Determination of 27 pesticides in wine by dispersive liquid–liquid microextraction and gas chromatography–mass spectrometry

    Determination of 27 pesticides in wine by dispersive liquid-liquid microextraction and gas chromatography–mass spectrometry Bo Chen, Feng-qi Wu, Wei-dong Wu, Bao-hui Jin, Li-qi Xie, Wen Feng, Gangfeng Ouyang PII: DOI: Reference:

S0026-265X(15)00273-8 doi: 10.1016/j.microc.2015.11.003 MICROC 2302

To appear in:

Microchemical Journal

Received date: Revised date: Accepted date:

19 September 2015 1 November 2015 1 November 2015

Please cite this article as: Bo Chen, Feng-qi Wu, Wei-dong Wu, Bao-hui Jin, Li-qi Xie, Wen Feng, Gangfeng Ouyang, Determination of 27 pesticides in wine by dispersive liquid-liquid microextraction and gas chromatography–mass spectrometry, Microchemical Journal (2015), doi: 10.1016/j.microc.2015.11.003

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Determination of 27 pesticides in wine by

PT

dispersive liquid-liquid microextraction and gas

SC

RI

chromatography–mass spectrometry

Bo Chena,b, Feng-qi Wub, Wei-dong Wub, Bao-hui Jinb, Li-qi Xieb, Wen Fengc, Gangfeng

MA

a

NU

Ouyanga*

MOE Key Laboratory of Aquatic Product Safety/KLGHEI of Environment and Energy

TE

D

Chemistry, School of Chemistry and Chemical Engineering, Sun Yat-sen University, Guangzhou 510275, China

Shenzhen Key Laboratory of Detection Technology for Food Safety/Shenzhen Entry-Exit

AC CE P

b

Inspection and Quarantine Bureau, Shenzhen 518067, China c

Guangzhou Fiber Product Testing Institute, Guangzhou 510220, China

*Corresponding

author.

Phone

and

fax:

+86-2084110953.

E-mail

address:

[email protected].

1

ACCEPTED MANUSCRIPT ABSTRACT A low solvent consumption method was developed to determine 27 different classes of

PT

pesticides (including organochlorine pesticide, organophosphorus pesticide, pyrethroid

RI

pesticide, fungicide, herbicide and acaricide) in wine using dispersive liquid-liquid

SC

microextraction (DLLME). Extraction parameters including type and volume of extraction solvent, type and volume of disperser solvent, salinity, pH , centrifugation

NU

time, vortex extraction time and wine volumes were optimized. A mixture of 60 μL chloroform (extraction solvent) and 940 μL acetonitrile (disperser solvent) was injected

MA

into 5 mL wine diluent. After shaking and centrifugation, the sedimented phase was transferred into a 200 μL glass insert and determined by gas chromatography-mass

TE

D

spectrometry method (GC-MS). The results demonstrated that the recoveries for all the pesticides spiked at three different levels ranged from 66.7 to 126.1 %. The intra-day

AC CE P

repeatabilities (RSDs) ranged from 2.0 to 27.2 %. The limits of detection ranged from 0.025 to 0.88 μg/L, and the limits of quantification ranged from 0.082 to 2.94 μg/L. The proposed method is very low cost, rapid and convenient, and could be an effective method for monitoring of multi-pesticide in wine.

Keywords:

Pesticide;

wine;

dispersive

liquid-liquid

microextraction;

gas

chromatography-mass spectrometry

2

ACCEPTED MANUSCRIPT

1. Introduction

PT

Wine is one of the most popular alcoholic beverage consumed around the world.

RI

Despite the wine industry is considered being environmentally safe, the cultivation of

SC

wine grapes and production of wine is associated with a large number of environmental concerns [1]. Pesticides are widely used in vineyards around the world in order to

NU

increase grape yields and quality [2]. Although there are alternative techniques to the use of pesticides, the use of these substances in grape crops is chosen, due to lower cost, time

MA

and ease of application [1]. During the process of wine production, pesticide residues in grape pulps are difficult to completely be removed, and may remain in the final drink,

TE

D

which can be a toxicological risk to the consumer [3, 4]. At present, over 9700 pesticides based on the 502 active ingredients have been

AC CE P

registered in China and over 16,000 pesticide formulations based on the 1055 active ingredients are labeled for use in the whole world [5]. Several pesticides are widely used in the treatment of diseases of grapes, such as folpet, fludioxonil, metalaxyl, thiophanate methyl, penconazol, pyrimethanil, procymidone and vinclozolin [6]. Considering pesticide residues not only cause potential health risks for the consumers, but also lead to a decrease in the wine quality, the sensitive analytical methods with screening ability are mandatory. The complexity of the matrix and the nature of the pesticide should be taken into account in the analysis of pesticides in wine samples. Gas chromatography-mass spectrometry (GC-MS) combines the high separation ability of GC and high selectivity and sensitivity of MS, and is still the most commonly used instrument for separating and identifying pesticide residues [7-9].

