Food Chemistry 150 (2014) 408–413
Contents lists available at ScienceDirect
Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Analytical Methods
A simple and sensitive single-step method for gas chromatography–mass spectrometric determination of fipronil and its metabolites in sugarcane juice, jaggery and sugar Thirumalaiandi Ramasubramanian a,⇑, Mariappan Paramasivam b, Ramabhadran Jayanthi a, Subramaniam Chandrasekaran b a b
Division of Crop Protection, Sugarcane Breeding Institute, Indian Council of Agricultural Research, Coimbatore 641007, Tamil Nadu, India Pesticide Toxicology Laboratory, Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
a r t i c l e
i n f o
Article history: Received 18 May 2012 Received in revised form 11 September 2013 Accepted 1 November 2013 Available online 12 November 2013 Keywords: Sugarcane juice Jaggery Sugar Fipronil Metabolites Residue
a b s t r a c t A simple and sensitive single-step method for gas chromatography–mass spectrometric determination of fipronil and its metabolites viz., fipronil desulfinyl, fipronil sulphide and fipronil sulphone in sugarcane juice, jaggery and sugar has been developed. Acetonitrile was superior to ethyl acetate in terms of selectivity, though they were on par with each other in terms of recoveries. This method does not require any cleanup as the PSA-based cleanup was on par with no-cleanup treatment. Interestingly, the recoveries of fipronil and its metabolites decreased with increased amounts of C18 from 10 to 50 mg/g of matrix. Matrix effects were insignificant and the limit of quantification was 0.005 lg/g. The recoveries of fipronil and its metabolites varied between 87.5% and 108.5% with the RSD of 0.2–5.3% for all the three matrices studied. This method has also been validated by monitoring fipronil and its metabolites in the retail outlet samples of sugarcane juice, jaggery and sugar. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction India is the world’s second largest producer of sugarcane. In 2011, it produced 355 million tonnes of sugarcane from 5.07 million hectares with an average productivity of 68 t/ha (Solomon, 2011). About 60–70% of the cane produced in the country is utilised for the production of sugar (Nair, 2011) and 25–30% is being utilised for the production of jaggery and khandsari (Singh, Singh, Anwar, & Solomon, 2011). Sugarcane juice is also a very popular beverage among the rural poor and is usually commercialised by street vendors. Though the experimental maximum yield of sugarcane is 325 t/ha, the national average hovers between 66 and 70 t/ha (Solomon, 2011). Insect pests are among the few important constraints which limit the productivity of sugarcane to a considerable extent across the Indian sub-continent. The early shoot borer, Chilo infuscatellus Snellen and the root borer, Emmalocera depresella Swinhoe are among the 20 major pests causing 22–33% and 35% loss in yield, respectively (Directorate of Sugarcane Development, 2012). Fipronil is one of the important broad-spectrum insecticides recommended for the management of C. infuscatellus and
⇑ Corresponding author. Mobile: +91 9442912010; fax: +91 4222472923. E-mail address: [email protected] (T. Ramasubramanian). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.11.004
E. depresella across the country to achieve the economic yield (Central Insecticide Board & Registration Committee, 2009). Fipronil, the phenylpyrazole insecticide [(±)-5-amino-1-(2,6-dichloro-a,a,a-trifluoro-p-tolyl)-4-trifluoromethylsulfinylpyrazole-3carbonitrile] blocks GABAA-gated chloride channels in the central nervous system of insects. Disruption of the GABAA receptors by fipronil prevents the uptake of chloride ions resulting in excess neuronal stimulation and death of the target insect (Cole, Nicholson, & Casida, 1993). Fipronil and its metabolites exhibit differential binding affinity for GABAA receptor subunits of insects and mammals. Fipronil has higher binding affinity for insect receptor complexes compared to mammalian complexes. The lower binding affinity for mammalian receptors enhances selectivity for insects and increases the margin of safety for humans and animals (Cole et al., 1993; Hainzl, Cole, & Casida, 1998; Ratra & Casida, 2001; Ratra, Kamita, & Casida, 2001). However, fipronil sulphone, the primary biological metabolite of fipronil has been reported to be 20 times more active at mammalian chloride channels than at insect chloride channels (Zhao, Yeh, Salgado, & Narahashi, 2005). Fipronil desulfinyl, the primary environmental metabolite (photo product) of fipronil is 9–10 times more active at the mammalian chloride channel than the parent compound and thus, reducing the selectivity between insects and humans (Hainzl & Casida, 1996; Hainzl et al., 1998). The residues of fipronil and its
T. Ramasubramanian et al. / Food Chemistry 150 (2014) 408–413
