ORIGINAL PAPER

Optimization of parameters for the alcoholic-assisted dispersiveliquid–liquid microextraction of estrogens in water

Panteha Shakeri • Zahra Mousavi Kiasari •

Mohammad Reza Hadjmohammadi •

Mohammad Hossein Fatemi

Received: 7 July 2013 / Accepted: 30 December 2013

� Iranian Chemical Society 2014

Abstract Extraction and determination of estrogens in

water samples were performed using alcoholic-assisted

dispersive liquid–liquid microextraction (AA-DLLME)

and high-performance liquid chromatography (UV/Vis

detection). A Plackett–Burman design and a central com-

posite design were applied to evaluate the AA-DLLME

procedure. The effect of six parameters on extraction

efficiency was investigated. The factors studied were vol-

ume of extraction and dispersive solvents, extraction time,

pH, amount of salt and agitation rate. According to

Plackett–Burman design results, the effective parameters

were volume of extraction solvent and pH. Next, a central

composite design was applied to obtain optimal condition.

The optimized conditions were obtained at 220 lL 1-oct-

anol as extraction solvent, 700 lL ethanol as dispersive

solvent, pH 6 and 200 lL sample volume. Linearity was

observed in the range of 1–500 lg L-1 for E2 and

0.1–100 lg L-1 for E1. Limits of detection were

0.1 lg L-1 for E2 and 0.01 lg L-1 for E1. The enrichment

factors and extraction recoveries were 42.2, 46.4 and 80.4,

86.7, respectively. The relative standard deviations for

determination of estrogens in water were in the range of

3.9–7.2 % (n = 3). The developed method was success-

fully applied for the determination of estrogens in envi-

ronmental water samples.

Keywords Alcoholic-assisted dispersive liquid–liquid

microextraction � Optimization � Estrogens � Experimental

design � Plackett–Burman design

Abbreviations

AA-DLLME Alcoholic-assisted dispersive liquid–liquid

microextraction

EDCs Endocrine disrupting chemicals

E1 Estrone

E2 17b-estradiol

DLLME Dispersive liquid–liquid microextraction

LLE Liquid–liquid extraction

SPE Solid-phase extraction

CPE Cloud point extraction

SBSE Stir bar sorptive extraction

SPME Solid-phase microextraction

PB Plackett–Burman design

CCF Central composite face-centered

ER Extraction recovery

EF Enrichment factor

ANOVA Analysis of variance

R2 Coefficient of determination

Introduction

The fact that some chemicals may disrupt the endocrine

systems in humans and animals has received considerable

attention in the scientific and public community. Such

chemicals are widely referred to as endocrine disrupting

chemicals (EDCs), and are on the agenda of many expert

groups, steering committees and panels of governmental

organizations, industries and academia throughout the

world. Exposure to EDCs may have little effect on the

exposed organism, but the offspring of that organism may

suffer drastic repercussions [1]. Recently, there has been a

growing worldwide concern on EDCs due to their high

toxicity. Among the EDCs known to effect people, the

P. Shakeri � Z. Mousavi Kiasari � M. R. Hadjmohammadi (&) �M. H. Fatemi

Faculty of Chemistry, University of Mazandaran, Babolsar, Iran

e-mail: hadjmr@umz.ac.ir

123

J IRAN CHEM SOC

DOI 10.1007/s13738-013-0403-5

most important ones are the natural estrogens, estrone (E1)

and 17b-estradiol (E2), which display higher estrogenic

capacities and have thousand times higher biological

potency than other compounds such as bisphenol A, al-

kylphenols and nonylphenols [2–5]. Therefore, the pre-

sence of E1 and E2 in aquatic environments will pose a

serious threat to the local organisms and human health [6].

