Food Chemistry 172 (2015) 585–595

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Food Chemistry journal homepage: www.elsevier.com/locate/foodchem

Analytical Methods

Development, validation and determination of multiclass pesticide residues in cocoa beans using gas chromatography and liquid chromatography tandem mass spectrometry Badrul Hisyam Zainudin a,⇑, Salsazali Salleh a, Rahmat Mohamed a, Ken Choy Yap b, Halimah Muhamad c a Analytical Services Laboratory, Chemistry and Technology Division, Malaysian Cocoa Board, Cocoa Innovative and Technology Centre, Lot 12621 Kawasan Perindustrian Nilai, 71800 Nilai, Negeri Sembilan, Malaysia b Advanced Chemistry Solutions, 43 Jalan Wangsa 1/2, Taman Wangsa Permai, 52200 Kuala Lumpur, Malaysia c Product Development and Advisory Services, Analytical and Quality Development Unit, Malaysian Palm Oil Board, No. 6 Persiaran Institusi, Bandar Baru Bangi, 43000 Kajang, Selangor, Malaysia

a r t i c l e

i n f o

Article history: Received 28 May 2014 Received in revised form 18 September 2014 Accepted 21 September 2014 Available online 28 September 2014 Keywords: Pesticide residues LC–MS/MS GC–MS/MS Cocoa beans Method validation

a b s t r a c t An efficient and rapid method for the analysis of pesticide residues in cocoa beans using gas and liquid chromatography–tandem mass spectrometry was developed, validated and applied to imported and domestic cocoa beans samples collected over 2 years from smallholders and Malaysian ports. The method was based on solvent extraction method and covers 26 pesticides (insecticides, fungicides, and herbicides) of different chemical classes. The recoveries for all pesticides at 10 and 50 lg/kg were in the range of 70–120% with relative standard deviations of less than 20%. Good selectivity and sensitivity were obtained with method limit of quantification of 10 lg/kg. The expanded uncertainty measurements were in the range of 4–25%. Finally, the proposed method was successfully applied for the routine analysis of pesticide residues in cocoa beans via a monitoring study where 10% of them was found positive for chlorpyrifos, ametryn and metalaxyl. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Currently, Malaysia is the largest cocoa processor in Asia and ranks fifth in the world with a volume of 293,000 tonnes in 2013 (International Cocoa Organization., 2014). Unfortunately, due to the shortage of locally produced beans (2809 tonnes), Malaysia had to import 311,608 tonnes of dried cocoa beans mainly from Indonesia and Africa to meet the growing local grindings requirement (Malaysian Cocoa Board, 2014). Hence, food safety played an important role in cocoa industry since the originality of the agrochemicals used in the exporting countries is unknown. The identification of pesticide residues in food with high fat content such as cocoa beans is a difficult and challenging task since the inherent complexity of the matrix could interfere in the determination and quantification of the targeted analyte of interests. Among other constituents contained in cocoa beans are high amount of fatty acids, fatty acid esters, phytosterols, tocopherols, sugar, polyphenols, theobromine, and caffeine. It is well known ⇑ Corresponding author. Tel.: +60 6 7999593; fax: +60 6 7941910. E-mail addresses: [email protected] (B.H. Zainudin), [email protected] (S. Salleh), [email protected] (R. Mohamed), [email protected] (K.C. Yap), [email protected] (H. Muhamad). http://dx.doi.org/10.1016/j.foodchem.2014.09.123 0308-8146/Ó 2014 Elsevier Ltd. All rights reserved.

that the main problem associated when dealing with these kinds of matrices for analysis is that dirty extracts with even a small amount of fats may disrupt the analytical column used in the experiment and harm the analytical instrument ion sources and detectors, and finally upsetting the correct analyte determination through signal suppression and enhancement. Hence, the development of sensitive, selective and reproducible analytical method and technique have always been a prerequisite for the achievement of high quality results in enforcement and monitoring programme (Pizzutti, de Kok, Hiemstra, Wickert, & Prestes, 2009). While the number of publications on the determination of pesticide residues in vegetables, fruits and other foodstuffs were extensive (van der Lee, van der Weg, Traag, & Mol, 2008), the number of papers dedicated to cocoa beans analysis is relatively limited (Rodríguez, Permanyer, Grases, & González, 1991; Hirahara et al., 2005; Guan, Brewer, & Morgan, 2009; Frimpong et al., 2012a; Paul, Lajide, Aiyesanmi, & Lacorte, 2012; Frimpong, Yeboah, Fletcher, Pwamang, & Adomako, 2012b; Ademola & Gideon, 2012). Rodríguez et al. (1991) studied the identification and determination of some organophosphorus and organochlorine pesticides in cocoa beans by gas chromatography mass spectrometry (GC–MS) using Universal Trace Residue Extractor (UNITREX). In