3

ACCEPTED MANUSCRIPT Sampling preparation steps are usually required to clean-up and pre-concentrate the analytes for determining pesticide residues in wine. Typical sample preparation methods

PT

employed for multi-class pesticide residues determination in wine are liquid liquid

RI

extraction (LLE) [10,11] , QuEChERS extraction [12,13] and solid phase extraction (SPE) [11,14]. Most of these methods are labor-intensive, time consuming, and require

SC

large volume of samples and organic solvents. In the past few decades, several

NU

miniaturized methods including membrane-assisted solvent extraction [15,16], solidphase microextraction (SPME) [17] and single drop microextraction (SDME) [18,19]

MA

were developed for extraction and concentration of trace pesticide residues. These microextraction methods are economical, environmental-friendly and effective. However, most

TE

D

of these methods need a long equilibrium time. In 2006, a more efficient dispersive liquid-liquid microextraction (DLLME) method

AC CE P

was developed [20]. During DLLME, the mixture of extraction solvent and disperser solvent was rapidly injected into aqueous sample, and a cloudy solution was formed. The large surface area between the extraction phase and aqueous sample resulted in short equilibrium time. The mixture was centrifuged after extraction, and the analytes in the settled phase are collected and analyzed by gas chromatography (GC) or high performance liquid chromatography (HPLC). DLLME has been widely applied in the extraction of organic compounds in food and water since it’s invention. As a kind of alcoholic beverage, DLLME can be easily applied in wine analysis without additional sample pretreatment. In 2007, DLLME was first applied in analysis of volatile phenols in the aroma of red wines [21]. After that, DLLME was applied in the determination of different kinds of organic compounds in wine, including chlorophenol [22, 23],

4

ACCEPTED MANUSCRIPT fungicide[24-26], ochratoxin A[27], phenolic acid [28], sulfonylurea herbicide [29], organophosphorus pesticide [30], triazine [30] and carbamate [31]. Most research

PT

mentioned above focused on one or two classes of pesticides, and was not suitable for

RI

multi-pesticide screening in wine. Therefore, the establishment of higher throughput multi-pesticide residue screening method gained more attention.

SC

In this work, a DLLME-GC-MS method was developed for the detection of 27

NU

different classes of pesticides (including organochlorine pesticide, organophosphorus pesticide, pyrethroid pesticide, fungicide, herbicide and acaricide) in wine. The pesticide

MA

class number covered in the proposed method was rarely reported. Several parameters of the DLLME procedure, including type and volume of extraction solvent, type and volume

were optimized.

AC CE P

2. Experimental

TE

D

of disperser solvent, salinity, pH, centrifugation time, vortex time and wine volumes,

2.1. Chemicals and wine samples All pesticides used were purchased from Dr. Ehrenstorfer (Germany). Individual pesticide stock solutions (1000 mg/L) were prepared in acetone. A composite stock standard solution of multiple pesticides was prepared in acetone, and the concentration of all pesticides was 1.0 mg/L. All the solutions were stored at -18 °C when not in use. Carbon tetrachloride, chloroform, methylbenzene, dichloroethylene and sodium chloride (NaCl) were analytical grade (Kemiou, Tianjin, China). Disperser solvents acetone, acetonitrile and methanol were HPLC grade obtained from Merck. Wine samples were obtained from local market in the city of Shenzhen (China) for the evaluation of the method proposed.

5

ACCEPTED MANUSCRIPT 2.2. Instrument A gas chromatography (Shimadzu QP2010 Ultra) equipped with a split/splitless

PT

injector system was used for separation and determination of pesticides. Shimadzu

RI

Chemstation (GCMS solution edition 2.70) was used for data collection/processing and

SC

GC-MS control. Ultra pure helium (99.999 %, Shente Industrial gases Co., Shenzhen, China) made to pass through a molecular sieve trap and oxygen trap was used as the

NU

carrier gas. A volume of 1 μL extract was injected in splitless mode. The injection port

MA

was held at 280°C. Separation was carried out on a Rtx-5MS (30 m × 0.25 mm × 0.25 m) capillary column. The oven temperature was programmed as follows: initial 40 °C and

D

held for 1min, then increased to 130 °C at the rate of 30 °C/min, ramped to 250 °C at the

TE

rate of 5 °C/min, finally ramped to 300 °C at the rate of 10 °C/min and held for 10 min. The MS spectrometric parameters were set as follows: electron impact ionization with 70

AC CE P

eV energy; ion source temperature, 230 °C and MS quadrupole temperature, 280 °C. The MS system was routinely set in selective ion monitoring (SIM) mode with a solvent delay of 3 min. Each pesticide residue was quantified based on peak area using one target and two qualifier ions, the dwell times were set depending on the peak widths of the analytes. Complete SIM parameters and retention times of the analytes are shown in Table 1. 2.3. Dispersive liquid-liquid microextraction procedure An aliquot of 5mL wine diluent (2 mL wine mixed with 3 mL water) was placed to a 15 mL glass tube with conical bottom. A mixture of 60 μL chloroform (extraction solvent) and 940 μL acetonitrile (disperser solvent) was rapidly injected into the sample tube and shaken gently by hand for a few seconds. After centrifuging at 3000 rpm for 5

6

ACCEPTED MANUSCRIPT min, sedimented phase was obtained and transferred into a 200 μL glass insert in a 2 mL vial. Then, 1 μL of the extract was injected into GC-MS system for analysis.

RI

PT

3. Results and discussion

SC

3.1. Chosen of chromatographic conditions

The chromatographic conditions were optimized according to GC-MS condition

NU

mentioned in Chinese national standard “Method for determination of 500 pesticides and

MA

related chemicals residues in fruits and vegetables - GC-MS method”(GB/T 19648-2006). In this paper, the capillary column used was Rtx-5MS (30 m × 0.25 mm × 0.25 m)

D

instead of Agilent DB-1701, and the SIM ions were re-optimized and shown in Table 1.

TE

With the optimized GC-MS condition, 27 pesticides showed good chromatographic behavior and were well separated. Fig. 1 showed a typical GC-MS chromatogram of 1

AC CE P

mg/L standard solution.

