Food Chemistry 168 (2015) 356–365

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Analytical Methods

Rapid determination of pesticide residues in fruits and vegetables, using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry P. Sivaperumal ⇑, P. Anand 1, L. Riddhi 2 Pesticide Toxicology Division, National Institute of Occupational Health, (Indian Council of Medical Research), Ahmedabad 380 016, India

a r t i c l e

i n f o

Article history: Received 30 December 2013 Received in revised form 5 July 2014 Accepted 13 July 2014 Available online 19 July 2014 Keywords: Multiresidue analysis Pesticide residues Ultra-high-performance liquid chromatography Time-of-flight mass spectrometry Solid-phase extraction

a b s t r a c t A multiresidue method, based on the sample preparation by solid-phase extraction cartridges and detection by ultra-high-performance liquid chromatography/time-of-flight mass spectrometry (UHPLC/TOF– MS), was used for the analysis of 60 pesticides in vegetable and fruit samples. Quantitation by UHPLC/ TOF–MS is accomplished by measuring the accurate mass of the protonated molecules [M+H]+. The mass accuracy typically obtained is routinely better than 2 ppm. The rates of recovery for pesticides studied were satisfactory, ranging from 74% to 111% with a relative standard deviation (RSD) of less than 13.2%, at concentrations below 10 lg kg 1. The method limit of quantification (MLOQ) for most compounds was below the MRLs established by the Food Safety Standard Authority of India and the European Union. The uncertainty was determined using repeatability, recovery and calibration curves data for each pesticide. The method illustrated is suitable for routine quantitative analyses of pesticides in food samples. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction India is the second largest producer of vegetables, having 13% of the world’s total vegetable production. Several food commodities, including fruits and vegetables, are contaminated with pesticides. Therefore, monitoring of pesticide residues in food commodities is necessary, in order to assess potential health risks and to fix the maximum residue limits (MRLs) for safe human consumption. About 240 pesticides are registered in India for the purpose of controlling undesirable pests and weeds in food crops (Central Insecticides Board (CIB), 2012, Sinha, Vasudev, & Rao, 2012). Pesticide residues have traditionally been monitored by GCbased multi-residue methods. However, many new polar and ionic pesticides cannot be determined directly by this method, due to their poor thermal stability or volatility (Cajka, Hajslova, Lacina, Mastovska, & Lehotay, 2008; Lacina, Urbanova, Poustka, & Hajslova, 2010; Pihlstrom, Blomkvist, Friman, Pagard, & Osterdahl, 2007). Pesticide analysis is not commonly carried out

⇑ Corresponding author. Tel.: +91 9904721778, +91 079 22688864/65 (O); fax: +91 079 22688864/65. E-mail addresses: [email protected] (P. Sivaperumal), patelanandv@ gmail.com (P. Anand), [email protected] (L. Riddhi). 1 Tel.: +91 9898232482, +91 079 22688864/65 (O). 2 Tel.: +91 9099616465, +91 079 22688864/65 (O). http://dx.doi.org/10.1016/j.foodchem.2014.07.072 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved.

using ultra-high-performance liquid chromatography/time-offlight mass spectrometry (LC–TOF/MS). The presence of matrix interferences in extracts can affect analyte quantification. Sample clean-up is necessary in order to remove matrix interferences, which may impair chromatographic performance and reduce instrument sensitivity. Solid-phase extraction simplifies the purification of the initial extract, reduces the volume of solvent consumed, and improves the method sensitivity (Anastassiades, Lehotay, Stajnbaher, & Schenck, 2003; Gonzalez-Rodríguez, Rial-Otero, Cancho-Grande, & SimalGandara, 2008; Hernando, Agüera, Fernández-Alba, Piedra, & Contreras, 2001; Yang et al., 2011). In recent times, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has become a useful technique in multiple residues analysis (Hernandez et al., 2006; Kovalczuk, Lacina, Jech, Poustka, & Hajslova, 2008; Pozo et al., 2007). High sensitivity and selectivity in detection of pesticide residues can be achieved by tandem mass analysers when operating in selective reaction monitoring mode. This approach allows optimisation of the parameters for each target analyte. However, it does not allow the identification of non-target compounds. Liquid chromatography with high-resolution time-of-flight mass spectrometry (LC– TOF/MS) can be used for target and non-target analysis of pesticide residues in food analysis (Ferrer & Thurman, 2007; Gilbert-Lopez, Garcia-Reyes, Ortega-Barrales, Molina-Diaz, & Fernández-Alba,

