Development and validation of a multiclass method ...

Food Additives & Contaminants: Part A

ISSN: 1944-0049 (Print) 1944-0057 (Online) Journal homepage: http://www.tandfonline.com/loi/tfac20

Development and validation of a multiclass method for the determination of antibiotic residues in honey using liquid chromatographytandem mass spectrometry Khaled El Hawari, Samia Mokh, Samah Doumyati, Mohamad Al Iskandarani & Eric Verdon To cite this article: Khaled El Hawari, Samia Mokh, Samah Doumyati, Mohamad Al Iskandarani & Eric Verdon (2017) Development and validation of a multiclass method for the determination of antibiotic residues in honey using liquid chromatography-tandem mass spectrometry, Food Additives & Contaminants: Part A, 34:4, 582-597, DOI: 10.1080/19440049.2016.1232491 To link to this article: http://dx.doi.org/10.1080/19440049.2016.1232491

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Date: 03 March 2017, At: 09:12

FOOD ADDITIVES & CONTAMINANTS: PART A, 2017 VOL. 34, NO. 4, 582–597 http://dx.doi.org/10.1080/19440049.2016.1232491

Development and validation of a multiclass method for the determination of antibiotic residues in honey using liquid chromatography-tandem mass spectrometry Khaled El Hawaria,c, Samia Mokha, Samah Doumyatib, Mohamad Al Iskandarania,b and Eric Verdonc a

CNRSL, Lebanese Atomic Energy Commission (LAEC), Laboratory for Analysis of Organic Compound (LAOC), Beirut, Lebanon; bFaculty of Public Health I, Lebanese University, Hadath, Beirut, Lebanon; cFrench Agency for Safety of Food, Environment and Occupational Health, Laboratory of Fougères, French National and European Union Reference Laboratory for Residues of Antimicrobial Veterinary Medicinal Products in Food from Animal Origin, Fougères, France ABSTRACT

ARTICLE HISTORY

A new, simple and fast method was developed for the determination of multi-class antibiotic residues in honey (sulfonamides, tetracyclines, macrolides, lincosamides and aminoglycosides). Separation and determination were carried out by LC-MS/MS. During sample preparation, various parameters affecting extraction efficiency were examined, including the type of solvent, pH, efficiencies of cleavage of N-glycosidic linkages by hydrochloric acid, ultrasonic extraction and its duration compared with shaking, along with dispersive SPE clean-up. Experiments with fortified samples demonstrated that 10 min of ultrasonic treatment with acidified methanol (HCl 2 M) followed by dispersive SPE clean-up with 50 mg PSA gave an effective sample preparation method for several classes of antibiotics in honey. Anhydroerythromycin A, erythromycin A enol ether and desmycosin were used as markers for the presence of erythromycin A and tylosin A in honey samples. The method was validated according to European Commission Decision (EC) No. 2002/657. The recoveries of analytes ranged from 85% to 111%. Repeatability and intra-laboratory reproducibility were < 20.6% and 26.8%, respectively. Decision limit (CCα) and detection capability (CCβ) ranged from 6 to 9 µg kg–1 and from 7 to 13 µg kg–1, respectively, except for streptomycin and neomycin, which showed slightly higher CCα at 25 µg kg–1 and CCβ at 34 µg kg–1. Finally, the method was applied to the honey test material 02270 through a FAPAS proficiency test (PT) for the determination of tetracyclines. PT results were obtained within a z-score range of ±2, proving that the validated method is suitable for routine analysis to ensure the quality of honey.

Received 9 June 2016 Accepted 11 August 2016

Introduction Veterinary medicinal products (VMPs) are widely used in farm practice to prevent and control animal diseases. However, incorrect practices can lead to the potential presence of VMP residues in food-producing animals and may cause risks to human health (Coffman 1999; McEwen & Fedorka-Cray 2002; Landers, Cohen et al. 2012). Many studies indicate that VMPs can accumulate in edible tissues, which can trigger allergic reactions in sensitive individuals. Furthermore, long-term exposure to low levels of VMPs could result in the development of antibiotic-resistant bacteria, which would no longer respond actively to drug treatment (Dayan 1993; Tollefson & Karp 2004; Graham et al. 2014). Therefore, the European Union (EU) (EU

KEYWORDS

Multi-class analysis; veterinary drugs; honey; LC-MS/MS; validation

Regulation No. 37/2010, 2009), USFDA (CFR-21 Part556 2015), and other international regulatory authorities regulate VMPs intended for use in food-producing animal production. In addition, they established MRLs for VMPs to monitor their levels in food and to ensure that these residues impose no health risks to consumers. In apiculture, beekeepers treat their hives with antibacterial agents (Reybroeck et al. 2012) against bacterial diseases such as American foulbrood (AFB) (Genersch 2010) and European foulbrood (EFB) (Forsgren 2010). However, in some countries, like the UK (BeeBase 2015) and New Zealand (Biosecurity Order 1998), when bee colonies are infected with AFB the hives must be destroyed by burning because spores are considered to be highly resistant and can

CONTACT Eric Verdon [email protected] French Agency for Safety of Food, Environment and Occupational Health, Laboratory of Fougères, 10B [email protected] Lebanese Atomic Energy rue Claude Bourgelat, Bioagropolis, Javené, F-35306 Fougères, France; Khaled El Hawari Commission (LAEC), Laboratory for Analysis of Organic Compound (LAOC), Airport Road, PO Box 11-8281, Beirut, Lebanon © 2016 Informa UK Limited, trading as Taylor & Francis Group

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Table 1. Tolerance levels (µg kg–1) for veterinary drugs in honey in several countries. Classes Streptomycin Tetracyclines Tetracycline Chlortetracycline Oxytetracycline Sulfonamides Macrolides Erythromycin Tylosin Lincomycin Penicillin

EU-RLs RCs 40 20 – – – 50 20 – – – –

Belgium PAsa 20 20 – – – 20 – – – – –

Canada MRLs 125 – 250 100 – 100 – 100 200 100 10

WRLsb 37.5 – 75 30 – 30 – 30 60 30 0.3

Australia MRLs – – – – 300 – – – – – –

India MRLs 10 – 10 10 10 10 – – 10 – –

Switzerland MRLs 10 – – – – 50 – – – – –

Notes: aPoints of action. Working residue limits.

