MS

Accepted Manuscript Development and validation of a multianalyte method for quantification of mycotoxins and pesticides in rice using a simple dilute and shoot procedure and UHPLC-MS/MS Lucas Pinto da Silva, Fernando Madureira, Eugênia de Azevedo Vargas, Adriana Ferreira Faria, Rodinei Augusti PII: DOI: Reference:

S0308-8146(18)31274-3 https://doi.org/10.1016/j.foodchem.2018.07.126 FOCH 23241

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

3 August 2017 26 April 2018 18 July 2018

Please cite this article as: da Silva, L.P., Madureira, F., de Azevedo Vargas, E., Faria, A.F., Augusti, R., Development and validation of a multianalyte method for quantification of mycotoxins and pesticides in rice using a simple dilute and shoot procedure and UHPLC-MS/MS, Food Chemistry (2018), doi: https://doi.org/10.1016/j.foodchem. 2018.07.126

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Development and validation of a multianalyte method for quantification of mycotoxins and pesticides in rice using a simple dilute and shoot procedure and UHPLC-MS/MS

Lucas Pinto da Silvaa, Fernando Madureirab, Eugênia de Azevedo Vargasb, Adriana Ferreira Fariaa, Rodinei Augustia*

a

Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de

Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil. b

Laboratórios de Controle de Qualidade e Segurança Alimentar (LACQSA), Avenida

Raja Gabaglia, 245, Setor H, Bairro Cidade Jardim, Belo Horizonte, MG, 30380-103, Brazil.

* Corresponding author. Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627, Pampulha, 31270-901, Belo Horizonte, Minas Gerais, Brazil. E-mail address: [email protected] (Rodinei Augusti).

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Abstract

In the present manuscript an analytical methodology for the simultaneous determination of ten mycotoxins and six pesticides in rice was developed. This methodology comprises the application of the dilute and shoot protocol followed by quantification via UHPLC-MS/MS. The methodology was validated and all figures of merit shown to be within the limits established by regulation. Hence, the recoveries for mycotoxins and pesticides were within the specified ranges. Precision was assessed by repeatability and intra-laboratory reproducibility with standard deviations smaller than or equal to 20 %. The limits of detection, quantification and decision as well as the detection capacity were determined by the analytical curves whereas the measurement uncertainty was established by applying the bottom-up approach. Finally, the current methodology was applied to samples of rice (n = 42) commercialized in Brazil and positive results were found in only two for deoxynivalenol and zearalenone.

Keywords: Dilute and shoot; Ultra-high performance liquid chromatography; Tandem mass spectrometry; Mycotoxins; Pesticides

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1. Introduction

Rice belongs to the angiosperm division, whose grains are constituted mainly by starch with a minor content of proteins, lipids, fibers and minerals. It is able to supply about 20% of the energy and 15% of the protein required by the human diet. Rice is of high social and economic importance, being currently cultivated and consumed all over the world. Approximately 165 million hectares are estimated to be dedicated to rice cultivation, which furnish approximately 740 million tons of grain per crop (Maclean, Dawe & Hettel, 2002). The protection of rice crops against weeds, pests and diseases is done not only by the adoption of modern management techniques but also by the use of pesticides. Mycotoxins are natural contaminants produced by fungi and are present in several foods of plant origin. The proliferation of filamentous fungi in the crop is triggered by elements such as the bioavailability of microorganisms strains, climatic adequacy, and competition among different colonies (Hussein & Brasel, 2001; Pitt, 2000). During the storage and transport of rice, the main factors that can cause contamination by mycotoxins are the concentration of strains, the mechanical damage suffered by the grains and the infestation of insects and pests. Pesticides are substances intended to prevent, destroy, attract, repel or control any pest, including undesirable species of plants or animals during the whole grains production step. These substances can also be administered to animals for an effective control of ectoparasites (Brazilian Government, 1989). Currently, more than 800 chemical compounds are used as pesticides, which are grouped in roughly 100 classes. For rice only, the European Union regulates the presence of about 460 compounds, mainly those of insecticidal, herbicidal and fungicidal action (International Program, 2010). 3