metabolites may be expected in the sugarcane juice and its downstream commercial products such as jaggery and sugar. Hence, development of methods for determination of residues of fipronil and its metabolites in sugarcane juice, jaggery and sugar deserves importance to ensure safety to people across the world and India in particular, as it is the largest consumer of sugar in the world. Few analytical methods have been reported for the determination of fipronil and its metabolites in different matrices such as soil, water, human urine (Vílchez, Prieto, Araujo, & Navalón, 2001), honeybees (Morzycka, 2002), honey (García-Chao et al., 2010), pollen (Kadar & Faucon, 2006) milk, cattle feed (Le Faouder et al., 2007), ovine plasma (Bichon, Richard, & Le Bizec, 2008), grape leaves, berries (Mohapatra et al., 2010), cotton lint and seed (Chopra, Chauhan, Kumari, & Dahiya, 2011). These methods have their own merits and demerits. Matrix solid-phase dispersion (MSPD) method developed by Morzycka (2002) was rapidly given up as it has been proved effective only for solid matrices such as honeybees. Kadar and Faucon (2006) developed a liquid chromatography–tandem mass spectrometry method characterised by cumbersome sample preparation steps for highly complex matrices like pollen. Le Faouder et al. (2007) developed a lengthy and laborious protocol for the determination of fipronil and its metabolites in milk and cattle feed. It involved extraction of target analytes with comparatively larger volume of solvents followed by a delipidation step and solid-phase extraction (SPE) cleanup. The SPE cleanup was performed in a single-step with atoll XC alone (for milk samples), or in two-steps (an additional step with florisil) for plant samples. This was followed by the highest level of concentration aimed at drastically reduce the limit of detection of the method. The SPE procedure of García-Chao et al. (2010) involved the costly vacuum manifold system for extraction of fipronil and its metabolites from honey and pollen matrices. The method developed by Vílchez et al. (2001) employed a specially designed solid-phase micro extraction (SPME) device made of fused-silica fibre coated with polymeric stationary phase (85 lm polyacrylate) for extraction of fipronil from very low volume of samples (0.5 g for soil, 64 mL for human urine and water samples). Bichon et al. (2008) developed a method meant for ultra-low volume of matrix (200 lL plasma) as the quantities of plasma available are often limited. Despite their good sensitivity, the suitability of these two methods only for low to ultra-low volume of matrices precludes their use for agricultural commodities, which require relatively larger sample size for adequate representation. The analytical methods developed for grape leaves, berries (Mohapatra et al., 2010), cotton lint and seed (Chopra et al., 2011) involved extraction of analytes with larger volume of solvents followed by the traditional liquid–liquid partitioning and column cleanup with adsorbents like florisil alone or with neutral alumina for final determination in GC–ECD. The limit of quantification of these two methods is comparatively higher (0.01 lg/g) besides the involvement of larger volume of organic solvents. Therefore, we first had to develop a rapid, simple and sensitive method for the determination of fipronil and its metabolites in sugarcane juice, jaggery and sugar.
2. Materials and methods 2.1. Chemicals and reagents Certified reference standards of fipronil (97.5%) and its metabolites viz., fipronil desulfinyl (97.8%), fipronil sulphide (98.8%) and fipronil sulphone (99.7%) were obtained from Bayer CropScience Ltd., India. The SPE sorbents primary secondary amine (BondesilPSA, 40 lm particle size) and Bondesil-C18 (40 lm particle size) were procured from Agilent Technologies, USA. The organic
409
solvents used in the study were of HPLC grade and purchased from Thomas Baker (Chemicals) Ltd., Mumbai, India. Analytical reagent grade anhydrous MgSO4 and NaCl were purchased from Merck India Ltd., Mumbai.
2.2. Preparation of standard solutions The stock solutions (1000 lg/mL) of fipronil and its metabolites were prepared by accurately weighing 10 mg of each analyte in volumetric flasks (certified A class) and dissolving in 10 mL of nhexane. These were stored in dark vials at 4 °C. A working standard mixture of 100 lg/mL was prepared by appropriate dilution of the stock solutions, from which the calibration standards (0.003–1.0 lg/mL) were prepared by serial dilution with n-hexane.
2.3. Sample preparation Sugarcane juice was extracted immediately after harvesting the 10 months-old crop (variety Co-86032) grown at Sugarcane Breeding Institute (Indian Council of Agricultural Research), Coimbatore (Tamil Nadu, India) using motorised sugarcane crusher. The sugarcane juice was filtered through Whatman No. 1 filter paper under mild-suction and used for analyses. Jaggery was also prepared from the same variety used for the extraction of juice, while the refined sugar was purchased from the retail market. Jaggery and sugar samples were ground to fine powder in mortar and then used for analyses.
2.3.1. Extraction A portion (10 g) of well-homogenised sample was placed into a 50 mL screw-capped oak ridge tube. The target compounds were extracted with 20 mL of the solvent (acetonitrile/ethyl acetate) as 10 mL of the extraction solvent was insufficient to get enough volume of organic phase from jaggery and sugar samples for downstream cleanup process. The oak ridge tube was closed tightly and shaken vigorously for 1 min to ensure better interaction between the solvent and sample. Then 1 g of NaCl and 4 g of MgSO4 (anhydrous) were added to the sample, vortexed for 1 min and centrifuged (Superspin R-V/FA; Plasto Crafts, Mumbai, India) at 5000 rpm for 10 min at room temperature. The supernatant (4 mL) was either concentrated under gentle stream of nitrogen (15 psi) in the Turbovap LV (Caliper Life Sciences, Russelsheim, Germany) at 40 °C and reconstituted in 1 mL of hexane for analysis in the GC–MS without cleanup or subjected to cleanup with the sorbents as detailed below.
