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Analytica Chimica Acta 591 (2007) 69–79

Dispersive liquid–liquid microextraction followed by high-performanceliquid chromatography-diode array detection as an efficientand sensitive technique for determination of antioxidants

Mir Ali Farajzadeh a, Morteza Bahram a, Jan Ake Jonsson b,∗a Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran

b Department of Analytical Chemistry, University of Lund, P.O. Box 124, 221 00 Lund, Sweden

Received 24 January 2007; received in revised form 15 March 2007; accepted 16 March 2007Available online 25 March 2007

bstract

Dispersive liquid–liquid microextraction (DLLME) and high performance liquid chromatography-diode array detection (HPLC-DAD) wasresented for extraction and determination of Irganox 1010, Irganox 1076 and Irgafos 168 (antioxidants) in aqueous samples. Carbon tetrachloridet microliter volume level and acetonitrile were used as extraction and dispersive solvents, respectively. The main advantages of method areigh speed, high enrichment factor, high recovery, good repeatability and extraction solvent volume at �L level. Limit of detection for analytess between 3 and 7 ng mL−1. One variable at a time optimization and response surface modeling were used to obtain optimum conditions for

icroextraction procedure and nearly same experimental conditions were obtained using both optimization methods. Recoveries in the ranges8–86% and 84–110% were obtained by one variable at a time and response surface modeling, respectively. Using tap water and packed water asatrices do not show any detrimental effect on the extraction recoveries and enrichment factors of analytes.2007 Elsevier B.V. All rights reserved.

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eywords: Dispersive liquid–liquid extraction; Antioxidant; High-performancesponse surface modeling

. Introduction

Plastic additives such as antioxidants, stabilizers and plas-icizers have a major influence in the processing and shelfife of plastics and are responsible for many of the proper-ies of these materials [1]. Plastics additives, which are presentn small amounts in plastics (generally ranging from 0.1 to%) are dispersed in the polymer matrix and prevent effectsike thermo-oxidative deterioration, which initiates cleavage andross-linking of the macromolecular chains and, consequently,he deterioration of the polymer [2].

Plastic additives have relatively low molecular weights andigration mechanisms into foods are often of concern. The

uropean Commission has adopted the policy of using restric-

tions (mostly specific migration limits, SMLs) to control theafety of food contact materials and articles. There are several

∗ Corresponding author. Tel.: +46 46 222 8169; fax: +46 46 222 4544.E-mail address: jan ake.jonsson@analykem.lu.se (J.A. Jonsson).

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003-2670/$ – see front matter © 2007 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2007.03.040

id chromatography; Sample preparation; One variable at a time optimization;

undred SMLs in Directive 2002/72/EC [3] and amendments,hich have been assigned to plastic monomers and additives.small number of analytical methods have been validated foreasuring migration of substances; most of these only apply to

ood owing to the complexity of foods. The determination oflastic additives in food matrices is associated with two mainifficulties. The first is the low detection level required, as theseubstances are present in small amounts. The second one is theiversity of potential interferences present in foodstuffs [4]. Ineneral, extraction techniques such as extraction with solvents5,6] and solid phase extraction [6,7] are used to clean up and pre-oncentrate the additives. Traditional extraction techniques useonsiderable volumes of expensive and toxic organic solvents.ecently Assadi and coworkers reported a new liquid–liquidxtraction technique namely “dispersive liquid–liquid microex-raction” (DLLME) which uses microliter volumes of extraction

olvent along with a few milliliters of dispersive solvents such asethanol, acetonitrile, acetone or THF. They applied this techni-

ue for preconcentration of organophosphorus pesticides [8] andolycyclic aromatic hydrocarbons [9] from aqueous samples.

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0 M.A. Farajzadeh et al. / Analyt

In this work, we examined the applicability of partial facto-ials design at five levels for simultaneous optimization of nineactors affecting the DLLME-HPLC-DAD analyzing proceduref three polymer additives. The “one variable at a time” opti-ization of the same analyses had shown that some parameters

ave no importance or at least have low significance. Therefore,e could select only the main factors for study but in order to

echeck the significance of each variable in model, as well inrder to evaluate applicability of fractional factorial design alline variables were included in optimization. MLR was used touild the optimization model.

. Experimental

.1. Chemicals

Irganox 1076 (Scheme 1) was obtained from Sigma (St.ouis, MO, USA). Irganox 1010 and Irgafos 168 (Scheme 1)ere gifts from Petrochemical Research and Technologyompany (Tehran, Iran) and used as received without fur-

her purification. Other chemicals such as methanol (HPLCrade), carbon tetrachloride, toluene, acetonitrile, tetrahydrofu-an (THF), sodium chloride and sodium dihydrogen phosphateere purchased from Merck (Darmstadt, Germany). All reagentater used was purified with a Milli-Q system (Bedford, MA,SA).