b

remain infectious for more than 35 years (Haseman 1961; Genersch 2010). So far there are no MRLs for antibiotic residues in honey (EU Regulation No. 37/ 2010, 2010), therefore the presence of VMPs in honey is not authorised. The European Union Reference Laboratories (EU-RLs) provide recommended concentrations (RCs) for the control of non-authorised substances like tetracyclines, sulfonamides, streptomycin and macrolides (erythromycin and tylosin) in honey (Table 1) in order to improve and harmonise the performance of the monitoring analytical methods (CRL Guidance 2007; Commission Decision (EC) No. 2003/181, 2003). Several countries like Canada (FDR 2005), Belgium (AFSCA 2016), India (EIC 2015), Australia (APVMA 2015) and Switzerland (DFI 1995) define their own tolerance levels for each class of antibacterial agents in honey (Table 1). Furthermore, the Codex Committee on Residues of Veterinary Drugs in Foods (CCRVDF) drafted guidance by JECFA (2013) for the establishment of MRLs in honey based on the ADI of VMP residues and their depletion studies in honey. Honey is a complex biological matrix that contains a high concentration of several sugars and other substances like vitamins, proteins, minerals, organic acids and enzymes (White 1971; Ball 2007). The composition of these substances can vary depending on the nectar source and other external factors such as seasonal and environmental conditions (White & Chichester 1978; Anklam 1998). These variations pose analytical challenges regarding sample processing and the analysis of trace contaminants in honey. One of these challenges is to remove interfering substances such as sugar, wax and pigments from honey extract prior to VMP residue analysis to reduce matrix effects (Kujawski & Namieśnik 2008). There are several other challenges for the analyst to overcome during the development of a multiclass method for analysis of VMPs in honey (Kujawski &

Namieśnik 2008). For example, sulfonamide residues in honey combine with reducing sugar to form N-glycoside bonds, which lead to poor recoveries for almost all sulfonamides that could be found in the sample (Kaufmann, Roth et al. 2002). For that reason, it is necessary to include a pretreatment hydrolysis step to break the sugar–sulfonamide bond. Studies have demonstrated that methanol (Bernal et al. 2009) and hydrochloric acid (Thompson & Noot 2005; Sajid et al. 2013; Tölgyesi, Berky et al. 2013; Dubreil-Chéneau et al. 2014) were the main reagents used to hydrolyse N-glycoside bonds in order to give better recovery. However, macrolides are not stable at acidic conditions (Skinner et al. 1993; Volmer & Hui 1998; Kim et al. 2004); they are usually extracted under basic conditions to avoid their degradation (Wang 2004; Benetti et al. 2006; Bogialli et al. 2007; Wang & Leung 2007). Erythromycin A degrades rapidly to anhydroerythromycin A in honey (Thompson & Van den Heever 2012), which is known to be an acidic matrix (the pH ranges from 3.4 to 6.0; Ball 2007). Other studies identified in honey samples desmycosin (tylosin B), the degradation product of tylosin A (Kochansky 2004; Adams et al. 2007; Thompson et al. 2007). Therefore, it is important to include not only the parent VMPs but also their metabolites or other transformation products when monitoring the use of their residues in honey. A similar phenomenon is observed for tetracyclines; these compounds can undergo structural epimerisation in acidic conditions (pH 2–6) (Anderson et al. 2005). Furthermore, they have a strong affinity to form complexes with divalent metal cations (Carlotti et al. 2012), which leads to inadequate recoveries during sample extraction processing. To improve recoveries, the interaction can be disrupted by adding EDTA to the extraction solvent because it has greater affinity to chelate cations than tetracyclines (Anderson et al. 2005). Another issue associated with the development of multiclass analytical method is associated with

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aminoglycosidic antibiotics. These VMPs are highly polar organic basic compounds that show practically no retention in reversed-phase LC, unless an ion-pairing reagent such as a perfluorocarboxylic acid is added to the mobile phase, also considering the suitable concentration to minimise ionisation suppression (Inchauspe & Samain 1984). Several studies have focused on the determination of VMPs in honey based on single-class residue methods (SRMs) which cover a limited number of analytes generally from the same family and similar chemical behaviour (Peres et al. 2010; Gajda et al. 2013; Tölgyesi, Berky et al. 2013; Dubreil-Chéneau et al. 2014). However, there is an increasing effort in the development of multi-class analytical methods capable of detecting a wide range of residues with different chemical and physical properties in the same sample (Kaufmann 2009; Kaufmann et al. 2011). Several multi-class analytical methods in honey are described in the literature (Debayle et al. 2008; Hammel et al. 2008; Lopez et al. 2008; Vidal et al. 2009; Hou et al. 2011; Bohm et al. 2012; Gómez-Pérez et al. 2012; Wang & Leung 2012; Wang, Shi et al. 2013; Galarini et al. 2015; Shendy et al. 2016). Most of these methods are based on SPE for sample preparation and purification, with the aim of enhancing the sensitivity of detection and reduce interferences. However, Hammel et al. (2008) describe four separate liquid–liquid extractions for the simultaneous determination of 42 antibiotics in honey prior to LC-ESI-MS/MS analysis. In addition, Wang and Leung (2012), Wang, Shi et al. (2013) and Shendy et al. (2016) reported the use of a QUECHERS method for quantification and confirmation of veterinary drugs in honey. The aim of this study is to develop and validate a simple multiclass method for identification and quantification of 21 antimicrobial VMPs in honey by LCESI-MS/MS. These drugs belong to several classes of antibiotics that include sulfonamides (SAs), macrolides (MAs), tetracyclines (TCs), lincosamides (LCs) and aminoglycosides (AMGs). Simple extraction and clean-up steps were investigated and optimised by using ultrasonic-assisted extraction and dispersive solid-phase extraction (d-SPE). Our objective is to determine the best conditions of extraction (pH and solvent) having in mind practical routine use including reduced sample preparation and detection time, without affecting the multi-analyte recoveries. Furthermore, mass spectrometric matrix effects were evaluated on a multi-floral honey extract during the validation procedure.

