Comprehensive Method Validation for the

Food Anal. Methods DOI 10.1007/s12161-017-1026-8

Comprehensive Method Validation for the Determination of 170 Pesticide Residues in Pear Employing Modified QuEChERS Without Clean-Up and Ultra-High Performance Liquid Chromatography Coupled to Tandem Mass Spectrometry Magali Kemmerich 1 & Gabrieli Bernardi 1 & Osmar D. Prestes 1 & Martha B. Adaime 1 & Renato Zanella 1

Received: 15 May 2017 / Accepted: 20 August 2017 # Springer Science+Business Media, LLC 2017

Abstract A fast ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) method was developed for quantitative determination of pesticide residues in pear. A fast modified acetate QuEChERS method without clean-up was used for sample preparation. Validation was performed according to SANTE guideline. Matrix effect results were significative for most part of compounds and thus a matrix-matched calibration was employed. The linear range of the method was from 2.5 to 100 μg kg−1. Recoveries were between 70 and 120% with precision ≤ 20%. Limit of quantification was 2.5 μg kg−1 for most compounds. Uncertainty results ranged from 22 to 49%. In real sample analyses, 21 compounds were quantified in concentrations between 3.3 and 1427 μg kg−1. Method proved to be simple, robust and effective to be applied in routine analysis. Keywords Pesticide residues . Pear . Validation criteria . QuEChERS . UHPLC-MS/MS

Introduction Inappropriate agricultural practice for the use of pesticides incur in residues occurrence that, at levels above the maximum residue limits (MRL), might be a risk to human health (Gibney et al. 2009). According to Food and Agriculture

* Renato Zanella [email protected] 1

Laboratory of Pesticide Residues Analysis (LARP), Chemistry Department, Federal University of Santa Maria, Santa Maria, RS 97105-900, Brazil

Organization (FAO), Bresidue^ is the fraction of a substance, its metabolites, conversion or reaction products, and impurities that remain in food from agricultural products and/or animals treated with these substances (FAO 2011). Each country has established the MRL for pesticide residues in food matrices (Global MRLs 2015) and maintain monitoring programs, in different extensions, to ensure food safety. To monitor pesticides occurrence in cultures, the optimal extraction procedure is the one that allows the determination of a higher number of compounds, called a multiresidue method. The sample preparation step and the operations involved can affect the result and thus need a suitable procedure to obtain the right information in the analysis (Fenik et al. 2011). Miniaturized methods such as solid-phase microextraction (SPME) (Xu et al. 2016), stirring bar sorptive extraction (SBSE) (Kawaguchi et al. 2013), and others like pressurized liquid extraction (PLE) (Vazquez-Roig and Picó 2015), microwave-assisted extraction (MAE) (Wang et al. 2016), matrix solid-phase dispersive (MSPD) (Carro et al. 2017), and ultrasonic extraction (USE) (Pan et al. 2008) have proven to be fast and efficient techniques for many types of food samples. However, the different physicochemical properties of the pesticides make the establishment of multiresidue methods difficult (Hyötyläinen 2009; Cabrera et al. 2012). With this purpose, Anastassiades et al. (2003) introduced the QuEChERS method (Quick, Easy, Cheap, Effective, Rugged, and Safe), developed for the extraction of a wide range of pesticides in fruits and vegetables. In the 1980s, the mini Luke method was developed, which is a miniaturization of the original Luke extraction method, consisting in the extraction with acetone, petroleum ether, and dichloromethane (Prestes et al. 2009). Methods of extraction that employ ethyl acetate have also been applied for pesticide multiresidue analyzes (Rajski et al. 2013; EURL-FV 2016).

Food Anal. Methods

As most part of the compounds are not sufficiently volatile to be analyzed by gas chromatography, the use of liquid chromatography (LC) has been remarkable for determination of pesticide residues (Masiá et al. 2016). In order to obtain an analysis with higher resolution, the ultra-high performance liquid chromatography (UHPLC) has been introduced in routine laboratories. Among the advantages of the UHPLC system compared to LC are the small sample volume used, increase in sensitivity and separation efficiency, facility to transfer a method developed in LC to UHPLC system, and the opportunity to analyze faster a larger number of samples (Kovalczuk et al. 2006). Allied to this, mass spectrometry (MS) has stood out for its versatility of use together with both techniques (Stachniuk and Fornal 2016). Method validation is an important requirement for chemical analysis to ensure the provision of data with the required quality. For this reason, validation has received considerable attention in the literature and regulatory agencies worldwide (Rambla-Alegre et al. 2012). There are different documents that describe the method validation requirements to support the validity of data reported from official controls on pesticide residues and these are complementary to the requirements presented by the ISO/IEC 17025. In this context, Codex Alimentarius guidelines contribution for safety, quality, and fairness of international food trade, pesticides, food additives, and contaminants are some of the issues discussed in Codex meetings (Codex Alimentarius 1993). On the other hand, SANTE guidance is intended for laboratories involved in official control of pesticide residues in food and feed in the European Union (SANTE 2015), and INMETRO guide presents the requirements for accredited Brazilian laboratories (INMETRO 2010). These guidelines require almost the same parameters for validation and sometimes differ in criteria of acceptance. Studies evaluating the differences between validation guidances for pesticide residues determination are rarely reported. Validation parameters such as sensitivity, linearity, specificity, trueness, precision, and limits of detection and quantification are commonly evaluated in pesticide residues determination by chromatography techniques (Codex Alimentarius 1993; SANTE 2015; INMETRO 2010). However, measurement of uncertainty which represents a combination of all determination steps and is presented in all guidelines (Dubus et al. 2003) is reported only in few works, since it requires a further processing of the validation data (Carneiro et al. 2013). Matrix effect (ME) that is considered an important validation parameter for many authors (Pizzutti et al. 2009; Ferrer et al. 2011) is slightly discussed in Codex Alimentarius guideline and do not appear in INMETRO guideline. Codex Alimentarius only suggests that ME can changes with time, sample, and column, but does not specify a significance criterion (Codex Alimentarius 1993) while SANTE stablished that ME is significant when higher than ± 20% (SANTE 2015).

Robustness is shown just in SANTE and INMETRO guidelines, however, with different approaches. SANTE demonstrates robustness through evaluation of mean recovery and within-laboratory reproducibility. Also indicates a comparison of mean recovery reproducibility of critical parameters (SANTE 2015). INMETRO is more specific and indicates the Youden test to achieve it (INMETRO 2010). Regarding the limit of detection (LOD), INMETRO specifies that LOD must be experimentally obtained, since it can vary with sample type and recommends the use of statistics, such as Student t test (INMETRO 2010). On the other hand, Codex Alimentarius (1993) determines LOD as the lowest calibration level and SANTE (2015) does not address this issue. In general, validation guidelines differ only slightly. Despite this, SANTE has been the most employed guideline for pesticide residues analysis validation in food using QuEChERS and liquid chromatography determination. This can be evidenced since SANTE was used in most publications in this area in the recent years (Paz et al. 2015; Golge and Kabak 2015; Andrade et al. 2015; Christia et al. 2015; Abad-Fuentes et al. 2015; Lichtmannegger et al. 2015; Botero-Coy et al. 2015; Lopes et al. 2015; Ferreira et al. 2015; Martins et al. 2016; Bernardi et al. 2016; Vázquez et al. 2016; Mol et al. 2016). Pear belongs to the Rosaceae family, Pyrus genus, and comprises more than 20 species. The most important Pyrus communis L. in Brazil is Pyrus pyrifolia (Burm) Nak and its hybrid. The main producers worldwide are Argentina, Portugal, and the USA, accounting for 113 (82%), 10 (7%), and 9 (6.5%) thousand tons produced per year, respectively (FAO 2011). In Brazil, there are relatively few species of pests that attack pear trees and the Brazilian Ministry of Agriculture, Livestock, and Supply has registered only 21 pesticides for pears cultivation. Thus, the aims of the present study were to develop a fast and effective extraction procedure for the determination of pesticide multiresidue in pear by UHPLC-MS/MS method, to validate the method according to the SANTE guideline and, also to compare and discuss the validation parameters and acceptance criteria with other guides used worldwide.

Experimental Chemicals and Instrumentation Pesticide standards were obtained from Dr. Ehrenstorfer (Augsburg, Germany) with purity from 92.5 to 99.9%. All pesticide stock solutions at 1000 mg L−1 were prepared in acetonitrile. From these stock solutions, a mixture at 5 mg L−1 was prepared in acetonitrile and then diluted to a 1 mg L−1 solution to prepare the analytical curves, in both solvent and in blank matrix extract.

Food Anal. Methods

Acetone, n-hexane, and acetonitrile HPLC grade (Mallinckrodt, USA); glacial acetic acid and methanol HPLC grade (J.T. Baker, Mexico); and formic acid 96.0% (Tedia, USA). Ultrapurified water was obtained with a MilliQ Direct UV3® system (Millipore, France). Anhydrous magnesium sulfate (99.8%), anhydrous sodium acetate, and sodium chloride were acquired from J.T. Baker (Mexico). Florisil® 60-100 mesh (J.T. Baker, USA), Bondesil C18, and PSA (Agilent Technologies, USA) were used. Other materials included nylon filters of 0.2 μm of porosity (Agilent Technologies, USA), 15 and 50 mL polypropylene (PP) tubes (Sarstedt, Germany), 2 mL Eppendorf microtube (Axygen Scientific, USA), and glass vials of 2 mL (Agilent, USA). It was also employed a vortex mixer QL-901 (Microtécnica, Brazil), analytical balances AUW-220D and UX-420H (Shimadzu, Japan), centrifuge Solab (Centribio, Brazil), automatic micropipettes with variable capacity (Brand, Germany), food processor (Walita, Brazil), and a common laboratory glassware. For the chromatographic analyses, a Waters Acquity UPLC system with Xevo TQ mass spectrometer equipped with electrospray source (Milford, USA) was employed. UHPLC-MS/MS Analyses The separation was achieved according to Kemmerich et al.’s (2015) optimized procedure, using an Acquity BEH C18 column (50 × 2.1 mm, 1.7 μm) with a mobile phase consisting of (A) an aqueous solution containing 2% (v/v) of methanol and (B) methanol, both containing 0.1% (v/v) of formic acid and ammonium formate 5 mmol L−1. The percentage of organic solution was changed linearly as follows: 0 min, 5% B; 7.75 min, 100% B; and 8.51 min, 5% B. The flow rate was 0.225 mL min−1, the total chromatographic run time was 10 min, the injection volume 10 μL, and the column temperature was set at 40 °C. Nitrogen 6.0 (desolvation gas) at 600 L h−1 and argon 6.0 (collision gas) at 0.15 mL min−1 were used. The MS parameters were optimized by infusion of individual pesticide solutions directly into the mass spectrometer. The most abundant ion (first transition) was selected for quantification and the second transition was used as a qualitative (identification) ion. MassLynx 4.1 software was used for data acquisition and system control. Sample Preparation Pear blank samples were produced and collected in Rio Grande do Sul state (south of Brazil). A portion of 1 kg was processed according to the Codex Alimentarius (CODEX 1999), removing only the stalk previously, processing and conditioning at −4 °C until use. The tests were conducted in triplicate at a concentration of 100 μg kg−1. The method optimization for pesticide multiresidue extraction in pear was

