Edward Malone

Geraldine Dowling

Chris Elliott

David Glenn Kennedy

Liam Regan

Glenn Kennedy

Confirmatory method for the determination of various acetylgestagens in animal kidney fat using liquid chromatography

Keywords: Drug residues -hormones, Veterinary drug residues -anabolic steroids, Veterinary drugs Food Types: Animal, Animal products -meat, Oils and fats Acetylgestagens, Kidney Fat, Mass Spectrometry, Decision Limit, Detection Capability

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Under European Union (EU) Council Directive 96/22/EC (Council Directive EC 96/22 1996), any substances exhibiting, thyreostatic, androgenic, oestrogenic or gestagenic effects are prohibited for use in food producing animals. The synthetic progestagens, medroxyprogesterone acetate (MPA), megestrol acetate (MGA), melengestrol acetate (MLA), chlormadinone acetate (CMA) and delmadinone acetate (DMA) exhibit gestagenic effects when animals are treated with these substances and are prohibited for use in animals reared for human consumption. Incidents of the illegal administration of gestagens have been previously reported in several European countries [START_REF] Rapp | Control of illegal Medroxyprogesterone acetateapplication in veal calves by residue analysis in adipose tissues using HPLC/RIA method[END_REF][START_REF] Daeseleire | Application of highperformance thin-layer chromatography and gas chromatography-mass spectrometry to the detection of new anabolic steroids used as growth promotors in cattle fattening[END_REF].

The acetylgestagen group of compounds exhibit lipophilic properties and hence are more likely to bio-accumulate in fatty, adipose tissues [START_REF] Krzeminski | Fate of radioactive melengestrol acetate in the bovine[END_REF]. Therefore the preferred matrix when testing for these compounds is usually kidney fat as it offers the greatest chance of detecting residues after illegal administration.

In order to maintain a zero tolerance policy with regards to these compounds suitably sensitive analytical methods are required. Low limits of detection are required especially for MPA as it has a minimum required performance level (MRPL) set by the EU at 1 µg kg -1 in porcine kidney fat (Commission Decision 2003/181 2003).

There is no MRPL set for MGA, MLA, CMA and DMA. However laboratories should be capable of monitoring at levels of 5 µg kg -1 which is the required performance level (RPL) set by the community reference laboratory (CRL Guidance Paper 2007).

