Rapid and Simultaneous Analysis of 360 Pesticides in Brown Rice

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Rapid and Simultaneous Analysis of 360 Pesticides in Brown Rice, Spinach, Orange, and Potato using Microbore GC-MS/MS Jonghwa Lee, Leesun Kim, Yongho Shin, Junghak Lee, Jiho Lee, Eunhye Kim, Joon-Kwan Moon, and Jeong-Han Kim J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b00576 • Publication Date (Web): 27 Mar 2017 Downloaded from http://pubs.acs.org on March 28, 2017

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Journal of Agricultural and Food Chemistry

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Manuscript for Journal of Agricultural and Food Chemistry.

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Articles

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Rapid and Simultaneous Analysis of 360 Pesticides in Brown Rice,

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Spinach, Orange, and Potato using Microbore GC-MS/MS

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Jonghwa Lee,1 Leesun Kim,2 Yongho Shin,1 Junghak Lee,1 Jiho Lee,1 Eunhye Kim,1 Joon-

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Kwan Moon,3 and Jeong-Han Kim1*

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Republic of Korea.

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of Korea

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Anseong 17579, Republic of Korea

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* Corresponding author. Phone: +82-2-880-4644. Fax: +82-2-873-4415. E-mail:

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[email protected].

Department of Agricultural Biotechnology, Seoul National University, Seoul 08826,

School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic

Department of Plant Life and Environmental Sciences, Hankyong National University,

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ACS Paragon Plus Environment


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ABSTRACT

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A multi-residue method for simultaneous and rapid analysis of 360 pesticides in

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representative agricultural produce (brown rice, orange, spinach, and potato) was developed

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using a modified QuEChERS procedure combined with gas chromatography tandem mass

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spectrometry (GC-MS/MS). Selected reaction monitoring transition parameters (e.g.,

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collision energy, precursor and product ions) in MS/MS were optimized to achieve the best

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selectivity and sensitivity for a wide range of GC amenable pesticides. A short (20 m)

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microbore (0.18 mm i.d.) column resulted in better signal-to-noise ratio with reduced

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analysis time than a conventional narrowbore column (30 m × 0.25 mm i.d.). The priming

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injection dramatically increased peak areas by masking effect on a new GC liner. Limit of

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quantitation was lower than 0.01 mg/kg and the correlation coefficients (r2) of matrix-

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matched standards was > 0.99 within the range of 0.0025–0.1 mg/kg. Acetonitrile with 0.1%

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formic acid without additional buffer salts was used for pesticide extraction, while only

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primary secondary amine (PSA) was used for dispersive solid phase extraction (dSPE)

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cleanup, to achieve good recoveries for most of the target analytes. The recoveries ranged

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from 70 to 120% with relative standard deviations of ≤ 20 % at 0.01 and 0.05 mg/kg

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spiking levels (n=6) in all samples, indicating acceptable accuracy and precision of the

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method. Seventeen real samples from the local markets were analyzed by the optimized

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method and 14 pesticides in incurred 11 samples were found at below the maximum residue

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limits.

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KEYWORDS: pesticide multi-residues, GC-MS/MS, QuEChERS, brown rice, orange,

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spinach, potato

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INTRODUCTION

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Pesticide residue level of agricultural produce is one of the most important issues

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because it is directly related with human health safety. To ensure that levels of pesticide

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residues on grain, fruit, and vegetable meet current tolerances or maximum residue limits

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(MRLs) of individual countries, they are strictly monitored by authorities. Therefore, a fast

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and simultaneous analytical method of many pesticide residues is critically in need because

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those residues in imported, exported or domestic foodstuff should be determined as rapidly

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as possible with reliable accuracy and precision for food safety.

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Conventional gas chromatography (GC) or high-performance liquid chromatography

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(HPLC) have several limitations in fast and simultaneous multi-residual analysis. Extensive

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partitioning and cleanup procedures are also required to remove co-extractives for base line

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separation of peaks on GC or HPLC, often causing low recoveries and precisions by loss of

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analytes. However, GC or LC combined with tandem mass spectrometry (MS/MS) plays a

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vital role recently in monitoring multi-residual pesticides in food matrices. Selected

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reaction monitoring (SRM, operating as “molecular cleanup”) by MS/MS enables the

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simultaneous analysis of hundreds of pesticides1-2 in a short time with high sensitivity and

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selectivity.

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For the GC analysis, a narrowbore (0.2 mm ≤ i.d. < 0.3 mm)3 column such as 30-m

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columns with 0.25 mm i.d. has been widely used for separation of various pesticides.