Materials and methods Chemicals, reagents and solutions Analytical standards of sulfamethoxazole (SMXZ), sulfamerazine (SMRZ), sulfathiazole (STZL), sulfadimethoxine (SDMX), sulfadimerazine (SDMZ), sulfachloropyridazine (SCPD), oxytetracycline (OTC), doxycycline (DC), chlortetracycline (CTC), tetracycline (TC), anhydrotetracycline (AH-TC), anhydrochlortetracycline (AH-CTC), 6-epidoxycycline (EPI-DC), 4epitetracycline (EPI-TC), demeclocycline (DMC), tylosin A (TYLA), erythromycin A (EMTC), anhydroerythromycin A (AH-ETMC), erythromycin A enol ether (ETMC enol), josamycine (JSMC), lincomycin (LNMC), dihydrostreptomycin (DH-STRP), streptomycin (STRP), neomycin (NEO), hygromycin (HGMC), deuterated sulfonamides sulfadoxine-D3 (SDX-d3) and sulfadimethoxine-D6 (SDMX-d6) were supplied by Sigma-Aldrich (Seelze, Germany). Desmycosine (TYLB) and 13C-labelled erythromycin (ETMC-c13) were obtained from Toronto Research Chemicals (North York, ON, Canada). Tobramycin (TBMC) was purchased from Dr. Ehrenstorfer (Augsburg, Germany); and 13C-labelled sulfathiazole (STZL-c13) from Witega (Berlin, Germany). LC-MS-grade acetonitrile (MeCN) and methanol (MeOH) were obtained from Honeywell Burdick & Jackson (Seelze, Germany). Ethylenediaminetetraacetic tetrasodium salt (Na4EDTA) were purchased from Sigma-Aldrich (Madrid, Spain) and heptafluorobutyric acid (HFBA) for ion-pairing chromatography from Fluka (St. Louis, MO, USA). Hydrochloric acid solution (HCl) at 37% was obtained from VWR international (Fontenay-sous-Bois, France). Primary secondary amine (PSA) sorbent was purchased from Agilent technologies (Waldbronn, Germany). Formic acid (FA) analytical grade was obtained from BDH Laboratory (Poole, UK). Deionised water with resistivity 18.2 Ωm was prepared with Barnstead-Easy pure II from Thermo Fisher Scientific (Hudson, NY, USA). pHs were measured with a pH meter: Thermo Orion 720A plus (Beverly, MA, USA). The sonication process was carried out using an Elma T 760DH ultrasonic bath with a frequency of 40 kHz (Singen, Germany). Individual stock standard solutions were prepared into appropriate organic solvents at a concentration of 500 µg ml–1. Only AH-CTC was prepared at 120 µg ml–1. MeOH was used to dissolve SA, TC, LC and MA standards. However, AMG stock standard solutions were dissolved in ultra-pure water with 1% FA using a polypropylene volumetric flask. All stock solutions were kept at –20°C, except for AMGs which

FOOD ADDITIVES & CONTAMINANTS: PART A

were stored at 4°C. Multi-compound intermediate solutions for each antibiotic class (except for aminoglycosides) were prepared in MeOH at 20 µg ml–1 by appropriate dilution of the stock solution. The intermediate solution for AMG analytes was prepared in ultra-pure water at 20 µg ml–1 (only NEO and STRP were prepared at 100 µg ml–1). For spiking purposes, three daily spiking solutions containing all analytes were prepared by diluting intermediate stock solutions in ultra-pure water to obtain the desired concentrations at 0.4, 0.6 and 0.8 µg ml–1, whereas for NEO and STRP they were prepared at concentrations of 2, 3 and 4 µg ml–1. The internal standard (IS) mixture prepared as an intermediate solution in ultra-pure water includes DMC, SDX-d3 and SDMX-d6 at 10 µg ml–1; ETMCc13, STZL-c13 and TBMC at 20 µg ml–1; as well as HGMC at 40 µg ml–1. Freshly prepared solutions were used by diluting 10 times the IS intermediate solution in water for spiking the samples. Sample extraction Honey samples (2 ± 0.05 g) were weighed into 50 ml disposable centrifuge tubes. Samples were spiked with 100 µl of the IS working solution and kept overnight in the dark. Afterwards, 2.5 ml of ultra-pure water were added to dissolve the honey by shaking for 1 min. Then, 2.5 ml of acidified MeOH (HCl, 2 mol l–1) were added; the sample was shaken by hand for 1 min and then sonicated for 10 min. The honey extract pH was adjusted to 2.0 by adding 460 ± 5 mg of Na4EDTA. The samples were shaken for 1 min and centrifuged for 10 min at RT at 4000 rpm using an Eppendorf 5810 (Hamburg, Germany). Following that, 1 ml of the extract was transferred to a centrifuge tube containing 50 ± 5 mg PSA for dispersive clean-up. The mixtures were shaken for 1 min and centrifuged for 10 min at RT at 4000 rpm. Finally, 500 µl of the purified extract were transferred into LC plastic vials for chromatography. Liquid chromatography-mass spectrometry (LC-MS) The chromatographic system consisting of an HPLC Agilent Technologies 1200 series (Morges, Switzerland) was coupled to an Agilent 6410 tandem mass spectrometer (Wilmington, DE, USA) set in positive mode electrospray ionisation (ESI). The analysis was conducted on a reversed-phase Zorbax SB C18 column (100 × 2.1 mm; 3.5 µm) obtained from Agilent. Three mobile phase components: A, ultra-pure deionised water with 100 mM HFBA; B, acetonitrile; and C, ultra-pure deionised water were used for

585

chromatographic separation according to the following gradient programme. The initial condition started from 10% of A, 5% of B, and 85% of C. Eluent A was kept unchanged at 10% during the analysis. Eluent C decreased from 85% to 50% over 10.5 min. After that, this percentage was linearly decreased to 25% within 1.5 min, then to 10% in 1 min. This composition was held for 1 min and increased to 85% in 0.5 min, followed by a re-equilibration time of 8.5 min. The flow rate was set at 0.5 ml min–1 and the total running time prior to re-injection was 23 min. The injection volume was 30 µl. To minimise the contamination of the system with sugar, a Valco valve was used to divert the column elution directly to waste from 0 to 4 min, and plug again to MS/MS from 4 to 23 min in each sample run. All antimicrobial VMPs were detected using ESI source in positive mode under the following working conditions: The source temperature was 350°C; desolvation nitrogen gas was at flow rate of 7 l h–1; capillary voltage was 4.0 kV; and nebuliser pressure was 30 psi. The collision energy (CIV) as well as the fragmentor for each analyte was optimised through direct injection of 0.5 µg ml–1 standard solution into an MS. Table 2 summarises the specific MS/MS parameters for the targeted analytes. From the MS/MS optimisation, the two most intense transitions per analyte were selected for operation in MRM mode and one transition (the most intense) was used for quantification.

