide are used for the treatment of chronic disease, they are continuously released to the surface water systems through industrial and sewage treatment plants at very low concentrations. Several studies have been reported for quantitation of repaglinide, using highly sophisticated, expensive, and sensitive techniques [6,7,8,9,10,11] due to the low concentrations of repaglinide in aqueous samples. Sample preparation and preconcentration are important to analyze the low concentrations of repaglinide. Solid-phase extraction and liquid phase extraction are the main common sample preparation techniques [12, 13]. Compared with other sample preparation techniques, dispersive liquid–liquid microextraction is widely used because it is rapid, simple, inexpensive, and eco-friendly. Dispersive liquid–liquid microextraction is a ternary solvent system in which only microliters of organic solvent as an extractant in addition to a disperser solvent are added to the aqueous sample to form a cloudy state [14]. Deep eutectic solvents could be used in dispersive liquid–liquid microextraction [15, 16]. The dispersion process improves the surface area contact between both the extractant and the aqueous sample and hence attains the equilibrium rapidly [17]. Several mechanical powers can assist the disperser to improve the dispersion process. Manual shaking is considered the simplest one, but it rarely achieves an effective dispersion and always needs to be combined with other dispersion methods. It results in poor stability of the dispersion and as a result a contradiction of the analysis results between the samples using the vortex mixing improves the dispersion and its uniformity [18]. Using the vortex mixing or other mechanical powers enhances the extraction efficiency and reaches equilibrium more rapidly [19].

Optimization of the experimental variables of vortex-assisted DLLME is critical to get the best conditions that give an acceptable response at the lowest possible concentration of the analyte. One-factor-at-a-time (OFAT) approach is used by most of the studies of analytical methods by studying the effect of each variable individually while keeping other variables constant. This approach consumes a lot of time, effort, and reagents. Moreover, it may not consider interactions between variables, which may affect the optimization results [20]. Recently, chemometric-assisted optimization of analytical methods has provided a theoretical basis for the processes and provides mathematical models that evaluate the independent parameters’ significance and interactions. This approach saves time, reagents and experimental work, extracts the more significant variables as well as determines what combination of the levels of variables achieve the best method performance [21,22,23].

In this paper, a vortex-assisted dispersive liquid–liquid microextraction (DLLME) method combined with HPLC/UV detection was optimized and validated for preconcentration and quantitation of repaglinide in an aqueous environmental sample (river water and tap water). The experimental variables that affect the method were evaluated and optimized with the aid of chemometrics design of experiment (DOE) using a quadratic integrated D-optimal design to achieve the best performance with the least time and effort. The method was compared with other reported methods and was found simpler, easier, faster, more sensitive than the reported HPLC/UV methods and comparable in performance to LC/MS without the need for such a sophisticated technique or experienced operators.


Apparatus and software

A Cyan-CL008benchtop centrifuge from Cypress Diagnostics (Langdorp, Belgium) was used for phase separation. The pH measurements were carried out using a Jenway 3510 pH-meter of Cole-Parmer (Saint Neots, UK). A vortex (2800 rpm) (London, UK) was used for sample dispersion. Optimization of the extraction procedures was performed with the aid of Stat-Ease’s Design Expert 8® software (Minneapolis, MN, USA). A Dionex UltiMate 3000 HPLC of Thermo Scientific™ (Sunnyvale, CA, USA) was used. The HPLC instrument included an LPG-3400SD quaternary pump, a WPS-3000TSL autosampler, a TCC-3000SD column thermostat, and a VWD-3000 variable wavelength detector. The HPLC was controlled by a computer using Chromeleon 7 software for data acquisition and processing.

Reagents and standards

Repaglinide (99.8%) and nateglinide (99.2%) were kindly supplied by Sigma Pharmaceutical Industries (Quwaysna, Egypt). Methanol (HPLC grade), acetonitrile (HPLC grade) and potassium dihydrogen phosphate were purchased from Merck (Darmstadt, Germany). 1-Octanol, 1-decanol, 1-undecanol and 1-dodecanol (analytical grade) were purchased from Sigma Aldrich (Darmstadt, Germany). Glycerol, propylene glycol, sodium hydroxide, and phosphoric acid were purchased from Piochem (6th of October, Egypt). Distilled water was used for the preparation of aqueous solutions. Drug-free human plasma from healthy volunteers (Blood Bank, Benha University Hospitals, Benha, Egypt) was used for the application of the vortex-assisted DLLME method. The plasma samples, stored at − 20 °C were thawed to room temperature before use.