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another study, Hirahara et al. (2005) reported a validation of multiresidue screening method for the determination of 186 pesticides in 11 agricultural products including cocoa beans using a combination of solvent extraction and solid phase extraction (SPE) clean-up with mini column (SAX/PSA). Guan et al. (2009) published a new approach to multiresidue pesticide determination in foods with high fat content using disposable pipette extraction (DPX) and determination with GC–MS. Several papers also appeared on the determination of organochlorine (Frimpong et al., 2012a; Paul et al., 2012), synthetic pyrethroid (Frimpong et al., 2012b) and organophosphorus (Ademola & Gideon, 2012) residues employing a combination of solvent extraction and SPE clean-up. Until now, there are very few studies on the method and level of pesticide residues in cocoa beans. Most of the current methods discussed above involve high volume of extraction solvent, long sample preparation time, and required specialised materials or instrumentation. Furthermore, some of the current methods are focused only to certain classes of pesticides. Therefore, the need for a fast, robust and efficient method for the determination of pesticide residues of different chemical classes in cocoa beans is evident. For this reason, the purpose of this work was to develop and validate an efficient and rapid method for the analysis of different classes of pesticide residues in cocoa beans. Finally, the optimised method was then applied in a real sample monitoring programme carried out on imported and domestic cocoa beans samples collected over two years from smallholders and Malaysian ports. 2. Material and methods 2.1. Reagents and materials HPLC grade acetonitrile and reagent grade formic acid were obtained from Merck (Darmstadt, Germany) while reagent grade ammonium formate was obtained from Sigma–Aldrich (St. Louis, USA). Water was purified through an Elga Purelab Option-Q system (High Wycombe, UK). Two mL mini-centrifuge tube containing 150 mg MgSO4, 50 mg C18, and 50 mg primary secondary amine (PSA) was purchased from Agilent Technologies (Palo Alto, USA). Pesticide reference standards of all analytes were purchased from Dr. Ehrenstorfer (Augsburg, Germany). Individual pesticide stock solutions (1000 mg L 1) were prepared in acetonitrile and kept at 20 °C in the dark. Mixed intermediate standard solutions (10 mg L 1 and 1 mg L 1) of multiple pesticides were prepared by diluting an appropriate volume of each individual stock standard solution in acetonitrile. All working solutions containing the target pesticides were prepared freshly by dilutions of the intermediate standard solution in acetonitrile and kept in scintillation vials at 4 °C in the refrigerator. 2.2. Cocoa beans samples for fortification Dried cocoa beans were obtained from Cocoa Research and Development Centre, Jengka. The samples were used as blanks, fortified samples for recovery assays and matrix-matched standards for calibration in the experiments. 10 g samples were weighed and transferred into 50 mL screw cap centrifuge tubes and fortified with 100 and 500 lL from the 1 mg L 1 intermediate standard solution. The samples were then allowed to stand at room temperature until analysis to give final spiking concentration levels of 10 and 50 lg/kg. 2.3. Extraction and clean-up procedure Ten g of dried cocoa beans was weighed into a 50 mL screw cap centrifuge tube. Then, 10 mL of acetonitrile was added and the mixture was vigorously shaken manually for 1 min and another

1 min using vortex mixer. After that, the mixture was centrifuged at 12,000 rpm for 5 min at 4 °C. From this extract, 3 types of experiment were conducted to study the clean-up effect on the final extracts. The first experiment involves no clean-up step. One mL of the supernatant was filtered through 0.2 lm PVDF filter into autosampler vial before analysis. The second experiment involves hexane partitioning of the extract. Two mL of the supernatant was taken out and mixed with 4 mL of n-hexane in a scintillation vial. The vial was vortex for 30 s and aliquot of 1 mL acetonitrile layer was filtered through 0.2 lm PVDF filter into autosampler vial before analysis. The final experiment consists of d-SPE clean-up using 150 mg MgSO4, 50 mg C18 and 50 mg PSA as sorbents. One mL of the supernatant was transferred into d-SPE tube. The tube was vortexed for 30 s. After centrifugation at 12,000 rpm for 5 min, an aliquot of 0.5 mL extract was filtered through 0.2 lm PVDF filter into autosampler vial before analysis. 2.4. Instrumentation 2.4.1. Liquid chromatography–triple quadrupole mass spectrometry analysis LC–MS/MS analysis was performed using a Perkin Elmer Flexar FX-15 ultra-high performance liquid chromatography (UHPLC) (Perkin Elmer, USA). It was equipped with a reversed-phase C18 analytical column of 50 mm  2.1 mm  1.9 lm particle size (Perkin Elmer, USA). The column oven temperature was set to 40 °C and the flow rate was 250 ll/min. Mobile phase A and B were water and acetonitrile each containing 5 mM ammonium formate and 0.1% formic acid respectively. The linear gradient programme was set as follows: 10% B to 95% B from 0–5 min, followed by 2 min elution time before re-equilibration back to 10% B for 3 min. The injection volume was 5 lL with a run time of 10 min. The UHPLC was hyphenated to a triple quadrupole mass spectrometer AB Sciex 3200 QTrap (Toronto, Canada) equipped with an electrospray ionisation interface set at positive mode. The interface heater was held at the temperature of 550 °C and an ion-spray (IS) voltage of 5500 eV. The nebulising gas (GS1), heating gas (GS2) and curtain gas pressures were set at 40, 40 and 10 psi, respectively during the whole analysis. Purified nitrogen gas was used as collision and spray gas. Analyst software version 1.5.2 was used for method development, data acquisition and data processing. 2.4.2. Gas chromatography–triple quadrupole mass spectrometry analysis GC–MS/MS analysis was performed using an Agilent 7890A GC equipped with an Agilent 7693B autosampler and an Agilent 7000B triple quadrupole mass spectrometry system (Agilent Technologies, Palo Alto, USA). HP-5MS 30 m  0.25 mm i.d.  0.25 lm film thickness was used for the chromatographic separation of the compounds. 1 lL injection volume was performed using a 7890A GC multimode inlet system operated in a cold-splitless injection mode. In this mode, injector temperature was ramped from 70 °C to 280 °C at 900 °C/min. He (99.999%) was used as carrier gas and quenching gas at a flow rate of 1.2 mL/min (constant flow) and 2.25 mL/min, respectively. Nitrogen (99.999%) was used as the collision gas at a flow rate of 1.5 mL/min. The initial oven temperature was 70 °C, with an initial time of 2 min. The oven was heated to 150 °C at 25 °C/min, then to 200 °C at 3 °C/min, followed by a final ramp at 8 °C/min to 280 °C. The final temperature was held for 10 min and the total run time was 41.867 min. The mass spectrometer was operated in electron impact ionisation (EI) mode. The temperatures of the transfer line, ion source, quadrupole 1 and quadrupole 2 were 280 °C, 300 °C,

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180 °C and 180 °C respectively. Agilent MassHunter B.05.00 software was used for instrument control and data analysis.