Validation procedure The method was validated on a blank multifloral honey material and according to the recommendations of Commission Decision (EC) No. 2002/657 (2002). For substances without MRL in honey but having an MRL in other animal matrices such as meat and milk, the European Commission recommends that one assesses method performance in honey at a concentration at least as low as that recommended by the EU-RLs guidance of 2007 (CRL Guidance Paper 2007). Therefore, adequate levels for spiking blank samples were chosen following these recommendations. Three levels of spiking were selected to evaluate the recoveries and corresponded to 1, 1.5 and 2 times the minimum recommended performance level (Commission Decision No. 2002/657, 2002). The first level was set at 20 µg kg–1 for all antibiotics, except for NEO and STRP at 100 µg kg–1. Method performance was evaluated for each analyte through the determination of the precision (repeatability and intra-laboratory reproducibility parameters), recoveries, decision limit (CCα),

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Table 2. Ionisation parameters for the analytes with chromatographic retention time. Class Sulfonamides

Retention time (min) 10.413

Precursor ion 253.8

STZL

7.325

255.9

STZL C13 (IS)b SMRZ

7.503 6.847

261.9 265.1

SDMZ

8.268

279.1

SCPD

10.069

284.9

SDMX

11.704

311.1

Lincosamides

SDX D3 (IS) SDMX D6 (IS) LNMC

10.3 12.06 9.950

314.1 317.1 407.1

Tetracyclines

AH-TC

14.523

427

DC

13.596

445.1

EPI-DC

13.413

445.1

TC

11.610

445.1

EPI-TC

11.136

445.1

AH-CTC

15.400

461.1

OTC

11.071

461.1

CTC

13.249

479

DMC (IS) TBMC (IS) HGMC (IS) STRP

12.58 12.69 10.07 10.213

464.9 468.2 528.2 582.2

DH-STRP

10.166

584.2

NEO

13.194

615.4

AH-ETMC

15.608

716.3

ETMC

14.763

734.4

ETMC enol

15.421

716.3

ETMC C13 (IS) TYLB

15.73 14.069

718.4 772.3

TYLA

15.031

916.2

JSMC

15.981

828.4

Amino-glycosides

Macrolides

Names SMXZ

Product ion 253.8 > 108a 253.8 > 155.9 255.9 > 155.9a 255.9 > 108 261.9 > 161.8a 265.1 > 108a 265.1 > 155.9 279.1 > 186.1a 279.1 > 108 284.9 > 155.9a 284.9 > 108 311.1 > 155.9a 311.1 > 108 314.1 > 155.9a 317.1 > 161.8a 407.1 > 126.1a 407.1 > 359.2 427 > 410a 427 > 154 445.1 > 428.1a 445.1 > 154 445.1 > 428.1a 445.1 > 154 445.1 > 409.8a 445.1 > 154 445.1 > 409.8a 445.1 > 427 461.1 > 444a 461.1 > 154 461.1 > 425.8a 461.1 > 443.1 479 > 443.8a 479 > 461.8 464.9 > 447.9a 468.2 > 163.1a 528.2 > 177.1a 582.2 > 263.1a 582.2 > 245.8 584.2 > 263.1a 584.2 > 246 615.4 > 160.9a 615.4 > 292.9 716.3 > 157.8a 716.3 > 557.9 734.4 > 157.8a 734.4 > 578 716.3 > 157.8a 716.3 > 557.9 718.4 > 160a 772.3 > 173.5a 772.3 > 131.6 916.2 > 173.6a 916.2 > 771.9 828.4 > 108.9a 828.4 > 173.9

Fragmentor (V) 120 120 80 80 100 100 100 120 120 120 120 120 120 100 120 80 80 100 100 120 120 100 100 120 120 100 100 100 100 120 120 100 100 140 100 140 140 140 120 120 140 140 120 120 160 160 140 140 120 180 180 200 200 200 200

CID (V) 25 10 10 25 10 30 10 15 30 10 25 20 30 15 20 35 15 15 25 15 30 15 30 15 30 15 10 15 30 20 10 20 15 15 20 30 30 40 30 40 30 20 30 10 25 15 30 15 30 30 35 40 30 45 35

(IS) used SDX D3 STZL C13 – STZL C13 STZL C13 SDX D3 SDMX D6 – – ETMC C13 DMC DMC DMC DMC DMC DMC DMC DMC – – – HGMC HGMC TBMC ETMC C13 ETMC C13 ETMC C13 – ETMC C13 ETMC C13 ETMC C13

Notes: aQuantification ion. Internal standard.

b

capacity of detection (CCβ), specificity, matrix effect and linear range. Blank honey samples (2 g) were fortified with 100 µl of IS working solution and 100 µl of spiking solution containing all the analytes at three different concentrations (20, 30 and 40 µg kg–1). However, for NEO and STRP the concentration levels were increased to 100, 150 and 200 µg kg–1. The spiked samples were kept overnight in the dark. Six replicates for each level were

analysed on three different days from different weeks. For the quantification results, a matrix-fortified calibration curve was prepared by extracting five blank samples (2 g) fortified at levels of 20, 40, 60, 80 and 100 µg kg–1 for all analytes, except for NEO and STRP at 100, 150, 200, 250 and 300 µg kg–1. For each set of runs, the analysis was performed by injecting the matrix-fortified calibration standards plus an additional blank sample twice at the start and at the end

FOOD ADDITIVES & CONTAMINANTS: PART A

of the samples sequence, resulting in a total number of 30 analyses carried out within 1 day.

