Analysis of Pesticide Residues in Brown Rice Using Modified

J Korean Soc Appl Biol Chem (2012) 55, 769−775 DOI 10.1007/s13765-012-2153-y ORIGINAL ARTICLE

Analysis of Pesticide Residues in Brown Rice using Modified QuEChERS Multiresidue Method Combined with Electrospray Ionization-Liquid Chromatography-Tandem Mass Spectrometric Detection

Zaw Win Min · Su-Myeong Hong · In-Cheol Yang · Hye-Young Kwon · Taek-Kyum Kim · Doo-Ho Kim

Received: 11 July 2012 / Accepted: 4 October 2012 / Published Online: 31 December 2012 © The Korean Society for Applied Biological Chemistry and Springer 2012

Abstract An efficient and modified Quick Easy Cheap Effective for routine analysis of residues of target pesticides in brown rice. Rugged and Safe (QuEChERS) method combined with liquid chromatography-electrospray ionization with tandem mass Keywords brown rice · high performance liquid spectrometric detection were evaluated for the analysis of residues chromatography-electro-spray ionization-tandem mass of 72 pesticides in brown rice including acidic sulfonylurea spectrometry · modified Quick Easy Cheap Effective Rugged herbicides. For extraction of pesticides and clean-up of the extract, and Safe · multiresidue analysis · sulfonylurea herbicides 1% formic acid in acetonitrile and dispersive solid phase extraction were used, respectively. Two fortified spikes at 50 and 200 µgL–1 levels were performed for recovery test. Mean recoveries of majority of pesticides at two spike levels ranged Introduction from 90 to 110% with standard error (Coefficient of Variation) less than 10%. The limits of detection and quantification ranged Cereal crops cover more than 60% of world agricultural production. –1 –1 from 0.24 to 19.92 µg L and 0.79 to 65.74 µg L , respectively. Among cereals, rice is the top most important commodity, and its Good linearity of calibration curves were achieved with R2> yield has increased by about 2% per year since 1990 (Pareja et al., 0.9943 within the observed concentration range (from 20 to 400 2011). The intensive use of agrochemicals (e.g. fertilizers and µg L–1). The modified method also provided satisfactory results pesticides) in rice production is one of the most important for sulfonylurea herbicides, which could not be determined methodologies to increase production yield per unit area. Fungi, properly with previously reported methods. This method was insects, and mites can affect rice production, because it is applied to determine residues of target pesticides in real samples. generally cultivated in a humid-wet land environment. Weed A total of 22 pesticides in 31 out of 40 tested samples were growth is also one of the major constraints on rice production observed. The highest concentration was observed for tricyclazole causing seriously reduced yield or even blocking the growth of at 1.17 mg L–1. This pesticide found in two brown rice samples rice. Consequently, several kinds of pesticides, insecticides, exceeded its MRL regulated for rice in Republic of Korea. Except fungicides, and herbicides have been used to prevent crops against this pesticide, concentrations of all observed pesticides were lower pest damage. Residues of several pesticides can be observed in than their MRLs. The results reveal that the method is applicable rice due to bad agricultural practices while applying these chemicals (Demont et al., 2009). Z. W. Min · S.-M. Hong (
) · I.-C. Yang · H.-Y. Kwon · T.-K. Kim · D.-H. The occurrence of pesticides not only affects the quality of rice Kim but also threatens human health and the environment. Therefore, Chemical Safety Division, Department of Agro-food Safety, Rural monitoring of pesticides residues in rice is an important issue to Development Administration, National Academy of Agricultural Science, Suwon 441-707, Republic of Korea ensure food safety. However, due to the complexity of the matrix E-mail: [email protected] and the low concentration of pesticides present in rice, analysis of 770 J Korean Soc Appl Biol Chem (2012) 55, 769−775 pesticides is difficult. Despite technological improvements in To prepare stock solutions of individual standard, all standards analytical tools, pre-treatment prior to instrumental analysis is were dissolved in MeCN to obtain 1000 mg L–1. The appropriate necessary to avoid interferences during the analysis of these volume of each stock solution was mixed together and diluted compounds (Gilbert-López et al., 2009). Although several with MeCN to obtain 20 mg L–1 mixture of all target pesticides methodologies were reported for the determination of pesticides in (Total =72). The 20 mg L–1 mixed-standard was diluted in MeCN cereals, only few are available for polished and unpolished rice for preparation of working standard solutions. especially using liquid chromatography with tandem mass Instrumentation and data analysis. HPLC-MS/MS analysis was spectrometry (HPLC-MS/MS) (Koesukwiwat et al., 2008; performed using an Agilent Technologies (USA) model 1200 Takatori et al., 2008; Lee et al., 2009; Liu et al., 2010; Mastovska series HPLC coupled to an Agilent 6410 triple-quadrupole