The environmental concentrations for these estrogens

are very low; therefore, a sensitive, selective and simple

method requires monitoring them in water [7]. Before

determination of these materials in water samples they

require a pretreatment technique. Many different pretreat-

ment techniques, such as liquid–liquid extraction (LLE) [8,

9], solid-phase extraction (SPE) [10, 11], solid-phase

microextraction (SPME) [12], stir bar sorptive extraction

(SBSE) [13] and cloud point extraction (CPE) [14] were

used for the extraction of estrogens. Unfortunately, the

traditional methods such as LLE and SPE require a large

consumption of organic solvents, sample volume and are

time consuming. Although SPME and SBSE are both sol-

vent-free techniques, the fibers of SPME are fragile,

expensive and have limited lifetime and sample carries

over is the other problem of this technique. For SBSE, an

additional desorption step is required when it couples with

HPLC. CPE uses surfactants for extraction thus the choices

of the surfactants often bring the nuisance to the analysis of

analytes using GC and HPLC [15–19]. Recently, a new

microextraction method, named dispersive liquid–liquid

microextraction (DLLME), introduced by Assadi et al. [20]

has been used as a powerful preconcentration technique for

extraction of a variety of compounds including estrogens

[21–23]. The main disadvantage of the common DLLME

technique is the use of chlorinated solvents as extraction

solvent that are potentially toxic to humans and the envi-

ronment. In addition, because the extraction solvent is

incompatible with liquid chromatography (LC), DLLME

extract cannot be injected directly to LC system for ana-

lysis. On the other hand, in the determination of some

important compounds, for example organochlorine pesti-

cides using DLLME-GC-electron capture detector, chlori-

nated extraction solvents have a very high solvent peak

which covers some analytes peaks. To develop the appli-

cability of the DLLME procedure, the alcoholic-assisted

dispersive liquid–liquid microextraction (AA-DLLME)

method was introduced in our laboratory [24]. The basic

criteria in AA-DLLME for selection of alcoholic solvents

as extraction and dispersive solvents are their less toxicity

and environmental greenness. In comparison of DLLME

and AA-DLLME, the former needs higher volumes of

dispersive solvent (in the mL range). Furthermore, the

tedious procedure of evaporation of extraction solvent in

DLLME, which may cause the loss of analyte, was elimi-

nated in the AA-DLLME procedure and the extraction

solvent can be directly injected into HPLC. Moreover, AA-

DLLME method is environmentally greener than other

DLLME procedures due to the use of alcoholic solvents

[25–27]. The main aim of the present work was to inves-

tigate and optimize the extraction conditions of AA-

DLLME procedure using Plackett–Burman factorial design

(PBD) and central composite face-centered (CCF) design.

Then, the developed method was used for analysis of

estrogens in water samples.

Experimental

Reagents and standards

Estrone and 17b-estradiol were purchased from Sigma–

Aldrich (St. Louis, MO, USA). 1-Octanol and 1-heptanol

were purchased from Fluka (Buches, Switzerland). Etha-

nol, methanol (HPLC-grade), acetonitrile (HPLC-grade),

2-ethyl-1-hexanol, sodium chloride, sodium hydroxide and

hydrochloric acid, were obtained from Merck (Darmstadt,

Germany). Double distilled deionized water was produced

by a Milli-Q system (Millipore, Bedford, MA, USA). Stock

solutions of estrogens (500.0 mg L-1) were prepared in

methanol and stored in the dark at 4 �C. The working

solutions were prepared daily by an appropriate dilution of

the stock solution in water. All solutions were filtered

through 0.45 lm membrane filters (Millipore, Bedford,

MA) prior to use.

