Validation of a Multiresidue Analysis Method for 379 Pesticides in

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Validation of a Multiresidue Analysis Method for 379 Pesticides in Human Serum Using Liquid Chromatography-Tandem Mass Spectrometry Yongho Shin, Jonghwa Lee, Ji-Ho Lee, Junghak Lee, Eunhye Kim, Kwang-Hyeon Liu, Hye Suk Lee, and Jeong-Han Kim J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b00094 • Publication Date (Web): 14 Mar 2018 Downloaded from http://pubs.acs.org on March 15, 2018

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Journal of Agricultural and Food Chemistry

Validation of a Multiresidue Analysis Method for 379 Pesticides in Human Serum Using Liquid Chromatography-Tandem Mass Spectrometry

Yongho Shin,1 Jonghwa Lee,1 Jiho Lee,1 Junghak Lee,1 Eunhye Kim,1 Kwang-Hyeon Liu,2 Hye Suk Lee,*,3 and Jeong-Han Kim*,1

1

Department of Agricultural Biotechnology and Research Institute of Agriculture and Life

Sciences, Seoul National University, Seoul 08826, Republic of Korea 2

BK21 Plus KNU Multi-Omics based Creative Drug Research Team, College of Pharmacy

and Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu 41566, Republic of Korea 3

College of Pharmacy, The Catholic University of Korea, Bucheon-si, Gyeonggi-do 14662,

Republic of Korea

Corresponding Authors *

(Jeong-Han Kim) Phone: +82-2-880-4644. Fax: +82-2-873-4415. E-mail:

[email protected]. *

(Hye Suk Lee) E-mail: [email protected].

1

ACS Paragon Plus Environment


1

ABSTRACT

2

A screening method for simultaneous analysis of 379 pesticides in human serum was

3

developed using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

4

Electrospray ionization with positive/negative switching mode of LC-MS/MS was adopted

5

and scheduled multiple reaction monitoring for each target compound was established. The

6

limit of quantitation was 10 ng/mL for 94.5% of the total pesticides and the correlation

7

coefficients of calibration were ≥0.990 for 93.9% of the pesticides. For the sample

8

preparation, scaled-down QuEChERS were used. Serum (100 µL) was extracted with

9

acetonitrile (400 µL), partitioned with magnesium sulfate (40 mg) and sodium chloride (10

10

mg), and the upper layer was used for analysis without further cleanup steps. For the

11

accuracy and precision tests, most of the pesticides showed excellent results in intra- and

12

inter-day conditions. In the recovery tests at 10, 50, and 250 ng/mL, 85.8-91.8% of all

13

target compounds satisfied the recovery range of 70-120% (relative standard deviation

14

≤20%).

15

KEYWORDS: pesticide, multiresidue, intoxication, serum, QuEChERS, LC-MS/MS

2


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INTRODUCTION

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Pesticides are used worldwide for the control of insects, microorganisms, fungi, and

18

other harmful pests in order to protect agricultural products. According to a U.S.

19

Environmental Protection Agency (EPA) report, world pesticide expenditure at the

20

producer level was $55,921 million in 20121. In the United States, $8,866 million was

21

reported on the same basis, corresponding to 16% of the world pesticide market1. In the

22

Republic of Korea, the Korea Crop Protection Association’s agrochemical book reported

23

that forwarding of pesticide was 19,798 metric tons in 20162.

24

Agriculture pesticide usage has contributed to improving productivity, protection of crop

25

losses/yield reduction, and food quality3. One of the major disadvantages, however, is that

26

pesticides cause acute poisoning problems to agricultural workers and the public if used

27

inappropriately. Acute intoxication symptoms caused by pesticides range from mild

28

symptoms such as nausea, headache, and paresthesia to fatalities4. Calvert and coworkers

29

investigated 3,271 cases of acute pesticide poisoning in the United States from 1998 to

30

2005 and reported that 2,334 (71%) were employed as farmworkers5. It was reported that

31

up to 25 million cases of pesticide intoxication may be experienced by agricultural workers

32

in the Asian developing country6. In the Republic of Korea, it was reported that 22.9% of

33

1,958 male farmers had experienced acute intoxication symptoms within 48 h after using

34

pesticides in 20107.

35

For identification of the pesticides causing poisoning problems, analytical methods have

36

been studied with human-derived samples such as blood (whole blood or serum)8-14, urine15-

37

16

38

that its volume does not vary substantially with water intake or other factors8. Therefore,

, hair17, and saliva18. Among biological samples, blood is a regulated fluid, which means

3



39

blood is available without further dilutions for determination of the internal concentration

40

of pesticides, whereas compensation techniques are required for urine and saliva samples. It

41

is also advantageous that pesticides are present in blood as parent compounds instead of

42

their metabolites as usually found in urine19, and blood has less risk of exogenous or

43

endogenous contamination compared to hair20. Because only a few milliliters of blood from

44

adults or less in the case of children can be obtained, analytical methods for a few tens or

45

several hundreds of microliters of blood samples have been developed and validated to

46

overcome the sample volume problem12-14. Serum analysis is usually preferred over whole

47

blood analysis, because serum has a minor matrix complexity, and is a more homogenous

48

material9-10. Therefore, one or more cleanup steps can be reduced with serum samples

49

compared to whole blood10-11.

50

In the case of pesticide intoxication, analysis of as many pesticides as possible with high

51

reliability and speed is important in order to identify unknown compounds for further

52

medical treatment and investigation. Traditional gas chromatography (GC) and high-

53

performance liquid chromatography (HPLC) have many limitations in specificity,

54

sensitivity, and speed for multiresidue analysis. Furthermore, both conventional instruments

55

require partitioning or cleanup procedures to remove interference, which takes a long time,

56

uses high volumes of solvents, and may remove target compounds during extensive cleanup

57

steps. Mass spectrometry combined with GC or HPLC systems can overcome these

58

limitations. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful

59

analytical technique for quantitative detection of a broad range of pesticides in short time

60

with simultaneous manner by operating in multiple reaction monitoring (MRM) mode in

61

biological monitoring21. LC-MS/MS has been employed for determining pesticides in 4


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serum with high sensitivity in many studies22-23.

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Purification of as many as possible pesticides from serum samples with high recovery of

64

target pesticides is also an important factor in sample preparation procedures. In various

65

cleanup procedures, column chromatography (CC)24-25, solid phase extraction (SPE)26-28,

66

liquid-liquid extraction (LLE)29, and protein precipitation (PP)22-23 with acetonitrile solvent

67

have been reported as representative preparation methods. CC and SPE are advantageous

68

for a few specific target compounds, but take much time and effort to conduct and have

69

some difficulty finding optimum elution condition in multiresidue analysis. LLE and PP are

70

more convenient than CC or SPE, but have similar drawbacks to CC or SPE and

71

interferences of serum may remain in the extract to cause problems during analysis. The

72

QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method is a preparation

73

method with high extraction efficiency and convenience in multiresidue analysis. Since the

74

first unbuffered QuEChERS analytical method for crops was developed in 200330, a

75

number of improved QuEChERS methods have been validated and applied. Among these

76

methods, AOAC 2007.0131 and EN 1566232 methods, in which buffer reagents are

77

contained for adjustment of sample pH, have been used widely with advantages in

78

extraction rate (recovery) of pH-dependent pesticides. Recently, QuEChERS methods have

79

been used for biological samples in clinical and forensic toxicology33-35.

80

In this study, a simultaneous multiresidue screening analytical method for 379 pesticides

81

in human serum was developed and validated using LC-MS/MS. The scheduled MRMs and

82

retention times (tR) for each analyte were optimized for qualification and quantitation

83

within 15 minutes per sample with high sensitivity. Three QuEChERS extraction methods

84

were compared and modified to use a very small sample volume (100 µL) without using 5



85

dispersive SPE (dSPE) in the cleanup procedure. With the optimized analytical method,

86

reasonable validation data (limit of quantitation (LOQ), linearity of calibration, accuracy

87

and precision, recovery, and matrix effect) for most pesticides were obtained. This fast and

88

convenient analytical method is applicable for biomonitoring of pesticide multiresidues in

89

serum samples from food toxicology, agricultural operator exposure, clinical and forensic

90

studies and investigation.

91

MATERIALS AND METHODS

92

Chemicals and Reagents. Individual pesticide standards (purity >98%) or stock

93

solutions (1,000 µg/mL) for quality control (QC) were obtained from Chemservice (West

94

Chester, PA, USA), Dr. Ehrenstorfer (Augsburg, Germany), Sigma-Aldrich (St, Louis, MO,

95

USA), Wako (Osaka, Japan), and Ultra Scientific (North Kingstown, RI, USA).