mass et al., 2010). spectrometer. Instrument parameters were: gas temp 350, gas flow –1 Polarity of pesticides and type of matrices has a great impact on 10 L min , and nebulizer 50 psi. N2 gas was used as nebulizer extraction of pesticides from food. Due to its simplicity, low cost, gas. Ionization was performed by electrospray ionization at relatively high efficiency, and minimal number of steps, Quick, positive mode. Standard mixtures of 72 pesticides and internal Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method standard, triphenylphosphate (TPP), were initially determined for has been developed as an alternative for sample preparation for precursor, and product ions, mass spectra, retention times, and the analysis of multiple pesticide residues in fruits and vegetables optimum voltages for fragmentor and collision cell at positive (Anastassiades et al., 2003). Several experimental works have ionization mode using full scan. Acquisition time, flow rate, and been published on improving the overall performance or applying gradient were also optimized using standard mixtures at multiple QuEChERS specifically to some difficult issues of pesticide reactions monitoring (MRM) mode. After satisfactory conditions residue analysis, adapting it as a template for the needs of users were determined, standard mixtures were tested again using (Lehotay, 2007; Payá et al., 2007; Nguyen et al., 2008). Recently, dynamic MRM (DMRM) mode at 1.2 min of delta retention time. several modifications have been introduced to the original Analyte separation was achieved by YMC-Pack Pro C18 RS, 3 method, aiming to improve the recoveries for some problematic µm, 100×3 mm i. d., (YMC Co., Ltd., Japan). Column temperature pesticides (Lehotay et al., 2005) or for the analysis of complicated was set at 40ºC. Mobile phase consisted of water buffered with 10 matrices (Mol et al., 2008; Asensio-Ramos et al., 2010; mM ammonium formate (solvent A) and MeCN (solvent B). The Koesukwiwat et al., 2010). HPLC gradient for the separation applied at 0.2 mL min–1 was: 0– HPLC-MS/MS has been proven to be a powerful technique for 2 min, 5–70% B; 2–10 min, 70% B; 10–15 min, 70–90% B; 15– the analysis of drugs and pesticides at trace concentration levels 20 min, 90–95% B. Initial condition (5% B) was re-established in owing to its high selectivity, precision, and sensitivity (Xia et al., 3 min and was maintained for 2 min. Post running time was set for 2009; Lei et al., 2010; Tölgyesi et al., 2010; Min et al., 2011). 5 min, resulting in a total run time of 30 min. Sample injection Among several ionization modes, electrospray ionization (ESI) volume was 10 µL. Data processing was performed with MassHunter has been proven to be reliable, robust, and sensitive (Lagana et al., Workstation data acquisition software using DMRM mode. Cycle 2000; Pico et al., 2000; Mazella et al., 2009). The present study time was 500 ms and min/max dwell was 10.39 ms/246.5 ms. explored an HPLC-ESI-MS/MS method for the determination of Optimized HPLC-MS/MS parameters are listed in Supplementary pesticide multiresidues using modified QuEChERS approach and Table 1. evaluated the method for extraction of pesticides from brown rice. Sample preparation. Paddy rice samples were subjected to The developed analytical method has been applied to real samples primary milling operation to remove husks. The resulting brown to provide scientific evidence for monitoring of pesticides in rice samples were grind in the grinder (Foss Analytical, Korea). brown rice. Poundered rice samples were kept in the freezer at –20oC until used. A 5 g of poundered samples were weighed into 50-mL empty polypropylene centrifuge tubes. Pesticide mixture (100 µL) Materials and Methods was spiked onto sample for recovery test samples and 100 µL of MeCN for matrix blanks and screening samples. A 100 µL of 10 Chemicals and standard stock solution preparation. All mg L–1 TPP standard was spiked onto all samples. The tubes were pesticides were obtained from Dr. Ehrenstorfer GmbH (Germany). left standing for 1 h to assure the penetration of pesticides and Gradient grade acetonitrile (MeCN) and glacial acetic acid TPP into sample matrix. Subsequently, 10 mL each of deionized (100%) were purchased from Merck KGaA (Germany). Formic water and 1% formic acid in MeCN were added into the centrifuge acid and ammonium acetate (>98 and 99% purities, respectively) tubes. Capped centrifuge tubes were horizontally laid down on the were from Sigma Aldrich (USA). Distilled water was deionized in reciprocating shaker and shaken for 1 h, followed by addition of Milli-Q system from Millipore (USA). Anhydrous MgSO4, 4 g anhydrous MgSO4 and 2 g NaCl into vials and hand-shaked primary secondary amine (PSA), and C18 were supplied by Restek vigorously for 2 min. Centrifugation at 3500 rpm was conducted (USA). Rice samples were collected from fields of farmers in to achieve solvent separation. Extract clean-up was done by Republic of Korea. vortex mixing 1 mL of supernatant with 150 mg anhydrous J Korean Soc Appl Biol Chem (2012) 55, 769−775 771