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2007). In quantification of pesticides in food and water, several researchers have used accurate mass identification of both target and non-target compounds by LC–TOF/MS (Gilbert-Lopez et al., 2010; Lacorte & Fernandez-Alba, 2006; Masia et al., 2013; Mezcua, Malato, Garcia-Reyes, Molina-Diaz, & Fernandez-Alba, 2009). Ultra-high-performance liquid chromatography time-of-flight mass spectrometry (UHPLC–TOF/MS) instrumentation provides sensitive full-scan acquisition, identification and confirmation of target and non-target analytes of pesticide residues in fruits and vegetables in a short run time. The use of sub-2 lm UHPLC column provides excellent chromatographic resolution and sensitivity. In this paper we have developed and validated a method for rapid multi-residue analysis in vegetables and fruits, using solid-phase extraction (SPE) followed by UHPLC–TOF/MS analysis. This method was simple with fast analysis time using a low volume of mobile phase. 2. Materials and methods 2.1. Instrumentation A UHPLC (Acquity; Waters Corporation, Milford, MA) system coupled with TOF/MS (Synapt; Waters) with UHPLCÒBEH C18 column (2.1  50 mm; 1.7 lm particle size; Waters) was used. In addition, an analytical weighing balance (AUX 220; Shimadzu, Kyoto, Japan), homogeniser (Tulip, Japan), rotary evaporator (Heidolph Instruments, Schwabach, Germany), centrifuge (Thermo Fisher Scientific Inc., Waltham, MA), TurboVap LV Evaporator (Zymark, Hopkinton, MA), and SPE vacuum manifold (Supelco, Bellefonte, PA) were used. 2.2. Chemicals and analytical standards LC–MS grade methanol, acetonitrile, water, anhydrous Na2SO4 (ACS, Certified) and NaCl (ACS, Certified) were obtained from Thermo Fisher Scientific. Sodium sulphate was heated at 650 °C for 4 h and kept in a desiccator until use. The lock-mass internal calibration standard leucine-enkephalin was obtained from Ultra Scientific (Kingstown, RI). The 60 analytical standards (purity > 99.9%) were obtained from AccuStandard, Inc. (New Haven, CT). The individual stock standard solutions of 200 mg L 1 of pesticides were prepared from 1000 mg L 1 original standards in LC–MS methanol; 5 mg L 1 intermediate mixture solutions were prepared from stock solutions. The working standard solutions were prepared from the intermediate solutions and used for method validation, quantification and confirmation of residues (see Table 1). All standards were stored at 5 °C. 2.3. Sample preparation The vegetable and fruit samples (n = 286) including brinjal, cabbage, cauliflower, guava, okra, onion, potato apple, banana, grape, mango orange and pomegranate were selected at random from the local markets at Ahmedabad, Anand, Surat, Navsari, Kheda, Narmada, Patan and Radhanpur, in the state of Gujarat, in the western part of India. The samples were chosen according to the consumption pattern of residents in the region, and the pesticides were selected according to the recommended use in different crops. The sample wet weight was 2 kg for small and medium sized fresh product and the unit sample weight was generally in the range of 15–250 g. The vegetable and fruit samples were prepared as an analytical sample for determination of pesticide residues according to the Codex Alimentarius (Volume 2A, Part 1: 2011) Commission (2011). A representative portion of the analytical

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sample was blended using a food processor and mixed thoroughly. The homogenised samples were stored at 20 °C. Before using, the samples were thawed at 5 °C overnight. The quantities of each sample are presented in Table 3.

2.3.1. Samples extraction process The of homogenised samples (10 g) were accurately weighed into 50-mL PTFE centrifuge tubes and mixed with 25 mL of acetonitrile-methanol mixture (90:10 v/v). The mixture was vortexed for 3 min, 5 g sodium chloride was added and vortexed for 3 min again. The mixture was centrifuged for 5 min at 5000 rpm, and the supernatants were transferred into a 50-mL round bottom flask and evaporated to dryness at 35 °C using a rotary evaporator.

2.3.2. Clean-up process Solid phase extraction (SPE) was carried out using graphitized carbon black (0.5 g) and primary secondary amine (0.5 g) (GCB/ PSA) in a 3.0 mL cartridge (Supelco, Bellefonte, PA). A layer (ca. 1 cm) of anhydrous sodium sulphate was added to the GCB/PSA column to remove traces of water from the eluate. The columns were washed with 5 mL of acetonitrile–methanol (95:5 v/v) mixture. Utmost care was taken not to allow the sorbent to dry out during the conditioning and sample loading steps. After the conditioning step, the extracted dry samples were re-dissolved in 2 mL acetonitrile–methanol mixture (95:5 v/v) and loaded onto the columns. The extracted samples were passed through the columns at a flow rate of 1 ml min 1. The retained analytes were eluted with 10 mL of acetonitrile-methanol (95:5 v/v) at a rate of 2 mL min 1. This eluent was collected in a 15-mL test tube and evaporated to near dryness using a Turbovap system. Finally, the residues were re-dissolved in 1 mL of methanol and 5 lL were injected into the UHPLC–TOF/MS.

2.4. Chromatographic analysis The analysis was performed using UHPLC–TOF/MS; the column temperature was maintained at 40 °C. The mobile phase consisted of 0.1% (v/v) formic acid in methanol (A) and 0.1% (v/v) formic acid in water (B). The initial mobile phase composition was 5% A for 0.1 min, following by a linear gradient to 100% A up to 4.29 min, and kept for 0.7 min at 95% A. The flow rate used was 0.5 mL/min and the UHPLC operating pressure was maintained at 6500 psi at initial gradient conditions, and the maximum pressure was maintained at less than 8000 psi. Only 5 lL of samples were injected during the experiments. The autosampler temperature was maintained at 8 °C. The UHPLC system was connected to TOF/MS, as mentioned above. The instrument was operated in positive electrospray ionisation mode (ESI+) with the capillary and sampling cone voltages of 80 and 30 V, respectively. The source and desolvation temperatures were maintained at 115 and 250 °C, respectively. Nitrogen was used as desolvation and cone gas at flow rates of 600 and 50 L h 1, respectively. The instrument was tuned using leucineenkephalin to provide a resolution higher than 11,000 FWHM (m/z 556.2771) in ESI+ and the total current ion chromatogram was acquired over the mass (m/z) range of 50–1000. The mass calibration in positive ionisation mode was performed using sodium formate solution (0.5 M). The mass accuracy was maintained within the whole acquisition period by using a lock spray with leucine-enkephalin as the internal reference compound. MassLynx 4.1 software was used for data acquisition and processing, whereas QuanLynx software was used for quantification and confirmation of the pesticide residues in the samples.