2.5. Analytical method validation and performance criteria Validation on the optimised analytical method for cocoa beans was performed as described in Document No. SANCO/12495/ 2011 (European Commission DG-SANCO., 2012). The method was tested to assess for validation parameters and criteria in terms of linearity, matrix effect, limit of quantification (LOQ), specificity, accuracy, precision and robustness. The calibration curves were plotted to obtain the linearity of the system at six calibration levels ranging between 5 and 100 ng/mL. The reagent-only calibration standards and matrix-matched calibration standards were used to assess the matrix effects. The LOQ was set at the minimum concentration than can be quantified with acceptable accuracy and precision. Specificity of the proposed method was assessed by analysing the response in both blank and control samples. The accuracy of the method was expressed in terms of average recoveries of spiked blank matrix at 10 and 50 ng/g concentration levels. Precision of the method was represented as relative standard deviation (RSD%) of within-laboratory reproducibility analyses. Robustness was assessed by making deliberate variations to the method (duration of manual shaking, vortex shaking and centrifugation), and the subsequent effects on method performance (accuracy, precision) were investigated. Uncertainty measurement was calculated individually for each pesticide following the guidance of EURACHEM/CITAC Guide CG 4 (Ellison & Williams, 2012).

2.6. Real samples Domestic cocoa beans samples for monitoring study were collected from local farmers, while imported beans were collected from ports and comprised beans from Indonesia, Cameroon, Nigeria, Venezuela, Ghana, Ecuador and Papua New Guinea. A total of 132 samples were collected quarterly in 2012 and 2013 and stored at 4 °C until analysis. Each sample was analysed in duplicate and adhered to the confirmation criteria as described in Document No. SANCO/12495/2011 (European Commission DG-SANCO, 2012).

3. Results and discussion 3.1. Optimisation of LC–MS/MS parameters Initially, a Q1 scan of the mass spectra was recorded to select the most abundant mass to charge ratio (m/z) ion using continuous infusion of each pesticide directly into the MS using syringe pump at a flow rate of 10 lL/min. In this study, the proton adduct [H+] of the molecular ion was chosen as the precursor ion for all analytes. Then, enhance product ion (EPI) scan was conducted to obtain the product mass spectra of the precursor ion. The first transition, which corresponds to the most abundant product ion was used for identification and quantification, while the second one for confirmation purpose. In order to obtain maximum sensitivity for the identification and quantification of the analytes, manual optimisation of the declustering potential (DP), collision energy (CE), entrance potential (EP) and collision exit potential (CXP) was performed for each analyte using 1 lg/mL solution of individual compounds in acetonitrile. Finally the presence of precursor and product ions was investigated using the multiple reaction monitoring (MRM) experiments with a dwell time of 50 ms. The optimised LC–MS/MS parameters were summarised in Table 1.

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3.2. Optimisation of GC–MS/MS parameters Optimisation of GC–MS/MS was performed as previously reported (Lozano et al., 2012). The MS/MS detection method was optimised firstly with individual injections in full-scan mode of each analyte at 1 lg/mL to obtain their retention times and to select the optimal precursor ions. The most intense ion with the highest m/z relationship was selected in most cases. In contrast to LC–MS/MS in which the proton adduct [H+] of the molecular ion was chose as the precursor ion in all cases, the pseudo-molecular ion is hardly used as a precursor ion in GC–MS/MS determination, due to the stronger ionisation occurring with electron impact. Then, product ion scan was conducted with various collision energies, ranging from 10 to 40 V, to obtain the best product ions from the selected precursor ions. The first transition, which corresponds to the most abundant product ion was used for quantification, while the second one for identification purpose. An 8-time-segments MRM method was developed with a solvent delay of 7 min. Finally, once a segment was adjusted, dwell time was increased for the less sensitive analytes by decreasing the dwell time for analytes with enough signal in order to obtain sufficient data points to perform acceptable and accurate quantification. The optimised GC–MS/MS parameters were summarised in Table 2. 3.3. Optimisation of extraction and clean-up procedure 3.3.1. Effect of hexane partitioning GC-ToF analysis of cocoa beans extracts revealed high amounts of interfering co-extractives such as alkaloids, fatty acids esters, phytosterols and tocopherols (data not shown). The total ion chromatogram showed a distinctly large quantity of interfering coextractives especially fatty acids such as propanoic, propenoic, butanoic, octenoic, hexanoic, heptanoic and tetradecanoic acid. In order to overcome this matrix interfering problem, the addition of a very non-polar solvent such as hexane in the extraction step has already been proved to be an efficient way to remove this kind of compounds especially fatty acids and fatty acid esters in baby food (Charlton & Jones, 2007) and later was successfully applied to honeybees and pollens (Wiest et al., 2011). In another study, liquid–liquid extraction using hexane with the aid of 20% aqueous sodium chloride (w/w) solution was able to significantly reduce the amount of matrix co-extracts in the clean-up step (Cajka et al., 2012). Considering this effect, we also evaluated the possibility of purifying the crude acetonitrile extracts with the addition of hexane to remove the matrix co-extracts. Unfortunately, the addition of hexane in the clean-up of the acetonitrile extracts did not help much to the recoveries of some of the pesticides studied (supplementary data). The differences in recoveries were performed using t-test at 95% confidence interval. Statistics showed that significant decrease in average recoveries (at 10 and 50 lg/kg) were observed when hexane partitioning was included in the clean-up step. This can be explained by the higher tendency of some of the pesticides to partition into the hexane layer. Since the recoveries of the pesticides represent an important analytical performance characteristics in the method validation, we concluded that hexane partitioning was not very efficient in cocoa beans matrices and hence rejected form the method development. 3.3.2. Effect of dispersive-SPE clean-up In this study, the extract of the solvent extraction was subjected to dispersive-SPE clean-up using 150 mg MgSO4, 50 mg C18 and 50 mg PSA as sorbents. Graphitised carbon black (GCB) was omitted from the dispersive-SPE since it is well known that GCB has high affinity to planar pesticides (chlorothalonil). Anhydrous MgSO4 was used to absorb micro quantities of water in the solvent