resulting normalized data for each pesticide was used for calibration curve and recovery calculation in order to improve data quality. Coefficient of variation (CV %) was calculated for each pesticide using standard deviation and mean recovery of two spiking levels. For selectivity study, blank extracts of sample matrix without spiking standard solution but with internal standard were analyzed in HPLC-MS/MS with the developed analytical method. Consecutive analyses of matrix-matched standards were also carried out to determine presence of false signals, if any, in the blank extract. Application of method to real samples. The developed method was applied to the analysis of residues of target pesticides in blind- incurred brown rice samples collected from various regions of Republic of Korea. Forty brown rice samples were used for this purpose. Sample preparation and quantitative analysis were conducted using the procedure described in Materials and Methods section. Duplicate samples were analyzed, and the amount of pesticides detected was determined using matrix- matched standard calibration curve.

Results and Discussion Fig. 1 Schematic diagram of sample preparation method Optimization of sample preparation method. The experiment was primarily aimed to monitor residual pesticides in brown rice MgSO4, 50 mg PSA, and 50 mg C18. After centrifugation at 12000 using a previously reported method (Koesukwiwat et al., 2010; rpm, 0.5 mL of supernatant was thoroughly mixed with 100 µL Maštovská et al., 2010). The method involved 10 mL MeCN as standard for use as matrix match calibration standards and with extraction solvent together with the same volume of water to 100 µL MeCN for the rest of the samples. The final sample assure swelling of dry rice sample. However recoveries of extract was filtered with 0.2-µm polytetrafluoroethylene (PTFE) sulfonylurea herbicides among target pesticides (total =72) were filter and injected onto HPLC-MS/MS. Schematic flow chart found to be very low (Fig. 2). Sulfonylurea herbicides are weak diagram on extraction and cleaning is presented in Fig. 1. All rice acids (pKa from 3 to 5). They are negatively charged when in samples were screened for pesticides using the above sample neutral and alkaline conditions. PSA used for cleaning-up of the preparation method. The samples free from pesticides were sample extract is a weak anion exchanger, thus negatively charged selected to use as control and recovery test samples. pesticides can interact with the sorbent due to their chemical For extraction with MeCN following the method reported by nature (Lehotay, 2007). This chemical interaction may result in Koesukwiwat et al. (2010), MeCN with no acidification was used lower recovery of sulfonylurea pesticides. To avoid low recovery as extraction solvent. Solvent separation and clean-up procedures of these pesticides, one research group did not perform dispersive were the same as described above. For extraction with acetic SPE clean-up step (Niell et al., 2010). However, Maštovská et al. buffer, 1% acetic acid in MeCN was used as extraction solvent as (2010) showed that the use of PSA is very effective in removing reported by Lehotay (2007). Except for the changes in extraction sample co-extractives such as carboxylic acids in the fatty sample solvent, the whole procedure was the same as described above. of rice. C18 is non-polar sorbent that more effectively retains trace Method validation. Method validation was carried out to amounts of lipids from the extract of fatty matrices (Lehotay et al., evaluate the performance of the method using the following 2005). Without using PSA and C18, greater matrix effect induced parameters: limit of detection (LOD), limit of quantification by fatty sample co-extractive will obviously interfere with (LOQ), selectivity, accuracy, and precision. LODs for each analyte chromatographic results. In addition, occurrence of lipids and fatty were calculated by considering a signal value three times higher co-extractive can reduce durability of HPLC column. Therefore, than that of background noise. LOQs were set at equivalent the method in which dispersive SPE clean-up is included and high concentration of signal to noise ratio 10. recovery of sulfonylurea pesticides is achieved should be developed. Recovery test using two spiking levels (50 and 200 µg L–1) On the other hand, Koesukwiwat et al. (2008) developed a were carried out for accuracy and precision study. The ratio of method to extract phenoxy acid herbicide from rice matrix using peak area of each pesticide to peak area of TPP was multiplied by 5% formic acid. The recoveries of pesticides were sufficiently 100 for both recovery test data and matrix matched standard. The high. The authors claimed that phenoxy acid analytes would be 772 J Korean Soc Appl Biol Chem (2012) 55, 769−775