.2. Standard solutions and real samples

Due to the limited solubility of Irgafos 168 in methanol,he polymer additives were initially dissolved in carbon tetra-hloride to prepare a stock solution (each 1000 �g mL−1). Thisolution is stable for at least 2 months at room temperature. Atandard solution of additives in methanol was prepared daily.or this purpose 200 �L stock solution was transferred to a1 mL glass vial and the solvent was evaporated in a water bath.he residue was dissolved in 10 mL HPLC grade methanol to

repare standard solution (each 20 �g mL−1). This solution wasnjected to the separation system each day (three times) for qual-ty control and the obtained peak areas were used in calculationf enrichment factors and recoveries. Working standard solution

Scheme 1. Chemical structure of the selected antioxidants.

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imica Acta 591 (2007) 69–79

as prepared by dilution of the above standard solution ineagent water (400 ng mL−1).

Tap water samples were collected fresh from our labora-ory. Packed waters were purchased from a local store (Lund,weden).

.3. HPLC-diode array detection (DAD) system

Separation and determination of Irganox 1010, Irganox076 and Irgafos 168 were carried out on an HPLC systemquipped with a photodiode array detector (Hewlett-Packarderies 1050) (Agilent, Wilmington, DE, USA) The instrumentas linked to an Hewlett-Packard ChemStation. All injectionsere performed manually with 26.5 �L sample loop. A Zor-ax Extend-C18 column (100 mm × 2.1 mm, 3.5 �m particleize) from Agilent was employed at room temperature. Pureethanol was used at a flow rate 0.5 mL min−1 as a mobile

hase. Detection was performed at λ = 210 nm.

.4. Dispersive liquid–liquid microextraction procedure

A 5.00 mL working standard solution (400 ng mL−1) waslaced in a 12-mL glass tube with conical bottom. Acetonitrile2 mL) as dispersive solvent, containing 40 �L carbon tetrachlo-ide as extraction solvent, was injected rapidly into the sampleolution by using a 5-mL syringe. The cloudy solution pro-uced was centrifuged for 5 min at 2000 rpm (Lab systems OyP 510-1, Helsinki, Finland). After centrifuging, the dispersedne droplets of carbon tetrachloride sedimented in the bottom of

est tube (about 30 �L). The sedimented phase was completelyransferred to another test tube with conical bottom using 100-L HPLC syringe and after evaporation of the solvent in a waterath, the residue was dissolved in 50 �L HPLC grade methanolnd injected into the separation system. All experiments wereerformed in duplicate and means of results were used in plottingf curves or in tables.

.5. Statistical software

The computer software EREGRESS, Essential Regressionnd Experimental Design for Chemists and Engineers [10–12],as used in this study.

. Results and discussion

In this study dispersive liquid–liquid microextraction com-ined with HPLC-DAD was used for preconcentration andetermination of the selected antioxidants in aqueous sam-les. To obtain a high recovery and enrichment factor, theffect of different factors such as type of dispersive and extrac-ion solvents their volumes, pH, salt addition, etc. were tested

sing both the one variable at a time and response surfaceodeling approaches. In order to study the mentioned fac-

ors, extraction recovery and enrichment factor have been used.qs. (1) and (2) were used for calculation of enrichment factor

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M.A. Farajzadeh et al. / Analyt

nd recovery:

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here EF, Csed and C0 are the enrichment factor, concentra-ion of analyte in sedimented phase and initial concentration ofnalyte in aqueous sample, respectively.

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here R, Vsed and Vaq are the extraction recovery, volume of sed-mented phase and volume of aqueous sample, respectively. Csedas calculated by comparing peak areas of concentrated solu-

ion with those of standard solution (20 �g mL−1) in methanolnjected to HPLC.

.1. One variable at a time optimization

In DLLME extraction, solvent(s) has to satisfy the followingariety of requirements: (1) its density should be higher thanater; (2) it should extract the analytes; (3) it should form a

loudy solution in the presence of a dispersive solvent whennjected to an aqueous solution (form very tiny droplets) andnally (4) it should show a good chromatographic behavior (it

hould not absorb radiation at the detection wavelength of ana-ytes and also it should not have elution strength higher than

obile phase used in the separation system). Among the solventsith density higher than water (mainly chlorinated solvents),

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ig. 1. Effect of dispersive solvent on the enrichment factor and recovery of antioxidmL; extraction solvent, 50 �L CCl4.

