Effective Monitoring of Fluxapyroxad and Its Three Biologically Active

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Article

Effective monitoring of fluxapyroxad and its three biologically active metabolites in vegetables, fruits, and cereals by optimized QuEChERS treatment based on UPLC-MS/MS Xixi Chen, Fengshou Dong, Jun Xu, Xingang Liu, Xiaohu Wu, and Yongquan Zheng J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b03253 • Publication Date (Web): 27 Oct 2016 Downloaded from http://pubs.acs.org on October 29, 2016

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Journal of Agricultural and Food Chemistry

Effective monitoring of fluxapyroxad and its three biologically active metabolites in vegetables, fruits, and cereals by optimized QuEChERS treatment based on UPLC-MS/MS Xixi Chen1, Fengshou Dong1*, Jun Xu1, Xingang Liu1, Xiaohu Wu1, Yongquan Zheng1,* 1

State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection,

Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China

* Correspondence: Prof. Fengshou Dong (Tel:+86-01-62815938, Fax: +86-01-62815938, E-mail: [email protected];)

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ACS Paragon Plus Environment


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Abstract

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Qualitative analysis and quantification of pesticide residue in foodstuff are essential to our health

3

in daily life, especially aimed at their metabolites, which may be more toxic and persistent. Thus, a

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valid analytical measure for detection of fluxapyroxad and its three metabolites (M700F002 (C-2),

5

M700F008 (C-8), M700F048 (C-48)) in vegetables (cucumber, tomato, and pepper), fruits (grape,

6

apple), (and cereals (wheat, rice) was developed by UPLC-MS/MS with negative ion mode.

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Among, targets compounds were extracted by acetonitrile contain 0.2% formic acid (V/V)and the

8

extractions were cleaned up by octadecylsilane sorbents. The limits of quantitation and

9

quantification were less than 0.14 µg kg-1 and 0.47 µg kg-1 in seven matrices. Furthermore,

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recoveries at levels of 0.01, 0.05 and 0.1mg kg-1 ranged from 74.9% to 110.5% with relative

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standard deviations ≤ 15.5% (n=5). The method is validated to be effective and robust for the

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routine supervising of fluxapyroxad and its metabolites.

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Keywords: Fluxapyroxad; metabolites; QuEChERS; UPLC-MS/MS

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Introduction

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Fluxapyroxad

(3-(difluoromethyl)-1-methyl-N-(3',

4’,

and

5’-trifluorobiphenyl-2-yl)

16

pyrazole-4-carboxamide) (Figure1) is a new synthetic broad-spectrum fungicide introduced in

17

2012 by BASF Group for the control of fungal diseases. It works by inhibiting succinate

18

dehydrogenase in complex II of the mitochondrial respiratory chain, resulting in inhibition of spore

19

germination, germ tubes and mycelia growth within the fungus target species. Fluxapyroxad is the

20

family of succinate-dehydrogenase-inhibitor (SDHI) fungicides.

21

generation SDHIs, fluxapyroxad was expected to be more effective against numerous fungal plant

22

pathogens. Preliminary reports suggest that the fluxapyroxad has high efficacy of against several fungi3,

23

4

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fungi on vegetables, fruits, and cereals.

1, 2

. Compared to the first

. And it has been registered on a global scale for prevention of Botrytis cinerea and several other

25

Generally, chemical pesticides will gradually degrade to form metabolites in the environment

26

through metabolism, hydrolysis, and photolysis approaches after their application. The metabolites

27

vary with the different matrices in the environment. For example, the major metabolites of

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fluxapyroxad in soil are M700F001, 3-(difluoromethyl)-1-methyl-1H-pyrazole-4-carboxylic acid

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(C-1) and M700F002, 3-(difluoromethyl)-1H-pyrazole-4-carboxylic acid (C-2), while M700F007,

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3-(difluoromethyl)-1-methyl-1H-pyrazole-4-carboxamide (C-7) in the surface water. And

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M700F002,

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3-(difluoromethyl)-N-(3’,4’,5’-trifluoro[1,1-biphenyl]-2-yl)-1H-pyrazole4-carboxamide

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and

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3-(difluoromethyl)-1-(beta-D-glucopyranosyl)-N-(3’,4’,5’-trifluoro[1,1-biphenyl]-2-yl)-1H-pyrazo

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le-4-carboxamide (C-48) (Figure1) are the dominant metabolites of fluxapyroxad in plants.

3-(difluoromethyl)-1H-pyrazole-4-carboxylic

acid

(C-2),

M700F008, (C-8),

M700F048,

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Besides, the research showed that the metabolites are present in significant amounts in some plant

37

matrices, which is of comparable toxicity with parent 5.