Fig. 2 Recoveries of sulfonylurea herbicides from brown rice sample Fig. 3 Recoveries of sulfonylurea herbicides from brown rice sample extracted with MeCN only and 1% acetic acid in MeCN extracted with 1% formic acid in MeCN pushed towards their protonated neutral forms by the addition of sample extract with modified method is comparable to the method formic acid into solvent extraction. This basic idea of acidification reported by Koesukwiwat et al. (2010), and better recoveries of of solvent was adapted for the development of QuEChERS based sulfonylurea herbicides are obtained with the modified method. sample preparation method to extract target pesticides including Method validation. Consequently, the method was validated for sulfonylurea herbicides from rice matrix. Extraction solvent all target pesticides in terms of selectivity, linearity, recovery, and (MeCN) was acidified using 1% acetic acid or 1% formic acid. precision. Fig. 4(A) and (B) demonstrate total ion chromatograms The recoveries of sulfonylurea herbicides (4 out of 7) were still of rice matrix blank extract and matrix-matched standard in rice low with 1% acetic acid (Fig. 2). On the other hand, the recoveries extract. As can be seen in the figures, no significant chromatographic of those herbicides were found to be satisfactory with 1% formic signals were observed in matrix blank sample at the same acid (Fig. 3). retention time of target pesticides. Signals at 12.178 min represent Matrix effect among the pesticides standards spiked in MeCN internal standard (TPP). Therefore, selectivity of the developed and in rice sample extracts (matrix-matched standards) prepared method is considered reliable for determining target pesticides. by reported method of Koesukwiwat et al. (2010) or the modified LODs were set at the equivalent concentration of pesticides method was assessed using respective calibration curves, and the calculated from the lowest concentrations that yield signal values response of detector to each pesticide was linear within the tested three times higher than that of the background noise. The method calibration range (Table 1). Statistical comparison between calibration gave LODs ranging from 0.24 to 19.92 µg L–1. LOQs were set at in MeCN and in each sample extract prepared by two different the equivalent concentration of pesticides calculated from the methods was carried out to compare slopes of the calibration lowest concentrations that yield a signal to noise ratio of 10. curves using two-tailed student-t test in Sigma-Plot 10.0, Systat LOQs ranged from 0.79 to 65.74 µg L–1. These LODs and LOQs Software, Inc. (USA) for calculating t and p values. are acceptably low for the analysis of target pesticides at the Sulfonylurea herbicides show significant matrix effect in both concentration below the regulated MRLs. matrix-matched standards (MMS) with respect to standards in For quantitative analysis, matrix-matched calibration standards MeCN, except bensulfuron-methyl. The calibration curve of were prepared by spiking known concentration of pesticides on bensulfuron-methyl in both types of extract and in MeCN solvent rice blank extract. Linear range was from 20 to 400 µg L–1 is not significantly different. MMS of flucetosulfuron and equivalents. The ratio of detector response to pesticides versus imazosulfuron in extract with reported method are significantly detector response to TPP (internal standard) was multiplied by different from standard in MeCN, whereas MMS in extract with 100 and plotted against pesticide concentrations to perform modified method shows no difference. However, there are no quantitative analysis. Good linearity of calibration curves was significant differences between MMS with reported method and observed with regression coefficient greater than 0.995 in all cases modified method for all herbicides when their calibration curves except cafenstrole, whose regression coefficient was 0.9943 are statistically compared. This reveals that the cleanness of (Supplementary Table 2). J Korean Soc Appl Biol Chem (2012) 55, 769−775 773