3.2. Optimization of extraction procedure There are some factors that affect the extraction process, including extraction solvent type and volume, disperser solvent type and volume, salinity, pH, centrifugation time, vortex extraction time and wine volume. 3.2.1. Selection of extraction solvent In a typical DLLME procedure, three factors should be considered for the selection of extraction solvent: higher density than wine, excellent extraction capability to the interested compounds, and good gas chromatography behavior. Wine diluent (5 mL, 2 mL wine mixed with 3 mL water) was used for the optimization, and 100 μL of

7

ACCEPTED MANUSCRIPT chloroform,

tetrachloride,

dichloroethylene,

and

mixture

of

tetrachloride

and

methylbenzene (50:50, v:v) were used as the extraction phases. Acetone (900 μL) was

PT

used as the disperser solvent. No sediment phase was obtained using dichloroethylene as

RI

the extraction solvent. The peak areas of all pesticides using other three extraction solvents were compared and shown in Fig. 2. Equal proportion mixture of tetrachloride

SC

and methylbenzene observed the highest peak area, however with poor chromatography

NU

behavior. The peak areas using chloroform as extraction solvent were higher than those

extraction solvent.

D

3.2.2. Selection of disperser solvent

MA

obtained with tetrachloride. Therefore, chloroform was selected as the optimum

TE

The disperser solvent should be miscible with both wine and extraction solvent.

According to this principle, acetone, acetonitrile and methanol were selected and

AC CE P

compared. 900 μL of each disperser solvent together with 100 μL of chloroform was mixed and injected into 5 mL of spiked wine sample. Too much precipitate was observed using methanol as disperser solvent and the sedimented phase was difficult to obtain. The peak areas of all pesticides using acetonitrile and acetone as disperser solvents were compared and shown in Fig. 3, and no significant difference was observed. However, larger deviations were observed using acetone as disperser solvent. Finally, acetonitrile was chosen as the optimum disperser solvent. 3.2.3. Effect of extraction solvent volume Lower volume of extraction solvent usually results in higher enrichment factor, and lower detection limit [20]. Some components of the wine matrix precipitated at the bottom of the tubes when using chlorinated solvents. Larger volumes of extractant would

8

ACCEPTED MANUSCRIPT provide a better separation between both phases, and enough volume of organic extract for GC-MS analysis [32]. In this experiment, 60 μL, 80 μL, and 100 μL extractant were

PT

compared. Extractant lower than 60 μL may cause difficulty in withdrawing sendimented

RI

solvent. The peak areas obtained using different extraction solvent volume were shown in Fig. 4. Results showed that with the increase of extractant volume, the peak areas of

SC

different pesticides gradually decreased. Therefore, 60 μL chloroform was selected as the

MA

3.2.4 Effect of disperser solvent volume

NU

optimum extraction solvent volume.

The disperser solvent volume directly affects the formation of cloudy solution. Larger

D

amount of disperser solvent improves the dispersion effect of the extraction solvent in

TE

wine. However, higher volume of disperser solvent may also reduce the recoveries of compounds, because it may increase the solubility of compounds in wine. In this

AC CE P

experiment, 60 μL chloroform (extraction solvent) with 440 μL, 940 μL, 1440 μL acetonitrile (disperser solvent) were compared. With the increase of acetonitrile, the precipitate in the bottom gradually decreased. There was too much precipitate and the sendimented phase was difficult to obtain when using 440 μL disperser solvent. So the peak areas using 940 μL and 1440 μL acetonitrile as disperser solvent were compared Results in Fig. 5 showed that for most compounds, the peak areas obtained with 940 μL disperser solvent were higher than those obtained with 1440 μL disperser solvent, except for isofenphos-methyl, fluvalinate and phosalone. Therefore, 940 μL was selected as the optimum disperser solvent volume. 3.2.5. Effect of salt addition

9

ACCEPTED MANUSCRIPT The solubility of the target analytes and organic extraction solvent in aqueous phase usually decreased with the increase of ionic strength, which may increase recovery.

PT

However, the volume of the sedimented phase may also be increased, which may decrease the enrichment factor [5]. In this experiment, 60 μL chloroform was used as

RI

extraction solvent, and 940 μL acetonitrile was used as disperser solvent. Extraction was

SC

performed with the adding of different amounts of NaCl (0%, 5%,10%; w/v) into the

NU

wine sample. Results in Fig. 6 showed that with the increase of salt addition, the peak areas significantly decreased, because of the increase of sedimented phase volume. So no

MA

salt was added in the following experiments.

D

3.2.6. Effect of pH, vortex extraction time, centrifugation time and wine volume

TE

Different wines have different pH, which may affect the extraction efficiency, so the

AC CE P

influence of pH should be investigated. Wine pH value ranged from 2.5 to 4.0 through real wine sample analysis, and most red wine showed higher pH value than white wine. In the study, wine samples adjusted to different pH value (2.50, 3.25, 4.00) were extracted using the above DLLME procedure, and no significant difference was observed (data not shown). So no further pH adjustment was needed. Shaking of the ternary DLLME mixture would be expected to increase the surface of contact between the aqueous phase and the tiny dispersed droplets of chloroform. In most study, the DLLME extraction time was very short. In this paper, gently shaking after injection, vortex extraction for 30 s and 60 s was compared, and no difference was observed (data not shown). So no extra vortex extraction was needed. Centrifugation was the most often used method to achieve separation of extractant droplets. Working at 3000 rpm for 5 min and 10 min, the peak area showed no difference

10

ACCEPTED MANUSCRIPT (data not shown). Therefore 5 min was selected as the centrifugation time. Larger wine volume could increase the sensitivity of the method, but also may

PT

increase matrix effect and cause difficulty in separation of extraction phase and wine. To

RI

optimize the wine volume, 1 mL, 2 mL, 3 mL, 4 mL and 5 mL of wine were diluted to 5 mL with water and extracted using DLLME. For some wines, more than 2 mL wine may

SC

cause too little sedimented phase to be able to transfer into glass insert. Therefore, 2 mL

MA

3.3. Evaluation of the method performance

NU

wine was selected.

