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 pesticideresidues in cocoa beans using gas chromatography and liquidchromatography tandem mass spectrometry

http://dx.doi.org/10.1016/j.foodchem.2014.09.1230308-8146/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +60 6 7999593; fax: +60 6 7941910.E-mail addresses: badrul@koko.gov.my (B.H. Zainudin), salsazali@koko.gov.my

(S. Salleh), rahmat@koko.gov.my (R. Mohamed), haruyapkc@gmail.com (K.C. Yap),halimah@mpob.gov.my (H. Muhamad).

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, Malaysiab Advanced Chemistry Solutions, 43 Jalan Wangsa 1/2, Taman Wangsa Permai, 52200 Kuala Lumpur, Malaysiac Product Development and Advisory Services, Analytical and Quality Development Unit, Malaysian Palm Oil Board, No. 6 Persiaran Institusi, Bandar Baru Bangi, 43000Kajang, Selangor, Malaysia

a r t i c l e i n f o a b s t r a c t

Article history:Received 28 May 2014Received in revised form 18 September 2014Accepted 21 September 2014Available online 28 September 2014

Keywords:Pesticide residuesLC–MS/MSGC–MS/MSCocoa beansMethod validation

An efficient and rapid method for the analysis of pesticide residues in cocoa beans using gas and liquidchromatography–tandem mass spectrometry was developed, validated and applied to imported anddomestic cocoa beans samples collected over 2 years from smallholders and Malaysian ports. The methodwas based on solvent extraction method and covers 26 pesticides (insecticides, fungicides, and herbi-cides) of different chemical classes. The recoveries for all pesticides at 10 and 50 lg/kg were in the rangeof 70–120% with relative standard deviations of less than 20%. Good selectivity and sensitivity wereobtained with method limit of quantification of 10 lg/kg. The expanded uncertainty measurements werein the range of 4–25%. Finally, the proposed method was successfully applied for the routine analysis ofpesticide residues in cocoa beans via a monitoring study where 10% of them was found positive for chlor-pyrifos, ametryn and metalaxyl.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Currently, Malaysia is the largest cocoa processor in Asia andranks fifth in the world with a volume of 293,000 tonnes in 2013(International Cocoa Organization., 2014). Unfortunately, due tothe shortage of locally produced beans (2809 tonnes), Malaysiahad to import 311,608 tonnes of dried cocoa beans mainly fromIndonesia and Africa to meet the growing local grindings require-ment (Malaysian Cocoa Board, 2014). Hence, food safety playedan important role in cocoa industry since the originality of theagrochemicals used in the exporting countries is unknown.

The identification of pesticide residues in food with high fatcontent such as cocoa beans is a difficult and challenging task sincethe inherent complexity of the matrix could interfere in the deter-mination and quantification of the targeted analyte of interests.Among other constituents contained in cocoa beans are highamount of fatty acids, fatty acid esters, phytosterols, tocopherols,sugar, polyphenols, theobromine, and caffeine. It is well known

that the main problem associated when dealing with these kindsof matrices for analysis is that dirty extracts with even a smallamount of fats may disrupt the analytical column used in theexperiment and harm the analytical instrument ion sources anddetectors, and finally upsetting the correct analyte determinationthrough signal suppression and enhancement. Hence, the develop-ment of sensitive, selective and reproducible analytical methodand technique have always been a prerequisite for the achieve-ment of high quality results in enforcement and monitoring pro-gramme (Pizzutti, de Kok, Hiemstra, Wickert, & Prestes, 2009).

While the number of publications on the determination of pes-ticide residues in vegetables, fruits and other foodstuffs wereextensive (van der Lee, van der Weg, Traag, & Mol, 2008), the num-ber 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 deter-mination of some organophosphorus and organochlorine pesti-cides 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 mul-tiresidue screening method for the determination of 186 pesticidesin 11 agricultural products including cocoa beans using a combina-tion of solvent extraction and solid phase extraction (SPE) clean-upwith mini column (SAX/PSA). Guan et al. (2009) published a newapproach to multiresidue pesticide determination in foods withhigh fat content using disposable pipette extraction (DPX) anddetermination with GC–MS. Several papers also appeared on thedetermination of organochlorine (Frimpong et al., 2012a; Paulet al., 2012), synthetic pyrethroid (Frimpong et al., 2012b) andorganophosphorus (Ademola & Gideon, 2012) residues employinga combination of solvent extraction and SPE clean-up.