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However, the residue determination of fluxapyroxad focused on the parent compound and there

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is no analytical method aiming at the metabolites in food matrices have been reported. Moreover,

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the maximum residue levels (MRLs) of fluxapyroxad have not been fully established in many

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countries and organizations and the need for a highly sensitive analytical method has been

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demanded, especially with the increasing attention in food safety in our daily life. Hence, it is

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imperative and crucial to establish a reliable, sensitive and effective analytical method for

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simultaneous measurement of fluxapyroxad and its three metabolites residue in plant samples

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including vegetables, fruits, and cereals for the effective monitoring of metabolic behavior and

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proper assessment of human exposure to fluxapyroxad through food products.

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At present, the reported analytical methods for measuring the residue of fluxapyroxad are scarce,

48

especially involved the simultaneous determination of the parent and its metabolites. The paper

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reported by Li et al. was concerned by fluxapyroxad and its metabolites, which is found in the

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environment matrices including soil and surface water6. Besides, only a few articles have studied

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the analytical determination of the parent fluxapyroxad in food with high performance liquid

52

chromatography/tandem mass spectrometry (HPLC-MS/MS)

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degradation behavior in soil samples9, 10. Therefore, no investigation focused on monitoring the

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parent and its metabolites in plant or foodstuff have been reported. It could be lack of effective and

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rugged analytical methods. The technique of QuEChERS method has been an attractive alternative

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for traditional sample pretreatment, especially in multipesticide residue analysis since 200311-14.

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Also, some reports demonstrated that the QuEChERS method using modified acetonitrile mixture

7,

4


8

and the evaluation of

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solution as extraction solvent has highly effective for the detection of pesticide residues in

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foodstuff15,

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(UPLC-MS/MS) has been shown to be a potent technique for pesticide residue detection in many

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matrices17, 18. And the use of Xevo TQ-S tandem Quadrupole Mass spectrometer, which equipped

62

with a novel StepWave ion guide, enable improved ion sampling in the source and ion transfer

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efficiency resulting the higher sensitive and stability.

16

. Moreover, ultraperformance liquid chromatography/tandem mass spectrometry

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Therefore, the objective of this paper was to build an approach for fluxapyroxad and its 3

65

metabolites in vegetables, fruits, and cereals by UPLC-ESI-MS/MS. A modification of the

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QuEChERS method based on the evaluation of the applicability for analysis of 4 target

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compounds in food samples was proposed, including the choice of extraction solvent and

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optimization of purification effect with different adsorbers such as primary secondary amine

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(PSA), octadecylsilane (C18), and graphitized carbon black (GCB). For all we know, this is the

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first report of an analytical method about determination fluxapyroxad and its three plant

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metabolites in food at the same time.

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Materials and methods

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Reagents and chemicals

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99.7% of Fluxapyroxad standard (, purity) was purchased from Dr. Ehrenstorfer (Augsburg,

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Germany). M007F002 (97.0%, purity), M007F008 (97.0%, purity) and M007F048 (97.0%, purity)

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were provided by Boyners Pharmaceutical Co. LTD (Shanghai, China). Chromatographic-grade

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acetonitrile and formic acid were obtained from Sigma-Aldrich, Steinheim, Germany. Others is

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analytical reagent including acetonitrile, formic acid, NaCl, MgSO4were provided by Bei-hua

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Fine-chemicals Co. (Beijing, PRC). A Milli-Q system (Bedford, MA, USA) is used to prepare

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ultra-pure water. The sorbents of C18 (40µm), PSA (40µm), and GCB (40µm) were provided by

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Agela Technologies Inc. (Beijing, China). .

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Stock standard solutions of fluxapyroxad and its metabolites (100 mg L -1) were diluted with

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chromatographic grade acetonitrile. The calibration graph of standard solution (5, 10, 20, 50, 100,

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and 200 µg L-1) were made through

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Likewise, the same concentrations range of matrix-matched standard solutions were obtained by

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adding an appropriate volume of blank sample extracts (cucumber, pepper, tomato, apple, grape,

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wheat, and rice) to each corresponding standard solution. All solutions were placed at 4 °C under

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dark condition, and no degradation occurred in 3 months. These seven blank samples were

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obtained from one experimental base in LangFang, Hebei province, where were not polluted by

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the target pesticide. Authentic samples were collected from a local market in Beijing. And all

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samples were homogenized, divided into subsamples, kept in the dark at -20 °C until analysis.

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Instrument

serial diluting the stock solution

with acetonitrile.