conducted evaluating the original (Anastassiades et al. 2003), acetate (Lehotay et al. 2005) and citrate (Anastassiades et al. 2007) QuEChERS methods, mini-Luke (Luke et al. 1981), ethyl acetate (Jansson et al. 2004), and official European Union Reference Laboratory for Pesticide Residues in Fruit & Vegetables (EURL-FV 2016) methods. For extract clean-up, dispersive solid-phase extraction (d-SPE) was evaluated with C18, PSA, and florisil sorbents, alone or in combination. The injection of the extract without clean-up was also tested. The extract dilution before injection was evaluated in the relation of 1:1, 1:4, and 1:9 (v/v) of extract:water. Optimized Method The validated extraction procedure consists in weighing 10 g of fruit sample in a 50 mL PP tube and after add 10 mL of acetonitrile containing 1% (v/v) acetic acid. The tube was vortexed for 1 min and then 4 g of anhydrous magnesium sulfate and 1.7 g of sodium acetate were added. Shaking the tube again for 1 min and then centrifuge at 3400 rpm (2420 g) for 8 min, in order to obtain a good phase separation. No clean-up was used. The extract was filtered with a 0.2-μm nylon syringe filter and then diluted 1:9 (v/v) with ultrapure water. Atrazine d-5 was added at 10 μg kg−1 before extraction procedure, as surrogate standard (SS). Triphenylphosphate was added before injection in the chromatographic system, as internal standard in a final concentration of 20 μg kg−1. Parameters and Criteria for the Validation Step The method developed in this work was validated in accordance to the SANTE (2015) guideline. Selectivity is the ability of the extraction, separation system, and detector to discriminate between the analyte and others compounds. It was evaluated by comparing the chromatograms obtained from blank and spiked samples. Linearity was checked from six concentration levels with criteria of residuals < ± 20%. The lowest calibration level (LCL) was equal or lower than the reporting limit (RL), and the RL higher than the limit of quantification (LOQ). It was also considered the drift between two calibration curves not being higher than 30%. Matrix effect (ME) was estimated comparing the slopes of curves prepared in matrix blank extract and solvent (acetonitrile). ME results were expressed in percent and the criteria for considering this effect significant was larger than ± 20%. Specificity is the response in reagent blank and blank control samples, with criteria of being < 30% of RL. Also, identification criteria for MS/MS was considered as a minimum of two product ion for compound, signal-noise (S/N) ≥ 3, and ion ratio variation between ± 30%. Trueness, defined as the average recovery for spike levels tested, with criteria of 70–120% of acceptance and associated repeatability (RSDr) ≤ 20% was adopted.

Food Anal. Methods

Precision is specified as RSDr for spike levels tested, with criteria of ≤ 20% of acceptance, also, the precision as intermediate precision (RSDip), derived from on-going method validation/verification, with criteria of ≤ 20% of acceptance. In this study, for trueness and precision evaluation, recovery assays in five different levels, 2.5, 5, 10, 25, and 100 μg kg−1 (n = 9), were performed. LOQ is the lowest spike level meeting the method performance criteria for trueness and precision, with criteria of being bellow the maximum residue limits (MRL). The method must be capable of providing acceptable mean recovery values (70–120%, with RSDr ≤ 20%). Robustness can be evaluated as average recovery and RSDip, derived from on-going method validation/verification, with criteria as above mentioned. The robustness was evaluated through recovery assays (n = 9) at 10 μg kg−1, with little variations in some critical UHPLC-MS/MS parameters, like as methanol concentration in mobile phase (from 2% to 1 and 3%) and methanol suppliers (J. T. Baker and Merck). The uncertainty associated with quantification step was estimated by the standard deviation(s) of n replicates (u = s/√(n)) of three spiked levels (u(c) = √(u12 + u22 + u32)) combined (u(c,t) = √(u(c)2 + u(t)2)) with uncertainty arising from method bias (u(t) = 100 − mean recovery)), multiplied by a coverage factor of 2 (U = 2.u(c,t)) (Ellison and Williams 2012). Application to Real Samples The cultivation of pears in Brazil represents only 0.5% of the total fruit of temperate climate produced. Nearly 50% of the fruits that are imported to Brazil are pears (Fachinello et al. 2011). Because of that, the method developed and validated in this work was applied for pesticide residues determination in 27 pear samples imported from Argentina and Chile.

Results and Discussion UHPLC-MS/MS Conditions In order to enable the separation of all analyzed compounds with good efficiency in a short time, UHPLCMS/MS conditions were optimized. The precursor and product ions were obtained by infusion of each compound at 0.5 mg L−1 in the MS system, varying cone voltage and collision energy. Table 1 shows precursor and products ions monitored and collision energies used in the chromatographic method by selected reaction monitoring (SRM) mode with positive ionization. A proper separation was achieved using the gradient elution selected. Although most compounds have similar retention time, MS facilitates the identification of compounds.

Extraction Procedure Optimization Based on the existing methods for pesticide multiresidue analysis (Anastassiades et al. 2003; EURL-FV 2016; Lehotay et al. 2005; Anastassiades et al. 2007; Luke et al. 1981; Jansson et al. 2004), six tests were conducted to optimize the extraction of pesticides in fruits: original, acetate and citrate QuEChERS, mini-Luke method, ethyl acetate, and official EURL method. As shown in Fig. 1, the QuEChERS method was the most appropriate, whereby the acetate one was more efficient, with 156 of the 170 compounds evaluated obtaining results of recovery and RSD in the acceptable range. Furthermore, the application of QuEChERS method and chromatographic techniques, such as liquid chromatography, minimize sample handling and decrease the detected concentration levels quantified. The results obtained in the clean-up step using d-SPE with C18, PSA, florisil, and without clean-up, did not shown a significant change in precision and trueness results (Fig. 2). Based on that, the possibility of proceeding the extraction without clean-up step was investigated. Since the UHPLC has reduced internal diameter of pipes and filters, they are much more susceptible to clogging than those employed in HPLC (Maldaner and Jardim 2012). Thus, in order to minimize this problem, a higher dilution of the final extract was evaluated. In general, it was observed that the higher the dilution factor, the better the peak shape and signal intensity of the compounds (Fig. 3). Thus, the dilution proportion of 1:9 (v/v) for extract:water was selected for injection in the UHPLC-MS/MS system. Among the years, extraction procedures for pesticide multiresidue determination in pear by LC-MS/MS have been developed. For example, ethyl acetate in the presence of sodium sulfate (Jansson et al. 2004), original (Núñez et al. 2012), and acetate QuEChERS (Munaretto et al. 2016) were used as extraction method for determination of 57, 100, and 96 compounds, respectively. Thus, in this work, we achieved a higher number of compounds (170) with a simple and efficient acidified acetonitrile extraction and a higher dilution of the extract with water, without the need of a clean-up step. Evaluation of Method Validation The optimized method was validated in accordance to SANTE (2015) guideline. Selectivity was ensured by the absence of interfering compounds with the same retention time, quantitation, and confirmation ions of each pesticide. Figure 4 shows a comparison of the chromatograms obtained from blank and spiked sample (10 μg kg−1) for dimethoate. Linearity was obtained from 0.75 to 100 μg kg−1, with determination coefficient (r2) ranging from 0.9901 to 0.9998 (Table 2). Most of the compounds

Food Anal. Methods Table 1 Retention time (tR), ion transitions of the selected pesticides, cone and collision energies, ion ratio, and Codex Alimentarius maximum residues limit in pear Compounds

tR (min)

Precursor ion (m/z)

Cone voltage (V)

First transition quantification

Second transition identification

Product ion (m/z)

CE (eV)

Product ion (m/z)

CE (eV)

Ion ratio

Codex MRL (mg kg−1)

Acetamiprid

3.5

223

23

126

20

56

15

0.1

NA

Ametryn Atrazina-d5 (S)

5.4 5.4

228 221

32 26

186 179

18 18

68 101

36 23

0.1 0.3

NA –

Atrazine Azaconazole

5.3 5.5

216 300

30 25

174 152

18 28

96 131

23 18

0.2 0.3

NA NA

Azimsulfuron

5.3

425

26

182

20

139

40

0.1

NA

Azinphos-ethyl Azinphos-methyl

6.2 5.6

346 318

10 12

132 160

16 8

77 261

36 8

0.3 0.2

NA 2

Azoxystrobin

5.8

404

17

329

30

372

15

0.6

NA

Benomyl Bentazone

6.7 4.7

291 241

13 12

160 199

28 12

192 107

16 26

0.5 0.2

NA NA

Bifentrin

8.2

440

15

166

42

181

20

0.6

NA

Bispyribac sodium Boscalid Bromuconazole Bupirimate Buprofezin

6.1 5.9 6.2 6.4 7.3

431 343 376 317 306

23 32 32 31 22

275 307 159 166 201

14 20 35 28 12

196 140 70 108 57

20 20 25 28 20

0.1 0.2 0.1 0.4 0.1

NA NA NA NA 6

Carbaryl Carbendazim

4.9 2.7

202 192

19 24

145 160

22 18

117 132

28 28

0.3 0.2

NA NA

Carbofuran Carbofuran-3-hydroxy Carboxin Chlorbromuron Chlorfenvinphos

4.7 3.5 4.9 5.9 6.7

222 238 236 293 359

25 25 25 24 18

165 163 143 204 99

16 16 16 18 30

123 181 87 182 155

16 10 22 16 12

0.7 0.8 0.1 0.5 1.0

NA NA NA NA NA

Chlorimuron ethyl Chlorpyrifos Chlorpyriphos-methyl Clofentezine Clorantraniliprole

6.0 7.5 7.0 6.8 5.6

415 350 322 303 482

25 27 23 19 20

186 97 125 102 451

15 32 20 35 22

83 198 289 138 284

40 20 16 22 14

0.2 0.4 0.8 0.4 0.8

NA NA NA NA NA

Clothianidin Cyanazine Cyazofamid Cymoxanil Cyproconazole Cyprodinil Deltamethrin Demeton-S-methyl-sulfon Desmedipham Diazinon Dichlorvos Diclofluanide Dicrotophos Difenoconazole Dimethenamid Dimethoate