Until now, there are very few studies on the method and level ofpesticide residues in cocoa beans. Most of the current methods dis-cussed above involve high volume of extraction solvent, long sam-ple preparation time, and required specialised materials orinstrumentation. Furthermore, some of the current methods arefocused only to certain classes of pesticides. Therefore, the needfor a fast, robust and efficient method for the determination of pes-ticide residues of different chemical classes in cocoa beans is evi-dent. For this reason, the purpose of this work was to develop andvalidate an efficient and rapid method for the analysis of differentclasses of pesticide residues in cocoa beans. Finally, the optimisedmethod was then applied in a real sample monitoring programmecarried out on imported and domestic cocoa beans samples col-lected 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 wereobtained from Merck (Darmstadt, Germany) while reagent gradeammonium 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 containing150 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 purchasedfrom Dr. Ehrenstorfer (Augsburg, Germany). Individual pesticidestock solutions (�1000 mg L�1) were prepared in acetonitrile andkept at �20 �C in the dark. Mixed intermediate standard solutions(10 mg L�1 and 1 mg L�1) of multiple pesticides were prepared bydiluting an appropriate volume of each individual stock standardsolution in acetonitrile. All working solutions containing the targetpesticides were prepared freshly by dilutions of the intermediatestandard solution in acetonitrile and kept in scintillation vials at4 �C in the refrigerator.

2.2. Cocoa beans samples for fortification

Dried cocoa beans were obtained from Cocoa Research andDevelopment Centre, Jengka. The samples were used as blanks, for-tified samples for recovery assays and matrix-matched standardsfor calibration in the experiments. 10 g samples were weighedand transferred into 50 mL screw cap centrifuge tubes and fortifiedwith 100 and 500 lL from the 1 mg L�1 intermediate standardsolution. The samples were then allowed to stand at room temper-ature until analysis to give final spiking concentration levels of 10and 50 lg/kg.

2.3. Extraction and clean-up procedure

Ten g of dried cocoa beans was weighed into a 50 mL screw capcentrifuge tube. Then, 10 mL of acetonitrile was added and themixture was vigorously shaken manually for 1 min and another

1 min using vortex mixer. After that, the mixture was centrifugedat 12,000 rpm for 5 min at 4 �C. From this extract, 3 types of exper-iment were conducted to study the clean-up effect on the finalextracts.

The first experiment involves no clean-up step. One mL of thesupernatant was filtered through 0.2 lm PVDF filter into autosam-pler vial before analysis.

The second experiment involves hexane partitioning of theextract. Two mL of the supernatant was taken out and mixed with4 mL of n-hexane in a scintillation vial. The vial was vortex for 30 sand aliquot of 1 mL acetonitrile layer was filtered through 0.2 lmPVDF filter into autosampler vial before analysis.

The final experiment consists of d-SPE clean-up using 150 mgMgSO4, 50 mg C18 and 50 mg PSA as sorbents. One mL of thesupernatant was transferred into d-SPE tube. The tube was vor-texed for 30 s. After centrifugation at 12,000 rpm for 5 min, an ali-quot of 0.5 mL extract was filtered through 0.2 lm PVDF filter intoautosampler vial before analysis.

2.4. Instrumentation

2.4.1. Liquid chromatography–triple quadrupole mass spectrometryanalysis

LC–MS/MS analysis was performed using a Perkin Elmer FlexarFX-15 ultra-high performance liquid chromatography (UHPLC)(Perkin Elmer, USA). It was equipped with a reversed-phase C18analytical column of 50 mm � 2.1 mm � 1.9 lm particle size (Per-kin Elmer, USA). The column oven temperature was set to 40 �Cand the flow rate was 250 ll/min. Mobile phase A and B werewater and acetonitrile each containing 5 mM ammonium formateand 0.1% formic acid respectively. The linear gradient programmewas set as follows: 10% B to 95% B from 0–5 min, followed by2 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. TheUHPLC was hyphenated to a triple quadrupole mass spectrometerAB Sciex 3200 QTrap (Toronto, Canada) equipped with an electro-spray ionisation interface set at positive mode. The interface heaterwas held at the temperature of 550 �C and an ion-spray (IS) voltageof 5500 eV. The nebulising gas (GS1), heating gas (GS2) and curtaingas pressures were set at 40, 40 and 10 psi, respectively during thewhole analysis. Purified nitrogen gas was used as collision andspray gas. Analyst software version 1.5.2 was used for methoddevelopment, data acquisition and data processing.

2.4.2. Gas chromatography–triple quadrupole mass spectrometryanalysis

GC–MS/MS analysis was performed using an Agilent 7890A GCequipped with an Agilent 7693B autosampler and an Agilent 7000Btriple quadrupole mass spectrometry system (Agilent Technolo-gies, Palo Alto, USA). HP-5MS 30 m � 0.25 mm i.d. � 0.25 lm filmthickness was used for the chromatographic separation of the com-pounds. 1 lL injection volume was performed using a 7890A GCmultimode inlet system operated in a cold-splitless injectionmode. In this mode, injector temperature was ramped from 70 �Cto 280 �C at 900 �C/min. He (99.999%) was used as carrier gasand quenching gas at a flow rate of 1.2 mL/min (constant flow)and 2.25 mL/min, respectively. Nitrogen (99.999%) was used asthe collision gas at a flow rate of 1.5 mL/min.