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The separation of the 4 compounds was conducted on a Waters ACQUITY HUPLC system

94

(Milford, MA, USA) with a Waters ACQUITY HUPLC HSS T3 column (50 mm×2.1 mm, 1.8 µm

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particle size), whose temperature was hold at 40 °C. The mobile phase was composed of water

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(solvent A) and acetonitrile (B) with

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follows: started with 10% component B (90% A), then increased linearly to 70% B (30% A) in 1

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min, held constant for 1.5 min, afterwards returned to the initial conditions in 0.1 min, and remain

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1.4 min for re-equilibrate, and whole run-time is 4 min. The injected volume was 3 µL. The

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elution order of 4 compounds were C-2 (1.06 min), C-48 (1.41min), C-8 (1.71 min), and

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fluxapyroxad (1.86 min).

a flow rate of 0.3 mL min-1. The gradient program was as

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Then the separated compounds were injected directly into a Waters Xevo TQS tandem mass

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spectrometer (Waters Corp, Milford, MA, USA) in the ESI- mode. 99.95% nitrogen and 99.99%

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argon were respectively used as the nebulizer gas and the collision gas with a pressure of 3.2×10-3

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mbar in the T-Wave cell. The measuring conditions for target compounds were optimized as below:

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2.8 kV for the capillary voltage and temperature of ion source and desolvation were kept at 120 °C

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and 350 °C, respectively. The gas flow of cone and desolvation were set as 50 L h-1 and 600 L h-1.

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Multi-reaction monitoring (MRM) was used to detect target pesticides with a dwell time of 36 ms.

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Related parameters of the MRM transition were also optimized individually for each target

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pesticide (Table 1). The Masslynx NT V.4.1 (Waters, USA) software was applied to process and

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acquire data .

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Sample treatment

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The frozen samples were thawed at the room temperature at first. Then, in total 10 g of blank

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matrices were weighed into a 50 mL polypropylene centrifuge tube with screw caps and were

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added a certain volumes of standard solution. Then the tubes containing the target pesticides were

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vortexed for 30 s and stand for 2 h at room temperature to guarantee the pesticide disperse evenly

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in the matrices. Next, 5 mL water (only for rice and wheat samples) and 10 mL acetonitrile contain

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0.2% formic acid (v/v) were added. The sample tubes were shaken with an oscillation frequency

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of 1350 min-1 for 5 min. Subsequently, 4 g anhydrous MgSO4 and 1 g NaCl were added. Next, the

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process of shaken was conducted again for 1 min and centrifuged for 5 min at RCF 2811 × g. Then,

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1.5 mL of the acetonitrile layer was transferred into a single-use centrifuge tube containing 50 mg

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C18 and 150 mg anhydrous MgSO4. And the samples were vortexed for 1 min and centrifuged at

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RCF 2400×g for 5 min. Afterwards, 1 mL aliquot of the resulting supernatant was transferred to

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a single-use 2 mL centrifuge tube to be dried with a gentle nitrogen stream and was then

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reconstituted with 1 mL work solution containing ACN and water (20:80, v/v). At last, the tube

126

was vortexed again for 1min, and the resulting solvent was filtered through 0.22 µm nylon syringe

127

filter into an autosampler vial for instrument analysis.

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Method validation

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The developed approach was validated to evaluate its performance including the parameters

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of specificity, accuracy, precision, linearity, matrix effect, and limit of detection (LOD), limit of

131

quantification (LOQ), and stability according to the conventional validation procedure.

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Blank samples were tested to vonfirm the absence of interfering peaks near the retention time

133

of the analytes. The calibration curve of standard solutions and different matrix-matched standard

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solutions were made ranging from 5 to 200µg L-1 to evaluate the linearity of the method. Table 2

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showed the data of the linear regression equations including slope, intercept, standard deviations

136

and the correlation index (R2). The LODs of the fluxapyroxad and its metabolites are the

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concentrations that produce a signal-to-noise (peak to peak) ratio of 3, whereas the LOQs are

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calculated based on a signal-to-signal ratio of 10.

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Spiked recoveries were performed to confirm the accuracy and precision of the method. Five

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replicates of the spiked samples at three levels of 0.01, 0.05 and 0.1 mg kg-1 of each compound

141

were conducted at 3 different days. The sample were treated follow the above-mentioned

142

procedure. The precision (RSD) was calculated by the intra- and inter-day assays.

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The stability of 4 pesticides was investigated both in the solvent and the matrix. The stability

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of the stock solutions and the spiked samples (0.05 mg kg-1) was tested monthly. All the samples

145

of the stability test were kept at -18 °C.

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Results and discussion

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UPLC-MS/MS Optimization

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. The MRM mode was applied to analysis the 4 compounds, also its parameters were optimized

149

to yield the highest response by injecting the individual pesticides.