A wide range of methods has been developed for the analysis of mycotoxins in food samples. Due to the complexity and diversity of the matrices analyzed, the analytical procedures are generally composed by extraction, clean-up, separation, detection, quantification and confirmation steps. The QuEChERS procedure (Quick, Easy, Cheap, Effective, Rugged and Safe) has been used in several multi-analytical methods, including the ones for the determination of mycotoxins and pesticides (De Dominicis, Commissati & Suman, 2012; Dzuman, Zachariasova, Veprikova, Godula & Hajslova, 2015; Romero-Gonzalez, Garrido Frenich, Martinez Vidal, Prestes & Grio, 2011). Extraction procedures using acetonitrile/ water or methanol/ water mixtures, which often do not require clean up steps, have been reported in the literature. Among these, the dilute and shoot methodology has been used (Malachova, Sulyok, Beltran, Berthiller, & Krska, 2014). In this work, a method for the determination of 10 mycotoxins (aflatoxin B1, aflatoxin B2, aflatoxin G1, aflatoxin G2, citreoviridine, ochratoxin A, fumonisin B1, fumonisin B2, deoxynivalenol and zearalenone) and 6 pesticides (bentazone, carbendazim, methyl chlorpyrifos, fipronil, metalaxyl and pencicuron) in rice is developed and later validated according to the SANCO/12571/2013 (European Commission, 2013) and 2002/657/CE (Commission Decision, 2002) regulations. The complete methodology involves the application of the dilute and shoot protocol and subsequent quantification of the analytes via ultra-high performance liquid chromatography coupled to sequential mass spectrometry (UHPLC-MS/MS). The pesticides were selected due to their wide use and also because their application in the rice production chain is allowed and regulated by the Brazilian and European inspection agencies.

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2. Materials and Methods

2.1. Reagents, Chemicals and Stock Solutions Standards of aflatoxins B1, B2, G1, G2, citreoviridine and ochratoxin A (Biopure Getzersdorf, Austria), fumonisin B1 and B2, deoxynivalenol, zearalenone and the pesticides bentazone, carbendazim, chlorpyrifos methyl, fipronil, metalaxil and pencicurom (Sigma-Aldrich, Saint Louis, USA) were acquired and used to prepare the stock solutions. The stock solutions were prepared at the following concentrations: aflatoxin B1 (10 mg mL-1), aflatoxin B2 (10 mg mL-1), aflatoxin G1 (10 mg mL-1), aflatoxin G2 (10 mg mL-1), citreoviridin (8 mg mL-1), ochratoxin A (50 mg mL-1), fumonisin B1 (50 µg mL-1), fumonisin B2 (50 mg mL-1), deoxynivalenol (40 mg mL-1), zearalenone (40 mg mL-1) and the pesticides (100 mg mL-1). Acetonitrile (Merck, Darmstadt, Germany) was the solvent used to prepare the stock solutions for all the analytes, excepting for fumonisin B1 for which a mixture of acetonitrile/water 1:1 v/v was employed. These stock solutions were kept under refrigeration at -20 °C. Glacial acetic acid and HPLC grade methanol (Merck, Darmstadt, Germany) were employed to prepare the mobile phase of the chromatographic runs.

2.2. Instruments and Apparatuses A mill (Retsch, Haan, Germany), tube shaker (Eberbach, Germany) and centrifuge (ThermoScientific, Waltham, USA) were used to the initial treatment of the rice grains. Micropipettes with capacities of 20 to 10000 μL (Gilson, Middleton, USA) and membranes with 0.22 μm porous diameter (ThermoScientific, Waltham, USA) were employed in several steps of the experimental procedure. Quantification was performed

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using a chromatographer (UHPLC system, Agilent model 1290, Santa Clara, USA) coupled to a mass spectrometer (Sciex model QTRAP 6500, Toronto, Canada). For the chromatographic runs, a C18 column (Zorbax RRHD, Agilent, Santa Clara, USA) with dimensions of 50 mm x 2.1 mm x 1.8 μm was used. The softwares (Analyst version 1.6.3 and MultiQuant version 3.0.2), both provided by Sciex, were used for data acquisition and processing.

2.3. Samples A blank sample was used to minimize the inherent variable composition of the rice grains. This was prepared from rice samples supplied by the National Agricultural and Livestock Laboratory, one among the laboratories maintained by the Ministry of Agriculture in Brazil. Hence, eight rice samples (including polished and integral grains) were combined to totalize 1 kg. The grains were then ground in the mill up to a mean particle size of 100 m and subsequently homogenized. A fraction of this sample of roughly 100 g was further milled to yield particles with an average size of 8 m. The grains of the real samples were also submitted to an identical milling procedure. The ground samples were then stored in inert plastic bottles and conditioned in a refrigerator (-8 ± 2 °C) until the time of analysis.

2.4. Dilute and Shoot Procedure In order to optimize the dilute and shoot procedure, a 23 factorial design was built. The following factors were evaluated at two distinct levels [(-1) and (+1)]: solvent composition, acetic acid concentration and volume of the extractive solution. The compositions of the solvent mixtures (acetonitrile and water) was 9:1 v/v (level -1) and

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8:2 v/v (level +1). The concentrations of acetic acid were 1 % v/v (level -1) and 2 % v/v (level +1) whereas the volumes of extractive solution were 8 mL (level -1) and 10 mL (level +1). For the performance of each test, 5 g of ground rice were weighed and an appropriate volume of the extractive phase (8 mL or 10 mL) was added. The mixture was vortexed for 1 min and then agitated in the shaker apparatus for additional 90 min. Subsequently, the mixture was centrifuged at 4000 rpm for 5 min and the supernatant filtered through a syringe filter (0.22 μm porous diameter). A general overview on the dilute and shoot procedure is schematically displayed in Figure 1.