The calibration curve was obtained by analyzing blank wine sample spiked at six

D

concentration levels. Good linearity of GC-MS response was found for all pesticides at

TE

concentrations within the test intervals, with linear regression coefficients (r2) higher than

AC CE P

0.990 (Table 2). The average recoveries and repeatability of the proposed method were evaluated by extracting six consecutive wine samples spiked at three different levels (2.5 μg/L, 5.0 μg/L,10.0 μg/L). The results (Table 2) showed that the recoveries for all the pesticides spiked at three different levels ranged from 66.7 to 126.1 %. The intra-day repeatabilities (RSDs) at three levels ranged from 2.0 to 27.2 %. The limits of detections ranged from 0.025 to 0.88 μg/L, and the limits of quantifications ranged from 0.082 to 2.94 μg/L. 3.4. Real sample analysis The established method was then applied to analyze the pesticides in 10 imported wine samples (5 red wines and 5 white wines) purchased from local supermarket (Shenzhen, China). Results showed that trace amount of pesticides, such as molinate, diphenylanmine, metalaxyl, myclobutanil and tebuconazole, were found in 9 wines. The

11

ACCEPTED MANUSCRIPT concentrations of the pesticides found were from 0.56 μg/L to 18.6 μg/L, and the highest concentration of pesticide detected was metalaxyl (18.6 μg/L) (Fig. 7).

PT

4. Conclusion

RI

In the present study, a new multi-pesticide sample preparation method coupled with

SC

GC-MS was developed for the analysis of 27 different classes of pesticide residues

NU

(including organochlorine pesticide, organophosphorus pesticide, pyrethroid pesticide, fungicide, herbicide and acaricide) in wine. The conditions for DLLME of pesticides in

MA

wine were investigated and optimized, and all 27 pesticides showed good recoveries and repeatability. Besides, the method only needs very small amount of solvent and can be

D

finished within few minutes. Thus, the developed DLLME method should be an attractive

AC CE P

TE

method for multi-pesticide analysis in wine sample.

Acknowledgment

This research was supported by projects of National Natural Science Foundation of China (21225731, 21477166), the NSF of Guangdong Province (S2013030013474), Shenzhen CIQ Science project (SZ2015209) and National Key Technology Support project (2012BAD29B01-5)

References [1]

K.L. Christ, R.L. Burritt, Critical environmental concerns in wine production: an integrative review, J. Clean. Prod. 53 (2013) 232-242.

[2]

J.P.D Anjos, J.B.D Andrade, Simultaneous determination of pesticide multiresidues in white wine and rosé wine by SDME/GC-MS, Microchem. J. 120 (2015) 69-76.

[3]

A. Economou, H. Botitsi, S. Antoniou, D. Tsipi, Determination of multi-class pesticides in wines by solid-

12

ACCEPTED MANUSCRIPT phase extraction and liquid chromatography-tandem mass spectrometry, J. Chromatogr. A 1216 (2009) 58565867. [4]

A. Garbi, V. Sakkas, Y.C Fiamegos, C.D. Stalikas, A. Triantafyllos, Sensitive determination of pesticides

PT

residues in wine samples with the aid of single-drop microextraction and response surface methodology, Talanta 82 (2010) 1286-1291.

B. Chen, B. Jin, R. Jiang, L. Xie, Y. Lin, W. Feng, G. Ouyang, Screening and quantification of 304 pesticides

RI

[5]

SC

and related organic pollutants in surface water using dispersive liquid–liquid microextraction coupled with gas chromatography-mass spectrometry, Anal. Meth. 6 (2014) 1743-1752.

B. Jin, L. Xie, Y. Guo, G. Pang, Multi-residue detection of pesticides in juice and fruit wine: A review of

NU

[6]

extraction and detection methods, Food Res. Int. 46 (2012) 399-409. M. Tankiewicz, C. Morrison, M. Biziuk, Multi-residue method for the determination of 16 recently used

MA

[7]

pesticides from various chemical groups in aqueous samples by using DI-SPME coupled with GC-MS, Talanta 107 (2013) 1-10.

P. Kaewsuya, W.E. Brewer, J. Wong, L.M. Stephen, Automated QuEChERS tips for analysis of pesticide

D

[8]

[9]

TE

residues in fruits and vegetables by GC-MS, J. Agric. Food Chem. 61(2013) 2299-2314. G. Pang, Y. Cao, J. Zhang, C. Fan, Y. Liu, X. Li, G. Jia, Z. Li, Y. Wu, T. Guo, Validation study on 660

AC CE P

pesticide residues in animal tissues by gel permeation chromatography cleanup/gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry, J. Chromatogr. A 1125 (2006) 1-30. [10] R.M. González-Rodríguez, B. Cancho-Grande, J. Simal-Gándara, Multiresidue determination of 11 new fungicides in grapes and wines by liquid–liquid extraction/clean-up and programmable temperature vaporization injection with analyte protectants/gas chromatography/ion trap mass spectrometry, J. Chromatogr. A 1216 (2009) 6033-6042.