The initial oven temperature was 70 �C, with an initial time of2 min. The oven was heated to 150 �C at 25 �C/min, then to 200 �Cat 3 �C/min, followed by a final ramp at 8 �C/min to 280 �C. The finaltemperature was held for 10 min and the total run time was41.867 min. The mass spectrometer was operated in electronimpact 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 soft-ware was used for instrument control and data analysis.

2.5. Analytical method validation and performance criteria

Validation on the optimised analytical method for cocoa beanswas performed as described in Document No. SANCO/12495/2011 (European Commission DG-SANCO., 2012). The method wastested to assess for validation parameters and criteria in terms oflinearity, matrix effect, limit of quantification (LOQ), specificity,accuracy, precision and robustness. The calibration curves wereplotted to obtain the linearity of the system at six calibration levelsranging between 5 and 100 ng/mL. The reagent-only calibrationstandards and matrix-matched calibration standards were usedto assess the matrix effects. The LOQ was set at the minimum con-centration than can be quantified with acceptable accuracy andprecision. Specificity of the proposed method was assessed by ana-lysing the response in both blank and control samples. The accu-racy of the method was expressed in terms of average recoveriesof spiked blank matrix at 10 and 50 ng/g concentration levels. Pre-cision of the method was represented as relative standard devia-tion (RSD%) of within-laboratory reproducibility analyses.Robustness was assessed by making deliberate variations to themethod (duration of manual shaking, vortex shaking and centrifu-gation), and the subsequent effects on method performance (accu-racy, precision) were investigated. Uncertainty measurement wascalculated individually for each pesticide following the guidanceof EURACHEM/CITAC Guide CG 4 (Ellison & Williams, 2012).

2.6. Real samples

Domestic cocoa beans samples for monitoring study were col-lected from local farmers, while imported beans were collectedfrom ports and comprised beans from Indonesia, Cameroon, Nige-ria, Venezuela, Ghana, Ecuador and Papua New Guinea. A total of132 samples were collected quarterly in 2012 and 2013 and storedat 4 �C until analysis. Each sample was analysed in duplicate andadhered to the confirmation criteria as described in DocumentNo. 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 selectthe most abundant mass to charge ratio (m/z) ion using continuousinfusion of each pesticide directly into the MS using syringe pumpat a flow rate of 10 lL/min. In this study, the proton adduct [H+] ofthe molecular ion was chosen as the precursor ion for all analytes.Then, enhance product ion (EPI) scan was conducted to obtain theproduct mass spectra of the precursor ion. The first transition,which corresponds to the most abundant product ion was usedfor identification and quantification, while the second one for con-firmation purpose. In order to obtain maximum sensitivity for theidentification and quantification of the analytes, manual optimisa-tion of the declustering potential (DP), collision energy (CE),entrance potential (EP) and collision exit potential (CXP) was per-formed for each analyte using 1 lg/mL solution of individual com-pounds in acetonitrile. Finally the presence of precursor andproduct ions was investigated using the multiple reaction monitor-ing (MRM) experiments with a dwell time of 50 ms. The optimisedLC–MS/MS parameters were summarised in Table 1.

3.2. Optimisation of GC–MS/MS parameters

Optimisation of GC–MS/MS was performed as previouslyreported (Lozano et al., 2012). The MS/MS detection method wasoptimised firstly with individual injections in full-scan mode ofeach analyte at 1 lg/mL to obtain their retention times and toselect the optimal precursor ions. The most intense ion with thehighest m/z relationship was selected in most cases. In contrastto LC–MS/MS in which the proton adduct [H+] of the molecularion was chose as the precursor ion in all cases, the pseudo-molec-ular ion is hardly used as a precursor ion in GC–MS/MS determina-tion, due to the stronger ionisation occurring with electron impact.Then, product ion scan was conducted with various collision ener-gies, ranging from 10 to 40 V, to obtain the best product ions fromthe selected precursor ions. The first transition, which correspondsto the most abundant product ion was used for quantification,while the second one for identification purpose. An 8-time-seg-ments MRM method was developed with a solvent delay of7 min. Finally, once a segment was adjusted, dwell time wasincreased for the less sensitive analytes by decreasing the dwelltime for analytes with enough signal in order to obtain sufficientdata points to perform acceptable and accurate quantification.The optimised GC–MS/MS parameters were summarised inTable 2.