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However, it generally requires more than 30 min run time for one sample. For faster

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analysis, a low pressure-GC method uses a short megabore column (e.g. 10 m × 0.53 mm

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i.d.) for multi-residual analysis with an advantage of large sample loading capacity but it

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requires an additional restriction column to maintain positive pressure in inlet. In addition, 3



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the negative influence on vacuum state of MS spectrometer caused by the large volume of

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carrier gas flow may give rise to increase detection limit.4 On the other hand, a microbore

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column (0.1 mm ≤ i.d. < 0.2 mm) was not widely studied for fast and simultaneous analysis

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despite its good performance with higher signal-to-noise (S/N) ratio3. The column also

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provides increased efficiency and higher sensitivity by reducing resistance to mass

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transfer.5 Furthermore, it can provide enough linear velocity with low carrier gas flow for

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narrow chromatographic bands. However, it should be noted that narrow peaks should be

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supported by a detector (e.g. MS/MS) with fast scan speed or cycle time (or loop time)

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because it requires sufficient data points across the peak width for reliable and repeatable

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chromatographic data. The GC-MS/MS instruments recently released are known to be

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capable of producing consistent chromatographic data by rapidly providing sufficient points

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for narrow peaks while maintaining quantitative accuracy.

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For multi-residual sample preparation combined with the MS or MS/MS techniques, the

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QuEChERS (quick, easy, cheap, effective, rugged, and safe) method has widely replaced

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the traditional sample treatment methods such as column chromatography or solid phase

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extraction (SPE) since it was first introduced by Anastassiades and coworkers in 2003.6-7

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Especially, dispersive SPE (dSPE) cleanup is simple but offers high accuracy and

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precision.1,8-9 The dSPE typically uses primary secondary amine (PSA) sorbent for removal

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of some organic acids, sugars, and fatty acid.7, 10 Optional sorbents such as graphitized

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carbon black (GCB)8, 11 and ChloroFiltr12-13 can be applied with PSA to remove pigment

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like chlorophyll or carotenoid. Recently, multi-walled carbon nanotube (MWCNT) was

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also introduced as co-sorbents for reduction of matrix interferences.14-15

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Currently, the combination of QuEChERS and the MS/MS technique is one of the most 4


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popular analytical approaches for the multi-residual analysis in various food matrices.16-18

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GC-MS/MS and LC-MS/MS can generally play a complimentary role for each other

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because some unique compounds are only amenable to one of the techniques. A wide range

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of multi-residual analysis using LC-MS/MS has been carried out, but with GC-MS/MS not

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many studies on multi-residual analysis (over 300 pesticides) were not performed9,

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while 541 pesticides were reported to be GC amenable on GC-MS by Pang G. et al..20

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Recent study on multi-residues in botanical samples identified 310 pesticides with GC-

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MS/MS but considered each isomer peak as an individual compound.21 In addition, not

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many studies have focused on the optimization of GC-MS/MS to increase the number of

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the GC amenable pesticides. Therefore, several GC and MS/MS conditions (e.g. column,

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selection of precursor and product ions) still need to be optimized for maximization of the

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number of GC amenable pesticides without compromising sensitivity and selectivity.

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This study aimed to develop a multi-residual screening method for simultaneous analysis

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of 360 GC amenable pesticides in representative food commodities (brown rice, orange,

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spinach, and potato) using GC-MS/MS. The QuEChERS extraction and dSPE cleanup

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procedures were modified for more practical and efficient analysis without compromising

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of these advantages or recoveries. Optimum instrumental conditions were established for

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enhancement of sensitivity and selectivity. In terms of limit of quantitation (LOQ),

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accuracy and precision, the developed method was validated before finally applied for the

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screening of multiclass pesticides in real samples collected at Korean local markets.

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MATERIALS AND METHODS

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Chemicals and Consumables. HPLC grade acetonitrile (ACN) was purchased from

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Fisher Scientific (Seoul, South Korea) while formic acid (for mass spectrometry) and acetic 5



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acid (purity >99.7%) from Sigma-Aldrich (St, Louis, MO, USA). QuEChERS extraction

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packets (4 g of magnesium sulfate, MgSO4 and 1 g of sodium chloride, NaCl), 2 mL dSPE

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tubes containing 25 mg of PSA and 150 mg of MgSO4 and dSPE containing GCB (2.5 and

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7.5 mg) were obtained from Restek (Bellefonte, PA, USA). The other QuEChERS salt

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packages including AOAC and EN15662 methods were also from Restek. The ChloroFiltr®

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dSPE tube (2 mL) containing PSA, MgSO4, and 50 mg of ChloroFiltr was from UCT

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(Bristol, PA, USA). Ceramic homogenizers to aid extraction were purchased from Agilent

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Technologies (Seoul, South Korea). Certified organic brown rice, orange, spinach, and

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potato for recoveries and real samples were obtained from the local markets.

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Analytical Standard. Analytical reference (over 360 compounds), internal and quality

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control standards with high purity ( > 98%) were purchased from Sigma-Aldrich (St, Louis,

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MO, USA), Chemservice (West Chester, PA, USA), Wako (Osaka, Japan), Dr. Ehrenstorfer

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(Augsburg, Germany) and Ultra Scientific (North Kingstown, RI, USA). Individual

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pesticide stock solutions (1000 µg/mL) were prepared in acetone or ACN and then 20

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groups of intermediate standard mixtures (containing about 20 pesticides for each group)

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were prepared at 50 µg/mL from each stock solution. Finally, working standard mixture at

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2 µg/mL was used for matrix-matched calibration standards (0.01–1.0 µg/mL) by serial

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dilution. An internal standard, tris(1, 3-dichloroisopropyl)phosphate (TDCPP) was prepared

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at 0.5 µg/mL and the quality control (QC) standards mixture containing triphenyl phosphate

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(TPP), alpha-BHC-d6 and chlorpyrifos-d10 were also prepared at 10 µg/mL.

