Chromatography-Tandem Mass Spectrometry as

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AUTHOR'S PROOF

Metadata of the article that will be visualized in OnlineFirst

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Article Title

Pesticide Residue Determination by Gas Chromatography-Tandem Mass Spectrometry as Applied to Food Safety Assessment on the Example of Some Fruiting Vegetables

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Springer Science+Business Media New York 2015 (This will be the copyright line in the final PDF)

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Food Analytical Methods

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Walorczyk Stanisław

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Institute of Plant Protection—National Research Institute

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Władysława Węgorka 20, Poznań 60-318, Poland

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[email protected]

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Kopeć

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Izabela Institute of Plant Protection—National Research Institute Władysława Węgorka 20, Poznań 60-318, Poland Szpyrka Ewa

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Institute of Plant Protection—National Research Institute

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Regional Experimental Station in Rzeszów

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Langiewicza 28, Rzeszów 35-101, Poland

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Received Revised

23 July 2015

AUTHOR'S PROOF 31 32

Accepted Abstract

14 August 2015

A multiresidue method for the analysis of over 140 multiclass pesticides in fruiting vegetables, based on quick, easy, cheap, effective, rugged, and safe (QuEChERS) sample preparation followed by gas chromatography-tandem mass spectrometry (GC-MS/MS), was established. In the validation study, the overall recoveries from spiked samples were 102 ± 7, 95 ± 7, and 95 ± 7 % with RSD values of 7 ± 3, 7 ± 4, and 7 ± 3 % at the spiking levels of 0.01, 0.05, and 0.5 mg kg−1, respectively, demonstrating fitness for purpose of the method. The limit of quantification (LOQ) was 0.01 mg kg−1 for more than 90 % of the target compounds. The analysis of over 300 samples of tomatoes, sweet peppers, and cucumbers was carried out in 2006–2014. Of these samples, 52 % contained pesticide residues but the results of the assessment of dietary exposure supported the conclusion that the presence of pesticide residues was unlikely to have a negative effect on the health of consumers. Although some of the pesticides detected in years 2006–2009 are no longer approved in the EU member countries (namely endosulfan, oxadixyl, procymidone, propargite, and tolylfluanid), the consumer dietary exposure was low and did not exceed 12 % of the acceptable daily intake (ADI) considering both adults’ and children’s diet. Regarding short-term exposure (acute), in only one case of procymidone in sweet pepper, the acute reference dose (ARfD) for children was exceeded by 139.6 % of the ARfD.

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Keywords separated by ' - '

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Foot note information

Pesticide residue analysis - Fruiting vegetables - Gas chromatography Tandem mass spectrometry - Dietary consumer exposure

AUTHOR'S PROOF

JrnlID 12161_ArtID 292_Proof# 1 - 24/08/2015

Food Anal. Methods DOI 10.1007/s12161-015-0292-6

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Pesticide Residue Determination by Gas Chromatography-Tandem Mass Spectrometry as Applied to Food Safety Assessment on the Example of Some Fruiting Vegetables Stanisław Walorczyk 1 & Izabela Kopeć 1 & Ewa Szpyrka 2

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Received: 23 July 2015 / Accepted: 14 August 2015 # Springer Science+Business Media New York 2015

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Abstract A multiresidue method for the analysis of over 140 multiclass pesticides in fruiting vegetables, based on quick, easy, cheap, effective, rugged, and safe (QuEChERS) sample preparation followed by gas chromatography-tandem mass spectrometry (GC-MS/MS), was established. In the validation study, the overall recoveries from spiked samples were 102± 7, 95±7, and 95±7 % with RSD values of 7±3, 7±4, and 7± 3 % at the spiking levels of 0.01, 0.05, and 0.5 mg kg−1, respectively, demonstrating fitness for purpose of the method. The limit of quantification (LOQ) was 0.01 mg kg−1 for more than 90 % of the target compounds. The analysis of over 300 samples of tomatoes, sweet peppers, and cucumbers was carried out in 2006–2014. Of these samples, 52 % contained pesticide residues but the results of the assessment of dietary exposure supported the conclusion that the presence of pesticide residues was unlikely to have a negative effect on the health of consumers. Although some of the pesticides detected in years 2006–2009 are no longer approved in the EU member countries (namely endosulfan, oxadixyl, procymidone, propargite, and tolylfluanid), the consumer dietary exposure was low and did not exceed 12 % of the acceptable daily intake (ADI) considering both adults’ and children’s diet. Regarding short-term exposure (acute), in only one case of procymidone in sweet pepper, the acute reference dose (ARfD) for children was exceeded by 139.6 % of the ARfD.

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Keywords Pesticide residue analysis . Fruiting vegetables . Gas chromatography . Tandem mass spectrometry . Dietary consumer exposure

* Stanisław Walorczyk [email protected] 1

Institute of Plant Protection—National Research Institute, Władysława Węgorka 20, 60-318 Poznań, Poland

2

Regional Experimental Station in Rzeszów, Institute of Plant Protection—National Research Institute, Langiewicza 28, 35-101 Rzeszów, Poland

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Introduction

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Fruiting vegetables including cucumber (Cucumis sativus L.), tomato (Lycopersicon esculentum Mill.), and sweet pepper (Capsicum L.) are vegetables formed from the fruits of the plants that bear them. They are among the most widely consumed vegetables (especially tomato) (http://www.aitanazionale.it/atti/070209/colvine.pdf). Although the crops are suited for pesticide-free (or organic) cultivation (Matyjaszczyk 2015), efficient and profitable crop protection most often relies on the use of synthetic pesticides (Popp et al. 2013). Fruiting vegetables can be adversely affected by numerous pests and diseases. The most serious pests are, among others, aphids, flea beatles, cutworms, hornworms, armyworms, leafrollers, weevils, and spider mites. Whereas the major diseases are, among others, mosaic, fusarium wilt, blossom-end rot, bacterial spot, bacterial canker, powdery mildew, alternaria leaf blight, leaf spots, downy mildew, and anthracnose (http://www.minrol.gov.pl/pol/Informacjebranzowe/Wyszukiwarka-srodkow-ochrony-roslin). Chemical control of the pests and pathogens represents a major part of the pest control measures necessary to achieve profitable crop yields. Hence, monitoring the pesticide residue levels in crops is of great importance in order to reassure food safety standards required by the legislation currently in force (Hanford et al. 2015). In general, many methods can be applied to conduct pesticide residue analyses in food crops, as demonstrated in reviews published recently (Fenik et al. 2011; GonzálezCurbelo et al. 2012). Since the introduction by Anastassiades

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AUTHOR'S PROOF


Food Anal. Methods

Experimental

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Samples

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Vegetable samples for pesticide residue analysis were either submitted by private customers or collected by authorized personnel representing government agencies. Upon receipt into the laboratory, the samples were comminuted, placed in plastic storage bags, and stored at −20 °C before proceeding with analysis. The results were obtained for 308 samples of

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Pesticide Residue Analysis

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Chemicals and Standards

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Certified pesticide standards and triphenylphosphate (internal standard) were purchased from Witko (Łódź, Poland). Stock solutions of approximately 1000 μg mL−1 were prepared in acetone. Purity of each standard was taken into account for calculating the actual concentration of the standard solution. A blend solution of all pesticides at the concentration of 5 μg mL−1 was prepared in acetone. The working standards needed for spiking samples and calibrating the GC-MS/MS were prepared by subsequent dilutions of the blended standard solution. Acetonitrile and acetone (for residue analysis) were purchased from Witko (Łódź, Poland). Toluene (for residue analysis) and formic acid (ACS grade) were purchased from Merck (Darmstadt, Germany). Anhydrous magnesium sulfate (reagent grade) and Supel Que Citrate (EN) tubes containing 4 g magnesium sulfate, 1 g sodium chloride, 0.5 g sodium citrate dibasic sesquihydrate, and 1 g sodium citrate tribasic dehydrate were purchased from Sigma-Aldrich Sp. z.o.o. (Poznań, Poland). Pure sodium chlorine was purchased from POCH (Gliwice, Poland) and Bondesil PSA (40 μm) bulk sorbent from Perlan Technologies (Warszawa, Poland).

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Preparation of Sample Extracts

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The sample preparation procedure was based on the modified citrate-buffered QuEChERS method (Payá et al. 2010). Briefly, a 10 g of sub-sample was weighted into a 50-mL polypropylene centrifuge tube. Acetonitrile (10 mL) and internal standard (TPP, 50 μL, 150 μg mL−1) were added, and the centrifuge tube was mixed for 5 min on a laboratory shaker. Hereafter, 4 g anhydrous magnesium sulfate, 1 g sodium chloride, 1 g trisodium citrate dihydrate, and 0.5 g disodium hydrogencitrate sesquihydrate were added, and the tube was manually shaken for 1 min, then centrifuged at 4500 rpm for 2.5 min. A 5 mL aliquot of the supernatant was transferred to a 15-mL polypropylene centrifuge tube containing 125 mg prostate-specific antigen (PSA) and 750 mg anhydrous magnesium sulfate. The tube was vortexed for 0.5 min and centrifuged at 4500 rpm for 2.5 min. A 1.5 mL aliquot of the supernatant was transferred into an autosampler vial containing 50 μL of 5 % formic acid in acetonitrile (which was added for stabilization of base-sensitive pesticides). The extract was evaporated under a stream of nitrogen and reconstituted in 1.5 mL toluene before injection into the GC-MS/MS.

144 145Q1 146 147 148 149 150 151 152 153 154 155 156Q2 157 158 159 160 161 162 163

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fruiting vegetables including tomatoes (173 samples), peppers (71 samples), and cucumbers (64 samples) originating from central-western Poland.

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et al. (2003), the quick, easy, cheap, effective, rugged, and safe (QuEChERS) approach to sample preparation, it has proven to be an efficient and convenient approach to analyze numerous pesticides and other contaminants in different types of agricultural products. The procedure involves the initial extraction by shaking with a solvent (typically acetonitrile), salt-out partitioning of water with salts (magnesium sulfate and buffering agents), and centrifugation, then dispersive-solid phase extraction (d-SPE) to retain matrix co-extractives but leave the analytes in the extract (Lehotay et al. 2010; Lehotay 2011; Bruzzoniti et al. 2014). A great advantage of the QuEChERS is the possibility to accommodate modifications depending on analyte properties, matrices, and available laboratory facilities (González-Curbelo et al. 2015). Regarding instrumental analysis, chromatographic techniques with different detection systems including triple quadrupole (TQ), ion trap (IT), quadrupole linear ion trap (QLIT), time of flight (TOF), and quadrupole time of flight (QTOF) have been established as powerful analytical tools for identification and quantification of pesticide residues (Botitsi et al. 2011). In particular, gas chromatography-tandem mass spectrometry (GC-MS/MS) using triple quadrupole mass analyzers has gained in importance in the last decade as a powerful technique for targeted analysis of pesticides in complex matrices due to its inherent capability to achieve a higher degree of selectivity, lower limits of quantification, and higher sample throughput compared with classical single stage GCMS/SIM analysis (Hernández et al. 2013). Despite the potentiality of chromatography-tandem mass spectrometry-based techniques, some doubts still exist about long-time applicability to routine analysis. The main objective of this work was to validate a sensitive and specific analytical method based on modified QuEChERS and GC-MS/MS and apply the method to long-term pesticide residue analyses in several different commodities under strict quality control conditions to demonstrate ruggedness of the proposed approach. Also, the potential risks to the consumers’ health associated with the dietary exposure to pesticide residues in the samples of cucumbers, sweet peppers, and tomatoes with special attention to the residues of pesticides that are no longer approved in the EU member countries were evaluated.