96

Ammonium formate, formic acid (LC-MS grade), acetic acid (HOAc, ≥99.7%), magnesium

97

sulfate anhydrous (MgSO4, ≥99.5%), sodium acetate anhydrous (NaOAc, ≥99.0%), sodium

98

citrate dibasic sesquihydrate (Na2HCitr · 1.5H2O, ≥99.0%), and sodium citrate tribasic

99

dihydrate (Na3Citrate · 2H2O, ≥99.0%) were purchased from Sigma-Aldrich. Sodium

100

chloride (NaCl, 99.0%) was obtained from Samchun (Gyeonggi-do, South Korea).

101

Methanol and acetonitrile (HPLC grade) were purchased from Fisher Scientific (Seoul,

102

South Korea). Ceramic homogenizers (2 mm) were purchased from Ultra Scientific.

103

Deionized water was prepared in house using LaboStar™ TWF UV 7 (Siemens, MA, USA).

104

Preparation of Standard Solutions. Individual pesticide stock solutions (1,000 µg/mL)

105

were prepared in acetonitrile. For pesticides that were difficult to dissolve at this

106

concentration level (e.g., carbendazim), acetone, methanol, or water were used instead of

107

acetonitrile or lower concentrations of stock solutions were prepared so that these 6


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components could be sufficiently dissolved. Then, a mixed standard solution was prepared

109

by taking a portion of each stock solution and mixing to give an individual pesticide

110

concentration of 10 µg/mL. This was diluted with acetonitrile to make the mixed working

111

standard solutions of lower concentrations for preparing calibration curves and using in

112

several validation procedures.

113

LC-MS/MS Parameters. LC-MS/MS analysis was carried out on a Shimadzu Nexera

114

X2 UHPLC system coupled to a Shimadzu LCMS-8050 triple quadrupole mass

115

spectrometer (Kyoto, Japan). The UHPLC system consisted of a degassing unit (DGU-

116

20A5R), solvent delivery module (LC-30AD), autosampler (SIL-30AC), and column oven

117

(CTO-20A). The separation was conducted on a Kinetex® C18 column (100 × 2.1 mm, 2.6

118

µm, Phenomenex, Torrance, CA, USA) coupled with SecurityGuard™ Ultra guard column

119

(Phenomenex) at 40 °C oven temperature. The flow rate was 0.2 mL/min and the injection

120

volume was 4 µL. Mobile phase A was 0.1% formic acid and 5 mM ammonium formate in

121

water while mobile phase B was 0.1% formic acid and 5 mM ammonium formate in

122

methanol. The gradient program for mobile phase B was started at 5%, 0.5 minutes

123

later %B was raised to 55% for 0.5 min, ramped to 95% for 7 min, held for 3 min, raised to

124

100% for 1 min, then dropped sharply to 5% for 0.1 min, and held for 2.9 min. The total

125

run time was 15.0 min.

126

In the mass spectrometer system, ionization of target analytes was performed by

127

electrospray ionization (ESI) with positive/negative switching mode. The interface,

128

desolvation line (DL), and heat block temperature were 300, 250, and 400 °C, respectively.

129

The heating gas (air), nebulizing (nitrogen), and drying gas (nitrogen) flow were 10, 3, and

130

15 L/min, respectively. The collision-induced dissociation (CID) gas was argon. For 7



131

MS/MS analysis, each standard solution (0.1-1 µg/mL) was injected without the column to

132

obtain a full scan spectrum (m/z 50-500 or m/z 100-1,000). A precursor ion (e.g., [M+H]+)

133

was selected from the spectrum data and subjected to collision with several collision energy

134

(CE) voltages to find the two product ions that showed the highest and the second highest

135

detection intensity. The former ion transition was used as a quantifier for quantitation of the

136

target compound, and the latter was used as a qualifier for its reference. This MRM was

137

scheduled by the retention time of each compound, and the MRM detection window was ±

138

0.5 min. Finally, dwell times (≥2.0 ms) were adjusted automatically based upon loop time

139

(0.12 s) for the maximized data acquisition. LabSolutions (version 5.72) as LCMS software

140

was utilized for data processing.

141

Optimization of Sample Preparation. Human serum (100 µL) was extracted with three

142

different QuEChERS extraction reagents scaled-down as follows: (A) original QuEChERS

143

procedure30 (400 µL of acetonitrile, 40 mg of MgSO4, and 10 mg of NaCl); (B) QuEChERS

144

of AOAC 2007.0131 (1% HOAc in acetonitrile (400 µL), 40 mg of MgSO4, and 10 mg of

145

NaOAc); and (C) QuEChERS of EN 1566232 (400 µL of acetonitrile, 40 mg of MgSO4, 10

146

mg of NaCl, 10 mg of Na3Citrate, and 5 mg of Na2HCitr). The extract from each method

147

was centrifuged and 200 µL of supernatants were mixed with 50 µL of acetonitrile for

148

matrix-matching. The serum sample was equivalent to 0.2 mL per mL of final extract.

149

Finally, 4 µL of the sample was analyzed by LC-MS/MS.

150

Final Established Sample Preparation. Human serum (100 µL) in a 2-mL

151

microcentrifuge tube was extracted with 400 µL of acetonitrile by shaking for 1 min at

152

1,200 rpm using a Geno Grinder (1600 MiniG SPEX Sample Prep, Metuchen, NJ, USA).

153

Forty milligrams of MgSO4 and 10 mg of NaCl were added under ice bath conditions to 8


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prevent heat caused by MgSO4. The tube was centrifuged for 5 min at 13,000 rpm (16,800

155

g) using microcentrifuge (17TR, Hanil Science, Seoul, Korea). The supernatant (200 µL)

156

was transferred into a 2-mL amber glass vial and mixed with 50 µL of acetonitrile for

157

matrix matching. Without further cleanup steps, 4 µL of the final extraction sample was

158

taken into LC-MS/MS for analysis of target analytes.

159

Validation of Analytical Methods. For determination of the LOQ and linearity of

160

calibration, matrix-matched procedure standard solutions at 10, 20, 50, 100, 150, and 250

161

ng/mL were analyzed. The minimum concentration satisfying a signal to noise ratio (S/N)

162

greater than 10 on the chromatogram was selected as the LOQ. The linearity of calibration

163

was evaluated (n=5) by the correlation coefficient (r2) of the calibration curve from 10 to

164

250 ng/mL. A weighting regression factor of 1/x was adopted to minimize calculation error

165

at low concentrations. Accuracy and precision tests were performed using QC samples at

166

10, 50, 150, and 250 ng/mL levels. The intra-day tests were conducted by analyzing five

167

replicates of each treated level in a single day. The inter-day tests were carried out by

168

analyzing one QC sample of each treated level per day for five separate days. To verify the

169

extraction efficiency of the preparation process, recovery tests at fortification levels of 10,

170

50, and 250 ng/mL were conducted. Five µL of mixed working standard solutions (200,

171

1,000, and 5,000 ng/mL) in acetonitrile were fortified in 100 µL of each blank serum,

172

respectively and the treated samples were prepared as the final established preparation

173

procedures (n=3). Recovery of target compounds was determined using calibration curves

174

of matrix-matched standards to compensate for matrix effects in LC-MS/MS analysis. The

175

matrix effect was also calculated by comparing the slope of the calibration curve of the

176

matrix-matched standards with that of the calibration curve of the solvent-only standards 9



177

using the following equation (1): Slope of matrix matched calibration curve Matrix effect, % = − 1 × 100 (1) Slope of solvent calibration curve

178

Safety Information. All pesticide standards and reagents used in this study were

179

handled according to the Material Safety Data Sheet (MSDS)’s safety instructions. For all

180

instrumentation, the manufacturer's safety information was followed and implemented.

181

RESULTS AND DISCUSSIONS

182

Optimization of Multiple Reaction Monitoring (MRM). For the determination of

183

MRM transition profiles, a full scan analysis was first performed with 400 pesticides. In

184

this step, 17 compounds (binapacryl, bromophos-methyl, chlorpropham, cyanophos,

185

cyfluthrin, dichlofluanid, dicofol, disulfoton, endosulfan-sulfate, ethalfluralin, isofenphos,

186

isofenphos-methyl, nitrothal-isopropyl, oxyfluorfen, parathion-methyl, silafluofen, and

187

spiromesifen) did not give a suitable quasi-molecular ion (precursor ion) and were excluded

188

while the remaining 383 components successfully produced precursor ions. Among them,

189

326 target compounds were [M+H]+ quasi-molecular ion form, 24 compounds were

190

[M+NH4]+ form, seven compounds (abamectin B1a, alanycarb, aldicarb, butocarboxim,

191

lepimectin A3, lepimectin A4, and pyribenzoxim) were [M+Na]+ form, and two compounds

192

(milbemectin A3 and milbemectin A4) were [M+H-H2O]+ form in the ESI positive mode.