Chromatographic conditions

A Thermo Hypersil® ODS C18 column (150 mm × 4.6 mm, 5 µm) was used as the stationary phase at 30 ℃. A phosphate buffer (10 mM, pH 2.5): acetonitrile (45:55, v/v) was used as a mobile phase with a flow rate of 1 mL/min in an isocratic elution mode. Detection was carried out at 210 nm with a run time of 10 min and an injection volume of 3µL.

Standard solutions and sample pretreatment

Standard stock solutions of repaglinide and nateglinide (internal standard) were prepared in methanol (0.5 and 1 mg/mL, respectively) and stored at 4 °C. A standard solution of repaglinide was prepared by diluting the stock solution with deionized water to the concentration of 1 µg/mL. Environmental water samples were collected from the Nile River (Egypt) and lab tap water, and stored in amber glass bottles at 4 °C. No filtration was applied to environmental water before microextraction. Environmental water samples were treated with a standard phosphoric acid solution or a standard sodium hydroxide solution to adjust the pH. Further dilutions were prepared by spiking the pretreated environmental water with repaglinide and nateglinide to obtain the working solutions of the desired concentrations.

Vortex-assisted DLLME procedures

A volume of 10 mL of environmental water (pH 8) spiked with repaglinide and nateglinide was mixed with 30 µL of 1-octanol (extractant) and 100 µL of acetonitrile (disperser) in a 15 mL screw cap plastic tube. The mixture was vortexed for 1 min for dispersion, then the sample was centrifuged for 5 min at 1792 G-force for phase separation. The upper 1-octanol layer was withdrawn using a 25µL Hamilton syringe, collected in a micro vial, and injected (3 µL) into the HPLC column. Vortex-assisted DLLME procedures were performed at room temperature (25 °C).

Calculation of enrichment factor (EF)

Several parameters can be used to evaluate the performance of the vortex assisted-DLLME method. The enrichment factor (EF) was used to assess the effect of experimental conditions on the efficiency of vortex assisted-DLLME and was calculated from the following equation:

$$EF= \frac{{\text{C}}_{\text{org}}}{{\text{C}}_{\text{aq}}}$$

where Corg and Caq are the concentration of repaglinide in the organic phase and the initial concentration of repaglinide in the aqueous sample, respectively [24].

Analytical performance of the developed method

Validation of the vortex-assisted DLLME HPLC/UV method according to ICH guidelines was done by spiking tap water samples with different concentration levels of repaglinide and a constant level of nateglinide. The calibration curve was constructed by plotting peak area ratios (repaglinide-to-nateglinide) versus concentrations of repaglinide (n = 6). Limits of detection (LOD) and quantitation (LOQ) were used for evaluation of the method sensitivity through determining the lowest concentration producing signal-to-noise ratio of 3 and 10, respectively. The selectivity of the method was evaluated by checking the chromatograms of blank samples for any interfering peaks at the same retention time of repaglinide. The selectivity was further confirmed by making minor modifications in the mobile phase composition (Acetonitrile:buffer ratio) and checking the chromatograms for the appearance of any unexpected peaks. The intra- and inter-day accuracy and precision (n = 3) were studied using spiked environmental water samples with repaglinide at concentrations of 10, 40, 70 ng/mL using 1000 ng/mL nateglinide as internal standard (IS). The accuracy and precision were expressed as % recovery and % RSD, respectively.

Results and discussion

DLLME has been extensively used for the preparation of various samples with different matrices including environmental water [25], soil [26], food [27], plasma [28, 29], serum [30], urine and saliva [31]. DLLME has been applied for determinations of pharmaceuticals [17], pesticides [32], insecticides [33], toxicants [34], and natural products [35]. In this work, a dispersive liquid–liquid microextraction methodology was combined with HPLC/UV detection for preconcentration and determination of repaglinide in environmental water samples. The continuous consumption of antidiabetic drugs increases the possibility of its presence in industrial and municipal sewage water. Repaglinide cannot be detected in aqueous samples without pretreatment due to its low concentrations. Vortex-assisted DLLME efficiency was affected by experimental conditions including extractant type and volume, disperser type and volume, and pH of the sample [36]. To attain the utmost enrichment of repaglinide, it was essential to optimize these variables. This optimization was performed on two sequential steps: studying the effects of different experimental variables to set the limits, and optimizing these conditions by simultaneous changes within the specified limits. The mentioned chromatographic conditions showed retention times of 8.3 and 5.5 min for repaglinide and nateglinide, respectively.