Results and discussion Optimisation of sample preparation Despite the fact that honey contains multiple compounds possibly responsible for matrix effects, antimicrobial VMPs also impose specific problems to overcome. SAs can form a very stable N-glycosidic bond with reducing sugars present in honey (Kaufmann, Roth et al. 2002). TCs can form chelating complex with metal ions and interact with a silica group in a reversed-phase analytical column. Therefore, they can elute with tailing peaks unsatisfactory for reliable quantification (Stolker & Brinkman 2005). MAs are acid sensitive which results in the degradation of ETMC (Thompson & Van den Heever 2012), JSMC (Skinner et al. 1993), and TYLA (Kochansky 2004; Thompson et al. 2007). For that reason, Thompson et al. (2007) recommended including both the parent ion and its major degradation products in LC-MS/MS analysis in order to verify the use of these antibiotics in apiculture. AMGs are highly polar compounds that cannot be satisfactorily retained on LC reversedphase unless an ion-pairing agent is added to the mobile phase at a suitable concentration (Inchauspe & Samain 1984). Our aim was to develop a simple extraction and clean-up method that covers several types of antimicrobial residues with different polarity in honey without affecting recoveries. Therefore, the choice of extraction solvent, effect of pH, acid hydrolysis pretreatment and ultrasonic extraction versus traditional shaking were evaluated. Furthermore, dispersive SPE clean up with PSA was assessed through determination of the remained amount of sugar content in honey. All experiments were performed in triplicate by spiking blank honey with all analytes at 100 µg kg–1. These samples were kept overnight in the dark to permit sufficient absorption of the different standards prior to extraction. In addition, recoveries for all analytes were estimated without correction for the losses during the sample preparation. To assess the possible matrix effects, the peak area of each analyte in spiked samples was compared with that obtained from analytes added to blank honey extract immediately prior to LC injection.

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Choice of extraction solvent in the antimicrobial multi-residue method The choice of the solvent for extraction is one of the most important parameters for the development of a multiclass residue method. The solvent must be compatible with the analytes of interest and amenable to the chromatographic separation system. Furthermore, another aspect to consider when selecting the extraction solvent is the safety of laboratory staff and for the environment, as well as its cost. In our study, MeOH and MeCN solvents were assessed for their ability to extract multiclass antibiotic residues. Only one parameter was changed during sample extraction, which is the type of solvent. In the first experiment, fortified honey samples (2 g) were dissolved in 2.5 ml of water, then 2.5 ml of pure organic solvent were added to a 50 ml centrifuge tube. The tubes were shaken for 1 min and ultra-sonicated for 30 min. Then the extract was treated with 1 g of Na4EDTA followed by centrifugation at 4000 rpm for 10 min. Finally, the extract was purified using 50 mg of PSA then injected into the LCMS/MS. Comparing MeOH with MeCN (Figure 1), MeOH was able to extract 16 of 31 compounds with recoveries higher than 60%. However, MeCN was suitable to extract macrolide residues from honey samples. Erythromycin A, aminoglycosides and sulfonamides were not extracted with MeOH or MeCN. Acetonitrile is more effective in avoiding sugar coextractive interferences from honey samples due to the sugaring-out phenomenon causing liquid–liquid partitioning between water and MeCN (Wang et al. 2008; Tsai et al. 2010). However, it was found to be an unsuitable solvent for sample preparation for multiclass analysis of antimicrobials in honey. This could be due to high solubility of polar compounds (aminoglycosides and tetracyclines) in the water fraction resulting in loss of these analytes during solvent partitioning in acetonitrile. Therefore, MeOH was assumed to be more suitable than MeCN for extracting antimicrobial residues in honey. For sulfonamides, an acid hydrolysis step must be implemented prior to extraction to release N-glycosidic bonds with the aim to avoid their underestimation. Wang et al. (2012) reported that a mixture of hydrochloric acid and methanol could increase the cleavage rate. ETMC was not detected in our experiment, although the measured pH after adding Na4EDTA during extraction was 10. The reason behind this observation is related to low pH (< 3) due to the

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Figure 1. Evaluation of MeOH and MeCN organic solvents in sample preparation for the analysis of antibiotic residues in honey: (a): SAs, (b) TCs, (c) MAs and LCs, and (d) AMGs.

addition of HFBA ion-pairing reagent during the chromatographic separation. This resulted in ETMC degradation into ETMC enol and AH-ETMC A. Therefore, these two analytes were chosen as markers for analysis of ETMC in honey (Thompson & van den Heever 2012). It should be noted that the same degradation process for ETMC IS (EMTC-c13) occurred with an intense peak having a pseudo-molecular ion m/z at 718.4, giving a daughter ion at m/z 160. Effect of pH The effect of pH on the extraction procedure was evaluated by adjusting its value by adding an appropriate amount of Na4EDTA to honey extract. In our

study, three pHs, 2, 5.1 and 10, were investigated. At pHs 2 and 5.1, acidified MeOH with 2 M HCl was added to the extract after dissolving honey with water. After sonication for 30 min, 460 mg or 1 g of Na4EDTA were added to adjust the pH to 2 and 5.1, respectively. However, for basic extraction at pH 10, pure MeOH and 1 g of Na4EDTA were used. The optimal pH for each antibiotic class was assessed from the recovery values (Figure 2). For sulfonamides, best recoveries were found at pHs 2 and 5.1; for tetracyclines and lincomycin, similar recoveries were obtained at all pHs. Acidification of honey sample is likely to disrupt the N-glycosidic linkage between sulfonamides and reduced sugar in honey. This increases

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Recovery %

100 80 60 40 20 0 5.1

2

10

pH Sulfonamides

Tetracyclines

Macrolides

Amino-glycosides

Figure 2. Influence of pH on the extraction of antibiotic residues in honey.