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Selection of GC Column and instrumental conditions. GC-MS/MS analysis was

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carried out on a Shimadzu GCMS-TQ8040 triple quadrupole system equipped with an

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AOC-20i auto-sampler (Kyoto, Japan). GC/MS solution software (version 4.3) was used for 6


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data processing. A solvent standard mixture (1 µL) at 0.1 µg/mL containing 360 analytes

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was injected onto narrowbore (Rxi-5Sil MS; 30 m × 0.25 mm i.d., 0.25 µm df), and

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microbore column (Rxi-5SIL MS; 20 m × 0.18 mm i.d., 0.18 µm film thickness, df)

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installed in the same GC system, respectively. Oven temperature program for the

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narrowbore was as follows: 70 °C for 2 min, up to 160 °C at 15 °C/min, then up to 260 °C

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at 5 °C/min and finally up to 300 °C at 15 °C/min (held for 8 min). Program for microbore

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column was as follows: 50 °C for 1 min and increased to 200 °C at 25 °C/min, then ramped

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to 300 °C at 10 °C/min (held for 8 min). Total run time was 25.0 min with microbore and

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38.7 min with narrowbore column. To effectively transfer the target analytes to the column,

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the pulsed injection was employed at pressure of 250 kPa with inlet temperature of 280 °C.

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Helium (≥ 99.999%) was used as carrier gas at a constant flow (1.0 mL/min) and argon was

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used as collision gas. The ion source and transfer line temperature were 230 and 280 °C

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respectively. The electron ionization energy was -70 eV and detector voltage was set at 1.4

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kV.

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For MS/MS analysis, two transitions (quantifier and qualifier) were chosen for scheduled

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SRM mode after automatic optimization procedure. The SRM detection window was ± 0.15

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min and dwell times were adjusted automatically based upon loop time (0.15 s) for the

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maximized data acquisition. Quantitation of individual compounds was performed using an

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internal standard based on peak area of quantifier transition. To choose a proper column for

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more efficient analysis, GC run time, peak shapes, and S/N ratios of representative

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pesticides obtained from the narrowbore column were compared with those from the

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microbore.

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Optimization of Sample Preparation. In order to optimize an extraction method, a 7



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preliminary test was performed with brown rice. The powdered sample (5.0 ± 0.1 g)

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fortified at 0.01 mg/kg (n=3) was extracted with different combination of extraction

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solvents, salts, and buffers based on QuEChERS method as follows. : (A) original method

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(ACN), (B) 0.1% formic acid in ACN, (C) 1% formic acid in ACN, (D) 0.1% acetic acid in

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ACN, (E) 1% acetic acid in ACN, (F) AOAC method,22 and (G) EN 15662 method.23 For

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cleanup procedure, general dSPE containing MgSO4 and PSA was employed. Each final

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extract (400 µL) from seven different extraction methods was mixed with ACN (100 µL)

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for matrix-matched quantification. Extraction efficiency of each system was calculated on

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the basis of the matrix-matched standards employing single-point calibration (0.005 mg/kg).

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For the optimization of dSPE cleanup, spinach containing a high amount of chlorophyll

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was used in comparison with several dSPE cleanup procedures. After spiking at 0.01 mg/kg,

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the homogenized spinach samples (n=3) were extracted with ACN (0.1% formic acid) and

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partitioned by MgSO4 and NaCl. After partitioning, supernatant (1 mL) were transferred to

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four different dSPE sorbents as follows: (a) general dSPE (PSA only) (b) 2.5 mg of GCB

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(c) 7.5 mg of GCB and (d) 50 mg of ChloroFiltr. All dSPE sorbents contained 25 mg of

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PSA and 150 mg of MgSO4. Cleanup efficiency of each dSPE sorbent was calculated based

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on the matrix-matched standards employing single-point calibration (0.005 mg/kg).

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Final Optimized Sample Preparation method. Frozen samples (brown rice, orange,

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spinach, and potato) were homogenized with dry ice into fine particles using a blender. The

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sample (10.0 ± 0.1 g) into a 50 mL centrifuge tube was fortified with pesticide standards at

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0.01 and 0.05 mg/kg, and the QC standard (50 µL) at 10 µg/mL was also added for

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monitoring the extraction efficiency. For brown rice, deionized water (5 mL) was added to

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5.0 ± 0.1 g of the powdered samples, allowing soaking for 1 h after fortification. ACN (10 8


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mL) with 0.1% formic acid was added to each tube and the tubes were vigorously shaken

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(1500 rpm) using a Geno Grinder (1600 MiniG SPEX Sample Prep, Metuchen, NJ, USA)

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for 1 min. And then, the tubes were cooled down in an ice bath to prevent the thermal

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degradation of some pesticides due to MgSO4 before adding the salt packet. After the

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mixture was shaken vigorously for another 1 min, the tube was centrifuged at 3500 rpm (5

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min). The supernatant (1 mL) was transferred into a general dSPE tube and vortexed (1 min)

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on a Multi Speed Vortex (MSV-3500, Biosan, Riga, Latvia) before centrifugation at 15000

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rpm (5 min). Finally, the supernatant (400 µL), ACN (50 µL), and an internal standard

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(TDCPP; 50 µL) at 0.5 µg/mL were transferred into a GC vial for GC-MS/MS injection.