imica Acta 591 (2007) 69–79 71

ichloromethane, chloroform and carbon tetrachloride wereested. On the other hand, the selection of a dispersive solvent isimited to solvents such as methanol, acetonitrile, tetrahydrofu-an and acetone, that are miscible with both water and extractionolvents. In this study all combinations of CH2Cl2, CHCl3 andCl4 (50 �L) as extraction solvents and methanol, acetonitrile,

etrahydrofuran (THF) and acetone (1 mL) as dispersive sol-ents were tested. In the case of CH2Cl2 as extraction solvent, awo-phase system was not observed with any studied dispersiveolvents when they were injected to 5 mL analytes solution inater. In the case of CHCl3 only with THF as dispersive sol-ent, a two-phase system was achieved. It should be noted thatn this case the volume of sedimented phase was 260 �L, whichs a reason for obtaining low EFs. Also it seems that Irgafos 168ecomposes in the presence of THF and no peak was observedn the chromatogram in its retention time after extraction (seeelow). With CCl4 as extraction solvent, a two-phase systemas formed with all four dispersive solvents. In Fig. 1 EF and Rere plotted as a function of type of dispersive solvent. As cane seen, EF for all analytes is high using methanol comparedo other solvents. In order to obtain relatively higher EF and Re have to also consider the volume of sedimented phase (20,7, 367 and 42 �L for methanol, acetonitrile, THF and acetone,espectively). By applying these volumes and obtaining R for the

nalytes, it can be concluded that acetonitrile has an advantagever the other solvents. For this reason carbon tetrachloride andcetonitrile were chosen as extraction and dispersive solvents,espectively, in the following studies.

ants. Extraction conditions: sample volume, 5 mL; dispersive solvent volume,

72 M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79

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ig. 2. Effect of dispersive solvent volume on the enrichment factor ofntioxidants. Extraction conditions: sample volume, 5 mL; dispersive solvent,cetonitrile; extraction solvent, 50 �L CCl4.

.1.1. Selection of dispersive solvent (acetonitrile) volumeIn order to study the effect of acetonitrile volume, the vol-

me was varied in the range 0–3 mL in 0.5-mL intervals. Thebtained results are shown in Fig. 2 as EF. By increasing theolume of acetonitrile EF and R increased till 1.5 mL. At higherolumes, EF remained constant whereas R was decreased. Itas observed that by increasing the volume of dispersive sol-ent, the sedimented phase volume decreased (15 �L in the casef 3 mL) and consequently recovery also decreased. From thebtained results 1.5 mL was chosen as an optimum volume forhe dispersive solvent.

.1.2. Selection of extraction solvent (carbon tetrachloride)olume

In order to study the effect of extraction solvent volume onhe performance of the presented DLLME procedure, differentolumes of carbon tetrachloride (20–100 �L at 10-�L inter-als) and a constant volume of dispersive solvent (acetonitrile,.5 mL) were tested. With less than 30 �L carbon tetrachloride

o two-phase system was observed. Figs. 3–5 show the variationf sedimented phase volume, enrichment factor and recoveryersus volume of extraction solvent, respectively. According toig. 3, by increasing the volume of extraction solvent from 30

ig. 3. Volume of sedimented phase vs. extraction solvent volume. Extrac-ion conditions: sample volume, 5 mL; dispersive solvent, 1.5 mL acetonitrile;xtraction solvent, CCl4.

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ig. 4. Effect of the volume of extraction solvent (CCl4) on the enrichmentactor of antioxidants. Extraction conditions: sample volume, 5 mL; dispersiveolvent, 1.5 mL acetonitrile.

o 100 �L, the volume of sedimented phase increases from 6 to5 �L. Also the recovery of analytes increased. But due to thencreasing sedimented phase volume at higher extraction solventolumes, the enrichment factor decreased in those volumes. Theolume of extraction solvent should be selected so that high EFnd R are obtained. In the range of 50–70 �L recoveries of ana-ytes are high and EFs are acceptable. In the following studies0 �L was selected as the optimal volume of extraction solvent.

To reduce the use of chlorinated solvent (CCl4) and study ofossibility of using less toxic solvents and with considerationf the fact that most organic solvents have density lower thanater, mixtures of toluene and carbon tetrachloride were tested

s extraction solvent. The fraction of toluene in the mixture wasseful if less than 70% because those mixtures have a densityigher than water and the extraction solvent is collected in theottom of extraction vessel after centrifuging. In Figs. 6 and 7,nrichment factor and volume of sedimented phase are plotteds a function of toluene fraction, respectively. From the resultsn Fig. 6 it can be seen that EF for Irganox 1010 and Irganox076 is the same in the presence and absence of toluene. In the

ase of Irgafos 168, it is constant till about 30% toluene and itsF decreases at higher fraction of toluene in extraction solvent.owever, the obtained results show that mixtures of other sol-ents with chlorinated solvents can be used for this purpose. The

ig. 5. Effect of the volume of extraction solvent (CCl4) on the recovery ofntioxidants. Extraction conditions: sample volume, 5 mL; dispersive solvent,.5 mL acetonitrile.