3.3. Optimisation of extraction and clean-up procedure

3.3.1. Effect of hexane partitioningGC-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 chro-matogram showed a distinctly large quantity of interfering co-extractives especially fatty acids such as propanoic, propenoic,butanoic, octenoic, hexanoic, heptanoic and tetradecanoic acid. Inorder to overcome this matrix interfering problem, the additionof a very non-polar solvent such as hexane in the extraction stephas already been proved to be an efficient way to remove this kindof compounds especially fatty acids and fatty acid esters in babyfood (Charlton & Jones, 2007) and later was successfully appliedto honeybees and pollens (Wiest et al., 2011). In another study,liquid–liquid extraction using hexane with the aid of 20% aqueoussodium chloride (w/w) solution was able to significantly reducethe amount of matrix co-extracts in the clean-up step (Cajkaet al., 2012). Considering this effect, we also evaluated the possibil-ity of purifying the crude acetonitrile extracts with the addition ofhexane to remove the matrix co-extracts. Unfortunately, the addi-tion of hexane in the clean-up of the acetonitrile extracts did nothelp much to the recoveries of some of the pesticides studied(supplementary data). The differences in recoveries wereperformed using t-test at 95% confidence interval. Statisticsshowed that significant decrease in average recoveries (at 10 and50 lg/kg) were observed when hexane partitioning was includedin the clean-up step. This can be explained by the higher tendencyof some of the pesticides to partition into the hexane layer. Sincethe recoveries of the pesticides represent an important analyticalperformance characteristics in the method validation, weconcluded that hexane partitioning was not very efficient in cocoabeans matrices and hence rejected form the method development.

3.3.2. Effect of dispersive-SPE clean-upIn this study, the extract of the solvent extraction was subjected

to dispersive-SPE clean-up using 150 mg MgSO4, 50 mg C18 and50 mg PSA as sorbents. Graphitised carbon black (GCB) was omit-ted from the dispersive-SPE since it is well known that GCB hashigh affinity to planar pesticides (chlorothalonil). AnhydrousMgSO4 was used to absorb micro quantities of water in the solvent

Table 1LC–MS/MS acquisition method parameters.

Analyte Retentiontime (min)

Q1 mass(m/z)

Q3 mass(m/z)

Declusteringpotential DP (V)

Entrancepotential EP (V)

CollisionEnergy CE (V)

Collision cell exitpotential CXP (V)

Ametryn 4.36 228.0 186.0 36.0 10.0 25.0 7.04.36 228.0 96.0 36.0 10.0 35.0 4.0

Chlorpyrifos 5.64 350.0 198.0 21.0 10.0 25.0 7.05.64 350.0 97.0 21.0 10.0 41.0 6.0

Cinosulfuron 3.85 414.0 183.2 36.0 8.0 21.0 4.03.85 414.0 83.1 36.0 8.0 57.0 4.0

Cyproconazole 4.74 292.1 70.1 56.0 10.0 41.0 4.04.74 292.1 125.1 56.0 10.0 39.0 4.0

Difenoconazole 5.46 406.0 251.1 61.0 12.0 31.0 4.05.46 406.0 111.1 61.0 12.0 77.0 4.0

Dimethoate 3.11 230.0 199.0 16.0 10.0 13.0 7.03.11 230.0 125.0 11.0 10.0 29.0 6.0

Fluazifop-butyl 5.55 384.0 282.0 51.0 10.0 27.0 10.05.55 384.0 328.0 46.0 10.0 23.0 12.0

Isazofos 4.9 314.0 120.0 41.0 10.0 35.0 6.04.9 314.0 162.0 41.0 10.0 21.0 7.0

Isoprocarb 4.17 194.0 95.0 20.0 10.0 20.0 17.04.17 194.0 137.2 51.0 10.0 17.0 8.0

Metalaxyl 4.18 280.0 220.0 46.0 10.0 19.0 8.04.18 280.0 160.0 51.0 10.0 31.0 6.0

Oxadixyl 3.67 279.0 219.0 46.0 10.0 17.0 8.03.67 279.0 133.0 41.0 10.0 29.0 6.0

Propoxur 3.79 210.0 111.0 11.0 10.0 19.0 6.03.79 210.0 168.0 6.0 10.0 11.0 7.0

Quinalphos 5.04 299.0 147.0 31.0 10.0 29.0 6.05.04 299.0 163.0 21.0 10.0 29.0 7.0

Quizalofop-ethyl 5.41 373.0 299.0 71.0 10.0 25.0 10.05.41 373.0 271.0 76.0 10.0 33.0 10.0

Terbuthylazine 4.61 230.0 174.0 41.0 10.0 23.0 7.04.61 230.0 104.0 41.0 10.0 43.0 6.0

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and by doing so, it will make the final acetonitrile extracts lesspolar and causing precipitation of certain polar matrix co-extracts.PSA represents a weak ion exchanger which mainly removes sug-ars, fatty acids, organic acids, and some pigments, while C18 canbe used for the reduction of lipids and non-polar interferences(Cajka et al., 2012). The recovery profiles of the effect of disper-sive-SPE clean-up were summarised in the bar chart as shown inFig. 1.