All compounds displayed

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abundant [M-H] – ions. And ESI in negative mode showed higher responses compared to that in

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positive mode. The confirmation can be conducted according to the retention time, the two

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selected ion transitions and their relative abundance. The optimum MS/MS conditions of

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fluxapyroxad and its three metabolites were obtained (Table 1).

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Optimization of chromatography

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The separation conditions were studied by injecting 5µL of the fluxapyroxad and its three

156

metabolites mixture working standard solutions at level of 0.05 mg kg-1, on the HSS T3 column

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(50 mm×2.1 mm, 1.8 µm particle size). However, the compound C-2 in working solution prepared

158

with ACN has extremely weak response. Thus, the optimization with ACN, ACN-0.2% formic

159

acid aqueous solution (10:90, V/V), and ACN-water (10:90, V/V) as working solvent was

160

conducted. Result showed that the last two kind of working solution is much better than that

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prepared with ACN, but there is no significant differences between the last two kinds of working

162

solution. And the same conclusion was arrived in the paper reported by Li et al. 6. However,

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different ratios between ACN and water were not further discussed in that work, which influence

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the retention behavior to varying degrees because of the respective different physicochemical

165

properties. As showed in figure 2, the response of the analyte changes with the solvent ratio

166

changes. And it allow the instrument to achieve the most suitable conditions for all compounds

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when there is 20% ACN in working solution. Therefore, the ACN-water (20:80, V/V) was finally

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selected to work solution. Furthermore, effect of different mobile phase compositions (ACN/water,

169

methanol/water, ACN/0.2% formic acid aqueous solutions) on the retention behave of analyte

170

were evaluated. The result showed that 4 compounds were well separated using ACN and water,

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and the retention time of this pesticide parent and its three metabolites was all less than 2.0 min.

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Optimization of the extraction solvents

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To obtain the optimal extraction effect of the target pesticides from different matrices, the

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extraction solvent were investigated. In the preliminary experiment, a low recovery (≤ 50%) of

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the metabolite C-2 was obtained for cucumber, pepper, wheat, and rice, while for apple, grape, and

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tomato the relatively satisfactory recovery were obtained with pure acetonitrile extraction. It may

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be because the latter created a slightly acidic environment in favor of the extraction of C-2. Then a

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low proportion of acid (0.2%, V/V) was mixed during the extract procedure to investigate if the

179

acidifying effect of extracting environment make the result different. Comparison study among the

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cucumber, wheat and tomato was be done, as showed in figure 3, the better recoveries were got

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than that using ACN for cucumber and wheat. Also, all the recoveries percentage of the target

182

compounds meet the requirement. Thus, the effect of this extract solution was investigated to other

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matrices. It was proved to be suitable for all target analytes in all matrices. From the consideration

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of the results and economic factors, ACN with 0.2% formic acid was finally selected as the

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extraction solution.

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Optimization of the cleanup procedure

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In order to get better clean sample reducing the interferences, three kinds of common used sorbent,

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PSA (50mg), C18 (50mg), and GCB (10mg) plus PSA (40 mg), were respectively selected in

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sample preparation to investigate the remove effect of impurities in all matrices. Different

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mechanism exist in different types of sorbent. PSA has a better advantage to remove polar

191

compounds like sugars and fatty acids, and C18

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compounds. And as for GCB, is mainly used to adsorb hydrophobic compounds, specifically the

193

better efficiency in removing pigment contents 19. Finally, the result showed that the recovery and

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RSD were both accepted for all the analytes while C18 was used in all matrices (Figure 4).

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However, for the metabolite C-2, the recovery was very low (< 70%) when PSA was used,

196

especially in the wheat and rice matrix treatment. Perhaps because chemical structure of C-2, such

197

as the carboxyl, made it easily to be absorbed by PSA. In the case of GCB (10mg), also the poor

198

result was obtained for C-2 and C-48. It is turned out that the GCB may increase the absorption of

199

metabolite C-48. Besides, because of the ultimate work solution including 80% water, there seems

200

no need to remove pigment. Therefore, the C18 (50mg) was selected to clean up the seven matrix

201

(apple, grape, cucumber, tomato, pepper, wheat and rice) in this study.

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Method validation

203

Linearity, LOD, and LOQ

204

There was no interferences in the retention time interval by determination the blank samples.

205

Different calibration curves (working standard solution, cucumber, tomato, pepper, apple, grape,

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wheat, and rice matrix) was prepared to make linear regression analysis within the concentration

207

range from5µg L -1to 200 µg L -1 for each analyte. The regression equation and coefficients (R2)

208

of all the matrix-matched curves and the standard solution curves were list (Table 2). And

209

satisfactory linearities were obtained for all compounds (R2≥0.996 in all cases). The LOQs (in

210

original samples) for fluxapyroxad and the three metabolites were assessed to be 0.02-0.47 µg kg

211

-1

suitable for extracting low or moderate polar

, which is based on five replicated extractions of analyses at lowest concentration levels. The

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LOQs value of this UPLC-MS/MS method was much less than previous reports methods 6-8.