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Table 1 UHPLC–TOF/MS accurate mass measurements of pesticides in mango matrix. Pesticide

Elemental composition

tr

Selection ion

m/z

Acephate Carbendazim Oxydemeton–methyl Thiabendazole Monocrotophos Dimethoate Oxycarboxin Thiacloprid Metoxuron Phosphamidon Dichlorvos Metribuzin Simazine Propoxur Bromacil Fenamiphos Sulfoxide Carboxin Methabenzthiazuron Isoproturon Clomazon Linuron Propanil Methiocarb Malathion Dimethomorph Ethoxysulfuron Triadimefon Myclobutanil Propetamphos Triazophos Fenarimol Etaconazole Metolachlor Coumachlor Quinalphos Iprobenfos Phenthoate Ediphenphos Penconazole Tebuconazole Diazinon Chlorfenvinphos Propiconazole Phosalone Hexaconazole Thiobencarb Methyl chlorpyrifos Difenoconazole Prallethrin Buprofezin Indoxacarb Pretilachlor Benfuracarb Profenophos Quizalofop ethyl Tetramethrin Abate Ethion Allethrin Chlorpyrifos

C4H10NO3PS C9H9N3O2 C6H15O4PS2 C10H7N3S C7H14NO5P C5H12NO3PS2 C12H13NO4S C10H9CIN4S C10H13ClN2O2 C10H19ClNO5P C4H7Cl2O4P C8H14N4OS C7H12ClN5 C11H15NO3 C9H13BrN2O2 C13H22NO4PS C12H13NO2S C10H11N3OS C12H18N2O C12H14ClNO2 C9H10Cl2N2O2 C9H9CI2NO C11H15NO2S C10H19O6PS2 C21H22ClNO4 C15H18N4O7S C14H16ClN3O2 C15H17ClN4 C10H20NO4PS C12H16N3O3PS C17H12Cl2N2O C14H15Cl2N3O2 C15H22ClNO2 C19H15ClO4 C12H15N2O3PS C13H21O3PS C12H17O4PS2 C14H15O2PS2 C13H15Cl2N3 C16H22ClN3O C12H21N2O3PS C12H14Cl3O4P C15H17Cl2N3O2 C12H15ClNO4PS2 C14H17Cl2N3O C12H16ClNOS C7H7Cl3NO3PS C19H17Cl2N3O3 C19H24O3 C16H23N3OS C22H17ClF3N3O7 C17H26ClNO2 C20H30N2O5S C11H15ClBrO3PS C19H17ClN2O4 C19H25NO4 C16H20O6P2S3 C9H22O4P2S4 C19H26O3 C9H11Cl3NO3PS

0.91 1.28 1.42 1.45 1.56 1.88 2.07 2.12 2.27 2.43 2.50 2.53 2.55 2.57 2.57 2.67 2.69 2.91 2.97 3.15 3.23 3.25 3.28 3.33 3.38 3.38 3.40 3.44 3.44 3.44 3.54 3.54 3.56 3.61 3.64 3.66 3.66 3.69 3.73 3.73 3.74 3.78 3.78 3.81 3.83 3.86 3.88 3.91 3.93 3.95 3.95 3.98 4.02 4.04 4.04 4.09 4.12 4.12 4.14 4.17

[M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+Na]+ [M+Na]+ [M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+H]+ [M+Na]+ [M+Na]+ [M+H]+ [M+Na]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+Na]+ [M+H]+ [M+Na]+ [M+H]+ [M+Na]+

206.0017 192.0773 247.0228 202.0439 246.0507 251.9894 290.0463 275.0134 229.0744 300.0768 220.9537 215.0967 202.0859 232.0950 283.0058 320.1085 236.0745 222.0701 207.1497 240.0791 249.0198 218.0139 248.0721 353.0258 388.1316 399.0374 294.1009 289.1220 304.0748 314.0728 331.0405 328.0620 284.1417 343.0737 299.0619 311.0847 343.0204 311.0329 284.0721 308.1530 305.1089 358.9774 342.0776 389.9766 314.0827 280.0539 343.8848 406.0725 323.1623 306.1640 528.0785 312.1730 411.1954 372.9430 373.0955 354.1681 466.9976 406.9774 303.1960 371.9160

2.5. Method validation Blank samples were selected for validation purposes. Different validation parameters were evaluated, including recovery, precision, linear range, method limits of detection (MDL) and quantification (MLOQ). Linearity was studied by evaluating the matrixmatched standard calibration curves for all targeted pesticides. Calibration curves were calculated with standards in blank samples at concentrations of 10, 25, 50, 100, 250, 500 and 750 lg L 1. Linear calibration curves were constructed by plotting the targeted

Calculated

m/z

Experimental

206.0016 192.0770 247.0226 202.0439 246.0511 251.9896 290.0462 275.0138 229.0744 300.0770 220.9535 215.0965 202.0859 232.0932 283.0060 320.1083 236.0742 222.0696 207.1497 240.0792 249.0200 218.0139 248.0721 353.0257 388.1313 399.0374 294.1014 289.1220 304.0750 314.0727 331.0423 328.0617 284.1413 343.0739 299.0618 311.0845 343.0201 311.0329 284.0723 308.1533 305.1091 358.9775 342.0774 389.9769 314.0824 280.0540 343.8845 406.0728 323.1626 306.1640 528.0782 312.1729 411.1957 372.9436 373.0958 354.1685 466.9973 406.9774 303.1957 371.9155