While the addition of C18 is quite beneficial in reducing thematrix interferences, the use of PSA could raise some concernsespecially to the recoveries of certain pesticides. Previous studiesreported the decreasing of recoveries of some pesticides thatattributed to the binding to PSA (Lozano et al., 2012;Mayer-Helm, 2009; Muhamad, Zainudin, & Abu Bakar, 2012). Inthis study, the most prominent effect of PSA was on the reducedrecovery rate (less than 20%) of cinosulfuron in LC–MS/MScompared to acetonitrile extract without clean-up. The possiblereason maybe because cinosulfuron contains acidic sulfonamidegroup which may react with basic PSA sorbent that contains aminogroups. When PSA was omitted, the recoveries obtained improvedconsiderably. On the other hand, most of the LC amenable pesticidesstudied gave acceptable recoveries and precision with or withoutdispersive-SPE clean-up except for acephate and methidathion.

In contrary, GC amenable pesticides gave noticeable resultswhen dispersive-SPE were used in the clean-up step. From theanalysis, 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 (caffeineand theobromine) and tocopherol (c-tocopherol) did not affect

much when dispersive-SPE was applied. As predicted from thechromatograms (not shown), heavy interferences in the extractswould 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-SPEclean-up and 13 of them gave recoveries of more than the accept-able value of 120%. This shows that the addition of PSA removedsome of the matrix compounds which caused signal enhancementin GC–MS/MS. In contrast, strong matrix suppression was observedfor ametryn when PSA was not present. Here, large matrix interfer-ences at the analyte retention time is likely to be the cause, sincethey can hinder the correct and accurate analyte integration andquantitation.

Besides using the full scan chromatograms, clean-up efficiencyof the method was also assessed through gravimetric measure-ments. In this study, the amount of co-extractives from the sam-ples into the extracts were measured gravimetrically at each stepof acetonitrile extraction and dispersive-SPE clean-up. Vials ofthe final solution were weighed before the addition of extracts.Then, each sample extracts obtained after acetonitrile extractionand dispersive-SPE clean-up were dried via N-evaporator under ahot water bath. The weight difference was recorded to estimatethe amount of co-extracted matrix in the initial and final extracts.From the results obtained, the amount of matrix co-extracted forcoca beans samples after acetonitrile extraction and dispersive-SPE 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 rep-resented 0.3% and 0.1% of the sample mass. Therefore, solventextraction using acetonitrile was able to remove 99.7% of the

Table 2GC–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 8.10 136.0 94.0 1 10 108.10 142.0 96.0 5 10