[Insert Figure 1]

2.5. Mass Spectrometry and Chromatography Optimization The main parameters for the electrospray ionization source (in the positive and negative ion modes) and the mass spectrometer (under MRM, Multiple Reaction Monitoring, conditions) were optimized by directly infusing a standard pool solution containing all the analytes (at 1 μg mL-1 each in acetonitrile with 0.1 % v/v acetic acid). For the optimization of the ion source parameters, the chromatographic mobile phase, composed by methanol/water (1:1 v/v) acidified with 0.1 % acetic acid, and the standard pool solution were simultaneously injected via a FIA (Flow Injection Analysis) system at flow rates of 300 μL min-1 and 10 μL min-1, respectively. Hence, the optimized ion spray voltages achieved were 5000 V and -4500 V for the positive and negative modes, respectively. Nitrogen was the gas used in the ion source at the following pressures: curtain gas (40 psi), gas 1 and gas 2 (45 psi). Finally, the source temperature was set to 500 oC.

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For the optimization of the MRM conditions, the standard pool solution and the mobile phase were directly infused into the ion source as just described. The optimized results for the mass spectrometer parameters are displayed in Table 1. The pool standard solution of the analytes was also injected into the chromatographic system to establish the optimized chromatographic conditions. The following parameters were thus evaluated: (a) chromatographic columns (C18 Gemini, Phenomenex, dimensions 150 mm x 4.6 mm x 5.0 μm and C18 Zorbax RRHD, Agilent, dimensions of 50 mm x 2.1 mm x 1.8 μm); (b) column temperature; (c) mobile phase composition (mixtures of acetonitrile/water and methanol/water, both acidified with 0.1 % v/v acetic acid); (d) mobile phase flow rate (200 to 400 μL min-1); (e) elution gradient; (f) injection volume (2 to 10 μL). At the end, the following optimal chromatographic conditions were established: column Zorbax RRHD with a pre-column (Phenomenex) at 30 oC and an injection volume of 5 μL. The mobile phase consisted of water with 0.5% v/v of acetic acid (A) and methanol with 0.5% v/v of acetic acid (B), which was infused at a flow rate of 350 µL min-1. The gradient started at 60.0% A up to 5 min and reduced to 10.0 % up to 5.5 min. This proportion of A was maintained up to 6.0 min and then raised to 60.0 % up to 7.0 min.

[Insert Table 1]

2.6. Validation Figures of merit (selectivity, linearity, veracity, precision, limit of detection, limit of quantification, limit of decision, detection capacity and measurement uncertainty) were estimated according to the validation protocol specified in the SANCO/12571/2013 (European Commission, 2013) and 2002/657/CE (Commission

8

Decision, 2002) directives. The uncertainty calculation was performed according to the Guide for the Expression of Measurement Uncertainty (Hall, 2008). The maximum tolerated limits (MTLs) and the maximum residue limits (MRLs) for mycotoxins and pesticides, respectively, were stipulated based on current European and Brazilian legislations (Brazilian Health, 2005, 2009, 2011; Commission Regulation, 2006a, 2006b, 2008, 2016; European Commission, 2008, 2013).

2.7. Selectivity Six blank samples were injected into the chromatographic system to verify the presence of interfering compounds. Acceptable selectivity was ascribed only if the interference signal was lower than 30 % of the detection limit for a given analyte.

2.8. Calibration curves and matrix effect Aiming at avoiding possible matrix effects, the mycotoxins and pesticides were quantified by using matrix matched calibration curves. Firstly, four pool standard solutions were prepared by diluting the stock solutions of the analytes. The first one, containing aflatoxin B1, aflatoxin B2, aflatoxin G1, aflatoxin G2, citreoviridin, ochratoxin A and zearalenone, was prepared in acetonitrile at the following concentrations, respectively: 0.2, 0.2, 0.4, 0.4, 0.4, 0.5 and 24.0 µg mL-1. The second pool standard solution, with fumonisin B1 (40.0 µg mL-1) and fumonisin B2 (36.0 µg mL-1), was prepared using a mixture of acetonitrile/water 1:1 v/v as solvent. The third standard pool solution was prepared by diluting the stock solution of deoxynivalenol with acetonitrile to a final concentration of 20.0 µg mL-1. The fourth standard pool solution with the pesticides (at 0.8 µg mL-1 each) was also prepared in acetonitrile.