Error mDa

ppm

0.1 0.3 0.2 0.0 0.4 0.2 0.1 0.4 0.0 0.2 0.2 0.2 0.0 1.8 0.2 0.2 0.3 0.5 0.0 0.1 0.2 0.0 0.0 0.1 0.3 0.0 0.5 0.0 0.2 0.1 1.8 0.3 0.4 0.2 0.1 0.2 0.3 0.0 0.2 0.3 0.2 0.1 0.2 0.3 0.3 0.1 0.3 0.3 0.3 0.0 0.3 0.1 0.3 0.6 0.3 0.4 0.3 0.0 0.3 0.5

0.5 1.6 0.8 0.0 1.6 0.8 0.3 1.5 0.0 0.7 0.9 0.9 0.0 7.8 0.7 0.6 1.3 2.3 0.0 0.4 0.8 0.0 0.0 0.3 0.8 0.0 1.7 0.0 0.7 0.3 5.4 0.9 1.4 0.6 0.3 0.6 0.9 0.0 0.7 1.0 0.7 0.3 0.6 0.8 1.0 0.4 0.9 0.7 0.9 0.0 0.6 0.3 0.7 1.6 0.8 1.1 0.6 0.0 1.0 1.3

pesticide peak areas ratio obtained against the concentration values. The recovery and repeatability of the method were evaluated by carrying out seven consecutive extractions (n = 7) of spiked samples at four concentration levels (10, 50, 100 and 200 lg kg 1) with 60 targeted pesticides. Linear regression was applied to establish method limits of detection (MDL) and quantification (MLOQ), which were calculated from the curve obtained from the recovery studies (Su, 1998, SANCO/12495/2011, 2012; Commission Decision 2002/657/EC, 2002; Jeffery, 1996). The sample concentrations were calculated using matrix-matched calibration standards. The

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uncertainty was determined for all of the pesticides, according to the procedures recommended by EURACHEM/CITAC and Quantifying Uncertainty in Analytical Measurement (2000). Three sources of uncertainty were taken into account: uncertainty associated with precision, bias and calibration curve. The uncertainty of measurement was obtained at the level of 10 lg kg 1 using validation data. 2.6. Determination of sample stability The stability of pesticides was evaluated during sample collection, handling, storage, freezing, thawing cycles and after the preparation of samples. The stability studies were carried out in two cycles of freeze ( 20 °C) and thaw (5 and 25 °C) and analyses were performed using fresh standards. The pesticide stability during the storage conditions was estimated after it was stored at 20 °C for 1, 5 and 10 days. All stability tests were performed at 10 lg kg 1 and seven replicates. It was considered that the samples were stable, when the mean level of recovery of multiple replicates was within ±10% of the spiked concentration, i.e., 10 lg kg 1. 2.7. Determination of method accuracy of fruit and vegetable samples An in-house laboratory comparison was performed for determination of the method accuracy. Bulk homogenised samples (100 g) were taken in a clean plastic container and tested for residue interferences, using the method described above. The sample was free from pesticide residues. For in-house laboratory comparison, 10 lg kg 1 pesticide standard solution was spiked into bulk sample, and was distributed amongst laboratory analysts, and accordingly, the Z-scores were calculated, using standard formulae (Edelgard, Luc Massart, & Johanna Smeyers, 2000). 2.8. Statistical analysis The statistical analysis was performed using SPSS 16.0 software (SPSS Inc., Chicago, IL). Summary statistics of different concentrations of pesticides in different commodities were tested. The uncertainty measurement, Z-scores and the method validation parameters were calculated using Excel 2007 (Microsoft). 3. Results and discussion 3.1. Chromatographic and analytical conditions 3.1.1. Sample extraction and clean-up procedure Selection of an extracting solvent, having proper polarity match with the analyte is beneficial to improve the process efficiency and also to minimise matrix interferences. In order to choose an appropriate extracting solvent, a comparison was made amongst three mixtures (90:10 v/v) of ethyl acetate:methanol, acetone:methanol and acetonitrile:methanol. The extractions that were carried out using ethyl acetate:methanol (90:10 v/v) and acetone:methanol (90:10 v/v), showed a marked difference in the recovery, ranging from 40% to 140%. The extracts were dirtier, high in pigments due to the high polarity of solvent. Also, the column efficiency was adversely affected after repetitive injection of the extracts into the chromatographic system. Pesticides were readily soluble in acetonitrile:methanol, which gave satisfactory recoveries between 73% and 111%. The solvent needed for the extraction was considerably less in quantity. Therefore, acetonitrile:methanol (90:10 v/v) was chosen for the sample extraction. In the sample clean-up process, SPE was used to remove matrix interference from the vegetable and fruit samples. For optimisation of SPE conditions florisil, GCB/PSA, C18 and NH2-LC sorbents were tested for the studied pesticides. The recovery ranges of sorbents