Instrumentation

The chromatographic separations were carried out on a 1525

solvent delivery system and a model 2487 UV/Vis selective

wavelength detector set at 280 nm, all from Waters (Mil-

ford, MA, USA). The analytical isocratic RP-HPLC sepa-

ration was performed on a C18 column (250 9 4.6 mm,

5 lm) from Dr. Maisch (Beim Brueckle, Germany) at room

temperature. Mobile phase was a mixture of acetonitrile:

water (50:50, v/v), with flow rate of 1.0 ml min-1. The

injection volume was 20 lL. A Hettich centrifuge model

UNIVERSAL 320 (Tuttlingen, Germany) was used to

accelerate the phase separation. A Jenway model 3030 pH

meter equipped with a combined glass–calomel electrode

was employed for pH measurement. The magnetic stirrer

used was MR 2002 (Heidolph, Germany). All statistical

analyses were performed with Statgraphics 5.1.

Alcoholic-assisted dispersive liquid–liquid

microextraction procedure

For AA-DLLME, 10 ml of aqueous standard (pH 6)

including the analytes (100 lg L-1) was poured into a

J IRAN CHEM SOC

123

specially designed glass cell (Fig. 1) containing a magnetic

stirring bar. A mixture of extraction solvent (220 lL,

1-octanol) and disperser solvent (700 lL, ethanol) was

rapidly injected into the sample solution by a Hamilton

syringe (Reno, NV, USA) while solution was being stirred

at 1,250 rpm. After the injection a cloudy solution was

formed, and the extraction solvent was floated on the neck

of glass cell. Afterward the cell was centrifuged for 10 min

at 3,000 rpm and a 100 lL Hamilton syringe was used to

remove the extracted layer and 30 lL of this phase was

injected into the HPLC system for quantification.

Calculation of enrichment factor, extraction recovery

and relative recovery

The enrichment factor (EF) during the AA-DLLME was

calculated according to the following equation:

EF ¼ Cf=Caq ð1Þ

The Cf is the final concentration of analyte in the

floating phase, and Caq is the initial analyte concentration

within the sample.

Recovery (R) was calculated according to the following

equation:

ER ¼ nf

�naq � 100% ¼ Vf

�Vaq

� �EFð Þ � 100%

¼ Vf

�Vaq

� �Cf

�Caq

� �� 100% ð2Þ

where nf and naq are the number of moles of analyte finally

collected in the extraction solvent and the number of moles

of analyte originally present in the sample, respectively. In

the above equation, Vf is the volume of floating extraction

solvent and Vaq is the volume of sample. Vf was determined

by bending the glass cell and gathering the floating organic

solvent by a micro liter syringe (conditions were kept

constant, so the same sample volume was obtained).

The relative recovery (RR %) calculated from the fol-

lowing equation:

RR %ð Þ ¼ Cfound � Crealð Þ=Caddedð Þ � 100 ð3Þ

where Cfound, Creal and Cadded are the concentration of

analyte after addition of known amount of standard to real

sample, the concentration of analyte in real sample and the

concentration of standard added to the real sample,

respectively.

Results and discussion

Selection of disperser and extracting solvents

To achieve good recovery for AA-DLLME of estrogens,

the selection of an appropriate mixture of extraction and

disperser solvents is very important. The extraction solvent

should have some properties to extract the analytes effi-

ciently such as lower density than water, low solubility in

water, extraction capability of interested compound and

good chromatographic behavior. In this work, three alco-

holic solvents including 2-ethyl-1-hexanol (density

0.834 g mL-1), 1-octanol (density 0.824 g mL-1) and

1-heptanol (density 0.819 g mL-1) were used as extraction

solvents. Disperser solvent should be miscible with both

water and extraction solvent and produce very fine droplet

of extraction solvent, when mixture of extraction and dis-

perser solvent was rapidly injected into the sample.

Methanol and ethanol, which have this ability, were

applied. All combinations of extraction and disperser sol-

vents were examined for finding the optimum solvents.

During this procedure other AA-DLLME factors were

maintained constant (150 lL of extraction solvent, 500 lL

of disperser solvent, stirring rate of 500 rpm, 10 % salt in

sample solution, pH 7 and 5 min extraction time). The

obtained results indicated that maximum extraction effi-

ciency was achieved using 1-octanol and ethanol as

extraction and disperser solvents, respectively.