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Table 1 LC–MS/MS acquisition method parameters. Analyte

Retention time (min)

Q1 mass (m/z)

Q3 mass (m/z)

Declustering potential DP (V)

Entrance potential EP (V)

Collision Energy CE (V)

Ametryn

4.36 4.36

228.0 228.0

186.0 96.0

36.0 36.0

10.0 10.0

25.0 35.0

7.0 4.0

Chlorpyrifos

5.64 5.64

350.0 350.0

198.0 97.0

21.0 21.0

10.0 10.0

25.0 41.0

7.0 6.0

Cinosulfuron

3.85 3.85

414.0 414.0

183.2 83.1

36.0 36.0

8.0 8.0

21.0 57.0

4.0 4.0

Cyproconazole

4.74 4.74

292.1 292.1

70.1 125.1

56.0 56.0

10.0 10.0

41.0 39.0

4.0 4.0

Difenoconazole

5.46 5.46

406.0 406.0

251.1 111.1

61.0 61.0

12.0 12.0

31.0 77.0

4.0 4.0

Dimethoate

3.11 3.11

230.0 230.0

199.0 125.0

16.0 11.0

10.0 10.0

13.0 29.0

7.0 6.0

Fluazifop-butyl

5.55 5.55

384.0 384.0

282.0 328.0

51.0 46.0

10.0 10.0

27.0 23.0

10.0 12.0

Isazofos

4.9 4.9

314.0 314.0

120.0 162.0

41.0 41.0

10.0 10.0

35.0 21.0

6.0 7.0

Isoprocarb

4.17 4.17

194.0 194.0

95.0 137.2

20.0 51.0

10.0 10.0

20.0 17.0

17.0 8.0

Metalaxyl

4.18 4.18

280.0 280.0

220.0 160.0

46.0 51.0

10.0 10.0

19.0 31.0

8.0 6.0

Oxadixyl

3.67 3.67

279.0 279.0

219.0 133.0

46.0 41.0

10.0 10.0

17.0 29.0

8.0 6.0

Propoxur

3.79 3.79

210.0 210.0

111.0 168.0

11.0 6.0

10.0 10.0

19.0 11.0

6.0 7.0

Quinalphos

5.04 5.04

299.0 299.0

147.0 163.0

31.0 21.0

10.0 10.0

29.0 29.0

6.0 7.0

Quizalofop-ethyl

5.41 5.41

373.0 373.0

299.0 271.0

71.0 76.0

10.0 10.0

25.0 33.0

10.0 10.0

Terbuthylazine

4.61 4.61

230.0 230.0

174.0 104.0

41.0 41.0

10.0 10.0

23.0 43.0

7.0 6.0

and by doing so, it will make the final acetonitrile extracts less polar and causing precipitation of certain polar matrix co-extracts. PSA represents a weak ion exchanger which mainly removes sugars, fatty acids, organic acids, and some pigments, while C18 can be used for the reduction of lipids and non-polar interferences (Cajka et al., 2012). The recovery profiles of the effect of dispersive-SPE clean-up were summarised in the bar chart as shown in Fig. 1. While the addition of C18 is quite beneficial in reducing the matrix interferences, the use of PSA could raise some concerns especially to the recoveries of certain pesticides. Previous studies reported the decreasing of recoveries of some pesticides that attributed to the binding to PSA (Lozano et al., 2012; Mayer-Helm, 2009; Muhamad, Zainudin, & Abu Bakar, 2012). In this study, the most prominent effect of PSA was on the reduced recovery rate (less than 20%) of cinosulfuron in LC–MS/MS compared to acetonitrile extract without clean-up. The possible reason maybe because cinosulfuron contains acidic sulfonamide group which may react with basic PSA sorbent that contains amino groups. When PSA was omitted, the recoveries obtained improved considerably. On the other hand, most of the LC amenable pesticides studied gave acceptable recoveries and precision with or without dispersive-SPE clean-up except for acephate and methidathion. In contrary, GC amenable pesticides gave noticeable results when dispersive-SPE were used in the clean-up step. From the analysis, it was found that fatty acids (butanoic acid, decanoic acid, heptanoic acid, hexanoic acid, and linoleic acid) and phytosterol (stigmasterol) were reduced significantly while alkaloids (caffeine and theobromine) and tocopherol (c-tocopherol) did not affect