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AUTHOR'S PROOF


Food Anal. Methods

A Varian CP-3800 gas chromatograph interfaced to a triple quadrupole mass spectrometer, model 1200 (Middelburg, Netherlands) was used. The instrument was equipped with electronic flow control (EFC), a 1079 universal capillary injector, and a CP-8400 autosampler. The compounds were separated on a DB-5 MS 30 m×0.25 mm×0.5 μm (Agilent Technologies, Folsom, CA, USA) capillary column connected to a 2 m×0.53 mm precolumn using a press-tight column connector. The carrier gas (helium) pressure was initially 11.6 psi (held for 3 min), then raised at 1.87 psi min−1 to 16 psi and at 0.6 psi min−1 to 24.9 psi (held for 10 min). The column oven temperature program was 80 °C (held for 3 min), then 30 °C min−1 to 150 °C and 10 °C min−1 to 300 °C (held for 10 min). Injection port temperature program was 250 °C for 1.5 min, then 200 °C min−1 to 300 °C (held for 20 min). Injection volume was 5 μL. The mass spectrometer was operated in electron impact ionization mode (electron energy 70 eV and filament current 50 μA). The temperatures of the transfer line, ion source, and manifold were 290, 270, and 40 °C, respectively. Argon was used as the collision gas with cell pressure 1.7 mTorr. The mass spectrometer was calibrated with perfluorotributylamine (PFTBA). Multiple reaction monitoring (MRM) transitions of precursor ions fragmenting into product ions under optimized collision energies were experimentally developed for each individual pesticide on the instrument used in this work. The GC-MS/MS conditions including optimized MRM transitions and other parameters are detailed in Table 1. MS Workstation software, version 6.6, was used for instrument control and data evaluation.

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Method Validation and Ongoing Verification

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The initial method validation was carried out on tomato. The recoveries were determined in six repetitions at the three spiking levels (0.01, 0.05, and 0.5 mg kg−1). The samples were spiked before proceeding with preparation of sample extracts. The average recoveries and relative standard deviations (RSD) were compared against the EU SANCO/12571/2013 acceptance criteria, according to which the average recovery should be in the range 70–120 % with RSD less or equal 20 % per spiking level (http://ec.europa.eu/food/plant/pesticides/ guidance_documents/docs/qualcontrol_en.pdf). The limit of quantification (LOQ) was set at the lowest spiking concentration that has been validated with satisfactory recovery and precision. The uncertainty of the measurement was estimated according to the “top-down” approach using the validation data (Walorczyk and Drożdżyński 2012). For ongoing method performance verification, a spiked sample (typically at 0.05 mg kg−1) was always analyzed with a batch of samples to check efficiency of the sample

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Assessment of Consumer Exposure

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Assessment of chronic dietary consumer exposure was done considering average pesticide residue results obtained in the analyzed samples, whereas the assessment of the acute consumer dietary exposure was done for samples containing pesticide residues above the MRLs as well as for pesticides being no longer approved for use in the EU member countries (http://ec.europa. eu/sanco_pesticides/public/?event=activesubstance. selection&language=EN). For this purpose, international estimated daily intake (IEDI) and international estimated shortterm intake (IESTI) values were calculated and compared against the relevant values of acceptable daily intake (ADI) and acute reference dose (ARfD) (http://apps.who.int/iris/bitstream/10665/ 44065/9/WHO_EHC_240_9_eng_Chapter6.pdf). For the assessment of chronic consumer dietary exposure, the IEDI values were calculated according to Eq. (1) (Szpyrka et al. 2013).

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preparation method and suitability of the GC/MS/MS instrument. Different matrices were selected for the determination of recoveries to challenge the applicability of the method. In this way, recoveries for all analyte-matrix combinations were collected on an ongoing basis. Also, Shewhart control charts were plotted for selected representative pesticides to evaluate reproducibility of the method with time.

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GC-MS/MS Analysis

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164

IEDI ¼

X Food chemical concetration Food consumption Body weight

ð1Þ

Chronic exposure was calculated based on the average results for individual pesticides considering data on estimated consumption of raw and semiprocessed agricultural commodities published by the World Health Organization (http://www. who.int/foodsafety/areas_work/chemicalrisks/ IEDIcalculation0217clustersfinal.xlsm). For the assessment of acute consumer dietary exposure, IESTI values were calculated according to Eqs. 2 or 3, depending on the food commodity, since the meal-sized portion, such as a single fruit or vegetable unit, might have a higher residue than the composite (i.e., when whole fruit or vegetable unit weight is above 0.025 kg). Therefore, in the case of tomato samples, where the edible unit weight of raw commodity is less than the weight of large portion, we used Eq. (2). IESTI ¼

U *HR*v þ ðLP−U Þ*HR Body wight

240 239 238 241 242 243 244 245 246 247 248 249 250 251 252 253 254

ð2Þ

where LP represents highest large portion reported at the 97.5th percentile of eaters (kg of food/day), HR highest residue (mg kg−1), U unit weight of the edible portion (0.1287 kg for tomato), and ν variability factor which is applied to the composite residue to estimate the residue level in a high-residue unit.

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AUTHOR'S PROOF


Food Anal. Methods t1:1

Table 1

t1:2

Pesticide

GC-MS/MS conditions Retention time window (min)

t1:3

MRM transitions, m/z (collision energy, eV) Quantification

Identification

MRM ratio (RSD, %)

Methamidophos Dichlorvos Acephate Propham Heptenophos Omethoate Tecnazene Propoxur Propachlor Diphenylamine Ethoprophos Phenmedipham Trifluralin Chlorpropham

7.21–7.47 7.39–7.45 9.19–9.51 9.55–9.64 10.76–10.85 11.03–11.21 11.15–11.21 11.16–11.25 11.24–11.31 11.48–11.59 11.49–11.58 11.55–11.75 11.62–11.68 11.68–11.79

141>95 (10) 185>93 (15) 136>94 (15) 137>93 (10) 124>89 (10) 125>79 (10) 203>83 (15) 110>64 (20) 120>77 (15) 169>167 (10) 158>97 (15) 167>135 (10) 306>264 (10)

141>94 (10) 185>109 (20) – 192>127 (15) – 156>110 (15) 215>142 (20) 152>110 (10) 176>57 (5) 169>168 (5) 158>114 (5) 167>122 (15) 264>160 (15)

31 (8) 24 (6) – 26 (16) – 88 (28) 93 (10) 52 (8) 27 (14) 90 (4) 51 (8) 49 (10) 87 (8)

171>127 (10)

213>171 (5)

31 (9)

t1:18 t1:19 t1:20 t1:21 t1:22 t1:23 t1:24 t1:25 t1:26 t1:27 t1:28 t1:29 t1:30 t1:31 t1:32 t1:33 t1:34

Monocrotophos Desmedipham HCH-alfa HCB Dimethoate Carbofuran Dicloran Simazine Atrazine HCH-beta Quintozene Diazinon Lindane Propyzamide Pyrimethanil Chlorothalonil

11.86–11.99 12.36–12.51 12.37–12.43 12.47–12.54 12.53–12.64 12.56–12.64 12.59–12.70 12.62–12.70 12.69–12.76 12.87–12.93 12.94–13.01 13.02–13.08 13.06–13.14 13.06–13.14 13.24–13.33 13.35–13.44

192>127 (10) 181>109 (10) 181>145 (20) 284>214 (35) 125>93 (10) 164>149 (5) 176>148 (10) 201>173 (5) 215>173 (5) 181>145 (20) 214>142 (25) 304>179 (10) 181>145 (20) 173>145 (10) 198>118 (30) 266>133 (35)

192>164 (5) 181>122 (10) 219>181 (10) 284>249 (30) – 164>131 (10) 206>176 (10) 201>186 (5) 215>172 (15) 219>181 (10 214>179 (10) 304>162 (10) 219>181 (10) 173>109 (25) 198>158 (25) 266>168 (35)

24 (14) 48 (12) 39 (8) 57 (4) – 61 (8) 83 (7) 42 (20) 25 (26) 49 (12) 53 (12) 16 (13) 43 (5) 94 (5) 39 (8) 28 (10)

13.56–13.62 13.80–13.87

238>166 (10) 125>93 (10)

238>72 (25) 224>125 (20)

21 (6) 7 (11)

t1:36 t1:37 t1:38 t1:39 t1:40 t1:41 t1:42 t1:43 t1:44 t1:45 t1:46 t1:47 t1:48 t1:49 t1:50

Metribuzin Chlorpyrifos-methyl Vinclozoline Spiroxamine 1 Parathion methyl Tolclofos-methyl Metalaxyl Carbaryl Prometryn Heptachlor Pirimiphos-methyl Fenitrothion Spiroxamine 2 Methiocarb Malathion

14.13–14.19 14.12–14.19 14.17–14.23 14.21–14.26 14.26–14.32 14.29–14.35 14.37–14.42 14.41–14.49 14.41–14.46 14.56–14.61 14.60–14.65 14.73–14.79 14.73–14.78 14.76–14.83 14.83–14.88

198>82 (20) 288>93 (20) 198>145 (15) 100>72 (5) 263>109 (25) 265>93 (25) 206>132 (15) 144>115 (20) 241>58 (10) 274>237 (20) 290>151 (15) 277>109 (25) 100>72 (5) 168>153 (10) 173>99 (15)

198>110 (5) 286>286 (10) 285>212 (10) 100>58 (10) 263>136 (10) 265>250 (20) 206>105 (15) 144>116 (10) 241>184 (10) 274>239 (20) 305>180 (10) 277>260 (10) 100>58 (10) 168>109 (20) 173>127 (10)

26 (16) 91 (5) 57 (12) 70 (6) 23 (12) 65 (6) 69 (15) 65 (4) 46 (6) 74 (7) 64 (10) 52 (29) 71 (3) 58 (5) 30 (9)