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Matrix effect

214

Matrix effects, is a major challenge existing in the MS detection, which is a reduction or

215

enhancement of the analyte response caused by matrix co-extractives 20.

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type of matrices, analyte, and the sample pretreatment procedure are all the influence factor of

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matrix effect 21. Therefore, the matrix effect of the optimized approach was studied in the seven

218

matrix by contrasting standard solutions with matrix-matched standard solutions. It was classified

219

to be strong matrix effect when the values were below -50% or above +50%

220

demonstrated that pronounced signal suppression was observed for the 4 compounds in all

221

matrices with the value range of -92.4 to -52.4% (Table 2). Generally, the inadequate removing of

222

endogenous compounds including phospholipids, fatty acid, saccharides, phenols and pigments is

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response for the occurrence of signal suppression or enhancement effect

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mechanism underlying influencing signal response are still not been fully understood and need

225

more research. Hence, external matrix-matched standards calibration was applied to eliminate the

226

matrix effect and get accurate results of all samples in this research.

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Precision and accuracy

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The precision and accuracy of the developed method was validated by recovery assays . RSD was

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used to describe the precision of a method, which assessed by the repeatability and reproducibility

230

studies. The intra-day precision (RSDa) was obtained by contrasting the standard deviation of the

231

recovery percentages on the same day. The inter-day precision (RSDb) was measured by analyzing

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the spiked samples on three different days. The average recovery values (74.9-110.5%) and RSD

233

values (below 16.3%) at three fortified concentration levels were both meet the requirement (Table

12


Instrument, different

23

22

. The results

. However, the related

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3). For fluxapyroxad, the mean recoveries ranged from 76.5% to 106.2% with 2.2-15.5% intra-day

235

RSD, while they were from 74.9% to 106.3% with 3.2-12.8% intra-day RSD for C-2, and from

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77.2% to 110.5% with 1.9%-10.0% intra-day RSD for C-48, and ranged from 79.1% to 110.3%

237

with 1.0% -15.2% for C-8. Generally, the intra-day RSDs (n=5) and inter-day RSDs (n=15) for the

238

developed approach ranged from 1.0% to 15.5% and 1.4%-16.3%, respectively (Table 3). In

239

summary, the recovery, precision, and accuracy of this method were satisfied for fluxapyroxad and

240

its metabolites. The typical chromatograms of the 4 analyte in apple and cucumber were presented

241

(Figure 5). In addition, stability analysis of the fluxapyroxad and its metabolites was carried on,

242

and no significant difference with time (P>0.05, student t-test) was found in the all matrices.

243

Application to actual samples

244

The simplicity and applicability of this validated method for the routine monitoring and

245

metabolic behaviors of the fluxapyroxad was further assessment through determine seven kinds of

246

food samples (cucumber, tomato, pepper, apple, grape, wheat, and rice) purchased from a local

247

market (Beijing, China). Neither of the targeted compounds was detected in the samples.

248

In conclusion, an effective and robust trace analytical method using UPLC-MS/MS in the ESI

249

negative mode for simultaneous detection of fluxapyroxad and its three metabolites in food matrix

250

has been successfully established and validated. The 4 compounds were eluted and separated

251

within 2.0 min with good specificity. And good recovery, linearity, low LOQs, accuracy, and

252

precision were obtained in this study. Although the seven matrices has strong matrix effect, the

253

error has been compensated through using matrix-matched solvent to calibration. The

254

determination results of practical samples using proposed method confirmed its reliability and

255

efficacy for the routine monitoring of fluxapyroxad and its metabolites residues.

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256

Abbreviations: RCF, relative centrifugal force; UPLC-MS/MS, ultraperformance liquid

257

chromatography/tandem mass spectrometry; MRM, Multi-reaction monitoring; PSA, primary

258

secondary amine; C18, octadecylsilane; GCB, graphitized carbon black; MRL, maximum residue

259

levels; SSE, signal suppression and enhancement

260 261 262 263

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LC–MS/MS method for the analysis of succinate dehydrogenase inhibitor fungicides

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6419-6427.

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

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multiresidue method employing acetonitrile extraction/partitioning and “dispersive

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sewage sludge using quick, easy, cheap, effective, rugged and safe extraction followed

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insecticidal organophosphates and carbamates. Journal of Agricultural and Food

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Chemistry 2007, 55, 10563-10571.