Table 1 Assessment of matrix effects using calibration data among standards preapared in MeCN and in rice extracts

Linear range 2 Matrix Effect Pesticides –1 Regression equation Type of Cal. STD R (mg L ) with A with B with C Azimsulfuron 0.0013−0.417 Y=(106.434±3.580)x–(6.757±0.565) Solvent only STD 0.9967 - - - 0.0013−0.417 Y=(147.106±2.601)x–(4.489±2.978) MMS in MeCN extract 0.9990 Yes** No - 0.0013−0.417 Y=(146.845±3.102)x–(2.235±1.849) MMS in FA extract 0.9996 Yes** - No Bensulfuron-m 0.0013−0.417 Y=(242.799±7.434)x–(14.682±2.701) Solvent only STD 0.9970 - - - 0.0013−0.208 Y=(317.758±27.960)x+(7.111±0.996) MMS in MeCN extract 0.9973 No No - 0.0013−0.417 Y=(262.387±9.744)x+(16.076±3.090) MMS in FA extract 0.9965 No - No Cinosulfuron 0.0013−0.417 Y=(170.413±2.953)x–(14.135±1.614) Solvent only STD 0.9947 - - - 0.0013−0.417 Y=(200.425±7.624)x+(6.238±5.897) MMS in MeCN extract 0.9978 Yes* No - 0.0013−0.417 Y=(188.327±5.679)x+(9.396±2.105) MMS in FA extract 0.9977 Yes* - No Cyclosulfamuron 0.0013−0.417 Y=(236.181±2.361)x+(9.245±2.558) Solvent only STD 0.9988 - - - 0.0013−0.052 Y=(450.717±23.882)x+(0.316±0.822) MMS in MeCN extract 0.9995 Yes** No - 0.0013−0.052 Y=(441.691±15.137)x+(1.626±2.001) MMS in FA extract 0.9967 Yes** - No Ethoxysulfuron 0.0013−0.417 Y=(142.271±5.812)x–(2.870±1.434) Solvent only STD 0.9994 - - - 0.0013−0.417 Y=(170.174±7.495)x+(4.542±2.846) MMS in MeCN extract 0.9986 Yes* No - 0.0013−0.417 Y=(170.387±1.199)x+(6.965±2.160) MMS in FA extract 0.9961 Yes* - No Flucetosulfuron 0.0013−0.417 Y=(57.513±1.103)x–(4.651±0.501) Solvent only STD 0.9959 - - - 0.0013−0.417 Y=(64.829±2.534)x+(0.585±0.561) MMS in MeCN extract 0.9996 No No - 0.0013−0.417 Y=(66.515±0.501)x+(1.353±0.989) MMS in FA extract 0.9987 Yes** - No Imazosulfuron 0.0013−0.417 Y=(22.068±0.268)x+(0.426±0.269) Solvent only STD 0.9997 - - - 0.0013−0.417 Y=(21.248±0.507)x+(1.379±0.347) MMS in MeCN extract 0.9968 - - - 0.0013−0.417 Y=(20.287±0.219)x+(1.322±0.221) MMS in FA extract 0.9960 Yes** - No Notes: *99% confident level, **99.9% confident level, A=Solvent only STD, B=MMS in Formic Acid extract, C=MMS in MeCN extract