M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79 73

Fig. 6. Influence of toluene volume fraction on the enrichment factor of selecteda5d

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ntioxidants obtained from DLLME. Extraction conditions: sample volume,mL; extraction solvent, 70 �L mixture of carbon tetrachloride and toluene;ispersive solvent, 1.5 mL acetonitrile.

esults show that till about 60% toluene the recovery of analytess not altered or even slightly improved. The decrease of recov-ry at 70% toluene is caused by a sharp decrease of the volumef sedimented phase. It should be noted that after extraction, therganic phase is in this case divided into two portions, of whichhe largest is located on the top of the aqueous phase (due toensity near to 1). Furthermore it should be mentioned that with0% toluene all organic phase was collected on the top of thequeous phase and DLLME became useless.

.1.3. Study of sample volumeFor this purpose 2.5, 5.0, 7.5 and 10.0 mL analyte solu-

ions (containing 400 �g mL−1 of each analyte) were selected asample size and the DLLME procedure using acetonitrile as dis-ersive solvent (0.75, 1.50, 2.25 and 3.00 mL, respectively) andarbon tetrachloride as extraction solvent was performed. The

esults (Fig. 8) show that by increasing sample volume the EFor analytes was also increased. This is evident from the fact thaty holding sample volume/dispersive solvent volume ratio at aonstant value, the volume of sedimented phase also remains

ig. 7. Sedimented phase volume vs. toluene percent. Extraction conditions:ample volume, 5 mL; extraction solvent, 70 �L mixture of carbon tetrachloridend toluene; dispersive solvent, 1.5 mL acetonitrile.

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ig. 8. Effect of sample volume on the enrichment factor of antioxidants. Extrac-ion conditions: dispersive solvent, 1.5 mL acetonitrile; extraction solvent, 70 �Larbon tetrachloride.

lmost constant. Therefore, by increasing sample volume anderforming DLLME the concentration of analytes in the sedi-ented phase and consequently, the EFs are also increased. Thus

ncreasing sample volume is ineffective on the recovery of theelected antioxidants.

.1.4. Other parametersThe effect of extraction time, centrifuging time, pH and ionic

trength on the DLLME of the analytes were also investigated.rom these parameters only ionic strength at levels of 0.05–0.2 godium chloride added to 5 mL sample solution had a significantnfluence on the EF and recovery of analytes. By increasingodium chloride in the range 0.05–0.5 g, the volume of theedimented phase was gradually increased from 60 to 160 �L.xtraction times of 0–10 min were tested by waiting after addingolvents and before centrifuging. This had no influence andndicated that extraction in DLLME is very fast.

.1.5. Selection of methanol volume in final step forissolution of residue

After extraction the organic phase is transferred to a test tubewith conical bottom) and dried in a water bath. The residues dissolved in mobile phase (methanol) and injected to HPLC.y this procedure a solvent exchange is performed. It shoulde noted that carbon tetrachloride as a sample solvent has noood chromatographic behavior in this study and causes peakroadening and high background signal. In order to optimizehe volume of methanol used in the dissolution of analytes 30,0, 100 and 200 �L methanol were selected. Fig. 9 shows theeak areas of analytes versus methanol volume. In plotting thisgure it is supposed that in the case of 200 �L, all analytesere dissolved and the loss of solvent in dissolving step and

ransferring of analytes solution from test tube to the HPLCystem is negligible. Therefore, practical and theoretical peakreas for 200 �L are considered the same. For other volumes,he theoretical values of peak areas were calculated from theolumes ratios (for example in the case of 100 �L methanolhe theoretical value is calculated as follows: (200/100) × peak

reas for 200 �L). It can be seen that 30 �L is no suitable volumeor methanol, because practical and theoretical peak areas areifferent due to solubility of analytes and evaporation problems.rom the reminding volumes tested, 50 �L was selected and

74 M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79

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ig. 9. Effect of methanol volume on the peak areas of analytes. Theoreticaleak areas were calculated by supposing equality of practical and theoreticaleak areas in 200 �L methanol.

sed in the study of analytical performance of the method inrder to obtain high EF and low limit of detection.