Various analytical methods exist that describe the analysis of gestagens in kidney fat, a wide array of extraction techniques have been applied for this purpose. They include solid phase extraction (SPE) (Lohmus et al 2007[START_REF] Impens | Analysis on residues of estrogens, gestagens and androgens in kidney fat and meat with gas chromatography-tandem mass spectrometry[END_REF][START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF][START_REF] Hageleit | A sensitive enzyme immunoassay (EIA) for the determination of melengestrol acetate (MGA) in adipose and muscle tissues[END_REF]; supercritical fluid extraction (SFE) [START_REF] Stolker | Determination of acetyl gestagenic steroids in kidney fat by automated supercritical fluid extraction and liquid chromatography iontrap mass spectrometry[END_REF] , accelerated solvent extraction (ASE) [START_REF] Hooijerink | Screening for gestagens in kidney fat using accelerated solvent extraction and liquid chromatography electrospray tandem mass spectrometry[END_REF] and matrix solid phase dispersion (MSPD) [START_REF] Rosen | Automated extraction of acetylgestagens from kidney fat by matrix solid phase dispersion[END_REF]. A rigorous clean-up is required when analysing for these compounds. Hexane wash steps as well as saponification have been described previously in the literature in order to remove some of the lipophilic interferences [START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF], Lohmus et al 2007). Other purification techniques used include SPE, which often occurs after hexane washing and/or saponification [START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF], Lohmus et al 2007), various types of cartridge chemistry are utilised to achieve. These include C 18 [START_REF] Daeseleire | Validation of multi-residue methods for the detection of anabolic steroids by GC-MS in muscle tissues and urine samples from cattle[END_REF][START_REF] Hooijerink | Screening for gestagens in kidney fat using accelerated solvent extraction and liquid chromatography electrospray tandem mass spectrometry[END_REF][START_REF] Hageleit | A sensitive enzyme immunoassay (EIA) for the determination of melengestrol acetate (MGA) in adipose and muscle tissues[END_REF], Silica+NH 2 [START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF] 2003). A number of analytical techniques have been published for the determination of acetylgestagens compounds; these include enzyme linked immunoassay (ELISA) [START_REF] Daxenberger | Detection of melengestrol acetate residues in plasma and edible tissues of heifers[END_REF], gas chromatography-mass spectrometry (GC-MS) [START_REF] Impens | Analysis on residues of estrogens, gestagens and androgens in kidney fat and meat with gas chromatography-tandem mass spectrometry[END_REF][START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF][START_REF] Stolker | The use of supercritical fluid extraction for the determination of steroids in animal tissues[END_REF][START_REF] Daeseleire | Validation of multi-residue methods for the detection of anabolic steroids by GC-MS in muscle tissues and urine samples from cattle[END_REF][START_REF] Daeseleire | Application of highperformance thin-layer chromatography and gas chromatography-mass spectrometry to the detection of new anabolic steroids used as growth promotors in cattle fattening[END_REF] and liquid chromatography-mass spectrometry (LC-MS) [START_REF] Stolker | Determination of acetyl gestagenic steroids in kidney fat by automated supercritical fluid extraction and liquid chromatography iontrap mass spectrometry[END_REF][START_REF] Hooijerink | Screening for gestagens in kidney fat using accelerated solvent extraction and liquid chromatography electrospray tandem mass spectrometry[END_REF], Lohmus et al 2007[START_REF] Mortensen | Confirmatory analysis of acetylgestagens in plasma using liquid chromatography-tandem mass spectrometry[END_REF]. Other matrices have also been investigated and there is published work on a variety of these. These include the analysis of MPA in bovine hair [START_REF] Rambaud | Study of 17-estradiol-3-benzoate, 17-methyltestosterone and medroxyprogesterone acetate fixation in bovine hair[END_REF]. Methods have been described to detect gestagens in injection sites that utilise high performance thin layer chromatography (HPTLC) and detection under UV light at 366 nm and GC-MS [START_REF] Daeseleire | Application of highperformance thin-layer chromatography and gas chromatography-mass spectrometry to the detection of new anabolic steroids used as growth promotors in cattle fattening[END_REF]. Work on the analysis of gestagens in bovine muscle using high performance liquid chromatography (HPLC) clean-up and fractionation followed by GC-MS analysis has also been published [START_REF] Daeseleire | Validation of multi-residue methods for the detection of anabolic steroids by GC-MS in muscle tissues and urine samples from cattle[END_REF]. Methods have been described that analyse gestagens from liver and muscle using GC-MS [START_REF] Marchand | Ultra trace detection of a wide range of anabolic steroids in meat by gas chromatography coupled to mass spectrometry[END_REF] and from bovine and porcine plasma using LC-MS/MS [START_REF] Mortensen | Confirmatory analysis of acetylgestagens in plasma using liquid chromatography-tandem mass spectrometry[END_REF] and from skin and meat by GC-MS [START_REF] Stolker | The use of supercritical fluid extraction for the determination of steroids in animal tissues[END_REF].

For Group A substances, mass spectrometry is one of the techniques deemed to The developed method was based on previously published work [START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF].

It was modified to reduce the analytical sample size required, to include the analysis of delmadinone acetate, to extend the range of species it is applicable to and replace detection using GC-MS/MS with LC-MS/MS.

Materials and methods

Materials and reagents

Water, methanol, ethyl acetate, acetonitrile and n-hexane, all (HPLC grade) were obtained from BDH (Merck, UK). Magnesium chloride, sodium hydroxide and ammonium acetate, all Analar grade were obtained from BDH (Merck, UK). MPA, MGA, MLA, CMA (Vetranal analytical standards) were obtained from Riedel de Haen (Sigma Aldrich, Ireland). DMA, MPA-d3, MGA-d3, MLA-d3 were obtained from RIVM (Bilthoven, The Netherlands). Primary stock standard solutions of MPA, MGA, MLA and CMA were prepared in methanol at a concentration of 1 mg mL -1 . Primary stock standard solutions of the deuterated internal standards as well as DMA were prepared by reconstitution of ampoules containing 0.1 mg with 1 mL of methanol yielding a concentration of 0.1 mg mL -1 . Stock standard solutions with a concentration of 1 mg mL -1 were stored in the dark at less than -20°C until use, up to a period of 12 months. A final working solution containing all the analytes, except internal standards, was prepared in methanol at a concentration of 300 ng mL -1 (for MGA, MLA, CMA and DMA) and 60 ng mL -1 (MPA) solutions of all analytes (stable for 6 months). A final internal standard working solution was prepared in methanol at a concentration of 600 ng mL -1 (stable for 6 months). All standards were stored at -20°C in the dark. Isolute TM CN solid phase extraction cartridges (6 mL, 500 mg) were obtained from Biotage (Biotage, UK).