[11] R.R. Otero, B.C. Grande, J.S. Gándara, Multiresidue method for fourteen fungicides in white grapes by liquid– liquid and solid-phase extraction followed by liquid chromatography–diode array detection, J. Chromatogr. A 992 (2003) 121-131. [12] X. Wang, M.J. Telepchak. Determination of Pesticides in Red Wine by QuEChERS Extraction, Rapid MiniCartridge Cleanup, and LC-MS-MS Detection, Lc Gc N. AM. 30 (2012) 912-930. [13] P. Payá, M. Anastassiades, D. Mack, I. Sigalova, B. Tasdelen, J. Oliva, A. Barba, Analysis of pesticide residues using the Quick Easy Cheap Effective Rugged and Safe (QuEChERS) pesticide multiresidue method in combination with gas and liquid chromatography and tandem mass spectrometric detection, Anal. Bioanal. Chem. 389 (2007) 1697-1714.

13

ACCEPTED MANUSCRIPT [14] G. Pang, C. Fan, Y. Liu, Y. Cao, J. Zhang, B. Fu, X. Li, Z. Li, Y. Wu, Multi-residue method for the determination of 450 pesticide residues in honey, fruit juice and wine by double-cartridge solid-phase

Food Addit. Contam. 23 (2006) 777-810.

PT

extraction/gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry,

[15] M. Moeder, C. Bauer, P. Popp, M.V. Pinxteren, T. Reemtsma, Determination of pesticide residues in wine by solvent

extraction

and

high-performance

chromatography–tandem mass

SC

spectrometry, Anal. Bioanal. Chem. 403 (2012) 1731-1741.

liquid

RI

membrane-assisted

[16] P. Viñas, N. Aguinaga, N. Campillo, M. Hernández-Córdoba, Comparison of stir bar sorptive extraction and

NU

membrane-assisted solvent extraction for the ultra-performance liquid chromatographic determination of oxazole fungicide residues in wines and juices, J. Chromatogr. A 1194 (2008) 178-183.

MA

[17] J. Martins, C. Esteves, T. Simões, C. Manuela, D.M. Cristina, Determination of 24 Pesticide Residues in Fortified Wines by Solid-Phase Microextraction and Gas Chromatography–Tandem Mass Spectrometry, J. Agric. Food Chem. , 59 (2011) 6847-6855.

D

[18] J.P. dos Anjos, J.B. de Andrade, Simultaneous determination of pesticide multiresidues in white wine and rosé

TE

wine by SDME/GC-MS, Microchem. J. 120 (2015) 69-76. [19] A. Garbi, V. Sakkas, Y.C. Fiamegos, C.D. Stalikas, A. Triantafyllos, Sensitive determination of pesticides

AC CE P

residues in wine samples with the aid of single-drop microextraction and response surface methodology, Talanta 82 (2010) 1286-1291.

[20] M. Rezaee, Y. Assadi, M.R. Milani Hosseini, E. Aghaee, F. Ahmadi, S. Berijani, Determination of organic compounds in water using dispersive liquid-liquid microextraction, J. Chromatogr. A 1116 (2006) 1-9. [21] L. Farina, E. Boido, F. Carrau, E. Dellacassa, Determination of volatile phenols in red wines by dispersive liquid–liquid microextraction and gas chromatography–mass spectrometry detection, J. Chromatogr. A 1157 (2007) 46-50. [22] C. Pizarro, C. Sáenz-González, N. Perez-del-Notario, J.M. González-Sáiz, Optimisation of a dispersive liquid– liquid microextraction method for the simultaneous determination of halophenols and haloanisoles in wines, J. Chromatogr. A 1217 (2010) 7630-7637. [23] Y. Fan, S. Hu, S. Liu, Salting-out assisted liquid–liquid extraction coupled to dispersive liquid–liquid microextraction for the determination of chlorophenols in wine by high - performance liquid chromatography, J. Sep. Sci. 37 (2014) 3662-3668.

14

ACCEPTED MANUSCRIPT [24] R. Montes, I. Rodríguez, M. Ramil, E. Rubí, R. Cela, Solid-phase extraction followed by dispersive liquid– liquid microextraction for the sensitive determination of selected fungicides in wine, J. Chromatogr. A 1216 (2009) 5459-5466.

PT

[25] S.P. Chu, W.C. Tseng, P.H. Kong, C.K. Huang, J.H. Chen, P.S. Chen, S.D. Huang, Up-and-down-shakerassisted dispersive liquid–liquid microextraction coupled with gas chromatography–mass spectrometry for the

RI

determination of fungicides in wine, Food Chem. 185(2015) 377-382.

SC

[26] W.C. Tseng, S.P. Chu, P.H. Kong, C.K. Huang, J.H. Chen, P.S. Chen, S.D. Huang, Water with Low concentration of surfactant in dispersed solvent-assisted emulsion dispersive liquid–liquid microextraction for

NU

the determination of fungicides in wine, J. Agric. Food Chem. 62 (2014) 9059-9065. [27] N. Arroyo-Manzanares, L. Gámiz-Gracia, A.M. García-Campaña, Determination of ochratoxin A in wines by

MA

capillary liquid chromatography with laser induced fluorescence detection using dispersive liquid–liquid microextraction, Food Chem. 135 (2012) 368-372.

[28] J. Li, S. Jia, S. J. Yoon, S.J. Lee, S.W. Kwon, J. Lee, Ion-pair dispersive liquid-liquid microextraction

D

solidification of floating organic droplets method for the rapid and sensitive detection of phenolic acids in

Anal. 45 (2016) 73-79.