.1.6. Study of dispersive solvent role on DLLMETo evaluate the dispersive solvent (in this study acetonitrile)

ole in DLLME, three experiments were performed: (1) extrac-ion was performed in the absence of acetonitrile with 70 �Larbon tetrachloride, (2) extraction was performed by 70 �Larbon tetrachloride after adding 1.5 mL acetonitrile to samplend finally (3) extraction was performed by the method used inhis study with 70 �L carbon tetrachloride dissolved in 1.5 mLcetonitrile. In experiments (1) and (2) the extraction vessels (12-L test tube with conic bottom) were closed with stopper and

haken at 200 rpm for 30 min. Fig. 10 shows the obtained results.dding acetonitrile to the extraction system improved the EF and

ecovery for all analytes, but DLLME proper (experiment 3) isore efficient compared with the case in which acetonitrile and

arbon tetrachloride were added in separate steps. It was notedhat the volume of sedimented phase in experiments (1) and (2)ere the same (≈40 �L) and in the case of experiment (3) itas about 60 �L. It seems that with DLLME the solubility of

xtraction solvent in aqueous sample was decreased or its lossas less in comparison with experiments (1) and (2). However,

he detailed explanation of the higher recovery and especiallyF in the DLLME experiment against the results obtained inxperiments (1) and (2) is difficult.

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able 1he variables and values used for partial factorial design

Variable name Coded factor levels

−2 (low) −1 Dispersion solvent (mL) 02 CCl4 (�L) 303 Toluene (�L) 04 Sample volume (mL) 2.55 Centrifuge time (min) 16 Centrifuge speed (rpm) 1000 157 Ionic strength (g NaCl) 0.008 Extraction time (min) 09 pH 2

ig. 10. EF obtained for selected additives in different procedures. Extractiononditions: sample volume, 5 mL; extraction solvent, 70 �L carbon tetrachlo-ide.

.1.7. Experimental design and response surface modelingNine parameters (factors) were used to optimize the DLLME

rocedure using experimental design. The variables and val-es of each level are shown in Table 1. Also Table 2 showshe experiment characteristics that have been used for modelonstruction. The response measured from these experimentsncluded recovery, sedimented phase volume (�L) and enrich-

ent factor (Table 3). Being the most important response, the recoveries were

hosen for building the model. It must be mentioned that the twoemaining response variables cannot be regressed to acquire aeaningful model (e.g. there is no direct dependency betweenost of factors and sedimented phase volume or enrichment

actor). Often, it is not known how many factors of a given set areeally significant contributors to a response. Also, there might benteractions and/or higher order effects to consider. Starting outy including all the possible regressors into the model equationight be a first choice. Usually, the model shows a very good fit

s judged by the R2 and mean squares error (M.S.E.). But thereould be a risk for over-fitting. Also, some of the regressionoefficients might be characterized by low significance [10].

In order to find the important factors and build a model toptimize the procedure, we start with a quadratic model without

e modeled, were checked using the function “Fit All” in theREGRESS software. The best R2 (model fitness) was obtained

or the model containing factors 1, 2, 4 and 9, representing the

1 0 +1 +2 (high)

1 1.5 2 340 50 70 10010 20 40 50

5 7.5 9 102 5 7 10

00 2000 2500 30000.05 0.10 0.30 0.501 2 5 104.5 5 7 10

M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79 75

Table 2List of experiments in the fractional factorial design for model optimization

Experiments Factor ID

X1 X2 X3 X4 X5 X6 X7 X8 X9

1 1.5 50 20 7.5 5 2000 0.1 2 72 1.5 30 0 10 2 3000 0.1 1 53 0 30 50 5 10 2000 0.05 1 104 0 100 10 10 5 1500 0.05 5 05 3 40 2 7.5 2 1500 0.3 10 106 1 100 20 5 2 2500 0.5 5 77 3 50 10 5 7 3000 0.3 2 08 1.5 40 10 9 10 2500 0.1 10 09 1 40 40 10 7 2000 0.5 10 2

10 1 70 50 9 5 3000 0.5 0 1011 1 100 40 7.5 10 3000 0 5 212 3 70 20 10 10 1000 0.3 0 713 2 50 50 10 1 2500 0 2 1014 1.5 100 50 2.5 7 1000 0.1 5 1015 3 100 0 9 1 2000 0.3 5 516 3 30 40 2.5 5 2500 0.3 1 217 0 70 0 7.5 7 2500 0.05 0 518 2 30 20 9 7 1500 0 1 719 0 50 40 9 2 1000 0.05 2 220 1.5 70 40 5 1 1500 0.1 0 221 2 70 10 2.5 2 2000 0 0 022 2 40 0 5 5 1000 0 10 523 1 30 10 7.5 1 1000 0.5 1 024 0 40 20 2.5 1 3000 0.05 10 725 1 50 0 2.5 10 1500 0.5 2 5