Lincosamides

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the extraction efficiency of these analytes at pHs 2 and 5.1 compared with 10, in which no acidic hydrolysis was applied. However, when analysing the sample at low pHs, loss of macrolide residues occurs, except for TYLB, which is correctly extracted. Figure 2 shows better recoveries for macrolides at basic pH; this observation is in line with results reported by Wang and Leung (2007). On the other hand, better extraction efficiency was observed for most studied aminoglycosides at pH 2, which decreased with increasing pH. Furthermore, a better signal-to-noise ratio was obtained when the sample was extracted at pH 2 compared with those at pHs 5.1 and 10 (Figure 3). Our results showed that the extraction efficiency of most analytes was improved by acidifying the samples at pH 2. However, that pH has a strong impact on macrolide antibiotics except for TYLB, leading to their degradation. Effect of acidic hydrolysis Several studies demonstrated that acidic hydrolysis is a crucial step to ensure the complete release of sulfonamides bonding sugar (Thompson & Noot 2005; Sajid et al. 2013; Tölgyesi, Berky et al. 2013; DubreilChéneau et al. 2014). However, Bernal et al. (2009) reported the possibility of using pure MeOH to break the N-glycosidic bond. However, Figure 1(a) shows that pure MeOH could not disrupt the bonding. This observation is likely attributable to the equilibration of our spiked samples overnight. Sajid et al. (2013) reported that methanol is adequate if the spiked honey were analysed within the same day immediately after spiking. But when the spiked sample was kept for more than 1 day, no recoveries were found. Wang et al.

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(2012) and Tölgyesi, Berky et al. (2013) reported the use of acidified methanol to release sugar-bonded sulfonamides in honey and, thus, improve their recoveries. To avoid the possible underestimation of sulfonamides in honey samples, the breaking efficiencies of N-glycosidic bond were investigated with 1, 2 and 4 M hydrochloric acid prepared in MeOH. The pH of extraction was maintained at 2 by adding the appropriate amount of Na4EDTA. The recoveries of analytes are displayed in Figure 4. It can be observed in Figure 4 (a) that there were approximately 5–10% higher recoveries for analysed sulfonamides at 2 M HCl than at 1 and 4 M. The cleavage efficiencies were 27–35% for 1 M HCl, 29–51% for 2 M HCl and 19–38% for 4 M HCl. We also observed that recoveries change depending on the amount of HCl used. For tetracyclines, better recoveries (60–110%) were obtained at 1 and 2 M HCl. Among tetracyclines only AH-TC (Figure 4(b)) shows an increase in recovery with increasing hydrochloric acid concentration, which leads to a partial degradation of EPI-TC and TC into AH-TC (Pena et al. 1998). On the other hand, recoveries for aminoglycosides (Figure 4(d)) with 4 M HCl are higher than those with 1 and 2 M, except for STRP and DH-STRP. Their maximum recoveries were found at 2 M HCl. Regarding macrolides, hydrochloric acid at 1 M generally provides better recoveries than 2 and 4 M. AH-ETMC and TYLB had recoveries of 100% and 108% respectively. However, poor recoveries (< 20%) were obtained for ETMC enol, EMTC-C13 and JSMC. When the concentration of HCl increased, AH-ETMC, ETMC enol, EMTC-C13 and JSMC were not detectable. As a result,

Figure 3. Improvement in the signal-to-noise ratio (SNR) of NEO (left) and TBMC (right) at different pHs: (a) pH 2, (b) pH 5.1 and (c) pH 10.

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Figure 4. Analyte recoveries with different amounts of HCl but equal pH (= 2): (a) SAs, (b) TCs, (c) MAs and LCs, and (d) AMGs.

it is recommended that one reach an optimised compromise among these different effects. To minimise the degradation of macrolides, extraction with 1 M HCl was the best choice, but recoveries of aminoglycosides as well as the efficiency of N-glycosidic bond decreased at that concentration. Therefore, in our final method, hydrochloric acid at 2 M was chosen when performing extractions of multiclass antimicrobials. Comparison of shaking versus sonication This experiment assessed the influence of mechanical shaking versus sonication on the extractability of antimicrobial residues in honey. The results were evaluated to study the efficiency of conventional shaking and ultrasonic-assisted extraction procedures through

recovery experiments. A conventional orbital shaker IKA KS 501 (Staufen, Germany) was used to shake honey samples at 200 rpm. The duration of extraction was set to 30 min for both treatments. Figure 5 displays the comparison of different extraction approaches. It can be observed that the extraction efficiency for most antimicrobials in honey using the sonication method were higher than conventional shaking. Therefore, ultrasonic extraction was considered throughout this work. It is important to note that the use of conventional shaking gave better results for AH-ETMC, which was easily extracted, while 30 min of ultrasonic extraction led to its degradation. We can conclude that all compounds except degradation products of erythromycin A give better recoveries using ultrasonic extraction.

Shaking VS ultrasonic 160 140 Recovery %

120 100 80 60 40 20 SMXZ STZL STZL C13 SMRZ SDMZ SCPD SDMX SDX D3 SDMX D6 LNMC AH-TC TC EPI-TC DC EPI-DC OTC AH-CTC DMC TBMC CTC HGMC STRP DH-STRP NEO AH-ETMC ETMC enol ETMC C13 TYLB JSMC

0

Shaking

Ultrasonic

Figure 5. Comparison of extraction techniques (shaking and ultrasonic) with extract antibiotic residues in honey.

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Figure 6. Influence of ultrasonication periods on extraction efficiency: (a) SAs, (b) TCs, (c) MAs and LCs, and (d) AMGs.

A second set of experiments was performed to investigate the efficiency of ultrasonic extraction at 10, 20 and 30 min. The effect of sonication time on the extraction of antibiotic residues in honey can be seen in Figure 6. It was found that the increase in ultrasonic extraction time from 10 to 30 min caused a decrease in many recovered antimicrobials. Taking into consideration the possible degradation of some analytes such as AH-ETMC and ETMC enol at higher extraction time (Figure 6(c)), the optimum sonication time was selected at 10 min. Regarding JSMC, the acid conditions adopted for sample preparation (pH 2) led to its degradation (Skinner et al. 1993). It was also shown that neither sonication nor hydrolysis improved its recovery (< 10%) during method development, thus JSMC was removed from the method. Sample clean-up Honey is a very complex matrix that contains interfering compounds (such as sugar, wax and pigments) that can be co-extracted during the extraction process. This can reduce the lifetime of the analytical column and interfere with antimicrobials detection. Therefore, a clean-up step after extraction is mandatory to minimise co-extracted substances from honey extract. A number of multi-class methods use SPE for cleaning honey extract (Debayle et al. 2008; Lopez et al. 2008; Vidal et al. 2009; Hou et al. 2011; Bohm et al. 2012; Galarini et al. 2015). The most common SPE sorbents include hydrophilic-lipophilic balanced (HLB), polymeric reversed phase, strong cation exchange and mixed mode (reversed-phase/strong cation) exchange.