Collision cell exit potential CXP (V)

much when dispersive-SPE was applied. As predicted from the chromatograms (not shown), heavy interferences in the extracts would result in the matrix enhancement effect of some pesticides. Fig. 1(B) revealed that 25 out of 31 pesticides analysed using GC– MS/MS showed signal enhancement without the dispersive-SPE clean-up and 13 of them gave recoveries of more than the acceptable value of 120%. This shows that the addition of PSA removed some of the matrix compounds which caused signal enhancement in GC–MS/MS. In contrast, strong matrix suppression was observed for ametryn when PSA was not present. Here, large matrix interferences at the analyte retention time is likely to be the cause, since they can hinder the correct and accurate analyte integration and quantitation. Besides using the full scan chromatograms, clean-up efficiency of the method was also assessed through gravimetric measurements. In this study, the amount of co-extractives from the samples into the extracts were measured gravimetrically at each step of acetonitrile extraction and dispersive-SPE clean-up. Vials of the final solution were weighed before the addition of extracts. Then, each sample extracts obtained after acetonitrile extraction and dispersive-SPE clean-up were dried via N-evaporator under a hot water bath. The weight difference was recorded to estimate the amount of co-extracted matrix in the initial and final extracts. From the results obtained, the amount of matrix co-extracted for coca beans samples after acetonitrile extraction and dispersiveSPE clean-up was 3.3 ± 0.6 mg/g (n = 3) and 1.3 ± 0.3 mg/g (n = 3) respectively, originated from 0.5 g of the sample. These values represented 0.3% and 0.1% of the sample mass. Therefore, solvent extraction using acetonitrile was able to remove 99.7% of the

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B.H. Zainudin et al. / Food Chemistry 172 (2015) 585–595 Table 2 GC–MS/MS acquisition method parameters. Analyte

Retention time (min)

Q1 mass (m/z)

Q3 mass (m/z)

Time segment

Collision Energy CE (V)

Dwell time (ms)

Acephate

8.10 8.10

136.0 142.0

94.0 96.0

1

10 5

10 10

Ametryn

17.90 17.90

227.0 227.0

58.1 170.1

3

10 10

5 5

Chlorothalonil

15.54 15.54

263.8 265.8

229.0 168.0

3

20 25

5 5

Chlorpyrifos

19.97 19.97

198.9 196.9

171.0 169.0

3

15 15

5 5

Cyfluthrin (II)

32.23 32.23

162.9 162.9

90.9 127.0

7

15 5

4 4

Cyfluthrin (III,IV)

32.38 32.38

162.9 162.9

90.9 127.0

7

15 5

4 4

k-Cyhalothrin

30.35 30.35

181.1 197.0

152.0 141.0

6

25 10

10 10

Cypermethrin

32.77 32.77

163.1 181.2

127.1 152.1

7

5 25

4 4

Cyproconazole

25.28 25.28

222.0 139.0

125.1 111.0

5

15 15

5 5

Deltamethrin

35.50 35.50

250.7 181.0

172.0 152.1

8

5 25

5 5

Difenoconazole I

34.77 34.77

322.8 324.8

264.8 266.8

8

15 15

5 5

Difenoconazole II

34.90 34.90

322.8 324.8

264.8 266.8

8

15 15

5 5

Dimethoate

13.39 13.39

86.9 92.9

46.0 63.0

2

15 10

20 20

Endosulphan I

23.22 23.22

241.0 207.0

206.0 172.0

4

15 15

5 5

Endosulphan II

25.53 25.53

207.0 241.0

172.0 206.0

5

15 15

5 5

Endosulphan sulphate

27.00 27.00

271.9 273.8

237.0 238.9

5

15 15

5 5

Fenvalerate I

34.02 34.02

167.0 181.0

125.1 152.1

8

5 20

5 5

Fenvalerate II

34.40 34.40

167.0 181.0

125.1 152.1

8

5 25

5 5

Fluazifop-butyl

25.64 25.64

281.9 281.9

91.0 238.0

5

20 20

5 5

Isazofos

15.79 15.79

161.0 161.0

119.1 146.0

3

5 5

5 5

Isoprocarb

9.69 9.69

121.0 136.0

77.1 121.1

1

20 10

10 10

Metalaxyl

18.09 18.09

234.0 234.0

146.1 174.1

3

20 10

5 5

Methidathion

22.90 22.90

144.9 144.9

85.0 58.1

4

5 15

5 5

Oxadixyl

26.17 26.17

163.0 132.0

132.1 117.1

5

5 15

5 5

Permethrin I

31.29 31.29

163.0 183.1

127.0 168.1

6

5 10

10 10

Permethrin II

31.47 31.47

162.9 182.9

127.1 168.1

6

5 10

10 10

Propoxur

10.98 10.98

110.0 152.0

63.0 110.0

1

25 10

10 10

Quinalphos

22.32 22.32

146.0 146.0

118.0 91.0

4

10 30

5 5

Quizalofop-ethyl

32.73 32.73

371.8 163.0

298.9 100.0

7

10 20

4 4

Terbuthylazine

14.53 14.53

228.9 214.0

173.1 71.0

2

5 20

10 10

Triadimenol

22.34 22.34

168.0 128.0

70.0 65.0

4

10 25

5 5

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B.H. Zainudin et al. / Food Chemistry 172 (2015) 585–595

Fig. 1. Comparison of percentage recoveries at 10 lg/kg (n = 20) spiking level between acetonitrile extracts and acetonitrile extracts + d-SPE clean-up using (A) LC–MS/MS and (B) GC–MS/MS.

