* Relative rea area, % =
Area in acetonitrile−based mobile phase Area inmethanol−based mobile phase × 100
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Figure 19. Peak area and repeatability by injection volume (n = 5).
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0.2 0.6 1.1 4.6 11.3
Optimization of cleanup procedure
In QuEChERS methodologies as well as other multiresidue analysis methods, pigments rich commodities such as leafy vegetables, carrots, and berries are challenging due to the coloured residues existed even after cleanup procedure.
Among them, a green matrix containing high chlorophyll like a spinach in this study could give rise to chromatographic problem. The additional absorbents like GCB has been used for the purpose of the removal of chlorophyll, but it also known to retain planar structured pesticides (Anastassiades, Lehotay, et al., 2003; Hayward et al., 2015; Koesukwiwat et al., 2010; Mol et al., 2007).
Although the previous studies have shown that the coextractives from chlorophyll-rich matrix did not affect quantitative results and chromatograms in GC-MS analysis (Lee et al., 2017; Lehotay, Maštovská, et al., 2005), there were not many studies about the effect on LC analysis. To check the necessity to eliminate the chlorophyll-coextractives and whether remained green matrix would affect to LC-MS/MS-amenable compounds, preliminary recovery test was performed by different amounts (2.5 and 7.5 mg) of GCB on spinach extracts.
Table 10 shows recoveries of representative pesticides in different dSPE absorbents. The pesticides were chosen on the basis of the reduced recovery rate as the amounts of GCB sorbents increased. The well-known pesticides for planar structured such as carbendazim, thiabendazole, and cyprodinil were decreased by addition of GCB, consistent with previous findings (Mol et al., 2007; Nie, Shui Miao, et al., 2015; Walorczyk, 2008; Wong et al., 2010). The pesticides containing aromatic moiety including phenylureas (forchlorfenuron and Thidiazuron) and benzoylureas (diflubenzuron and teflubenzuron) were also adsorbed on GCB. In addition, it was noticeable that the best result was obtained in PSA cleanup without GCB in terms of the frequency satisfying the
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recoveries between of 70-120% with RSD ≤ 20%. No great difference in the frequency was observed compared with additional 2.5 mg of GCB, but the average recoveries was more close to 100% in PSA-only cleanup. Because there were also no effect on chromatographic separations or peak shapes, therefore, the dSPE containing PSA and MgSO4 was chosen as cleanup procedure for method validation study.
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Table 10. Recoveries of representative pesticides from the cleanup with different types of dSPE sorbents in spinach (100 ng/g spiking level, n = 3).
Pesticides
Number of pesticides (percentage, %)
282 (91.0) 281 (90.6) 262 (84.5)
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Method validation
Recovery test was carried out to validate the analytical method by spiking 332 pesticides into three blank samples (brown rice, orange, and spinach) at fortification levels of 10 and 50 ng/g (n = 5). The calibrations curves for quantitation were obtained by matrix-matched calibration from each sample matrices. The correlation coefficient (r2) were obtained from the calibration curves of each commodities in each recovery test. High levels of linearity (r2), with ≥ 0.99 were achieved in most of compounds, ranging from 1 to 100 ng/g.
(Table S5). When the LOQs were defined as the minimum concentrations having an S/N ≥ 10, the 85.8% (265 compounds) of pesticides had 1 ng/g of LOQs (Figure 20). In most case the LOQs were satisfied less than or equal to the 10 ng/g. But two pesticides (atrazine and inabenfide) had 20 ng/g of LOQ due to the low sensitivity.
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Figure 20. Distribution of limit of quantitation in LC-MS/MS analysis.
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0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0
1.0 2.0 5.0 10.0 20.0
85.8
5.5 7.1 1.3
Percentage of pesticides, % 0.6
Limit of quantitation (ng/g)
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The results of recovery tests were evaluated according to acceptability criteria of DG-SANTE guidelines (Hanot et al., 2015). The recoveries in three commodities are summarized in Figure 21. In the low spiking level (10 ng/g), 86.8–88.7% of pesticides met the validation criteria with recoveries in the range of 70–120% and RSD ≤ 20%. Slightly better satisfactory frequencies (91.9–
96.1%) were obtained in the high spiking level (10 ng/g), owing to improved recoveries of several pesticides having low signal intensity. There were no significantly differences between different in the types of matrices while the good and excellent recovery results were achieved.