LC conditions

The LC system consisted of a model Finnigan TSQ Quantum Ultra (MS) coupled to a Finnigan Surveyor LC Pump and a Finnigan Surveyor Autosampler (Thermo Electron Corporation, CA, USA). A gradient LC system (Table 1) using 0.5 mM ammonium acetate:methanol (70:30, v/v Mobile phase A) and 0.5 mM ammonium acetate:methanol (5:95, v/v Mobile phase B) at a flow of 0.300 mL min -1 , was used to separate the analytes on a Phenomenex Luna 3 µm C 18 (100 x 2.0 mm) (Phenomenex, UK) column equipped with a Phenomenex Luna 3 µm C 18 guard column (10.0 × 2.0 mm I.D.). The column temperature was maintained at 45 °C. Data acquisition and integration were performed using XCalibur version 1.4 chromatographic management software (Thermo Electron Corporation, CA, USA). A linear gradient (Table 1) was used, the total runtime was 28 min.

MS/MS parameters

The analysis was performed using electrospray ionization operated in positive polarity and data was gathered using multiple reaction monitoring (MRM) mode. Two transitions per compound were monitored (one transition for the internal standards) and the collision voltages were optimised as shown (Table 2). The MS/MS detector conditions were as follows: Ion mode ESI+; sheath gas pressure 60 psi; auxiliary gas pressure 30 psi; capillary temperature 350 o C; source CID -10 V; collision pressure 1.5 torr; collision gas argon.

Kidney fat samples

Bovine, porcine, ovine and equine kidney fat samples were received at our laboratory within 24 h of animal slaughter for testing as part of the National Residue Control Plan. The samples are stored at -20 °C until analysis. Kidney fat ran in previous batches and found to contain no detectable residues of MPA, MGA, MLA, DMA or CMA was used as negative control material.

Sample extraction and clean-up

A fat tissue sample (approximately 10 g) sliced into very fine pieces was transferred into a small glass funnel previously packed with glass wool; this funnel was then placed into a conical flask. This was then placed for at least three hours into an oven (65°C). A portion (3 g) of the molten fat is then weighed into a polypropylene centrifuge tube (15 mL), 50 µL of the mixed internal standard is added at this time, corresponding to a concentration of 10 µg kg -1 . Samples are fortified at levels corresponding to 5, 7.5 and 10 µg kg -1 for MGA, MLA, CMA, DMA and 1, 1.5 and 2 µg kg -1 for MPA by adding 50, 75 and 100 µL portions of a 300 ng mL -1 solution of and the supernatant is transferred to a clean polypropylene tube. The sample pellet is re-extracted with acetonitrile (8 mL) using the same procedure as above and the supernatants are combined and dried under nitrogen with heat (55 o C) to an approximate volume of 7 mL. N-hexane (5 mL) is then added to the samples and they are shaken (1 min) and centrifuged (3500 rpm, 10 min, 4 o C). The upper hexane layer is then removed and discarded and the acetonitrile extracts are evaporated to dryness under nitrogen with heat (55 o C). The extract is then reconstituted in n-hexane (5 mL) and vortexed (1 min), the saponification step is then carried out by the addition of 0.1 M sodium hydroxide (NaOH) (1.4 mL) followed by of 1 M magnesium chloride (MgCl 2 ) (850 µL) This is then shaken gently (30 seconds) and placed in an oven (60 o C) to aid saponification. The samples are then removed and centrifuged (3500 rpm, 10 min, 4 o C), the upper hexane layer is then removed and transferred to a clean 15 ml glass test tube.

Solid Phase Extraction

The sample extracts are purified by SPE using Isolute CN SPE cartridges. Sample extracts are loaded under gravity onto the cartridges (preconditioned with ethyl acetate (3 mL) and n-hexane (5 mL). The cartridges are then eluted (2 x 2.5 mL) with ethyl acetate :n-hexane (90:10, v/v). The eluates are then evaporated to dryness under

Matrix-Matched Calibration

Matrix matched calibration curves were prepared and used for quantification. Control kidney fat previously tested and shown to contain no residues was prepared as above (2.4). One kidney fat sample is used for each calibration standard level. Samples were fortified at levels corresponding to 0, 0.5, 1, 2, 3, 4 and 8 µg kg -1 MPA and 0, 2.5, 5, 10, 15, 20 and 40 µg kg -1 MGA, MLA, DMA, CMA by adding 0, 25, 50, 100, 150, 200 and 400 µL portions of a 300 ng mL -1 standard solution of MGA, MLA, DMA, CMA and 60 ng mL -1 of MPA. After fortification, samples were held for 15 min prior to the extraction procedure as described above (2.5). Calibration curves were prepared by plotting the response factor (area of the analyte/area of internal standard) as a function of analyte concentration (0 to 8  µg kg -1 for MPA) and (0 to 40  µg kg -1 for MGA, MLA, DMA and CMA) to quantify samples.

