Anal. Chem. 1999, 71, 2417-2422
A Systematic Approach To Optimize Solid-Phase Microextraction. Determination of Pesticides in Ethanol/Water Mixtures Used as Food Simulants R. Batlle, C. Sa´nchez, and C. Nerı´n*
Department of Analytical Chemistry, Centro Polite´ cnico Superior, Universidad de Zaragoza, C/Marı´a de Luna, 3, E-50015 Zaragoza, Spain
The optimization of solid-phase microextraction (SPME) of several organochlorine and organophosphorus pesticides is presented, and the influence of variables is discussed. The optimized method is applied to several selected ethanol/water mixtures used as food simulants, and the influence of the ethanol content on the SPME performance is also discussed. Detection limits ranging from 0.02 to 0.04 ng/g for water simulant and from 38.7 to 205.5 ng/g for 95% ethanol simulant were obtained. The relative standard deviation (% RSD) was 98%), chlorpyrifos, procymidone, malathion, endosulfan-beta, tolclofos-methyl, vinclozolin, 4,4′dichlorobenzophenone, bromopropylate, and tetradifon, were obtained from Dr. S. Ehrenstorfer (Augsburg, Germany). o,p-DDE was Certified Reference Material (Terdington, U.K.) and chlorobenzilate was supplied by Rie¨del de Haen (Seelze, Germany). Lindane (γ-hexachlorocyclohexane), used as the SPME internal standard, was also supplied by Rie¨del de Haen and 2,4,5,2′,3′,4′hexachlorobiphenyl (PCB 138) used as the LLE internal standard was from ChemService (West Chester, PA). All the solvents used were from Merck (Darmstadt, Germany) and were Suprapur Quality for gas chromatography. Sodium chloride, ammonium chloride, and ammonium sulfate were also from Merck (pro-analysis quality). Sodium hydrogen phosphate (>99% purity) was supplied by Probus (Barcelona, Spain). Agricultural recycled film used in the migration study was supplied by the Empresa de Gestio´n Mediambiental, EGMASA (Sevilla, Spain). The postconsumer material is mainly low-density polyethylene (LDPE, ∼90% w/w) and ethylene-vinyl acetate copolymer (EVA, ∼10% w/w). Sample Preparation. 0.1 g of a methanol standard solution containing ∼20 µg/g of all the pesticides under study was added to 13 g of the simulants to give a final concentration of ∼250 ng/g for the optimization procedure. The pesticides were selected 2418 Analytical Chemistry, Vol. 71, No. 13, July 1, 1999
according to those detected in the postconsumer plastic material.16 For the calibration experiments, the appropriate amount of the standard solution was added plus 0.07 g of lindane selected as the internal standard (final concentration, ∼300 ng/g). For the migration-tests analysis, 13 g of simulant were analyzed plus the same amount of internal standard. In the kinetic study, 0.1 g of an ethanol standard solution of pesticides containing about 13 µg/g of each pesticide was added to 13 g of the simulants to obtain a final concentration of ∼100 ng/g of each. The appropriate amount of internal standard, lindane, was added to give a final concentration of ∼150 ng/g. For the 95% ethanol simulant, 0.25 g of the standard solution and 0.09 g of lindane solution were added, because of its detection limits in this case. The sorption times used in these experiments were 2, 5, 10, 20, 40, and 80 min. The rest of the SPME conditions were fixed at the optimum values found. If necessary, the salt amount was added immediately after sample preparation and allowed to dissolve and homogenize for at least 5 min with a magnetic stirrer. Solid-Phase Microextraction Optimization. As was previously mentioned, most SPME developers optimize extraction methods one parameter at a time3. This sequential univariate strategy