On the other hand, the sulfonylurea pesticides including bensulfuron-methyl, cyclosulfamuron, ethametsulfuron-methyl, ethoxysulfuron, thifensulfuron-methyl, and tribenuron-methyl gave poor recoveries. The results were consistent with previous studies, in which sulfonylurea pesticides showed undesirable recoveries using QuEChERS methods. This could be explained by the acidic properties of sulfonylurea pesticides, which can be easily adsorbed by PSA. To increase the recoveries of the sulfonylurea pesticides, Kaczyński and Łozowicka (2017) added chitin for the purpose of purification with citrate buffered QuEChERS, skipping the PSA cleanup procedure.
The imidazolinone pesticides (imazamox, imazapic, imazaquin, and imazethapyr), one of the acidic pesticides, were showed low recoveries (< 20%) with high RSDs. Jadhav et al. (2015) previously reported that the ethyl acetate extraction with control of pH or citrate buffered QueChERS without PSA cleanup helped to increase the recoveries of acidic pesticides such as imazethapyr and imazosulfuron. Because these pesticides are stabilized to non-ionised form in acidic condition resulting in remaining in the organic layer in the extraction step. The cleanup utilized combination of Z-Sep+ with PSA after acetate buffered extraction has been used to enhance the recovery (Kiljanek et
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al., 2016).
Interestingly, about 200% of methomyl recoveries were observed at brown rice while thiodicarb were almost disappeared with not-detectable residue. This results was assumed to be due to the unstable property of thiodicarb converting into methomyl (Jones et al., 1989; Wu et al., 2013). Since the good results were observed in the orange and spinach without any enhanced or decreased recoveries, it is noteworthy that grains may help hydrolysis of thiodicarb into methomyl. For other compounds, many polar pesticides (e.g., asulam, cyromazine, haloxyfop, mecoprop-P, and penoxsulam) also showed poor recoveries in this study. The alternative methods like the QuPPe-Method (Anastassiades et al., 2016) by acidified methanolic extraction skipping the PSA cleanup thought to be helpful to improve the recovery of polar pesticides.
Details of the recovery results including LOQ and linearity (r2) data in all of the tests can be found in Table S5.
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Figure 21. Percentages of pesticides satisfying the recovery rates of 70-120% and RSD≤20% at 0.01 and 0.05 mg/kg spike levels, using the optimized method in this study.
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0.0 20.0 40.0 60.0 80.0 100.0
Brown rice Orange Spinach
81.6 85.2
82.2 89.5
94.6 92.2
Frequency, %
10 ng/g 50 ng/g
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Matrix effects
The peak response obtained from LC-MS/MS may be affected by co-elution of matrix components. Recent sample preparation methods prior to instrumental analysis tend to eliminate the minimum matrix interferences as possible in order to reduce the loss of recovery of target compounds. Consequently, the presence of matrix co-extracts leads to increase the possibility of matrix effect and inaccurate quantitation. The compensation method by matrix-matched calibration has been a widely used alternative way to overcome matrix effect.
It should be noted that it is difficult to prepare the exactly same matrix with the target sample even though it is same kinds of commodities in routine analysis.
Therefore, it is important to understand the tendency of matrix effect in each compound.
The matrix effects were determined by comparing the peak area between solvent-only standard and matrix matched standards (brown rice, orange, and spinach). According to the equation mentioned in the method section, a positive value of ME indicates signal enhancement, whereas a negative value indicates signal suppression. Figure 22 shows the distribution of MEs in three matrices. The matrix effects were evenly spread across the each range. In the case of brown rice and spinach, 87.4 and 74.1% of pesticides showed soft matrix effect (< ±20%) (He, Chen, et al., 2015), which is acceptability criteria according to SANTE guideline (European Commission, 2015). However, high percentage of pesticide (61%) in orange was calculated under -20% of matrix effect, indicating significant matrix-induced suppression.