the target pesticides was achieved with <10% standard error, showing good precision. Both accuracy and precision are inter- day accuracy and precision, because different spiking levels were conducted at different days. The results of pesticide recovery at each spike level, average recovery of two spike levels, standard deviation and CV% are presented in Supplementary Table 2 together with their respective values of LODs and LOQs. Application to real sample. In order to evaluate the effectiveness and applicability of the developed method, real samples collected from various places of Republic of Korea were analyzed for determination of the pesticide residues. Total 40 samples were collected, and each sample was analyzed in duplicate using the developed method (Table 2). A total of 22 pesticides were observed from the 31 brown rice samples, among which fungicide tricyclazole was the most frequently found pesticide followed by etofenprox, ferimzone, and hexaconazole. The concentrations of Fig. 4 Total ion chromatograms of (A) rice matrix blank and (B) matrix- those pesticides varied between 0.001 and 1.168 mg L–1. The matched standards highest concentration was found in tricyclazole. Observed concentration of this pesticide was as high as 1.168 mg L–1 and To determine recoveries of target pesticides with the modified was out of linear range. Therefore, the final extract of rice sample method, fortified spikes at 50 and 200 µg L–1 were conducted. The containing tricyclazole was diluted five times with MeCN to keep concentration of each pesticide was calculated using matrix- the concentration within the linear range. Tricyclazole is a matched standard calibration curve. Average recovery of both fungicide commonly used in rice cultivation. It has systemic spike levels ranges from 74 to 125%. Recoveries of the majority action and is absorbed rapidly by roots, and translocate through of pesticides fall between 90 and 110% (Figs. 5 and 6). Standard the plant. The concentration of all observed pesticides ranged error (CV %) was calculated base on average recovery and from 0.0003 to 1.168 mg L–1. Table 2 summarized the results of standard deviation of two spike levels. Satisfactory recovery of all pesticides found in real samples. The concentrations of tricyclazole 774 J Korean Soc Appl Biol Chem (2012) 55, 769−775

Table 2 Pesticides observed in real rice samples (n=2 for each sample) along with frequency, concentration range, and their MRLs regulated for rice in Republic of Korea Frequency Conc. range MRL No Pesticide –1 –1 observed (mg L ) (mg L ) 1 Acetamiprid 1 0.262 0.30 2 Azoxystrobin 3 0.002–0.015 1.00 3 Bensulfuron-methyl 2 0.005–0.006 0.02 4 Cafenstrole 2 0.0003–0.008 0.05 5 Carbendazim 2 0.001–0.002 0.05 6 Carpropamid 1 0.022 1.00 7 Dinotefuran 2 0.026–0.017 1.00 8 Edifenphos 2 0.021–0.035 0.20 9 Etofenprox 8 0.001–0.296 1.00 10 Fenoxaprop-ethyl 3 0.001–0.0015 0.05 11 Ferimzone 7 0.009–0.448 0.70 12 Hexaconazole 6 0.002–0.085 0.30 13 Imidacloprid 1 0.013 0.05 14 Indoxacarb 1 0.006 0.10 15 Iprobenfos 5 0.002–0.019 0.20 16 Methoxyfenozide 4 0.004–0.008 1.00 17 Orysastrobin 1 0.019 0.30 Fig. 5 Average recoveries of target pesticides group 1 (36 out of 72) 18 Pencycuron 4 0.003–0.005 0.30 analyzed by developed method. Data are mean values of two spike levels 19 Propiconazole 1 0.057 0.10 –1 (50 and 200 µg L ) 20 Tebuconazole 1 0.011 0.05 21 Thifluzamide 1 0.091 0.10 22 Tricyclazole 18 0.003–1.168* 0.70 Note: *Exceed MRLs regulated for rice in Republic of Korea