3.2 4.5 6.4 3.7 6.2 6.6 7.8 2.7 5.5 6.7 4.8 6.3 3.1 7.0 6.0 3.5

250 241 325 199 292 226 523 263 301 305 221 330 238 406 276 230

15 28 17 14 27 47 16 20 25 20 23 30 17 37 17 12

132 214 108 111 125 93 281 169 136 97 109 123 193 251 244 125

18 17 20 18 24 33 18 17 22 35 22 17 10 25 14 20

169 96 261 128 70 108 506 121 182 169 79 141 112 111 168 199

12 25 10 8 18 25 11 17 10 22 34 18 10 60 26 10

0.9 0.1 0.2 0.5 0.6 0.8 0.4 0.3 0.9 0.7 0.1 0.5 0.6 0.1 0.4 0.7

NA NA NA NA NA NA NA NA NA NA NA 5 NA NA NA 1

Food Anal. Methods Table 1 (continued) Compounds

tR (min)

Precursor ion (m/z)

Cone voltage (V)

First transition quantification Product ion (m/z)

Second transition identification CE (eV)

Product ion (m/z)

Ion ratio


CE (eV)

Dimethomorph

6.0

388

30

165

30

301

20

0.6

NA

Dimoxystrobin Diniconazole

6.5 7.0

327 326

21 37

116 70

21 25

205 159

10 34

0.6 0.2

NA NA

Diuron Dodemorph

5.5 5.6

233 282

27 31

72 116

18 21

46 98

14 28

0.2 0.4

NA NA

Epoxiconazole

6.3

330

25

101

50

121

22

0.3

NA

Ethiofencarb sulfone Ethiofencarb sulfoxide

3.1 3.2

258 242

19 19

107 107

18 18

201 185

5 8

0.4 0.5

NA NA

Ethiprole Ethoprophos

6.1 6.4

414 243

12 18

351 97

25 31

397 131

9 20

0.7 0.8

NA NA

Ethoxysulfuron Etofenprox

6.3 8.1

399 394

25 17

218 107

24 43

261 177

16 15

0.9 0.4

NA 0.6

Ethopabate Fenamiphos Fenarimol Fenazaquin Fenbuconazole

4.4 6.5 6.3 7.8 6.4

238 304 331 307 337

18 27 37 27 29

206 202 268 57 125

11 36 22 25 36

164 217 81 161 70

20 24 34 19 20

0.3 0.5 0.3 0.8 0.4

NA NA NA NA NA

Fenhexamid Fenoxycarb

6.3 6.5

302 302

32 19

97 88

22 20

55 116

38 11

0.2 0.7

NA NA

Fenpropathrin Fenpropimorph Fenpyroximat Fenthion Fluazafop-p-butyl

7.5 5.8 7.7 6.6 7.2

350 304 422 279 384

15 41 23 25 27

97 147 366 169 282

34 28 15 16 22

125 57 138 247 328

14 30 32 13 16

0.4 0.1 0.2 0.8 0.5

NA NA NA NA NA

Flufenoxuron Fluquinconazole Fluroxypyr Flusilazole

7.8 6.2 4.6 6.6

489 376 255 316

31 37 19 27

158 307 181 165

22 30 22 28

141 349 209 247

46 18 16 18

0.9 0.7 1.0 0.9

NA NA NA NA

Flutolanil Flutriafol Furaltadone hydrochloride Furathiocarb Furazolidone Hexaconazole Hexythiazox Imazalil Imidacloprid Iprovalicarb Kresoxim-methyl Linuron Lufenuron Malathion Mecarbam Mephosfolan Mepronil Metalaxyl

6.0 5.3 1.9 7.2 2.6 6.8 7.5 5.3 3.2 6.2 6.6 5.8 7.5 6.0 6.3 4.6 6.1 5.4

324 302 325 383 226 314 353 297 256 321 314 249 511 331 330 270 270 280

23 23 16 22 26 31 21 31 23 19 15 25 20 12 12 25 27 15

65 123 100 195 139 70 168 159 175 119 206 160 141 99 227 140 91 192

40 29 26 18 15 22 26 22 20 16 7 18 50 24 8 24 44 17

262 70 252 252 95 159 228 69 209 203 116 181 158 127 97 75 119 220

18 18 17 12 14 28 14 22 15 10 12 16 25 12 35 22 28 13

0.2 0.7 1.0 0.7 0.8 0.4 0.8 0.2 1.0 0.8 0.5 0.1 0.7 1.0 0.3 0.1 0.5 0.7

NA NA NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA


tR (min)

Precursor ion (m/z)

Cone voltage (V)

First transition quantification Product ion (m/z)

Second transition identification CE (eV)

Product ion (m/z)

CE (eV)

Ion ratio


Metconazole

6.8

320

29

70

22

125

36

0.2

NA

Methamidophos Methidathion

0.7 5.5

142 303

17 10

93.9 145

12 10

125 85

13 20

0.1 0.5

NA 1

Methiocarb sulfone Methiocarb sulfoxide

3.1 3.4

258 242

22 17

107 122

38 28

122 185

19 14

0.1 0.4

NA NA

Methomyl

2.5

163

17

106

10

88

10

1.0

0.3

Methoxyfenozide Metobromuron

6.1 5.2

369 259

25 22

149 170

18 20

313 148

8 15

0.4 0.5

NA NA

Metolachlor Metoxuron

6.4 4.2

284 229

17 20

176 72

25 18

252 156

15 25

0.5 0.2

NA NA

Metribuzin Metsulfuron methyl

4.6 4.9

215 382

33 22

131 167

18 16

89 199

20 22

0.7 0.2

NA NA

Mevinphos Monocrotophos Monolinuron Nitenpyram Nuarimol

3.9 2.8 5.0 2.4 5.8

225 224 215 271 315

13 15 23 22 37

127 127 99 125 252

15 16 34 25 22

193 98 126 225 81

8 12 22 12 28

0.3 0.2 0.4 0.6 0.2

NA NA NA NA NA

Omethoate Oxadixyl

1.9 3.4

214 279

16 31

125 132

22 34

183 –

11 –

0.8 1.0

1 NA

Oxamyl Oxifluorfen Paraoxon ethyl Parathion Parathion-methyl

2.3 7.2 5.3 6.5 2.6

237 362 276 292 264

12 27 28 25 29

72 237 220 236 109

10 24 17 14 22

90 316 248 110 79

10 14 16 33 36

0.4 0.3 0.1 0.1 0.3

NA NA NA NA NA

Penconazole Pencycuron Pendimethalin Phosmet

6.6 6.9 7.5 5.6

284 329 282 318

25 30 12 19

159 125 212 160

34 22 10 22

70 125 194 77

16 40 17 46

0.5 1.0 0.2 0.1

NA NA NA NA

Picoxystrobin Piperonyl butoxide Pirimicarb Pirimiphos-ethyl Pirimiphos-methyl Procymidone Profenofos Prometryn Propanil Propiconazole Propyzamide Propoxur Protiophos Pymetrozine Pyraclostrobin Pyrazophos Pyrazosulfuron-ethyl Pyridaben

6.5 7.3 3.5 7.3 6.8 6.5 7.2 6.0 5.9 6.7 6.1 4.7 7.9 1.9 6.8 6.8 6.3 7.8

368 356 239 334 306 284 373 242 218 342 256 210 345 218 388 374 415 365

10 17 25 30 25 33 25 26 31 37 27 12 20 28 20 33 22 19

145 119 182 198 108 256 128 158 162 159 190 111 241 105 163 194 182 147

22 37 15 23 32 17 40 25 16 34 15 16 18 20 25 32 20 24

205 177 72 182 164 67 303 200 127 69 168 79 194 222 83 309

10 11 18 25 22 28 20 17 22 22 10 30 12 22 45 12

0.9 0.2 0.1 0.2 0.7 0.2 0.1 0.4 0.5 0.1 1.0 0.5 1.0 0.1 0.9 0.9 0.1 0.8

NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA


tR (min)

Precursor ion (m/z)

Cone voltage (V)

First transition quantification

Second transition identification

Product ion (m/z)

CE (eV)

Product ion (m/z)

CE (eV)

Ion ratio


Pyridaphenthion

6.1

341

31

189

22

92

34

0.2

NA

Pyridate Pyrifenox

8.0 6.2

379 295

19 29

207 93

18 22

351 67

10 60

0.2 0.1

NA NA

Pyrimethanil Pyriproxifen

5.7 6.2

200 295

42 23

107 93

24 14

82 96

24 14

0.3 0.5

NA NA

Quinalphos

6.5

299

15

97

30

163

24

0.8

NA

Quinoxyfen Simazine

7.4 4.6

308 202

52 34

162 96

44 22

197 124

32 16

0.7 0.6

NA NA

Spinosad A Spirodiclofen

6.9 7.9

733 411

47 22

142 313

31 13

98 71

59 13

0.1 0.1

NA NA

Tebuconazole Tebufenozide

6.7 6.5

308 353

31 12

70 133

22 20

125 297

40 8

0.2 0.7

1 NA

Tebufenpyrad Terbuthylazine Tetraconazole Thiabendazole Thiacloprid

7.3 5.9 6.3 3.0 3.9

334 230 372 202 253

43 28 32 42 32

117 174 159 175 126

34 16 30 25 20

145 96 70 131 90

28 28 20 30 40

1.0 0.1 0.2 0.6 0.1

NA NA NA NA NA

Thiamethoxam Thiodicarb

2.7 5.1

292 355

19 17

132 88

22 16

211 108

12 16

0.3 0.4

NA NA

Thiobencarb Thiophanate-methyl Tolylfluanid Triadimefon Triadimenol

6.9 4.63 6.6 6.1 6.2

258 343 347 294 296

21 23 16 22 12

125 151 137 69 70

17 21 28 20 10

100 311 238 197 99

12 11 10 15 15

0.1 0.2 0.8 0.6 0.1

NA NA NA NA NA

Triazophos Triasulfuron Trichlorfon Tridemorph

6.2 5.8 3.4 6.7

314 404 257 298

22 25 20 43

162 372 257 98

35 15 5 34

119 – 127 57

18 – 17 28

0.3 1.0 0.1 0.7

NA NA NA NA

Triphenylphosphate (IS) Trifloxystrobin Triflumizole Triflumuron Triticonazole Tylosin Vamidothion

6.7 7.0 7.1 6.8 6.3 5.4 3.5

327 409 346 359 318 917 288

40 25 13 23 20 57 17

152 145 278 139 70 174 118

37 40 10 35 16 40 28

215 186 278 156 125 101 146

28 16 10 16 35 45 10

0.8 0.8 1.0 0.9 0.2 0.1 0.4

NA NA NA NA NA NA

CE collision energy, S surrogate, IS internal standard, Codex Codex Alimentarius, MRL maximum residues limit, NA not allowed

presented a significant matrix effect (above ± 20%), as can be seen in Table 2. So matrix-matched calibration (MMC) was performed with analyte solutions prepared in matrix blank extracts, to compensate this effect in the determination step. The identification criteria that ensures the method specificity are all compounds had two product ion, S/N ≥ 3 and ion ration varying from 0.1 to 1.0.