Selection of extractant type and volumes

Suitable extractants should have certain characteristics to achieve the desired purpose of use. These characteristics include high safety, environmental benignness, low miscibility with the aqueous phase, the ability to extract the target analyte, and compatibility with the analytical instrument [37]. High density halogenated hydrocarbon solvents such as chloroform and dichloromethane usually were used but they are highly toxic [38]. Other low-density solvents such as long-chain alcohols are suitable alternative extractants [39]. Based on preliminary experiments, it was found that long-chain alcohols such as 1-octanol, 1-decanol, 1-undecanol, and 1-dodecanol were good extractants of repaglinide (Additional file 1: Fig. S1) so, they were chosen to be studied using experimental design optimization. The volume of extractant was also critical for the enrichment of the analyte. Increasing the extractant volume may lead to decreasing the enrichment factor due to the dilution effect [40]. Decreasing the volume beyond 30 µL lead to decreasing the available volume for analysis [38] so, volumes ranging from 30 to 150 µL were chosen for the subsequent optimization.

Selection of disperser type and volumes

The main requirement of a suitable disperser is its miscibility with both the extractant solvent and aqueous phase to ensure efficient contact between both of them [41]. Introducing new dispersers like glycerol and propylene glycol enhances the green aspects of analysis. Based on preliminary experiments, methanol, acetonitrile, glycerol, and propylene glycol showed reasonable extraction efficiency (Additional file 1: Fig. S2), while acetone and ethanol were excluded as they showed overlapping peaks at the same retention time of the analyte. This could be due to the presence of impurities in acetone and ethanol. Volumes ranging from 100 to 500 µL were chosen for subsequent optimization. Volumes lower than 100 µL and higher than 500 µL were tried but they showed low enrichment factors (Additional file 1: Fig. S3). Low volumes of disperser are not enough to disperse the extractant in the aqueous phase while high volumes may enhance the miscibility of the extractant in the aqueous phase making the phase separation difficult [36] or it may be due to increasing the solubility of repaglinide in water which minimizing the extraction efficiency [24].

Selection of pH range

Ionizable drugs are greatly affected by pH, hence optimization of pH during vortex-assisted DLLME procedures is critical. The extraction of the drug is affected by the degree of ionization which in turn depends on pH. The best pH is the one that ensures that most of the drug molecules are in the unionized form with sufficient hydrophobicity to be more extractable in the organic phase [40, 41]. Repaglinide has two pKa values of 6.20 (basic) and 3.96 (acidic) for the amine and the carboxylate groups, respectively [42]. So, no specific pH within the HPLC working range could be selected to make the two functional groups of repaglinide in the unionized forms. For this reason, a wide range of pH values from 2 to 8 was chosen for further optimization.

Optimization of extraction procedures

Method optimization in DLLME could be performed using either the classical one factor at a time approach [43] or chemometrics methods including response surface methodology [44]. In response surface methodology, the different experimental variables are studied simultaneously which saves time, effort and consider potential interactions between variables. Accordingly, the results of the preliminary study were used as a base for further optimization of vortex-assisted DLLME procedures of repaglinide using response surface methodology, with aid of Design Expert 8® StatEase software. The model studied the interaction between the different variables: type of extractant, the volume of extractant, type of disperser, volume of the disperser, pH of the sample. The developed model decreased the time required to reach the optimum conditions in comparison with the one factor at a time approach. Besides, the model helped to predict enrichment factors at any given condition and the critical experimental variables.

The five variables (extractant type, extractant volume, disperser type, disperser volume, and sample pH) were varied over a program-suggested 58 runs using a quadratic integrated D-optimal design (Additional file 1: Table S1). The levels of each variable were chosen based on the previous results, as shown in Table 1. The desirability criteria were set to select the conditions that achieve the maximum response, where the latter was defined as the EF of each run. The desirability function attempts to reach operating conditions that guarantee compliance with the criteria of the involved response. The d values could be between 0 and 1, where 0 indicated undesirable conditions while 1 could hypothetically be attained at fully desirable conditions [21, 45].