is time-consuming and rarely known to be effective for determining a true or operational optimum within a reasonable period of time. An optimization method which, by carrying out 16 randomized experiences allows work with six experimental variables (four quantitative and two qualitative)17 was applied in this paper. The method is a composite design, made by combination of a 2n factorial design for quantitative variables optimization superposed on Latin squares for qualitative optimization. The factors and the values selected are shown in Table 1 and Table 2 shows the experimental matrix generated, for water analysis optimization. A different matrix, but identical in form, was used to optimize the analysis in each simulant. As can be seen in Table 1, four quantitative variables were selected, according to scientific literature and previous experiments. The effect of pH was not taken into account, according to scientific-literature results.6 However, the use of different salts provides basic and acidic conditions, which did not significantly affect the extraction of pesticides by the fiber. Qualitative variables, so-called because a continuous numeric variation is not possible for them, are also listed in Table 1. The influence of the variables has been described in detail6,18,19 and will not be examined here, but a short discussion on them will be presented in Results and Discussion. Matrix evaluation was carried out by comparison of each single variable with the mean of the whole set of experimental results, noted as 100%. Obviously, higher results indicate a positive influence of the factor, whereas lower values show a negative tendency. The optimum conditions obtained are listed in Table 3. (16) Nerı´n, C.; Batlle, R.; Cacho, J. Proceedings of International Symposium of Analytical Methodology in the Environmental Field, Bilbao, Spain, 1996. (17) Akhnazarova, S.; Kafarov, V. Experiment Optimization in Chemistry and Chemical Engineering; Mir, Moscow, 1982. (18) Arthur, C. L.; Potter, D. W.; Buchholz, K. D.; Motlagh, S.; Pawliszyn, J. LCGC 1992, 10, 656-668. (19) Zhang, Z.; Yang, M. J.; Pawliszyn, J. Anal. Chem. 1994, 66, 844A-853A.
Table 1. Factors, Levels, and Codification in the Experimental Design levels variable
high (+)
low (-)
qualitative factors I. sorption tempa II. desorption tempb III. sorption time IV. salt concn
60 °C max temp - 10 °C 20 min 7.5% w/w
room temp min temp + 10 °C 5 min 0% w/w
quantitative factors V. salt identity
1. Na2HPO4 2. NH4Cl 3. NaCl 4. (NH4)2SO4 A. 65-µm CW/CVB B. 7-µm PDMS C. 100-µm PDMS D. 85-µm PA
VI. fiber identity
a Room temperature was maintained in the experiments between 17 and 20 °C. b Maximum and minimum temperatures were selected according to manufacturer’s specifications (Supelco).
Table 2. Design Matrix Generateda run
I
II (°C)
III (min)
IV (%)
V
VI
1 (2) 2 (10) 3 (16) 4 (5) 5 (12) 6 (9) 7 (13) 8 (7) 9 (1) 10 (15) 11 (3) 12 (11) 13 (8) 14 (4) 15 (14) 16 (6)
room 50 room room room 50 50 room room room 50 room 50 50 50 50
210 230 210 300 230 210 300 260 210 300 210 310 300 210 310 250
5 5 5 5 20 5 5 5 20 20 20 20 20 20 5 20
0 0 10 0 0 10 0 10 10 0 0 10 0 10 10 10
1 2 3 4 4 3 2 1 2 1 4 3 3 4 2 1
A B C D B A D C C D A B D C B A
a Numbers in parentheses correspond to randomized order of experiments.
Table 3. Optimum SPME Conditions Found for Simulant Analysis (Spiking level: 250 ng/g)a simulant
I
II
III
IV
V
VI
distilled water 15% (v/v) EtOH 30% (v/v) EtOH 50% (v/v) EtOH 95% (v/v) EtOH
+ +
+ + + + +
+ + + + +
+ + + -
4 4 4 no salt no salt
C C C D D
a
For identification of levels (+) (-), fiber and salt identity, see Table
1.