Table 3 Average recovery (%), RSD (%), method LOQ (lg/kg), measurement uncertainty (%) and MRLs obtained by acetonitrile extraction and d-SPE clean-up of cocoa beans samples, spiked at 10 and 50 lg/kg, and analysed by LC–MS/MS and GC–MS/MS. Analyte

MRLa (lg/kg)

LC–MS/MS

10 lg/kg (n = 20)

50 lg/kg (n = 14)

Rec (%)

RSD (%)

Rec (%)

RSD (%)

106.7 91.7 101.1 105.0 – 106.1 104.3 107.0 117.1 96.6 118.7 110.6 108.5 105.9 84.0 89.0 97.3 113.4 109.0 98.9 102.8 101.1 95.6 109.4 98.5 98.3 95.6 107.3 105.5 104.3 92.3 105.8

12 4 12 7 – 9 11 10 5 3 8 10 3 7 14 9 7 7 11 3 4 6 5 6 4 11 6 8 6 3 2 6

103.1 86.4 119.3 94.0 – 94.6 113.6 104.5 104.7 92.2 106.2 100.5 99.2 101.9 83.2 83.9 92.4 107.8 104.8 94.5 98.8 95.2 90.4 103.5 93.7 88.3 86.5 101.9 98.3 95.8 87.1 101.4

18 6 6 6 – 17 12 7 4 3 11 2 2 5 9 6 8 6 3 5 3 8 5 5 5 7 4 13 6 4 3 7

LOQ (lg/kg)

10 10 10 10 – 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10

Measurement uncertainty (%)

10 lg/kg (n = 20)

50 lg/kg (n = 14)

Rec (%)

RSD (%)

Rec (%)

RSD (%)

18 5 17 12 – 21 16 10 16 5 19 8 6 8 23 11 8 13 12 5 6 7 6 8 7 9 7 10 7 6 4 7

50.0 80.1 – 88.5 102.1 – – – – 96.0 – 93.5 – 82.9 – – – – – 84.2 80.6 81.6 90.3 83.5 86.1 – – 85.4 95.9 78.4 85.8 –

107 7 – 20 10 – – – – 9 – 7 – 8 – – – – – 7 7 6 7 29 11 – – 9 10 14 7 –

41.9 82.2 – 84.0 95.1 – – – – 89.6 – 92.7 – 79.4 – – – – – 83.0 81.0 82.0 87.3 84.1 84.5 – – 79.2 86.8 80.9 82.5 –

52 5 – 7 7 – – – – 4 – 3 – 8 – – – – – 5 7 4 4 33 5 – – 14 5 5 4 –

LOQ (lg/kg)

Measurement uncertainty (%)

n.q 10 – 10 10 – – – – 10 – 10 – 10 – – – – – 10 10 10 10 n.q 10 – – 10 10 10 10 –

n.q 10 – 25 10 – – – – 13 – 7 12 – – – – – 8 9 9 11 n.q 14 – – 18 12 21 11 –

200 200 50 50 100 100 100 100 50 100 50 100 100 100 100 100 100 50 50 100 50 100 200 100 1000 – – 50 100 100 500 200

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Acephate Ametryn Chlorothalonil Chlorpyrifos Cinosulfuron Cyfluthrin (II) Cyfluthrin (III,IV) k-Cyhalothrin Cypermethrin Cyproconazole Deltamethrin Difenoconazole I Difenoconazole II Dimethoate Endosulphan I Endosulphan II Endosulphan Sulphate Fenvalerate I Fenvalerate II Fluazifop-butyl Isazofos Isoprocarb Metalaxyl Methidathion Oxadixyl Permethrin I Permethrin II Propoxur Quinalphos Quizalofop–ethyl Terbuthylazine Triadimenol

GC–MS/MS

n.q.: not qualifying for quantitation criteria. a Food Regulations (2010).

591

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matrix co-extracted, while the additional dispersive-SPE clean-up removed another 0.1%. 3.4. Method validation GC–MS/MS analysis with the clean-up step and LC–MS/MS analysis without the clean-up step were chose as the final method for validation study. Good linearity was achieved for most of the pesticides studied using LC–MS/MS and GC–MS/MS with correlation coefficients better than 0.990. However, acephate and methidathion gave R values below 0.990. As previously mentioned in Section 3.3.2, both analytes gave rather low signal intensity at lower concentration levels with high baseline noise when using LC–MS/MS. Fortunately, both pesticides gave good linearity with R values of more than 0.990 when using GC–MS/MS. The observed effect of an increase or decrease in

detector response of pesticides in matrix extracts compared with the same pesticides present in just pure solvent was studied in order to assess matrix effect of cocoa beans extracts. In this study, only minor matrix effects were observed for LC–MS/MS analysis. As for GC–MS/MS, same good results were also achieved after clean-up step with dispersive-SPE was introduced. However, matrix effects are well known to be variable with time and condition of instrument used. Therefore, quantification using matrix-matched standards was opted rather than pure solvent standards. Table 3 displays the method performance and validation parameters for acetonitrile extraction and dispersive-SPE cleanup of cocoa beans employing LC–MS/MS and GC–MS/MS. Limit of quantification (LOQ) values were determined as the lowest concentration of the analyte that has been validated with acceptable accuracy (70–120%) and precision (RSD < 20%) by applying the

Fig. 2. LC–MS/MS extracted ion chromatogram (34 MRM transitions) of (A) blank cocoa beans extract and (B) spiked cocoa beans at 10 lg/kg.