193

Twenty three pesticides were [M-H]- form and dithianon showed an ion form [M·]- in the

194

ESI negative mode. After CID step, quantifier and qualifier ions were selected depending

195

on intensity. After MRM optimization steps, retention time and sensitivity of target

196

compounds were verified using both the solvent-only standards (acetonitrile) and matrix-

197

matched standard solutions. However, folpet was rejected after this step due to its poor

198

response in both standard types. The details of MRM transitions, CEs, and retention times 10


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for all target compounds are presented in Supporting Table S1.

200

Optimization of Sample Extraction Step. Since the first unbuffered original

201

QuEChERS analytical method was reported in 200330, the AOAC 2007.01 method

202

containing acetate buffers and the EN 15662 method containing citrate buffers have been

203

developed to improve recovery efficiency for pH-dependent pesticides31-32. The entire or a

204

portion of these three types of QuEChERS methods have been utilized or modified

205

depending on the characteristics of the pesticides and sample matrices in many analytical

206

studies36. In this study, the sample size was reduced to 100 µL, and therefore three

207

representative QuEChERS extraction methods were downsized to methods (A), (B), and (C)

208

to verify the extraction efficiency at a fortification level of 250 ng/mL with two matrix-

209

matched standards of 100 and 250 ng/mL. Bensultap, dithianon, and the acidic flonicamid

210

metabolite TFNG [N-(4-trifluoromethylnicotinoyl)glycine] were again rejected because

211

they could not be recovered. Except for the rejected four analytes (folpet, bensultap,

212

dithianon, and TFNG), the remaining 379 pesticides were selected as the final research

213

analytes (Table 1). The total ion chromatogram (TIC) for the 379 target analytes in serum

214

sample is shown in Figure 1. There were no false positives in non-fortified serum samples,

215

and no overlaps were observed between pesticides in fortified samples.

216

The number of pesticides that satisfied the recovery range from 70 to 120% with relative

217

standard deviation (RSD) below 20% based on the criteria of SANTE/11813/201737 and

218

their percentage ratio for each extraction method are shown in Table 2. There was no

219

significant difference in the number of analytes satisfying the recovery criteria between the

220

three methods. For methods (A), (B), and (C), 344 (90.8%), 341 (90.0%), and 341 (90.0%)

221

of the total 379 pesticides satisfied the recovery criteria with method (A), the unbuffered 11



222

condition, showing slightly higher recovery than the others. From the optimization

223

experiment results, the final preparation method using method (A) (downsized original

224

QuEChERS) was established. In addition, further cleanup steps, such as dSPE, were

225

discarded in this treatment method to prevent the loss of labile target analytes and minimize

226

the analysis time.

227

Method Validation. With the final established analytical method for the 379 pesticides,

228

method validation of five validation parameters was conducted, including limit of

229

quantitation (LOQ), linearity of calibration, accuracy and precision, recovery, and matrix

230

effect.

231

Limit of Quantitation (LOQ). The LOQ was defined as the lowest concentration or mass

232

of the analyte that was validated with acceptable accuracy37. One way to determine the

233

LOQ is to verify that the S/N on the chromatogram is greater than 1038. For the LOQs

234

shown in Table 3, 358 compounds of the total pesticides (94.5% of the total analytes) had

235

LOQs of 10 ng/mL in serum. It is remarkable that a sufficiently low LOQ level was

236

obtained with reliable selectivity while analyzing 379 pesticides simultaneously. The ratio

237

of pesticides satisfying LOQ 10 ng/mL was higher than Dulaurent et al. (2010)’s screening

238

analysis of more than 300 pesticides in blood using MS2 and MS3 mode of LC-IT-MS39.

239

Among the remaining 21 (5.5%) components with LOQs higher than 10 ng/mL, the LOQ

240

levels of 11 (2.9%), 7 (1.8%), and 3 (0.8%) analytes were determined at 25, 50, and 100

241

ng/mL, respectively. These LOQs are sufficiently low enough to detect cases of acute

242

pesticide poisoning because pesticide concentrations in blood have been reported to be

243

from a few tens of ng/mL to several tens of µg/mL in most acute poisoning cases40-43. These

244

results demonstrate that this analytical method can sufficiently determine pesticides from an 12


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unknown sample without further concentration of the sample. Supporting Table S2 shows

246

the detailed LOQ results for the pesticides.

247

Linearity of Calibration. Calibration was defined as the determination of the relationship

248

between the observed signal from the target analyte in the sample extract and known

249

quantities of the analyte prepared as standard solutions37. The degree of dependence

250

established between the two variables can be expressed by the correlation coefficient (r2).

251

Before the correlation coefficients of target analytes were determined, linear ranges were

252

investigated. There were various linear ranges (Table 4) because each analyte had a

253

different LOQ and upper limit of quantitation (concentration of the highest calibration

254

standard). Most of the compounds (92.4%) had a linear range from 10 to 250 ng/mL, which

255

was the largest range in the analytical method. However, 19 compounds (5.0%) showed

256

linear ranges from their LOQ to 250 ng/mL (9 for 25-250 ng/mL, 7 for 50-250 ng/mL, and

257

3 for 100-250 ng/mL). For the remaining 10 (2.6%) components, a linear range could not

258

be drawn to 250 ng/mL due to the saturation effect of the signal at higher concentrations,

259

resulting in linear ranges from their LOQ to 150 ng/mL for 8 analytes (6 for 10-150 ng/mL

260

and 2 for 25-150 ng/mL) and 2 for 10-100 ng/mL.

261

For the correlation coefficient (r2) of the 379 target compounds (Table 5), 356 pesticides

262

(93.9%) had r2 greater than 0.990, indicating that most of the pesticides had a quantitative

263

property with good linearity within these linear ranges. Correlation coefficients for 17

264

compounds (4.5%) were within 0.980-0.990, and four pesticides were within 0.900-0.980.

265

It was expected that these ranges of correlation coefficients were also acceptable for the

266

multiresidue screening method. Diafenthiuron and tolyfluanid had somewhat poor

267

correlation coefficients (0.835 and 0.879, respectively). The detailed results for the linear 13



268

ranges and correlation coefficients (r2) of calibrations are presented in Supporting Table

269

S2.

270

Accuracy and Precision. Accuracy was defined as the degree of closeness of the

271

determined value to the nominal or known true value, and precision as the closeness of

272

agreement among a series of measurements obtained from multiple sampling of the same

273

homogenous sample44. The accuracy value was calculated as the average of the measured

274

values (%) for the replicates (n=5) at each level, and its precision was expressed as RSD

275

(%).

276

For the verification of accuracy and precision values of 379 pesticides at a glance, the

277

representative results at a QC level of 150 ng/mL were shown by using scatter plots of

278

intra- and inter-day tests (Figure 2). In both of intra- and inter-day, most of the pesticides

279

were located in a square zone of accuracy; 80-120% and RSD; 0-20% showing excellent

280

accuracies and precisions. Only a few pesticide such as aldicarb, bifenox, diafenthiuron,

281

imazamox, thiocyclam in intra-day and diafenthiuron, imazamox, inabenfide, lepimectin

282

A3, nitrapyrin, parathion, thiocyclam, trifluralin in inter-day were out of the zone. There

283

was no relationship between there compounds and those of chemical group or tR.

284

To summarize and evaluate accuracy and precision results including all QC levels, data

285

were statistically processed based on reasonable criteria. According to the criteria of

286

Guidance for Industry: Bioanalytical Method Validation44, accuracy values are 80-120%

287

(RSD ≤20%) at the LOQ level and 85-115% (RSD ≤15%) at higher levels. In this study,

288

there were four LOQs (10, 25, 50, and 100 ng/mL) for each analyte depending on their

289

sensitivity and the LOQ for most of the pesticides (94.5%) was 10 ng/mL. Therefore, the

290

former criterion of accuracy and precision was applied for the 10 ng/mL QC level, and the

291

latter was applied for the other (50, 150, and 250 ng/mL) QC levels to reduce the 14


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complexity of reorganizing data.