Ametryn 17.90 227.0 58.1 3 10 517.90 227.0 170.1 10 5

Chlorothalonil 15.54 263.8 229.0 3 20 515.54 265.8 168.0 25 5

Chlorpyrifos 19.97 198.9 171.0 3 15 519.97 196.9 169.0 15 5

Cyfluthrin (II) 32.23 162.9 90.9 7 15 432.23 162.9 127.0 5 4

Cyfluthrin (III,IV) 32.38 162.9 90.9 7 15 432.38 162.9 127.0 5 4

k-Cyhalothrin 30.35 181.1 152.0 6 25 1030.35 197.0 141.0 10 10

Cypermethrin 32.77 163.1 127.1 7 5 432.77 181.2 152.1 25 4

Cyproconazole 25.28 222.0 125.1 5 15 525.28 139.0 111.0 15 5

Deltamethrin 35.50 250.7 172.0 8 5 535.50 181.0 152.1 25 5

Difenoconazole I 34.77 322.8 264.8 8 15 534.77 324.8 266.8 15 5

Difenoconazole II 34.90 322.8 264.8 8 15 534.90 324.8 266.8 15 5

Dimethoate 13.39 86.9 46.0 2 15 2013.39 92.9 63.0 10 20

Endosulphan I 23.22 241.0 206.0 4 15 523.22 207.0 172.0 15 5

Endosulphan II 25.53 207.0 172.0 5 15 525.53 241.0 206.0 15 5

Endosulphan sulphate 27.00 271.9 237.0 5 15 527.00 273.8 238.9 15 5

Fenvalerate I 34.02 167.0 125.1 8 5 534.02 181.0 152.1 20 5

Fenvalerate II 34.40 167.0 125.1 8 5 534.40 181.0 152.1 25 5

Fluazifop-butyl 25.64 281.9 91.0 5 20 525.64 281.9 238.0 20 5

Isazofos 15.79 161.0 119.1 3 5 515.79 161.0 146.0 5 5

Isoprocarb 9.69 121.0 77.1 1 20 109.69 136.0 121.1 10 10

Metalaxyl 18.09 234.0 146.1 3 20 518.09 234.0 174.1 10 5

Methidathion 22.90 144.9 85.0 4 5 522.90 144.9 58.1 15 5

Oxadixyl 26.17 163.0 132.1 5 5 526.17 132.0 117.1 15 5

Permethrin I 31.29 163.0 127.0 6 5 1031.29 183.1 168.1 10 10

Permethrin II 31.47 162.9 127.1 6 5 1031.47 182.9 168.1 10 10

Propoxur 10.98 110.0 63.0 1 25 1010.98 152.0 110.0 10 10

Quinalphos 22.32 146.0 118.0 4 10 522.32 146.0 91.0 30 5

Quizalofop-ethyl 32.73 371.8 298.9 7 10 432.73 163.0 100.0 20 4

Terbuthylazine 14.53 228.9 173.1 2 5 1014.53 214.0 71.0 20 10

Triadimenol 22.34 168.0 70.0 4 10 522.34 128.0 65.0 25 5

B.H. Zainudin et al. / Food Chemistry 172 (2015) 585–595 589

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/MSand (B) GC–MS/MS.

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Table 3Average 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 andGC–MS/MS.

Analyte GC–MS/MS LC–MS/MS MRLa (lg/kg)

10 lg/kg (n = 20) 50 lg/kg (n = 14) LOQ (lg/kg) Measurementuncertainty (%)

10 lg/kg (n = 20) 50 lg/kg (n = 14) LOQ (lg/kg) Measurementuncertainty (%)

Rec (%) RSD (%) Rec (%) RSD (%) Rec (%) RSD (%) Rec (%) RSD (%)

Acephate 106.7 12 103.1 18 10 18 50.0 107 41.9 52 n.q n.q 200Ametryn 91.7 4 86.4 6 10 5 80.1 7 82.2 5 10 10 200Chlorothalonil 101.1 12 119.3 6 10 17 – – – – – – 50Chlorpyrifos 105.0 7 94.0 6 10 12 88.5 20 84.0 7 10 25 50Cinosulfuron – – – – – – 102.1 10 95.1 7 10 10 100Cyfluthrin (II) 106.1 9 94.6 17 10 21 – – – – – – 100Cyfluthrin (III,IV) 104.3 11 113.6 12 10 16 – – – – – – 100k-Cyhalothrin 107.0 10 104.5 7 10 10 – – – – – – 100Cypermethrin 117.1 5 104.7 4 10 16 – – – – – – 50Cyproconazole 96.6 3 92.2 3 10 5 96.0 9 89.6 4 10 13 100Deltamethrin 118.7 8 106.2 11 10 19 – – – – – – 50Difenoconazole I 110.6 10 100.5 2 10 8 93.5 7 92.7 3 10 7 100Difenoconazole II 108.5 3 99.2 2 10 6 – – – – – 100Dimethoate 105.9 7 101.9 5 10 8 82.9 8 79.4 8 10 12 100Endosulphan I 84.0 14 83.2 9 10 23 – – – – – – 100Endosulphan II 89.0 9 83.9 6 10 11 – – – – – – 100Endosulphan Sulphate 97.3 7 92.4 8 10 8 – – – – – – 100Fenvalerate I 113.4 7 107.8 6 10 13 – – – – – – 50Fenvalerate II 109.0 11 104.8 3 10 12 – – – – – – 50Fluazifop-butyl 98.9 3 94.5 5 10 5 84.2 7 83.0 5 10 8 100Isazofos 102.8 4 98.8 3 10 6 80.6 7 81.0 7 10 9 50Isoprocarb 101.1 6 95.2 8 10 7 81.6 6 82.0 4 10 9 100Metalaxyl 95.6 5 90.4 5 10 6 90.3 7 87.3 4 10 11 200Methidathion 109.4 6 103.5 5 10 8 83.5 29 84.1 33 n.q n.q 100Oxadixyl 98.5 4 93.7 5 10 7 86.1 11 84.5 5 10 14 1000Permethrin I 98.3 11 88.3 7 10 9 – – – – – – –Permethrin II 95.6 6 86.5 4 10 7 – – – – – – –Propoxur 107.3 8 101.9 13 10 10 85.4 9 79.2 14 10 18 50Quinalphos 105.5 6 98.3 6 10 7 95.9 10 86.8 5 10 12 100Quizalofop–ethyl 104.3 3 95.8 4 10 6 78.4 14 80.9 5 10 21 100Terbuthylazine 92.3 2 87.1 3 10 4 85.8 7 82.5 4 10 11 500Triadimenol 105.8 6 101.4 7 10 7 – – – – – – 200

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

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etal./Food

Chemistry

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585–595

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matrix co-extracted, while the additional dispersive-SPE clean-upremoved another 0.1%.