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The final concentration of each sample was 0.8 g/mL (0.4 g/mL for brown rice).

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Priming effects. Untreated spinach (10 g) sample was prepared by the final optimized

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extraction procedure. After changing a new deactivated liner, solvent standard mixtures

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(0.1 µg/mL) were analyzed by GC-MS/MS three times. Then, spinach extracts were

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injected with the same GC conditions for priming treatment, followed by solvent standard

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mixture injection again. The priming extracts and solvent standard mixture were injected

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alternately more than two times. The signal intensities obtained from six solvent standard

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mixtures and priming runs were compared to investigate the priming effect on inlet system.

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Validation of Analytical Methods. Method validation was conducted based on the

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criteria of the document SANTE/11945/2015.24 Accuracy and precision of the optimized

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method were evaluated using mean recovery rate (%) and relative standard deviation

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(RSD, %) respectively, at fortification levels 0.01 and 0.05 mg/kg. Since matrix

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components generally result in signal enhancement or suppression, matrix-matched

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standards are used to solve such problems by mixing solvent standard solution and blank 9



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matrix extracts. Therefore, in this study, the matrix-matched standards for calibration (1,

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2.5. 5, 10, 25, 50, and 100 µg/kg) were prepared by adding the solvent standard solution,

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quality control and internal standards to blank matrix extracts. To minimize calculation

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error at low concentration, a weighting regression factor of 1/x was used for quantitation.

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Linearity, limit of quantitation (LOQ) were evaluated by each matrix-matched calibration.

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Matrix-dependent LOQ was determined to be the lowest concentration having S/N ratio of

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a quantifier ion peak above 10.

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Because errors can occur during sample preparation procedures including extraction,

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partitioning, and cleanup, three QC standards (TPP, alpha-BHC-d6, and chlorpyrifos-d10)

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were used as quality control of the data. When recoveries for the QC standards were in the

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range of 80–120%,25 it meant that sample preparation was considered to be properly

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performed. TDCPP was used as an internal standard for quantitation.

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Matrix Effects (ME). ME (%) was calculated as average percent suppression or

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enhancement by comparing slope of calibration curve of the matrix-matched standards with

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that of the solvent only calibration curve in ACN, using the following equation: ME, % =

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Slope of matrix matched calibration curve − 1 × 100 Slope of solvent only calibration curve

RESULTS AND DISCUSSIONS

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Selected reaction monitoring (SRM) optimization. Total number of 360 pesticides was

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chosen after full scan and SRM optimization of initially selected 392 pesticides including

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several metabolites and isomers. Full scan spectrum of individual compound was obtained

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in the mass range of 50–500 m/z. Compounds of no detection (e.g., chloridazone,

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pymetrozine, cyromazine, TCMTB, and inabenfide) or poor response (lufenuron,

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fluoroimide, bistrifluron, anilazine, trinexapac-ethyl, flupyradifuron, cycloprothrin, 10


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probenazole, allethrin, and bioresmethrin) or very low m/z value of base peak ion (73 m/z;

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methoprene) were excluded from the target analytes in this step.

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Based on selectivity rather than signal intensity, specific precursor ions were then selected

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out of the following ions; (1) the base ion with the highest intensity in the scan spectrum

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and (2) the ions with higher specificity to separate the target compound from other

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neighboring pesticides or interferences. For most of the target analytes, several ions could

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be potentially used as one of the precursor ions, but ions with the higher mass (m/z > 200)

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preferred to be selected, whenever available, because those ions generally produced the

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highest S/N ratios for their product ions26 to give minimum matrix interferences. The most

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selective and sensitive transition was used for quantifier and the second most selective was

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for qualifier. In the selected target analytes, the diastereoisomers (17 pesticides) giving two

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or more peaks (e.g., cypermethrin, dimethomorph, and fenvalerate) were quantitated by the

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sum of each peak area. Interestingly, chlorobenzilate (m/z 325.2) and chloropropylate (m/z

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339.2) were not distinguished by SRM transitions due to the analogous structure (one

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methyl group differences) and patterns of fragmentation with the same retention time. The

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details of SRM transitions, collision energies and retention times for 360 pesticides are

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presented in Supporting Table S1.

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Selection of GC Column. To find out an efficient column in terms of better peak shape,

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sensitivity, and shorter analysis time, two columns of 30-m narrowbore (0.25 mm)9-11 and

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20-m microbore (0.18 mm) were compared using solvent standard solution (mixture of 360

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pesticides) at 0.1 µg/mL. The 20-m column saved 15 min of the analysis time compared

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with the 30-m column based on the latest-eluting compound, as expected. Figure 1 shows

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individual chromatograms of representative compounds (profuralin, fenthion, procymidone, 11



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and bifenthrin) obtained from the 30-m (Figure 1-I) and the 20-m column (Figure 1-II).