Table 3List of responses for the presented experiments in Table 2

Experiments Irganox 1010 Irganox 1076 Irgafos 168

Sedimented phasevolume (�L)

EFa Rb Sedimented phasevolume (�L)

EFa Rb Sedimented phasevolume (�L)

EFa Rb

1 51 72.2 49.1 51 103 70.0 51 35.0 23.82 12 239 28.7 12 273 32.8 12 211 25.33 50 1.2 10.2 50 18.9 18.9 50 9.07 9.074 52 4.68 2.90 52 11.4 7.05 52 4.76 2.955 0 0 0 0 0 0 0 0 06 110 13.7 30.2 110 38.3 84.3 110 13.4 29.57 0 0 0 0 0 0 0 0 08 31 88.0 30.3 31 134 46.2 31 85.4 29.49 60 61.1 36.7 60 66.1 39.6 60 28.7 17.2

10 112 31.6 39.6 112 39.9 49.7 112 22.8 28.411 142 36.3 68.7 142 50.2 95.0 142 26.7 50.612 37 114 42.1 37 132 49.0 37 88.7 32.813 80 65.1 52.1 80 75.2 60.2 80 45.4 36.314 0 0 0 0 0 0 0 0 015 103 77.8 89.0 103 80.1 91.7 103 54.7 62.616 0 0 0 0 0 0 0 0 017 65 12.5 10.8 65 14.3 12.4 65 11.0 9.5118 40 79.4 35.3 40 97.4 43.3 40 73.4 32.619 50 5.18 2.88 50 10.8 5.99 50 6.06 3.3720 115 31.8 73.3 115 39.6 91.0 115 20.0 46.021 0 0 0 0 0 0 0 0 022 30 169 101 30 175 105 30 138 82.923 28 87.0 32.5 28 110 41.0 28 80.7 30.124 55 14.3 31.4 55 13.5 29.7 55 7.40 16.325 0 0 0 0 0 0 0 0 0

a Enrichment factor.b Recovery.

7 ica Chimica Acta 591 (2007) 69–79

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actors having the most important effect on the response (recov-ry) for all three additives. Instead of (as usual) making a new setf experiments to simultaneously optimize the significant vari-bles, we tried to build a meaningful model as much as possiblesing the original data.

In order to properly access a quadratic term, a minimum ofhree levels of each factor is required. Because we had used

ore than three levels for each variable subject to optimizationTable 1), response surface modeling (RSM) might be suitableo find the optimum (or optima). Therefore by including interac-ion parameters between factors we tried to obtain a good fitted

odel.Using the Essential Regression and Experimental Design

oftware [11], repeated forward and backward stepwise regres-ion was employed to generate the fitting equations for eachdditive at 95% confidence level. When using standardized data,he size of the coefficients signifies how well the coefficients R2

djusted and R2 predicted can be used to evaluate model fitting.n order to prevent over-fitting, five randomly designed sampleshat have not been used in model construction were consequentlyvaluated with the model coefficients. The main characteristicsf the constructed model are presented in Table 4.

The plots of predicted response versus calculated (Fig. 11)how that the residual values are significantly low. This allows uso further use the response surface as a predictive tool to obtainesponses over the whole parameter uncertainty range.

From the coefficients (Table 4) and their values (not shown)he following results could be concluded: Factors 1, 2, 4 and 9ave significant effect on the response and the reminding factorsave low significance and factors 6 and 7 have no importancefor Irgafos 168 factor 7 a little negative effect was observed).he most important result is that there are only two significant

nteractions; between factors 1–4 and factors 2–4 (Table 4). Alson the case of Irgafos 168 there is a small interaction betweenactors 2–3.

.1.8. Selection of optimized conditionsThe selection of optimized method conditions is possible

rom experimental data set. In Table 2, each experiment set cor-esponds to a set of method conditions. In Table 3 each set of

esponses corresponds to a specific experiment set. Therefore,he optimized method conditions can be chosen based on theesponses that meet the requirements. For example if the crite-ia are set as follows: recovery near to 100%, sediment phase

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dditive R2 Standard error Durbin-Watson d Equations th

rganox 1010 0.906 12 1.36 Response (%+ b5*Factor1b9*Factor4*

rganox 1076 0.867 13 2.20 Response (%+ b5*Fac9*Fb10*Fac2*Fa

rgafos 176 0.950 8.4 1.97 Response (%b6*Fac4*Facb10*Fac1*Fa

ig. 11. Plot of predicted response vs. the calculated response for Irganox 1010a), Irganox 1076 (b) and Irgafos 168 (c).

olume between 30 and 50 �L (due to using HPLC-DAD alongith 26.5 �L injection loop for separation and determination of

nalytes) and enrichment factor as high as possible, the condi-ions that meet these requirements are achieved in experimentumber 22. Therefore these conditions can be selected for furthertudy.