Table 1 The levels of variables used in the optimization process

The enrichment factors (EF) of different extractant/disperser combinations were calculated by the developed model, as shown in Fig. 2. This simultaneous optimization considered the possible interactions between variables and help reach the actual optimum. The results revealed that the best combination consisted of 1-octanol as an extractant with acetonitrile as a disperser. It also showed that 1-octanol was always the best extractant, no matter which disperser was used. It is worth mentioning that glycerol showed comparable results with methanol, which highlights the potential use of glycerol as a green disperser. Based on the optimization results, three variables had substantial effects on the EFs. These variables included the extractant type, the extractant volume, and the pH, while the other two variables (the disperser type and volume) had observably lower effects on EFs (Additional file 1: Fig. S4).

Fig. 2
figure 2

The effect of interaction between the type of extractant and the type of disperser on the enrichment factor. ACN: acetonitrile, MeOH: methanol, GLY: glycerol, PPG: propylene glycol

As expected, the EFs were inversely proportional to the extractant volumes due to the possible dilution effect, with the maximum EF achieved using 30μL. Increasing the disperser volumes decreased the EFs due to the expected increase in the volume of extractant/disperser mixture after phase separation. Using pH 8 led to the greatest enrichment factor at which the amine functional group (basic pKa = 6.20) of repaglinide would be in the unionized form, which was preferably extractable. As shown in Fig. 3, the highest d value was attained under the experimental conditions of 30 µL of 1-octanol (extractant) and 100 µL of acetonitrile (disperser) at pH 8.

Fig. 3
figure 3

Response curves of the optimized conditions for the experimental variables of extractant volume (µL), disperser volume (µL), pH

The model was evaluated by the fraction of design space (FDS) that could give an estimate of the response with enough precision. FDS needs to be higher than 80% for the model to be considered reliable [46]. FDS was estimated by the software (Additional file 1: Fig. S5) and was found to be 88.2% with a standard error of 0.978 at a 95% confidence level. The model predictability was tested by plotting the actual EFs versus the predicted EFs (Additional file 1: Fig. S6), satisfactory results were obtained. The Model had an F-value of 4.38, which implied that the model was significant. The P-values (0.0031) was less than 0.0500, which indicated that the model terms were significant, with only a 0.31% chance that an F-value this large could occur due to noise.

The optimum conditions were tried to extract repaglinide from environmental waters using vortex-assisted DLLME and were compared with the untreated aqueous samples. Nateglinide was used as an internal standard to improve the method’s accuracy and precision. Figure 4 shows the chromatographic separation of repaglinide before and after vortex-assisted DLLME using the optimum condition. The samples treated with vortex-assisted DLLME were markedly concentrated with an EF of 480.

Fig. 4
figure 4

Chromatogram of repaglinide A before and B after vortex assisted-DLLME. Microextraction conditions: extractant type: octanol, extractant volume: 30μL, disperser type: acetonitrile, disperser volume: 100 μL, vortex time: 1 min, centrifugation time: 5 min, diluent: pH = 8

Analytical performance of the vortex-assisted DLLME HPLC/UV method

Under the optimized conditions, the developed vortex-assisted DLLME HPLC/UV method was validated for linearity, specificity, limits of detection and quantitation, intra- and inter-day accuracy and precision using spiked samples. The method showed good linearity in the range of 1–100 ng/mL with a correlation coefficient of (r) of 0.9969 (n = 6). The LOD was found to be 0.4 ng/mL with a signal-to-noise ratio of 3 (Table 2) while the LOQ was found to be 1 ng/mL with a signal-to-noise ratio of 10. The selectivity of the method was shown by the absence of any peaks in the same retention time of repaglinide in blank chromatograms and chromatograms of other chromatographic conditions. The method reliability was assessed via using control charts of repaglinide retention times and areas under curves of the internal standard. As shown in Additional file 1: Fig. S7, the results showed that none of the reported data exceeded the control limits and more than 95% of the data were below the warning limits. The % recovery (found concentration/ spiked concentration × 100) was used for the evaluation of accuracy while the precision was evaluated by %RSD. The intra-day accuracy and precision (n = 3) were found to be 99.46% and 1.20%RSD while inter-day accuracy and precision (n = 3) were found to be 99.57% and 0.13% RSD (Table 3).

Table 2 Regression parameters for determination of repaglinide by the proposed method
Table 3 Intra and inter-day accuracy and precision (n = 3) for determination of repaglinide by the proposed method


The validated developed vortex-assisted DLLME method was applied for the determination of repaglinide in environmental water using the optimum conditions. River water (The Nile) and tap water were spiked with 5, 20, and 100 ng/mL. Application of the method showed acceptable % recovery as shown in Table 4 and showed that the method could be applied in the analysis of repaglinide in water samples with high sensitivity and acceptable accuracy and precision. It should be noted that the method was validated in aqueous samples for the determination of repaglinide in environmental water.