Trueness and precision were achieved in the evaluated spike levels 2.5, 5, 10, 25, and 100 μg kg−1 for almost all compounds, with recovery results between 70 and 120% and RSD ≤ 20%. For limit of quantification (LOQ), it was considered the lowest spiked level with acceptable trueness and recovery. Therefore, 90% of studied compounds presented LOQ of 2.5 μg kg−1 (Table 2). However, for lower levels (2.5 and 5.0 μg kg−1), some compounds did not present satisfactory

Food Anal. Methods Fig. 1 Extraction tests performed with blank spiked at 100 μg kg−1, number of compounds with recoveries between 70 and 120%, RSD ≤ 20%

180

Number of compounds

160 140 120 100 80 60 40 20 0 Original QuEChERS

results. For this reason, pesticides such as bentazone, fluroxypyr, furaltadone hydrochloride, parathion, pirimicarb, procymidone, triflumizole, and triflumuron presented LOQ of 5.0 μg kg−1, and azinphos-ethyl, clofentezine, cyazofamid, fenoxycarb, oxadixyl, oxifluorfen, parathion-methyl, pyraclostrobin, and triadimenol presented LOQ of 10.0 μg kg−1. In spite of the dilution used, better peak shape were obtained and the LOQ values are in the proper range and similar to other works (LÓPEZ et al. 2014; JARDIM et al. 2014) and attend to the MRL for the pesticides analyzed (Table 1). Considering small modifications from

180 160

Number of compounds

Fig. 2 Clean-up tests performed with blank spiked at 100 μg kg−1, number of compounds with recoveries between 70 and 120%, RSD ≤ 20%

140 120 100 80 60 40 20 0

Acetate QuEChERS

Citrate QuEChERS

Mini-Luke

ethyl acetate

EURL official

validated method, as methanol concentration in mobile phase (1 and 3%) and methanol suppliers (J.T. Baker and Merck), the precision of obtained results in the recovery tests shown a satisfactory robustness of the proposed method, with recoveries from 70 to 120% and RSD ≤ 19% (Table 3). Table 2 presents the results of uncertainty associated with the quantification step, calculated on basis of trueness and repeatability. The average uncertainty of 170 compounds was 39%, ranging from 22 to 49%. According to SANTE, an expanded uncertainty of 50% is considered the default value of assessment of pesticide residues analysis in food.

Food Anal. Methods

a

b

c Acetamiprid

Carbendazin

Fig. 3 Total ion chromatogram for acetamiprid and carbendazim prepared in blank of pear matrix (concentration of 100 μg L−1) and diluted in water in proportion of (A) 1:9, (B) 1:4, and (C) 1:1 (v/v)

Application to Real Samples All the 27 analyzed samples presented at least 1 compound. Table 4 presents the results of pesticide residues found (LOQ) in the analyzed samples. The less contaminated sample was S4, which presented only acetamiprid at 6.7 μg kg−1. In total, 21 compounds were quantified in concentrations between 3.3 and 1427 μg kg−1: acetamiprid, azinphos-methyl, carbaryl, carbendazim, chlorpyrifos, clorantraniliprole, difenoconazole, fenpropathrin, imazalil, malathion, methomyl, metribuzin, piperonyl butoxide, pirimiphos-methyl, pyrifenox, pyrimethanil, pyriproxifen, spirodiclofen, thiabendazole, thiacloprid, and

Fig. 4 UHPLC-MS/MS total ion chromatogram (TIC) of the compound dimethoate obtained from the analysis of (A) spiked sample at 10 μg kg−1) and (B) blank sample

thyophanate-methyl. The compounds most frequently found in the samples were the insecticides clorantraniliprole (19 samples ranging from 3.6 to 59.5 μg kg−1) and acetamiprid (18 samples ranging from 3.7 to 29.5 μg kg−1). Highest concentrations were found for the fungicides thiabendazole (1427 μg kg−1) and pyrimethanil (492 μg kg−1). Sample 10 presented the maximum number of residue compounds, with eight pesticides in concentrations between 3.7 and 171 μg kg−1. Sample 6 presented the higher sum of compounds concentrations, in a total of 516 μg kg−1 from six pesticides. All concentrations are in accordance to Brazilian MRL, with exception of methomyl, metribuzin, pirimiphos-methyl, and thiacloprid that were quantified but are not allowed to be

r2

0.9987 0.9994 0.9991 0.9977 0.9994 0.9914 0.9931 0.9957 0.9942 0.9983 0.9964 0.9949 0.9939 0.9929 0.9963 0.9969 0.9983 0.9928 0.9946 0.9951 0.9967 0.9932 0.9948 0.9951 0.9984 0.9914 0.9977 0.9965 0.9972 0.9919 0.9980 0.9977 0.9914 0.9903 0.9984 0.9952 0.9928 0.9997 0.9974 0.9984 0.9985 0.9978 0.9979 0.9953

Acetamiprid Ametryn Atrazine Azaconazole Azimsulfuron Azinphos-ethyl Azinphos-methyl Azoxystrobin Bentazone Bifentrin Bispiribaque sodio Boscalid Bupirimate Bromuconazole Buprofezin Carbaryl Carbendazima Carbofuran Carbofuran-3-hydroxy Carboxin Chlorbromuron Chlorfenvinphos Chlorimuron ethyl Chlorpyrifos Chlorpyriphos-methyl Clofentezine Clorantraniliprole Clothianidin Cyanazine Cyazofamid Cymoxanil Cyproconazole Cyprodinil Deltamethrin Demeton-S-methyl-sulfon Desmedipham Diazinon Dichlorvos Diclofluanide Dicrotophos Difenoconazole Dimethenamid Dimethoate Dimethomorph

LOQ (μg kg−1)

2.5 2.5 2.5 2.5 2.5 10 2.5 2.5 5.0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 10 2.5 2.5 2.5 10 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

Matrix effect

−24 −5 −13 29 71 8 −15 2 107 4 12 −21 −38 −14 −4 −24 −28 −29 −7 −10 −18 −31 72 −102 −76 −15 −23 17 −24 −21 30 −7 70 1 −4 9 −12 −4 −20 13 −5 −27 −9 −10 102 (8) 115 (6) 101 (6) 83 (15) 83 (8) – 87 (10) 96 (14) – 117 (12) 93 (9) 101 (19) 103 (14) 95 (17) 75 (18) 103 (13) 101 (3) 112 (10) 91 (14) 86 (7) 100 (13) 106 (18) 113 (13) 82 (18) 120 (15) – 103 (15) 72 (20) 82 (16) – 95 (14) 90 (18) 105 (19) 105 (9) 104 (13) 89 (9) 88 (10) 95 (15) 108 (20) 99 (15) 120 (7) 1096 (14) 105 (18) 95 (16)

89 (9) 96 (2) 90 (11) 86 (4) 94 (7) – 114 (12) 105 (8) 70 (8) 92 (19) 95 (10) 98 (4) 72 (1) 96 (16) 75 (16) 107 (16) 92 (5) 94 (2) 88 (9) 76 (7) 109 (13) 114 (8) 81 (3) 76 (8) 87 (14) – 98 (9) 70 (9) 102 (7) – 92 (8) 111 (10) 103 (12) 109 (12) 99 (4) 93 (10) 77 (11) 93 (10) 77 (15) 88 (5) 112 (13) 83 (12) 70 (12) 87 (7)

86 (6) 94 (3) 93 (5) 92 (8) 92 (10) 120 (17) 100 (13) 93 (7) 75 (20) 110 (11) 101 (4) 112 (8) 95 (14) 101 (16) 89 (10) 85 (6) 88 (3) 88 (7) 94 (16) 73 (5) 87 (13) 95 (18) 105 (5) 83 (18) 91 (9) 104 (17) 99 (11) 82 (8) 89 (4) 84 (18) 77 (12) 116 (16) 100 (16) 95 (17) 93 (3) 92 (9) 88 (16) 90 (9) 83 (16) 79 (16) 102 (9) 90 (10) 88 (10) 86 (8)

83 (2) 85 (3) 84 (5) 89 (2) 87 (2) 100 (16) 83 (6) 77 (2) 71 (20) 102 (9) 94 (3) 107 (1) 90 (11) 100 (16) 88 (4) 72 (3) 80 (2) 81 (2) 90 (13) 70 (2) 87 (17) 87 (17) 93 (9) 84 (16) 76 (13) 98 (16) 93 (5) 98 (7) 80 (5) 110 (9) 74 (2) 91 (9) 89 (4) 113 (5) 85 (6) 88 (4) 73 (3) 85 (7) 72 (19) 70 (5) 88 (11) 83 (3) 83 (8) 90 (4)

25 76 (4) 81 (4) 79 (3) 82 (5) 84 (4) 80 (20) 80 (9) 80 (7) 72 (19) 82 (18) 85 (5) 87 (3) 85 (4) 91 (3) 83 (7) 70 (5) 74 (2) 72 (4) 81 (3) 70 (5) 75 (5) 94 (8) 87 (5) 81 (14) 77 (15) 115 (10) 88 (8) 84 (7) 75 (6) 93 (17) 70 (3) 87 (11) 88 (11) 89 (7) 78 (2) 81 (7) 72 (11) 80 (3) 71 (10) 71 (4) 87 (6) 76 (7) 72 (3) 87 (7)

100 120 (6) 78 (18) 91 (17) 93 (13) 102 (17) – 120 (20) 96 (16) – 85 (15) 120 (10) 98 (20) 99 (13) 98 (20) 116 (20) 71 (19) 102 (15) 93 (18) 108 (20) 89 (18) 85 (19) 71 (18) 91 (15) 119 (18) 117 (3) – 111 (9) 109 (14) 96 (19) – 97 (18) 117 (20) 76 (19) 119 (18) 97 (18) 81 (20) 86 (5) 106 (17) 120 (18) 84 (18) 120 (10) 80 (16) 119 (2) 96 (13)

109 (10) 88 (14) 94 (15) 105 (12) 97 (17) – 107 (14) 91 (18) 106 (7) 109 (18) 105 (15) 83 (19) 74 (19) 109 (20) 96 (20) 74 (8) 106 (12) 107 (11) 115 (20) 70 (9) 85 (20) 73 (17) 77 (11) 102 (12) 79 (6) – 76 (18) 82 (15) 115 (18) – 103 (13) 119 (15) 82 (20) 74 (19) 98 (18) 99 (16) 97 (13) 92 (17) 118 (20) 76 (15) 103 (18) 89 (20) 106 (8) 86 (5)

5.0 103 (15) 90 (16) 91 (15) 95 (11) 100 (20) 120 (13) 108 (7) 91 (19) 74 (19) 86 (13) 98 (18) 99 (15) 70 (17) 90 (20) 93 (19) 71 (3) 98 (10) 89 (15) 99 (16) 80 (20) 70 (20) 72 (19) 89 (11) 83 (18) 70 (13) 92 (18) 76 (8) 99 (18) 101 (7) 93 (20) 75 (10) 106 (12) 95 (14) 103 (16) 102 (12) 89 (17) 92 (16) 99 (16) 72 (15) 100 (14) 86 (13) 97 (19) 91 (16) 99 (15)