Method conditions can also be optimized using theesponse surface, based on the optimized equation found usingREGRESS software.

Using the obtained equations one will be able to find opti-ize condition(s) by response surface modeling. For example

igs. 12 and 13 show the interaction between factors 1, 2 andfor Irganox 1010 and Irganox 1076. Also Fig. 14 presents the

esponse surface obtained for Irgafos 168. These figures showhe interaction between the mentioned factors (see figures) when

at were achieved using EREGRESS for optimization

) = b0 + b1*Factor2 + b2*Factor3 + b3*Factor5 + b4*Factor1*Factor1*Factor4 + b6*Factor2*Factor2 + b7*Factor2*Factor4 + b8*Factor3*Factor3 +Factor4 + b10*Factor5*Factor5 + b11*Factor8*Factor8 + b12*Factor9*Factor9) = b0 + b1*Fac1*Fac4 + b2*Fac1*Fac1 + b3*Fac4*Fac4 + b4*Fac4ac9 + b6*Fac9 + b7*Fac3*Fac3 + b8*Fac5*Fac5 + b9*Fac2*Fac4 +c2 + b11*Fac8*Fac8) = b0 + b1*Fac3 + b2*Fac5 + b3*Fac9 + b4*Fac1*Fac4 + b5*Fac2*Fac4 +4 + b7*Fac5*Fac5 + b8*Fac9*Fac9 + b9*Fac2*Fac3 +c1 + b11*Fac7*Fac7 + b12*Fac2*Fac2 + b13*Fac1 + b14*Fac8*Fac8

M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79 77

tccetEbtv

3b

ob

amfaatotma

TO

XXXXXXXXX

Fig. 12. Response surface of factors 2–4 and 1–4 for Irganox 1010.

he remaining factors have been kept on the fixed ones using theonstructed model by EREGRESS software. Finally, optimumonditions can be selected from the obtained model for furtherxaminations (Table 5). The EREGRESS software has poten-ial to find optimum responses using solver function available inxcel® software [12]. In Table 5, optimum values obtained byoth methods (response surface modeling and one variable at aime methods) are shown. These data indicate that the optimumalues obtained by both methods are in very good agreement.

.2. Performing DLLME at optimum conditions obtainedy two optimizing methods and comparison of their results

DLLME was performed on the selected additives under theptimum conditions obtained by response surface modeling andy one variable at a time method and the results expressed as EF

tdao

able 5ptimum conditions obtained from response surface modeling and one variable at a t

Variable Name Optimum values obtaine

Response surface model

1 Dispersive solvent (mL) 1.3–2 mL depending on2 CCl4 (�L) >30 �L depending on fa3 Toluene (�L) 0 �L4 Sample volume (mL) 4–8 mL regards to factor5 Centrifuge time (min) Effectless6 Centrifuge rate (rpm) Effectless7 Ionic strength (g NaCl) 0 g (small negative effec8 Extraction time (min) Very low effect9 pH 4–6 (low effect)

Fig. 13. Response surface of factors 2–4 and 1–4 for Irganox 1076.

nd recovery are summarized in Table 6. With response surfaceodeling the conditions of experiment 22 (Table 2) were used

or further study. With the one variable at a time method 1.5 mLcetonitrile as dispersive solvent, 70 �L carbon tetrachlorides extraction solvent, 5 mL sample solution, 2000 rpm for cen-rifuge speed, 5 min for centrifuge time, 0.2 g NaCl as saltingut agent and pH = 4.5 were used. The data in Table 6 show thathe obtained results for DLLME in the case of response surface

odeling are better than those of one variable at a time methodnd higher EFs and recoveries are obtained. It should be noted

hat the relatively lower recovery for Irgafos 168 is due to its oxi-ation [13]. The presence of an additional peak in retention timebout 11 min in chromatogram is a good indication of oxidationf Irgafos 168.

ime methods

d by

ing One variable at a time method

factor 4 volume 1.5 mLctor 4 volume 50–70 �L

Effectless1 and 2 volume 5 mL

EffectlessEffectless

t for Irgafos 168 was observed) 0.05–0.1 g (low effect)EffectlessWithout buffer

78 M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79

3

ma

Table 6Comparison of results obtained for MLLDE of selected additives by responsesurface modeling and one variable at a time optimization

Compound One variable at a time Response surface modeling

EF ± S.D.a R ± S.D.b EF ± S.D. R ± S.D.