293

As shown in Figure 3, the percentages of pesticides satisfying the criteria at 10 ng/mL

294

were 87.3 and 86.5% in the intra- and inter-day tests, respectively. At the 50, 150, and 250

295

ng/mL levels, the percentages meeting the criteria were 91.3, 97.6, and 96.0% in the intra-

296

day test and 90.8, 96.0, and 94.7% in the inter-day test, respectively. Most of the pesticides

297

satisfied the accuracy and precision (RSD) criteria, and the proportions of pesticides

298

meeting the criteria in intra-day were slightly higher than those of inter-day. In the case of

299

tolyfluanid, its poor linearity did not affect its quantitation property, showing an excellent

300

accuracy (85.1-106.0%) with an acceptable precision (6.0-14.7%) in intra- and inter-day

301

tests. Four pesticides (bifenox, diafenthiuron, imazamox, and nitrapyrin) did not satisfy the

302

criteria at all QC levels in the intra- and inter-day tests. In the case of bifenox, nitrapyrin,

303

and imazamox, the accuracy values were 68.8-129.8% (RSD; 10.1-33.6%) within those of

304

linear ranges. This indicated that those pesticides had acceptable accuracy and precision

305

ranges in the screening analysis. Diafenthiuron had poor accuracy (116.5-184.1%) and

306

precision results (RSD; 40.8-62.3%) in both tests, due to the poor linearity of the calibration

307

(r2 = 0.835) and instrumental reproducibility. Therefore, diafenthiuron should be

308

determined by qualitative confirmation rather than quantitative confirmation when

309

analyzing an unknown serum sample. The accuracy and precision of diafenthiuron has been

310

reported as excellent with buffered QuEChERS approaches in tomatoes, evaluated with

311

recovery test, by using LC-MS/MS45. Because there is no literature on the analysis of

312

diafenthiuron in serum or blood to our knowledge, further research to improve accuracy

313

and precision of diafenthiuron in serum is needed.

314

The results confirmed that the true concentration value for most pesticides in serum can 15



315

be determined excellently and reliably, and is also valid over several days. Therefore, using

316

the established preparation method and instrumental condition, biomonitoring for pesticide

317

multiresidues can be conducted simultaneously in agricultural or other cases of pesticide

318

intoxication. The detailed accuracy and precision results of each pesticide are presented in

319

Supporting Table S3.

320

Recovery. Recovery was defined as the proportion of the analyte remaining at the point

321

of final determination, following its addition immediately prior to extraction37. A greater

322

recovery rate could increase the sensitivity of target analytes. Recovery can also be a

323

parameter of trueness (accuracy) for the analytical method. Generally, a recovery rate of

324

70-120% (RSD ≤20%) is an acceptable trueness range37. It was already verified that

325

pesticide multiresidues can be effectively recovered with unbuffered original QuEChERS

326

extraction salts using the criteria in the above section.

327

For the verification of recovery rates of 379 pesticides at a glance, the recovery data at a

328

fortification level of 50 ng/mL were presented according to representative chemical groups

329

(Figure 4). All of the compounds belonging to the neonicotinoid, strobilurin, triazine,

330

triazole groups and most of the pesticides classified as the avermectin/spinsyn, carbamate,

331

organophosphate, pyrethroid, urea, and others/unclassified groups showed excellent

332

recovery range (70-120%). However, half of the pesticides belonging to the

333

aryloxyalkanoic/aryloxyphenoxypropionic acid and most of the imidazolinone pesticides

334

showed lower recovery ranges (20%). As shown in Table 6, 85.8, 90.2,

341

and 91.8% of target analytes satisfied the recovery range of 70-120% with RSD ≤20% at

342

fortification levels of 10, 50, and 250 ng/mL, respectively. The percentages of pesticides

343

with a recovery rate of less than 70% were 3.4-6.6% at all fortification levels. Only 1.8%

344

and 1.4% of pesticides had a recovery rate greater than 120% at 10 and 250 ng/mL of the

345

treated level and no pesticide included at 50 ng/mL. The overall recovery results were

346

similar with those in Kim et al. (2014) using mini-QuEChERS (AOAC 2007.1 buffer salts)

347

for whole blood analysis, in which approximately 83% and 11% of 215 pesticides had a

348

recovery range of 80-100% and 100-150% respectively33. However, among the pesticides

349

with a recovery rate under 60%, acephate, aldicarb, dimepiperate, diphenylamine, EPN,

350

fluazinam, methamidophos, omethoate, pyridalyl, teflubenzuron, and triclopyr showed a

351

better recovery rate (72.3-114.2%) in our study33, most likely because cleanup was omitted

352

after extraction in the sample preparation procedure.

353

From the results, 18 compounds were verified as out of the recovery range of 70-120%

354

with RSD ≤20% at all fortification levels (Table 7). Two compounds (inabenfide and

355

nitrapyrin) had high recovery rates (123.4 and 173.9%, respectively). The reason for the

356

recovery rates exceeding 120% despite the matrix-matching was that low sensitivity of

357

inabenfide and nitrapyrin (LOQ; 100 ng/mL) and insufficient calibration points caused

358

quantitation error. In contrast, 16 compounds showed a low recovery range of 15.4-66.2%

359

or poor recovery RSD (20.5-52.7%). Among the pesticides with low recovery rates, nine

360

compounds (2,4-D, imazamox, imazapic, imazaquin, imazethapyr, MCPA, mecoprop-P, 17



361

quinmerac, and the flonicamid metabolite TFNA [4-trifluoromethyl nicotinic acid]) were

362

acidic compounds or zwitterion48-49. Therefore, it is thought that those analytes were

363

present as ionic forms in the serum at almost neutral pH, and some of them remained in the

364

water layer at the partitioning step and were not recovered. It has been reported that the

365

recovery rate for some of these pH-dependent compounds increased by extracting with an

366

organic acid or an acidic buffer in many matrices including blood33, 50-51. Our study verified

367

that these pesticides obtained a greater recovery rate when using method (B) (AOAC

368

2007.1) or (C) (EN 15662) in the optimizing sample preparation step (Supporting Figure

369

S1). Although the pesticides listed in Table 7 were out of the recovery criteria range, the

370

accuracy and precision were excellent except for a few analytes, such that screening of the

371

pesticides is not a problem.

372

In conclusion, most of the pesticides were well recovered at all treated levels in this

373

study. The recovery rate of pH-dependent compounds could be increased by adjusting the

374

sample pH. The detailed recovery results of each compound are listed in Supporting Table

375

S4.

376

Matrix effect. The matrix effect was defined as the influence of one or more co-extracted

377

compounds from the sample on the measurement of the analyte’s concentration or mass37.

378

In LC-MS/MS, the matrix effect, which may cause some quantitation problems due to peak

379

enhancement or suppression, is a common phenomenon with biological samples. One

380

technique for minimizing the matrix effect is sample dilution21, 52. In this study, therefore,

381

100 µL of the serum sample was extracted with four times larger extraction solvent volume

382

(400 µL of acetonitrile). The extract solution was also partitioned into an organic solvent

383

layer and water layer using MgSO4 and NaCl to remove polar compounds from the organic 18


Page 18 of 45

Page 19 of 45


384

solvent layer that may affect the matrix effect. The matrix effect of each compound was

385

expressed as enhancement (> 0%) or suppression (< 0%) by comparing the slope of the

386

matrix-matched calibration curve with that of the solvent-only calibration curve.

387

The average matrix effect for all target pesticides was -6.8%, which means that the

388

response on LC-MS/MS for most compounds was somewhat suppressed by the matrix. For

389

a further detailed evaluation, the results were divided into six ranges and three groups

390

(Figure 5), corresponding to a soft effect when the value was within -20% to 0% or 0% to

391

20%, a medium effect within -50% to -20% or 20% to 50%, and a strong effect below -50%

392

or above 50%, according to Ferrer et al. (2011)53 and Kmellár et al. (2008)54. The number

393

of pesticides with a soft effect (between -20% and 20%) was 349 (92.1%), which was

394

considered as no matrix effect53. Therefore, those compounds in this group were not prone

395

to be affected by serum, indicating that solvent-only calibration could be possible for

396

quantitation. The compounds within the medium and strong effect groups were 25 (6.6%)

397

and 5 (1.3%), respectively. For those analytes susceptible to influences of the serum matrix,

398

it is necessary to make quantitation data using matrix-matched calibration to avoid

399

enhancement or suppression of responses.

400

The matrix effect data and tR of each pesticide were plotted on a scatter plot (Figure 6)

401

for the verification of a relationship between the two variables. The scatter graph showed

402

that matrix effects of most pesticides were within the soft effect zone during the time of

403

first pesticide elution to 9 minutes. From 9 minutes to the time of the last pesticide elution,

404

however, more than 50% of the compounds were out of soft effect zone, showing large

405

instrumental signal suppression.

406

In conclusion, using sample dilution in the preparation step, most of the pesticides 19



407

showed a very small matrix effect, regarded as no effect by the human serum matrix during

408

quantitation. Only a few compounds with a large matrix effect need alternative approaches

409

such as matrix-matching or standard addition method in the quantitative process in serum.

410

The result of the matrix effect for each pesticide in serum is given in Supporting Table S4.