Our work evaluated a simple clean-up approach based on d-SPE. PSA was chosen for clean-up since both sugars and fatty acids can be removed from sample extract through hydrogen bonding (Anastassiades, Lehotay et al. 2003; Orso, Martins et al. 2014). Our experiment was performed to investigate the impact of PSA on the purification of 1 ml honey extract along with the recovery of analytes using different amounts of PSA sorbent: 50, 100, 150 and 200 mg. An aliquot from each purified extract was taken for the analysis of sugar content (glucose and fructose) using LC with a refractive index detector (RID). As can be seen in Figure 7, 50 mg of PSA were effective at removing about 20–30% of the tested sugar. However, increasing the amount of PSA above 50 mg does not provide a further decrease in sugar content. In addition, all target analytes achieved better recoveries when 50 mg of PSA were used (Figure 8). Note that a significant loss in the

Figure 7. Clean-up capability of PSA sorbent to remove the coextracted hydrophilic component mainly in sugar.

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Figure 8. Effect of using PSA sorbent with different amounts on analyte recoveries.

recoveries of aminoglycoside residues was observed with increased amounts of PSA. This might be attributed to its capacity to form hydrogen bonds with polar compounds. Therefore, 50 mg PSA was chosen as the most effective clean-up technique.

Validation of the method After method development, validation of the method was performed in accordance with European Commission Decision (EC) No. 2002/657/EC (2002). Identification of antibiotics is confirmed by the presence of transition ions at the correct retention times (±2.5%) compared with the corresponding standards. Two MRM transitions are monitored for each analyte earning four identification points, as described in Commission Decision (EC) No. 2002/657 (2002, table 5). Furthermore, the measured ion ratio (least intense versus the most intense signal) was compared with those obtained from fortified honey samples and, thus, must fall within the criteria described in Commission Decision (EC) No. 2002/657/EC (2002, table 4). To verify the absence of potential interfering substances at the retention time of the analysed antibiotic, blank honey samples came from more than 20 different origins from different regions. They were included over the 3 days of validation throughout the sample queues on LC-MS/MS. These blanks showed no interfering peaks overlapping with the analytes in MRM mode, indicating adequate specificity for the analysis of the 21 antimicrobial residues in honey.

Linearity was determined by using a matrix-fortified linear calibration curve. Blank honey samples were spiked with an incremental amount of analytes prior to sample extraction at five concentration levels: 100–300 µg kg–1 for NEO and STRP, and 20–100 µg kg–1 for the other compounds. ISs were spiked into blank honey samples, also at a fixed amount for all levels (DMC, SDX-d3 and SDMX-d6 at 50 µg kg–1; ETMC-c13, STZL-c13 and TBMC at 100 µg kg–1; as well as HGMC at 200 µg kg–1). The calibration curve showed good linearity with the coefficient of determination (r2) > 0.988 (Table 3).

Table 3. Results of CCα, CCβ and coefficient of determination (r2) for each analyte. Analytes SMXZ SMRZ STZL SDMX SDMZ SCPD OTC DC CTC TC AH-TC AH-CTC EPI-DC EPI-TC TYLB AH-ETMC ETMC enol LNMC DH-STRP STRP NEO

Linear range (µg kg–1) 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 20–100 100–300 100–300

r2 CCα (µg kg–1) CCβ (µg kg–1) 0.992 8 11 0.994 7 9 0.993 7 10 0.992 8 10 0.995 6 8 0.991 9 11 0.996 5 7 0.994 7 9 0.993 7 10 0.992 8 11 0.993 8 10 0.988 10 13 0.993 8 10 0.993 8 10 0.991 9 11 0.992 8 11 0.994 7 9 0.991 0.991 0.990 0.989

9 9 24 25

12 11 32 33

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CCα and CCβ were estimated according to ISO11843-2 (2000), which allows the determination of both parameters using a linear calibration curve. The decision limit CCα means the limit at and above which it can be concluded with an error probability (α = 1% for non-authorised substances) that a sample is non-compliant (Commission Decision (EC) No. 2002/657/EC, 2002). Commission Decision (EC) No. 2002/657 defines that a CCα of a non-authorised substance must be lower than the recommended limit, and CCβ must be lower or equal to this recommended limit. As shown in Table 3, CCα and CCβ values for the studied analytes range from 6 to 25 µg kg–1 and from 7 to 33 µg kg–1, respectively. The highest CCα and CCβ values were obtained for NEO and STRP antibiotics; however, both decision limits and detection capabilities met the conditions of EU-RL guidance (CRL Guidance 2007). The recovery and precision of each antibiotic (except NEO and STRP) were determined on spiked blank honey samples at three different concentrations of 20, 30 and 100 µg kg–1. For NEO and STRP the spiked levels were 100, 150 and 200 µg kg–1. The recoveries were estimated and corrected automatically from the matrix-fortified calibration curve. Table 4 provides satisfactory results for all tested antimicrobials where the average recoveries ranged between 85% and 111% at the three concentrations. These values fell within the range of acceptable bias (between –20% and 10%) when samples are spiked at a concentration

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higher than 10 µg kg–1 (Commission Decision (EC) No. 2002/657/EC, 2002). In terms of precision, repeatability (CVr) and intralaboratory reproducibility (CVR) experiments on three different days were investigated at three spiking levels, and the results are shown in Table 4. We can observe that CVr and CVR values for all antimicrobials range from 5.8% to 20.6% and from 6% to 26.8%, respectively. Commission decision (EC) No. 2002/657 requires that CVR for samples spiked at concentrations < 100 µg kg–1 should be as low as possible (targeting below 23%). Our CVR values for all analytes were below 23%, except for SMRZ (CVR = 24.2%) and AH-EMTC (CVR = 26.8%) at 20 µg kg–1. Therefore, our method satisfactorily fulfils the requirements for intra-laboratory reproducibility. Finally, the matrix effect was evaluated by comparing calibration sets prepared by spiking analytes in solvent and by spiking analytes in extracted blank honey at the same concentrations. Signal enhancement or suppression are generally due to in-source ionising competition of the targeted analytes with co-extractive components in honey matrix samples from sugars primarily, and also from carbohydrate fibres. Matrix effects (ME%) were calculated by comparing the slope obtained for each analyte in honey matrix (am) with that of the same analyte in solvent (as), using the following formulae (Economou et al. 2012):  ME% ¼

 am x 100 as  1

Table 4. Method validation data of the developed method. 20 μg kg–1 Analyte SMXZ SMRZ STZL SDMX SDMZ SCPD OTC DC CTC TC AH-TC AH-CTC EPI-DC EPI-TC TYLB AH-ETMC ETMC enol LNMC DH-STRP NEO STRP