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varied widely; for example, florisil, GCB/PSA, C18 and NH2-LC were 40–147%, 73–111%, 60–140% and 25–75%, respectively. In the case of GCB/PSA, the pigments could be efficiently absorbed from the sample matrix and recoveries were greater than 70% for nearly all pesticides. Accordingly, GCB/PSA SPE column was selected for the clean-up of residues from fruit and vegetable samples. Whilst the SPE column elution system was chosen amongst several other commonly used systems, the relative percentage of methanol was arrived at by performing recovery tests with 1%, 2.5% and 5% of methanol in acetonitrile. Most of the residues were quantitatively recovered using 5% of methanol in acetonitrile, and hence it is used for column elution. An increase in the eluent strength did not have any significant effect on the amount of coextracted sample matrix. The efficiency of the clean-up procedure was also shown, since no detrimental effects on UHPLC column performance were observed after analysis of nearly 500 samples. Finally, recoveries in the range of 73–111% were obtained when the elution solvent was acetonitrile:methanol (95:5 v/v). 3.1.2. UHPLC–TOF/MS parameter optimisation The LC separation of the target species was achieved in less than 5 min, obtaining satisfactory resolution with average peak widths of 10 s. This was better than the typical analytical column with average peak width of 20–40 s at baseline. Compared to the previously reported methods (Garcia-Reyes, Hernando, Ferrer, MolinaDíaz, & Fernández- Alba, 2007; Mol et al., 2008; Taylor, Keenan, Reid, & Fernández, 2008), the use of a small particle size column provides several advantages, e.g., the total run time is very short, less volume of organic solvent is used, and average base-line peak width is reduced twofold, which leads to increase in analyte S/N ratio, and thus, improves the limits of detection of the method. It was also noted that the use of this column type is fully compatible with non-high pressure systems. These advantages, are therefore, worthy to be exploited, using conventional HPLC instrumentation, since the operating pressure of the column is typically less than 500 bar. The instrumental parameters, like drying and nitrogen flow rates, vaporiser and drying temperatures, and capillary voltage were optimised for better sensitivity of the instrument. These parameters in the studied ranges did not affect the signal of the analytes significantly, except for the fragmentor voltage, which ranged from 50 to 200 V, and was optimum at 80 V. Ferrer, García-Reyes, Mezcua, Thurman, and Fernanndez-Alba (2005) noted that the fragmentor voltage provides information on the characteristics of fragmentation of pesticides and the accurate mass of each fragmentation together with its elemental composition. The optimised in-source fragmentation in LC–TOF/MS is useful for identification of pesticides. In order to establish criteria for spectrometric identification and confirmation of organic residues and contaminants, the 2002/657/EC, 2002 European Commission decision addresses the need to obtain three identification points to confirm organic residues (Ferrer et al., 2005). 3.1.3. Accurate mass measurements One of the main attributes of TOF instruments, making them an attractive analytical technique, is their accurate mass measurement, which gives the elemental composition of parent and fragment ions. The reliability of the screening method depends heavily on the ruggedness of the TOF instrument, in order to provide consistently accurate mass measurements within a fixed mass error tolerance. Typically, the measurement of accurate masses within 5 ppm is widely accepted for the verification of the elemental composition. The TOF system used for this work has demonstrated mass accuracy values of 0.99 of all the pesticides. Table 2 shows the MDLs and MLOQs obtained for each pesticide. The MDLs and MLOQ values obtained ranged from 0.3 to 3.8 lg kg 1 and 0.8 to 11.8 lg kg 1, respectively (Table 2). Most components presented linearity, MDL and MLOQs values below the MRLs established by the Food Safety Standard Authority of India and the European Union (Barr et al., 2002; Commission Decision 2002/657/EC, 2002; SANCO/12495/ 2011, 2012). To evaluate the matrix effect in vegetable and fruit samples, the matrix match calibration and dual spray ESI source (reference and standard) technique was used to reduce the matrix effect. The relative recoveries for analytes in samples in comparison with the results obtained from spiked samples at the same concentrations were calculated after performing the proposed method. According to the obtained results, a relatively strong matrix effect was observed when the sample was applied without matrix match calibration and dual spray technique, but by applying the technique, there was no significant effect on the recovery performance of the method. Sample dilution, matrix-matched calibration, and analyte protectants are amongst the effective ways to reduce interfering compounds, and, in turn, diminish matrix effects (Ferrer, Lozano, Aguera, Giron, & Fernandez-Alba, 2011; Rahman, Abd ElAty, & Shim, 2013). 3.2.2. Accuracy, precision and uncertainty The study of method recovery was carried out for three levels of concentration (0.05, 0.1 and 0.2 lg kg 1) by comparing the concentration of each pesticide obtained after extraction and concentration in fortified matrix. The method precision is expressed as the repeatability (RSD, %) of the recovery determinations at the four different spiking levels (10, 50, 100 and 200 lg kg 1). The recovery averages and the values of relative standard deviations (RSD, %) of the individual pesticides are shown in Table 3. All pesticides studied showed recovery rates ranging from 74% to 111% with a relative standard deviation less than 15%. The SANCO Guidelines recommend recovery percentages of 70–120% and RSD 6 20% (European Commission, 2009; Commission Decision 2002/657/ EC, 2002; SANCO/12495/2011, 2012). The multi-residue method was previously used by Chu et al. (2007) for the extraction of pesticides from soybean samples, using LC/TOF–MS. A recovery percentage between 60% and 120% was obtained, with an RSD of 2.17–13.54%. Nguyen, Yu, Lee, and Lee (2008) achieved satisfactory recoveries (80–115%) for 107 pesticides in cabbage and radish. Sinha et al. (2012) using the QuEChERS method and LC–MS/MS for the determination of 18 pesticides in banana obtained recovery percentages between 94% and 103% with RSD below 10%. The uncertainty of measurement represents a quantitative indicator of the reliability of the analytical results, expressed as a range which is estimated to be the real value, usually associated with a confidence level. In an analytical procedure, the uncertainty about the result may arise from many possible sources, including sampling, matrix effects, environmental conditions, glassware, method of measurement and random variation. In this work, the combined relative uncertainty (Uc) and expanded uncertainty (Ue) was