Method validation

For estimation of accuracy, blank kidney fat samples were fortified with MGA, MLA, DMA and CMA at 5, 7.5 and 10 µg kg -1 which is 1, 1.5 and 2 times the (RPL) of 5 µg kg -1 set for these compounds by the Community Reference Laboratory and fortified with MPA at 1, 1.5 and 2 µg kg -1 which is 1, 1.5 and 2 times the (MRPL) set by the EU. Six replicate test portions, at each of the three fortification levels, were analysed. Analysis of the 18 test portions was carried out on three separate occasions.

For the estimation of the precision of the method, repeatability and within-laboratory reproducibility was calculated. The decision limit (CCα) of the method was calculated according to the calibration curve procedure using the intercept (value of the response factor, y, where the concentration, x is equal to zero) and 2.33 times the standard error of the intercept for a set of data with 6 replicates at 3 levels. The detection capability (CCβ) was calculated by adding 1.64 times the standard error to the CCα.

Results and Discussion

Preliminary experiments:-

The LC-MS/MS method was developed to provide confirmatory data for the analysis of animal kidney fat for MPA, MGA, MLA, DMA and CMA. An excellent response was noted for each of the analytes using electrospray ionisation in positive polarity and this was the chosen ionization mode for this work. The MS/MS fragmentation conditions were investigated and collision energies were optimised, the fragmentation patterns observed were similar to those found in previously published work [START_REF] Hooijerink | Screening for gestagens in kidney fat using accelerated solvent extraction and liquid chromatography electrospray tandem mass spectrometry[END_REF][START_REF] Mortensen | Confirmatory analysis of acetylgestagens in plasma using liquid chromatography-tandem mass spectrometry[END_REF]. For a method to be deemed confirmatory in accordance with Commission Decision 2002/657/EC four identification points are required. These identification points are obtained by monitoring one precursor ion and two transition products (corresponding to strong and weak ion transitions). The precursor and product transitions for each of the acetylgestagen compounds assayed for are listed in Table 2 as well as their associated collision energy.

The extraction and sample purification steps in the method described in this paper are based on those described in a previously published work by [START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF].

However in this paper we have downscaled the sample size from 5 grams to 3 grams, included an extra analyte, delmadinone acetate as well as extended the method to also MPA, MGA, MLA, DMA and CMA were chromatographed on a Phenomenex Luna C 18 , 100 x 3mm, 3 µm column, with retention times of 18.5, 18.2, 18.6, 17.7 and 18.3 min respectively. Deuterated analogues of MPA, MLA and MGA were used as internal standards for these compounds. It was found also that MLA-d3 corrected best for CMA and that MGA-d3 was best at correcting for DMA so these were chosen as their corresponding internal standards. chlormadinone 37 Cl acetate was obtained, in order to be used as an I.S. for chlormadinone acetate. However during development it was noted that in samples only fortified with the various I.S. compounds that quite a large response was found in the chlormadinone acetate trace that originated from chlormadinone 37 Cl acetate, hence it was decided to omit using this as an internal standard.

Validation study

Validation of the method was according to procedures described in Commission Decision 2002/657/EC covering specificity, calibration curve linearity, recovery (accuracy), precision, decision limit (CCα) and detection capability (CCβ).

Specificity

The technique of LC-MS/MS itself offers a high degree of selectivity and specificity.

To establish the selectivity/specificity of the method, a variety of fat samples from different species (bovine, ovine, porcine and equine) were fortified with MPA, MGA,

Linearity of the response

The linearity of the chromatographic response was tested with matrix matched curves using 7 calibration points in the concentration range of 0 to 40 µg kg -1 for MGA, MLA, DMA and CMA and 0 to 8 µg kg -1 for MPA. The regression coefficients (r 2 ) for all the calibration curves used in this study were ≥ ≥ ≥ ≥ 0.997. A plot of residuals was also carried out to further demonstrate the linearity of the response. For each observed value of x (concentration), the residual is the observed y value (response factor) minus the corresponding fitted Y. The residual was then plotted against the concentration;

this is shown in Figure 3 for each of the five analytes. The residuals were acceptable, they have a random scatter rather than any systematic pattern and also they are normally distributed with an approximate mean of zero. The accuracy of the method was determined using kidney fat samples fortified at 5.0, 7.5 and 10.0 µg kg -1 for MGA, MLA, DMA and CMA and 1, 1.5 and 2 µg kg -1 for MPA. The resulting values were calculated from matrix matched calibration curves.

Accuracy

Mean corrected recovery (n = 18) of the analyte, determined in three separate assays (Table 3) was between 98 and 100 % for MPA, MGA, MLA, DMA and CMA.