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To build the calibration curves, a blank extract was prepared by applying the optimized extraction procedure on 5 g of ground rice, as previously described. Hence, 1800 μL of the blank extract was fortified with 50 μL of each pool standard solution to a final volume of 2000 μL. Increasing volumes of this solution (25, 50, 100, 250, 500 and 900 µL) was transferred to distinct vials whereas decreasing volumes of the blank extract (975, 950, 900, 750, 500 and 100 μL) were added to each of them, respectively, to yield six distinct concentration levels of the calibration curves. These solutions were prepared in triplicate and injected into the chromatographic system in duplicate, during three distinct days. A total of 18 data for each point of the calibration curves was therefore collected. These six concentration levels were as following: aflatoxin B1 and aflatoxin B2 (0.3, 0.5, 1.0, 2.5, 5.0 and 9.0 µg kg-1); aflatoxin G1, aflatoxin G2, citreoviridin and ochratoxin A (0.5, 1.0, 2.0, 5.0, 10.0 and 18.00 µg kg-1); bentazon. carbendazin. chlorpyrifos methyl. fiproniol. metalaxil and pencicuronto (1.0, 2.0, 4.0, 10.0, 20.0 and 38.0 µg kg-1); deoxynivalenol and zearalenone (25.0, 50.0, 100.0, 250.0, 500.0 and 900.0 µg kg-1); fumonisin B1 and fumonisin B2 (50.0, 100.0, 200.0, 500.0, 1000.0 and 1800.0 µg kg-1)

2.9. Linearity Firstly, the Grubbs test was applied to identify outliers on the chromatographic data (peak areas). Normal statistical distribution of residues was assessed by the RyanJoiner test, which was confirmed for all analytes. Regression was performed by the weighted least squares method (MMQP) using dispersion as a weighting factor. This procedure was adopted due to the heterocedastic behavior of the instrumental response. The linear adjustment quality was evaluated by applying the t-test. The Durbin-Watson

10

independence test was applied to prove the non-existence of leverage points along the calibration curve (Ministry of Agriculture, 2015).

2.10. Trueness and Precision The method accuracy was estimated via the attainment of recovery rates. Precision was assessed by the repeatability and intra-laboratory reproducibility results. For the recovery and accuracy evaluation, six blank samples were fortified and extracted at three concentration levels and on three distinct days. For the fortification experiments, concentrations of 0.5, 1.0 and 1.5 times the MRL or MTL of each analyte were chosen: aflatoxin B1 and aflatoxin B2 (0.5; 1.0 and 1.5 µg kg-1); aflatoxin G1, aflatoxin G2 and citreoviridin (1.0; 2.0 and 3.0 µg kg-1); ochratoxin A (1.5; 3.0 and 4.5 µg kg-1); bentazone. carbendazim. chlorpyrifos methyl. fipronil. metalaxil and pencicuron (2.0; 4.0 and 6.0 µg kg-1); deoxynivalenol and zearalenone (50.0; 100.0 and 150.0 µg kg-1); fumonisin B1 and fumonisin B2 (100.0; 200.0 and 300.0 µg kg-1). When these limits were not available, it was adopted 4.0 µg kg-1 as the limit (MRL or MTL) for calculation purposes. Mean recovery values were considered acceptable only if within the 70-110 % range. As acceptability criteria for the precision tests, the values calculated by the Horwitz equation (Eq. 1) (Ministry of Agriculture, 2015) were used as the limit for each fortification level. Hence, the relative standard deviations obtained under conditions of reproducibility should not exceed those obtained by the Horwitz function (Eq. 1). For reproducibility, the relative standard deviations should not be higher than 2/3 of the value provided by the Horwitz function (Eq. 1).

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where RSDHorwitz = Horwitz relative standard deviation; C = concentration in the mass fraction (µg kg-1).

2.11. Limit of Detection (LOD), Limit of Quantification (LOQ), and The LOD (Eq. 2), LOQ (Eq. 3), CCα (Eq. 4) and CCβ (Eq. 5) were calculated by means of the regression parameters obtained from the calibration curves.

where Sa = standard deviation of the intercept; b = slope; μMRL or μMTL = combined uncertainty at the MRL or MTL concentration levels.

2.12. Measurement uncertainty (uc) The measurement uncertainty (uc) was calculated using the bottom-up methodology (Ministry of Agriculture, 2015). The main sources of uncertainty concerning the validated analytical method were those arising from the calibration curve, the weighing sample, the measurement of the extraction solution as well as the correction factors for recovery, dilution and reproducibility. Hence, the measurement uncertainty was calculated using the measurement function (Eq 6), in which the analyte concentration (C) was expressed in μg kg-1.