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Table 2 UHPLC–TOF/MS method performances for pesticide residues (mango matrix). Pesticide

tra

Calibration r

Acephate Carbendazim Oxydemeton-methyl Thiabendazole Monocrotophos Dimethoate Oxycarboxin Thiacloprid Metoxuron Phosphamidon Metribuzin Dichlorvos Bromacil Propoxur Simazine Fenamiphos Sulfoxide Carboxin Methabenzthiazuron Isoproturon Clomazon Linuron Propanil Methiocarb Malathion Dimethomorph Ethoxysulfuron Triadimefon Propetamphos Triazophos Myclobutanil Fenarimol Etaconazole Metolachlor Coumachlor Quinalphos Iprobenfos Phenthoate Ediphenphos Penconazole Tebuconazole Chlorfenvinphos Diazinon Chlorfenvinphos Hexaconazole Phosalone Thiobencarb Methyl chlorpyrifos Prallethrin Difenoconazole Indoxacarb Pretilachlor Buprofezin Benfuracarb Quizalofop ethyl Tetramethrin Profenophos Allethrin Ethion Abate Chlorpyrifos

0.91 1.28 1.42 1.45 1.56 1.88 2.07 2.12 2.27 2.43 2.50 2.53 2.55 2.57 2.57 2.67 2.69 2.91 2.97 3.15 3.23 3.25 3.28 3.33 3.38 3.38 3.40 3.44 3.44 3.44 3.54 3.54 3.56 3.61 3.64 3.66 3.66 3.69 3.73 3.73 3.74 3.78 3.78 3.81 3.83 3.86 3.88 3.91 3.93 3.95 3.95 3.98 4.02 4.04 4.04 4.09 4.12 4.12 4.14 4.17

2b

0.9996 0.9988 0.9998 0.9995 0.9964 0.9941 0.9988 0.9981 0.9992 0.9925 0.9992 0.9998 0.9996 0.9985 0.9994 0.9997 0.9991 0.9991 0.9983 0.9994 0.9994 0.9976 0.9968 0.9921 0.9993 0.9976 0.9985 0.9996 0.9976 0.9955 0.9967 0.9971 0.9981 0.9997 0.9934 0.9987 0.9955 0.9929 0.9984 0.9978 0.9988 0.9982 0.9992 0.9985 0.9996 0.9994 0.9953 0.9993 0.9960 0.9988 0.9993 0.9992 0.9976 0.9994 0.9993 0.9921 0.9993 0.9965 0.9988 0.9965

S/Nc

% RSDd

MDLe

LOQf

% Recoveryg

Uh (%)

30 13 4 34 3 12 10 6 6 25 67 6 3 16 10 17 33 31 25 19 9 3 24 17 9 19 38 26 18 8 24 42 15 26 88 79 55 85 81 92 64 27 37 12 66 3 14 43 13 23 58 72 15 26 34 39 5 13 51 16

5.3 2.5 1.7 2.5 8.5 5.7 2.4 1.8 2.7 1.8 3.0 4.6 5.3 4.6 2.1 1.5 3.3 2.9 2.8 2.6 2.9 2.8 1.4 5.9 1.2 0.9 1.8 1.5 3.5 2.5 1.2 2.5 1.7 2.1 6.6 3.4 3.9 3.8 2.9 1.9 2.6 1.8 3.2 2.5 2.5 3.2 6.5 2.5 2.1 3.3 2.4 4.1 2.3 1.4 2.3 5.8 2.4 5.8 1.6 3.9

2.9 2.6 0.9 3.3 2.8 3.2 2.8 3.1 2.8 1.9 1.6 1.6 3.8 2.1 2.8 2.1 1.5 1.7 3.3 1.3 1.9 3.1 1.3 2.8 3.3 0.7 2.6 1.8 2.1 2.8 3.3 3.5 2.3 1.4 3.1 3.7 2.3 3.4 3.4 2.1 1.2 1.3 2.4 1.1 1.9 3.7 3.5 0.3 2.0 1.6 1.6 3.2 2.9 0.5 1.2 3.3 1.3 3.2 1.1 2.3

9.1 8.4 2.9 10.5 8.9 10.1 8.8 9.9 8.9 6.1 5.0 4.5 11.8 6.8 9.1 6.6 4.7 5.5 10.3 4.2 6.3 9.8 4.1 8.9 10.3 2.5 8.4 5.6 7.8 7.7 10.5 10.9 7.4 4.4 9.9 11.8 7.2 11.4 10.9 6.5 3.9 4.3 7.6 3.5 5.9 11.7 10.8 0.8 6.5 5.2 5.2 10.0 9.1 1.5 3.9 10.5 4.1 9.9 3.6 7.4

97 ± 3 111 ± 1 105 ± 1 108 ± 2 101 ± 4 103 ± 3 74 ± 2 100 ± 2 97 ± 2 83 ± 1 102 ± 1 74 ± 2 102 ± 2 103 ± 1 101 ± 1 108 ± 1 96 ± 1 85 ± 2 102 ± 2 95 ± 1 101 ± 1 98 ± 2 98 ± 1 81 ± 2 103 ± 1 99 ± 1 106 ± 2 96 ± 1 87 ± 3 101 ± 1 104 ± 1 87 ± 3 99 ± 1 102 ± 1 96 ± 3 107 ± 2 101 ± 2 85 ± 1 103 ± 2 99 ± 1 106 ± 2 97 ± 1 105 ± 1 99 ± 1 91 ± 1 99 ± 4 86 ± 4 102 ± 1 100 ± 1 103 ± 1 100 ± 1 107 ± 2 102 ± 2 101 ± 1 105 ± 1 87 ± 3 101 ± 1 73 ± 4 100 ± 1 76 ± 2