Table 4 Application of vortex assisted-DLLME for determination of repaglinide in environmental water

Comparison of the proposed method and other reported methods

The sensitivity of the proposed vortex-assisted DLLME method was compared to different reported methods for the determination of repaglinide in different matrices in terms of LODs and LOQs (Table 5). The comparison shows that vortex-assisted DLLME can reach comparable levels of sensitivity with mass spectroscopy techniques, which is sophisticated, expensive, and requires highly expert technicians. Vortex-assisted DLLME does not require time, high consumption of organic solvents, or expensive and special cartridges such as liquid–liquid extraction (LLE) and solid phase extraction (SPE). In other words, vortex-assisted DLLME is a simple, rapid, cheap, and eco-friendly method.

Table 5 Comparison between the proposed method and other reported methods for repaglinide determination in different matrices


A vortex assisted-dispersive liquid–liquid microextraction coupled with HPLC/UV methodology was developed and validated for preconcentration and determination of repaglinide in environmental samples. The optimization of the experimental conditions was carried out using a factorial design model which allows studying the interaction between the various factors affecting the method efficiency in addition to saving time and effort. The response surface optimization provided a more efficient method for studying the variables simultaneously. The optimized conditions provided the highest possible enrichment factor. Vortex-assisted DLLME method offers a lot of advantages including a short time of analysis, simple procedures, and low consumption of organic solvents, which makes the method more economic and environmentally benign. The validation of the proposed method proved its sensitivity, accuracy, and reproducibility. The method was applied for environmental samples like river water and tap water providing reliable %recoveries. As well as it was applied to plasma and could be extended to be applied in the biological analysis of repaglinide.

Availability of data and materials

Not applicable.



Dispersive liquid–liquid microextraction


Enrichment factor


Fraction of design space


  1. McLeod HL, Evans WE. Clinical pharmacokinetics and pharmacodynamics of epipodophyllotoxins. Cancer Surv. 1993;17:253–68.

    CAS  PubMed  Google Scholar 

  2. Ruzilawati AB, Wahab MSA, Imran A, et al. Method development and validation of repaglinide in human plasma by HPLC and its application in pharmacokinetic studies. J Pharm Biomed Anal. 2007;43:1831–5.

    CAS  Article  PubMed  Google Scholar 

  3. Culy CR, Jarvis B. Repaglinide: A review of its therapeutic use in type 2 diabetes mellitus. Drugs. 2001;61:1625–60.

    CAS  Article  Google Scholar 

  4. Halling-Sørensen B, Nors Nielsen S, Lanzky PF, et al. Occurrence, fate and effects of pharmaceutical substances in the environment- A review. Chemosphere. 1998;36:357–93.

    Article  PubMed  Google Scholar 

  5. Radjenovic J, Petrovic M, Barceló D. Analysis of pharmaceuticals in wastewater and removal using a membrane bioreactor. Anal Bioanal Chem. 2007;387:1365–77.

    CAS  Article  PubMed  Google Scholar 

  6. Wang M, Miksa IR. Multi-component plasma quantitation of anti-hyperglycemic pharmaceutical compounds using liquid chromatography-tandem mass spectrometry. J Chromatogr B. 2007;856:318–27.

    CAS  Article  Google Scholar 

  7. Petsalo A, Turpeinen M, Pelkonen O, Tolonen A. Analysis of nine drugs and their cytochrome P450-specific probe metabolites from urine by liquid chromatography-tandem mass spectrometry utilizing sub 2 μm particle size column. J Chromatogr A. 2008;1215:107–15.

    CAS  Article  PubMed  Google Scholar 

  8. Lakshmi KS, Rajesh T. Separation and quantification of eight antidiabetic drugs on a high-performance liquid chromatography: its application to human plasma assay. ISRN Pharm. 2011;2011:1–7.

    CAS  Article  Google Scholar 

  9. Chen W, Wang L, Van Berkel GJ, et al. Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry. J Chromatogr A. 2016;1439:137–43.

    CAS  Article  PubMed  Google Scholar 

  10. Truong QK, Mai XL, Lee JY, et al. Simultaneous determination of 14 oral antihyperglycaemic drugs in human urine by liquid chromatography-tandem mass spectrometry. Arch Pharm Res. 2018;41:530–43.