TE

wine samples using liquid chromatography combined with a core-shell particle column, J. Food Compos.

AC CE P

[29] A. Gure, F.J. Lara, A.M. García-Campaña, N. Megersa, M. del Olmo-Iruela, Vortex-assisted ionic liquid dispersive liquid–liquid microextraction for the determination of sulfonylurea herbicides in wine samples by capillary high-performance liquid chromatography, Food Chem. 170 (2015) 348-353. [30] G. Cinelli, P. Avino, I. Notardonato, M.V. Russo, Ultrasound-vortex-assisted dispersive liquid–liquid microextraction coupled with gas chromatography with a nitrogen–phosphorus detector for simultaneous and rapid determination of organophosphorus pesticides and triazines in wine, Anal. Methods, 6 (2014) 782-790. [31] D. Moreno-González, J.F. Huertas-Pérez, A.M. García-Campaña, J.M. Bosque-Sendra，L. Gámiz-Gracia, Ultrasound-assisted surfactant-enhanced emulsification microextraction for the determination of carbamates in wines by ultra-high performance liquid chromatography–tandem mass spectrometry, J. Chromatogr. A 1315 (2013) 1-7. [32] T. Rodríguez-Cabo, I. Rodríguez, M. Ramil, R. Cela, Dispersive liquid–liquid microextraction using nonchlorinated, lighter than water solvents for gas chromatography–mass spectrometry determination of fungicides in wine, J. Chromatogr. A 1218 (2011) 6603-6611.

15

ACCEPTED MANUSCRIPT Figure Captions Figure 1. GC-MS chromatogram of 27 pesticides. Peak numbers correspond to: (1)

PT

molinate, (2) diphenylanmine, (3) ethoprophos, (4) sulfotep, (5) cadusafos, (6) terbufos,

(13) pendimethalin,

(14) phosfolan,

(15)procymidone, (16)

SC

isofenphos-methyl,

RI

(7) isazofos, (8) vinclozolin, (9) alachlor, (10) metalaxyl, (11) tridimefon, (12)

profenofos, (17)myclobutanil, (18) flusilazole, (19) nitrofen, (20)propiconazole-1, (21)

NU

propiconazole-2, (22)tebuconazole, (23)bromopropylate, (24)phosalone, (25) mirex, (26)fenbuconazole, (27)difenoconazole, (28)fluvalinate.

MA

Figure 2. The effect of different extracting solvent on the peak area of pesticides obtained from DLLME.

from DLLME.

TE

D

Figure 3. The effect of different disperser solvent on the peak area of pesticides obtained

AC CE P

Figure 4. Comparison of the peak area of pesticides obtained from DLLME using different volume of extraction solvent. Figure 5. Comparison of the peak area of pesticides obtained from DLLME using different volume of disperser solvent. Figure 6. Comparison of peak area of pesticides obtained from DLLME using different amounts of salt addition. Figure 7. GC-MS chromatogram of a real red wine sample.

16

ACCEPTED MANUSCRIPT Figure 1.

SC

RI

2

26

100000 11

3 4,5

50000 89 10

0 12

14

16

18

20

22

23 2122 20

24

Time (min)

26

25

27 28

24

28

30

32

34

TE

D

10

18 15 17 13 19 14 16

12

MA

6 7

NU

1

AC CE P

Peak area

150000

PT

200000

17

40000

20000

0

-20000

D

TE

AC CE P

60000

molinate diphenylanmine ethoprophos sulfotep terbufos vinclozolin isazofos alachlor metalaxyl tridimefon isofenphos-methyl pendimethalin phosfolan procymidone profenofos myclobutanil flusilazole nitrofen propiconazole-1 propiconazole-2 tebuconazole bromopropylate phosalone mirex fenbuconazole difenoconazole fluvalinate cadusafos

Peak area 80000

100000

120000

160000

PT tetrachloride+methylbenzene

140000

RI

SC

NU

MA

ACCEPTED MANUSCRIPT

Figure 2.

chloroform

tetrachloride

18

molinate diphenylanmine ethoprophos sulfotep terbufos vinclozolin isazofos alachlor metalaxyl tridimefon isofenphos-methyl pendimethalin phosfolan procymidone profenofos myclobutanil flusilazole nitrofen propiconazole-1 propiconazole-2 tebuconazole bromopropylate phosalone mirex fenbuconazole difenoconazole fluvalinate cadusafos

D

TE

0

AC CE P

20000

100000

PT

RI

80000

SC

60000

40000

NU

MA

Peak area

ACCEPTED MANUSCRIPT

Figure 3.

120000 acetonitrile

acetone

19

D

TE

AC CE P molinate diphenylanmine ethoprophos sulfotep terbufos vinclozolin isazofos alachlor metalaxyl tridimefon isofenphos-methyl pendimethalin phosfolan procymidone profenofos myclobutanil flusilazole nitrofen propiconazole-1 propiconazole-2 tebuconazole bromopropylate phosalone mirex fenbuconazole difenoconazole fluvalinate cadusafos

50000

0

100000

150000

200000

250000

PT

RI

SC

NU

MA

Peak area

ACCEPTED MANUSCRIPT

Figure 4. 300000

60μL 80μL 100μL

20

D

TE

AC CE P

PT

RI

200000 180000 160000 140000 120000 100000 80000 60000 40000 20000 0

SC

NU

MA

molinate diphenylanmine ethoprophos sulfotep terbufos vinclozolin isazofos alachlor metalaxyl tridimefon isofenphos-methyl pendimethalin phosfolan procymidone profenofos myclobutanil flusilazole nitrofen propiconazole-1 propiconazole-2 tebuconazole bromopropylate phosalone mirex fenbuconazole difenoconazole fluvalinate cadusafos

Peak area

ACCEPTED MANUSCRIPT

Figure 5.