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complete analytical method. In this study, LOQ for all pesticides studied were set at 10 lg/kg for both LC–MS/MS and GC–MS/MS analysis since it was the lowest spiking level with acceptable accuracy and precision. This value is lower than national MRLs (Food Regulations, 2010). Therefore, it can be concluded that both LC– MS/MS and GC–MS/MS method are sensitive enough to quantify all pesticides studied in cocoa beans. Selectivity and specificity of the instruments are defined as the ability of the extraction, the clean-up, the separation and the detection method to discriminate between the analyte and other compounds, and at the same time capable of providing signals that effectively identify the analyte (European Commission DG-SANCO, 2012). In order to do that, the selectivity of the analytical method in this work was determined by comparing the chromatograms of a blank matrix solution with the spiked matrix solution (Figs. 2 and 3). Since multiple reaction monitoring (MRM) analysis was applied in this study using LC–MS/MS and GC–MS/MS, we can see that the analytes of interest were well separated from the other components present in the extracts and hence allowed the differentiation and quantification of the analytes. In the meantime, the method specificity was demonstrated using retention time matching and ion ratios between the analyte in the sample extract and calibration standard. The tolerance for retention time is ±0.5% for GC and ±2.5% for LC. Meanwhile, maximum permitted tolerance for relative ion ratio is ±20%. This showed that the analytical method and subsequent detectors used are highly selective and highly specific. Duration of extraction that consists of shaking, vortex mix, and centrifuge time may significantly affect the efficiency of extraction. The effect of these parameters was checked with robustness/ ruggedness test and the optimised conditions should be kept constant as far as possible. During the initial study, variations in the extraction parameters previously mentioned generally had little effect on the mean recoveries and RSDs. This showed that the method was adequately robust to be successfully applied by inexperienced technicians.

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As demonstrated in Table 3, the recoveries of the majority of the analytes at 10 and 50 lg/kg were ranged between 78.4–102.1% for LC–MS/MS and 83.2–119.3% for GC–MS/MS with RSDs below 20% for most cases. Since acephate gave unacceptable recovery values and RSDs, it was deemed to be not quantifiable and omitted from the LC–MS/MS analysis. The same thing goes to methidathion, which obtained acceptable recoveries, but poor precision (>20%). However, it is fascinating to know that both pesticides gave acceptable recoveries and RSDs when GC–MS/MS was used. One would think that acephate should give better results in LC, but we had to resort to adding it to the list of analytes in GC–MS/MS in overall monitoring programme. The change in mobile phase or final extracts solution could rectify this problem in LC analysis though. From the 26 pesticides studied, 25 can be quantified using GC–MS/ MS, whereas LC–MS/MS only managed to cover 15 pesticides. The rest of the pesticides that can only be analysed using GC–MS/MS mostly belong to organochlorine and pyrethroid group. Pyrethroid pesticides are known to pose difficulties in multiresidue analysis due to their highly non-polar nature and lower signal intensities (Koesukwiwat, Lehotay, Miao, & Leepipatpiboon, 2010). The only pesticide that cannot be analysed using GC–MS/MS is cinosulfuron. 3.5. Estimation of measurement uncertainty In the presented study, the ‘‘bottom-up’’ approach was used for estimation of combined standard uncertainty. By using this approach, it was found that uncertainty of extraction which comprises two components – (i) repeatability of extraction and (ii) uncertainty of extraction recovery, were shown to represent the main source of combined standard uncertainty. On the other hand, uncertainties associated with calibration (uncertainties of weighing or diluting standards, uncertainties of purity of standards) were not so important. The relative expanded uncertainty was then calculated by using the coverage factor k = 2 at 95% confidence level. Combined standard uncertainties associated with the described analytical method ranged for individual compounds from 7% to

Fig. 3. GC–MS/MS extracted ion chromatogram (62 MRM transitions) of (A) blank cocoa beans extract and (B) spiked cocoa beans at 10 lg/kg.

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25% for LC–MS/MS and 4% to 23% for GC–MS/MS as shown in Table 3. Additionally, a Student’s t test was used to determine whether the mean recovery is significantly different from 100%. From the results obtained, it was found that the calculated t values were greater than the critical value tcrit, hence recoveries obtained in the validation data were significantly different from 100%. In this case, it was recommended to include the recovery in the calculation of the results. 3.6. Real samples monitoring The effectiveness of the developed method was applied to routine monitoring analysis of imported and domestic cocoa beans samples collected from 2012 to 2013 from smallholders and Malaysian ports. 132 dried cocoa beans samples were analysed using GC–MS/MS and confirmed using LC–MS/MS method. Among those samples were from local smallholders (Malaysia), Indonesia, Cameroon, Nigeria, Venezuela, Ghana, Ecuador and Papua New Guinea. During the monitoring programme, most of the residue levels detected are within the 1–10 lg/kg. The results are consistent with the study conducted by Pizzutti et al. (2009), where this could be explained by the fact samples were mostly taken from big shipments, usually representing large, mixed lots or consignments. Overall, there were 3 different pesticides found in cocoa beans with concentration of more than 10 lg/kg. Chlorpyrifos, ametryn and metalaxyl were detected in 14 positive samples, ranged from 10 to 200 lg/kg. The most prevalent compound was chlorpyrifos, which was found in 9 samples. It was found that some samples contained residues around MRL (50 lg/kg) and two samples exceeded the MRL, with concentrations of 149 and 200 lg/kg. In Japan, several consignments of cocoa beans have been denied entry into the country since the new legislation on MRLs came into effect in May 2006 by the Japanese Ministry of Health, Labour and Welfare (MHLW) (Ministry of Health, Labour and Welfare, http:// www.mhlw.go.jp/english/topics/foodsafety/positivelist060228/ introduc-tion.html). According to the progress report from the International Cocoa Organization (ICCO) regarding on the action programme of pesticide residues, the most notable active ingredients detected included pirimiphos-methyl, chlorpyrifos and 2,4-D (ICCO Progress Report, http://www.icco.org/about-us/international-cocoa-agreements/cat_view/30-related-documents/34-pestsand-diseases.html). Data form Chocolate and Cocoa Association of Japan (CCAJ) showed that from 2006 to 2010, 51 cases of chlorpyrifos residues violation occurred in Japan for imported cocoa beans with minimum and maximum concentration of 60 lg/kg and 1760 lg/kg, respectively (Kaminaga, 2011). On the other hand, in this study, residues detected for ametryn and metalaxyl were well below the national MRLs. From the monitoring results, it can be concluded that in order to reduce consumer’s risk to pesticide residues in Malaysia, frequent monitoring programme should be undertaken to cocoa beans samples especially for imported beans since the originality of the agrochemicals used in the exporting countries is unknown. 4. Conclusions According to Malaysian Food Act and Regulations (Food Regulations, 2010), there are a total of 34 pesticides allowed to be use in cocoa plantation with national MRLs regulated. Out of these 34 pesticides, 26 pesticides were covered in this study. The rest of the pesticides were either belong to difficult pesticides (dithiocarbamate, fosetyl ammonium, MSMA) or highly polar pesticides (glufosinate, glyphosate, paraquat), hence require specific single residue method (SRM). Reliable, efficient and rapid sample extraction and clean-up technique based on acetonitrile extraction and dispersive-SPE was successfully developed and validated to