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where A = chromatographic peak area; a = intercept of the calibration curve; b = slope of the calibration curve; Vextration = volume of the extractive solution (mL); Msample = sample mass (kg); FCrec = correction factor due to systematic error corrected by recovery; FCdil = correction factor due to dilution; Crepro = correction due to internal reproducibility. From Eq. 6, the coefficients of sensitivity for each of the identified uncertainty source was calculated according to Eqs. 7 to 14.

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3. Results and Discussion

3.1. Method optimization The MRM mode was used and the analytes ionized in both positive and negative modes. For each analyte, transitions for quantification and confirmation were monitored (Table 1). The relative standard deviations of the ratio between the chromatographic peak areas of the quantification and confirmation transitions met the recommendations of the Directive 2002/657/EC (Commission Decision, 2002). In order to optimize the chromatographic separation, some instrumental parameters were studied individually. Firstly, two separate chromatographic columns were tested both with a C18 phase: Phenomenex Gemini and Agilent Zorbax RRHD operating in line with a C18 precolumn (see Experimental section for more details). It was observed that both chromatographic columns were able to provide separation with similar efficiency. However, the latter chromatographic column was selected because it provided a shorter run time. The influence of the column temperature on the chromatographic separation was evaluated at three distinct values: 20; 30 and 35 °C. Since no significant difference in the separation was observed due to temperature variation, 30 °C was selected due to a faster oven stabilization at this value. Methanol and acetonitrile were evaluated as solvents for the mobile phase. An appropriate resolution was verified under both conditions. However, the chromatographic peaks of the fumonisins B1 and B2 presented an undesirable asymmetry when acetonitrile was used. Methanol was thus chosen as the mobile phase for the chromatographic method. To minimize ionization suppression effects and also to preserve the mass spectrometer clean for a longer period of time, the diverter valve was triggered at the run time window from 0.0 to 0.5 min. This procedure allowed the discharge of hydrophilic substances extracted from the 14

matrix thus avoiding their entrance into the chromatographic system. Finally, the elution gradient was optimized in order to obtain adequate resolution and run time. The optimized conditions (previously exposed) yielded chromatograms with high signal-tonoise relationship, which resulted in quite sensitive analyses (Figure 2).

[Insert Figure 2]

3.2. Dilute and shoot procedure: optimization The experimental conditions for the dilute and shoot procedure was also optimized. The chromatographic area for aflatoxin B1 was adopted as a convenient response, since this analyte has the lowest tolerated upper limit among all the analytes evaluated herein. The best condition was achieved by adding 10 mL of the extractive solution composed by acetonitrile/water 8:2 v/v and acetic acid at 2 % v/v to 5 g of the pulverized rice sample (Figure 1).

3.3. Validation Analysis of blank sample (Figure 3) confirmed the method selectivity, as no interfering compounds were detected at a signal-to-noise ratio of up 3:1 in the retention times of the pesticides and mycotoxins. However, it was found that the blank sample was naturally contaminated with the pesticides bentazone, fipronil and zearalenone, but at very low levels. This finding was expected indeed because these pesticides are widely applied on rice crops and thus the attainment of real blank (uncontaminated) samples is a quite difficult task.

[Insert Figure 3]

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3.4. Linearity and matrix effect After the removal of outliers upon the application of the Grubbs trial, the residues followed a normal distribution as verified by the Ryan-Joiner test (Ministry of Agriculture, 2015). Because of the heteroscedastic behavior of the instrumental response variances, the MMQP regression was thus applied on the experimental data. The quality of the linear fit was confirmed by the excellent determination coefficients (r2) obtained (> 0.999), as displayed in Table 2. Moreover, the t-test applied on each set of data confirmed the adequacy of the linear model since the tvalue >> tcritical (6.4 for n = 6 with 95 % confidence level) for each analyte (Table 2).

3.5. Trueness Trueness was estimated by determining the recoveries of spiked blank samples at three concentration levels, with six replicates per level. The assays were repeated on three distinct days under reproducibility conditions. All recovery rates obtained herein fall in the suitable range of 70 and 110 % (Table 3).

3.6. Precision To confirm the analytical performance, the Horwitz relative standard deviation (RSDHorwitz) values were used as the limit for the RSD. The limit for the RSD under repeatability conditions was established as two thirds of the RSDHorwitz values, according to the recommendations of EC/657/2002 (Commission Decision, 2002). It can be observed (Table 3) that reproducibility is within the limits recommended by the regulations adopted at the three levels for each compound.