3.4. Method validation

GC–MS/MS analysis with the clean-up step and LC–MS/MSanalysis without the clean-up step were chose as the finalmethod for validation study. Good linearity was achieved formost of the pesticides studied using LC–MS/MS and GC–MS/MSwith correlation coefficients better than 0.990. However,acephate and methidathion gave R values below 0.990. As previ-ously mentioned in Section 3.3.2, both analytes gave rather lowsignal intensity at lower concentration levels with high baselinenoise when using LC–MS/MS. Fortunately, both pesticides gavegood linearity with R values of more than 0.990 when usingGC–MS/MS. The observed effect of an increase or decrease in

Fig. 2. LC–MS/MS extracted ion chromatogram (34 MRM transitions) of (

detector response of pesticides in matrix extracts compared withthe same pesticides present in just pure solvent was studied inorder to assess matrix effect of cocoa beans extracts. In thisstudy, only minor matrix effects were observed for LC–MS/MSanalysis. As for GC–MS/MS, same good results were also achievedafter clean-up step with dispersive-SPE was introduced.However, matrix effects are well known to be variable with timeand condition of instrument used. Therefore, quantification usingmatrix-matched standards was opted rather than pure solventstandards.

Table 3 displays the method performance and validationparameters for acetonitrile extraction and dispersive-SPE clean-up of cocoa beans employing LC–MS/MS and GC–MS/MS. Limit ofquantification (LOQ) values were determined as the lowest con-centration of the analyte that has been validated with acceptableaccuracy (70–120%) and precision (RSD < 20%) by applying the

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 pesticidesstudied were set at 10 lg/kg for both LC–MS/MS and GC–MS/MSanalysis since it was the lowest spiking level with acceptable accu-racy and precision. This value is lower than national MRLs (FoodRegulations, 2010). Therefore, it can be concluded that both LC–MS/MS and GC–MS/MS method are sensitive enough to quantifyall pesticides studied in cocoa beans.

Selectivity and specificity of the instruments are defined as theability of the extraction, the clean-up, the separation and thedetection method to discriminate between the analyte and othercompounds, and at the same time capable of providing signals thateffectively identify the analyte (European Commission DG-SANCO,2012). In order to do that, the selectivity of the analytical methodin this work was determined by comparing the chromatograms ofa blank matrix solution with the spiked matrix solution (Figs. 2 and3). Since multiple reaction monitoring (MRM) analysis was appliedin this study using LC–MS/MS and GC–MS/MS, we can see that theanalytes of interest were well separated from the other compo-nents present in the extracts and hence allowed the differentiationand quantification of the analytes. In the meantime, the methodspecificity was demonstrated using retention time matching andion ratios between the analyte in the sample extract and calibra-tion standard. The tolerance for retention time is ±0.5% for GCand ±2.5% for LC. Meanwhile, maximum permitted tolerance forrelative ion ratio is ±20%. This showed that the analytical methodand subsequent detectors used are highly selective and highlyspecific.

Duration of extraction that consists of shaking, vortex mix, andcentrifuge 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 keptconstant as far as possible. During the initial study, variations inthe extraction parameters previously mentioned generally hadlittle effect on the mean recoveries and RSDs. This showed thatthe method was adequately robust to be successfully applied byinexperienced technicians.

Fig. 3. GC–MS/MS extracted ion chromatogram (62 MRM transitions) of (

As demonstrated in Table 3, the recoveries of the majority of theanalytes at 10 and 50 lg/kg were ranged between 78.4–102.1% forLC–MS/MS and 83.2–119.3% for GC–MS/MS with RSDs below 20%for most cases. Since acephate gave unacceptable recovery valuesand RSDs, it was deemed to be not quantifiable and omitted fromthe 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 accept-able recoveries and RSDs when GC–MS/MS was used. One wouldthink that acephate should give better results in LC, but we hadto resort to adding it to the list of analytes in GC–MS/MS in overallmonitoring programme. The change in mobile phase or finalextracts 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. Therest of the pesticides that can only be analysed using GC–MS/MSmostly belong to organochlorine and pyrethroid group. Pyrethroidpesticides are known to pose difficulties in multiresidue analysisdue to their highly non-polar nature and lower signal intensities(Koesukwiwat, Lehotay, Miao, & Leepipatpiboon, 2010). The onlypesticide 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 forestimation of combined standard uncertainty. By using thisapproach, it was found that uncertainty of extraction which com-prises two components – (i) repeatability of extraction and (ii)uncertainty of extraction recovery, were shown to represent themain source of combined standard uncertainty. On the other hand,uncertainties associated with calibration (uncertainties of weigh-ing or diluting standards, uncertainties of purity of standards) werenot so important. The relative expanded uncertainty was then cal-culated by using the coverage factor k = 2 at 95% confidence level.Combined standard uncertainties associated with the describedanalytical method ranged for individual compounds from 7% to

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 themean recovery is significantly different from 100%. From the resultsobtained, it was found that the calculated t values were greater thanthe critical value tcrit, hence recoveries obtained in the validationdata were significantly different from 100%. In this case, it was rec-ommended to include the recovery in the calculation of the results.