Precision

The usefulness of suitable isotopically labelled internal standards is demonstrated in the excellent repeatability and within-laboratory reproducibility obtained for MPA, MGA and MLA (Table 3). No deuterated analogue was available for CMA or DMA, however these compounds are structurally very similar to the other gestagen compounds which had deuterated analogues, so these internal standards also worked very well for CMA and DMA. In the case of CMA, MLA-d3 was used as the corresponding internal standard and in the case of DMA, MGA-d3 was used as the corresponding internal standard. The precision of the method, expressed as RSD values for the within-lab reproducibility at the three levels of fortification (5.0, 7.5 and 10.0 µg kg -1 for MGA, MLA, DMA and CMA and 1, 1.5 and 2 µg kg -1 for MPA) was less than 6 %.

CCα and CCβ

The decision limit is defined as the limit above which it can be concluded with an error probability of α, that a sample contains the analyte. In general, for prohibited substances an α value equal to 1 % is applied. The detection capability is the smallest content of the substance that may be detected, identified and quantified in a sample, with a statistical certainty of 1-β, were β = 5 %. CCα and CCβ were calculated using the intercept (value of the signal, y, were the concentration, x is equal to zero) and the standard error of the intercept for a set of data with 6 replicates at 3 levels on three separate days (5.0, 7.5 and 10.0 µg kg -1 for MGA, MLA, DMA and CMA and 1, 1.5 and 2 µg kg -1 ). Blank kidney fat was fortified at 1, 1.5 and 2 times the minimum required performance level of 1 µg kg -1 for MPA and 5 µg kg -1 for MGA, MLA, DMA and CMA. CCα is the concentration corresponding to the intercept + 2.33 times the standard error of the intercept. CCα values of 0.12, 0.48, 0.40, 0.54 and 0.63 µg kg -1 were achieved for MPA, MGA, MLA, CMA and DMA respectively. CCβ is the concentration corresponding to the signal at CCα + 1.64 times the standard error of the intercept (i.e the intercept + 3.97 times that standard error of the intercept). CCβ values of 0.20, 0.81, 0.68, 0.92 and 1.07 µg kg -1 were achieved for MPA, MGA, MLA, CMA and DMA respectively.

Measurement Uncertainty

The measurement of uncertainty was estimated by taking into account the within laboratory reproducibility over days 1, 2 and 3 as well as considering the repeatability on day 4 due to matrix effects caused by different species (bovine, ovine, porcine and equine) and different animals. These two variability's were combined and multiplied by a coverage factor of three to give an overall figure for the uncertainty of the measurement. This approach of using the within laboratory reproducibility as a good estimator of measurement of uncertainty is taken from the SANCO/2004/2726rev1 document (SANCO 2004(SANCO /2726rev1 2004)). It recommends using the within laboratory reproducibility and using a coverage factor of 2.33 to estimate expanded uncertainty, however as it was felt that not all the environmental and other factors that could be varied over the course of the validation were examined, it was felt that a coverage factor of 2.33 may underestimate the true uncertainty of the method. So a value of 3 was chosen instead to give a more realistic value for the true uncertainty, this approach was acceptable to the ISO17025 (ISO/IEC 17025 2005) auditors as well.

Evaluation

The method developed in this study has been used to monitor for the presence of MPA, MGA, MLA, CMA and DMA residues in kidney fat of bovine, porcine, ovine and equine kidney fat as part of the National Monitoring Plan in Ireland in some of, 2007 and all of 2008. The method has been carried out by different analysts over a period of two years under varying environmental conditions and the method was

shown to be robust. The method has been accredited by the Irish National Accreditation Board in accordance with ISO17025 standard which is the main internationally recognised standard used by testing and calibration laboratories.

Samples of incurred porcine kidney fat received from the Community Reference Laboratory (CRL), RIVM, Bilthoven, The Netherlands as part of a proficiency scheme. Figure 4 shows a chromatogram with both strong and weak transitions for MPA for one of these incurred kidney fat samples of at a measured concentration of 1.2 µg kg -1 . These samples were tested using the method described here and were found to yield satisfactory results. In two samples containing incurred MPA, the described method was able to detect and confirm its presence and also the calculated concentrations were close to the CRL's assigned values. In fact when the method's measurement of uncertainty is applied, the assigned values falls within the possible range of concentrations given by this method, no z-scores for this proficiency scheme have been released by the CRL yet. Also the described method found no detectable peaks in the negative control sample sent by the CRL.

  provide sufficient confirmatory data under Commission Decision 2002/657/EC (Commission Decision 2002/657/EC 2002). LC-MS has an advantage over GC-MS analysis in the analysis of acetylgestagens as this mode of detection avoids the time consuming and often non robust step of having to hydrolyze and derivatise samples prior to analysis. of this work was to develop a multi-residue LC-MS/MS confirmatory method. This method should be capable of simultaneously identifying and quantifying five acetylgestagen compounds in kidney fat of bovine, ovine, porcine and equine species. The method should be validated according to Commission Decision 2002/657/EC. and establish a measurement of uncertainty for the analytical method developed. The second aim of the work was to accredit the method according to the IS0 17025 standard (ISO/IEC 17025 2005). A third aim was to implement the method into the National Monitoring Plan in Ireland to monitor for misuse of these substances under the requirements of Council Directive 96/23/EC (Council Directive EC 96/23 1996).