Although the degree of matrix effect was not much higher than those of GC-MS/MS results (Lee et al., 2017; Lozano et al., 2014), more matrix-dependent results were obtained. Matrix effects in LC–MS/MS cause because of co-eluting interference interacting with the pesticides in the electrospray ionization
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process, producing suppression or enhancement of the signal compared to the signal of the analyte injected in solvent (Lozano, Kiedrowska, Scholten, de Kroon, de Kok and Fernández-Alba, 2016). As it also described in the literature (European Commission, 2015; Niell et al., 2014), this can be explained that matrix effect in LC-MS/MS, unlike in GC-MS/MS, depends on co-elution of target analyte with coextracts that could be vary between different commodities. These results identified again the necessity of matrix-matched calibration using the equivalent matrix as possible for proper quantitation of compounds.
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Figure 22. Graphical comparison of the absolute values of matrix effects (ME) results.
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7.1
Real sample analysis
It should be noted that the Korean Ministry of Food and Drug Safety is now prepared for introducing a positive list system for the most protective regulation of pesticide residues on all the agricultural produce or commodities coming 2018 to keep up with the current worldwide trend. In the case of tropical fruits, it has already begun to apply. Unlike the traditional system, the new system would not allow the pesticides (with ≥ 0.01 mg/kg level) which are not registered or does not have maximum residue limit (MRL) for a specific item.
The optimized method was applied to real sample analysis to prove the effectiveness. The apple, easily purchasable, which is one of the most frequently consumed fruits was selected for real sample analysis. In addition, it was also considered that the different commodity is more suitable to check the possibility to apply in routine analysis. A total of 16 inorganic apple samples were collected from different markets. The certificated organic apple sample was used for quality control and matrix-matched calibration. For quality control on apple analysis, pesticides mixture containing 332 pesticides was spiked at concentration of 20 ng/g (n = 3). The detected pesticides and QC results of each pesticides are shown Table 11. A total of 19 pesticides were detected and most of them are insecticides except for four fungicides. On the whole, all samples contained one or more pesticide residues. Except for one sample contained only one pesticide, all of the samples had 4-10 kinds of pesticide multiresidues. Out of the 16 samples, none of the pesticides were detected above the maximum residue limit (MRL) of Korea (Korean Pesticides MRLs in Food; 2016;, 2016).
Most frequently found pesticides were etofenprox (87.5%), carbendazim (81.3%), and tebuconazole (75.0%) in detected samples but, with low residue
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levels compared with its MRLs. Meanwhile, thiophanate-methyl, which is known to degrade into carbendazim in the environment (Fan et al., 2013), was not detected in any samples. It was reported that as soon as thiophanate-methyl was applied to plant, converted into carbendazim, providing fungicidal activity (Buchenauer et al., 1973; Cycoń et al., 2011).
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Table 11. Results of real sample analysis by LC-MS/MS in a total of 16 apple samples. The detected pesticides were summarized with maximum residue limit in Korea and QC results of each pesticides.
No. Pesticide QC results
Notes: aAverage recovery (Rec.) and relative standard deviation (RSD) at 20 ng/g (n = 3). bMaximum residue limit on apple
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Conclusion
This research presented a rapid and efficient simultaneous multiresidue method for 500 pesticides by GC-MS/MS and LC-MS/MS in food matrices.
Multiple reaction monitoring parameters such as quantifier ion, qualifier ion, and collision energy were carefully optimized to obtain high selectivity and sensitivity, resulting in a final screen of 360 pesticides by GC-MS/MS and 332 pesticides by LC-MS/MS. To make the GC-MS/MS technique more practical for routine multiresidual analysis, short and microbore column (20 m length, 0.18 mm i.d.), priming injection, pressure pulse injection (PPI), and automated adjustment of retention time function (AART) were employed, giving improvement of peak sensitivity and shorter analytical time (within 20 min).
The modified QuEChERS (0.1 % formic acid in acetonitrile) extraction gave fewer co-extracts in the final extract and higher acceptable recovery results than other QuEchERS approaches. The dSPE cleanup with additional sorbents (GCB and ChloroFiltr) decreased the recovery of some pesticides having planar and aroma moiety, remained chlorophyll in final extracts did not cause an adverse effect on chromatographic and quantitation results. Final optimized method was successfully validated in terms of accuracy, precision, selectivity, and sensitivity. The applicability of some pesticides, which has not been studied was also evaluated from the validation study. Finally, the developed methodology was applied to the analysis of real samples for testing the applicability of the method.
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