    CAS  Article  PubMed  Google Scholar 

  11. Lam YH, Leung MT, Ching CK, Mak TWL. Simultaneous detection of 24 oral antidiabetic drugs and their metabolites in urine by liquid chromatography-tandem mass spectrometry. J Chromatogr B. 2020;1141: 122020.

    CAS  Article  Google Scholar 

  12. Nemati M, Tuzen M, Farazajdeh MA, et al. Development of dispersive solid-liquid extraction method based on organic polymers followed by deep eutectic solvents elution; application in extraction of some pesticides from milk samples prior to their determination by HPLC-MS/MS. Anal Chim Acta. 2022;1199: 339570.

    CAS  Article  PubMed  Google Scholar 

  13. Farajzadeh MA, Nemati M, Altunay N, et al. Experimental and density functional theory studies during a new solid-phase extraction of phenolic compounds from wastewater samples prior to GC–MS determination. Microchem J. 2022;177: 107291.

    CAS  Article  Google Scholar 

  14. Rezaee M, Yamini Y, Faraji M. Evolution of dispersive liquid-liquid microextraction method. J Chromatogr A. 2010;1217:2342–57.

    CAS  Article  PubMed  Google Scholar 

  15. Afshar Mogaddam MR, Farajzadeh MA, Tuzen M, et al. Organic solvent-free elevated temperature liquid-liquid extraction combined with a new switchable deep eutectic solvent-based dispersive liquid-liquid microextraction of three phenolic antioxidants from oil samples. Microchem J. 2021;168: 106433.

    CAS  Article  Google Scholar 

  16. Nemati M, Tuzen M, Altunay N, et al. Development of sodium hydroxide-induced homogenous liquid-liquid extraction-effervescent assisted dispersive liquid-liquid microextraction based on deep eutectic solvents; Application in the extraction of phytosterols from cow cream samples. J Food Compos Anal. 2022;106: 104291.

    CAS  Article  Google Scholar 

  17. Mansour FR, Danielson ND. Solvent-terminated dispersive liquid-liquid microextraction: A tutorial. Anal Chim Acta. 2018;1016:1–11.

    CAS  Article  PubMed  Google Scholar 

  18. Li MJ, Zhang HY, Liu XZ, et al. Progress of extraction solvent dispersion strategies for dispersive liquid–liquid microextraction. Chinese J Anal Chem. 2015;43:1231–40.

    CAS  Article  Google Scholar 

  19. Seebunrueng K, Santaladchaiyakit Y, Srijaranai S. Vortex-assisted low-density solvent-based demulsified dispersive liquid-liquid microextraction and high-performance liquid chromatography for the determination of organophosphorus pesticides in water samples. Chemosphere. 2014;103:51–8.

    CAS  Article  PubMed  Google Scholar 

  20. Petridis NP, Sakkas VA, Albanis TA. Chemometric optimization of dispersive suspended microextraction followed by gas chromatography-mass spectrometry for the determination of polycyclic aromatic hydrocarbons in natural waters. J Chromatogr A. 2014;1355:46–52.

    CAS  Article  PubMed  Google Scholar 

  21. Stalikas C, Fiamegos Y, Sakkas V, Albanis T. Developments on chemometric approaches to optimize and evaluate microextraction. J Chromatogr A. 2009;1216:175–89.

    CAS  Article  PubMed  Google Scholar 

  22. Beldean-Galea MS, Vial J, Thiébaut D, Coman MV. Analysis of multiclass organic pollutant in municipal landfill leachate by dispersive liquid-liquid microextraction and comprehensive two-dimensional gas chromatography coupled with mass spectrometry. Environ Sci Pollut Res. 2020;27:9535–46.

    CAS  Article  Google Scholar 

  23. Alipanahpour Dil E, Asfaram A, Sadeghfar F. Magnetic dispersive micro-solid phase extraction with the CuO/ZnO@Fe3O4-CNTs nanocomposite sorbent for the rapid pre-concentration of chlorogenic acid in the medical extract of plants, food, and water samples. Analyst. 2019;144:2684–95.

    CAS  Article  PubMed  Google Scholar 

  24. Rezaee M, Assadi Y, Milani Hosseini MR, et al. Determination of organic compounds in water using dispersive liquid-liquid microextraction. J Chromatogr A. 2006;1116:1–9.

    CAS  Article  PubMed  Google Scholar 

  25. Behbahani M, Najafi F, Bagheri S, et al. Coupling of solvent-based de-emulsification dispersive liquid-liquid microextraction with high-performance liquid chromatography for simultaneous simple and rapid trace monitoring of 2,4-dichlorophenoxyacetic acid and 2-methyl-4-chlorophenoxyacetic acid. Environ Monit Assess. 2014;186:2609–18.