2.3.2. Cleanup Dispersive solid phase extraction (d-SPE) cleanup with PSA or C18 was compared with no-cleanup (no sorbent) in terms of analyte recovery and interference from the study matrices (sugarcane juice, jaggery and sugar). After centrifugation at 5000 rpm for 10 min as described in the extraction, 8 mL of supernatant was transferred to 15 mL centrifuge tubes containing varied levels of PSA (10, 25 and 50 mg/g of matrix) and anhydrous MgSO4 (150 mg/g of matrix). Simultaneously, C18 was also evaluated at varied levels (10, 25 and 50 mg/mL) along with MgSO4 (150 mg/g of matrix). The extract was vortexed for 30 s and then centrifuged at 3000 rpm for 10 min. The supernatant (4 mL) was transferred to turbo tube and concentrated to near dryness under a gentle stream of nitrogen in the Turbovap LV as described earlier. The residue was reconstituted in 1 mL of hexane and thus, the amount of sample in the final extract was equivalent to 2 g/mL.
410
T. Ramasubramanian et al. / Food Chemistry 150 (2014) 408–413
2.4. GC–MS analysis Residues of fipronil and its metabolites were detected and quantified in Shimadzu GC 2010 gas chromatograph equipped with QP 2010 plus mass spectrometer. DB-1MS fused silica capillary column (30 m 0.25 mm 0.25 lm; Agilent Technologies, USA) was used for the separation of target analytes. The oven temperature was programmed as follows: 160 °C (1 min), 3 °C/min to 200 °C (2 min) and finally 4 °C/min to 250 °C (4 min). AOC-20s auto sampler and AOC-20i auto injector of the GC–MS system were employed to inject 1 lL of sample in split mode (1:5) at 250 °C. Ultrapure grade helium (99.9999%) (Bhuruka Gases Ltd., Bengaluru) was used as the carrier gas at constant flow rate of 2 mL/ min. The mass spectrometer was operated in electron ionisation (EI) mode with ionisation energy of 70 eV. The interface and the ion source temperatures were kept at 250 and 200 °C, respectively. The target and qualifier ions were chosen by injecting fipronil and its metabolites individually under the same chromatographic conditions using full scan with mass/charge ratio (m/z) of 50–500 (Table 1). Full-scan mode was used to determine cleanup effects and selected ion monitoring (SIM) mode was used for recovery experiments. The target compounds were confirmed through similarity match with NIST library (version 2.0). 2.5. Method validation The analytical method was validated as per the single laboratory validation approach (European Commission., 2010; Thompson, Ellison, & Wood, 2002). The performance of the method was evaluated by considering the validation parameters viz., linearity, limit of detection (LOD), limit of quantification (LOQ), matrix effect, recovery and repeatability. All the analyses were performed using the same blank samples. 2.5.1. Linearity Calibration curves for all of the target analytes were drawn by plotting the peak area against the concentration of the corresponding calibration standards at seven calibration levels ranged between 0.003 and 1.0 lg/mL. 2.5.2. Sensitivity Limit of detection (LOD) and the limit of quantification (LOQ) of each analyte were determined by considering a signal-to-noise ratio of 3 and 10, respectively with reference to the background noise obtained from the blank sample. 2.5.3. Matrix effects The area of the standard diluted with blank matrix extract was compared to the area of the standard diluted with solvent (n-hexane). The matrix effect was determined by the following equation: Matrix effect (%) = (peak area of matrix standard peak area of solvent standard) 100/peak area of solvent standard (Kanrar, Mandal, & Bhattacharyya, 2010). The positive and negative values of the matrix effect signify the matrix-induced enhancement and suppression, respectively.
2.5.4. Recovery and repeatability The recovery experiments were carried out on untreated matrices by fortifying 10 g of samples (sugarcane juice, jaggery and sugar) in six replicates with the mixture of fipronil and its metabolites separately at three concentration levels, i.e., 0.01, 0.05 and 0.1 lg/g, and extracting by the method described above. The precision of the method in terms of repeatability was determined on the basis of relative standard deviation (RSD%). 2.6. Statistics All statistical analyses including the Duncan’s Multiple Range Test (DMRT) were performed in SPSS Statistics version 17.0. 3. Results and discussion 3.1. Optimisation of the extraction procedure Acetonitrile and ethyl acetate were very effective in extracting fipronil and its metabolites from sugarcane juice matrix. The recoveries of fipronil and its metabolites were in the range of 89.5–106.9% and 94.7–104.4% when extracted with acetonitrile and ethyl acetate, respectively. In the present study ethyl acetate could extract 94.2% of fipronil from sugarcane juice as against only about 60% from sugarcane honey (Sampaio, Tomasini, Cardoso, Caldas, & Primel, 2012). The chromatogram of acetonitrile-extracted blank sample did not show any interference peaks in the retention times of the target compounds whereas, the chromatogram of ethyl acetate-extracted blank sample showed an interference peak in the retention time of fipronil sulphide (12.78 min). Hence, acetonitrile has been chosen for further downstream analyses, though the extraction efficiency of both the solvents was on par with each other. The physicochemical and practical advantages of acetonitrile over ethyl acetate in pesticide residue analyses have already been discussed in detail by earlier workers (Anastassiades, Lehotay, Štajnbaher, & Schenck, 2003; Lehotay et al., 2010; Maštovská & Lehotay, 2004). Acetonitrile was shown to be the most advantageous solvent for extraction of pesticide residues from food, and ethyl acetate was found to be second best (Anastassiades et al., 2003; Maštovská & Lehotay, 2004). The results obtained in the present study were also in favour of acetonitrile. Acetonitrile was used to extract fipronil and its metabolites from other two matrices namely jaggery and sugar as it was found effective in extracting the target compounds from sugarcane juice. 3.2. Cleanup The recoveries of fipronil and its metabolites were in the range of 87.4–105.3% when the samples were processed without any cleanup (Table 2). Among the two sorbents (PSA and C18) tested at three levels (10, 25 and 50 mg), PSA was superior to C18 at all the three levels. The recoveries of fipronil and its metabolites increased marginally with increased amounts of PSA from 10 to 50 mg/g of matrix (sugarcane juice). Lehotay et al. (2010) used twice as much PSA (50 mg/g of matrix) as the original QuEChERS protocol (Anastassiades et al., 2003), and stated that such minor
Table 1 GC–MS parameters and calibration data for the determination of fipronil and its metabolites. Compound
Retention time (min.)