20


Page 20 of 45

Page 21 of 45


411

SUPPORTING INFORMATION

412

The optimized LC-MS/MS parameters; Limits of quantitation (LOQs), linear ranges of

413

calibration, and correlation coefficients (r2); Validation data with accuracy and precision;

414

Validation data with recovery test and matrix effects in LC-MS/MS (Table S1-S4);

415

Recovery results of three different extraction methods for pH-dependent pesticides that

416

showed lower recovery rate in the validation test (Figure S1).

417 418

FUNDING SOURCES

419

This research was supported by the Bio and Medical Technology Development Program of

420

the National Research Foundation (NRF) and funded by the Korean government

421

(MSIP&MOHW) (NO. NRF-2015M3A9E1028325).

422 423

NOTES

424

The

authors

have

no

competing

financial

21


interests

to

declare.


425 426

REFERENCES (1)

Atwood, D.; Paisley-Jones, C. Pesticides Industry Sales and Usage: 2008-2012

427

Market Estimates; United States Environmental Protection Agency (U.S. EPA):

428

Washington, D.C., 2017.

429 430 431 432 433 434 435

(2)

KCPA. Agrochemical Year Book [in Korean]; Korea Crop Protection Association:

Seoul, South Korea, 2017. (3)

Aktar, M. W.; Sengupta, D.; Chowdhury, A. Impact of pesticides use in agriculture:

Their benefits and hazards. Interdiscip. Toxicol. 2009, 2, 1-12. (4)

Thundiyil, J. G.; Stober, J.; Besbelli, N.; Pronczuk, J. Acute pesticide poisoning: A

proposed classification tool. Bull. W. H. O. 2008, 86, 205-209. (5)

Calvert, G. M.; Karnik, J.; Mehler, L.; Beckman, J.; Morrissey, B.; Sievert, J.;

436

Barrett, R.; Lackovic, M.; Mabee, L.; Schwartz, A.; Mitchell, Y.; Moraga-McHaley, S.

437

Acute pesticide poisoning among agricultural workers in the United States, 1998–2005. Am.

438

J. Ind. Med. 2008, 51, 883-898.

439 440 441

(6)

Jeyaratnam, J. Acute pesticide poisoning: a major global health problem. World

Health Stat. Q. 1990, 43, 139-144. (7)

Kim, J.-H.; Kim, J.; Cha, E. S.; Ko, Y.; Kim, D. H.; Lee, W. J. Work-related risk

442

factors by severity for acute pesticide poisoning among male farmers in South Korea. Int. J.

443

Environ. Res. Public Health 2013, 10, 1100-1112.

444

(8)

Barr, D. B.; Barr, J. R.; Maggio, V. L.; Whitehead, R. D.; Sadowski, M. A.; Whyatt,

445

R. M.; Needham, L. L. A multi-analyte method for the quantification of contemporary

446

pesticides in human serum and plasma using high-resolution mass spectrometry. J.

447

Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2002, 778, 99-111. 22


Page 22 of 45

Page 23 of 45


448

(9)

Gill, U. S.; Schwartz, H. M.; Wheatley, B. Development of a method for the

449

analysis of PCB congeners and organochlorine pesticides in blood/serum. Chemosphere

450

1996, 32, 1055-1061.

451

(10) Hernández, F.; Pitarch, E.; Beltran, J.; López, F. J. Headspace solid-phase

452

microextraction in combination with gas chromatography and tandem mass spectrometry

453

for the determination of organochlorine and organophosphorus pesticides in whole human

454

blood. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2002, 769, 65-77.

455

(11) Lacassie, E.; Dreyfuss, M. F.; Gaulier, J. M.; Marquet, P.; Daguet, J. L.; Lachâtre, G.

456

Multiresidue determination method for organophosphorus pesticides in serum and whole

457

blood by gas chromatography–mass-selective detection. J. Chromatogr. B: Biomed. Sci.

458

Appl. 2001, 759, 109-116.

459

(12) Mostafa, A.; Medley, G.; Roberts, D. M.; Mohamed, M. S.; Elshanawani, A. A.;

460

Roberts, M. S.; Liu, X. Simultaneous quantification of carbamate insecticides in human

461

plasma by liquid chromatography/tandem mass spectrometry. J. Chromatogr. B: Anal.

462

Technol. Biomed. Life Sci. 2011, 879, 2234-2238.

463

(13) Saito, T.; Miura, N.; Namera, A.; Miyazaki, S.; Ohta, S.; Oikawa, H.; Inokuchi, S.

464

Rapid determination of polar and non-polar pesticides in human serum, using mixed-mode

465

C-C18 monolithic spin column extraction and LC–MS/MS. Chromatographia 2013, 76,

466

781-789.

467

(14) Wittsiepe, J.; Nestola, M.; Kohne, M.; Zinn, P.; Wilhelm, M. Determination of

468

polychlorinated biphenyls and organochlorine pesticides in small volumes of human blood

469

by high-throughput on-line SPE-LVI-GC-HRMS. J. Chromatogr. B: Anal. Technol.

470

Biomed. Life Sci. 2014, 945, 217-224. 23



471

(15) Cortéjade, A.; Kiss, A.; Cren, C.; Vulliet, E.; Buleté, A. Development of an

472

analytical method for the targeted screening and multi-residue quantification of

473

environmental contaminants in urine by liquid chromatography coupled to high resolution

474

mass spectrometry for evaluation of human exposures. Talanta 2016, 146, 694-706.

475

(16) Montesano, M. A.; Olsson, A. O.; Kuklenyik, P.; Needham, L. L.; Bradman, A.;

476

Barr, D. B. Method for determination of acephate, methamidophos, omethoate, dimethoate,

477

ethylenethiourea and propylenethiourea in human urine using high-performance liquid

478

chromatography-atmospheric pressure chemical ionization tandem mass spectrometry. J.

479

Exposure Sci. Environ. Epidemiol. 2007, 17, 321-330.

480

(17) Kavvalakis, M. P.; Tzatzarakis, M. N.; Theodoropoulou, E. P.; Barbounis, E. G.;

481

Tsakalof, A. K.; Tsatsakis, A. M. Development and application of LC–APCI–MS method

482

for biomonitoring of animal and human exposure to imidacloprid. Chemosphere 2013, 93,

483

2612-2620.

484

(18) Lee, C.-W.; Su, H.; Chen, P.-Y.; Lin, S.-J.; Shiea, J.; Shin, S.-J.; Chen, B.-H. Rapid

485

identification of pesticides in human oral fluid for emergency management by thermal

486

desorption electrospray ionization/mass spectrometry. J. Mass Spectrom. 2016, 51, 97-104.

487

(19) Wessels, D.; Barr, D. B.; Mendola, P. Use of biomarkers to indicate exposure of

488

children to organophosphate pesticides: Implications for a longitudinal study of children's

489

environmental health. Environ. Health Perspect. 2003, 111, 1939-1946.

490

(20) Altshul, L.; Covaci, A.; Hauser, R. The relationship between levels of PCBs and

491

pesticides in human hair and blood: Preliminary results. Environ. Health Perspect. 2004,

492

112, 1193-1199.

24


Page 24 of 45

Page 25 of 45


493

(21) Hernández, F.; Sancho, J. V.; Pozo, O. J. Critical review of the application of liquid

494

chromatography/mass spectrometry to the determination of pesticide residues in biological

495

samples. Anal. Bioanal. Chem. 2005, 382, 934-946.

496 497

(22) Dong, X.; Shi, Z.; Liang, S.; Sun, H. Rapid simultaneous determination of herbicides in human serum by UPLC-ESI-MS. Anal. Methods 2014, 6, 6939-6947.

498

(23) Sancho, J. V.; Pozo, O. J.; Hernández, F. Direct determination of chlorpyrifos and

499

its main metabolite 3,5,6-trichloro-2-pyridinol in human serum and urine by coupled-

500

column liquid chromatography/electrospray-tandem mass spectrometry. Rapid Commun.

501

Mass Spectrom. 2000, 14, 1485-1490.

502

(24) Azandjeme, C. S.; Delisle, H.; Fayomi, B.; Ayotte, P.; Djrolo, F.; Houinato, D.;

503

Bouchard, M. High serum organochlorine pesticide concentrations in diabetics of a cotton

504

producing area of the Benin Republic (West Africa). Environ. Int. 2014, 69, 1-8.

505 506

(25) Genuis, S. J.; Lane, K.; Birkholz, D. Human elimination of organochlorine pesticides: Blood, urine, and sweat study. Biomed Res. Int. 2016, 2016, 1-10.

507

(26) Cao, L.-L.; Yan, C.-H.; Yu, X.-D.; Tian, Y.; Zou, X.-Y.; Lu, D.-S.; Shen, X.-M.