    CAS  Article  PubMed  Google Scholar 

  26. Hu S, Song Y, Bai X, et al. Derivatization and Solidification of Floating Dispersive Liquid-phase Microextraction for the Analysis of Kanamycin in Wastewater and Soil by HPLC with Fluorescence Detection. CLEAN - Soil, Air, Water. 2014;42:364–70.

    CAS  Article  Google Scholar 

  27. Petrarca MH, Ccanccapa-Cartagena A, Masiá A, et al. Comparison of green sample preparation techniques in the analysis of pyrethrins and pyrethroids in baby food by liquid chromatography-tandem mass spectrometry. J Chromatogr A. 2017;1497:28–37.

    CAS  Article  PubMed  Google Scholar 

  28. Mabrouk MM, Soliman SM, El-Agizy HM, Mansour FR. Ultrasound-assisted dispersive liquid-liquid microextraction for determination of three gliflozins in human plasma by HPLC/DAD. J Chromatogr B Anal Technol Biomed Life Sci. 2020;1136: 121932.

    CAS  Article  Google Scholar 

  29. Hammad MA, Kamal AH, Kannouma RE, Mansour FR. Vortex-Assisted Dispersive Liquid-Liquid Microextraction Coupled with Deproteinization for Determination of Nateglinide in Human Plasma Using HPLC/UV. J Chromatogr Sci. 2021;59:297–304.

    CAS  Article  PubMed  Google Scholar 

  30. Taheri S, Jalali F, Fattahi N, et al. Sensitive determination of methadone in human serum and urine by dispersive liquid-liquid microextraction based on the solidification of a floating organic droplet followed by HPLC-UV. J Sep Sci. 2015;38:3545–51.

    CAS  Article  PubMed  Google Scholar 

  31. Bulatov A, Medinskaia K, Aseeva D, et al. Determination of antipyrine in saliva using the dispersive liquid-liquid microextraction based on a stepwise injection system. Talanta. 2015;133:66–70.

    CAS  Article  PubMed  Google Scholar 

  32. Pinto MI, Sontag G, Bernardino RJ, Noronha JP. Pesticides in water and the performance of the liquid-phase microextraction based techniques. A review Microchem J. 2010;96:225–37.

    CAS  Article  Google Scholar 

  33. Ccanccapa-Cartagena A, Masiá A, Picó Y. Simultaneous determination of pyrethroids and pyrethrins by dispersive liquid-liquid microextraction and liquid chromatography triple quadrupole mass spectrometry in environmental samples. Anal Bioanal Chem. 2017;409:4787–99.

    CAS  Article  PubMed  Google Scholar 

  34. Akramipour R, Golpayegani MR, Gheini S, Fattahi N. Speciation of organic/inorganic mercury and total mercury in blood samples using vortex assisted dispersive liquid-liquid microextraction based on the freezing of deep eutectic solvent followed by GFAAS. Talanta. 2018;186:17–23.

    CAS  Article  PubMed  Google Scholar 

  35. Yan Y, Chen X, Hu S, Bai X. Applications of liquid-phase microextraction techniques in natural product analysis: A review. J Chromatogr A. 2014;1368:1–17.

    CAS  Article  PubMed  Google Scholar 

  36. Mansour FR, Danielson ND. Solidification of floating organic droplet in dispersive liquid-liquid microextraction as a green analytical tool. Talanta. 2017;170:22–35.

    CAS  Article  Google Scholar 

  37. Kocúrová L, Balogh IS, Šandrejová J, Andruch V. Recent advances in dispersive liquid-liquid microextraction using organic solvents lighter than water. A review Microchem J. 2012;102:11–7.

    CAS  Article  Google Scholar 

  38. Mansour FR, Khairy MA. Pharmaceutical and biomedical applications of dispersive liquid-liquid microextraction. J Chromatogr B. 2017;1061–1062:382–91.

    CAS  Article  Google Scholar 

  39. Yeong Hwang T, Mee Kin C, Ling Shing W. A review on extraction solvents in the dispersive liquid-liquid microextraction Malaysian. J Anal Sci. 2018;22:166–74.