Factors screening

Screening design includes examining different factors for

the main effects to reduce the number of factors. A par-

ticular type of such designs is PBD [28]. This design is

very useful for preliminary studies or in initial optimization

steps. In PBD the interactions can be completely ignored,

so the main effects are calculated with a reduced number ofFig. 1 Schematic figure for container of AA-DLLME

J IRAN CHEM SOC

123

experiments. Based on the preliminary experiments, it was

suggested that six factors including volume of extracting

and disperser solvents, amount of salt in sample solution,

pH, extraction time and stirring rate can affect the AA-

DLLME efficiency. Based on the PBD, each factor was

examined at two levels: -1 for low level and ?1 for high.

Table 1 indicates the levels of six studied factors in this

work together with design matrix of PBD design. As can be

seen six assigned variables were screened in 12 experi-

mental runs and the recovery was taken as experimental

response (Table 1). The Statgraphics 5.1 software was used

to analyze the experimental results. The analysis of vari-

ance (ANOVA) method was used to evaluate main effects

of parameters. The normalized results of the experimental

design were evaluated at a 5 % of significance and ana-

lyzed by Standardized Pareto chart (Fig. 2). The vertical

line on the plot judges the effects that are statistically

significant (p \ 0.05). The bars, extending beyond the

line, correspond to the effects that are statistically, sig-

nificant at the 95 % confidence level. According to these

results, pH and extraction solvent volume were selected

as the important factors in extraction of estrogens by

AA-DLLME method. To optimize experimental condi-

tions, a CCF design was performed by these factors.

Other factors including disperser solvent volume,

extraction time and stirring rate were considered as

insignificant factors in the studied range, therefore, their

levels were kept constant and were determined based on

their sign on Pareto chart.

Optimization of AA-DLLME conditions

To investigate and validate process parameters affecting

the extraction of estrogens and exact optimization of the

extraction condition, the three levels, CCF was applied.

The total number of experiments (N) is determined by the

following equation

N ¼ 2f þ 2f þ N0 ð4Þ

In this equation f is the number of factors and N0 is the

number of replicates at central point. The resulted design

had four factorial points, four star points and four center

points. The examined levels of the factors and the design

matrix are given in Table 2. The resulting 12 experiments

were carried out randomly, using 10 mL of spiked water

samples, containing 100 lg L-1 of analytes and using

Table 1 The experimental variables, levels, design matrix and

response of the Plackett–Burman design

Variables (Symbols) Low High

Volume of extraction solvent

(A) (lL)

100 300

Volume of dispersion solvent

(B) (lL)

500 700

Amount of salt (C) (% w/v) 0 10

pH (D) 3 12

Extraction time (E) (min) 0 10

Stirring rate (F) (rpm) 100 1,250

No. Parameters ER %

A B C D E F

1 -1 1 1 -1 1 -1 57

2 1 1 1 -1 1 1 86

3 1 1 -1 1 -1 -1 77

4 -1 1 1 1 -1 1 55

5 -1 -1 -1 -1 -1 -1 73

6 -1 -1 -1 1 1 1 24

7 1 -1 -1 -1 1 1 90

8 1 -1 1 1 -1 1 70

9 -1 1 -1 -1 -1 1 64

10 1 -1 1 -1 -1 -1 65

11 1 1 -1 1 1 -1 49

12 -1 -1 1 1 1 -1 21

Fig. 2 Pareto charts of the main

effects obtained from the

Plackett–Burman design

J IRAN CHEM SOC

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1-octanol as extraction solvent and ethanol (700 lL) as

disperse solvent, 1 min extraction time and 1,250 rpm

stirring rate. The recoveries of the analyte were introduced

as the response. The ANOVA of obtained results is shown

in Table 3. A p value\0.05 in the ANOVA table indicates

the statistical significance of an effect at 95 % confidence

level. The lack of fit (LOF) p value of 0.559 implies that

the LOF is not significant relative to the pure error. The

best fitted model to these data is as the following

Recovery% ¼ 89:125 �2:691ð Þþ 8:333 �2:407ð ÞA� 8:833 �2:407ð ÞD� 12:875 �3:610ð ÞA2