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Pirimicarb t1:35 Formothion

F

t1:4 t1:5 t1:6 t1:7 t1:8 t1:9 t1:10 t1:11 t1:12 t1:13 t1:14 t1:15 t1:16 t1:17

AUTHOR'S PROOF


Food Anal. Methods t1:51 Table 1 (continued) Retention time window (min)

t1:52

t1:71 t1:72 t1:73 t1:74 t1:75 t1:76 t1:77 t1:78 t1:79 t1:80 t1:81 t1:82 t1:83

Heptachlor exo-epoxide Quinalphos Procymidone Triadimenol 1 Heptachlor endo-epoxide Captan Thiabendazole Triadimenol 2 Folpet Methidathion Hexythiazox Mepamipyrim Endosulfan-alfa

t1:84 t1:85 t1:86 t1:87 t1:88

Napropamid Fludioxonil Imazalil Hexaconazole Profenofos

t1:89 t1:90 t1:91 t1:92 t1:93 t1:94 t1:95 t1:96 t1:97 t1:98 t1:99

Iprovalicarb 1 Bupirimate DDE-pp′ Krezoxim-methyl Myclobutanil Flusilazole Iprovalicarb 2 Buprofezin Dieldrin Cyproconazole Nitrofen

14.93–14.98 15.03–15.08 15.03–15.08 15.14–15.19 15.18–15.23 15.20–15.25 15.26–15.31 15.31–15.36 15.44–15.50 15.70–15.73 15.68–15.74 15.70–15.75 15.75–15.80 15.84–15.88

224>123 (10) 314>258 (15) 238>162 (10) 128>70 (10) 336>218 (15) 291>81 (25) 208>111 (20) 263>191 (30) 139>111 (10) 367>213 (25) 212>139 (10) 252>162 (15) 225>224 (10) 213>121 (10)

167>124 (10) 314>286 (15) 238>133 (25) 128>110 (5) 336>183 (30) 291>109 (20) 208>127 (15) 263>193 (30) 139>75 (10) 367>215 (25) 212>125 (15) 252>191 (15) 225>208 (15) 255>213 (10)

37 (10) 19 (8) 51 (3) 48 (22) 49 (9) 95 (8) 83 (5) 94 (7) 75 (6) 34 (17) 71 (13) 37 (23) 17 (5) 19 (8)

15.86–15.90 15.87–15.92 15.89–15.94 15.93–15.97

248>157 (25) 269>161 (15) 159>131 (10) 238>137 (10)

248>192 (15) 323>267 (20) 159>132 (10) –

52 (4) 50 (9) 82 (7) –

16.05–16.10 15.93–16.39 16.12–16.16 16.11–16.16 16.14–16.18 16.21–16.25 16.21–16.29 16.27–16.31 16.32–16.37 16.37–16.42 16.49–16.54 16.63–16.68 16.81–16.85

353>254 (15) 146>118 (10) 283>96 (10) 168>70 (15) 237>141 (25) 264>79 (10) 201>174 (15) 168>70 (15) 260>130 (10) 145>85 (10) 227>149 (10) 222>220 (20)

353>282 (15) 157>129 (10) 283>67 (20) 128>65 (20) 237>143 (25) – 201>130 (25) 128>65 (20) 260>102 (30) – 184>115 (15) 222>207 (15)

54 (27) 49 (30) 42 (5) 88 (9) 95 (27) – 16 (9) 98 (11) 47 (6) – 68 (35) 67 (6)

16.81–16.86 16.85–16.90 16.88–16.93 16.92–16.97 16.97–17.02

241>206 (10) 271>72 (10) 248>127 (30) 173>145 (15) 256>159 (20) 339>269 (15)

272>237 (15) 271>128 (5) 248>154 (20) 215>173 (5) 214>172 ((10) 339>188 (25)

60 (10) 39 (9) 61 (4) 66 (6) 53 (32) 54 (6)

16.99–17.03 17.09–17.13 17.09–17.14 17.09–17.13 17.12–17.16 17.13–17.17 17.16–17.20 17.18–17.22 17.34–17.38 17.55–17.59 17.59–17.64

158>98 (10) 273>108 (15) 246>176 (25) 206>116 (5) 179>125 (10) 233>165 (15) 158>98 (10) 175>132 (10) 263>191 (30) 222>125 (25) 202>139 (20)

158>116 (10) 273>193 (10) 318>248 (15) 206>131 (10) 179>90 (25) 233>152 (15) 158>116 (10) 190>175 (5) 263>193 (30) 222>82 (15) 283>162 (15)

81 (28) 97 (5) 32 (11) 79 (9) 50 (8) 69 (4) 70 (11) 45 (19) 92 (8) 70 (2) 54 (5)

U

Penconazole t1:68 Chlorfenvinphos t1:69 Mecarbam t1:70 Tolylfluanid

Identification

F

Dichlofluanid Chlorpyrifos Metolachlor Fenpropimorph Tetraconazole Parathion Triadimefon Aldrin Dicofol Fipronil Thiamethoxam Pendimethalin Cyprodinil Isofenphos

MRM ratio (RSD, %)

Quantification

N C O R R EC TE D

t1:53 t1:54 t1:55 t1:56 t1:57 t1:58 t1:59 t1:60 t1:61 t1:62 t1:63 t1:64 t1:65 t1:66 t1:67

MRM transitions, m/z (collision energy, eV)

PR O O

Pesticide

AUTHOR'S PROOF


Food Anal. Methods t1:100 Table 1 (continued)

t1:101 17.77–17.81 17.83–17.87 17.86–17.19 17.92–17.97 17.96–18.00 17.99–18.03 18.12–18.15 18.09–18.38 18.26–18.48 18.37–18.58 18.51–18.62 18.51–18.62 18.37–18.58 18.58–18.64

t1:116 DDT-pp′ t1:117 Endosulfan sulfate t1:118 Fenhexamid t1:119 Propargite

18.64–18.71 18.68–18.72 18.71–18.74 18.79–18.74

t1:132 Bromuconazole 2 t1:133 Metconazole t1:134 Tetradifon t1:135 Cyhalothrin-lambda 1 t1:136 Phosalone t1:137 Triticonazole t1:138 Pyriproxyfen t1:139 Azinphos-methyl t1:140 Acrinathrin 1 t1:141 Cyhalothrin-lambda 2 t1:142 Acrinathrin 2 t1:143 Fenarimol t1:144 Azinphos-ethyl t1:145 Bitertanol t1:146 Permethrin 1 t1:147 Permethrin 2 t1:148 Prochloraz

18.79–18.94 18.93–18.96 19.15–19.18 19.37–19.41 19.50–19.54 19.56–19.59 19.57–19.61 19.60–19.64 19.63–19.67 19.72–19.76 19.90–19.94 19.92–19.96

19.93–19.96 19.95–19.99 20.11–20.15 20.16–20.21 20.20–20.24 20.23–20.27

U

t1:120 Tebuconazole t1:121 TPP (I.S.) t1:122 Epoxiconazole t1:123 Bifenthrin t1:124 Bromuconazole 1 t1:125 Phosmet t1:126 Bromopropylate t1:127 Fenpropathrin t1:128 Methoxychlor t1:129 Tebufenpyrad t1:130 Fenazaquin t1:131 Flurtamone

20.30–20.34 20.33–20.38 20.35–20.39 20.35–20.39 20.43–20.47 20.83–20.87 20.94–20.99 21.36–21.40 21.36–21.40 21.51–21.56 21.67–21.73

MRM ratio (RSD, %)

Quantification

Identification

263>191 (30) 231>129 (20) 163>132 (5) 235>165 (20) 241>170 (20) 235>165 (20) 257>162 (10) 190>130 (5) 176>146 (10) 259>69 (10) 307>237 (15) 209>182 (10) 259>69 (10)

263>193 (30) 231>175 (10) 163>117 (15) 235>199 (20) 241>206 (10) 235>199 (20) 161>106 (15) 186>145 (15) – 259>173 (15) 237>208 (15) 347>172 (20) 259>173 (15)

94 (11) 65 (5) 34 (11) 16 (4) 72 (8) 20 (4) 14 (26) 61 (10) – 34 (13) 93 (30) 83 (2) 33 (22)

153>153 (5) 235>165 (20) 272>237 (15) 301>97 (15) 350>81 (15)

153>136 (10) 235>199 (20) 241>206 (10) – 350>201 (5)

26 (16) 17 (32) 30 (12) – 32 (32)

250>125 (20) 326>169 (30) 192>138 (10) 181>165 (20) 295>173 (10) 160>77 (20) 341>183 (15) 265>210 (10) 227>141 (35) 276>171 (15) 145>117 (10)

250>70 (10) – 192>111 (25) 181>166 (20) 295>175 (10) 160>133 (10) 341>185 (20) 265>89 (25) 227>169 (30) 318>131 (10) 160>145 (10)

68 (7) – 62 (2) 96 (4) 55 (11) 59 (6) 91 (2) 93 (13) 77 (6) 62 (7) 47 (2)

333>120 (10) (15) 295>173 (10) 250>125 (10) 229>201 (15) 197>141 (10) 182>111 (25) 235>182 (10)

199>157 295>175 (10) 250>145 (10) 229>199 (15) 197>161 (5) 367>182 (10) 235>217 (10)

25 (6) 57 (4) 69 (18) 58 (25) 58 (25) 31 (6) 58 (5)

136>96 (5) 160>104 (10) 181>152 (20) 197>141 (10) 181>152 (20) 251>139 (15) 160>104 (10) 170>141 (15) 165>91 (10) 165>91 (10)

185>129 (5) 160>132 (10) 208>181 (15) 197>161 (5) 208>181 (15) 251>111 (25) 160>132 (10) 170>115 (30) 165>127 (5) 165>127 (5)

10 (12) 69 (17) 35 (9) 34 (5) 43 (10) 45 (2) 68 (8) 94 (3) 53 (10) 52 (9)

310>70 (15)

310>85 (10)

28 (10)

N C O R R EC TE D

t1:102 Endrin t1:103 Ethion t1:104 Oxadixyl t1:105 DDD-pp′ t1:106 Endosulfan-beta t1:107 DDT-op′ t1:108 Triazophos t1:109 Trifloxystrobin t1:110 Benalaxyl t1:111 Propiconazole 1 t1:112 Quinoxyfen t1:113 Fluopicolide t1:114 Propiconazole 2 t1:115 Lenacil


F

Retention time window (min)

PR O O

Pesticide

AUTHOR'S PROOF


Food Anal. Methods t1:149 Table 1 (continued) Retention time window (min)

t1:150

t1:169 Famoxadon t1:170 Dimethomorph 2 a

261 262 263 264 265 266 267 268 269

165>127 (5) 165>127 (5) 165>127 (5) 165>127 (5) 165>127 (5) 165>127 (5) 165>127 (5) 140>112 (10) 167>125 (10) 132>77 (20) 167>125 (10) 323>265 (10) 264>176 (10) 323>265 (10)