m/z for quantification
m/z for qualifier ions
Linear equation
Coefficient of determination (R2)
Fipronil desulfinyl Fipronil sulphide Fipronil Fipronil sulphone
9.51 12.78 13.29 16.67
388 351 367 383
333, 353, 369, 385,
y = 184366x 571.9 y = 308177x + 38.3 y = 204848x 184.6 y = 212452x 505.0
0.9989 0.9999 0.9998 0.9998
281, 420, 213, 255,
213 255 255 241
411
T. Ramasubramanian et al. / Food Chemistry 150 (2014) 408–413 Table 2 Cleanup capabilities of sorbents at different levels in sugarcane juice fortified with 0.1 lg/g of the analytes. Recovery (%)A
Sorbent amount (mg/g of matrix)
PSA
No cleanup 10 25 50 SEMB
C18
Fipronil desulfinyl
Fipronil sulphide
Fipronil
Fipronil sulphone
Fipronil desulfinyl
Fipronil sulphide
Fipronil
Fipronil sulphone
89.5b 91.3b 93.7a 94.3a 0.5
97.9b 99.4b 100.2b 103.8a 0.6
87.4c 87.9c 89.2b 90.9a 0.3
105.3a 106.3a 107.5a 109.7a 0.8
89.5a 78.3b 74.4c 62.8d 1.8
97.9a 86.1b 84.2b 72.6c 1.6
87.4a 74.4b 72.5c 60.9d 1.5
105.3a 97.1b 82.8c 75.2d 2.3
Note: Means followed by same alphabets (a, b, c, d) are not significantly different from each other based on Duncan’s Multiple Range Test (DMRT). A Mean of six replications. B SEM = standard error of means.
Fig. 1. Matrix-induced suppression or enhancement of fipronil and its metabolites.
Table 3 Recovery and repeatability of fipronil and its metabolites from fortified samples of sugarcane juice, jaggery and sugar. Compound
Fipronil desulfinyl
Fipronil sulphide
Fipronil
Fipronil sulphone
Spiked level (lg/g)
0.01 0.05 0.10 0.01 0.05 0.10 0.01 0.05 0.10 0.01 0.05 0.10
Sugarcane juice (n = 3)
Jaggery (n = 3)
Recovery (%)
RSD (%)
Recovery (%)
RSD (%)
Recovery (%)
RSD (%)
95.0 94.1 99.0 97.3 92.0 95.6 92.4 88.9 89.5 108.5 99.8 105.5
2.4 1.9 3.5 1.9 2.6 1.0 2.6 2.4 2.2 2.0 2.7 1.6
90.4 89.0 98.2 94.2 96.9 100.0 88.9 91.9 94.6 97.6 95.6 106.9
3.8 0.6 1.0 1.4 3.9 1.3 0.2 3.4 1.9 1.1 1.0 2.0
101.5 92.7 90.2 94.2 96.6 97.4 91.1 87.5 89.7 103.7 96.3 101.0
1.5 3.2 6.5 2.5 5.3 0.7 3.7 2.9 0.6 1.2 1.8 1.7
adjustments in the sorbent amounts have little impact on pesticide recoveries unlike pH and solvent. Conversely, the recoveries of fipronil and its metabolites decreased significantly with increased amounts of C18 from 10 to 50 mg/g of matrix (Table 2). It has been shown that the addition of C18 along with PSA (each @ 50 mg/g of matrix) had no adverse effect on the recoveries of analytes rather it could only improve, but not harm the d-SPE performance (Lehotay et al., 2010). On contrary to this, the recoveries of fipronil and its metabolites were significantly lesser in C18-based cleanup as compared to the nocleanup protocol. Moreover, the recoveries of fipronil and its primary environmental metabolite, fipronil desulfinyl were below
Sugar (n = 3)
the acceptable ranges (
Contents lists available at ScienceDirect
Food Chemistry journal homepage: www.elsevier.com/locate/foodchem
Analytical Methods
A simple and sensitive single-step method for gas chromatography–mass spectrometric determination of fipronil and its metabolites in sugarcane juice, jaggery and sugar Thirumalaiandi Ramasubramanian a,⇑, Mariappan Paramasivam b, Ramabhadran Jayanthi a, Subramaniam Chandrasekaran b a b
Division of Crop Protection, Sugarcane Breeding Institute, Indian Council of Agricultural Research, Coimbatore 641007, Tamil Nadu, India Pesticide Toxicology Laboratory, Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
a r t i c l e
i n f o
Article history: Received 18 May 2012 Received in revised form 11 September 2013 Accepted 1 November 2013 Available online 12 November 2013 Keywords: Sugarcane juice Jaggery Sugar Fipronil Metabolites Residue
a b s t r a c t A simple and sensitive single-step method for gas chromatography–mass spectrometric determination of fipronil and its metabolites viz., fipronil desulfinyl, fipronil sulphide and fipronil sulphone in sugarcane juice, jaggery and sugar has been developed. Acetonitrile was superior to ethyl acetate in terms of selectivity, though they were on par with each other in terms of recoveries. This method does not require any cleanup as the PSA-based cleanup was on par with no-cleanup treatment. Interestingly, the recoveries of fipronil and its metabolites decreased with increased amounts of C18 from 10 to 50 mg/g of matrix. Matrix effects were insignificant and the limit of quantification was 0.005 lg/g. The recoveries of fipronil and its metabolites varied between 87.5% and 108.5% with the RSD of 0.2–5.3% for all the three matrices studied. This method has also been validated by monitoring fipronil and its metabolites in the retail outlet samples of sugarcane juice, jaggery and sugar. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction India is the world’s second largest producer of sugarcane. In 2011, it produced 355 million tonnes of sugarcane from 5.07 million hectares with an average productivity of 68 t/ha (Solomon, 2011). About 60–70% of the cane produced in the country is utilised for the production of sugar (Nair, 2011) and 25–30% is being utilised for the production of jaggery and khandsari (Singh, Singh, Anwar, & Solomon, 2011). Sugarcane juice is also a very popular beverage among the rural poor and is usually commercialised by street vendors. Though the experimental maximum yield of sugarcane is 325 t/ha, the national average hovers between 66 and 70 t/ha (Solomon, 2011). Insect pests are among the few important constraints which limit the productivity of sugarcane to a considerable extent across the Indian sub-continent. The early shoot borer, Chilo infuscatellus Snellen and the root borer, Emmalocera depresella Swinhoe are among the 20 major pests causing 22–33% and 35% loss in yield, respectively (Directorate of Sugarcane Development, 2012). Fipronil is one of the important broad-spectrum insecticides recommended for the management of C. infuscatellus and
⇑ Corresponding author. Mobile: +91 9442912010; fax: +91 4222472923. E-mail address: [email protected] (T. Ramasubramanian). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.11.004
E. depresella across the country to achieve the economic yield (Central Insecticide Board & Registration Committee, 2009). Fipronil, the phenylpyrazole insecticide [(±)-5-amino-1-(2,6-dichloro-a,a,a-trifluoro-p-tolyl)-4-trifluoromethylsulfinylpyrazole-3carbonitrile] blocks GABAA-gated chloride channels in the central nervous system of insects. Disruption of the GABAA receptors by fipronil prevents the uptake of chloride ions resulting in excess neuronal stimulation and death of the target insect (Cole, Nicholson, & Casida, 1993). Fipronil and its metabolites exhibit differential binding affinity for GABAA receptor subunits of insects and mammals. Fipronil has higher binding affinity for insect receptor complexes compared to mammalian complexes. The lower binding affinity for mammalian receptors enhances selectivity for insects and increases the margin of safety for humans and animals (Cole et al., 1993; Hainzl, Cole, & Casida, 1998; Ratra & Casida, 2001; Ratra, Kamita, & Casida, 2001). However, fipronil sulphone, the primary biological metabolite of fipronil has been reported to be 20 times more active at mammalian chloride channels than at insect chloride channels (Zhao, Yeh, Salgado, & Narahashi, 2005). Fipronil desulfinyl, the primary environmental metabolite (photo product) of fipronil is 9–10 times more active at the mammalian chloride channel than the parent compound and thus, reducing the selectivity between insects and humans (Hainzl & Casida, 1996; Hainzl et al., 1998). The residues of fipronil and its
T. Ramasubramanian et al. / Food Chemistry 150 (2014) 408–413
metabolites may be expected in the sugarcane juice and its downstream commercial products such as jaggery and sugar. Hence, development of methods for determination of residues of fipronil and its metabolites in sugarcane juice, jaggery and sugar deserves importance to ensure safety to people across the world and India in particular, as it is the largest consumer of sugar in the world. Few analytical methods have been reported for the determination of fipronil and its metabolites in different matrices such as soil, water, human urine (Vílchez, Prieto, Araujo, & Navalón, 2001), honeybees (Morzycka, 2002), honey (García-Chao et al., 2010), pollen (Kadar & Faucon, 2006) milk, cattle feed (Le Faouder et al., 2007), ovine plasma (Bichon, Richard, & Le Bizec, 2008), grape leaves, berries (Mohapatra et al., 2010), cotton lint and seed (Chopra, Chauhan, Kumari, & Dahiya, 2011). These methods have their own merits and demerits. Matrix solid-phase dispersion (MSPD) method developed by Morzycka (2002) was rapidly given up as it has been proved effective only for solid matrices such as honeybees. Kadar and Faucon (2006) developed a liquid chromatography–tandem mass spectrometry method characterised by cumbersome sample preparation steps for highly complex matrices like pollen. Le Faouder et al. (2007) developed a lengthy and laborious protocol for the determination of fipronil and its metabolites in milk and cattle feed. It involved extraction of target analytes with comparatively larger volume of solvents followed by a delipidation step and