Migration Testing. The migration testing procedure has already been described.15 So, only a brief description of the procedure will be reported. The plastic material, with a known surface area and mass, is placed in a 20-mL glass vial filled with the appropriate amount of simulant for 10 days at 40 °C (EC legislated conditions20-22). After
the test, the plastic was removed, and the simulant was analyzed according to the procedure described above. Liquid-Liquid Extraction. Two different procedures were applied depending on the ethanol content in the simulant. For the 95% ethanol simulant, after the migration test, all the simulant was filtered through a Whatman 1P/S filter, and aqueous extract was separated. The ethanolic extract was evaporated to dryness under a nitrogen stream, and the residue was dissolved in n-hexane. For the other simulants (distilled water, 15%, 30%, and 50% v/v ethanol), the simulants after the test were extracted with n-hexane (1:5 w/w). Ammonium sulfate (1.8, 1.3, 0.8, and 0.5 g, respectively) was added to the funnel to break the emulsion. The mixture was sequentially extracted two times in an ultrasonic bath for 10 min, and the organic phase was separated. This organic phase was dried in a sodium sulfate column and filtered through a PTFE syringe filter of 0.2-µm pore size. The organic extract was concentrated under a nitrogen stream up to 1 mL. In both cases, after adding the internal standard, PCB 138, the solution was analyzed by GC-ECD. RESULTS AND DISCUSSION Experimental Design. As was mentioned above, an experimental design working with four quantitative and two qualitative factors was applied. Sorption-temperature influence depends on both analyte and simulant characteristics. For the more aqueous simulants (distilled water, 15% and 30% ethanol) there is no significant variation with all the recovery percentages in the range of 85-115%, with only two exceptions, procymidone, with a positive influence of 117%, and bromopropylate, with a negative tendency (77.5%). So, room temperature was adopted for these analyses. For the more ethanolic simulants, the effect of temperature increase is positive, with average recoveries between 125 and 300% depending on the compounds. Therefore, a sorption temperature of 60 °C was established for these analyses. The experimental results are related to the evolution of the equilibrium coefficient between the fiber and the sample matrix. All the analytes under study have higher solubility in ethanol than in water, so the partition equilibrium moves to the simulant when the ethanol content increases. A higher sorption temperature could help to overcome, to some extent, this effect and allow achievement of a better response for the analytes. Desorption temperature shows better results at the higher values studied. The positive effect is higher for some analytes such as bromopropylate and tetradifon which are retained more in the chromatographic system. So, working temperature was established at the higher value achievable, taking into account fiber degradation and carryover phenomena. It is well-known that sorption processes are affected by ionic strength, which can be regulated by salt addition. Two aspects have to be taken into account: identity and concentration of the salt added. The former affects the pH and solubility parameters while the later establishes the ionic strength. (20) EC Council Directive 82/711/EEC. Off. J. Eur. Comm. 1985, L297, 2630. (21) EC Commission Directive 93/8/EEC. Off. J. Eur. Comm. 1993, L90, 2225. (22) EC Commission Directive 97/48/EC. Off. J. Eur. Comm. 1997, L222, 1015.
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Figure 1. Effect of the ethanolic concentration in the simulant on the average signal for all the analytes (spiking level: 250 ng/g). Values represent relative signal expressed as percentage of distilled-water reponse.
The compounds under study did not show similar behavior as a function of salt addition. It can be highlighted that the presence of sulfate (7.5% w/w) gave results 2-6 times higher than the other combinations for the analytes vinclozolin, tolclofosmethyl, malathion and chlorpyriphos. For the rest of compounds, the results in the presence and those in the absence of salt were comparable (85-97%). Working with the more ethanolic simulants, the problem is that in the standard experimental conditions (7.5 w/w salt), the dissolution step is time-consuming. So, different amounts of salt (5, 3, and 1%) were tested, but the results obtained were similar to those obtained without salt addition, so no salt was added in the analysis of these simulants. The most important factor affecting the extraction rate is, as was mentioned above, the affinity of the compounds for the fiber coating. So, the selection of an appropriate stationary phase is the heart of the whole optimization process. In the present work, we tested the four fibers listed in Table 1. As could be expected, 100- and 85-µm fibers show the best performance for all the matrixes. Both of them are proposed in the scientific literature for pesticide analysis. However, the nonpolar PDMS 100-µm fiber shows better results for the more aqueous simulants, and the slightly polar 85µm PA works better for the more ethanolic matrixes. This fact will be related to the relationship between polarity of fibers and simulants. Matrix Influence. As Table 3 shows, the optimum values obtained for the more aqueous simulants are identical. Nevertheless, the method performance is not equal for all the simulants. Moreover, as Figure 1 shows, the signal obtained for each pesticide decreases with the presence of ethanol in the solution. This decrease became dramatic when the percentage of ethanol in the simulant was higher than 15% (v/v). For purposes of comparison, Figures 2 and 3 show the chromatograms obtained for a SPME analysis of all the pesticides at a 50 ng/g spiking level in distilled water and in simulant with 30% ethanol content. As can be seen, the analytes are wellseparated with the only exceptions being clorobenzilate and endosulfan (II), but even in this case, there is no problem in quantifying the compounds, by using several different characteristic masses from their mass spectra. The selected masses for quantitation were 111, 139, and 251 for chlorobenzilate and 159, 195, and 237 for endosulfan (II). 2420 Analytical Chemistry, Vol. 71, No. 13, July 1, 1999
Figure 2. Analysis of a 50 ng/g aqueous spiked sample. For peak identification, see Table 4.