206>151 (20) 206>151 (20) 206>151 (20) 206>151 (20) 181>152 (20) 181>152 (20) 181>152 (20) 140>76 (20) 181>127 (25) 164>132 (20) 181>127 (25) 265>139 (25) 264>148 (15) 265>139 (25)

42 (29) 33 (14) 37 (10) 29 (8) 65 (3) 60 (8) 61 (2) 90 (4) 5 (5) 64 (1) 5 (5) 69 (7) 42 (10) 73 (8)

25.28–25.36 25.73–25.82 26.07–26.16 26.45–26.54

253>172 (5) 253>172 (5) 344>156 (30) 301>165 (10)

253>174 (10) 253>174 (10) 344>329 (10) 301>139 (10)

95 (22) 87 (9) 90 (9) 20 (8)

26.62–26.69 27.26–27.35

330>193 (25) 301>165 (10)

330>196 (25) 301>139 (10)

55 (6) 20 (8)

Average retention time ± 3 × standard deviation (n=12)

In the second case, the formula used for calculation was based on the assumption that the first unit contains residues at the [HR × ν] level and the next ones contain residues at the HR level, which represents the residue in the composite from the same lot as the first one. Therefore, for cucumbers and sweet peppers, we used Eq. (3) because meal-sized portion of edible units of raw commodity exceeds large portion weight. This formula is based on the assumption that there is only one consumed unit, and it contains residues at the [HR × ν] level. IESTI ¼

270 271 272 273 274 275 276 277 278 279 280 281 282 283

22.02–22.07 22.15–22.20 22.25–22.29 22.30–22.35 22.53–22.58 22.67–22.73 22.77–22.83 22.80–22.86 24.14–24.21 24.28–24.37 24.53–24.60 25.11–25.20 25.21–25.28 25.25–25.33

U

Q3

Identification

N C O R R EC TE D

t1:166 Deltamethrin 2 t1:167 Azoxystrobin t1:168 Dimethomorph 1

MRM ratio (RSD, %)

Quantification

F

t1:151 Cyfluthrin 1 t1:152 Cyfluthrin 2 t1:153 Cyfluthrin 3 t1:154 Cyfluthrin 4 t1:155 Cypermethrin 1 t1:156 Cypermethrin 2 t1:157 Cypermethrin 3 t1:158 Boscalid t1:159 Fenvalerate 1 t1:160 Pyraclostrobin t1:161 Fenvalerate 2 t1:162 Difenoconazole 1 t1:163 Indoxacarb t1:164 Difenoconazole 2 t1:165 Deltamethrin 1


PR O O

Pesticide

LP HR ν Body weight

ð3Þ

where LP represents highest large portion reported at the 97.5th percentile of eaters (kg of food/day), HR highest residue (mg kg−1), and ν variability factor which is applied to the composite residue to estimate the residue level in a high-residue unit. IEDI and IESTI values were calculated for the two population groups: adults with an average body weight of 60 kg and children with an average body weight of 15 kg. Consumption data for the IEDI and IESTI assessment (consumption, large portion, and unit weight) were derived from WHO database (http://www.who.int/foodsafety/areas_work/chemical-risks/ IEDIcalculation0217clustersfinal.xlsm; http://www.who.int/ foodsafety/areas_work/chemical-risks/IESTI_calculation15_ model_final.xlsm). The average variability factor was assumed

to be equal to 3 as it has been accepted by the Joint Meeting of the FAO Panel of Experts on Pesticide Residues in Food and the Environment and the WHO Core Assessment Group on Pesticide Residues (http://whqlibdoc.who.int/ehc/WHO_ EHC_240_9_eng_Chapter6.pdf). The calculated IEDI and IESTI values were compared against the ADI and ARfD values as recommended by the European Commission, the European Food Safety Authority (EFSA), and the Standing Committee on the Food Chain and Animal Health (SCoFCAH) (http://ec.europa.eu/sanco_pesticides/public/ index.cfm?event=homepage&language=EN; http://www.efsa. europa.eu/en/efsajournal/doc/2565.pdf). For a few substances, namely fenvalerate, oxadixyl, and propargite, there were no available ARfD values; therefore, we accepted the ADI values for calculation of the acute consumer dietary exposure.

284 285 286 287 288 289 290 291 292 293 294 295 296 297 298

Results and Discussion

299

The Choice of Analytical Method

300

To prepare sample extracts for the GC-MS/MS analysis, we employed the well-known QuEChERS sample preparation technique which has been proven to be an efficient approach to analyze a wide range of pesticides and other contaminants

301 302 303 304

AUTHOR'S PROOF


Food Anal. Methods

Validation and Ongoing Verification of Analytical Performance

354 355

In this work, consideration was given to 143 active substances of plant protection products (a total of 159 target analytes

F

352 353

including the isomers and metabolites). The validation results are shown in Table 2. Accuracy in terms of trueness and precision (average recovery and RSD) was evaluated at 0.01, 0.05, and 0.5 mg kg−1 spiking levels in tomato, in six replicates per each spiking level. The vast majority of the target compounds exhibited high percentage of recovery with average values in the acceptance range of 70–120 % with RSD≤ 20 % as required by the EU SANCO/12571/2013 Guidance Document (http://ec.europa.eu/food/plant/pesticides/ guidance_documents/docs/qualcontrol_en.pdf). The exceptions were chlorothalonil and captan, but these compounds are well known as being problematic in the QuEChERS-based methods (Lehotay et al. 2010). Nevertheless, the recoveries of captan and chlorothalonil were higher than 50 % allowing for practical quantification of the results with, to some degree, poorer accuracy and higher uncertainty. According to the EU SANCO/12571/2013 guidance document, when the average recovery is lower than 70 % but consistent, it may be accepted for problematic compounds. On the other hand, it must be highlighted that the rest of the pesticides under study (over 140 pesticides), yielded highly satisfactory recovery and associated RSD values. Overall, the recoveries were 102±7, 95±7, and 95±7 % with RSD values of 7±3, 7± 4, and 7±3 % at the three spiking levels of 0.01, 0.05, and 0. 5 mg kg−1, respectively, demonstrating fitness for purpose of the validated method. Following to the EU SANCO/12571/2013 guidelines, the level of quantification (LOQ) was set as the lowest spiking level at which the acceptance criteria for average recovery in the range of 70–120 % and RSD≤20 % were met, and this was 0.01 mg kg−1 for the majority of the analytes, i.e., more than 90 % (Table 2). The uncertainty of the measurement was estimated by topdown approach using the overall recovery and precision data. The major uncertainty source in the uncertainty budget were repeatability of recoveries from the spiked samples and uncertainty of the recovery calculated from rectangular distribution whereas other uncertainty sources including preparation of calibration standards as well as weighing and volumetric steps of the analytical procecure had a negligible contribution to the total uncertainty (typically less than 1 %) (Walorczyk and Drożdżyński 2012). The expanded uncertainties were calculated with coverage factor k=2 and confidence level 95 %. The most problematic pesticide captan gave larger uncertainty value (>50 %) due to lower recovery and poorer repeatability, but for all other analytes, the expanded uncertainty was notably less than a default value of 50 % recommended by the EU SANCO/12571/2013 guidance document, demonstrating suitability of the method for the intended application. During routine use of the method, confirmation of its performance in terms of ruggedness, i.e., long-term performance with different matrices, batches of reagents, calibration curves, etc., was obtained from the application of a strict quality

PR O O

in many types of agricultural products (Lehotay 2011). But, the method was adjusted to our needs and available laboratory resources. To enable the extraction solvent to penetrate better the plant tissues and ensure complete transfer of the analytes from naturally contaminated samples, we applied mechanical shaking for an extended time of 5 min (compared with 30 s of manual shaking in some other versions of the QuEChERS) (Lehotay et al. 2010). Hereafter, for the dispersive-solid phase extraction (d-SPE cleanup), we employed a vortexing device (Multi Reax) allowing simultaneous shaking of 26 centrifuge tubes of 15 mL volume. In this way, the sample throughput was increased compared with the situation when we used our standard vortexing device for shaking only a single centrifuge tube at a time. In addition to that, an operator’s hand was not exposed to vibrations of vortex shaker. After d-SPE cleanup with PSA and magnesium sulfate, we added formic acid in acetonitrile (5 % v/v, 50 μL) to stabilize base-sensitive pesticides including captan, chlorothalonil, dichlofluanid, folpet, and tolyfluanid being susceptible to degradation in contact with basic PSA sorbent (Lehotay et al. 2005). Consistent recoveries of these problematic pesticides were obtained in this study as indicated in Table 2. Even for captan and chlorothalonil, the recoveries were typically below 70 % but were always above 50 %. Before injection into the GC-MS/MS, we exchanged the solvent from acetonitrile to toluene. Although acetonitrile can be accepted as a medium for the GC injection, it is not an ideal solvent because it is characterized by high expansion coefficient and poor focusing of chromatographic peaks due to its high polarity (Maštovská and Lehotay 2004). Toluene was used as the exchange solvent taking advantage of its properties including miscibility with acetonitrile, good solubility for wide range of pesticides, and good responses of pesticides yielding tailing peaks. In addition to that, very low volatility of toluene (boiling point 111 °C at 1 atm) makes it a highly suitable solvent for long-term storage of pesticide standards and sample extracts. To overcome a limitation of a maximum volume of 1–2 μL for injection with classical splitless technique, we employed a technique known as concurrent solvent recondensation-large volume injection (CSR-LVI). We used a Carbofrit packed liner and a retention gap (2 m×0.53 mm ID) to force a pressure drop and acceleration of the sample transfer into the retention gap and thus enable injections of larger volumes of solvent (5 μL) with liquid band formation (Walorczyk 2012). This allowed us to obtain good sensitivity and stable analyte responses, as demonstrated by the repeatability and reproducibility data detailed in the following section.