10 94 (9) 88 (4) 94 (6) 105 (6) 95 (10) 93 (16) 99 (15) 89 (4) 94 (11) 92 (15) 94 (13) 115 (12) 90 (10) 100 (9) 84 (12) 86 (17) 99 (12) 86 (9) 90 (14) 78 (20) 98 (16) 75 (20) 81 (10) 91 (16) 103 (20) 71 (17) 99 (17) 92 (17) 102 (9) 70 (16) 86 (10) 96 (12) 105 (11) 98 (18) 99 (11) 93 (6) 73 (19) 96 (14) 87 (8) 100 (14) 88 (9) 105 (12) 96 (13) 90 (12)

25

2.5

10

2.5

5.0

Spiked levels (μg kg−1) Trueness, Recovery % (Intermediate precision, RSDip%)

Spiked levels (μg kg−1) Trueness, Recovery % (Repeatability, RSDr %)

Determination coefficient (r2), matrix effect, LOQ, trueness, precision, and uncertainty results for the analyzed compounds

Compounds

Table 2

84 (7) 83 (8) 87 (12) 86 (6) 86 (4) 77 (9) 89 (12) 87 (11) 79 (10) 94 (11) 84 (11) 87 (7) 82 (14) 92 (15) 85 (16) 98 (11) 86 (5) 81 (8) 76 (12) 76 (19) 107 (8) 76 (19) 80 (10) 78 (8) 120 (14) 79 (20) 78 (16) 87 (9) 85 (7) 87 (4) 82 (13) 81 (10) 85 (10) 84 (15) 91 (6) 92 (12) 78 (13) 87 (10) 72 (10) 81 (11) 85 (12) 88 (17) 89 (16) 80 (10)

100 32 32 29 45 44 32 46 43 29 47 35 44 49 48 46 42 45 38 43 47 42 44 35 37 43 37 40 41 37 39 41 47 43 36 36 36 48 41 47 44 33 36 47 43

U (%)

Food Anal. Methods

r2

0.9992 0.9959 0.9969 0.9986 0.9936 0.9977 0.9969 0.9901 0.9951 0.9974 0.9932 0.9934 0.9958 0.9932 0.9969 0.9979 0.9974 0.9915 0.9982 0.9987 0.9925 0.9920 0.9972 0.9955 0.9969 0.9965 0.9973 0.9977 0.9968 0.9972 0.9960 0.9911 0.9950 0.9984 0.9957 0.9950 0.9926 0.9995 0.9974 0.9998 0.9955 0.9916 0.9968 0.9978

Compounds

Dimoxystrobin Diniconazole Diuron Dodemorph Epoxiconazole Ethiofencarb sulfone Ethiofencarb sulfoxide Ethiprole Ethoprophos Ethoxysulfuron Etofenprox Ethopabate Fenamiphos Fenarimol Fenazaquin Fenbuconazole Fenhexamid Fenoxycarb Fenpropathrin Fenpropimorph Fenpyroximat Fenthion Fluazafop-P-butyl Flufenoxuron Fluquinconazole Fluroxypyr Flusilazole Flutolanil Flutriafol Furaltadone hidrocloride Furathiocarb Furazolidone Hexaconazole Hexythiazox Imazalil Imidacloprid Iprovalicarb Kresoxim-methyl Linuron Lufenuron Malathion Mecarbam Mephosfolan Mepronil

Table 2 (continued)

6 −5 −3 1 −20 −15 4 21 8 164 40 109 −5 −101 −2 −22 55 315 −6 202 97 12 336 −17 39 1 −103 26 55 1 −40 −22 34 −8 15 −4 −15 2 1450 −10 −3 −32 1 −16

Matrix effect

2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 10 2.5 2.5 2.5 2.5 2.5 2.5 2.5 5.0 2.5 2.5 2.5 5.0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

LOQ (μg kg−1)

72 (20) 118 (9) 90 (20) 106 (13) 89 (13) 97 (12) 88 (17) 96 (11) 117 (15) 102 (17) 111 (15) 102 (13) 120 (16) 114 (9) 72 (19) 117 (6) 119 (16) – 117 (8) 94 (14) 75 (17) 111 (17) 110 (11) 80 (4) 101 (13) – 111 (19) 108 (9) 102 (19) – 89 (14) 120 (13) 108 (15) 114 (17) 119 (4) 88 (6) 92 (19) 117 (13) 84 (18) 84 (6) 95 (20) 110 (10) 96 (9) 111 (15)

85 (12) 103 (18) 82 (1) 86 (20) 86 (5) 92 (11) 78 (14) 98 (10) 92 (11) 110 (7) 83 (9) 90 (10) 100 (12) 108 (17) 71 (13) 117 (7) 115 (18) – 100 (19) 93 (6) 73 (12) 119 (20) 92 (11) 95 (15) 116 (4) 73 (19) 114 (10) 101 (5) 108 (19) 113 (19) 82 (13) 115 (6) 73 (15) 76 (4) 109 (15) 85 (7) 92 (7) 108 (17) 91 (16) 114 (5) 70 (16) 92 (8) 80 (5) 95 (1)

84 (15) 105 (13) 92 (2) 93 (8) 100 (10) 85 (7) 84 (9) 82 (9) 85 (7) 94 (15) 85 (10) 84 (10) 99 (11) 108 (16) 85 (16) 92 (14) 117 (19) 86 (13) 82 (10) 89 (17) 82 (8) 102 (15) 94 (7) 98 (8) 116 (8) 71 (20) 101 (10) 99 (6) 96 (12) 115 (17) 85 (14) 117 (10) 81 (12) 87 (18) 93 (13) 83 (11) 96 (13) 90 (15) 86 (13) 112 (15) 81 (20) 88 (20) 87 (6) 100 (5)

86 (4) 94 (15) 81 (4) 87 (4) 95 (5) 79 (4) 79 (5) 92 (8) 80 (17) 89 (11) 77 (15) 81 (4) 77 (11) 85 (11) 80 (3) 106 (19) 100 (11) 100 (6) 76 (6) 85 (7) 77 (15) 109 (6) 77 (3) 98 (6) 96 (2) 72 (15) 92 (7) 89 (2) 88 (2) 112 (18) 90 (4) 115 (3) 80 (7) 95 (2) 78 (10) 85 (10) 88 (10) 103 (10) 87 (7) 91 (12) 74 (11) 76 (13) 79 (2) 87 (3)

25

100 85 (12) 81 (7) 79 (4) 84 (8) 93 (6) 75 (4) 71 (4) 70 (6) 73 (9) 82 (7) 78 (6) 72 (4) 72 (7) 75 (11) 81 (9) 75 (9) 79 (9) 74 (13) 85 (5) 81 (5) 78 (7) 90 (12) 84 (4) 90 (4) 85 (11) 78 (9) 76 (9) 78 (6) 82 (4) 119 (5) 85 (3) 117 (4) 77 (8) 78 (9) 73 (4) 77 (4) 85 (6) 94 (12) 75 (3) 99 (15) 70 (10) 71 (7) 74 (6) 78 (3)

110 (9) 71 (15) 95 (18) 76 (12) 85 (19) 105 (12) 116 (14) 111 (10) 98 (14) 111 (7) 108 (14) 93 (16) 85 (13) 92 (20) 73 (18) 116 (2) 120 (6) – 112 (3) 95 (20) 72 (16) 70 (19) 108 (19) 114 (10) 96 (6) – 114 (18) 78 (19) 97 (16) – 89 (11) 111 (5) 81 (17) 110 (18) 109 (16) 98 (20) 113 (19) 92 (3) 77 (16) 114 (14) 120 (18) 113 (18) 113 (13) 73 (19)

92 (18) 118 (18) 95 (13) 87 (13) 87 (13) 93 (19) 85 (17) 120 (11) 74 (14) 89 (18) 101 (20) 98 (15) 107 (12) 77 (8) 87 (16) 70 (18) 88 (14) – 86 (20) 94 (12) 88 (9) 97 (20) 91 (15) 102 (15) 87 (13) 86 (14) 103 (20) 84 (18) 89 (19) 104 (18) 106 (14) 82 (15) 112 (3) 120 (19) 87 (13) 110 (7) 78 (17) 94 (1) 96 (20) 104 (20) 70 (14) 103 (13) 94 (11) 70 (13)

5.0 101 (11) 72 (9) 92 (18) 80 (3) 91 (16) 96 (14) 93 (12) 93 (20) 84 (20) 100 (15) 112 (12) 94 (12) 99 (17) 80 (8) 73 (7) 75 (15) 73 (19) 73 (20) 93 (20) 87 (16) 100 (15) 70 (10) 98 (16) 89 (6) 99 (19) 86 (14) 103 (16) 90 (15) 90 (17) 83 (17) 101 (10) 90 (14) 119 (20) 108 (16) 83 (16) 87 (17) 104 (18) 120 (15) 84 (16) 97 (11) 73 (20) 120 (13) 94 (12) 86 (9)

10 79 (15) 111 (18) 95 (12) 91 (17) 93 (17) 106 (19) 100 (16) 120 (20) 76 (12) 82 (16) 95 (11) 91 (8) 89 (15) 97 (14) 82 (8) 101 (12) 93 (15) 119 (9 71 (20) 93 (10) 90 (6) 70 (12) 98 (9) 88 (16) 93 (20) 72 (15) 82 (11) 103 (13) 83 (17) 105 (17) 107 (13) 105 (18) 88 (16) 95 (14) 70 (11) 92 (11) 74 (14) 94 (18) 106 (3) 115 (20) 73 (20) 104 (13) 89 (6) 118 (8)

25

2.5

10

2.5

5.0



85 (15) 100 (20) 83 (5) 89 (10) 76 (10) 86 (6) 92 (5) 100 (12) 84 (16) 78 (10) 101 (17) 84 (11) 90 (16) 79 (13) 101 (12) 73 (14) 88 (13) 107 (20) 88 (7) 86 (7) 85 (12) 76 (19) 89 (16) 83 (6) 83 (9) 79 (13) 85 (10) 99 (8) 86 (13) 96 (8) 98 (12) 98 (11) 104 (15) 100 (7) 89 (11) 83 (12) 84 (15) 107 (20) 91 (7) 92 (15) 77 (16) 95 (14) 83 (8) 104 (15)

100 40 37 43 34 37 44 36 39 41 40 42 41 40 38 40 39 40 45 22 32 41 42 35 40 37 48 35 24 41 38 48 41 44 36 32 37 43 38 44 44 42 42 34 32

U (%)