508

Determination of polychlorinated biphenyls and organochlorine pesticides in human serum

509

by gas chromatography with micro-electron capture detector. J. Chromatogr. Sci. 2012, 50,

510

145-150.

511

(27) Chang, C.; Luo, J.; Chen, M.; Wu, K.; Dong, T.; He, X.; Zhou, K.; Wang, L.; Chen,

512

D.; Zhou, Z.; Wang, X.; Xia, Y. Determination of twenty organophosphorus pesticides in

513

blood serum by gas chromatography-tandem mass spectrometry. Anal. Methods 2016, 8,

514

4487-4496.

25



515

(28) Fan, R.; Zhang, F.; Wang, H.; Zhang, L.; Zhang, J.; Zhang, Y.; Yu, C.; Guo, Y.

516

Reliable screening of pesticide residues in maternal and umbilical cord sera by gas

517

chromatography-quadrupole time of flight mass spectrometry. Sci. China: Chem. 2014, 57,

518

669-677.

519

(29) Bosch de Basea, M.; Porta, M.; Alguacil, J.; Puigdomènech, E.; Gasull, M.; Garrido,

520

J. A.; López, T. Relationships between occupational history and serum concentrations of

521

organochlorine compounds in exocrine pancreatic cancer. Occup. Environ. Med. 2011, 68,

522

332-338.

523

(30) Anastassiades, M.; Lehotay, S. J.; Štajnbaher, D.; Schenck, F. J. Fast and easy

524

multiresidue method employing acetonitrile extraction/partitioning and “dispersive solid-

525

phase extraction” for the determination of pesticide residues in produce. J. AOAC Int. 2003,

526

86, 412-431.

527

(31) Lehotay, S. J. Determination of pesticide residues in foods by acetonitrile extraction

528

and partitioning with magnesium sulfate: Collaborative study. J. AOAC Int. 2007, 90, 485-

529

520.

530

(32) Foods of plant origin-determination of pesticide residues using GC-MS and/or LC-

531

MS/MS following acetonitrile extraction/partitioning and clean-up by dispersive SPE-

532

QuEChERS-method. EN 15662, 2008.

533

(33) Kim, H.-s.; Kim, J.; Suh, J. H.; Han, S. B. General unknown screening for

534

pesticides in whole blood and Korean gastric contents by liquid chromatography–tandem

535

mass spectrometry. Arch. Pharm. Res. 2014, 37, 1317-1324.

26


Page 26 of 45

Page 27 of 45


536

(34) Plassmann, M. M.; Schmidt, M.; Brack, W.; Krauss, M. Detecting a wide range of

537

environmental contaminants in human blood samples—combining QuEChERS with LC-

538

MS and GC-MS methods. Anal. Bioanal. Chem. 2015, 407, 7047-7054.

539

(35) Usui, K.; Hayashizaki, Y.; Minagawa, T.; Hashiyada, M.; Nakano, A.; Funayama,

540

M. Rapid determination of disulfoton and its oxidative metabolites in human whole blood

541

and urine using QuEChERS extraction and liquid chromatography–tandem mass

542

spectrometry. Leg. Med. 2012, 14, 309-316.

543 544 545

(36) Rejczak, T.; Tuzimski, T. A review of recent developments and trends in the QuEChERS sample preparation approach. Open Chem. 2015, 13, 980-1010. (37) Guidance document on analytical quality control and validation procedures for

546

pesticide

547

https://ec.europa.eu/food/sites/food/files/plant/docs/pesticides_mrl_guidelines_wrkdoc_201

548

7-11813.pdf (accessed February, 24).

549 550

residues

analysis

in

food

and

feed

SANTE/11813/2017.

(38) De Bièvre, P.; Günzler, H.; Eds. Validation in Chemical Measurement, Springer: New York, 2005.

551

(39) Dulaurent, S.; Moesch, C.; Marquet, P.; Gaulier, J.-M.; Lachâtre, G. Screening of

552

pesticides in blood with liquid chromatography–linear ion trap mass spectrometry. Anal.

553

Bioanal. Chem. 2010, 396, 2235-2249.

554 555

(40) Hikiji, W.; Yamaguchi, K.; Saka, K.; Hayashida, M.; Ohno, Y.; Fukunaga, T. Acute fatal poisoning with Tolfenpyrad. J. Forensic Leg. Med. 2013, 20, 962-964.

556

(41) Lacassie, E.; Marquet, P.; Gaulier, J.-M.; Dreyfuss, M.-F.; Lachâtre, G. Sensitive

557

and specific multiresidue methods for the determination of pesticides of various classes in

558

clinical and forensic toxicology. Forensic Sci. Int. 2001, 121, 116-125. 27



Page 28 of 45

559

(42) Lee, S.-K.; Ameno, K.; In, S.-W.; Yang, W.-K.; Koo, K.-S.; Yoo, Y.-C.; Kubota, T.;

560

Ameno, S.; Ijiri, I. Acute fatal poisoning cases due to furathiocarb ingestion. Forensic Sci.

561

Int. 1999, 101, 65-70.

562

(43) Miyazaki, T.; Yashiki, M.; Kojima, T.; Chikasue, F.; Ochiai, A.; Hidani, Y. Fatal

563

and non-fatal methomyl intoxication in an attempted double suicide. Forensic Sci. Int. 1989,

564

42, 263-270.

565

(44) Guidance

for

Industry:

Bioanalytical

Method

Validation.

566

https://www.fda.gov/downloads/drugs/guidances/ucm368107.pdf (accessed November, 09).

567

(45) Golge, O.; Kabak, B. Evaluation of QuEChERS sample preparation and liquid

568

chromatography–triple-quadrupole mass spectrometry method for the determination of 109

569

pesticide residues in tomatoes. Food Chem. 2015, 176, 319-332.

570

(46) Jia, W.; Chu, X.; Ling, Y.; Huang, J.; Chang, J. High-throughput screening of

571

pesticide and veterinary drug residues in baby food by liquid chromatography coupled to

572

quadrupole Orbitrap mass spectrometry. J. Chromatogr. A 2014, 1347, 122-128.

573

(47) Mol, H. G. J.; Plaza-Bolaños, P.; Zomer, P.; de Rijk, T. C.; Stolker, A. A. M.;

574

Mulder, P. P. J. Toward a generic extraction method for simultaneous determination of

575

pesticides, mycotoxins, plant toxins, and veterinary drugs in feed and food matrixes. Anal.

576

Chem. 2008, 80, 9450-9459.

577 578

(48) Turner, J. A. The Pesticide Manual: A World Compendium, 17th ed.; British Crop Production Council: Alton, Hampshire, England, 2015.

579

(49) Chemicalize.org. https://chemicalize.com (accessed November, 09).

580

(50) Carneiro, R. P.; Oliveira, F. A. S.; Madureira, F. D.; Silva, G.; de Souza, W. R.;

581

Lopes, R. P. Development and method validation for determination of 128 pesticides in 28


Page 29 of 45


582

bananas by modified QuEChERS and UHPLC–MS/MS analysis. Food Control 2013, 33,

583

413-423.

584

(51) Pareja, L.; Cesio, V.; Heinzen, H.; Fernández-Alba, A. R. Evaluation of various

585

QuEChERS based methods for the analysis of herbicides and other commonly used

586

pesticides in polished rice by LC–MS/MS. Talanta 2011, 83, 1613-1622.

587

(52) Panuwet, P.; Hunter, R. E.; D’Souza, P. E.; Chen, X.; Radford, S. A.; Cohen, J. R.;

588

Marder, M. E.; Kartavenka, K.; Ryan, P. B.; Barr, D. B. Biological matrix effects in

589

quantitative

590

biomonitoring. Crit. Rev. Anal. Chem. 2016, 46, 93-105.

tandem

mass

spectrometry-based

analytical

methods:

Advancing

591

(53) Ferrer, C.; Lozano, A.; Agüera, A.; Girón, A. J.; Fernández-Alba, A. R.

592

Overcoming matrix effects using the dilution approach in multiresidue methods for fruits

593

and vegetables. J. Chromatogr. A 2011, 1218, 7634-7639.

594

(54) Kmellár, B.; Fodor, P.; Pareja, L.; Ferrer, C.; Martínez-Uroz, M. A.; Valverde, A.;

595

Fernandez-Alba, A. R. Validation and uncertainty study of a comprehensive list of 160

596

pesticide residues in multi-class vegetables by liquid chromatography–tandem mass

597

spectrometry. J. Chromatogr. A 2008, 1215, 37-50.