U

N C O R R EC TE D

305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351

356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408

AUTHOR'S PROOF



Table 2 Validation data by GC-MS/MS analysis of spiked tomato samples: limit of quantification (LOQ), recovery, relative standard deviation (RSD), and measurement uncertainty

t2:2

Pesticide

t2:3

LOQ

Recovery (RSD), %

U, % (k=2)

mg kg−1

0.01 mg kg−1

0.05 mg kg−1

0.5 mg kg−1

Overall

Acephate

0.05

–

96 (10)

81 (9)

88 (13)

27

t2:5 t2:6 t2:7 t2:8 t2:9 t2:10 t2:11 t2:12 t2:13 t2:14 t2:15 t2:16 t2:17 t2:18 t2:19 t2:20 t2:21 t2:22

Acrinathrin 1 Acrinathrin 2 Aldrin Atrazine Azinphos-ethyl Azinphos-methyl Azoxystrobin Benalaxyl Bifenthrin Bitertanol Boscalid Bromopropylate Bromuconazole 1 Bromuconazole 2 Bupirimate Buprofezin Captan Carbaryl

0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.1 0.01

98 (7) 96 (6) 88 (15) 106 (10) 113 (8) – 114 (6) – 103 (2) 105 (6) 107 (18) 105 (7) 99 (5) 101 (5) 100 (12) 106 (6) – 104 (8)

96 (4) 96 (6) 98 (4) 91 (11) 93 (6) 76 (10) 95 (3) 99 (6) 96 (4) 97 (3) 95 (4) 103 (5) 101 (4) 98 (5) 93 (7) 100 (12) 65 (37) 94 (7)

96 (3) 96 (5) 86 (7) 93 (7) 98 (9) 82 (8) 107 (9) 98 (5) 95 (3) 101 (3) 104 (4) 101 (2) 100 (6) 101 (6) 100 (7) 107 (11) 65 (29) 85 (8)

96 (5) 96 (5) 91 (11) 97 (11) 102 (11) 79 (10) 105 (10) 99 (6) 98 (5) 101 (5) 102 (12) 103 (5) 100 (5) 100 (5) 98 (9) 105 (10) 65 (32) 94 (11)

10 11 22 22 23 23 20 11 10 10 23 10 10 10 19 20 67 22

t2:23 t2:24 t2:25 t2:26

Carbofuran Chlorfenvinphos Chlorothalonil

0.01 0.01 0.05

100 (8) 103 (5) –

84 (12) 97 (5) 54 (15)

84 (6) 93 (8) 72 (11)

89 (12) 97 (7) 63 (19)

24 15 44

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

105 (10) 104 (8) 103 (7) 110 (6) 113 (7) 115 (7) 115 (4) 99 (3) 110 (6) 106 (5) 105 (8) 101 (5) 101 (6) 95 (6) 92 (6)

90 (8) 94 (6) 96 (8) 100 (6) 100 (3) 100 (4) 99 (2) 100 (3) 101 (4) 94 (6) 101 (3) 98 (6) 97 (6) 98 (4) 92 (4)

92 (7) 94 (6) 89 (10) 107 (6) 107 (3) 96 (6) 100 (6) 97 (4) 109 (6) 100 (7) 100 (6) 103 (6) 96 (6) 99 (2) 91 (5)

96 (11) 97 (8) 96 (10) 106 (7) 107 (7) 103 (10) 105 (8) 99 (4) 107 (6) 100 (7) 102 (6) 101 (6) 98 (6) 97 (4) 92 (5)

22 16 20 14 14 20 17 7 13 15 12 11 12 8 10

DDT-op′ DDT-pp′ Deltamethrin 1 Deltamethrin 2 Desmedipham Diazinon Dichlofluanid Dichlorvos

0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01

95 (8) 97 (6) 136 (12) 104 (4) 99 (14) 93 (11) 64 (7) 99 (6)

95 (5) 97 (4) 93 (9) 94 (8) 89 (8) 89 (9) 78 (12) 88 (20)

90 (8) 89 (11) 117 (11) 94 (8) 95 (6) 91 (8) 80 (9) 96 (2)

94 (7) 94 (8) 115 (19) 97 (8) 94 (11) 91 (9) 74 (14) 94 (12)

15 17 39 16 22 19 31 24

Dicloran

0.01

100 (11)

89 (10)

90 (5)

93 (11)

22

t2:41 t2:42 t2:43 t2:44 t2:45 t2:46 t2:47 t2:48 t2:49

PR O O

N C O R R EC TE D

Chlorpropham Chlorpyrifos Chlorpyrifos-methyl Cyfluthrin 1 Cyfluthrin 2 Cyfluthrin 3 Cyfluthrin 4 Cyhalothrin-lambda Cypermethrin 1 Cypermethrin 2 Cypermethrin 3 Cyproconazole Cyprodinil DDD-pp′ DDE-pp′

U

t2:27 t2:28 t2:29 t2:30 t2:31 t2:32 t2:33 t2:34 t2:35 t2:36 t2:37 t2:38 t2:39 t2:40

F

t2:4

AUTHOR'S PROOF


Food Anal. Methods t2:50 Table 2 (continued)

Dicofol Dieldrin Difenoconazole 1 Difenoconazole 2 Dimethoate Dimethomorph 1 Dimethomorph 2 Diphenylamine Endosulfan-alfa Endosulfan-beta Endosulfan sulfate Endrin Epoxiconazole Ethion Ethoprophos Famoxadone Fenarimol Fenazaquin

t2:70 t2:71 t2:72 t2:73 t2:74 t2:75 t2:76

Fenhexamid Fenitrothion Fenpropathrin Fenpropimorph Fenvalerate 1 Fenvalerate 2 Fipronil Fludioxonil Fluopicolide Flurtamone Flusilazole Folpet Formothion HCB HCH-alfa HCH-beta Heptachlor Heptachlor endo-epoxide

t2:88 t2:89 t2:90 t2:91 t2:92 t2:93 t2:94 t2:95 t2:96 t2:97 t2:98

Heptachlor exo-epoxide Heptenophos Hexaconazole Hexythiazox Imazalil Indoxacarb Iprovalicarb 1 Iprovalicarb 2 Isofenphos Krezoxim-methyl Lenacil

mg kg−1

0.01 mg kg−1

0.05 mg kg−1

0.5 mg kg−1

Overall

0.01 0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

100 (5) 97 (12) 96 (5) 99 (6) – 104 (8) 104 (5) 110 (7) 100 (16) 108 (6) 99 (4) 99 (6) 99 (3) 102 (3) 104 (9) 108 (6) 102 (4) 94 (5)

95 (6) 91 (5) 92 (5) 95 (4) 94 (9) 96 (3) 96 (2) 101 (7) 93 (12) 97 (5) 96 (7) 98 (9) 98 (3) 100 (7) 92 (8) 98 (5) 99 (3) 95 (3)

98 (4) 91 (7) 99 (5) 100 (4) 89 (9) 102 (7) 103 (7) 95 (11) 91 (7) 96 (8) 94 (9) 92 (9) 97 (4) 98 (6) 91 (6) 102 (5) 101 (7) 98 (5)

98 (5) 93 (9) 96 (6) 98 (5) 92 (9) 101 (7) 101 (6) 102 (10) 95 (13) 100 (8) 96 (7) 96 (8) 98 (3) 100 (5) 96 (10) 103 (7) 101 (5) 96 (4)

11 18 12 10 19 13 12 20 25 16 14 17 7 11 20 13 10 9

0.01 0.01 0.01 0.01 0.01 0.01

92 (9) 102 (11) 108 (8) 101 (6) 105 (6) 104 (6)

93 (9) 97 (10) 93 (6) 95 (6) 98 (2) 99 (2)

98 (7) 93 (7) 102 (4) 92 (8) 105 (3) 102 (3)

94 (8) 97 (10) 101 (9) 96 (7) 103 (5) 102 (5)

16 20 18 15 11 9

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

98 (15) 99 (3) 105 (5) 101 (6) 96 (8) 92 (16) 102 (5) 97 (5) 112 (7) 102 (7) 97 (8) 93 (14)

98 (7) 98 (3) 101 (6) 97 (3) 95 (5) 97 (10) 85 (7) 88 (10) 94 (11) 97 (9) 96 (10) 99 (11)

93 (7) 99 (4) 101 (6) 106 (4) 99 (4) 81 (18) 84 (8) 82 (9) 93 (4) 91 (7) 85 (11) 91 (5)

96 (10) 98 (3) 102 (6) 101 (6) 97 (7) 90 (16) 90 (11) 89 (10) 99 (12) 97 (9) 93 (11) 94 (11)

21 7 11 11 14 32 23 22 23 17 22 22

0.01 0.01 0.05 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01

104 (12) 106 (4) – – 101 (12) 105 (8) 104 (5) 102 (12) 103 (7) 103 (15) 112 (7)

102 (8) 91 (10) 100 (9) 94 (17) 93 (9) 96 (4) 90 (10) 98 (10) 98 (10) 91 (10) 98 (3)

95 (3) 91 (5) 94 (3) 95 (18) 96 (7) 99 (9) 96 (2) 100 (6) 95 (4) 98 (6) 101 (5)

100 (9) 96 (10) 97 (7) 94 (17) 97 (10) 100 (8) 97 (9) 100 (9) 98 (8) 97 (12) 104 (8)

18 20 15 34 20 16 17 19 15 24 15

U

t2:77 t2:78 t2:79 t2:80 t2:81 t2:82 t2:83 t2:84 t2:85 t2:86 t2:87

U, % (k=2)

F

t2:52 t2:53 t2:54 t2:55 t2:56 t2:57 t2:58 t2:59 t2:60 t2:61 t2:62 t2:63 t2:64 t2:65 t2:66 t2:67 t2:68 t2:69

Recovery (RSD), %

PR O O

t2:51

LOQ

N C O R R EC TE D

Pesticide

AUTHOR'S PROOF


Food Anal. Methods t2:99 Table 2 (continued) Recovery (RSD), %

t2:100

mg kg−1

0.01 mg kg−1

0.05 mg kg−1

0.5 mg kg−1

Overall

t2:101 Lindane t2:102 Malathion t2:103 Mecarbam t2:104 Mepamipyrim t2:105 Metalaxyl t2:106 Metconazole t2:107 Methamidophos t2:108 Methidathion t2:109 Methiocarb t2:110 Methoxychlor t2:111 Metolachlor t2:112 Metribuzin t2:113 Monocrotophos t2:114 Myclobutanil t2:115 Napropamid t2:116 Nitrofen t2:117 Omethoate t2:118 Oxadixyl

0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

106 (11) 102 (8) – 103 (7) 94 (11) 111 (12) 89 (11) 107 (7) 107 (4) 103 (3) 104 (9) 113 (11) 105 (4) 101 (4) 103 (7) 91 (11) 107 (8) 107 (4)

96 (11) 95 (9) 88 (9) 106 (7) 91 (15) 90 (8) 85 (7) 94 (4) 94 (8) 102 (2) 99 (7) 101 (9) 86 (10) 93 (5) 96 (6) 92 (6) 83 (10) 96 (3)

91 (6) 96 (8) 94 (4) 97 (5) 94 (6) 104 (8) 81 (5) 92 (9) 93 (8) 92 (13) 94 (6) 102 (2) 84 (10) 98 (4) 95 (9) 95 (8) 84 (8) 104 (6)