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The chromatograms from the 20-m column showed higher peak height and much higher

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S/N compared with the 30-m column conditions, resulting in much lower LOQ.

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Furthermore, despite the short runtime (20 min) for 360 pesticides, sufficient data points (>

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15) for each peak were obtained due to the fast scan speed. Judging from these results, the

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microbore column was chosen in this study for higher sensitivity and shorter rum time.

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Priming effects. In the GC system, it is typically observed that the target molecules in

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solvent standards could be adsorbed to the active sites (e.g., silanol group) of a liner or

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column, causing low responses.27 As a different technology from analyte protectants, the

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DG SANTE guideline24 recommended that, after a new column or a new inlet liner is

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installed, a couple of matrix-matched blanks should be injected before running matrix-

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matched standards because the blank would deactivate the GC system, leading to the

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maximized transmission of target compounds to the detector. This phenomenon occurs due

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to “priming effects”, where matrices in the samples can mask the active sites of a new

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column or inlet liner.27-28 In this study, solvent standards, blank spinach and orange extracts

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were injected into the GC system after a new inlet liner (deactivated) was installed to

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evaluate the practical priming effect. The results showed that when the standard mixture

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was injected after the spinach extract injections, the peak responses of all the target

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compounds increased compared with that from the first injection after a new liner was

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installed (Figure 2), however, priming effect was not observed after the orange extract

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injections (Supporting Figure S1). These findings indicated that the active sites in a new

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GC liner seemed to be effectively masked by chlorophyll or other ingredients with

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nonvolatile property. It should be also noted that the increased peak response maintained 12


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over the analysis time even after one priming injection. Therefore, a priming injection after

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changing a liner must be essential to achieve analytical results with higher sensitivity and

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precision. In this study, a priming injection with spinach extracts was always applied after a

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GC liner was changed.

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Modification of Sample Extraction Solvent. Citrate (EN15662)23 and acetate buffered

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QuEChERS methods (AOAC 2007.1)22 are usually used to efficiently extract hundreds of

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pesticides including base-sensitive pesticides, and several modified QuEChERS methods

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were also reported.16,

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extraction solvent to protect pH-sensitive pesticides.32-34 In addition, several studies used

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formic acid to stabilize the pesticide in standard working solution or final extracts.12, 18, 32, 35

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In this study, therefore, addition of formic acid to extraction solvent was evaluated to

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increase the recovery efficiency of the target pesticides.

29-31

Formic as well as acetic acid10 was used as an additive in

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Table 1 shows the ratio of pesticides, which satisfied the recovery ranges between 70–

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120% with RSD ≤ 20% in the applied extraction procedures. Original method A, 0.1%

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formic acid method B, and citrate buffered method G showed relatively better recoveries

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and repeatability than the others did. No great differences in the number of the analyte with

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satisfactory rates were also observed, showing 93.6% (337 analytes) from method A, 93.9%

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(338 analytes) from method B, and 93.6% (337 analytes) from method G. On the other

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hand, ACN extract with 1% formic or 1% acetic acid gave undesirable tailing peaks for

288

some compounds such as penycuron, simetryn and terbutryn. Flusilazole exhibited broad

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peaks (Supporting Figure S2) with unstable recoveries. This result was consistent with the

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previous studies, reporting that PSA may react with acid rather than absorb matrix

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interferences.10, 36 A high amount of acid in extraction solvent possibly prevented PSA from 13



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removing matrix interferences, leading to deterioration of peak shapes.

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Base sensitive pesticides such as tolylfluanid, dichlofluanid and chlorothalonil, still

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showed poor recoveries despite lowered pH in extraction solvent by AOAC method and 1%

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acetic or formic acid. For example, recoveries of tolylfluanid from AOAC method, 1%

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formic and acetic acid (25.3, 18.4 and 26.4%, respectively) were slightly higher than those

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from other extraction solvents (< 10%) but still gave poor recoveries (< 30%). Therefore,

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ACN with 0.1% formic acid was selected as optimized extraction solvent because the

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appropriate amount of formic acid in extracting solvent was expected to give stable and

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consistent results for applying various sample types without additional buffer salts.

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Optimization of Sample Cleanup with dSPE. After extraction solvent was optimized

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as above, various dSPE sorbents were tested for optimum cleanup. Several types of dSPE in

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QuEChERS are generally adopted as cleanup procedures for robust and simple pesticide

304

analysis. Green samples with a high amount of chlorophyll (e.g. lettuce, spinach or tea) are

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challenging in multi-pesticide analysis because the chlorophyll in final extracts may cause

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chromatographic problem as well as increased GC maintenance cost.12,

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pigments like chlorophyll, GCB sorbent has been used11, 38-41and recently, ChloroFiltr was

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introduced for the same purpose.12, 42 In this study, using spinach (a representative green

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sample) extract with ACN with 0.1% formic acid, four different types of dSPE sorbents

310

were investigated to compare their effectiveness in terms of removing co-extractives from

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green matrices, chromatographic separation, and recoveries.

37

To remove

312

It was observed that the green color of chlorophyll was greatly decreased in final extracts

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by GCB or ChloroFiltr (data not shown) as expected but at the same time, recoveries of

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some pesticides including planar pesticides (e.g., chinomethionat, coumaphos, cyprodinil, 14


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dimethipin, hexachlorobenzene and pentachlorothioanisole) were greatly reduced (Table 2).