598

29



Page 30 of 45

FIGURE CAPTIONS Figure 1. TIC obtained by LC-MS/MS analysis of matrix-matched standard in human serum with 379 pesticides at 100 ng/mL (4 µL injection) (a) and TIC of control (nonfortified) serum sample (b). Figure 2. Scatter plots for 379 pesticides to show accuracies and precisions (RSD) in intraday (a) and inter-day (b) tests (at 150 ng/mL of QC level). Figure 3. Percentage of 379 pesticides satisfying the accuracy values within 80-120% (RSD ≤20%) at 10 ng/mL and within 85-115% (RSD ≤15%) at 50, 150, and 250 ng/mL in the intra-day (grey bars) and inter-day (dark grey bars) tests using the final established method. Figure 4. Distribution to show recovery values for 379 pesticides classified into the representative chemical groups (at 50 ng/mL of fortification level). Figure 5. Distribution of matrix effects (%) for 379 pesticides in human serum samples. Figure

6.

Scatter

plot

to

show

tR

and

matrix

30


effect

of

379

pesticides.

Page 31 of 45


TABLES Table 1. List of the Final Research 379 Pesticides Based on the Chemical Groups. Chemical group (No. of compounds) Aryloxyalkanoic/ Aryloxyphenoxypropionic acid (12) Avermectin/ Spinosyn (11) Carbamate (42)

Imidazolinone (5) Neonicotinoid (7) Organophosphate (64)

Pyrethroid (14) Strobilurin (8) Triazine (12) Triazole (26)

Compound name 2,4-D, Clomeprop, Cyhalofop-butyl, Diclofop-methyl, Fenoxaprop-p-ethyl, Haloxyfop, Haloxyfop-R-Methyl, MCPA, Mecoprop-P, Metamifop, Propaquizafop, Quizalofop-ethyl Abamectin B1a, Emamectin B1a, Emamectin B1b, Lepimectin A3, Lepimectin A4, Milbemectin A3, Milbemectin A4, Spinetoram (XDE-175-J), Spinetoram (XDE-175-L), Spinosyn A, Spinosyn D Alanycarb, Aldicarb, Asulam, Bendiocarb, Benfuracarb, Benthiavalicarb-isopropyl, Butocarboxim, Carbaryl, Carbofuran, Carbosulfan, Cycloate, Dazomet, Di-allate, Diethofencarb, Dimepiperate, Esprocarb, Ethiofencarb, Fenobucarb (BPMC), Fenothiocarb, Fenoxycarb, Furathiocarb, Iprovalicarb, Isoprocarb, Methiocarb, Methomyl, Metolcarb, Molinate, Oxamyl, Pebulate, Phenmedipham, Pirimicarb, Promecarb, Propamocarb, Propham, Propoxur, Pyributicarb, Thiobencarb, Thiodicarb, Tri-allate, Trimethacarb, Vernolate, XMC Fenamidone, Imazamox, Imazapic, Imazaquin, Imazethapyr Acetamiprid, Clothianidin, Dinotefuran, Imidacloprid, Nitenpyram, Thiacloprid, Thiamethoxam Acephate, Anilofos, Azamethiphos, Azinphos-ethyl, Azinphos-methyl, Bensulide, Cadusafos, Carbophenothion, Chlorfenvinphos, Chlorpyrifos, Chlorpyrifos-methyl, Demeton-S-methyl, Diazinon, Dichlorvos, Dicrotophos, Dimethoate, Dimethylvinphos, Edifenphos, EPN, Ethion, Ethoprophos, Etrimfos, Fenamiphos, Fenthion, Fonofos, Fosthiazate, Imicyafos, Iprobenfos, Isazofos, Isoxathion, Malathion, Mecarbam, Methamidophos, Methidathion, Mevinphos, Monocrotophos, Omethoate, Oxydemeton-methyl, Parathion, Phenthoate, Phorate, Phosalone, Phosmet, Phosphamidon, Phoxim, Piperophos, Pirimiphos-ethyl, Pirimiphos-methyl, Profenofos, Prothiofos, Pyraclofos, Pyrazophos, Pyridaphenthion, Quinalphos, Sulprofos, Tebupirimfos, Terbufos, Tetrachlorvinphos, Thiometon, Tolclofos-methyl, Triazophos, Tribufos, Trichlorfon, Vamidothion Bifenthrin, Cycloprothrin, Cyhalothrin-lambda, Cypermethrin, Deltamethrin, Etofenprox, Fenpropathrin, Fenvalerate, Flucythrinate, Fluvalinate, Halfenprox, Permethrin, Phenothrin, Tralomethrin Azoxystrobin, Fluacrypyrim, Kresoxim-methyl, Metominostrobin, Orysastrobin, Picoxystrobin, Pyraclostrobin, Trifloxystrobin Ametryn, Atrazine, Cyanazine, Dimethametryn, Hexazinone, Metribuzin, Prometryn, Propazine, Simazine, Simetryn, Terbuthylazine, Terbutryn

Amisulbrom, Azaconazole, Bitertanol, Cafenstrole, Carfentrazone-ethyl, Cyproconazole, Difenoconazole, Diniconazole, Epoxiconazole, Fenbuconazole, Fluquinconazole, Flusilazole, Hexaconazole, Imibenconazole, Metconazole, Myclobutanil, Paclobutrazol, Penconazole, Propiconazole, Simeconazole, Tebuconazole, Tetraconazole, Triadimefon, Triadimenol, Triticonazole, Uniconazole

31



Table 1. (Continued) Chemical group (No. of compounds) Urea (34)

Others/ Unclassified (144)

Compound Name Azimsulfuron, Bensulfuron-methyl, Chlorfluazuron, Chlorimuron-ethyl, Chlorotoluron, Chlorsulfuron, Cyclosulfamuron, Daimuron, Diafenthiuron, Diflubenzuron, Diuron, Ethametsulfuron-methyl, Ethoxysulfuron, Flucetosulfuron, Flufenoxuron, Forchlorfenuron, Halosulfuron-methyl, Hexaflumuron, Imazosulfuron, Isoproturon, Linuron, Lufenuron, Metazosulfuron, Methabenzthiazuron, Metobromuron, Nicosulfuron, Novaluron, Pencycuron, Rimsulfuron, Teflubenzuron, Thidiazuron, Thifensulfuron-methyl, Tribenuron-methyl, Triflumuron Acibenzolar-S-methyl, Alachlor, Allidochlor, Ametoctradin, Amitraz, Benfuresate, Bentazone, Benzobicyclon, Benzoximate, Bifenazate, Bifenox, Boscalid, Bromacil, Bromobutide, Bromoxynil, Bupirimate, Buprofezin, Butachlor, Butafenacil, Carbendazim, Carboxin, Carpropamid, Cartap, Chinomethionat, Chlorantraniliprole, Chloridazon, Chromafenozide, Cinmethylin, Clethodim, Clofentezine, Clomazone, Cyazofamid, Cyflufenamid, Cymoxanil, Cyprodinil, Cyromazine, Diflufenican, Dimethachlor, Dimethenamid, Dimethomorph, Diphenamid, Diphenylamine, Dithiopyr, Ethaboxam (EBX), Ethoxyquin, Etoxazole, Famoxadone, Fenarimol, Fenazaquin, Fenhexamid, Fenoxanil, Fenpyroximate, Fentrazamide, Ferimzone, Fipronil, Flonicamid, Fluazinam, Flubendiamide, Fludioxonil, Flufenacet, Flumiclorac-pentyl, Flumioxazin, Fluopicolide, Fluopyram, Flusulfamide, Flutolanil, Fluxapyroxad, Hexythiazox, Imazalil, Inabenfide, Indanofan, Indoxacarb, Iprodione, Isoprothiolane, Isopyrazam, Lactofen, Mandipropamid, Mefenacet, Mefenpyr-diethyl, Mepanipyrim, Mepronil, Metalaxyl, Methoxyfenozide, Metolachlor, Metrafenone, Napropamide, Nitrapyrin, Nuarimol, Ofurace, Oxadiazon, Oxadixyl, Oxaziclomefone, Pendimethalin, Penoxsulam, Penthiopyrad, Picolinafen, Pretilachlor, Probenazole, Prochloraz, Propachlor, Propanil, Propisochlor, Propyzamide, Pymetrozine, Pyrazolynate, Pyrazoxyfen, Pyribenzoxim, Pyridaben, Pyridalyl, Pyridate, Pyrifenox, Pyrifluquinazon, Pyrimethanil, Pyrimidifen, Pyriminobac-methyl E, Pyriminobac-methyl Z, Pyrimisulfan, Pyriproxyfen, Pyroquilon, Quinmerac, Quinoclamine, Saflufenacil, Sethoxydim, Spirodiclofen, Spirotetramat, Sulfoxaflor, TCMTB, Tebufenozide, Tebufenpyrad, TFNA [4-trifluoromethyl nicotinic acid], Thenylchlor, Thiabendazole, Thiazopyr, Thifluzamide, Thiocyclam, Thiophanate-methyl, Tiadinil, Tolfenpyrad, Tolylfluanid, Triclopyr, Tricyclazole, Triflumizole, Trifluralin, Zoxamide