98 (11) 98 (9) 91 (7) 102 (7) 93 (10) 102 (13) 85 (8) 97 (10) 98 (9) 99 (9) 99 (8) 105 (10) 92 (13) 97 (5) 98 (8) 92 (8) 91 (15) 102 (6)

23 17 16 15 21 25 19 19 18 17 17 19 26 11 16 17 30 12

0.01 0.01 0.01 0.01 0.05 0.05

96 (14) 100 (11) 98 (8) 107 (9) –

95 (7) 91 (7) 101 (5) 89 (8) 94 (8)

94 (6) 88 (12) 94 (6) 90 (10) 96 (5)

95 (9) 93 (11) 98 (7) 95 (12) 95 (7)

19 22 13 25 14

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

– 109 (3) 108 (4) 95 (6) 101 (5) 103 (13) 93 (6) 98 (10) 109 (9) 103 (5) 103 (5) 104 (12) 103 (5)

101 (4) 86 (5) 98 (3) 91 (7) 97 (8) 100 (12) 92 (5) 98 (5) 98 (6) 99 (6) 89 (13) 94 (13) 91 (12)

100 (2) 98 (3) 95 (10) 86 (7) 98 (12) 92 (14) 94 (14) 94 (8) 98 (3) 96 (8) 91 (5) 98 (10) 92 (3)

100 (3) 98 (10) 101 (8) 91 (7) 99 (8) 98 (13) 93 (9) 97 (8) 102 (8) 100 (7) 94 (10) 99 (12) 95 (9)

6 21 16 16 17 26 18 15 16 13 20 23 19

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

100 (5) 104 (9) 104 (4) 99 (8) 92 (13) 110 (9) 100 (4) 118 (2) 97 (7) 96 (7) 106 (6)

100 (7) 100 (5) 91 (11) 90 (10) 97 (13) 106 (7) 94 (5) 105 (9) 100 (3) 91 (10) 90 (13)

101 (5) 102 (6) 91 (6) 93 (5) 98 (5) 92 (11) 99 (9) 94 (5) 100 (5) 88 (6) 94 (6)

100 (6) 102 (7) 95 (10) 94 (8) 96 (11) 102 (11) 98 (7) 106 (11) 99 (5) 92 (8) 96 (11)

11 13 20 17 21 23 14 22 10 17 21

t2:125 Phenmedipham t2:126 Phosalone t2:127 Phosmet t2:128 Pirimicarb t2:129 Pirimiphos-methyl t2:130 Prochloraz t2:131 Procymidone t2:132 Profenofos t2:133 Prometryn t2:134 Propachlor t2:135 Propargite t2:136 Propham t2:137 Propiconazole 1 t2:138 Propiconazole 2 t2:139 Propoxur t2:140 Propyzamide t2:141 Pyraclostrobin t2:142 Pyrimethanil t2:143 Pyriproxyfen t2:144 Quinalphos t2:145 Quinoxyfen t2:146 Quintozene t2:147 Simazine

PR O O

N C O R R EC TE D

t2:119 Parathion t2:120 Parathion methyl t2:121 Penconazole t2:122 Pendimethalin t2:123 Permethrin 1 t2:124 Permethrin 2

U, % (k=2)

F

LOQ

U

Pesticide

AUTHOR'S PROOF


Food Anal. Methods t2:148 Table 2 (continued) Recovery (RSD), %

t2:149

mg kg−1

0.01 mg kg−1

0.05 mg kg−1

0.5 mg kg−1

Overall

t2:150 Spiroxamine 1 t2:151 Spiroxamine 2 t2:152 Tebuconazole t2:153 Tebufenpyrad t2:154 Tecnazene t2:155 Tetraconazole t2:156 Tetradifon t2:157 Thiabendazole t2:158 Thiamethoxam t2:159 Tolclofos-methyl t2:160 Tolylfluanid t2:161 Triadimefon t2:162 Triadimenol 1 t2:163 Triadimenol 2 t2:164 Triazophos t2:165 Trifloxystrobin t2:166 Trifluralin t2:167 Triticonazole

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01

105 (6) 103 (5) 99 (6) 105 (5) 98 (8) 106 (7) 93 (8) 97 (5) 107 (8) 105 (6) 69 (12) 99 (11) 105 (8) 91 (7) 107 (3) 118 (9) 108 (11) 98 (8)

92 (10) 95 (5) 99 (2) 105 (2) 93 (13) 92 (12) 100 (3) 90 (7) 91 (15) 98 (8) 83 (9) 95 (12) 94 (7) 91 (7) 98 (3) 95 (5) 96 (10) 100 (6)

93 (9) 94 (9) 101 (6) 99 (5) 91 (4) 100 (2) 100 (3) 100 (9) 100 (7) 90 (8) 83 (12) 94 (5) 96 (4) 97 (4) 98 (7) 96 (2) 91 (7) 102 (7)

97 (10) 97 (8) 100 (5) 103 (5) 94 (9) 100 (10) 98 (6) 95 (8) 100 (12) 98 (9) 78 (14) 96 (10) 98 (8) 93 (6) 101 (6) 103 (12) 98 (12) 100 (7)

21 15 10 10 19 19 12 16 23 19 30 19 16 13 12 25 23 13

0.01

102 (8)

90 (11)

92 (8)

95 (10)

20

t2:168 Vinclozoline

U, % (k=2)

PR O O

F

LOQ

N C O R R EC TE D

Pesticide

control scheme. Since the appropriate reference materials for pesticide residue analysis are rarely available, the accuracy during long-time use of the method was checked by the analysis of spiked samples analyzed together with batches of samples. For spiking experiments, different commodities were selected to challenge the applicability of the method in the presence of differing matrix co-extractives. Shewhart control charts were plotted for selected representative pesticides, and they did not evidence drifts of the data, demonstrating good reproducibility of the method with time. As an example, Fig. 1 shows control charts obtained for DDT-pp′ (a problematic pesticide) and tebuconazole (a chromatographically stable pesticide). The action limits were set to ± 2 × and ± 3 × standard deviation of the recoveries obtained at the spiking concentration of 0.05 mg kg−1. Also, according to the EU SANCO/12571/2013 document, a practical default range of 60–140 % for individual recoveries was used as the acceptance range for the recoveries, which stands for an ideal recovery of 100 % ± 2 × maximum RSD of 20 %. All the results fell inside the action limits, and corrective actions were not necessary. This clearly demonstrated satisfactory ruggedness of the applied methodology.

431

Occurrence of Pesticide Residues in Fruiting Vegetables

432 433

The method was applied to analyze 308 samples of fruiting vegetables including tomatoes (173 samples), sweet peppers

U

409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430

(71 samples), and cucumbers (64 samples). The samples originated from central-western Poland. Overall, 48 % of the samples were free of the pesticide residues whereas the residues above LOQs were found in 52 % of the samples, of which 39 % (19 % of all samples) contained multiple pesticide residues (up to four compounds in one sample). In the case of cucumber, sweet pepper, and tomatoes, there were 34, 54, and 58 % samples containing pesticide residues, respectively (Fig. 2). Of the target analytes, 28 were found in the analyzed samples, i.e., 18 % of all target analytes (Table 3), of which 10 different pesticides were found in cucumbers, 14 in peppers, and 19 in tomatoes. Overall, the most frequently detected pesticides were azoxystrobin, chlorothalonil, procymidone, and boscalid being determined in 17, 12, 8, and 7 % of all analyzed samples, respectively. MRL concentrations set by the Regulation (EC) No. 396/2005 were exceeded in the case of procymidone in cucumbers, sweet peppers, and tomatoes (2, 28, and 3 % samples, respectively), chlorpyrifos in cucumbers (2 % samples), oxadixyl in sweet peppers (3 % samples), and tolylfluanid in tomatoes (0.6 % samples). The pesticide with the highest MRL exceedance was procymidon detected in tomato at the concentration of 1.3 mg kg−1. Overall, 10 % of analyzed samples (cucumbers, sweet peppers, and tomatoes) contained pesticide residues exceeding the EU MRLs in force (http:// ec.europa.eu/sanco_pesticides/public/?event= homepage&language=EN).

434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460

AUTHOR'S PROOF


N C O R R EC TE D

PR O O

F

Food Anal. Methods

U

Fig. 1 Shewhart control charts for quality control samples containing tebuconazole (a chromatographically stable pesticide) and DDT-pp′ (a problematic pesticide) at a spiked concentration of 0.05 mg kg−1

Fig. 2 Occurrence of pesticide residues in analyzed samples

It should be highlighted that the main role MRLs are put in force is to regulate trade in pesticide-treated crops and assure the safety of consumers (Ambrus 2015). Occurrence of residues above the MRL indicates that a pesticide had been incorrectly used, i.e., the applied dose was too high, preharvest interval was not respected, or an unauthorized pesticide was used. Such food products containing pesticide residues exceeding MRLs cannot be placed on the market. Although exceeding the MRL does not necessarily imply a risk to the health of consumers, the law prohibits suppliers from circulating agricultural products containing more than a specified amount of a pesticide residue, and thus, the MRL serve as a kind of trading standard (Drogué and DeMaria 2012). For trading purposes, measurement uncertainty is not usually employed when determining if the MRL exceedance has occurred (Walorczyk 2014). But in the case of monitoring programs, the default expanded uncertainty of 50 %, as recommended by the EU SANCO/12571/2013 guidance document, should be taken into account before an enforcement

461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479

AUTHOR'S PROOF


Food Anal. Methods t3:1 t3:2

Table 3 Summary of pesticide residues detected in analyzed samples of fruiting vegetables

Range, mg kg−1

Average, mg kg−1

% Samples

% Samples >MRL

Azoxystrobin Chlorothalonil Procymidone Boscalid Famoxadone

0.01–1.1 0.01–1.6 0.01–1.3 0.01–0.24 0.01–0.05

0.06 0.17 0.17 0.06 0.03

17.0 12.0 8.1 7.0 4.9

– – 8.1 – –

t3:8 t3:9 t3:10 t3:11 t3:12 t3:13 t3:14 t3:15 t3:16 t3:17

Bifenthrin Cyprodinil Fludioxonil Pyrimethanil Chlorpyrifos Cypermethrin Dimethomorph Metalaxyl Pyraclostrobin Cyhalothrin-lambda

0.01–0.09 0.01–0.53 0.01–0.38 0.01–0.06 0.01–0.11 0.01–0.04 0.01–0.02 0.02–0.12 0.02–0.06 0.01–0.08

0.05 0.09 0.67 0.03 0.05 0.02 0.01 0.07 0.03

4.2 3.6 2.9 2.6 1.3 1.3 1.3 1.3 1.3

– – – – 0.3 – – – –

t3:18 t3:19 t3:20 t3:21 t3:22 t3:23 t3:24 t3:25

Pyriproxyfen Endosulfana Fenhexamid Oxadixyl Cyfluthrin Fenvalerate Fluopicolide Hexythiazox

0.01–0.07 0.01–0.01 0.07–0.39 0.01–0.02 0.03 0.02 0.02 0.01

0.04 0.04 0.01 0.23 0.01 0.03 0.02 0.02 0.01

1.0 1.0 0.6 0.6 0.6 0.3 0.3 0.3 0.3

– – 0.6 – 0.6 – – – –

Pendimethalin Propargite Tebuconazole Tolylfluanid

0.01 0.09 0.02 0.08

0.01 0.09 0.02 0.08

0.3 0.3 0.3 0.3

– – – 0.3

t3:26 t3:27 t3:28 t3:29

PR O O

N C O R R EC TE D

t3:3 t3:4 t3:5 t3:6 t3:7

a

F

Pesticide

Sum of endodulfan-alpha, endosulfan-beta, and endosulfan sulfate expressed as endosulfan

action is followed. This effectively means that the measured concentration in a sample is above the MRL with a confidence level of 95 % when the result—uncertainty > MRL (http://ec. europa.eu/food/plant/pesticides/guidance_documents/docs/ qualcontrol_en.pdf). Considering this kind of interpretation, 7. 1 % of the samples analyzed in this work were non-compliant (compared with 10 % when simply comparing the results with the MRLs).