30 20 40 30 25 25 20 30 30 10 30 40 30 20 30 20 10 20 20 20 30 30 20 40 30 10 20 30 40 20 20 40 20 20 38 30 20 30 30 20 30 20 20 20 30 25 30 25 20 30 20 30 40 40 10 40 20 42 20 20

Equation y = 0.0516x + 0.0697 y = 0.1127x 0.2790 y = 0.0443x 0.4464 y = 0.1741x + 0.04061 y = 0.0584x + 0.9694 y = 0.1995x + 3.3246 y = 0.0828x + 0.1239 y = 0.0685x 0.4109 y = 0.0818x + 0.4554 y = 1.1921x + 3.9816 y = 0.3261x + 3.3690 y = 0.0262x + 0.0045 y = 0.0209x + 0.0930 y = 1.0789x 0.8023 y = 0.3661x + 1.3716 y = 0.9998x 0.5100 y = 0.1283x + 0.6904 y = 0.1145x + 0.8928 y = 0.3436x + 0.2906 y = 0.0867x + 0.5456 y = 0.0244x + 0.0649 y = 0.0099x + 0.1301 y = 0.0859x + 0.0212 y = 0.1873x + 2.8740 y = 0.0336x + 0.2938 y = 0.4372x + 8.1628 y = 0.1099x + 0.4327 y = 0.2114x + 0.5202 y = 0.2877x + 6.7742 y = 0.2685x + 8.7963 y = 0.1219x + 0.5524 y = 0.2219x + 1.5445 y = 0.0699x + 0.8723 y = 0.0971x + 0.3021 y = 0.1023x + 1.8409 y = 0.3423x + 0.6511 y = 0.2143x + 0.4862 y = 0.0312x + 0.1644 y = 0.1956x + 0.4251 y = 5.0595x + 4.4618 y = 0.1109x + 0.5073 y = 0.9632x + 0.9388 y = 0.1924x + 0.3112 y = 0.1944x + 0.4055 y = 0.1043x + 0.2979 y = 0.0138x + 0.0326 y = 0.2189x + 0.1843 y = 0.4730x + 0.8338 y = 0.1591x + 0.1726 y = 0.0325x + 0.1665 y = 0.4176x 1.0390 y = 0.4307x + 0.4256 y = 0.0844x + 1.9895 y = 0.0813x + 0.1239 y = 0.1795x + 0.0621 y = 0.1283x + 1.5276 y = 0.1559x + 0.3952 y = 0.0306x 0.6982 y = 0.0448x + 0.1625 y = 0.0040x + 0.0404

d,e,f,g a b c d e f g h

Seven replicates (n = 7). Retention time of the compound. Coefficient of determination. Signal-to-noise ratio. % Relative standard deviation. Method detection limit (lg kg 1). Limit of quantification (lg kg 1). % Recovery (n = 7). Uncertainty estimated for a level of confidence 95% (k = 2) at a concentration 10 lg kg

determined using repetition, recovery and calibration curves data for each pesticide (Table 2). The Ue values ranged from 10% to 40%. Oxydemeton-methyl, Dichlorvos, Malathion, Triazophos,

1

.

Etaconazole, Benfuracarb, Quizalofop ethyl and Profenophos showed highest levels of uncertainty (40%). The repeatability was the largest contribution to the measurement uncertainty. Other

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P. Sivaperumal et al. / Food Chemistry 168 (2015) 356–365 Table 3 Application of the method to vegetable and fruit samples. Matrix

Pesticide

Min (lg kg

1

)

Max (lg kg

1

)

⁄ EU MRL (lg kg 1)

⁄⁄

FSSAI MRL (lg kg 1)

No. of samples > MRL (⁄EU)

No. of samples > MRL (⁄⁄FSSAI)

Apple (n = 32)

Acephate Benfuracarb Carbendazim Dimethoate Ethion Monocrotophos Profenofos

14 46 1304 34 59 33 30

892 73 1325 144 71 209 32

20 – – – – – –

– – 5000 2000 2000 200 –

4 (13%) – – – – – –

– – – – – 1 (3%) –

Banana (n = 16)

Acephate Difenoconazole Dimethoate Monocrotophos

133 62 144 49

904 62 147 167

20 – – –

– – 2000 2000

2 (13%) – – –

– – – –

Brinjal (n = 26)

Acephate

124

1198

20

–

6 (23%)

–

Cauliflower (n = 25)

Acephate Chlorpyrifos Monocrotophos

222 30 10

1556 39 145

20 50 –

– 10 200

8 (32%) – –

– 3 (12%) –

Cabbage (n = 32)

Acephate Chlorpyrifos Ethion Monocrotophos Profenofos

122 19 56 55 29

1170 32 61 91 157

20 1000 – – –

– 10 1000 200 –

9 (28%) – – – –

– – – – –

Grape (n = 25)

Acephate Monocrotophos

298 9

1559 154

20 –

– 200

7 (28%) –

– –

Guava (n = 12)

Acephate Carbendazim Monocrotophos

84 11 138

117 1455 177

20 – –

– – 1000

2 (17%) – –

– – –

Mango (n = 6)

Acephate

Okra (n = 25)