þ 8:250 �2:948ð ÞAD� 13:375 �3:610ð ÞD2

ð5ÞR ¼ 0:961; SE ¼ 5:89; F ¼ 14:5

The criteria for the evaluation of descriptive capability

of a polynomial were correlation coefficient (R), Fisher

ratio value (F) and standard error of estimate (SE). The

correlation coefficient value was 0.961 that indicates the

model could explain 96.1 % of the variability in the

response. Figure 3 shows the response surface of these

experiments based on the above model. According to

Eq. 5, though the sign of extraction solvent volume is

positive its quadratic term has a negative sign, therefore,

the intermediate-high value of 1-octanol was selected as

the optimum volume. The pH as mentioned before has

negative sign; therefore intermediate-low value was

selected. Based on these results the optimum experimental

conditions were: 220 lL 1-octanol and pH -6. When these

optimum conditions were tested it was found that they

effectively provided the highest extraction recovery for

analytes.

Method evaluation

Figures of merit

Under the optimum condition, limits of detection (LODs),

linear range (LR), intra-day precision (repeatability) and

inter-day precision (reproducibility), extraction recovery

and EF of the AA-DLLME method for the estrogens were

obtained and shown in Table 4. LOD was determined

Table 2 Factors, value level, design matrix and responses for the

central composite face-centered

Variables (symbol) Levels

-1 0 1

Volume of extraction solvent (lL) (A) 100 200 300

pH (D) 3 7.5 12

No. Parameters

Volume of extraction solvent pH ER %

1 0 1 68

2 0 0 90

3 -1 1 39

4 0 0 89

5 -1 -1 76

6 1 -1 72

7 1 1 68

8 0 0 83

9 0 -1 80

10 -1 0 62

11 0 0 98

12 1 0 87

Table 3 The results of analysis of variance for CCF design

Source Sum of

squares

df Mean

squares

F valuea p value

prob [ Fb

A 416.667 1 416.667 11.99 0.0134

D 468.167 1 468.167 13.47 0.0105

AA 442.042 1 442.042 12.72 0.0118

AD 272.25 1 272.25 7.83 0.0312

DD 477.042 1 477.042 13.73 0.0100

Residual 208.542 6 34.7569

LOFc 94.5417 3 31.5139 0.83 0.5593

Pure

error

114.0 3 38.0

df degrees of freedoma Test for comparing model variance with residual (error) varianceb Probability of seeing the observed F value if the null hypothesis is

truec The variation of the data around the fitted model

Fig. 3 Response surfaces for estrogens using the central composite

face-centered design obtained by plotting of the extraction solvent

volume vs. pH

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123

based on a signal-to-noise ratio of three. LODs were

0.1 lg L-1 for E2 and 0.01 lg L-1 for E1. LR was in the

range of 1–500 lg L-1 for E2 and 0.1–100.0 lg L-1 for

E1. The coefficient of determination (r2) was 0.9618 and

0.9046. Relative standard deviations (RSD) were used to

determine the intra-day precision and inter-day precision of

the method. In this way, consecutive extraction of three

aqueous samples spiked at 100 lg L-1 (working standard

Table 4 Figures of merit of the proposed method for the analysis of

estrogens

E2 E1

LOD (lg L-1) 0.1 0.01

Linear range (lg L-1) 1–500 0.1–100

r2 0.9618 0.9046

EF ± SDa 42.2 ± 0.5 46.4 ± 6.4

ER ± SDb 80.4 ± 3.2 86.7 ± 6.3

RSD (%) (intra-day, n = 3) 3.9 7.2

RSD (%) (inter-day, n = 3) 9.7 8.3

a Mean enrichment factor ± standard deviation (n = 3)b Mean extraction recovery ± standard deviation (n = 3)