488

Food Safety Assessment

489 490 491 492 493 494 495 496 497

As already mentioned, MRL represents the maximum concentration of a pesticide residue being legally permitted in food commodities (MacLachlan and Hamilton 2010), and since September 2008, the MRLs are set on an EU-wide basis under Regulation No. 396/2005 (http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri= CONSLEG:2005R0396:20121026). Current MRLs are based on EU uses of pesticides approved in accordance with Regulation No. 1107/2009 (http://eur-

U

480 481 482 483 484 485 486 487

l e x . e u r o p a . e u / L e x U r i S e r v / L e x U r i S e r v. d o ? u r i = OJ:L:2009:309:0001:0050:EN:PDF). Although some of the pesticides detected in years 2006–2009 are no longer approved for use in the EU member countries, namely endosulfan, oxadixyl, procymidone, propargite, and tolylfluanid, we performed the assessment of dietary consumer exposure considering chronic (longterm) and acute (short-term) exposure in order to determine if the intake of pesticide residues could exceed the health-based limits (ADI/ARfd) (Renwick 2002; Nougadère et al. 2014). The results are detailed in Tables 4 and 5. Assessment of the dietary exposure was carried out combining food consumption data with data on the concentration of chemicals in food. The resulting dietary exposure estimate was then compared with the relevant health-based guidance value for the pesticide residue of concern as part of the risk characterization considering both acute or chronic exposures. Acute exposure covered a period of up to 24 h whereas long-term exposure covered average daily exposure over

498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517

AUTHOR'S PROOF



Table 4

t4:2

Type of vegetable

t4:4

Assessment of chronic dietary exposure to pesticide residues in analyzed samples of fruiting vegetables Consumption, adults ADI, mg body Intake, mg body Usage Average weight−1 day−1 weight−1day−1 residue, mg kg−1 and children, kg person−1 day−1 Adults Children

% ADI

Adults Children

F

0.01

0.01103

0.2

0.000002

0.000007

0.00

0.00

Bifenthrin Chlorpyrifos Chlorothalonil Cyprodinil Endosulfana Fludioxonil Metalaxyl Procymidone Pyrimethanil Alpha-cypermethrin Azoxystrobin Bifenthrin Chlorpyrifos Chlorothalonil Cypermethrin Esfenvalerate Fenvalerate Hexythiazox

I, A I, A F F I, A F F F F I F I, A I, A F I, A I I, A I, A

0.01 0.04 0.03 0.02 0.01 0.01 0.07 0.01 0.02 0.04 0.02 0.02 0.06 0.06 0.03 0.01 0.01 0.01

0.01103 0.01103 0.01103 0.01103 0.01103 0.01103 0.01103 0.01103 0.01103 0.00153 0.00153 0.00153 0.00153 0.00153 0.00153 0.00153 0.00153 0.00153

0.015 0.01 0.015 0.03 0.006 0.37 0.08 0.0028 0.17 0.015 0.2 0.015 0.01 0.015 0.05 0.02 0.0125 0.03

0.000002 0.000007 0.000006 0.000004 0.000002 0.000002 0.000013 0.000002 0.000004 0.000001 0.000001 0.000001 0.000002 0.000002 0.000001 0.0000003 0.0000003 0.0000003

0.000007 0.000029 0.000022 0.000015 0.000007 0.000007 0.000051 0.000007 0.000015 0.000004 0.000002 0.000002 0.000006 0.000006 0.000003 0.000001 0.000001 0.000001

0.01 0.07 0.04 0.01 0.03 0.00 0.02 0.07 0.00 0.01 0.00 0.00 0.02 0.01 0.00 0.00 0.00 0.00

0.05 0.29 0.15 0.05 0.12 0.00 0.06 0.26 0.01 0.03 0.00 0.01 0.06 0.04 0.01 0.00 0.01 0.00

t4:23 t4:24 t4:25 t4:26 t4:27 t4:28 t4:29 Tomato t4:30 t4:31 t4:32 t4:33 t4:34

Lambda-cyhalothrin Oxadixyl Pendimethalin Procymidone Propargite Pyrimethanil Alpha-cypermethrin Azoxystrobin Beta-cyfluthrin Bifenthrin Boscalid Chlorothalonil

I F H F A F I F I I, A F F

0.06 0.01 0.01 0.2 0.09 0.03 0.01 0.07 0.03 0.08 0.06 0.19

0.00153 0.00153 0.00153 0.00153 0.00153 0.00153 0.06831 0.06831 0.06831 0.06831 0.06831 0.06831

0.005 0.01 0.125 0.0028 0.01 0.17 0.015 0.2 0.003 0.015 0.04 0.015

0.000002 0.0000003 0.0000003 0.000005 0.000002 0.000001 0.000011 0.000080 0.000034 0.000091 0.000068 0.000216

0.000006 0.000001 0.000001 0.000020 0.000009 0.000003 0.000046 0.000319 0.000137 0.000364 0.000273 0.000865

0.03 0.00 0.00 0.18 0.02 0.00 0.08 0.04 1.14 0.61 0.17 1.44

0.12 0.01 0.00 0.73 0.09 0.00 0.30 0.16 4.55 2.43 0.68 5.77

t4:35 t4:36 t4:37 t4:38 t4:39 t4:40

Cyprodinil Dimethomorph Fludioxonil Famoxadone Fenhexamid Fluopicolide

F F F F F F

0.12 0.01 0.09 0.03 0.23 0.02

0.06831 0.06831 0.06831 0.06831 0.06831 0.06831

0.03 0.05 0.37 0.012 0.2 0.08

0.000137 0.000011 0.000102 0.000034 0.000262 0.000023

0.000546 0.0000046 0.000410 0.000137 0.001047 0.000091

0.46 0.02 0.03 0.28 0.13 0.03

1.82 0.09 0.11 1.14 0.52 0.11

t4:41 t4:42 t4:43 t4:44 t4:45 t4:46 t4:47

Lambda-cyhalothrin Procymidone Pyraclostrobin Pyriproxyfen Pyrimethanil Tebuconazole Tolylfluanid

I F F, P I F F F, A

0.01 0.07 0.03 0.04 0.04 0.02 0.08

0.06831 0.06831 0.06831 0.06831 0.06831 0.06831 0.06831

0.005 0.0028 0.03 0.10 0.17 0.03 0.1

0.000011 0.000080 0.000034 0.000046 0.000046 0.000023 0.000091

0.000046 0.000319 0.000137 0.000182 0.000182 0.000091 0.000394

0.23 2.85 0.11 0.05 0.03 0.08 0.09

0.91 11.38 0.45 0.18 0.11 0.30 0.36

PR O O

N C O R R EC TE D

A acaricide, F fungicide, H herbicide, I insecticide, P plant growth regulator a

F

Azoxystrobin

t4:5 t4:6 t4:7 t4:8 t4:9 t4:10 t4:11 t4:12 t4:13 t4:14 Sweet pepper t4:15 t4:16 t4:17 t4:18 t4:19 t4:20 t4:21 t4:22

U

Cucumber

Pesticide

Sum of endosulfan-alpha, endosulfan-beta, and endosulfan sulfate expressed as endosulfan

t4:3

AUTHOR'S PROOF


Food Anal. Methods

t5:2

Type of vegetable

Assessment of acute dietary exposure to pesticide residues in analyzed samples of fruiting vegetables Pesticide

Residue, mg kg−1

ARfD, mg kg bw−1

t5:3 t5:4

Adults LP, kg person−1

Cucumber

t5:5 t5:6 t5:7 Sweet pepper t5:8 t5:9 t5:10 t5:11 Tomato t5:12

Intake, % ARfD

LP, kg person−1

Intake, mg kg bw−1

Intake, % ARfD

0.07

0.1

0.31320

0.00110

1.096

0.15191

0.00213

2.127

Endosulfan Procymidon Fenvaleratea Oxadixyla Procymidone Propargitea Procymidone Tolylfluanid

0.01 0.01 0.01 0.02 1.3 0.09 0.19 0.08

0.02 0.012 0.02 0.01 0.012 0.01 0.012 0.25

0.31320 0.31320 0.03161 0.03161 0.03161 0.03161 0.37507 0.37507

0.00016 0.00016 0.00002 0.00003 0.00210 0.00014 0.00200 0.00084

0.783 1.305 0.079 0.316 17.517 1.422 16.690 0.337

0.15191 0.15191 0.06298 0.06298 0.06298 0.06298 0.28929 0.28929

0.00030 0.00030 0.00013 0.00025 0.01675 0.00113 0.00692 0.00292

1.519 2.532 0.630 2.519 139.604 11.336 57.707 1.166

Due to unavailability of ARfD value, we accepted the ADI value for the calculations

content than a composite portion. Therefore, we considered two cases where the unit size was less than the large portion (tomato) or greater than the large portion (cucumber and sweet peppers) (http://whqlibdoc.who.int/ehc/WHO_EHC_240_9_ eng_Chapter6.pdf)). If the evaluated consumer exposure values did not exceed 100 % of the ARfD value, they were considered to be acceptable and not constituting a threat to health. It was only exceeded in the case of one sample of sweet pepper. As seen in Table 5, the highest value of acute exposure occurred for procymidone in the case of sweet pepper and tomato and it was exceeded once for children consuming sweet pepper (139.6 % ARfD) in the case of the highest determined pesticide residue concentration of 1.3 mg kg−1. If ARfD is exceeded, it may be a human health concern and necessary steps should be taken to prevent the sale of such commodity. But, it must also be highlighted that as a rule, there are two or three digit safety margins between the dose with a biological effect determined in animal experiments and the toxicological limit derived from it (Solecki et al. 2005). Except to the aforementioned, for the rest of the samples with pesticide residues above the MRLs and/or for detection of unapproved pesticides, the IESTI values were notably lower than 100 % amounting from 0.1 to 57.7 % ARfD, so the risk was considered to be acceptable.