Irganox 1010 64.0 ± 6.63 82.8 ± 8.49 201 ± 16 100.5 ± 8.44Irganox 1076 74.0 ± 7.68 86.3 ± 9.98 220 ± 13 110 ± 5.77I

2fo

3

tcitptTsg1tip4a

3t

TQ

A

I

I

I

Fig. 14. Response surface of factors 2–4, 1–4 and 2–3 for Irgafos 168.

.3. Analytical performance

Analytical characteristics were obtained under the opti-um conditions obtained by response surface modeling and

re given in Table 7. Linearity was observed over the range

amhu

able 7uantitative results of DLLME-HPLC-DAD of selected antioxidants

nalyte LRa (ng mL−1) rb

rganox 1010 20–100 0.991100–1600 0.993

rganox 1076 20–100 0.987100–1600 0.992

rgafos 168 20–100 0.981100–1600 0.997

a LR, linear range.b r, correlation coefficient.c LOD, limit of detection for S/N = 3.d LOQ, limit of quantification (ng mL−1).e RSD, relative standard deviation (C = 400 ng mL−1, n = 6).

rgafos 168 60.3 ± 6.27 78.4 ± 8.15 168 ± 7 84.1 ± 3.53

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

0–1600 ng mL−1. Limits of detections are 3, 7 and 5 ng mL−1

or Irganox 1010, Irganox 1076 and Irgafos 168, respectively,n the basis of three times of signal-to-noise ratio.

.4. Real water samples

Different mineral water samples packed in polymeric con-ainers were tested for possible presence of the studied additivesontent (migrated from container) and none of them was detectedn these samples. To test the applicability and accuracy ofhe proposed method in real samples analysis, tap water andacked water were selected as matrix and analytes were addedo them in three levels and the DLLME method was performed.he obtained results were compared with those obtained frompiked distilled water. The relative recoveries of analytes areiven in Table 8. The obtained relative recoveries are between00 and 111%, which indicates that matrix does not influencehe proposed DLLME method for preconcentration of antiox-dants from water samples. Chromatograms of packed waterrior to and after spiking of additives at the concentration level00 ng mL−1 of each analytes along with concentrated solutionre shown in Fig. 15.

.5. Comparison of DLLME with other sample preparationechniques

DLLME has a short extraction time, higher enrichment factor

nd quantitative recovery, and lower solvent consumption. Theainly competing method (traditional liquid–liquid extraction)

as lower enrichment factor and higher solvent consumption. Itses about 10 mL or more solvent and EF for analytes is about 10

LODc (ng mL−1) LOQd R.S.D. (%)e

3 10 1.98

7 20 5.90

5 15 5.27

M.A. Farajzadeh et al. / Analytica Chimica Acta 591 (2007) 69–79 79

Table 8Relative recovery in tap water and packed water relative to distilled water

Compound Concentration (�g L−1) Tap water Packed water

Relative recovery R ± S.D.a Relative recovery R ± S.D.a

Irganox 1010400 121 108 ± 11.4 107 104 ± 4.28600 105 105800 98.9 98.8

Irganox 1076400 122 111 ± 9.54 115 103 ± 9.99600 105 97.8800 106 97.6

Irgafos 168400 123 110 ± 11.8 105 100 ± 4.53600 107800 100

a Mean recovery ± standard deviation.

Fig. 15. HPLC chromatogram of packed water (A); packed water spiked withselected antioxidants at concentration level 400 �g mL−1 of each before per-fss

oiAhc

4

pamr

rmmrtw

R

[

[

orming DLLME (B) and after performing DLLME (C). Extraction conditions:ample volume, 5 mL; extraction solvent, 40 �L carbon tetrachloride; dispersiveolvent, 2 mL acetonitrile.

r less in most cases, whereas DLLME uses extraction solventn the microliter range with higher EF (about 200 in this study).lso with solid phase extraction (SPE) it is possible to obtainigher EF such as the presented technique, but it is very time-onsuming in comparison with DLLME.

. Conclusion

A dispersive liquid–liquid microextraction procedure was

resented for concentration of the selected antioxidants fromqueous samples. HPLC-DAD was used in separation and deter-ination of analytes. The DLLME method is very efficient,

apid and repeatable. Enrichment factors about 200 times and

[

[

96.199.1

ecovery nearly 100% were attainable in this study. Two opti-ization approaches (one variable at a time and response surfaceodeling) were compared and the best results were found with

esponse surface modeling. No matrix effect was observed whenhe proposed DLLME technique was applied to tap and packedater samples spiked with the analytes.

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