Food Anal. Methods

r2

0.9981 0.9953 0.9958 0.9958 0.9901 0.9990 0.9985 0.9915 0.9989 0.9956 0.9978 0.9931 0.9914 0.9941 0.9924 0.9980 0.9978 0.9971 0.9971 0.9943 0.9991 0.9993 0.9990 0.9964 0.9982 0.9991 0.9908 0.9996 0.9988 0.9979 0.9967 0.9928 0.9985 0.9921 0.9992 0.9996 0.9972 0.9973 0.9969 0.9954 0.9953 0.9998 0.9967 0.9984

Compounds

Metalaxyl Metconazole Methamidophos Methidathion Methiocarb sulfone Methiocarb sulfoxide Methomyl Methoxyfenozide Metobromuron Metolachlor Metoxuron Metribuzin Metsulfuron methyl Mevinphos Monocrotophos Monolinuron Nitenpyram Nuarimol Omethoate Oxadixyl Oxamyl Oxifluorfen Paraoxon etil Parathion Parathion-methyl Penconazole Pencycuron Pendimethalin Phosmet Picoxystrobin Piperonyl butoxide Pirimicarb Pirimiphos-ethyl Pirimiphos-methyl Procymidone Profenofos Prometryn Propanil Propiconazole Propyzamide Propoxur Protiophos Pymetrozine Pyraclostrobin

Table 2 (continued)

19 −13 35 −22 −21 −28 14 17 −7 −31 −65 71 −21 −26 −2 8 −11 −11 −70 −11 368 −8 −6 17 5 −7 74 5 −6 −30 −52 −17 −13 52 38 −15 −3 2 −33 −39 −80 1 −1 34

Matrix effect

2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 10 2.5 10 2.5 5.0 10 2.5 2.5 2.5 2.5 2.5 2.5 5.0 2.5 2.5 5.0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 10

LOQ (μg kg−1)

87 (19) 83 (17) 105 (6) 74 (9) 103 (17) 118 (14) 118 (9) 70 (11) 94 (17) 94 (11) 108 (9) 93 (16) 102 (15) 90 (13) 96 (15) 100 (9) 93 (16) 87 (13) 102 (7) – 117 (8) – 95 (5) – – 92 (20) 83 (19) 106 (4) 93 (15) 89 (14) 117 (120) – 86 (18) 84 (16) – 71 (10) 103 (15) 116 (18) 77 (7) 76 (20) 94 (15) 104 (13) 81 (13) –

77 (16) 81 (3) 73 (11) 73 (12) 98 (8) 77 (2) 80 (7) 73 (1) 84 (13) 88 (19) 86 (15) 99 (14) 83 (8) 90 (14) 84 (13) 80 (16) 77 (14) 84 (16) 90 (11) – 85 (3) – 86 (2) 105 (15) – 106 (7) 84 (10) 71 (14) 78 (19) 90 (2) 90 (16) 75 (2) 74 (4) 101 (7) 103 (20) 76 (17) 83 (1) 96 (9) 82 (12) 78 (20) 72 (13) 81 (6) 70 (5) –

85 (11) 99 (9) 73 (10) 88 (14) 96 (15) 89 (11) 73 (20) 92 (4) 90 (5) 89 (11) 87 (9) 72 (13) 87 (12) 82 (11) 76 (8) 89 (6) 71 (11) 89 (13) 81 (3) 77 (13) 73 (13) 81 (16) 90 (6) 104 (19) 115 (3) 106 (8) 90 (20) 105 (8) 92 (5) 91 (17) 110 (7) 70 (13) 88 (9) 94 (19) 81 (20) 80 (14) 89 (11) 99 (9) 86 (14) 86 (9) 81 (12) 84 (8) 79 (11) 72 (19)

80 (12) 83 (5) 71 (2) 80 (14) 81 (11) 81 (9) 70 (13) 88 (8) 83 (6) 87 (6) 78 (1) 70 (14) 79 (4) 75 (3) 80 (3) 83 (8) 70 (7) 86 (14) 81 (7) 80 (19) 70 (14) 118 (17) 89 (2) 114 (13) 72 (2) 109 (14) 95 (3) 96 (17) 87 (7) 90 (1) 95 (7) 73 (7) 89 (7) 86 (12) 117 (13) 97 (6) 82 (7) 88 (11) 93 (8) 70 (20) 74 (6) 88 (20) 76 (1) 76 (9)

25

100 73 (4) 87 (7) 70 (12) 71 (14) 73 (7) 71 (7) 73 (20) 85 (11) 80 (4) 74 (12) 70 (2) 70 (9) 72 (7) 71 (4) 71 (2) 76 (2) 71 (6) 78 (3) 73 (20) 71 (17) 70 (8) 93 (13) 80 (3) 90 (17) 85 (11) 100 (13) 94 (9) 83 (13) 83 (6) 101 (18) 97 (7) 82 (19) 85 (6) 76 (9) 102 (7) 90 (2) 77 (5) 77 (4) 86 (3) 79 (20) 70 (6) 73 (20) 70 (1) 73 (13)

83 (15) 107 (18) 114 (9) 98 (10) 117 (14) 120 (14) 78 (4) 80 (11) 104 (19) 103 (18) 120 (9) 70 (19) 93 (20) 119 (8) 116 (19) 99 (15) 105 (16) 73 (19) 86 (11) – 119 (1) – 107 (19) – – 115 (20) 83 (19) 87 (10) 104 (16) 102 (16) 105 (8) – 120 (17) 110 (16) – 75 (16) 77 (2) 82 (11) 79 (11) 112 (19) 70 (14) 74 (18) 73 (7) –

73 (9) 80 (19) 92 (12) 70 (20) 78 (16) 92 (15) 79 (14) 78 (17) 84 (20) 109 (2) 94 (7) 116 (5) 94 (17) 83 (13) 106 (16) 70 (5) 103 (16) 77 (16) 77 (15) – 106 (20) – 106 (15) 110 (15) – 89 (13) 81 (18) 119 (16) 83 (11) 71 (14) 92 (18) 118 (5) 110 (9) 98 (5) 71 (6) 113 (15) 93 (7) 91 (4) 103 (13) 79 (16) 77 (13) 70 (18) 70 (5) –

5.0 71 (12) 86 (8) 86 (13) 105 (20) 70 (10) 106 (13) 74 (20) 72 (20) 74 (11) 98 (17) 83 (18) 120 (20) 98 (9) 92 (17) 98 (14) 75 (12) 101 (20) 96 (16) 85 (16) 72 (9) 99 (12) 120 (3) 107 (13) 106 (11) 71 (20) 93 (14) 82 (20) 120 (5) 82 (13) 79 (14) 86 (19) 78 (17) 95 (12) 100 (14) 92 (15) 102 (18) 96 (15) 103 (12) 83 (12) 87 (12) 86 (17) 105 (8) 72 (15) 83 (13)

10 83 (14) 70 (20) 86 (12) 86 (20) 86 (16) 100 (12) 70 (18) 77 (12) 86 (17) 95 (8) 88 (13) 73 (17) 80 (7) 81 (13) 93 (14) 89 (6) 70 (8) 105 (16) 78 (16) 81 (7) 71 (8) 118 (18) 93 (8) 100 (18) 70 (20) 114 (19) 100 (20) 117 (14) 102 (7) 105 (17) 89 (15) 70 (19) 85 (13) 81 (18) 118 (19) 90 (19) 92 (17) 92 (17) 100 (14) 96 (18) 89 (12) 101 (10) 73 (6) 81 (15)

25

2.5

10

2.5

5.0



90 (11) 78 (14) 84 (10) 78 (14) 98 (15) 85 (7) 87 (14) 70 (8) 79 (10) 101 (18) 80 (12) 74 (10) 82 (6) 92 (12) 86 (17) 80 (9) 94 (18) 87 (13) 74 (9) 77 (16) 90 (13) 71 (19) 84 (6) 89 (19) 90 (18) 115 (16) 91 (10) 99 (19) 90 (9) 78 (17) 85 (12) 106 (17) 84 (12) 85 (13) 77 (8) 86 (15) 85 (9) 82 (11) 77 (5) 100 (15) 87 (17) 110 (10) 72 (4) 108 (14)

100 39 44 41 45 42 42 42 46 35 33 33 47 39 40 43 29 39 41 43 49 41 28 25 45 38 27 39 37 38 34 31 44 31 49 35 43 34 28 38 46 37 38 37 45

U (%)

Food Anal. Methods

26 164 −1 234 5 −10 70 −10 26 −40 19 203 −25 −32 16 −19 15 −30 −27 −2 1 12 −22 9 4 −25 11 60 3 5 12 −16 −8 −26 −41 −17 −24

Matrix effect

2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 10 2.5 2.5 2.5 2.5 2.5 5.0 5.0 2.5 2.5 2.5

LOQ (μg kg−1)

77 (18) 117 (4) 70 (10) 107 (9) 102 (17) 114 (14) 101 (10) 96 (17) 88 (12) 76 (18) 111 (14) 77 (20) 108 (13) 81 (13) 70 (20) 96 (18) 109 (9) 117 (9) 104 (5) 102 (16) 119 (10) 91 (19) 89 (20) 110 (5) 97 (10) 97 (18) – 119 (9) 111 (6) 120 (4) 107 (15) 104 (4) – – 101 (15) 106 (19) 117 (9)

89 (6) 118 (15) 73 (13) 103 (17) 84 (12) 92 (9) 89 (7) 78 (1) 93 (8) 72 (5) 89 (16) 75 (10) 108 (10) 88 (10) 81 (2) 87 (15) 85 (3) 107 (9) 96 (8) 91 (9) 98 (6) 74 (18) 112 (6) 82 (1) 70 (19) 91 (14) – 96 (8) 97 (10) 100 (2) 70 (2) 90 (14) 101 (2) 73 (12) 90 (16) 106 (16) 102 (2)

88 (16) 104 (7) 77 (10) 98 (10) 90 (9) 93 (10) 90 (5) 76 (8) 73 (11) 84 (9) 77 (6) 85 (16) 99 (18) 90 (9) 80 (15) 94 (17) 91 (6) 112 (16) 86 (3) 88 (2) 93 (8) 87 (10) 84 (15) 82 (5) 72 (11) 100 (18) 77 (10) 93 (8) 100 (5) 95 (9) 81 (20) 89 (18) 97 (10) 86 (20) 77 (14) 79 (18) 98 (9)

91 (16) 96 (10) 74 (1) 89 (8) 96 (7) 85 (4) 86 (3) 71 (9) 75 (2) 90 (10) 78 (8) 97 (6) 80 (7) 101 (6) 103 (11) 83 (19) 84 (2) 92 (12) 82 (4) 83 (2) 89 (3) 80 (11) 86 (8) 77 (3) 94 (8) 102 (18) 72 (14) 77 (2) 83 (2) 91 (3) 71 (11) 70 (3) 79 (4) 94 (9) 101 (15) 87 (6) 85 (8)