552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576

Conclusions

577

As a general conclusion, it may be stated that pesticide residue levels at or below the MRL would not give intakes that exceed the ADI/ARfD values, but, despite this, there is a public concern over such residues. To protect the health of consumers, especially vulnerable groups, such as children, continuous

578 579 580 581 582

N C O R R EC TE D

the entire lifetime (http://whqlibdoc.who.int/ehc/WHO_ EHC_240_9_eng_Chapter6.pdf). As seen in Table 4, for most of the pesticides, chronic exposure was very low and did not exceeded 1 % ADI for both adults and children. The IEDI values were calculated based on the average values of the results for individual substances, thus taking into account the frequency of detecting the individual substances the chronic exposure could be considered to be even lower. The highest values of IEDI were obtained for the tomato because of high level of consumption of tomato by Polish consumers (i.e., 0.06831 kg person−1 day−1). In particular for procymidone, which was frequently found in analyzed samples, the IEDI values amounted to 2.8 and 11.4 % ADI considering the diet with the associated body weight of adults and children, respectively. But, it must be noted that if the consumer exposure values did not exceed 100 % of the ADI, they should have been considered to be acceptable and not constituting a threat to the health of consumers. The assessment of the acute consumer dietary exposure was performed for samples containing pesticide residues above the MRL and pesticides not approved for use in the EU member countries according to the legislation currently in force (http://ec.europa.eu/sanco_pesticides/public/?event= homepage&language=EN). The obtained results are detailed in Table 5. For acute exposure assessment, additional information was required on residues in single samples or individual unit crops. If such detailed data were not available, concentrations in single samples were derived from composite samples taken from a lot by applying a variability factor in order to take into account the differences in chemical concentrations in sample increments or unit crops. Calculations of the acute dietary exposure differed depending on different cases considering the situation where the meal-sized portion as a single fruit or vegetable unit might had a higher residue

U

518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551

Intake, mg kg bw−1

Chlorpyrifos

bw body weight, LP large portion a

Children

F

Table 5

PR O O

t5:1

AUTHOR'S PROOF


Food Anal. Methods

Acknowledgments Skillful assistance of laboratory technical staff is greatly appreciated. Compliance with Ethical Standards

Funding This work was supported in part by Ministerstwo Nauki i Szkolnictwa Wyższego (Ministry of Science and Higher Education), project IDs POZ-03 and POZ-07.

Conflict of Interest Stanisław Walorczyk declares that he has no conflict of interest. Izabela Kopeć declares that she has no conflict of interest. Ewa Szpyrka declares that she has no conflict of interest. Ethical Approval This article does not contain any studies with human participants or animals performed by any of the authors.

U

615 616 617 618 619

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Drogué S, DeMaria F (2012) Pesiticide residues and trade, the apple of discord? Food Policy 37:641–649. doi:10.1016/j.foodpol.2012.06. 007 Fenik J, Tankiewicz M, Biziuk M (2011) Properties and determination of pesticides in fruits and vegetables. Trends Anal Chem 30:814–826. doi:10.1016/j.trac.2011.02.008 González-Curbelo MÁ, Herrera-Herrera AV, Ravelo-Pérez LM, Hernández-Borges J (2012) Sample preparation methods for pesticide residue analysis in cereals and derivatives. Trends Anal Chem 38:32–51. doi:10.1016/j.trac.2012.04.010 González-Curbelo MÁ, Socas-Rodríguez B, Herrera-Herrera AV, González-Sálamo J, Hernández-Borges J, Rodríguez-Delgado MÁ (2015) Evolution and applications of the QuEChERS method. Trends Anal Chem. doi:10.1016/j.trac.2015.04.012 Handford CE, Elliott CT, Campbell K (2015) A review of the global pesticide legislation and the scale of challenge in reaching the global harmonization of food safety standards. Integr Environ Assess Manag 9999:1–12. doi:10.1002/ieam.1635 Hernández F, Cervera MI, Portolés T, Beltrán J, Pitarch E (2013) The role of GC-MS/MS with triple quadrupole in pesticide residue analysis in food and the environment. Anal Methods 5:5875–5894. doi:10. 1039/C3AY41104D Lehotay SJ (2011) QuEChERS sample preparation approach for mass spectrometric analysis of pesticide residues in food. Methods Mol Biol 747:65–91. doi:10.1007/978-1-61779-136-9_4 Lehotay SJ, Maštovská K, Lightfield AR (2005) Use of buffering and other means to improve results of problematic pesticides in a fast and easy method for residue analysis of fruits and vegetables. J AOAC Int 88:615–629 Lehotay SJ, Son KA, Kwon H, Koesukwiwat U, Fu W, Mastovska K, Hoh E, Leepipatpiboon N (2010) Comparison of QuEChERS sample pesticide residues in fruits and vegetables. J Chromatogr A 1217: 2548–2560. doi:10.1016/j.chroma.2010.01.044 MacLachlan DJ, Hamilton D (2010) Estimation methods for maximum residue limits for pesticides. Regul Toxicol Pharmacol 58:208–218. doi:10.1016/j.yrtph.2010.05.012 Maštovská K, Lehotay SJ (2004) Evaluation of common solvents for gas chromatographic analysis and stability of multiclass pesticide residues. J Chromatogr A 1040:259–272. doi:10.1016/j.chroma.2004. 04.017 Matyjaszczyk E (2015) Prevention methods for pest control and their use in Poland. Pest Manag Sci 71:485–491. doi:10.1002/ps.3795 Nougadère A, Merlo M, Héraud F, Réty J, Truchot E, Vial G, Cravedi J-P, Leblanc J-C (2014) How dietary risk assessment can guide risk management and food monitoring programmes: the approach and results of the French observatory on pesticide residues (ANSES/ ORP). Food Control 41:32–48. doi:10.1016/j.foodcont.2013.12.025 Payá P, Anastassiades M, Mack D, Sigalova I, Tasdelen B, Oliva J, Barba A (2010) Analysis of pesticide residues using the quick easy cheap effective rugged and safe (QuEChERS) pesticide multiresidue method in combination with gas and liquid chromatography and tandem mass spectrometric detection. Anal Bioanal Chem 389:697–1714. doi:10.1007/s00216-007-1610-7 Popp J, Pető K, Nagy J (2013) Pesticide productivity and food security. A review. Agron Sustain Dev 33:243–255. doi:10.1007/s13593-0120105-x Renwick AG (2002) Pesticide residue analysis and its relationship to hazard characterization (ADI/ARfD) and intake estimations (NEDI/NESTI). Pest Manag Sci 58:1073–1082. doi:10.1002/ps.544 Solecki R, Davies L, Dellarco V, Dewhurst I, van Raaij M, Trischer A (2005) Guidance on setting of acute reference dose (ARfD) for pesticides. Food Chem Toxicol 43:1569–1593. doi:10.1015/j.fct. 2005.04.005 Szpyrka E, Kurdziel A, Słowik-Borowiec M, Grzegorza M, Matyaszek A (2013) Consumer exposure to pesticide residues in apples from the

PR O O

monitoring of pesticide residues in agricultural commodities is needed and the pesticide residue levels above MRLs as well as incidents of detection of unapproved pesticides have to be investigated on a case-by-case basis to determine if the intake could exceed the health-based limits. The control of pesticide residues requires reliable analytical results which can be obtained by applying validated methods with further verification of method performance on an ongoing basis. In this work, we presented our approach to longtime pesticide residue analysis in fruiting vegetables by using a modified QuEChERS extraction then GC-MS/MS determination. The analyses were carried out under strict quality control conditions to maintain utmost analytical performance, which is needed to obtain accurate results when MRL is exceeded and/or an incident of unapproved pesticide detection occurs. In the present work, we proved the potential of GCMS/MS as a key tool for the quantification and confirmation of a high number of pesticides on a long-term basis application to real samples with excellent selectivity, sensitivity, and accuracy.

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Ambrus Á (2015) International harmonization of food safety assessment of pesticide residues. J Agric Food Chem. doi:10.1021/jf505854w Anasassiades M, Lehotay SJ, Stajnbaher D, Schenck FJ (2003) Fast and easy multiresidue method employing acetonitrile extraction/ partitioning and “dispersive solid-phase extraction” for the determination of pesticide residues in produce. J AOAC Int 86:412–431 Botitsi HV, Garbis SD, Economou A, Tsipi DF (2011) Current mass spectrometry strategies for the analysis of pesticides and their metabolites in food and water matrices. Mass Spectrom Rev 30:907– 939. doi:10.1002/mas.20307 Bruzzoniti MC, Checchini L, De Carlo RM, Orlandini S, Rivoira L, Del Bubba M (2014) QuEChERS sample preparation for the determination of pesticides and other organic residues in environmental matrices: a critical review. Anal Bioanal Chem 406:4089–4116. doi:10. 1007/s00216-014-7798-4

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AUTHOR'S PROOF


Food Anal. Methods multiresidue pesticide analysis in blackcurrants including studies of matrix effects and estimation of measurement uncertainty. Talanta 120:106–113. doi:10.1016/j.talanta.2013.11.087 Walorczyk S, Drożdżyński D (2012) Improvement and extension to new analytes of a multi-residue method for the determination of pesticides in cereals and dry animal feed using gas chromatographytandem quadrupole mass spectrometry revisited. J Chromatogr A 1251:219–231. doi:10.1016/j.chroma.2012.06.055

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region of south-eastern Poland. Environ Monit Assess 185:8873– 8878. doi:10.1007/s10661-013-3219-y Walorczyk S (2012) Gas chromatographic-tandem mass spectrometric analysis of pesticides residues in produce using concurrent solvent recondensation-large volume injection. J Chromatogr A 1222:98– 108. doi:10.1016/j.chroma.2011.12.012 Walorczyk S (2014) Validation and use of a QuEChERS-based gas chromatographic-tandem mass spectrometric method for

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AUTHOR'S PROOF AUTHOR QUERIES AUTHOR PLEASE ANSWER ALL QUERIES.

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Q1. Payá et al. 2007 has been changed to Payá et al. 2010 as per the reference list. Please check if okay. Q2. ”Prostate-specific antigen” was provided as the definition for ”PSA”. Please check and change as necessary. Q3. Footnote labeled ”a” was not cited in table body. Please check.