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(14) Kamel, A., Refined Methodology for the Determination of Neonicotinoid

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Pesticides and Their Metabolites in Honey Bees and Bee Products by Liquid

314

Chromatography−

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Agricultural and Food Chemistry 2010, 58, 5926-5931.

316

(15) Lehotay, S. J.; Tully, J.; Garca, A. V.; Contreras, M.; Mol, H.; Heinke, V.;

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Anspach, T.; Lach, G.; Fussell, R.; Mastovska, K., Determination of pesticide residues

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in foods by acetonitrile extraction and partitioning with magnesium sulfate:

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collaborative study. J. AOAC Int. 2007, 90, 485-520.

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(16) Arienzo, M.; Cataldo, D.; Ferrara, L., Pesticide residues in fresh-cut vegetables

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from integrated pest management by ultra performance liquid chromatography

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coupled to tandem mass spectrometry. Food Control 2013, 31, 108-115.

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(17) Nevado, J. J. B.; Peñalvo, G. C.; Robledo, V. R., Advantages of using a modified

324

orthogonal sampling configuration originally designed for LC–ESI-MS to couple CE

325

and MS for the determination of antioxidant phenolic compounds found in virgin

326

olive oil. Talanta 2010, 82, 548-554.

327

(18) Muratovic, A. Z.; Hagstrom, T.; Rosen, J.; Granelli, K.; Hellenas, K.-E.,

328

Quantitative Analysis of Staphylococcal Enterotoxins A and B in Food Matrices Using

329

Ultra High-Performance Liquid Chromatography Tandem Mass Spectrometry

Tandem

Mass

Spectrometry

(LC-MS/MS)†.

17


Journal

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(UPLC-MS/MS). Toxins 2015, 7, 3637-3656.

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(19) Liu, X.; Xu, J.; Dong, F.; Li, Y.; Song, W.; Zheng, Y., Residue analysis of four

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diacylhydrazine insecticides in fruits and vegetables by Quick, Easy, Cheap, Effective,

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Rugged,

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chromatography coupled to tandem mass spectrometry. Anal. Bioanal. Chem. 2011,

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401, 1051-1058.

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(20) Liu, X. G.; Xu, J.; Dong, F. S.; Li, Y. B.; Song, W. C.; Zheng, Y. Q., Residue

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analysis of four diacylhydrazine insecticides in fruits and vegetables by Quick, Easy,

338

Cheap, Effective, Rugged, and Safe (QuEChERS) method using ultra-performance

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liquid chromatography coupled to tandem mass spectrometry. Analytical and

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Bioanalytical Chemistry 2011, 401, 1051-1058.

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(21) Famiglini, G.; Palma, P.; Pierini, E.; Trufelli, H.; Cappiello, A., Organochlorine

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pesticides by LC-MS. Anal. Chem. 2008, 80, 3445-3449.

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(22) Li, M. M.; Liu, X. G.; Dong, F. S.; Xu, J.; Kong, Z. Q.; Li, Y. B.; Zheng, Y. Q.,

344

Simultaneous determination of cyflumetofen and its main metabolite residues in

345

samples of plant and animal origin using multi-walled carbon nanotubes in dispersive

346

solid-phase extraction and ultrahigh performance liquid chromatography-tandem mass

347

spectrometry. Journal of Chromatography A 2013, 1300, 95-103.

348

(23) Matuszewski, B.; Constanzer, M.; Chavez-Eng, C., Strategies for the assessment

349

of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS.

350

Analytical chemistry 2003, 75, 3019-3030.

and

Safe

(QuEChERS)

method

using

ultra-performance

18


liquid

Page 19 of 30


351

Funding

352

This work was financially supported by National Key Research and Development Program of

353

China (2016YFD0200204).

354

Notes

355

The authors declare no conflicts of interest.

356

19



357

Figure captions

358

Figure 1

359

The chemical structure of fluxapyroxad and its three plant metabolites

360

Figure 2

361

Influence of the ACN/water ratio on the response of fluxapyroxad and three metabolites

362

Figure 3

363

Comparison of recoveries of fluxapyroxad and its metabolites in cucumber, wheat and tomato

364

samples (0.05 mg kg -1) with pure acetonitrile and 0.2% formic acid in extraction solvents (n=5)

365

Figure 4

366

Effect of different sorbents (PSA, C18, GCB+PSA) for targeted fungicides in different matrix at

367

0.05 mg kg -1 level (n=5)

368

Figure 5

369

Typical UPLC-MS/MS MRM chromatograms of fluxapyroxad and its metabolites at the spiked

370

level of 50 µg kg-1. (A) Spiked apple sample, (A-1) apple matrix standard, and (A-2) blank apple