A modified QuEChERS method combined with HPLC-ESI- MS/MS detection for the analysis of pesticide residues in rice was developed. The developed method provided satisfactory results for sulfonylurea herbicides which could not be determined properly with the reported method. The developed method also provided comparable matrix effect with the reported method. The developed method was validated for the determination of 72 pesticides commonly used in rice cultivation. Results showed good performance of the developed method, including good selectivity, high recovery with low standard error, and low LODs were observed. Effectiveness of the developed method was evaluated on real samples of rice. The results show that the developed method can be effectively applied to routine analysis of residues of target pesticides in brown rice.

Acknowledgments This study was carried out with the support of “Research Program for Agricultural Science & Technology Development (Project No. PJ008498)”, National Academy of Agricultural Science, Rural Fig. 6 Average recoveries of target pesticides group 2 (36 out of 72) Development Administration, Republic of Korea. analyzed by developed method. Data are mean values of two spike levels (50 and 200 µg L–1) References in two brown rice samples exceeded their MRL regulated for rice in Republic of Korea. Except for this pesticide, none of the Anastassiades M, Lehotay SJ, Štajnbaher D, and Schenck FJ (2003) Fast and pesticide concentrations were above their respective MRLs. easy multiresidue method employing acetonitrile extraction/partitioning J Korean Soc Appl Biol Chem (2012) 55, 769−775 775