  , CMA, DMA and 60 ng mL -1 solution of MPA. After fortification, samples are held for 15 min prior to extraction. Acetonitrile (8 mL) is added and the samples are placed into the oven to allow the fat to become molten again and hence mix the acetonitrile. After 15 minutes the samples are removed from the oven and shaken vigorously (30 seconds). They are then centrifuged (3500 rpm, 10 min, 4 o C)

  heat (55 o C) and reconstituted in Mobile Phase A (200 µL) (70:30 0.5 mM ammonium acetate: methanol), vortexed (1 min) and transferred to LC vials.

  equine species. The determination step described in this work is carried out by LC-MS/MS as opposed to GC-MS/MS[START_REF] Impens | Faster analysis of anabolic steroids in kidney fat by downscaling the sample size and using gas chromatography-tandem mass spectrometry[END_REF] and yields lower CCα and CCβ values for all of the analytes examined.

  of the other analytes, also the data obtained was used in calculating the measurement of uncertainty) and their non-fortified equivalent samples were also analysed. No interfering peaks were observed at the retention time for any of the analytes (Figure1). Additionally samples were fortified with a variety of steroidal compounds including α and β trenbolone, diethylstilbestrol, dienestrol, hexestrol, dexamethasone, betamethasone, flumethasone, methylboldenone, methyltestosterone and fluoxymesterone. No interferences were observed in the retention windows of MPA, MGA, MLA, DMA and CMA in chromatograms when fortified with these substances.

Figure 1 .Figure 3 .

 13 Figure 1. (a) Negative control kidney fat (b) Kidney fat fortified with 0.5 µg kg -1 MPA and 2.5 µg kg -1 of MGA, MLA, CLA and DLA respectively.

Figure 4 .

 4 Figure 4. Incurred sample of kidney fat at a concentration of 1.2 µg kg -1 . (a) Strong Transition (m/z) 387.0>327.2 and (b) Weak Transition (m/z) 387.0>285.2

  and CN cartridges(Impens et al

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Table 1 :

 1 LC gradient profile for determination of MPA, MGA, MLA, DLA and CMA.

	Time	Component A	Component B
	(min)	(%)	(%)
	0	90	10
	6.5	90	10
	10.0	35	65
	20.0	35	65
	22.5	90	10
	28.0	90	10
	Component A:	0.5 mM ammonium acetate buffer + methanol (70:30, v/v).
	Component B:	0.5 mM ammonium acetate buffer + methanol (5:95, v/v).

Table 2 :

 2 MS/MS parameters for determination of each of the investigated analytes and their associated deuterated analogues.

	Page 27 of 28	Food Additives and Contaminants
	Compound	Transition	Polarity	Collision	Retention
		(m/z)		Energy (eV)	Time (min)
	Medroxyprogesterone Acetate	387.0>327.2* 387.0>285.2	Positive	15 18	18.5
	Megestrol Acetate	385.3>325.1* 385.3>267.3	Positive	19 15	18.2
	Melengestrol Acetate	397.3>337.3* 397.3>279.2	Positive	20 15	18.6
	Delmadinone Acetate	403.2>205.1* 403.2>181.1	Positive	19 21	17.7
	Chlormadinone Acetate	405.0>308.9* 405.0>344.9	Positive	17 13	18.3
	Medroxyprogesterone Acetate-d3	390.0>330.2	Positive	15	18.4
	Megestrol Acetate-d3	388.3>328.2	Positive	15	18.2
	Melengestrol Acetate-d3	400.2>340.2	Positive	15	18.6

* Denotes strong transition http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Table 3 :

 3 CCα, CCβ, Within-laboratory Reproducibility RSD (%), Accuracy (%) and Measurement of Uncertainty (%) for each analyte.

						Page 28 of 28
	Compound	CCα	CCβ	RSD	Measurement	Accuracy
		(µ µ µ µg kg -1 )	(µ µ µ µg kg -1 )	(%)	of Uncertainty	(%)
					(%)
	Medroxyprogesterone Acetate	0.12	0.20	4.4	16	99.8
	Megestrol Acetate	0.48	0.81	5.2	16	99.1
	Melengestrol Acetate	0.40	0.68	4.2	18	98.4
	Delmadinone Acetate	0.63	1.07	4.7	26	98.4
	Chlormadinone Acetate	0.54	0.92	4.4	26	98.4

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Acknowledgements

This work was funded by The State Laboratory, Co. Kildare in Ireland. The authors would like to thank the staff at The State Laboratory, Co. Kildare for their practical assistance.