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3.7. LOD, LOQ, CCa and CCβ The LOD, LOQ, CCα and CCβ were calculated as described in item 2.6.5. When the calculated LOD and LOQ values were smaller than those indicated by the calibration curve, the first and second lowest concentration levels were considered for the LOD and LOQ, respectively. For the calculation of CCα and CCβ, the values presented in the EC/1881/2006 regulation (Commission Regulation, 2006a) for mycotoxins were used as MTL. For pesticides, the MRL established by the European Commission and authorized monographs maintained by the Brazilian agency ANVISA were used. As no legislation provides MTL for citreoviridine and fumonisins B1 and B2 in rice, the level of 10 μg kg-1 was used as MTL for calculation purposes. The calculated or adopted values for LOD, LOQ, CCα and CCβ are shown in Table 2.

3.8. Measurement uncertainty The measurement uncertainty (uc) for each analyte was calculated by using the bottom-up approach. The sensitivity coefficients were obtained by applying Equations 7 to 14. Table 2 shows the expanded measurement uncertainty (U) for each compound, calculated at the LOQ level. It should be noted that the results obtained are in conformity with the minimum value determined by the European Commission 401/2006/EC (Commission Regulation, 2006b) and the Analytical Quality Assurance Manual (Ministry of Agriculture, 2015).

3.9. Occurrence study For the occurrence study, 42 rice samples provided by the Brazilian Ministry of Agriculture, Livestock, and Supply were analyzed. Among these samples, only two 17

were contaminated with deoxynivalenol (above the limit of quantification), with concentrations of 62 ± 19 μg kg-1 and 157 ± 60 μg kg-1. Moreover, in only one sample zearalenone was detected at a concentration level of 67 ± 25 μg kg-1. However, as this value lies in the uncertainty interval, this can be classified as a non-violated sample.

[Insert Table 2]

[Insert Table 3]

4. Conclusions

A methodology that employs a simple procedure (dilute and shoot) combined with UHPLC-MS/MS was developed for the simultaneous determination of mycotoxins and pesticides in rice. The entire procedure was validated according to the rigid directives from two distinct guidelines. Hence, these results indicated that the methodology is sufficiently selective, sensitive, reliable, repetitive and reproducible to be applied in real samples. Moreover, the matrix-matched calibration curves shown a linear behavior for all analytes in a wide dynamic range. It was also found that the main uncertainty source of the method is the calibration curve. As a proof-of-principle, the methodology was applied to the analysis of real rice samples, which showed to possess low levels of contamination for these pesticides and mycotoxins. The method therefore can be easily applied for quality control and safety of rice samples, according to the main standards of analytical quality. Finally, the use of so such simple procedure (dilute and shoot) makes the methodology described herein potentially applicable to other analytes and matrixes. Studies regarding this exciting possibility are underway in our laboratory. 18

Acknowledgements

The authors thank the Brazilian agencies FAPEMIG and CNPq for the financial support and fellowships.

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Contaminants, Pesticides. Guidance Document on Analytical Quality Control and Validation Procedures for Pesticides Residues Analysis in Food and Feed. Bruxelles, Belgium. European Commission Regulation (EC) No. 839/2008 of 31 July 2008 (2008). Amends Regulation (EC) Nr. 396/2005 of the European Parliament and of the Council as regards Annexes II, III and IV on maximum residue levels of pesticides in or on certain products. Official Journal of the European Union, 51 (2008), p. L 234. Hall, B. D. (2008). Evaluating methods of calculating measurement uncertainty. Metrologia, 45, L5-L8. Hussein, H. S., & Brasel, J. M. (2001). Toxicity, metabolism, and impact of mycotoxins on humans and animals. Toxicology, 167(2), 101-134. International Program on Chemical Safety (2010). The WHO recommended classification of pesticides by hazard and guidelines to classification 2009. Geneva: International Programme on Chemical Safety. Maclean, J. L., Dawe, D. C., & Hettel, G. P. (2002). Rice almanac : source book for the most important economic activity on earth (3rd ed.). Oxon, U.K.: CABI Pub. Malachova, A., Sulyok, M., Beltran, E., Berthiller, F., & Krska, R. (2014). Optimization and validation of a quantitative liquid chromatography-tandem mass spectrometric method covering 295 bacterial and fungal metabolites including all regulated mycotoxins in four model food matrices. Journal of Chromatography A, 1362, 145-156.

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Ministry of Agriculture, Livestock and Supply of Brazil (2015). Available in: http://www.agricultura.gov.br/assuntos/laboratorios/arquivos-publicacoeslaboratorio/manual-de-garantia-qualidade-analitica.pdf, accessed in July, 2017. Romero-Gonzalez, R., Garrido Frenich, A., Martinez Vidal, J. L., Prestes, O. D., & Grio, S. L. (2011). Simultaneous determination of pesticides, biopesticides and mycotoxins in organic products applying a quick, easy, cheap, effective, rugged and

safe

extraction

procedure

and

ultra-high

performance

liquid

chromatography-tandem mass spectrometry. Journal of Chromatography A, 1218 (11), 1477-1485.