solid-phase extraction (SPE) cleanup. The SPE cleanup was performed in a single-step with atoll XC alone (for milk samples), or in two-steps (an additional step with florisil) for plant samples. This was followed by the highest level of concentration aimed at drastically reduce the limit of detection of the method. The SPE procedure of García-Chao et al. (2010) involved the costly vacuum manifold system for extraction of fipronil and its metabolites from honey and pollen matrices. The method developed by Vílchez et al. (2001) employed a specially designed solid-phase micro extraction (SPME) device made of fused-silica fibre coated with polymeric stationary phase (85 lm polyacrylate) for extraction of fipronil from very low volume of samples (0.5 g for soil, 64 mL for human urine and water samples). Bichon et al. (2008) developed a method meant for ultra-low volume of matrix (200 lL plasma) as the quantities of plasma available are often limited. Despite their good sensitivity, the suitability of these two methods only for low to ultra-low volume of matrices precludes their use for agricultural commodities, which require relatively larger sample size for adequate representation. The analytical methods developed for grape leaves, berries (Mohapatra et al., 2010), cotton lint and seed (Chopra et al., 2011) involved extraction of analytes with larger volume of solvents followed by the traditional liquid–liquid partitioning and column cleanup with adsorbents like florisil alone or with neutral alumina for final determination in GC–ECD. The limit of quantification of these two methods is comparatively higher (0.01 lg/g) besides the involvement of larger volume of organic solvents. Therefore, we first had to develop a rapid, simple and sensitive method for the determination of fipronil and its metabolites in sugarcane juice, jaggery and sugar.
2. Materials and methods 2.1. Chemicals and reagents Certified reference standards of fipronil (97.5%) and its metabolites viz., fipronil desulfinyl (97.8%), fipronil sulphide (98.8%) and fipronil sulphone (99.7%) were obtained from Bayer CropScience Ltd., India. The SPE sorbents primary secondary amine (BondesilPSA, 40 lm particle size) and Bondesil-C18 (40 lm particle size) were procured from Agilent Technologies, USA. The organic
409
solvents used in the study were of HPLC grade and purchased from Thomas Baker (Chemicals) Ltd., Mumbai, India. Analytical reagent grade anhydrous MgSO4 and NaCl were purchased from Merck India Ltd., Mumbai.
2.2. Preparation of standard solutions The stock solutions (1000 lg/mL) of fipronil and its metabolites were prepared by accurately weighing 10 mg of each analyte in volumetric flasks (certified A class) and dissolving in 10 mL of nhexane. These were stored in dark vials at 4 °C. A working standard mixture of 100 lg/mL was prepared by appropriate dilution of the stock solutions, from which the calibration standards (0.003–1.0 lg/mL) were prepared by serial dilution with n-hexane.
2.3. Sample preparation Sugarcane juice was extracted immediately after harvesting the 10 months-old crop (variety Co-86032) grown at Sugarcane Breeding Institute (Indian Council of Agricultural Research), Coimbatore (Tamil Nadu, India) using motorised sugarcane crusher. The sugarcane juice was filtered through Whatman No. 1 filter paper under mild-suction and used for analyses. Jaggery was also prepared from the same variety used for the extraction of juice, while the refined sugar was purchased from the retail market. Jaggery and sugar samples were ground to fine powder in mortar and then used for analyses.
2.3.1. Extraction A portion (10 g) of well-homogenised sample was placed into a 50 mL screw-capped oak ridge tube. The target compounds were extracted with 20 mL of the solvent (acetonitrile/ethyl acetate) as 10 mL of the extraction solvent was insufficient to get enough volume of organic phase from jaggery and sugar samples for downstream cleanup process. The oak ridge tube was closed tightly and shaken vigorously for 1 min to ensure better interaction between the solvent and sample. Then 1 g of NaCl and 4 g of MgSO4 (anhydrous) were added to the sample, vortexed for 1 min and centrifuged (Superspin R-V/FA; Plasto Crafts, Mumbai, India) at 5000 rpm for 10 min at room temperature. The supernatant (4 mL) was either concentrated under gentle stream of nitrogen (15 psi) in the Turbovap LV (Caliper Life Sciences, Russelsheim, Germany) at 40 °C and reconstituted in 1 mL of hexane for analysis in the GC–MS without cleanup or subjected to cleanup with the sorbents as detailed below.