and “dispersive solid-phase extraction” for the determination of pesticide introduction GC-TOFMS and UPLC-MS/MS techniques. J Agric Food residues in produce. J AOAC Int 86, 412–31. Chem 58, 5959–72. Asensio-Ramos M, Hernandez-Borges J, Ravelo-Pe rez LM, and Rodriguez- Mazella N, Delmas F, Delest B, Méchin B, Madigou C, Allenou JP et al. Delgado MA (2010) Evaluation of a modified QuEChERS method for (2009) Investigation of the matrix effects on a HPLC-ESI-MS/MS the extraction of pesticides from agricultural, ornamental and forestall method and application for monitoring triazine, phenylurea and soils. Anal Bioanal Chem 396, 2307–19. chloroacetanilide concentrations in fresh and estuarine waters. J Environ Demont M, Rodenburg J, Diagne M, and Diallo S (2009) Ex ante impact Monit. 11, 108–15. assessment of herbicide resistant rice in the Sahel. Crop Protection 28, Min ZW, Lee J-Y, Son K-A, Im G-J, and Hong S-M (2011) Development and 728–36. validation of a quick easy cheap effective rugged and safe-based multi- Gilbert-López B, Garæia-Reyes JF, and Molina-Díaz A (2009) Sample residues analysis method for persimmon, grape and pear using liquid treatment and determination of pesticide residues in fatty vegetable chromatography-tandem mass spectrometry. J Korean Soc Appl Biol matrices: A review. Talanta 79, 109–28. Chem 54, 771–7. Koesukwiwat U, Lehotay SJ, Mastovska K, Dorweiler KJ, and Mol HGJ, Bolanos P, Zomer P, de Rijk TC, Stolker AAM, and Mulder PPJ Leepipatpiboon N (2010) Extension of the QuEChERS method for (2008) Toward a generic extraction method for simultaneous pesticide residues in cereals to flaxseeds, peanuts, and doughs. J Agric determination of pesticides, mycotoxins, plant toxins, and veterinary Food Chem 58, 5950–8. drugs in feed and food matrixes. Anal Chem 80, 9450–9. Koesukwiwat U, Sanguankaew K, and Leepipatpiboon N (2008) Rapid Nguyen TD, Han EM, Seo MS, Kim SR, Yun MY, Lee DM et al. (2008) A determination of phenoxy acid residues in rice by modified QuEChERS multi-residue method for the determination of 203 pesticides in rice extraction and liquid chromatography-tandem mass spectrometry. Anal paddies using gas chromatography/mass spectrometry. Anal Chim Acta Chim Acta 626, 10–20. 619, 67–74. Lagana A, Fago G, Marino A, and Penazzi VM (2000) Liquid Niell S, Pareja L, Geis Asteggiante L, Cesio MV, and Heinzen H (2010) chromatography mass spectrometry tandem for multiresidue Comparison of extraction solvents and conditions for herbicide residues determination of selected post-emergence herbicides after soil column in milled rice with liquid chromatography-diode array detection analysis extraction. Anal Chim Acta 415, 41–56. (LC-DAD). Food Addit Contam 27, 206–11. Lee SJ, Park HJ, Kim W, Jin JS, El-Aty AMA, Shim JH et al. (2009) Pareja L, Fernández-Alba AR, Cesio V, and Heinzen H (2011) Analytical Multiresidue analysis of 47 pesticides in cooked wheat flour and methods for pesticide residues in rice. Trends Anal Chem 30, 270–91. polished rice by liquid chromatography with tandem mass spectrometry. Payá P, anastassiades M, Mack D, Sigalova I, Tasdelen B, Oliva J et al. (2007) Biomed Chromatogr 23, 434–42. Analysis of pesticide residues using the Quick Easy Cheap Effective Lehotay SJ (2007) Determination of pesticide residues in foods by acetonitrile Rugged and Safe (QuEChERS) pesticide multiresidue method in extraction and partitioning with magnesium sulfate: Collaborative study. combination with gas and liquid chromatography and tandem mass J AOAC Int. 90, 485–520. spectrometric detection. Anal Bioanal Chem 389, 1697–714. Lehotay SJ, Maštovská K, and Lightfield AR (2005) Use of buffering and Pico Y, Font G, Molto JC, and Manes J (2000) Pesticide residue determination other means to improve results of problematic pesticides in a fast and in fruit and vegetables by liquid chromatography-mass spectrometry. J easy method for residue analysis of fruits and vegetables. J AOAC Int 88, Chromatogr A 882, 153–73. 615–29. Takatori S, Okihashi M, Okamoto Y, Kitagawa Y, Kakimoto S, Murata H et Lehotay SJ, Maštovská K, and Yun SJ (2005) Evaluation of two fast and easy al. (2008) A rapid and easy multiresidue method for the determination of methods for pesticide residue analysis in fatty food matrixes. J AOAC Int pesticide residues in vegetables, fruits, and cereals using liquid 88, 630–8. chromatography/tandem mass spectrometry. J AOAC Int 91, 871–83. Lei YQ, Zhang WY, Li HD, Yan MA, and Zhu RH (2010) Determination of Tölgyesi A, Verebey Z, Sharma VK, Kovacsics L, and Fekete J (2010) Ziprasidone by UPLC-MS-MS and Its Application to a Pharmacokinetic Simultaneous determination of corticosteroids, androgens, and Study of Chinese Schizophrenics. Chromatographia. 72, 975–9. progesterone in river water by liquid chromatography-tandem mass Liu S, Zheng Z, Wei F, Ren Y, Gui W, Wu H et al. (2010) Simultaneous spectrometry. Chemosphere 78, 972–9. determination of seven neonicotinoid pesticide residues in food by Xia X, Li XW, Ding SY, Zhang SX, Jiang HY, Li JC et al. (2009) ultraperformance liquid chromatography tandem mass spectrometry. J Determination of 5-nitroimidazoles and corresponding hydroxy Agric Food Chem 58, 3271–8. metabolites in swine kidney by ultra-performance liquid chromatography Maštovská K, Dorweiler KJ, Lehotay SJ, Wegscheid JS, and Szpylka KA coupled to electrospray tandem mass spectrometry. Anal Chim Acta 637, (2010) Pesticide multiresidue analysis in cereal grains using modified 79–86. QuEChERS method combined with automated direct sample