22

Figure Captions

Figure 1. Schematic representation of the dilute and shoot procedure applied to the analysis of mycotoxins and pesticides in rice samples. Figure 2. Extracted ion chromatograms (XIC) for each analyte (mycotoxins and pesticides) obtained under the optimized conditions of analysis. Figure 3. Typical chromatograms obtained during the analysis of the blank samples.

23

24

Aflatoxin B1

Aflatoxin B2

Aflatoxin G1

Aflatoxin G2

Chlorpyrifos Bentazone

Carbendazim

methyl

Citreoviridin

Deoxinivalenol

Fumonisin B1

Fumonisin B2

Fipronil

Metalaxil

Ochratoxin A

Pencicuron

Zearalenone

25

Aflatoxin B1

Aflatoxin B2

Bentazone

Carbendazim

Aflatoxin G1

Aflatoxin G2

Chlorpyrifos Citreoviridin methyl

Deoxinivalenol

Fumonisin B1

Fumonisin B2

Fipronil

Metalaxil

Ochratoxin A

Pencicuron

Zearalenone

26

Table 1. Optimized parameters of the mass spectrometer employed in the analysis of the mycotoxins and pesticides in rice samples. Analyte

Polarity

Aflatoxin B1

Positive

Aflatoxin B2

Positive

Aflatoxin G1

Positive

Aflatoxin G2

Positive

Citreoviridin

Positive

Chlorpyrifos methyl

Positive

Deoxynivalenol

Positive

Fumonisin B1

Positive

Fumonisin B2

Positive

Ochratoxin A

Positive

Carbendazim

Positive

Metalaxil

Positive

Pencicuron

Positive

Zearalenone

Negative

Bentazone

Negative

Fipronil

Negative

Q1 (m/z)

Q3 (m/z)

DP

EP

CE

CXP

312.8

285.1

122

10

32

15

312.8

241.0

122

10

50

15

315.0

258.9

30

10

40

15

315.0

287.0

30

10

35

25

328.8

199.9

70

10

54

15

328.8

242.9

70

10

37

15

331.0

189.0

115

10

55

15

331.0

245.2

115

10

41

15

403.0

138.8

131

10

29

18

403.0

315.0

131

10

10

16

321.5

289.7

37

10

22

18

321.5

211.8

37

10

39

19

297.1

249.0

60

10

14

16

297.1

231.0

60

10

16

16

722.2

334.3

151

10

55

14

722.2

352.2

151

10

49

16

706.2

336.2

126

10

49

14

706.2

318.2

126

10

51

14

404.0

239.0

60

10

33

14

404.0

102.0

60

10

101

16

192.0

160.0

60

10

25

10

192.0

132.0

50

10

44

12

280.0

248.0

25

10

16

30

280.0

220.0

25

10

18

25

329.0

125.0

70

10

30

10

329.0

218.0

60

10

46

12

317.0

174.8

-170

-10

-28

-15

317.0

272.9

-170

-10

-22

-21

239.0

197.0

-60

-10

-28

-15

239.0

132.0

-60

-10

-34

-10

434.8

330.0

-85

-10

-23

-20

434.8

250.0

-85

-10

-38

-23

27

Quantitative transitions are underlined. DP = declustering potential (V); EP = entrance potential (V); CE = collision energy (manufacturer unit); CXP = collision cell exit potential (V).

28

Table 2. Results for linearity, LOD, LOQ, CCα, CCβ and uncertainty arising from the validation of the analytical method developed for the analysis of the mycotoxins and pesticides in rice samples. MT t-

L

value

or

a

MR

Interce Analyte

LO Slope

R2

pt

LO

Um CCα

D

Q

0.3

0.5

CCβ

uc ax

L Aflatoxin

19414.

0.99

1981.7

550.6

B1

2

99

Aflatoxin

15856.

0.99

408.5 B2 Aflatoxin

788.3 8

99

10762.

0.99

2

2b 4

Aflatoxin -56.7

b

99

5

0.99

2149.

99 118769

94189.

0.99

.1

5

99

15571.

0.99

Bentazone

0.5

2.3

0.5

1.0

2.3

12.

32.

3

0

6.5

32.

2.7

1.0

2.5

31.

3.1 8

100

500c 99

4

6.1 0.5

1.9

108.

117.

19.

23.

9

7

3

4

17.

23.

787.

1074 2

4

6.3

4951.

10096. 9

1

2.6

-

m

32.

3

337.9

Carbendazi

12. 2.5

0

2b

4482.5

G2

0.3

2.2

3310.

666.4 G1

2b

1.5

5.1

5

2

.4

3400

3801

18.

19.

.7

.5

0

2

23.1

36.2

3.8

25.