2.3.2. Cleanup Dispersive solid phase extraction (d-SPE) cleanup with PSA or C18 was compared with no-cleanup (no sorbent) in terms of analyte recovery and interference from the study matrices (sugarcane juice, jaggery and sugar). After centrifugation at 5000 rpm for 10 min as described in the extraction, 8 mL of supernatant was transferred to 15 mL centrifuge tubes containing varied levels of PSA (10, 25 and 50 mg/g of matrix) and anhydrous MgSO4 (150 mg/g of matrix). Simultaneously, C18 was also evaluated at varied levels (10, 25 and 50 mg/mL) along with MgSO4 (150 mg/g of matrix). The extract was vortexed for 30 s and then centrifuged at 3000 rpm for 10 min. The supernatant (4 mL) was transferred to turbo tube and concentrated to near dryness under a gentle stream of nitrogen in the Turbovap LV as described earlier. The residue was reconstituted in 1 mL of hexane and thus, the amount of sample in the final extract was equivalent to 2 g/mL.
410
T. Ramasubramanian et al. / Food Chemistry 150 (2014) 408–413
2.4. GC–MS analysis Residues of fipronil and its metabolites were detected and quantified in Shimadzu GC 2010 gas chromatograph equipped with QP 2010 plus mass spectrometer. DB-1MS fused silica capillary column (30 m 0.25 mm 0.25 lm; Agilent Technologies, USA) was used for the separation of target analytes. The oven temperature was programmed as follows: 160 °C (1 min), 3 °C/min to 200 °C (2 min) and finally 4 °C/min to 250 °C (4 min). AOC-20s auto sampler and AOC-20i auto injector of the GC–MS system were employed to inject 1 lL of sample in split mode (1:5) at 250 °C. Ultrapure grade helium (99.9999%) (Bhuruka Gases Ltd., Bengaluru) was used as the carrier gas at constant flow rate of 2 mL/ min. The mass spectrometer was operated in electron ionisation (EI) mode with ionisation energy of 70 eV. The interface and the ion source temperatures were kept at 250 and 200 °C, respectively. The target and qualifier ions were chosen by injecting fipronil and its metabolites individually under the same chromatographic conditions using full scan with mass/charge ratio (m/z) of 50–500 (Table 1). Full-scan mode was used to determine cleanup effects and selected ion monitoring (SIM) mode was used for recovery experiments. The target compounds were confirmed through similarity match with NIST library (version 2.0). 2.5. Method validation The analytical method was validated as per the single laboratory validation approach (European Commission., 2010; Thompson, Ellison, & Wood, 2002). The performance of the method was evaluated by considering the validation parameters viz., linearity, limit of detection (LOD), limit of quantification (LOQ), matrix effect, recovery and repeatability. All the analyses were performed using the same blank samples. 2.5.1. Linearity Calibration curves for all of the target analytes were drawn by plotting the peak area against the concentration of the corresponding calibration standards at seven calibration levels ranged between 0.003 and 1.0 lg/mL. 2.5.2. Sensitivity Limit of detection (LOD) and the limit of quantification (LOQ) of each analyte were determined by considering a signal-to-noise ratio of 3 and 10, respectively with reference to the background noise obtained from the blank sample. 2.5.3. Matrix effects The area of the standard diluted with blank matrix extract was compared to the area of the standard diluted with solvent (n-hexane). The matrix effect was determined by the following equation: Matrix effect (%) = (peak area of matrix standard peak area of solvent standard) 100/peak area of solvent standard (Kanrar, Mandal, & Bhattacharyya, 2010). The positive and negative values of the matrix effect signify the matrix-induced enhancement and suppression, respectively.
2.5.4. Recovery and repeatability The recovery experiments were carried out on untreated matrices by fortifying 10 g of samples (sugarcane juice, jaggery and sugar) in six replicates with the mixture of fipronil and its metabolites separately at three concentration levels, i.e., 0.01, 0.05 and 0.1 lg/g, and extracting by the method described above. The precision of the method in terms of repeatability was determined on the basis of relative standard deviation (RSD%). 2.6. Statistics All statistical analyses including the Duncan’s Multiple Range Test (DMRT) were performed in SPSS Statistics version 17.0. 3. Results and discussion 3.1. Optimisation of the extraction procedure Acetonitrile and ethyl acetate were very effective in extracting fipronil and its metabolites from sugarcane juice matrix. The recoveries of fipronil and its metabolites were in the range of 89.5–106.9% and 94.7–104.4% when extracted with acetonitrile and ethyl acetate, respectively. In the present study ethyl acetate could extract 94.2% of fipronil from sugarcane juice as against only about 60% from sugarcane honey (Sampaio, Tomasini, Cardoso, Caldas, & Primel, 2012). The chromatogram of acetonitrile-extracted blank sample did not show any interference peaks in the retention times of the target compounds whereas, the chromatogram of ethyl acetate-extracted blank sample showed an interference peak in the retention time of fipronil sulphide (12.78 min). Hence, acetonitrile has been chosen for further downstream analyses, though the extraction efficiency of both the solvents was on par with each other. The physicochemical and practical advantages of acetonitrile over ethyl acetate in pesticide residue analyses have already been discussed in detail by earlier workers (Anastassiades, Lehotay, Štajnbaher, & Schenck, 2003; Lehotay et al., 2010; Maštovská & Lehotay, 2004). Acetonitrile was shown to be the most advantageous solvent for extraction of pesticide residues from food, and ethyl acetate was found to be second best (Anastassiades et al., 2003; Maštovská & Lehotay, 2004). The results obtained in the present study were also in favour of acetonitrile. Acetonitrile was used to extract fipronil and its metabolites from other two matrices namely jaggery and sugar as it was found effective in extracting the target compounds from sugarcane juice. 3.2. Cleanup The recoveries of fipronil and its metabolites were in the range of 87.4–105.3% when the samples were processed without any cleanup (Table 2). Among the two sorbents (PSA and C18) tested at three levels (10, 25 and 50 mg), PSA was superior to C18 at all the three levels. The recoveries of fipronil and its metabolites increased marginally with increased amounts of PSA from 10 to 50 mg/g of matrix (sugarcane juice). Lehotay et al. (2010) used twice as much PSA (50 mg/g of matrix) as the original QuEChERS protocol (Anastassiades et al., 2003), and stated that such minor
Table 1 GC–MS parameters and calibration data for the determination of fipronil and its metabolites. Compound
Retention time (min.)