Acephate Ethion Monocrotophos

23

100

20

–

2 (33%)

–

682 52 10

2774 57 11

– – –

– 1000 200

– – –

– – –

Onion (n = 19)

Propoxur Dimethoate

89 37

96 147

– –

– 2000

– –

– –

Orange (n = 6)

Acephate Monocrotophos

130 141

294 184

– –

– 200

– –

– –

Pomegranate (n = 8)

Acephate Benfuracarb Chlorpyrifos Ethion Monocrotophos

24 45 13 13 8

1517 46 39 66 253

– – 50 – –

– – 500 2000 1000

– – – – –

– – – – –

Potato (n = 29)

Acephate Ethion Monocrotophos Profenofos

1309 56 41 38

2634 65 131 164

– – – –

– 1000 50 –

– – – –

– – 2 (7%) –

Tomato (n = 25)

Acephate Dimethoate Ethion Monocrotophos Profenofos

20 32 52 29 16

1219 48 59 207 161

10 20 – – –

– 2000 1000 200 –

5 (20%) 2 (8%) – – –

– – – 6 (24%) –

Note: ⁄European Union (2012) and

⁄⁄

Food Safety Standard Authority of India (2011). Ministry of health and family welfare (Food Safety and Standards Authority of India).

sources, such as purity of the reference standard make small contributions to the uncertainty values.

seven replicates. The percent deviation in concentration was stable, since the values were within 10% of the sample concentration.

3.2.3. Stability test The short-term stability of pesticides in food commodities was assessed over 3 h by comparison with the initial measurement concentrations. The standard samples were stable after storage at 20 °C for one month. Freeze/thaw stability experiments indicated that the pesticides were stable for three freeze/thaw cycles (1, 5 and 10 days). After preparation, the samples were found to be stable for 10 days at 4 °C. The deviations of concentrations were lees then 20% of the quality control samples. All stability tests were performed on the quality control concentrations, using samples in

3.2.4. Determination of method accuracy The method accuracy was investigated at a concentration level of 10 lg kg 1 by intra-laboratory comparison. The analytical recovery was assessed by comparing the chromatograms of calibration standards with spiked sample solutions. For most of the compounds, the recovery values ranged between 70% and 108%. The accuracy was also tested by an intra-laboratory comparison test. The mean values obtained in the present method and the target values were very near to spiked concentration and the obtained Z-scores were 0.5 to 0.6, which was excellent and within range (Edelgard et al., 2000). This finding is additional convincing

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P. Sivaperumal et al. / Food Chemistry 168 (2015) 356–365

evidence of the high efficiency of the present method and its suitability for the determination of 60 pesticide residues in vegetable and fruit matrices. 3.3. Analysis of vegetable and fruit samples To verify the effectiveness of the method, 286 vegetable and fruit of samples were analysed. Only residues of the pesticides Acephate, Propoxur, Benfuracarb, Carbendazim, Chlorpyrifos, Difenoconazole, Dimethoate, Ethion, Monocrotophos, Myclobutanil and Profenofos were found in samples. Detailed data of the pesticide residues detected in more than one sample are shown in Table 3. The results showed that the most frequently detected pesticides were Acephate, Ethion, Monocrotophos and Profenophos, for which the detection range was from 14 to 2774, 13 to 71, 8 to 253 and 16 to 164 lg kg 1, respectively. The overall result revealed that 83.6% of samples were below the maximum residual limit prescribed by the EU (2012) and 96% of samples were below the maximum residual limit prescribed by Food Safety Standard Authority of India (2011). Srivastava, Trivedi, Srivastava, Lohani, and Srivastava (2011) conducted a similar study and the results indicated that 23 pesticides were detected from a total number of 48 pesticides analysed in the samples, with the range varying from 5 to 12,350 lg kg 1. These concentrations were very high, as compared to the present study. Most studies carried out in India are concerned with organochlorine pesticide residues in food commodities (Bhanti & Taneja, 2005; Kumari, Kumar, & Kathpal, 2001; Mandal & Singh, 2010). However, the present study covered the maximum number of pesticide residues in different samples. 4. Conclusions This is a novel multi-residue analysis method, developed and validated, using SPE with UHPLC-TOF/MS for sensitive identification and quantification of a large number of pesticide residues in vegetable and fruit samples. A mixture of acetonitrile:methanol was proved to be an effective solvent for extracting multi-level pesticides. The percentage recoveries, RSD, and S/N ratios obtained were well within the prescribed analytical limits of different regulatory authorities. Having the merits of shorter run time (5 min) and less consumption of mobile phase (2.5 mL), solid-phase extraction with GCB/PSA was used for clean-up and isolation of pesticides, and further determination of pesticide residues was performed using UHPLC–TOF/MS. This analytical method can routinely be applied for analysis of pesticides in vegetable and fruit samples, in order to regulate the MRL of pesticides. The present analysis was limited to only 286 fruit and vegetable samples, which did not contain a significant pesticide load; however, analysis of a large sample size might be required to establish whether vegetable and fruit consumption in the region poses a pesticiderelated health risk to the public. Acknowledgments The authors are grateful to the Director, National Institute of Occupational Health, Ahmedabad, for his valuable suggestions during the execution of the study. The authors acknowledge the financial assistance of the Indian Council of Medical Research for the project (IRIS ID NO. 2011-02530). References Anastassiades, M., Lehotay, S. J., Stajnbaher, D., & Schenck, F. J. (2003). Fast and easy multiresidue method employing acetonitrile extraction/partitioning and

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