940 μL

1440 μL

21

20000

D

TE

0

molinate diphenylanmine ethoprophos sulfotep terbufos vinclozolin isazofos alachlor metalaxyl tridimefon isofenphos-methyl pendimethalin phosfolan procymidone profenofos myclobutanil flusilazole nitrofen propiconazole-1 propiconazole-2 tebuconazole bromopropylate phosalone mirex fenbuconazole difenoconazole fluvalinate cadusafos

AC CE P

120000

140000

180000

160000

PT

RI

SC

100000

80000

NU

60000

40000

MA

Peak area

ACCEPTED MANUSCRIPT

Figure 6. 200000

0% 5% 10%

22

ACCEPTED MANUSCRIPT

PT

Figure 7.

RI

100000 90000

metalaxyl

60000

NU

50000 40000 30000

MA

20000 metalaxyl

10000 0 14

16

18

20

22

24

Time (min)

TE

D

12

myclobutanil

AC CE P

Peak area

70000

SC

80000

23

ACCEPTED MANUSCRIPT

tR

SIM ion(m/z)

Pesticides

SIM ion(m/z)

molinate

10.40

126,187,98

procymidone

20.33

283,255,285

diphenylanmine

11.90

169,168,167

profenofos

22.154

339,374,297

ethoprophos

12.14

158,200,242

myclobutanil

22.58

179,288,150

sulfotep

13.15

322,202,238

flusilazole

22.72

233,206,315

cadusafos

13.15

159,213,270

nitrofen

23.02

283,253,202

terbufos

14.84

231,153,288

propiconazole

25.02, 25.24

259,173,261

vinclozolin

17.04

285,212,198

tebuconazole

25.60

250,163,252

isazofos

15.82

161,257,285

bromopropylate

26.96

341,183,339

alachlor

17.31

188,237,269

phosalone

28.23

182,367,154

metalaxyl

17.55

206,249,234

mirex

28.486

272,274,237

tridimefon

18.99

208,210,181

fenbuconazole

30.96

129,198,125

isofenphos-methyl

19.85

121, 199 ,241

difenoconazole

33.20

323,265,325

pendimethalin

20.06

252,220,162

fluvalinate

33.61

250,252,181

phosfolan

20.17

AC

PT ED

MA

NU

SC

RI

tR

CE

Pesticides

PT

Table 1. GC-MS conditions of 27 pesticides

255,227,196

24

ACCEPTED MANUSCRIPT

(2.5μg/L)

(5.0μg/L)

Linearity (r2, μg/L) Intra-day Recovery

Spiked level C

RI

Pesticides

Spiked level B

(10.0μg/L)

Intra-day

Recovery

Intra-day

LOD

LOQ

(μg/L)

(μg/L)

Recovery

RSD

0.9948(0.2~25.0)

94.3

8.4

104.3

11.4

102.7

2.6

0.057

0.19

diphenylanmine

0.9926(0.2~25.0)

87.4

11.4

71.5

17.9

95.9

7.2

0.025

0.082

ethoprophos

0.9955(0.2-25.0)

98.9

8.4

107.7

7.7

102

2.3

0.068

0.23

sulfotep

0.9993(0.5~25.0)

96.2

11

112.2

11.9

112.8

4.6

0.14

0.50

terbufos

0.9984(0.2~25.0)

98.2

12

113.6

13.2

115.3

3.3

0.064

0.21

vinclozolin

0.9937(1.0~25.0)

82.1

18.2

103.3

13.5

100.4

3

0.24

0.80

isazofos

0.9984(2.5~25.0)

66.7

24.5

102.5

10.4

87

1.9

0.15

0.50

alachlor

0.9982(0.5~25.0)

85.9

11.8

99.3

8.8

96.5

3.1

0.089

0.30

metalaxyl

0.9983(2.5~25.0)

115.6

8.5

88.8

11.8

99.1

3.8

0.60

2.00

tridimefon

0.9904(0.5~25.0)

92.4

11.1

104.1

9.3

97.1

1.6

0.16

0.54

0.9902(0.5~25.0)

98.8

10.9

99.4

7.5

101.5

8.9

0.19

0.63

pendimethalin

0.9998(1.0~25.0)

93.9

10.5

99.3

20.3

100.4

5

0.28

0.94

phosfolan

0.9940(2.5~25.0)

118.5

4.8

120.4

16.2

107.1

7.6

0.83

2.50

procymidone

0.9994(0.2~25.0)

107.2

11

117.4

10.8

105.4

4

0.094

0.31

wine

CE

Red

PT ED

molinate

MA

RSD

AC

NU

RSD

SC

Wine

Spiked level A

PT

Table 2. Linearity, average recovery, intra-day repeatability, LODs and LOQs obtained with the DLLME method in spiked wine.

isofenphosmethyl

25

0.9988(0.5~25.0)

101.2

16.6

101.7

21.9

myclobutanil

0.9973(0.2~25.0)

84

15.2

95.3

10.7

flusilazole

0.9965(0.2~25.0)

84

15

95.8

12.9

nitrofen

0.9995(2.5~25.0)

117

9.8

115.1

14.9

propiconazole-1

0.9970(0.5~25.0)

91.6

18.3

98.9

propiconazole-2

0.9970(0.5~25.0)