30 μg kg–1

Rec (%) 94 98 100 109 101 106 100 98 97 97 102 85 100 97 95 100 97 103 97

CVr (%) 15.0 19.8 19.2 9.1 12.5 17.3 10.9 8.4 10.8 9.3 14.2 16.9 8.8 8.3 17.1 20.6 17.1 13.5 17.8

CVR (%) 16.9 24.2 17.6 10.1 12.6 18.2 12.3 8.4 11.1 13.4 16.1 16.4 8.2 8.0 20.0 26.8 20.5 15.7 17.2

Rec (%) 98 91 104 102 96 96 103 104 104 100 100 93 104 98 92 103 99 102 109

98.2 99.1

100 μg kg–1 16.7 12.6

17.9 14.1

96.5 94.4

CVr (%) 14.0 14.5 16.4 6.5 11.1 9.2 7.8 11.6 9.7 10.3 7.3 10.9 6.0 8.0 8.4 9.5 10.6 9.5 19.0 150 μg kg–1 14.6 8.9

40 μg kg–1 CVR (%) 16.3 16.1 17.2 9.0 12.1 10.0 14.8 10.8 9.4 13.1 7.2 20.6 6.0 8.1 10.7 20.1 20.9 11.0 21.3

Rec (%) 90 96 107 102 98 93 103 103 98 101 102 93 103 98 111 102 109 100 105

16.0 9.9

84.7 108.2

CVr (%) 7.3 12.6 12.3 8.0 10.4 12.5 10.9 8.0 11.0 9.0 7.8 10.8 7.2 8.0 10.2 8.7 13.4 10.7 19.0 200 μg kg–1 13.3 17.1

CVR (%) 11.4 13.2 11.8 7.3 11.0 11.8 12.0 13.0 13.1 9.1 9.7 13.2 11.5 7.5 13.2 18.3 24.4 12.2 20.6 14.3 21.7

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Matrix Effect in Honey 80 60 40

ME %

20 0 -20 -40 -60 -80

Figure 9. Matrix effects for the target compounds.

Figure 9 shows a significant matrix suppression ranging from –26% to –80% for sulphonamides, tetracyclines, lincosamides, STRP and DH-STRP. As for AHETMC and NEO, they showed tolerable matrix effects (< 20%). On the other hand, only two analytes (ETMC enol and TYLB) showed matrix enhancement ranging from 35% to 55%. Matrix-matched calibration was different from the one obtained in solvent, indicating a significant matrix effect for these analytes in honey. Therefore, matrix-fortified standard calibration was used in quantification to appropriately compensate these suppression and enhancement effects.

Quality control materials To assess the quality of the test results obtained through the validated method, an inter-laboratory proficiency test scheme was carried out for the determination of antibiotic residues in honey. Food Analysis Performance Assessment Scheme (FAPAS) honey test material 02270 containing tetracyclines was used. The identities of these analytes and their concentrations in honey were provided only with the final report issued by FAPAS. The analytes were identified as chlortetracycline and doxycycline at concentrations within the ranges 3.3–8.5 µg kg–1 (assigned value of 5.9 µg kg–1) and 41.6–106.9 µg kg–1 (assigned value of 74.2 µg kg–1), respectively. Our obtained concentrations were 58 µg kg–1 for doxycycline and 5 µg kg–1 for chlortetracycline with z-scores of –1 and –0.69, respectively, where the acceptable z-score range is –2 to 2. This was a good indicator that our developed and validated method provides reliable quantification results to determine tetracycline residues in honey. Further participation in proficiency testing is anticipated to

complement the ongoing external quality control of our method.

Conclusions A straightforward sample preparation was developed for the determination of multi-class antimicrobial residues including sulphonamides, tetracyclines, lincosamides and macrolides in honey using LC-ESI-MS/MS. Up to 24 samples can be extracted in less than 2 h in one step, offering a high-throughput multi-residue analytical method at an acceptable cost and time for analysis. In our study, methanol was found to be the most effective solvent for extraction of a wide range of antimicrobial residues in honey. The use of acid hydrolysis (HCl 2 M) during sample treatment is essential to disrupt N-glycosidic sulphonamide bonds forming with the honey-reducing sugars. Our experiment demonstrated the need to decrease the pH of honey samples to 2 in order to achieve good recoveries for most of the studied analytes. However, the risk of losing acidic-sensitive antimicrobials such as macrolides still occurs. As a result, degradation products of tylosin A and erythromycin A were selected as better marker residues to monitor the presence of these analytes in honey samples. d-SPE with PSA 50 mg allows the removal of 20–30% of coextractive sugars from honey extract; however, further addition of PSA sorbent reduces the recovery of aminoglycoside residues. The developed method was validated according to the recommended criteria of Commission Decision (EC) No. 2002/657 and satisfactory performance data were obtained for most studied analytes. Specificity, linearity, recovery, precision, CCα and CCβ were tested

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successfully. They were in compliance with European Union legislation requirements, demonstrating the fitness of this method for selected antimicrobials. Further work will be carried out by including this method in a surveillance monitoring programme dedicated to determine antimicrobial residues potentially found in different types of honey in Lebanon.

Acknowledgment The authors thank the Reference Laboratory of Fougeres from the French Agency for Safety of Food, Environment and Occupational Health for its continuous scientific support throughout the project.

Disclosure statement No potential conflict of interest was reported by the authors.

Funding This work was supported by the Lebanese National Council for Scientific Research (CNRSL); the Lebanese Atomic Energy Commission (LAEC); and the Reference Laboratory of ANSES-Fougeres (ANSES).

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