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This result was consistent with previous studies, showing that GCB interacted with not only

317

chlorophyll but also planar structure pesticides.7, 39, 41, 43 The planar solvents (e.g., toluene)

318

are known to enhance the recoveries of planar pesticide in GCB cleanup8,

319

undesirable matrix impurities may be also extracted.44 Furthermore, it should be noted that

320

addition of large amount of acid can have an even larger effect on the clean-up procedure

321

than toluene, as shown by Lehotay45. After ChlorFiltr cleanup, some pesticides such as

322

bendiocarb, edifenphos, ethiofencarb, formothion, pentachlorothioanisole and quintozene

323

were not even recovered, and many compounds showed deteriorated peak shapes possibly

324

due to interference from ChlorFiltr. If those pesticides are not target analyte in the analysis,

325

GCB or ChloroFiltr can be used as an effective chlorophyll-remover.

10, 43

but the

326

Considering acceptance criteria for accuracy and precision (70–120%, RSD ≤ 20 %), the

327

general dSPE containing PSA gave a slightly higher satisfactory ratio for overall pesticides.

328

The percentage of acceptance criteria were 93.8% in PSA only, followed by 2.5 mg of GCB

329

with PSA (92.2 %), 7.5 mg of GCB with PSA (85.5 %) and ChloroFiltr with PSA (74.5 %).

330

To demonstrate whether the remaining chlorophyll and interferences in the sample

331

extract affect analysis after the simple general dSPE containing PSA clean-up, spinach

332

samples (0.01 mg/kg of matrix-matched standard) were injected into GC 50 times,

333

consecutively. The results (Supporting Figure S3) showed that the average area of 360

334

pesticides was not changed with 3.0% of RSD. These data indicated that co-extractives

335

(chlorophyll) from the spinach would not affect repeatability or recoveries in routine

336

analysis as Lehotay et al. reported that the chlorophyll had no influence on the GC-MS

337

analysis.10 Furthermore, it was expected that chlorophyll in extracts may help to keep the 15



338

priming effects mentioned above. As a result, it was confirmed that the general dSPE

339

containing PSA and MgSO4 was the best cleanup option for this multi-residual analysis.

340

Finally, the general dSPE was used for the method validation in this study.

341

Method Validation. To validate the optimized sample treatment method (ACN with 0.1%

342

formic acid extraction and clean-up with the general dSPE containing PSA and MgSO4),

343

recoveries of each compound were investigated by fortifying 360 pesticides into four

344

untreated samples (brown rice, orange, spinach, and potato) at 0.05 and 0.01 mg/kg (n=6).

345

Matrix-dependent LOQ and linearity (r2) were calculated by matrix-matched calibration

346

from each commodity (Supporting Table S2). For the most pesticides (349, 344, 349, and

347

347 in brown rice, orange, spinach, and potato, respectively) out of 360 compounds, LOQs

348

were < 0.01 mg/kg. Twelve pesticides (binapacryl, captafol, captan, carbofuran,

349

carbosulfan, cinmethylin, dichlofluanid, hexythiazox, imazalil, propargite, tolfenpyrad, and

350

vernolate) were not separated properly due to matrix interference, degradation and broad

351

and tailing peak shape. Base-sensitive and thermally unstable compounds (e.g., captan,

352

captafol, dichlofluanid, and imazalil) gave no detectable peaks at low concentration in all

353

the matrices, which was consistent with the results from previous studies.10, 32, 36, 41, 46 The

354

linear correlation coefficients (r2) were > 0.99 within the range of 1–100 µg/kg for all the

355

pesticides except chlorothalonil, etridiazole, fluazinam, nitrapyrin, phenothrin, and

356

problematic compounds mentioned in LOQ estimation.

357

Based on the acceptability criteria of DG-SANTE guideline24, the recoveries in four

358

commodities were summarized in Figure 3. The percentages (of 360 pesticides) satisfied

359

the validation criteria (recovery; 70 to 120% with RSD < 20%) were in the range of 88.6–

360

95.3% (0.01 mg/kg level) and 93.6–97.2% (0.05 mg/kg level), indicating that excellent 16


Page 16 of 38

Page 17 of 38


361

results were achieved. Details of the accuracy and precision data in all of the samples and

362

analytes are also presented in Supporting Table S3.

363

Because of low sample amount (5 g) of brown rice comparing to the other crop samples

364

(10 g), recoveries from brown rice sample at the low spiking level (0.01 mg/kg level)

365

showed a relatively low frequency (88.6%). For example, at the low spiking level in brown

366

rice, some compounds such as bendiocarb, ethiofencarb, fosthiazate, phosphamidon,

367

tetrachlorvinphos, and zoxamide gave low signal intensities, resulting in insufficient

368

recoveries, however these pesticides at higher spiking level (0.05 mg/kg) were successful

369

quantified.