89.9

17.8

99.1

tebuconazole

0.9956(0.5~25.0)

87.7

14.9

91.4

bromopropylate

0.9976(0.5~25.0)

96.8

14.4

phosalone

0.9978(0.2~25.0)

118.6

18.9

mirex

0.9979(0.5~25.0)

92.5

fenbuconazole

0.9938(0.2~25.0)

73.5

difenconazole

0.9974(2.5~25.0)

105

fluvalinate

0.9977(2.5~25.0)

103.1

cadusafos

0.9951(0.5~25.0)

molinate

0.9963(0.5~25.0)

diphenylanmine

0.9981(0.2~25.0)

White

ethoprophos

wine

0.44

1.46

94.8

3.2

0.098

0.33

97.4

4

0.039

0.13

108.9

3.6

0.85

2.84

13

102.5

4.9

0.44

1.47

12.6

101.6

4.1

0.21

0.71

13.8

95.6

4.3

0.16

0.55

104.6

21.9

109.4

6.8

0.14

0.46

85.3

15.2

114.3

6.4

0.037

0.12

17.2

88.9

8.6

103.4

6

0.096

0.32

7.4

89

9.8

107.1

3.5

0.059

0.20

19.6

101.6

16.2

112.1

4.6

0.43

1.45

15.5

82.8

15.2

102

3.1

0.66

2.22

95.9

10.1

105.2

6.7

101.6

3.8

0.36

1.19

74.1

11.8

107.8

7.7

105.1

1.3

0.098

0.33

100.5

9.2

107.8

4.2

112

3.5

0.036

0.12

0.9946(0.2-25.0)

82.1

4

109.4

4.4

100

7.4

0.059

0.20

sulfotep

0.9928(0.2~25.0)

84.5

2

101.6

6.1

97

7.4

0.065

0.22

terbufos

0.9919(0.2~25.0)

82.9

4.8

105.2

4.9

100.2

6

0.046

0.15

vinclozolin

0.9951(0.5~25.0)

76.9

9.2

103.2

5.7

102.5

7.1

0.22

0.73

SC

NU

MA

PT ED

CE

112.1

RI

profenofos

PT

5.6

AC

ACCEPTED MANUSCRIPT

26

0.12

0.41

PT

isazofos

0.9946(0.5~25.0)

90

7

107.4

6.5

alachlor

0.9940(0.5~25.0)

80.5

4.1

103.2

3.3

metalaxyl

0.9989(0.5~25.0)

124.2

5.7

73.8

10.2

tridimefon

0.9959(0.5~25.0)

79.9

7.2

109

2.7

97.3

8.1

0.072

0.24

114.8

6.3

0.058

0.19

91.1

9.1

0.17

0.58

0.9920(0.2~25.0)

126.1

6.8

121.7

97.8

11.5

0.066

0.22

pendimethalin

0.9998(0.5~25.0)

86.6

6.8

97.6

6.3

90.9

8.5

0.13

0.43

phosfolan

0.9952(2.5~25.0)

82.6

14.4

100.4

9.3

92.1

13.1

0.88

2.94

procymidone

0.9953(0.5~25.0)

76

6.5

101.7

3.9

97.5

10.2

0.097

0.33

profenofos

0.9975(1.0~25.0)

87.9

14.5

111.2

6.3

93

10.6

0.34

1.12

myclobutanil

0.9945(0.2~25.0)

78.3

6.9

105.3

4.7

95.8

10.4

0.068

0.23

flusilazole

0.9926(0.2~25.0)

79.9

6.7

104.6

4.5

93.6

11

0.034

0.11

nitrofen

0.9974(1.0~25.0)

69.6

10.3

87.5

4.6

83.5

8.8

0.31

1.04

propiconazole-1

0.9931(0.5~25.0)

87.6

3.6

106

5.6

89.4

13.9

0.35

1.17

propiconazole-2

0.9916(0.5~25.0)

73.7

4.3

101.6

6.8

90.3

10.4

0.16

0.53

tebuconazole

0.9989(0.5~25.0)

77.8

8.9

99.5

5.5

87

10.9

0.12

0.38

bromopropylate

0.9989(0.2~25.0)

74.5

7.1

98.1

5.7

89.3

9.9

0.066

0.22

phosalone

0.9951(0.2~25.0)

84.6

6.7

96

10.6

73

13.5

0.076

0.26

mirex

0.9996(0.5~25.0)

74.3

13

114.1

3.2

105.2

8.2

0.070

0.23

fenbuconazole

0.9988(0.2~25.0)

81.2

17.2

93.6

18.4

71.6

13.2

0.030

0.099

MA

PT ED

CE

15.6

NU

methyl

SC

isofenphos-

101.5

RI

10.2

AC

ACCEPTED MANUSCRIPT

27

0.9990(1.0~25.0)

92.3

10

108.5

9.7

84

11.5

0.35

1.18

fluvalinate

0.9999(2.5~25.0)

78.5

27.2

118.4

9.5

101.5

7.0

0.69

2.30

cadusafos

0.9951(0.5~25.0)

83.4

3.9

105.2

5.8

100.2

6.6

0.20

0.67

AC

CE

PT ED

MA

NU

SC

RI

difenconazole

PT

ACCEPTED MANUSCRIPT

28

ACCEPTED MANUSCRIPT

PT

Highlights

AC

CE

PT ED

MA

NU

►The method was low cost, rapid and convenient.

SC

► The method covered different classes of pesticides in one analysis.

RI

► A sensitive DLLME method was developed to determine 27 pesticides in wine.

29