    Article  Google Scholar 

  40. Ahmadi-Jouibari T, Fattahi N, Shamsipur M. Rapid extraction and determination of amphetamines in human urine samples using dispersive liquid-liquid microextraction and solidification of floating organic drop followed by high-performance liquid chromatography. J Pharm Biomed Anal. 2014;94:145–51.

    CAS  Article  PubMed  Google Scholar 

  41. Guiñez M, Martinez LD, Fernandez L, Cerutti S. Dispersive liquid-liquid microextraction based on solidification of floating organic drop and fluorescence detection for the determination of nitrated polycyclic aromatic hydrocarbons in aqueous samples. Microchem J. 2017;131:1–8.

    CAS  Article  Google Scholar 

  42. Mandić Z, Gabelica V. Ionization, lipophilicity and solubility properties of repaglinide. J Pharm Biomed Anal. 2006;41:866–71.

    CAS  Article  PubMed  Google Scholar 

  43. Albaseer SS, Rao RN, Swamy YV, Khagga M. In-syringe dispersive liquid-liquid microextraction with liquid chromatographic determination of synthetic pyrethroids in surface water. Artic J Anal Sci Technol. 2012;3:143–50.

    CAS  Article  Google Scholar 

  44. Ghaedi M, Roosta M, Khodadoust S, Daneshfar A. Application of optimized vortex-assisted surfactant-enhanced DLLME for preconcentration of thymol and carvacrol, and their determination by HPLC-UV: response surface methodology. J Chromatogr Sci. 2015;53:1222–31.

    CAS  Article  PubMed  Google Scholar 

  45. Vera Candioti L, De Zan MM, Cámara MS, Goicoechea HC. Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta. 2014;124:123–38.

    CAS  Article  Google Scholar 

  46. Mansour FR, Kirkpatrick CL, Danielson ND. Separation of aliphatic and aromatic carboxylic acids by conventional and ultra-high-performance ion-exclusion chromatography. Chromatographia. 2013;76:603–9.

    CAS  Article  Google Scholar 

  47. Hess C, Musshoff F, Madea B. Simultaneous identification and validated quantification of 11 oral hypoglycaemic drugs in plasma by electrospray ionisation liquid chromatography-mass spectrometry. Anal Bioanal Chem. 2011;400:33–41.

    CAS  Article  PubMed  Google Scholar 

  48. Tatiparthi R, Duraiswamy D, Bannoth CK. Method development and validation of metformin and repaglinide in rabbit plasma by RP-HPLC. FABAD J Pharm Sci. 2010;35:69–75.

    Google Scholar 

  49. Mahrouse MA, Lamie NT. Experimental design methodology for optimization and robustness determination in ion-pair RP-HPLC method development: Application for the simultaneous determination of metformin hydrochloride, alogliptin benzoate and repaglinide in tablets. Microchem J. 2019;147:691–706.

    CAS  Article  Google Scholar 

  50. Gandhimathi M, Ravi TK, Renu SK. Determination of repaglinide in pharmaceutical formulations by HPLC with UV Detection. Anal Sci. 2003;19:1675–7.

    CAS  Article  PubMed  Google Scholar 

  51. Soni LK, Narsinghani T, Jain M. Development and validation of RP-HPLC method for simultaneous estimation of metformin hydrochloride and repaglinide in tablet dosage form. J Liq Chromatogr Relat Technol. 2012;35:385–92.

    CAS  Article  Google Scholar 

  52. Abdelhamid NS, Elsaady MT, Ali NW, Abuelazem WG. Simultaneous Determination of Repaglinide, Metformin hydrochloride and Melamine by New HPLC and HPTLC Chromatographic Methods. Anal Chem Lett. 2019;9:418–29.

    CAS  Article  Google Scholar 

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AHK participated in the study design and the discussion of the results and revised the manuscript. MAH participated in the study design and the discussion of the results and revised the manuscript. REK conducted the practical work, participated in the discussion of the results and the preparation and writing of the manuscript. FRM proposed the study design, participated in the results discussion, manuscript preparation and revision. All authors read and approved the final manuscript.

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Correspondence to Fotouh R. Mansour.

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Additional file 1:

Data of the model development, optimization and validation.

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Kamal, A.H., Hammad, M.A., Kannouma, R.E. et al. Response surface optimization of a vortex-assisted dispersive liquid–liquid microextraction method for highly sensitive determination of repaglinide in environmental water by HPLC/UV. BMC Chemistry 16, 33 (2022).

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  • Repaglinide
  • Nateglinide
  • Vortex-assisted DLLME
  • Chemometrics
  • Response Surface Optimization