Figure 3. Analysis of a 50 ng/g 30% (v/v) ethanol/water spiked sample. For peak identification, see Table 4.
The Figures show a considerable decrease in the analyte signal. This fact has a clear explanation. When the percentage of ethanol in the simulant increases, due to pesticide solubility, the partition equilibrium is displaced to the simulant. This fact has a direct influence on the partition coefficient, K, which diminishes. Anyway, for our purposes the method characteristics shown in Table 4 are appropriate. The influence of the organic content is not only the signal decreasing explained above. Probably, the most interesting effect is focused on the sorption time necessary to reach the equilibrium. Figure 4, parts a and b show the kinetic behavior of vinclozolin as a representative compound. The figures were obtained by plotting the reponse factor (signal/concentration (ng/g) ratio) versus sorption time (min). As can be seen, working with distilled water and with the simulant 15% ethanol, the maximum signal is found at 40 min sorption time, and the maximum is found at 20 min for 30% ethanol and at 10 min for the more ethanolic simulants, and in these cases after this time the signal decreases. So, the time necessary to get a maximal extraction has a close relationship with the percentage of ethanol. It can be highlighted that, working with the matrixes in which the analytes have the higher solubility, longer sorption times have a negative effect on the extraction procedure. We think that this behavior can be explained as follows. There are two effects which have influence in the process. First, one has to remember that the partitioning coefficient (K) represents a concentration ratio (i.e., masses ratio) according to
Table 4. Detection Limits (DL) and Precision, Expressed as ng of Pesticide/g of Simulanta distilled water
15% ethanol
30% ethanol
50% ethanol
95% ethanol
analyte
DL
% RSDb
DL
% RSD
DL
% RSD
DL
% RSD
DL
% RSD
1. vinclozolin 2. tolclofos methyl 3. malathion 4. chlorpyriphos 5. 4,4′-dichlorobenzophenone 6. procimidone 7. o,p-DDE 8. chlorobenzilate 8. endosulfan (II) 9. bromopropylate 10. tetradifon
0.1 0.02 0.2 0.08 0.02 0.11 0.03 0.4 0.3 0.04 0.1
4.9 6.1 5.9 4.2 6.1 4.8 4.6 5.6 7.1 6.0 5.9
0.3 0.06 0.7 0.5 0.06 0.4 0.05 1.0 0.9 0.2 0.3
5.6 6.1 6.4 4.4 7.0 5.5 4.8 8.2 6.7 6.2 6.1
13.1 1.8 47.6 13.3 12.5 13.8 0.4 20.9 18.8 2.5 5.5
6.4 6.7 7.0 4.9 7.6 5.7 5.0 8.5 9.5 6.3 6.4
64.4 37.7 85.5 91.0 91.1 75.4 8.7 79.4 97.0 56.5 65.4
9.9 8.4 12.9 6.1 15.6 13.2 14.6 9.8 12.2 11.7 12.2
169.9 94.2 205.5 120.0 166.5 186.4 38.7 127.0 146.8 86.2 151.7
15.9 9.8 19.9 14.1 16.6 17.2 16.7 12.8 15.2 12.7 16.1
a Detection limits defined as the concentration of an analyte in a sample which gives rise to a peak with a signal/noise (S/N) ratio of 3. b % RSD calculated from six SPME extractions.