m/z for quantification
m/z for qualifier ions
Linear equation
Coefficient of determination (R2)
Fipronil desulfinyl Fipronil sulphide Fipronil Fipronil sulphone
9.51 12.78 13.29 16.67
388 351 367 383
333, 353, 369, 385,
y = 184366x 571.9 y = 308177x + 38.3 y = 204848x 184.6 y = 212452x 505.0
0.9989 0.9999 0.9998 0.9998
281, 420, 213, 255,
213 255 255 241
411
T. Ramasubramanian et al. / Food Chemistry 150 (2014) 408–413 Table 2 Cleanup capabilities of sorbents at different levels in sugarcane juice fortified with 0.1 lg/g of the analytes. Recovery (%)A
Sorbent amount (mg/g of matrix)
PSA
No cleanup 10 25 50 SEMB
C18
Fipronil desulfinyl
Fipronil sulphide
Fipronil
Fipronil sulphone
Fipronil desulfinyl
Fipronil sulphide
Fipronil
Fipronil sulphone
89.5b 91.3b 93.7a 94.3a 0.5
97.9b 99.4b 100.2b 103.8a 0.6
87.4c 87.9c 89.2b 90.9a 0.3
105.3a 106.3a 107.5a 109.7a 0.8
89.5a 78.3b 74.4c 62.8d 1.8
97.9a 86.1b 84.2b 72.6c 1.6
87.4a 74.4b 72.5c 60.9d 1.5
105.3a 97.1b 82.8c 75.2d 2.3
Note: Means followed by same alphabets (a, b, c, d) are not significantly different from each other based on Duncan’s Multiple Range Test (DMRT). A Mean of six replications. B SEM = standard error of means.
Fig. 1. Matrix-induced suppression or enhancement of fipronil and its metabolites.
Table 3 Recovery and repeatability of fipronil and its metabolites from fortified samples of sugarcane juice, jaggery and sugar. Compound
Fipronil desulfinyl
Fipronil sulphide
Fipronil
Fipronil sulphone
Spiked level (lg/g)
0.01 0.05 0.10 0.01 0.05 0.10 0.01 0.05 0.10 0.01 0.05 0.10
Sugarcane juice (n = 3)
Jaggery (n = 3)
Recovery (%)
RSD (%)
Recovery (%)
RSD (%)
Recovery (%)
RSD (%)
95.0 94.1 99.0 97.3 92.0 95.6 92.4 88.9 89.5 108.5 99.8 105.5
2.4 1.9 3.5 1.9 2.6 1.0 2.6 2.4 2.2 2.0 2.7 1.6
90.4 89.0 98.2 94.2 96.9 100.0 88.9 91.9 94.6 97.6 95.6 106.9
3.8 0.6 1.0 1.4 3.9 1.3 0.2 3.4 1.9 1.1 1.0 2.0
101.5 92.7 90.2 94.2 96.6 97.4 91.1 87.5 89.7 103.7 96.3 101.0
1.5 3.2 6.5 2.5 5.3 0.7 3.7 2.9 0.6 1.2 1.8 1.7
adjustments in the sorbent amounts have little impact on pesticide recoveries unlike pH and solvent. Conversely, the recoveries of fipronil and its metabolites decreased significantly with increased amounts of C18 from 10 to 50 mg/g of matrix (Table 2). It has been shown that the addition of C18 along with PSA (each @ 50 mg/g of matrix) had no adverse effect on the recoveries of analytes rather it could only improve, but not harm the d-SPE performance (Lehotay et al., 2010). On contrary to this, the recoveries of fipronil and its metabolites were significantly lesser in C18-based cleanup as compared to the nocleanup protocol. Moreover, the recoveries of fipronil and its primary environmental metabolite, fipronil desulfinyl were below
Sugar (n = 3)
the acceptable ranges (