3.6. Real samples monitoring

The effectiveness of the developed method was applied to rou-tine monitoring analysis of imported and domestic cocoa beanssamples collected from 2012 to 2013 from smallholders andMalaysian ports. 132 dried cocoa beans samples were analysedusing GC–MS/MS and confirmed using LC–MS/MS method. Amongthose samples were from local smallholders (Malaysia), Indonesia,Cameroon, Nigeria, Venezuela, Ghana, Ecuador and Papua NewGuinea. During the monitoring programme, most of the residuelevels detected are within the 1–10 lg/kg. The results are consis-tent with the study conducted by Pizzutti et al. (2009), where thiscould be explained by the fact samples were mostly taken from bigshipments, usually representing large, mixed lots or consignments.Overall, there were 3 different pesticides found in cocoa beans withconcentration of more than 10 lg/kg. Chlorpyrifos, ametryn andmetalaxyl were detected in 14 positive samples, ranged from 10to 200 lg/kg. The most prevalent compound was chlorpyrifos,which was found in 9 samples. It was found that some samplescontained residues around MRL (50 lg/kg) and two samplesexceeded the MRL, with concentrations of 149 and 200 lg/kg. InJapan, several consignments of cocoa beans have been denied entryinto the country since the new legislation on MRLs came into effectin May 2006 by the Japanese Ministry of Health, Labour and Wel-fare (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 theInternational Cocoa Organization (ICCO) regarding on the actionprogramme of pesticide residues, the most notable active ingredi-ents detected included pirimiphos-methyl, chlorpyrifos and2,4-D (ICCO Progress Report, http://www.icco.org/about-us/interna-tional-cocoa-agreements/cat_view/30-related-documents/34-pests-and-diseases.html). Data form Chocolate and Cocoa Association ofJapan (CCAJ) showed that from 2006 to 2010, 51 cases of chlorpyri-fos residues violation occurred in Japan for imported cocoa beanswith minimum and maximum concentration of 60 lg/kg and1760 lg/kg, respectively (Kaminaga, 2011). On the other hand, inthis study, residues detected for ametryn and metalaxyl were wellbelow the national MRLs. From the monitoring results, it can beconcluded that in order to reduce consumer’s risk to pesticide res-idues in Malaysia, frequent monitoring programme should beundertaken to cocoa beans samples especially for imported beanssince the originality of the agrochemicals used in the exportingcountries is unknown.

4. Conclusions

According to Malaysian Food Act and Regulations (FoodRegulations, 2010), there are a total of 34 pesticides allowed tobe use in cocoa plantation with national MRLs regulated. Out ofthese 34 pesticides, 26 pesticides were covered in this study. Therest of the pesticides were either belong to difficult pesticides(dithiocarbamate, fosetyl ammonium, MSMA) or highly polar pes-ticides (glufosinate, glyphosate, paraquat), hence require specificsingle residue method (SRM). Reliable, efficient and rapid sampleextraction and clean-up technique based on acetonitrile extractionand dispersive-SPE was successfully developed and validated to

simultaneously analyse 26 pesticides of different chemical classesin cocoa beans. The diversity of the selected pesticides highlightsthe need of applying both gas and liquid chromatography hyphen-ated to triple quadrupole mass spectrometry for consistent andreliable monitoring programme. The analytical method gave satis-factory recoveries with good precision for most of the pesticidesstudied. However, there are some problematic pesticides such asacephate and methidathion, which gave poor recoveries and preci-sion using LC–MS/MS. Nevertheless, the problem was resolved byusing GC–MS/MS instead. Therefore, a clear conclusion can bemade that both LC amenable and GC amenable pesticides react dif-ferently to dispersive-SPE clean-up. In LC–MS/MS, matrix interfer-ences were not prominent and the addition of PSA in the clean-upstep would result in the loss of cinosulfuron. Meanwhile, PSAplayed a critical role in GC–MS/MS especially in terms of matrixeffects. According to Lehotay et al. (2010), GC–MS matrix effectswere more dependent on the condition of the instrument thanon the method or matrix. They also stated that a combination ofmatrix enhancement for pesticides susceptible to degradation onactive sites occurs in GC at the same time as matrix diminishmenteffects due to build-up of non-volatile materials in the inlet. Forthis reason, dispersive-SPE clean-up is imperative in GC–MS/MSanalysis, while acetonitrile extraction without dispersive-SPEclean-up is the method of choice for LC–MS/MS analysis in thisstudy. The method was applied to 132 cocoa beans samples via amonitoring programme from 2012 to 2013 and 10% of them wasfound 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 Gen-eral of the MCB for permission to publish this paper. The authorsare also highly indebted to the Regulatory and Quality ControlDivision for providing the monitoring samples for analysis.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foodchem.2014.09.123.

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