370

Apart from the problematic pesticides in LOQ estimation and linearity calculation, some

371

pesticides including carbaryl, chlorothalonil, cafenstrole, cyflumetofen, and folpet gave

372

lower recoveries in all the crop samples. Chlorothalonil is known to be degraded in

373

QuEChERS methods due to its base-labile property9-10, 37 and it was reported that the ethyl

374

acetate extraction or acetone extraction with EDTA as a stabilizing reagent could improve

375

the recoveries of chlorothalonil.47-48 Carbamate pesticides (e.g., carbaryl and carbosulfan)

376

easily break down on GC injector, causing relatively low responses and unstable recoveries

377

in GC-MS/MS. Therefore, it was concluded that these pesticides are more LC amenable as

378

reported in the other studies. 25, 43, 49-50 Ethoxyquin and fluazinam also gave poor recoveries

379

in all the matrices except orange due to matrix interferences, as previously reported.36, 38, 51

380

Real samples including domestic or imported agricultural produce (brown rice (5),

381

spinach (3), oranges (4), and potatoes (5)) were analyzed using the optimized method. Out

382

of 360 pesticides, 14 pesticides were identified in 11 incurred samples (Supporting Table

383

S4). No pesticide was detected in potato samples. The levels of pesticides identified in all 17



384

samples were lower than MRLs by Korean legislation.52 Trace levels of several pesticides

385

including azoxystrobin, difenoconazole, fenoxanil, tebuconazole, thifluzamide, triazophos,

386

hexaconazole, and isoprothiolane were found in four brown rice samples. Dimethomorph

387

was detected in all the spinach samples (0.004–7.60 mg/kg). Indoxacarb was also found in

388

one spinach sample with the highest concentration (0.81 mg/kg).

389

Matrix Effect. ME (%) is a major concern in GC-MS/MS because it has been observed

390

significantly due to interaction between active sites of liner or column and target analytes or

391

matrix components. The ME depends on the nature of pesticide, extraction method, and

392

analytical instrument as well as the sample matrix.53-55

393

To evaluate the ME, the slope of each matrix-matched calibration curve for individual

394

pesticides was compared with that from solvent-only calibration curve. A positive value of

395

ME was considered as signal enhancement, whereas negative value was considered as

396

signal suppression. Figure 4 shows the distribution of MEs of the target analytes in four

397

matrices. Absolute values of ME are listed in Supporting Table S3. Except a few

398

pesticides, the significant enhancement was observed for the most of the target analytes in

399

all the matrices. When the MEs are divided into several ranges (1–300%) (Figure 4), many

400

out of 360 pesticides analyzed in brown rice (40% out of 360 pesticides), orange (38%),

401

and spinach (38%) were in the 50–100% ME ranges. However, 54% of the target pesticides

402

in potato was in the range of 0–50%, indicating relatively low MEs. This was also

403

consistent with other studies showing that potato gave a relatively low ME in the GC

404

analysis.41, 56 The strong enhancements (more than 100% of ME value) were observed for

405

43, 40, 37, and 23% of the target compounds in brown rice, orange, spinach, and potato,

18


Page 18 of 38

Page 19 of 38


406

respectively. These results strongly confirmed the importance of matrix-matched

407

calibration for proper quantitation of compounds. High matrix effects in GC-MS/MS were not surprising as previous studies showed.18, 41,

408 409

57

410

and metal ion), leading to decreased signal intensity. On the other hand, matrix components

411

could shield active sites and help to deliver pesticide to GC detector. Consequently,

412

increased peak intensities were obtained in matrix matched standard. The use of analyte

413

protectant (AP) has been known to compensate the matrix effect, providing improved peak

414

intensity and shape. However, we did not use the AP because it requires additional

415

analytical step (adding AP solution) and specific management for a GC syringe to prevent

416

contamination by AP as well as it could increase the undesirable complexity in

417

chromatograms.41 Because pesticide-free matrices or the exactly same matrix as the target

418

sample is not always available for preparation of matrix-matched standards in routine

419

analysis, AP would play an important role in decreasing matrix effects.

420

Supporting Information Available: The optimized GC-MS/MS parameters; Matrix-

421

dependent limits of quantitation (LOQs), linearity, and matrix effects (MEs); Validation

422

data and matrix effects (MEs); Result of real sample analysis (Table S1-S4), Relative peak

423

area of orange extracts for priming effects; SRM chromatograms in different extraction

424

solvents; Distribution of relative peak area during 50 consecutive injections of spinach

425

matrix matched standard (Figure S1-S3).

Many active sites presented in the GC system might interact with analytes (e.g. silanol

19



426

FUNDING SOURCES

427

This study was carried out under the financial support by the Korean Ministry of Food and

428

Drug Safety (13162foodsafety010).

429

NOTES

430

The authors declare no competing financial interest.

20


Page 20 of 38

Page 21 of 38

431 432 433 434


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(56) Uclés, S.; Belmonte, N.; Mezcua, M.; Martínez, A. B.; Martinez-Bueno, M. J.;

621

Gamón, M.; Fernández-Alba, A. R., Validation of a multiclass multiresidue method and

622

monitoring results for 210 pesticides in fruits and vegetables by gas chromatography-triple

623

quadrupole mass spectrometry. J. Environ. Sci. Health, Part B 2014, 49, 557-568.