Mercedes Ana Peltzer
email: mercedes.peltzer@ua.es

Jorge Wagner

Alfonso Jiménez

Migration study of carvacrol as natural antioxidant in High Density Polyethylene for active packaging

Keywords: Antioxidants, Food contact materials, Migration, Packaging -active migration, active packaging, carvacrol, high density polyethylene

The migration behaviour of low molecular weight compounds from food packaging materials is one of the key issues in assessing the possibility of use in such applications.

The aim of this work was to study the migration of carvacrol (1% and 2% w/w) when added to high density polyethylene. All materials were exposed to the food simulants olive oil and distilled water separately at 40ºC and 25ºC. Three significant variables influencing the migration process were considered: incubation temperatures, initial concentration of antioxidant and type of simulant (oil and aqueous). The amount of carvacrol migrating to olive oil was significantly higher than in water and this was because of the higher solubility of this antioxidant in oil. Experimental results agreed reasonably well with those obtained by the application of a simple model derived from Fick's Second Law. Carvacrol could therefore be used in active packaging formulations as its release from the polymer matrix can be controlled.

Introduction

Food packaging is intended to protect food from environmental influences, such as microbial or chemical degradation, air (oxygen), radiation or water. The use of plastic materials compared to glass and metal containers for food packaging shows many advantages: polymer-based packaging systems are more flexible, show reduced weight and less energy is required for their production and processing. However, plastic materials also show some drawbacks in their use in contact with food. For instance, they are generally less heat resistant, more permeable with respect to gases and water, and diffusion of low molecular weight compounds, such as monomers or additives, into foodstuff can take place.

The increased use of plastic packaging materials and their possible interaction with food have created the need for more research in this field (Sendón-García et al. 2006[START_REF] Perez-Bonilla | Isolation and identification of radical scavengers in olive tree (Olea europaea) wood[END_REF]. It is essential to have suitable analytical methods to determine substances able to migrate from packaging materials to different foodstuff at low concentrations.

Traditional antioxidants used in polymers are often synthetic compounds, mainly phenol derivatives, and in some cases they may change organoleptic food quality and even promote toxicity in the case of their migration (Garde et al. 1998). Nevertheless, the use of some synthetic antioxidants in polymeric materials is questionable from the toxicity and environmental point of view. Side effects of some synthetic antioxidants frequently used in food processing, such as butylated hydroxytoluene (BHT) and butylated hydroxyanisole (BHA) have been documented and this has stimulated the substitution of synthetic antioxidants by natural ones (Till et al. 1982, Schwope et al. 1987, Tawfik and Huyghebaert 1999[START_REF] Perez-Bonilla | Isolation and identification of radical scavengers in olive tree (Olea europaea) wood[END_REF][START_REF] Cruz | Studies of mass transport of model chemicals from packaging into and within cheeses[END_REF]. Natural antioxidants are rising as a valid alternative as they are non-toxic compounds (Strandberg and Albertsson 2006) with lower risk of being hazardous to foodstuff.

Natural products have served as an important source of drugs since ancient times. In recent years, a renewed interest in obtaining biologically active compounds from natural sources has been observed. Plants are rich source of natural antioxidants and some of them, such as tocopherols (vitamin E), ascorbic acid (vitamin C) and carotenoids are substances of major significance in human physiology as well as in food industry (Mallegol et al. 2001, Suffield et al. 2004[START_REF] Oral | Characterization of irradiated blends of α-tocopherol and UHMWPE[END_REF][START_REF] Dimitrios | Sources of natural phenolic antioxidants[END_REF][START_REF] Perez-Bonilla | Isolation and identification of radical scavengers in olive tree (Olea europaea) wood[END_REF]. Oregano was reported to possess strong antioxidant activity, better than BHA [START_REF] Pezo | Development of an automatic multiple dynamic hollow fibre liquid-phase microextraction procedure for specific migration analysis of new active food packagings containing essential oils[END_REF].