5 Chlorpyrifo

0.99 305.5

931.0

s methyl Citreoviridi

300 250.8

4188.0

0.99

6.9

0d

98 1679.5

2.1

3601.

10

0.9

2.8

29

MT t-

L

Interce Analyte

Slope

2

R

value

LO

D

Q

or

pt a

LO

Um CCα

CCβ

uc ax

MR L

n

99

Deoxynival

5

0.99 1161.6

0 750

26.

52.

1716

2682

15.

29.

b

2

5

.4

.7

7

2

12.

32.

51.

103 7

0

99

5

.1

2.9

32.

10.077

0.99

45.

90.

.4

99

3

6

18181.

0.99

1.4

4.6

2425.7

enol

739.9 99

Fumonisin

0.99 18144.

3006.8

B1

813.9

10

10.3

10.6

6 Fumonisin 48787. B2

733.6

10

18.8

27.6

15.7

21.3

0

9

Fipronil

2019. 10b

329.4

-

2

99

0

76955.

0.99

18431

Metalaxil

50 5399.7

6

-187.4

4709.1

Ochratoxin A 12780.

24441

99

.3

0.99

1116. 3b

99

7

0.99

2710.

Zearalenone

8.4

8856.6

3837.4

99

8

0.99

15283

1.2

0.9

3.1

.2

0

102.

15.

25.

6

1

0

8.8

32.

76.3

3.6

4.1

67.0

23.

84.0 5

29.

58.

75b 99

6

9.5 50

7

0.6

4.6

25.

0

e

Pencicuron

1.4

15.

101. 88.2

3

6

30. 5.3

4

8

30

a

tcritical = 6,38 (n= 6; 95 % confidence level);

b

Commission Regulation, 2006a;

c

Brazilian Health, 2009; d European Commission, 2008; e Commission Regulation, 2008; R2 = correlation coefficient; MTL = maximum tolerated limit (µg kg-1); MRL = maximum residue limit (µg kg-1); LOD = limit of detection (µg kg-1); LOQ = limit of quantification (µg kg-1); CC = decision limit (µg kg-1); CC = detection capability (µg kg-1); uc = measurement uncertainty (%); U = expanded measurement uncertainty (%).

31

Table 3. Results for recovery and precision arising from the validation of the analytical method applied to the analysis of the mycotoxins and pesticides in rice samples. Recovery (%) Analyte

Aflatoxin B1

Aflatoxin B2

Aflatoxin G1

Aflatoxin G2

Bentazone

Carbendazim

Chlorpyrifos methyl Citreoviridin

Deoxynivalenol

Fumonisin B1

Fumonisin B2

Reproducibilit

Repeatability (%)

1°

2°

3°

level

level

level

RS

RS

RS

D 1º

D 2º

D 3º

day

day

day

12.

11.

RSDHorwi

RS

RSDHorwi

tz

D

tz

89.5

95.7

92.4

93.6

99.4

93.9

98.2

99.2

101.0

4.1

6.5

7.2

6.1

92.0

100.2

101.1

8.5

3.9

7.9

7.4

89.0

106.0

97.8

13.

12.

16.

0

4

2

98.1

99.9

96.9

4.1

5.2

7.7

6.8

89.9

92.7

95.6

6.3

3.1

13.6

97.8

102.8

103.7

7.2

7.8

9.5

94.9

96.7

98.4

5.8

8.8

16.3

85.9

89.2

90.0

95.8

103.4

102.2

4 10. 2

13. 4 12. 9 17. 6 15. 9 9.4

6.9

y (%)

5.8

8.6

9.3

4 12. 2

15. 2 3.2

11.2

10.2

19.5

13.4

8.0

33. 3 33. 3 30. 0 30. 0 23. 7 23. 7 23. 7 21. 3 30. 2 15. 0 15. 4

49.9

49.9

44.9

44.9

35.5

35.5

35.5

32.0

45.3

22.4

23.1

32

Fipronil

Metalaxil

Ochratoxin A

Pencicuron

Zearalenone

95.5

99.0

97.6

91.1

99.0

96.7

97.7

104.6

99.6

93.9

102.5

100.9

92.7

99.9

98.5

6.2 12. 3 18. 3 15. 6 12. 0

13.

16.

1

2

6.1

9.1

10.4

7.9

9.7

12.0

3.1

5.2

9.3

5.0

4.8

10.5

11.8

23. 7 23. 7 28. 2 23. 7 16. 7

35.5

35.5

42.4

35.5

25.1

33

Highlights



A methodology to determine pesticides and mycotoxins in rice was developed.



The method makes use of the dilute and shoot procedure and UHPLC-MS/MS.



The method was validated according to the EC/657/2002 normative.



The method was applied in the analysis of rice commercialized in Brazil.



These real rice samples shown to possess quite low levels of these analytes.

34