Validation of a Multiresidue Analysis Method for 379 Pesticides in

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New Analytical Methods

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].

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ABSTRACT

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

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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%).

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

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INTRODUCTION

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

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

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

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

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

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

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

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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

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

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inappropriately. Acute intoxication symptoms caused by pesticides range from mild

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

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

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

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

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

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

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pesticides in 20107.

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

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

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16

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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

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

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

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

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

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

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

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

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

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

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

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compared to whole blood10-11.

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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convenient analytical method is applicable for biomonitoring of pesticide multiresidues in

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

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studies and investigation.

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MATERIALS AND METHODS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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several validation procedures.

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

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

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

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

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

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

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

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

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

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

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

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

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run time was 15.0 min.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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was utilized for data processing.

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

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

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

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

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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RESULTS AND DISCUSSIONS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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.

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Optimization of Sample Extraction Step. Since the first unbuffered original

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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and no overlaps were observed between pesticides in fortified samples.

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

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

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

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

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

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

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

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

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

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

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the analysis time.

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

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

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

230

effect.

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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the detailed LOQ results for the pesticides.

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

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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).

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

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

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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

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

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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,

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

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

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

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

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

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

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

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multiresidue screening method. Diafenthiuron and tolyfluanid had somewhat poor

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

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

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S2.

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

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

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

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

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

275

(%).

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

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

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

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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,

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

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

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

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

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

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

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

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

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

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

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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.

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

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

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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

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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

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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

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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

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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.

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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

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interests

to

declare.

Journal of Agricultural and Food Chemistry

425 426

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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

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effect

of

379

pesticides.

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Journal of Agricultural and Food Chemistry

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

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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

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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

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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

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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 -

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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.

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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)

Journal of Agricultural and Food Chemistry

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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

Aryloxyalkanoic acid

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

Aryloxyalkanoic acid

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

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N.D.

a

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Journal of Agricultural and Food Chemistry

FIGURE GRAPHICS Figure 1

Intensity

(a)

Intensity

(b

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Figure 2

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Journal of Agricultural and Food Chemistry

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

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Journal of Agricultural and Food Chemistry

Figure 4

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Journal of Agricultural and Food Chemistry

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 (