371

sample; (B) spiked cucumber sample, (B-1) cucumber matrix standard, and (B-2) blank cucumber

372

sample

20


Page 20 of 30

Page 21 of 30


Table Table 1: Experimental Parameters and LC-MS/MS Conditions of the Fluxapyroxad and Its Metabolitesa

a

Compound

Molecular formula

MW

tR (min)

Ion source

CV (V)

Quantification ion transition

CE1 (eV)

Confirmatory ion transition

CE2 (eV)

Fluxapyroxad

C18H12F5N3O

380.12

1.86

ESI-

44

248.12

21

131.09

24

C-2

C5H4F2N2O2

161.00

1.06

ESI-

28

141.00

8

97.10

20

C-8

C17H10F5N3O

366.17

1.71

ESI-

14

326.16

16

346.20

8

C-48

C23H20F5N3O6

528.29

1.41

ESI-

78

326.16

28

346.19

24

a

MW, molecular weight; tR, retention time; CV, cone voltage; CE, collision energy

21



Page 22 of 30

Table 2: Comparison of Matrix-matched Calibration and Solvent Calibration with All of the Ranges at 5-200 µg L-1 for Fluxapyroxad and 3 Plant Metabolites Compound

Fluxapyroxad

C-2

C-48

C-8

a

Matrix

Regression equation

Slope ratio a

R2

Matrix effect b (%)

LOD

LOQ -1

(µg kg )

(µg kg-1)

acetonitrile

y = 4351.5x - 22821

0.9992

-

-

-

-

cucumber

y = 1272.9x + 2692.7

0.9998

0.29

-70.7

0.024

0.079

tomato

y = 1242.3x - 59.494

0.9996

0.29

-71.5

0.057

0.190

pepper

y = 533.08x - 797.25

0.9994

0.12

-87.7

0.069

0.230

apple

y = 2072x - 5081.9

0.9993

0.48

-52.4

0.006

0.021

grape

y = 1511.4x - 3950.8

0.9996

0.35

-65.3

0.052

0.174

rice

y = 1997.4x - 9293.3

0.9991

0.46

-54.1

0.017

0.057

wheat

y = 779.22x + 3006.4

0.9990

0.18

-82.1

0.021

0.070

acetonitrile

y = 468.44x - 5079.6

0.9991

-

-

-

-

cucumber

y = 44.191x + 64.25

0.9992

0.09

-90.6

0.139

0.464

tomato

y = 114.86x - 149.88

0.9989

0.25

-75.5

0.080

0.265

pepper

y = 65.937x - 98.594

0.9990

0.14

-85.9

0.035

0.117

apple

y = 106.16x - 97.303

0.9994

0.23

-77.3

0.009

0.031

grape

y = 50.691x - 76.494

0.9997

0.11

-89.2

0.045

0.150

rice

y = 65.973x - 250.41

0.9993

0.14

-85.9

0.142

0.474

wheat

y = 58.716x + 702.53

0.9960

0.13

-87.5

0.082

0.273

acetonitrile

y = 3371.1x - 20668

0.9993

-

-

-

-

cucumber

y = 619.45x - 79.827

0.9989

0.18

-81.6

0.007

0.025

tomato

y = 257.15x - 319.8

0.9998

0.08

-92.4

0.025

0.082

pepper

y = 336.46x - 517.43

0.9991

0.10

-90.0

0.016

0.054

apple

y = 861.95x - 987.52

0.9994

0.26

-74.4

0.007

0.025

grape

y = 340.93x - 833.47

0.9991

0.10

-89.9

0.009

0.030

rice

y = 465.06x - 1131.4

0.9993

0.14

-86.2

0.043

0.142

wheat

y = 269.72x + 574.64

0.9991

0.08

-92.0

0.014

0.046

acetonitrile

y = 5527x - 25326

0.9991

-

-

-

-

cucumber

y = 894.59x + 2503

0.9990

0.16

-83.8

0.020

0.066

tomato

y = 1362.5x - 2601.3

0.9995

0.25

-75.3

0.112

0.373

pepper

y = 876.8x - 1949.9

0.9998

0.16

-84.1

0.020

0.065

apple

y = 2258.6x - 5438.8

0.9998

0.41

-59.1

0.006

0.022

grape

y = 1898.3x - 4443

0.9996

0.34

-65.7

0.053

0.177

rice

y = 2507.8x - 8333.4

0.9981

0.45

-54.6

0.016

0.052

wheat

y = 468.44x - 5079.6

0.9993

0.08

-91.5

0.027

0.089

Slope ratio = matrix/acetonitrile.