Figure 4. Effect of the ethanolic concentration of the matrix on the reponse factor. (a) Distilled water, 15 and 30% (v/v) ethanol/water content. (b) 50 and 95% (v/v) ethanol/water content.
the expression proposed by Arthur and Pawliszyn23
K)
Cs Ms V 1 ) × C1 M1 Vs
(1)
where Ms and Ml are the amounts of analytes in the stationary phase and in the liquid and Vs and Vl are the volumes of these media. When the K value dimishes, Ms diminishes too, so fiber saturation occurs at lower values and, assuming that diffusion processes of the analytes from the matrix to the fiber interace are independent from the ethanol content, the time necessary to reach the equilibrium is shorter, as experimental results demonstrate. (23) Arthur, C. L.; Pawliszyn, J. Anal. Chem. 1990, 62, 2145-2153.
On the other hand, the signal decrease at sorption times longer than the equilibrium is related with a sorption/desorption equilibrium. The higher the ethanol content, the higher the analyte solubility, and simultaneously, the signal decreases faster, as can be seen in Figure 4 parts a and b. LLE Comparison. The optimized SPME method has been applied to the analysis of pesticide migration from a postconsumer polymeric material to selected food simulants. Three independent migration series were analyzed, each one containing two different experiments. For quantification, most of the work reported on the scientific background is carried out by using the external-standard calibration mode. In our case, we have applied an internal-standard mode, using lindane as the standard. However, standard selection in this case is not an easy goal and must be discussed. An internal standard must have a good reproducibility and repeatability, must not show irreversible sorption in the fiber, and must have a signal/concentration ratio equal or similar to those of the analytes. All the hexachlorocyclohexanes, aldrin, endrinaldehyde, heptachlor, and PCB 52, were tested. The best results, according to the selection criteria mentioned above, are obtained for lindane. Calibration curves were carried out the same day as the sample analysis, because the fiber performance is not constant with time. These curves were obtained by plotting the analyte/internal standard reponse ratio versus the ratio of their concentrations. Table 5 shows the results obtained using both SPME and LLE methods. Both series were made with different chromatographic systems: GC-MS was used for SPME studies and GC-ECD for LLE experiments. However, this situation does not affect the performance of both techniques under study, SPME and LLE, because GC-ECD is more sensitive than GC-MS for the analysis of the compounds listed in Table 5. As can be seen, the results are highly correlated with the only exceptions being chlorpyriphos and tetradifon in the more ethanolic simulants. Moreover, the results obtained for SPME are more reasonable and consistent with the evolution tendency within the simulants. However, in the rest of the cases the differences obtained lack statistical significance, as was demonstrated by using the T-test.24 Analytical Chemistry, Vol. 71, No. 13, July 1, 1999
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Table 5. Migration Test Results: Solid-Phase Microextraction (S) and Liquid-Liquid Extraction (L)a
analyte vinclozolin tolclofos-methyl malathion chlorpyriphos 4,4′-dichlorobenzophenone o,p-DDE chlorobenzilate procymidone endosulfan (II) bromopropylate tetradifon
distilled water
15% ethanol
30% ethanol
50% ethanol
95% ethanol
S
L
S
L
S
L
S
L
S
L
0.09 0.04 nd 0.02 0.03 nd 0.01 0.05 0.01 0.02 0.10
0.11 0.03 0.01 0.01 nd nd nd 0.01 nd nd 0.09
0.37 0.08 0.20 0.06 0.04 nd 0.10 0.80 0.05 0.10 0.16
0.36 0.04 0.19 0.01 0.01 nd 0.06 0.77 0.03 nd 0.07
0.49 0.12 0.25 0.08 0.06 nd 0.14 0.94 0.07 0.45 0.36
0.51 0.07 0.22 0.01 0.05 nd 0.10 0.88 0.05 0.36 0.07
0.49 0.10 0.21 0.10 0.12 nd 0.12 0.99 0.05 1.08 0.54
0.48 0.08 0.22 0.02 0.08 nd 0.10 0.92 0.05 1.03 0.08
0.56 0.22 0.26 0.12 0.42 nd 0.16 1.24 0.12 2.36 0.68
0.53 0.24 0.26 0.10 0.40 nd 0.13 1.29 0.10 2.47 0.11
a Results expressed as µg of pesticide/dm2 of plastic material. All the results are the average of six determinations. % RSD