25

100 79 (12) 90 (7) 71 (4) 82 (4) 80 (7) 86 (2) 79 (2) 70 (4) 76 (9) 77 (3) 72 (5) 86 (15) 76 (5) 95 (5) 84 (10) 83 (8) 78 (5) 86 (4) 77 (2) 79 (3) 75 (4) 75 (5) 74 (3) 72 (10) 74 (11) 79 (6) 71 (17) 76 (4) 89 (9) 82 (5) 86 (9) 81 (11) 70 (8) 78 (12) 86 (14) 85 (10) 81 (4)

111 (13) 84 (15) 98 (20) 88 (15) 87 (20) 77 (20) 79 (17) 71 (20) 112 (15) 86 (20) 119 (9) 95 (20) 114 (6) 100 (4) 99 (4) 78 (20) 105 (17) 73 (14) 110 (13) 94 (16) 95 (10) 118 (14) 77 (18) 115 (5) 72 (18) 119 (14) – 85 (16) 73 (18) 95 (16) 116 (18) 103 (16) – – 115 (14) 114 (12) 117 (19)

77 (8) 97 (16) 73 (5) 103 (17) 81 (14) 70 (20) 77 (18) 89 (20) 87 (13) 87 (14) 77 (18) 96 (14) 113 (13) 115 (12) 107 (16) 78 (18) 87 (18) 108 (9) 94 (114) 92 (9) 101 (20) 102 (8) 70 (2) 82 (7) 74 (14) 80 (6) – 79 (9) 100 (18) 87 (19) 119 (7) 80 (16) 74 (18) 71 (13) 70 (20) 84 (19) 105 (12)

5.0 90 (15) 104 (19) 93 (17) 120 (14) 98 (12) 73 (7) 81 (11) 79 (20) 78 (16) 84 (15) 72 (13) 116 (16) 93 (14) 118 (11) 85 (14) 78 (11) 85 (18) 75 (6) 106 (8) 98 (11) 111 (10) 93 (17) 97 (13) 76 (16) 78 (16) 84 (17) 93 (8) 92 (11) 88 (15) 91 (10) 114 (4) 89 (14) 76 (18) 113 (19) 99 (6) 70 (18) 90 (17)

10 95 (15) 99 (9) 88 (11) 116 (15) 91 (15) 71 (7) 94 (16) 71 (12) 81 (14) 80 (6) 80 (14) 75 (15) 87 (12) 117 (11) 82 (14) 74 (7) 88 (16) 104 (13) 96 (9) 93 (7) 100 (8) 100 (19) 81 (17) 80 (10) 83 (12) 99 (17) 73 (7) 99 (17) 92 (11) 94 (15) 76 (130 76 (6) 74 (7) 77 (17) 73 (20) 84 (11) 89 (12)

25

2.5

10

2.5

5.0



Carbendazin expressed as sum of benomyl plus carbendazim; RSD relative deviation standard

0.9993 0.9985 0.9903 0.9977 0.9969 0.9992 0.9985 0.9973 0.9949 0.9996 0.9913 0.9928 0.9988 0.9937 0.9936 0.9975 0.9979 0.9939 0.9978 0.9975 0.9987 0.9932 0.9974 0.9934 0.9912 0.9973 0.9924 0.9980 0.9946 0.9932 0.9923 0.9919 0.9950 0.9931 0.9939 0.9934 0.9948

Pyrazophos Pyrazosulfuron-ethyl Pyridaben Pyridaphenthion Pyridate Pyrifenox Pyrimethanil Pyriproxifen Quinalphos Quinoxyfen Simazine Spinosad A Spirodiclofen Tebuconazole Tebufenozide Tebufenpyrad Terbuthylazine Tetraconazole Thiabendazole Thiacloprid Thiamethoxam Thiodicarb Thiobencarb Thiophanate-methyl Tolylfluanid Triadimefon Triadimenol Triazophos Triasulfuron Trichlorfon Tridemorph Trifloxystrobin Triflumizole Triflumuron Triticonazole Tylosin Vamidothion

a

r2

Compounds

Table 2 (continued)

95 (15) 87 (18) 81 (11) 96 (14) 94 (14) 75 (10) 87 (11) 80 (12) 103 (15) 83 (17) 76 (10) 86 (17) 81 (13) 84 (10) 85 (18) 81 (13) 87 (9) 90 (11) 87 (7) 87 (5) 90 (6) 106 (18) 87 (11) 78 (11) 83 (15) 84 (10) 106 (16) 105 (14) 89 (9) 85 (6) 84 (9) 80 (9) 78 (12) 82 (16) 79 (19) 96 (18) 82 (8)

100 47 26 49 32 36 31 30 39 42 38 35 40 48 40 44 42 30 34 28 24 23 37 42 29 36 35 36 31 34 34 34 38 36 48 41 41 28

U (%)

Food Anal. Methods

Food Anal. Methods Table 3 Results of variations from different UHPLC-MS/MS parameters for robustness evaluation of pesticide residues determination

Compounds

Results of recovery % (RSD %) Methanol in mobile phase (supplier 1)

Methanol (supplier 2)

1%

2%

3%

Acetamiprid

83 (2)

109 (10)

94 (9)

Ametryn Atrazine

93 (16) 94 (11)

98 (10) 89 (18)

88 (14) 108 (7)

Azaconazole Azimsulfuron

94 (11) 83 (19)

108 (10) 70 (10)

95 (10) 74 (19)

Azinphos-ethyl

99 (13)

91 (16)

86 (13)

Azinphos-methyl Azoxystrobin

81 (4) 84 (5)

81 (10) 70 (13)

105 (15) 94 (13)

Bentazone Bifentrin

82 (5) 102 (17)

83 (18) 99 (17)

87 (7) 90 (10)

Bispyribac sodium

80 (10)

84 (12)

90 (16)

Boscalid Bupirimate

86 (6) 100 (9)

71 (19) 89 (15)

91 (17) 105 (12)

Bromuconazole Buprofezin Carbaryl

85 (16) 71 (3) 94 (3)

97 (17) 96 (14) 72 (19)

100 (10) 109 (10) 86 (4)

Carbendazima Carbofuran Carbofuran-3-hydroxy

87 (3) 119 (18) 76 (18)

82 (18) 87 (9) 102 (9)

77 (9) 89 (12) 83 (8)

Carboxin Chlorbromuron Chlorfenvinphos Chlorimuron ethyl

99 (18) 96 (19) 75 (10) 93 (10)

82 (13) 87 (4) 85 (10) 96 (12)

94 (6) 72 (3) 81 (2) 74 (2)

Chlorpyrifos Chlorpyriphos-methyl

82 (10) 117 (10)

91 (6) 90 (11)

70 (2) 93 (18)

Clofentezine Clorantraniliprole Clothianidin

80 (5) 70 (3) 93 (17)

91 (3) 88 (4) 86 (5)

98 (18) 103 (16) 86 (5)

Cyanazine Cyazofamid Cymoxanil Cyproconazole Cyprodinil Deltamethrin

88 (11) 91 (9) 85 (6) 94 (8) 87 (17) 81 (14)

80 (10) 90 (14) 75 (10) 73 (17) 85 (19) 102 (12)

89 (17) 92 (17) 72 (15) 71 (4) 72 (3) 76 (7)

Demeton-S-methyl-sulfon Desmedipham Diazinon Dichlorvos Diclofluanide Dicrotophos Difenoconazole Dimethenamid Dimethoate Dimethomorph Dimoxystrobin Diniconazole

87 (5) 77 (15) 84 (16) 93 (5) 84 (7) 98 (18) 78 (13) 92 (12) 96 (14) 87 (8) 88 (9) 81 (11)

77 (11) 79 (6) 90 (13) 87 (6) 88 (17) 79 (15) 90 (12) 95 (12) 100 (10) 91 (17) 93 (10) 95 (11)

85 (12) 87 (7) 79 (4) 91 (16) 86 (6) 92 (5) 76 (12) 89 (7) 73 (3) 81 (7) 80 (3) 72 (19)




1%

2%

3%

Diuron

89 (16)

82 (16)

92 (18)

Dodemorph Epoxiconazole

86 (13) 76 (15)

78 (6) 89 (18)

72 (9) 87 (13)

Ethiofencarb sulfone

77 (8)

85 (13)

85 (19)

Ethiofencarb sulfoxide Ethiprole

70 (18) 92 (8)

93 (16) 87 (16)

100 (11) 85 (5)

Ethoprophos Ethoxysulfuron

77 (15) 89 (11)

96 (13) 89 (10)

100 (6) 85 (7)

Etofenprox

77 (11)

92 (18)

78 (7)

Ethopabate

72 (4)

86 (5)

84 (4)

Fenamiphos

81 (9)

94 (15)

109 (6)

Fenarimol

75 (11)

84 (8)

79 (13)

Fenazaquin Fenbuconazole

75 (9) 96 (6)

95 (5) 75 (4)

90 (15) 85 (10)

Fenhexamid Fenoxycarb Fenpropathrin Fenpropimorph

89 (6) 86 (14) 84 (18) 82 (11)

79 (5) 73 (9) 88 (7) 73 (10)

96 (8) 83 (17) 98 (11) 70 (19)

Fenpyroximat Fenthion Fluazafop-p-butyl Flufenoxuron Fluquinconazole

105 (17) 99 (17) 94 (12) 82 (8) 79 (13)

87 (16) 90 (6) 89 (19) 98 (12) 70 (11)

89 (16) 70 (10) 93 (10) 83 (6) 88 (14)

Fluroxypyr Flusilazole Flutolanil Flutriafol Furaltadone hydrochloride

73 (14) 93 (15) 96 (2) 90 (4) 89 (11)

88 (16) 100 (7) 92 (11) 90 (14) 86 (10)

78 (9) 78 (6) 76 (9) 104 (18) 87 (3)

Furathiocarb Furazolidone Hexaconazole Hexythiazox Imazalil Imidacloprid Iprovalicarb Kresoxim-methyl Linuron Lufenuron Malathion Mecarbam Mephosfolan Mepronil Metalaxyl Metconazole Methamidophos Methidathion

87 (13) 81 (17) 108 (16) 110 (7) 78 (17) 85 (17) 74 (14) 70 (6) 82 (4) 82 (15) 85 (3) 73 (4) 76 (13) 98 (15) 95 (8) 79 (14) 77 (12) 79 (10)

74 (13) 95 (10) 88 (9) 91 (15) 93 (19) 87 (7) 71 (14) 94 (1) 70 (14) 114 (14) 94 (11) 80 (7) 120 (19) 85 (10) 84 (15) 91 (7) 94 (18) 73 (20)

73 (4) 71 (2) 84 (16) 85 (6) 75 (3) 94 (12) 70 (10) 91 (12) 74 (6) 113 (18) 73 (19) 73 (9) 92 (12) 107 (18) 89 (6) 104 (13) 86 (9) 83 (14)




1%

3%

2%

Methiocarb sulfone

74 (10)

115 (10)