simultaneously analyse 26 pesticides of different chemical classes in cocoa beans. The diversity of the selected pesticides highlights the need of applying both gas and liquid chromatography hyphenated to triple quadrupole mass spectrometry for consistent and reliable monitoring programme. The analytical method gave satisfactory recoveries with good precision for most of the pesticides studied. However, there are some problematic pesticides such as acephate and methidathion, which gave poor recoveries and precision using LC–MS/MS. Nevertheless, the problem was resolved by using GC–MS/MS instead. Therefore, a clear conclusion can be made that both LC amenable and GC amenable pesticides react differently to dispersive-SPE clean-up. In LC–MS/MS, matrix interferences were not prominent and the addition of PSA in the clean-up step would result in the loss of cinosulfuron. Meanwhile, PSA played a critical role in GC–MS/MS especially in terms of matrix effects. According to Lehotay et al. (2010), GC–MS matrix effects were more dependent on the condition of the instrument than on the method or matrix. They also stated that a combination of matrix enhancement for pesticides susceptible to degradation on active sites occurs in GC at the same time as matrix diminishment effects due to build-up of non-volatile materials in the inlet. For this reason, dispersive-SPE clean-up is imperative in GC–MS/MS analysis, while acetonitrile extraction without dispersive-SPE clean-up is the method of choice for LC–MS/MS analysis in this study. The method was applied to 132 cocoa beans samples via a monitoring programme from 2012 to 2013 and 10% of them was found positive for chlorpyrifos, ametryn and metalaxyl. Acknowledgements The authors would like to thank the Malaysian Cocoa Board (MCB) for financially supporting this work and the Director General of the MCB for permission to publish this paper. The authors are also highly indebted to the Regulatory and Quality Control Division for providing the monitoring samples for analysis. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foodchem.2014. 09.123. References Ademola, F. A., & Gideon, A. I. (2012). Determination of organophosphorus pesticide residue in some selected cocoa farms in Idanre, Ondo State, Nigeria. Electronic Journal of Environmental, Agricultural and Food Chemistry, 11, 118–127. Cajka, T., Sandy, C., Bachanova, V., Drabova, L., Kalachova, K., Pulkrabova, J., & Hajslova, J. (2012). Streamlining sample preparation and gas chromatography– tandem mass spectrometry analysis of multiple pesticide residues in tea. Analytica Chimica Acta, 743, 51–60. Charlton, A. J. A., & Jones, A. (2007). Determination of imidazole and triazole fungicide residues in honeybees using gas chromatography–mass spectrometry. Journal of Chromatography A, 1141, 117–122. Ellison, S. L. R., & Williams, A. (2012). EURACHEM/CITAC Guide CG 4, Quantifying Uncertainty in Analytical Measurement (3rd ed.). European Commission DG-SANCO. (2012). Method validation and quality control procedures for pesticide residues analysis in food and feed, No. SANCO/12495/ 2011. Food Regulations (2010). Food ACT 1983 (Act 281) and regulations. Selangor: International Law Book Services. Frimpong, S., Yeboah, P., Fletcher, J. J., Adomako, D., Osei-Fosu, P., Acheampong, K., Gbeddy, G., Doyi, I., Egbi, C., Dampare, S., & Pwamang, J. (2012a). Organochlorine pesticides levels in fermented dried cocoa beans produced in Ghana. Elixir Agriculture, 44, 7280–7284. Frimpong, S. K., Yeboah, P. O., Fletcher, J. J., Pwamang, J., & Adomako, D. (2012b). Assessment of synthetic pyrethroids pesticides residues in cocoa beans from Ghana. Elixir Food Science, 49, 9871–9875. Guan, H., Brewer, W. E., & Morgan, S. L. (2009). New approach to multiresidue pesticide determination in foods with high fat content using disposable pipette extraction (DPX) and gas chromatography–mass spectrometry (GC-MS). Journal of Agricultural and Food Chemistry, 57, 10531–10538.

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