b

Matrix effect (%) = ((slope matrix - slope solvent)/slope solvent) ×100

22


Page 23 of 30


Table 3: Accuracy and Precision of the Proposed Method in the Seven Studied Matrices at Three Spiked Levels Intra-day (n=5)

Inter-day (n=3)

spiked fluxapyroxad matrix

level recovery

RSD

(%)

(%)

C-2 a

C-48

recovery

RSD

(%)

(%)

a

C-8

recovery

RSD

(%)

(%)

a

fluxapyroxad

recovery

RSD

(%)

a

recovery

RSD

(%)

(%)

C-2 b

C-48

recovery

RSD

(%)

(%)

b

C-8 recovery

RSDb

(%)

(%)

(%)

recovery

RSD

(%)

(%)

b

(µg/kg) 10

84.6

6.0

74.9

5.1

85.8

3.5

85.8

6.0

89.9

8.4

75.7

8.1

82.4

3.5

83.7

7.1

50

91.6

4.2

94.9

4.7

90.8

3.0

92.2

3.6

81.2

1.9

80.5

7.1

79.9

2.2

82.1

1.1

100

81.0

2.2

81.2

7.3

79.9

2.0

82.1

1.0

91.6

3.8

93.2

4.9

90.6

3.0

92.3

3.6

10

78.8

6.7

106.3

3.2

77.6

5.6

97.4

12.7

90.8

14.8

99.7

8.7

89.6

14.6

102.1

10.0

50

94.2

11.0

104.4

8.7

102.8

8.6

96.7

10.0

95.2

9.1

101.0

10.0

102.2

6.7

96.6

7.7

100

101.7

2.7

98.5

3.7

97.5

6.2

110.3

4.5

91.4

13.3

91.0

11.6

90.1

10.2

96.7

15.8

10

99.6

13.2

85.3

8.6

107.3

5.5

99.8

12.7

88.9

16.3

90.6

13.8

98.9

13.2

88.0

12.7

apple

grape

50

103.5

8.8

101.7

7.9

110.5

5.1

96.2

12.2

93.3

14.3

94.1

10.0

99.2

13.4

87.1

14.6

100

96.1

7.1

84.9

10.7

89.7

10.0

90.4

9.0

91.3

7.1

82.6

11.6

95.6

9.8

84.8

10.2

10

86.5

5.6

97.9

4.4

77.2

3.9

89.9

10.3

89.6

6.2

87.5

13.5

87.0

12.3

88.0

10.1

50

93.4

9.0

101.0

8.4

94.4

5.6

89.4

15.2

86.6

10.1

94.1

11.4

89.9

6.8

82.2

13.6

100

91.9

12.2

96.9

8.6

79.9

6.1

91.4

11.7

91.6

12.6

89.1

13.4

84.6

9.5

87.9

10.7

10

88.0

15.5

81.6

12.8

92.6

7.8

92.6

14.5

87.5

12.9

83.3

13.2

84.8

11.2

81.1

10.7

50

80.5

9.4

78.0

7.4

79.4

5.5

82.6

9.7

90.8

14.0

92.6

14.8

88.1

15.3

82.7

9.9

100

76.5

5.4

82.4

5.9

84.5

6.2

79.1

10.2

85.4

12.1

89.7

12.1

84.1

5.6

80.8

8.8

cucumber

tomato

pepper

23



Page 24 of 30

10

98.1

7.2

90.9

7.3

83.3

3.0

95.8

8.4

97.2

6.0

88.7

7.5

83.2

4.2

93.8

8.1

50

88.3

11.1

96.0

7.7

102.3

8.4

83.5

3.4

97.2

10.6

87.7

12.2

96.1

1.4

92.5

11.7

100

101.0

7.0

103.3

5.5

92.4

8.0

94.9

7.3

98.5

7.6

103.1

5.7

91.2

6.6

93.5

6.4

10

106.2

6.8

80.3

9.7

84.9

1.9

92.0

12.0

105.4

7.8

80.2

9.2

81.0

6.3

92.3

11.1

50

90.7

7.4

84.8

6.5

100.0

7.1

103.0

5.1

89.2

5.7

82.3

8.8

101.0

7.9

105.8

6.7

100

83.1

6.9

92.1

5.4

87.4

7.4

83.4

6.8

94.7

13.9

92.8

5.2

89.6

8.4

90.9

13.2

wheat

rice

24


Page 25 of 30


Figure 1

25



Figure 2

26


Page 26 of 30

Page 27 of 30


Figure 3

27



Figure 4

28


Page 28 of 30

Page 29 of 30


Figure 5

29



Table of Contents Graphic (TOC)

30


Page 30 of 30

Effective Monitoring of Fluxapyroxad and Its Three Biologically Active

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