84 (10)

Methiocarb sulfoxide Methomyl

98 (14) 70 (8)

72 (10) 84 (10)

86 (8) 78 (14)

Methoxyfenozide

70 (5)

120 (10)

100 (12)

Metobromuron Metolachlor

77 (15) 70 (7)

94 (7) 83 (13)

86 (20) 106 (13)

Metoxuron Metribuzin

76 (2) 73 (20)

93 (10) 88 (13)

78 (16) 98 (17)

Metsulfuron methyl

86 (14)

92 (12)

78 (4)

Mevinphos

93 (13)

80 (7)

70 (14)

Monocrotophos

94 (18)

93 (14)

86 (14)

Monolinuron

80 (9)

71 (7)

90 (17)

Nitenpyram Nuarimol

78 (16) 96 (16)

81 (11) 74 (12)

89 (2) 100 (13)

Omethoate Oxadixyl Oxamyl Oxifluorfen

118 (18) 71 (8) 87 (13) 89 (15)

70 (13) 85 (11) 83 (6) 70 (9)

72 (2) 93 (14) 70 (10) 99 (19)

Paraoxon ethyl Parathion Parathion-methyl Penconazole Pencycuron

76 (9) 90 (1) 95 (7) 90 (2) 102 (7)

78 (1) 71 (4) 79 (4) 80 (3) 73 (19)

91 (10) 106 (17) 102 (7) 100 (18) 107 (13)

Pendimethalin Phosmet Picoxystrobin Piperonyl butoxide Pirimicarb

77 (4) 82 (7) 99 (12) 77 (16) 106 (11)

120 (3) 86 (19) 110 (16) 79 (14) 86 (19)

89 (13) 71 (20) 87 (10) 83 (19) 78 (17)

Pirimiphos-ethyl Pirimiphos-methyl Procymidone Profenofos Prometryn Propanil Propiconazole Propyzamide Propoxur Protiophos Pymetrozine Pyraclostrobin Pyrazophos Pyrazosulfuron-ethyl Pyridaben Pyridaphenthion Pyridate Pyrifenox

106 (15) 100 (14) 78 (17) 89 (15) 90 (19) 77 (8) 82 (11) 83 (13) 74 (1) 77 (10) 96 (7) 71 (9) 96 (9) 93 (17) 100 (8) 78 (11) 74 (7) 85 (18)

75 (16) 92 (15) 82 (11) 93 (7) 100 (14) 110 (10) 89 (12) 81 (15) 77 (8) 88 (15) 73 (5) 73 (7) 81 (14) 81 (11) 75 (10) 91 (15) 80 (12) 94 (16)

83 (11) 92 (17) 100 (15) 71 (10) 77 (18) 86 (10) 84 (15) 81 (14) 116 (16) 76 (10) 72 (15) 87 (18) 87 (12) 103 (13) 105 (8) 77 (13) 83 (13) 73 (7)




1%

2%

3%

Pyrimethanil

90 (11)

91 (16)

84 (15)

Pyriproxifen Quinalphos

87 (9) 98 (11)

82 (4) 79 (10)

95 (3) 73 (7)

Quinoxyfen

92 (12)

93 (8)

83 (6)

Simazine Spinosad A

78 (5) 101 (6)

88 (10) 74 (6)

97 (7) 95 (15)

Spirodiclofen Tebuconazole

76 (5) 83 (19)

73 (13) 76 (1)

96 (14) 81 (17)

Tebufenozide

84 (10)

96 (10)

84 (17)

Tebufenpyrad

87 (13)

81 (13)

83 (12)

Terbuthylazine

95 (10)

117 (11)

78 (18)

Tetraconazole

77 (18)

79 (2)

85 (14)

Thiabendazole Thiacloprid

100 (4) 94 (14)

90 (10) 76 (9)

73 (14) 85 (18)

Thiamethoxam Thiodicarb Thiobencarb Thiophanate-methyl

118 (14) 101 (10) 76 (16) 70 (2)

97 (6) 72 (5) 92 (11) 80 (9)

94 (16) 119 (14) 91 (3) 89 (9)

Tolylfluanid Triadimefon Triadimenol Triazophos Triasulfuron

83 (2) 77 (2) 80 (11) 75 (4) 77 (3)

76 (13) 113 (19) 84 (19) 99 (6) 78 (12)

70 (3) 86 (9) 77 (2) 71 (17) 94 (9)

Trichlorfon Tridemorph Trifloxystrobin Triflumizole Triflumuron

92 (11) 73 (7) 93 (14) 74 (3) 102 (18)

84 (11) 79 (19) 74 (14) 85 (16) 93 (8)

119 (7) 71 (13) 74 (7) 70 (8) 87 (6)

Triticonazole Tylosin Vamidothion

74 (11) 91 (10) 81 (4)

95 (16) 89 (14) 90 (17)

86 (14) 100 (18) 82 (8)

a

Carbendazim expressed as a sum of benomyl plus carbendazim; RSD relative deviation standard

applied in pear culture (MAPA 2011). In relation to the Codex MRL values of the detected pesticides, only azinphos-methyl and methomyl were in accordance to their established MRL. The other 19 pesticides found are not allowed for pear. Figure 5 presents a UHPLC-MS/MS SRM chromatogram of the compound pyrimethanil, quantified at 98.2 μg kg−1, obtained from the analysis of sample 14. The signal from the two selected transitions permitted the adequate quantification of this compound in the real sample. Han et al. (2015) studied the presence of pesticide residues in peel, pulp, and paper bag in pear production in China, and found residues of chlorpyrifos (10.0 to 846 μg kg−1), malathion (5.8 to 239 μg kg −1 ), and difenoconazole (65 to

105 μg kg−1) in pear peel and pyrimethanil in pear pulp (0.3 to 235 μg kg−1) and pear peel (2.1 to 1859 μg kg−1). Zhi-xia et al. (2015) studied the presence of pesticide residues in Chinese pears; likewise, they found insecticide residues of acetamiprid (1.0 to 159.2 μg kg−1), chlorpyrifos (20.2 to 945.0 μg kg−1), fenpropathrin (3.0 to 53.02 μg kg−1), and methomyl (11.5 to 303.6 μg kg−1); fungicide residues of carbendazim (0.4 to 576.8 μg kg−1), difenoconazole (6.1 to 1662.0 μg kg −1 ), imazalil (43.0 to 101.0 μg kg −1 ), pyrimethanil (1.1 to 1903.5 μg kg−1), and thiophanatemethyl (31.6 to 289.8 μg kg−1); and acaricide residue of spirodiclofen (19.1 μg kg−1). The intense presence of pesticide residues in pear samples, demonstrated in this and other

Food Anal. Methods Table 4

Results of the analyses in the real samples

Compounds

Concentration found in the samples (μg kg−1) S1

S2

S3

S4

11.1

16.7

6.7

S5

S6

S7

S8

6.7

10.5

61.1

Acetamiprid Azinphos-methyl Carbaryl Carbendazima Chlorpyrifos Clorantraniliprole Difenoconazole Fenpropathrin Imazalil Malathion Methomyl Metribuzin Piperonyl butoxide Pirimiphos-methyl Pyrifenox Pyrimethanil Pyriproxifen Spirodiclofen Thiabendazole Thiacloprid Tiophanate-methyl

178 3.8 15.1

Compounds

Concentration found in the samples (μg kg−1)

Acetamiprid Azinphos-methyl Carbaryl Carbendazima Chlorpyrifos Clorantraniliprole Difenoconazole Fenpropathrin Imazalil Malathion Methomyl Metribuzin Piperonyl butoxide Pirimiphos-methyl Pyrifenox Pyrimethanil Pyriproxifen Spirodiclofen Thiabendazole Thiacloprid Tiophanate-methyl a

S9

S10

S11

S12

S13

S14

3.7

4.2

6.3

6.1

5.5

10.4

3.8

4.3

6.2 33.7 18.2 18.1

19.9 59.5

4.5

3.9

3.41 14.1 9.9

14.3

4.9 4.1

15.8 3.8

3.7

5.8

5.9

3.5 8.5

4.7

6.3

8.0 6.8

5.5

34.6

9.0

171

1427 31.3

414

4.3 7.6 5.3

366

307

98.2

3.3 8.0

113

S15

S16

5.1

29.5

6.6 26.2 4.3

S17

37.8 118

3.6

S18

18.8

S19

S20

6.0

4.5

S21

S22

S23

162

125 5.8

S24

4.3

S25

S26

4.3

5.1

S27

3.4 7.3 4.5

8.0

14.5

10.2

7.1 7.1

3.7

7.1

18.9 3.8

3.6

6.3

17.3

8.9 9.7 4.9 3.6

3.9

5.1 3.3 6.5

4.2

234

104 3.6

49.1 9.2 18.1

34.8

161

6.3 15.0

434 22.6 145

5.5 279

5.1

17.8

13.2 62.8

15.7 20.9

3.6

492

4.1 4.4

4.3 7.9

436 3.9

Carbendazim expressed as a sum of benomyl plus carbendazim

works, emphasize the great concern about the presence of pesticide residues in food worldwide.

Conclusion Fig. 5 UHPLC-MS/MS SRM chromatogram of the compound pyrimethanil obtained from the analysis of sample 14, quantified at 98.2 μg kg−1

In this study, an approach based on the extraction efficacy of pesticide residues from pear samples with acetonitrile was developed to allow the analysis by UHPLC-MS/MS. Satisfactory

Food Anal. Methods

validation results were obtained employing a modification of QuEChERS acetate method and a final dilution of ten times in water prior injection in the chromatographic system. The method has sufficient sensitivity to be applied in samples from routine analysis and monitoring programs based on the low LOQ of 2.5 μg kg−1 for majority of compounds. The method was validated according to SANTE (2015) and its criteria were applied and compared to Codex Alimentarius and INMETRO. All guidelines cover a similar set of parameters (linearity, trueness, precision, robustness, and uncertainty), although no specific acceptability ranges are specified in some guidelines (like as LOD in SANTE). In this study, a comprehensive validation was performed for pear samples. For some parameters, only one of the guidelines indicates acceptance criteria: SANTE for significant matrix effect, for example. Finally, samples analyzed by the validated method showed a big concern about pear contamination, since 21 compounds were quantified in concentration between 3.3 and 1427 μg kg−1. Acknowledgements The authors acknowledge the financial support and fellowship granted by the Brazilian agencies CNPq, CAPES, and FINEP. Funding This study was funded by the National Council of Scientific and Technological Development (CNPq), Brazil. Compliance with Ethical Standards Conflict of Interest Magali Kemmerich declares that he has no conflict of interest. Gabrieli Bernardi declares that he has no conflict of interest. Osmar D. Prestes declares that he has no conflict of interest. Martha B. Adaime declares that he has no conflict of interest. Renato Zanella declares that he 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. Informed Consent Informed consent was obtained from all individual participants included in the study.

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