32


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Table 2. The Number of Pesticides with Recoveries between 70–120% with RSDs below 20% in the Recovery Test from Different Extraction Methods for 379 Pesticides in 100 µL of Human Serum (Fortification Level at 250 ng/mL, n=3). Type of method A

Preparation method scaled-down from Original method

B C

Extraction solvent

Extraction reagent

No. of analytes 344

% of analytes 90.8

acetonitrile (400 µL)

MgSO4 (40 mg) NaCl (10 mg)

AOAC 2007.01

1% HOAc in acetonitrile (400 µL)

MgSO4 (40 mg) NaOAc (10 mg)

341

90.0

EN 15662

acetonitrile (400 µL)

MgSO4 (40 mg) NaCl (10 mg) Na3Citrate (10 mg) Na2HCitr (5mg)

341

90.0

33



Page 34 of 45

Table 3. Distribution of LOQs from 10 to 100 ng/mL for 379 Pesticides. LOQ (ng/mL)

No. of analytes

% of analytes

10 25 50 100 Sum

358 11 7 3 379

94.5 2.9 1.8 0.8 100.0

34


Page 35 of 45


Table 4. Distribution of Linear Ranges for 379 Pesticides. Linear range (ng/mL) 10-250

No. of analytes 350

% of analytes 92.4

Remarks

25-250

9

2.4

50-250

7

1.8

100-250

3

0.8

Inabenfide, Nitrapyrin, Thiocyclam

10-150

6

1.6

25-150

2

0.5

Asulam, Benfuracarb, Bentazone, Cafenstrole, Fluxapyroxad, Mecoprop-P Bifenox, Diafenthiuron

10-100 Sum

2 379

0.5 100.0

Abamectin B1a, Aldicarb, Butocarboxim, Imazamox, Iprodione, MCPA, Milbemectin A3, Propham, Terbufos Diphenylamine, Fenvalerate, TFNA, Thiometon, Tolylfluanid, Tralomethrin, Trifluralin

Carbosulfan, Dazomet -

35



Table 5. Distribution of Correlation Coefficients (r2) of Calibration for 379 Pesticides. No. of % of Remarks r2 ≥0.990 0.980-0.990

analytes 356 17

analytes 93.9 4.5

0.900-0.980

4

1.1

20% 0-20% >20% 0-20% >20% 0-20% >20%

30-50% 50-70% 70-120% >120% N.D.a Sum a

10 ng/mL 1 (0.3) 0 (0.0) 1 (0.3) 1 (0.3) 7 (1.8) 3 (0.8) 325 (85.8) 13 (3.4) 5 (1.3) 2 (0.5) 21 (5.5) 379 (100.0)

Treated Level No. of analytes (%) 50 ng/mL 3 (0.8) 1 (0.3) 3 (0.8) 3 (0.8) 13 (3.4) 2 (0.5) 342 (90.2) 8 (2.1) 0 (0.0) 0 (0.0) 4 (1.1) 379 (100.0)

Not determined due to out of linear range.

37


250 ng/mL 0 (0.0) 0 (0.0) 6 (1.6) 0 (0.0) 10 (2.6) 0 (0.0) 348 (91.8) 0 (0.0) 4 (1.1) 1 (0.3) 10 (2.6) 379 (100.0)


Page 38 of 45

Table 7. Pesticides for Which Recovery Test Results were not within 70-120% (RSD ≤20%) at All Treated Levels (10, 50, and 250 ng/mL), and Intra-day Accuracy Results with RSD. Treated level Chemical group

No. 1

Compound name 2,4-D

Aryloxyalkanoic acid

2

Abamectin B1a

Avermectin

3

Bifenox

Nitrophenyl ether

10 ng/mL Recovery, Accuracy, % % (RSD, %) (RSD, %) 58.7 (10.0) 107.0 (12.7) N.D.a

N.D.a

a

N.D.

a a

N.D.

a

4

Diafenthiuron

Urea

N.D.

N.D.

5

Etofenprox

Pyrethroid

52.3 (5.9)

96.8 (2.7)

a

a

50 ng/mL Recovery, Accuracy, % % (RSD, %) (RSD, %) 52.2 (14.8) 93.9 (12.6)

250 ng/mL Recovery, Accuracy, % % (RSD, %) (RSD, %) 60.7 (8.9) 104.1 (12.5)

38.2 (58.8)

60.0 (11.8)

58.9 (22.7)

92.2 (35.6) 83.8 (20.1)

98.1 (10.7)

Remarks pKa 2.73 (20-25 ℃)48 -

N.D.

a

N.D.a

-

a

a

-

57.5 (26.0)

116.5 (62.3)

N.D.

55.8 (3.6)

88.2 (6.3)

59.5 (7.2)

85.2 (13.3)

N.D.

-

15.4 (47.8)

68.8 (32.7)

34.9 (9.5)

72.6 (19.9)

Zwitterion48

6

Imazamox

Imidazolinone

N.D.

N.D.

7

Imazapic

Imidazolinone

31.4 (11.4)

105.0 (13.2)

21.6 (7.1)

85.3 (9.7)

31.8 (9.4)

80.9 (10.5)

Zwitterion48

8

Imazaquin

Imidazolinone

52.8 (27.0)

93.9 (14.0)

46.3 (6.3)

92.1 (7.9)

52.6 (0.7)

89.3 (6.0)

9

Imazethapyr

Imidazolinone

56.6 (6.1)

97.0 (13.9)

41.7 (4.2)

92.0 (4.5)

54.1 (3.0)

88.1 (12.0)

pKa 3.8 (20-25 ℃)48 Zwitterion48

123.4 (16.5)

102.8 (8.0)

-

66.2 (10.9)

90.1 (4.4)

pKa 3.73 (20-25 ℃)48

a

a

10

Inabenfide

Unclassified

N.D.

N.D.

11

MCPA


N.D.a

N.D.a

N.D.

a

48.3 (20.5)

N.D.

a

99.4 (9.8)

a

pKa 3.78 (20-25 ℃)48 -

12

Mecoprop-P


60.0 (7.0)

97.0 (11.3)

48.7 (30.0)

74.8 (16.4)

N.D.

13

Nitrapyrin

Unclassified

N.D.a

N.D.a

N.D.a

N.D.a

173.9 (44.2)

110.3 (14.7)

14

Quinmerac

Quinolinecarboxylic acid

25.1 (6.6)

88.9 (6.7)

29.3 (6.3)

98.8 (10.2)

35.3 (3.2)

85.1 (8.0)

25.5 (15.9)

100.0 (14.5)

32.6 (2.3)

87.6 (14.3)

N.D.a

49.0 (13.0)

85.8 (12.1)

pKa 4.32 (20-25 ℃)48 pKa 2.62 b, 3.99 b (calculated)49 -

104.6 (14.7)

31.0 (15.4)

85.3 (8.8)

-

131.2 (12.4)

63.2 (10.7)

99.6 (12.9)

-

a

a

15

TFNA

Nicotinic acid

N.D.

N.D.

16

Thiocyclam

Nereistoxin analogue

N.D.a

N.D.a

N.D.a

a

N.D.

a

N.D.

a

N.D.

a

72.0 (52.7)

17 18

Tolylfluanid Tralomethrin

Phenylsulfamide Pyrethroid

N.D.

a

N.D.

a

Not determined due to out of linear range. b Calculated values using Chemicalize.org by ChemAxon. 38


N.D.

a

Page 39 of 45


FIGURE GRAPHICS Figure 1

Intensity

(a)

Intensity

(b

39



Figure 2

40


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Page 41 of 45


Figure 3

97.6

100.0 90.0

87.3

86.5

91.3

90.8

96.0

96.0

94.7

Frequency, %

80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 Intra-day Inter-day Intra-day Inter-day Intra-day Inter-day Intra-day Inter-day 10 ng/mL

50 ng/mL

150 ng/mL

41


250 ng/mL


Figure 4

42


Page 42 of 45

Page 43 of 45


Figure 5

Number of compounds

350

313 (82.6%)

300 250 200 150 100 50

3 (0.8%)

36 (9.5%)

18 (4.7%)

7 (1.8%)

2 (0.5%)

20%-50% Medium

>50% Strong

0 50%) Medium (20% to 50%) Soft (-20% to 20%) Medium (-20% to -50%) Strong (

Validation of a Multiresidue Analysis Method for 379 Pesticides in

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