624

(57) Lozano, A.; Rajski, Ł.; Uclés, S.; Belmonte-Valles, N.; Mezcua, M.; Fernández-

625

Alba, A. R., Evaluation of zirconium dioxide-based sorbents to decrease the matrix effect in

626

avocado and almond multiresidue pesticide analysis followed by gas chromatography

627

tandem mass spectrometry. Talanta 2014, 118, 68-83.

628

29



629

FIGURE CAPTIONS

630

Figure 1. SRM TIC of 360 pesticides at 0.01 µg/mL solvent standard mixture (a) and

631

individual chromatograms of profuralin (b), fenthion (c), procymidone (d), and bifenthrin (e)

632

corresponding to 30-m narrow bore (I) and 20-m microbore column (II).

633 634

Figure 2. Priming effects after replacing a new inlet liner in GC. Relative peak area of

635

solvent standard mixture (0.1 mg/kg) before (grey bars) and after (dark grey bars) spinach

636

extract injections. The intensities were compared after each area was normalized by the

637

area of the first injection as 100%. The top arrows indicated the sequence of sample

638

injection.

639 640

Figure 3. Percentages of pesticides satisfying the recovery rates of 70-120% and RSD≤20%

641

at 0.01 and 0.05 mg/kg spike levels, using the optimized method in this study.

642 643

Figure 4. Distribution of matrix effects (MEs) in each commodity. The MEs were assessed

644

by the slope ratios of the matrix-matched calibration curves to solvent-only calibration

645

curves.

30


Page 30 of 38

Page 31 of 38


646

TABLES

647 648 649 650

Table 1. Number of pesticides and percentages with recoveries between 70–120 % with RSD ≤ 20% in recovery results from different extraction solvents for brown rice sample (spiked at 0.01 m/kg, n=3). no. of method

extraction solvent

A

added salts and buffers in partitioning

no. of analytes

% of analytes

ACN

337

93.6

B

0.1% formic acid in ACN

338

93.9

C

1% formic acid in ACN

297

82.5

D

0.1% acetic acid in ACN

321

89.2

E

1% acetic acid in ACN

295

81.9

F

1% acetic acid in ACN

286

79.4

G

ACN

337

93.6

4 g of MgSO4 and 1 g of NaCl

1.5 g of sodium acetate and 6 g of MgSO4 4 g of MgSO4, 1 g of NaCl, 1.5 g of trisodium citrate dehydrate, and 0.5 g of disodium hydrogen citrate sesquihydrate

31



Page 32 of 38

651

Table 2. Recovery results for representative pesticides including planar pesticides in

652

spinach matrix (0.01 mg/kg spiking level, n=3) from clean-up with different types of dSPE

653

sorbents. pesticdes

PSA

bendiocarb

49.1 (16.3)

carbofuran chinomethionat

a

a

107.5 (3.9)

96.3 (4.4)

90.3 (5.3)

81.1 (5.7)

70.7 (8.5)

60 (16.9)

26.9 (27.6)

41.3 (51.5)

100.7 (7.8)

87.4 (6.2)

72.3 (12.9)

58.6 (19.5)

99.3 (1.3)

94.1 (3.1)

80.9 (11.4)

81.6 (16)

a

102.5 (3.3)

97.3 (3.8)

91 (4.1)

61.5 (7.7)

a

dimethipin

84.4 (7.9)

84.5 (16.3)

76.3 (4.9)

65.7 (3.9)

edifenphos

98 (8.4)

73.3 (7.1)

54.6 (44)

-b

ethiofencarb

81.9 (5)

68 (13.8)

44.1 (29.9)

-b

formothion

83.1 (4.6)

56.3 (27)

47.3 (21.2)

-b

coumaphos cyprodinil

a

87.7 (5.4)

77.5 (11.9)

68.3 (16.1)

83.1 (0.7)

a

77.3 (6.8)

64.7 (22.7)

65.5 (22.6)

-b

Quintozene % of pesticides (70-120%, ≤20% RSD)

83.1 (5.6)

69.8 (12.2)

58.4 (23.7)

-b

93.8

92.2

85.5

74.5

hexachlorobenzene

pentachlorothioanisole a

654 655

PSA+ 50 mg ChloroFiltr -b

a

chlorothalonil

a

Recovery, % (RSD, %) PSA+ PSA+ 2.5 mg GCB 7.5 mg GCB 44.7 (22.2) 32.6 (36.9)

b

Planar structure pesticides, Pesticides which couldn’t be quantitated due to matrix interference.

32


Page 33 of 38


656

FIGURE GRAPHICS

657

Figure 1

658

33



659

Figure 2

660 661

34


Page 34 of 38

Page 35 of 38

662


Figure 3

663

35



664

Page 36 of 38

Figure 4 brown rice

orange

spinach

potato

60 54

Percentages of pesticides, %

50 40 40

38 38

30 20 21

21

20 12

19 17 17

17 16

14

11 7 7 6 5

10 4 0

7

2 2

0

300

Page 37 of 38

666


GRAPHIC FOR TABLE OF CONTENTS

667

37



222x138mm (150 x 150 DPI)


Page 38 of 38

Rapid and Simultaneous Analysis of 360 Pesticides in Brown Rice

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