Table 5 Comparison of AA-LLME (LC-UV) with other methods for

extraction and determination of estrogens

Methods Compound DOL

(lg L-1)

LR

(lg L-1)

Reference

SPE (LC–UV) E2 0.0781 0.1–300 [10]

E1 0.0162 0.1–400

SBSE

(LC-UV)

E2 1.0 0.005–0.050 [13]

E1 1.0 0.005–0.050

CPE (LC-UV) E2 0.32 1–90 [14]

E1 0.25 1–192

DLLME-SFO

(LC-UV)

E2 0.8–1.4 5–1,000 [15]

E1 1.0–1.6 5–1,000

DLLME

(LC-UV)

E2 0.01 0.03–500 [22]

E1 0.01 0.03–500

DLLME

(LC-FLD)

E2 0.002 0.01–0.3 [23]

AA-DLLME

(LC-UV)

E2 0.1 1.0–500 The present

methodE1 0.01 0.1–100

DLLME-SFO dispersive liquid–liquid microextraction with solidifi-

cation of floating organic drop, FLD fluorescence detection

Table 6 Analytical results of estrogens determination in water

samples

Samples Compound Found

(lg L-1)

Added

(lg L-1)

RR

(%)

RSD (%)

(n = 3)

Siahrood E2 ND 100 60.3 7.6

E1 118 100 97.6 2.6

Telar E2 ND 100 78.4 6.7

E1 99 100 92.5 4.8

ND not detected

Fig. 4 HPLC (UV/Vis) chromatograms of estrogens in Siahrood

river water using AA-DLLME method under optimum microextrac-

tion condition, mobile phase: acetonitrile, water (50:50, v/v) with a

flow rate of 1 ml min-1, monitored at 280 nm a before spiking and

b after spiking with 100 lg L-1

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123

solution) was performed in a day and three continual days

to evaluate the intra-day and inter-day precision of the

estrogens recovery. Results are shown in Table 4.

A comparison of the present method with other

approaches reported in the literature for speciation of

estrogens in water samples is given in Table 5. In com-

parison with other methods, in the present method, 1-oct-

anol (as extraction solvent) and ethanol (as disperser

solvent) used were safer than the solvents those used in the

other methods [10, 13–15, 22, 23]. Furthermore, AA-

DLLME has compatible figures of merit. The method

developed in this work is proposed as a proper alternative

to more expensive instrument for estrogens determination

at trace levels. This methodology is a reproducible, simple

and low cost technique and with no requirement for further

instrumentation.

Real water sample analysis

Environmental water samples from Telar river water

(Ghaemshahr, Iran) and Siahrood river water (Ghaemshahr,

Iran) were used for recovery studies indicating the spiking

level. The relative recoveries and RSD % of the method

were in the range of 60.3–97.6 % and 2.6–7.6, respectively

(Table 6). The chromatograms of Siahrood river water

(a) and the spiked ( lgL-1) Siahrood river water (b) are

shown in Fig. 4a, b (k = 280 nm).

Conclusion

In the present study, the AA-DLLME technique followed

by HPLC–UV was applied for extraction and determination

of E1 and E2 in aqueous solutions. This new extraction

mode of DLLME with alcohols as extraction and disperser

solvents reduces the exposure danger to the toxic solvent

used in the conventional extraction procedure. According

to the Plackett–Burman design results, the important

parameter was determined. To obtain the optimum oper-

ating conditions that yield maximum efficiency, response

surface methodology (RSM) was used. Based on the

obtained result, intermediate-high value of 1-octanol vol-

ume and intermediate-low value of pH yielded the highest

recovery. The developed procedure is fast, simple and

effective for pretreatment of E1 and E2 in environmental

water samples with good linearity and repeatability.

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