Compounds responsible of the antioxidant activity of oregano were among other polyphenols, carvacrol and tymol (Ladrón de Guevara et al. 2002[START_REF] Pezo | Development of an automatic multiple dynamic hollow fibre liquid-phase microextraction procedure for specific migration analysis of new active food packagings containing essential oils[END_REF]) and no harmful effects should potentially arise from possible interactions between the additives and foodstuff [START_REF] Cheah | Natural antioxidant extract from galangal (Alpinia galanga) for minced beef[END_REF]. The health benefits of vegetables are mostly due to the antioxidant compounds in phytochemicals. These natural compounds can be also considered valid alternatives to synthetic antioxidants for the stabilization of polymers used for food packaging. However, phenols obtained from plants have not been completely studied because of the complexity of their chemical nature and the sometimes complicated Active packaging, including the concept of release of packaging components to a foodstuff, has received considerable attention in recent years [START_REF] Dongen | European Standpoint to Active Packaging-Legislation, Authorization, and Compliance Testing[END_REF]. In particular, the

2007).

In this framework, the formulation of active packaging systems with controlled migration of natural antioxidants to foodstuff may result in prolonged shelf-life and preservation of quality of the product (Bounatirou et al. 2007[START_REF] Nakatani | [END_REF][START_REF] Ben Arfa | Antimicrobial activity of carvacrol related to its chemical structure[END_REF].

Therefore, the incorporation of carvacrol in materials intended to be in contact with foodstuff is an attractive option for consumers and also for packaging manufacturers.

The aim of this work was to study of the release of carvacrol from high density

Materials and methods

Materials and sample preparation

High-density polyethylene, HDPE (ALCUDIA ® C-200RB, Repsol-YPF, Spain) was used as polymer matrix. Each sample consisted of 2 kg (approximately) of HDPE mixed with two concentrations of carvacrol (98%, Sigma-Aldrich, Spain) 1wt% and 2wt%, in separated samples. In addition, a control sample (pure HDPE) without the addition of any antioxidant was prepared. Carvacrol was dissolved in absolute ethanol (Merck KgaA, Germany) before their addition to the polymer.

Polymer and antioxidant were blended in an orbital mixer during 20 min at 23 ± 2ºC. Then, each mixture was processed in a twin-screw extruder operated at 25 rpm and 22 rpm at feeding (MD 30 Bausano, Italy) with seven temperature zones. These temperatures were set at 120/130/150/150/155/160/160ºC. After the extrusion process, samples were pelletized and compression moulded (Robima, S.A., Valencia, Spain) in order to obtain 307cm 2 sheets to carry out migration tests. Samples were moulded by compression at 150ºC and 0.45 kPa during 15 min. Temperature and incubation times were changed in order to simulate actual storing conditions for foodstuff. Sheets of 5 cm 2 area and 4 mm thickness were immersed completely in 10 mL of simulant in their both sides and incubated at the chosen temperature for 30 days. Extracts of simulant in contact with HDPE stabilized with carvacrol were taken at 2, 5, 10, 20 and 30 days of incubation in order to follow the release of additive with time.

Migration study

GC-MS

Gas chromatography coupled to mass spectrometry (GC-MS) was used for identification and quantitative analysis of carvacrol in olive oil. Samples were previously diluted in cyclohexane (1:10) before the injection to the chromatograph and further introduced (1 µL) into an Agilent (HP/Agilent Technologies 6890N) GC equipped with a TRB-5MS capillary column (30m x 0.25 mm i.d, 0.25µm film thickness, 95% dimethyl-5% diphenyl polysiloxane, Teknokroma, Spain). Helium was used as carrier gas with flow rate 1 mL min -1 in splitless injection method. The injector temperature was 280ºC. Temperature oven conditions were: initial temperature 60ºC, holding at this temperature for 2 min, with further temperature increase to 300ºC at 10ºC min -1 , held isothermally for 15 min. The mass spectrometer (Agilent Technologies 5973N) was operated in ionization mode with impact electron (70eV) and selected ion monitoring (SIM) mode. Figure 1a shows the GC-MS chromatogram of a carvacrol standard (2mg kg -1 ). Only one peak with a retention time of 11.2 minutes was observed indicating the excellent specificity of the method for the determination of this analyte. Figure 1b shows the Mass spectrum of this peak. The ions monitored for carvacrol were: m/z 77, m/z 91, m/z 107, m/z 135, m/z 150 as they were characteristic of this compound according to previous measurements carried out in scan mode. We could observe that m/z 135 ion was the most abundant, corresponding to the loss of a methyl group from the carvacrol molecule. In consequence, the quantification in all samples studied by GC-MS was carried out by using this ion.

(Figure 1) Calibration curve was obtained for carvacrol by injection of known amounts of this compound dissolved in cyclohexane. Samples and calibration standards were spiked with 0.5 mg kg -1 of BHT (2,6-di-tert-butyl-4-methylphenol) as internal standard (IS). The ions monitored for BHT were m/z 205 and m/z 220, and the quantification was carried out with m/z 205 as it was the most abundant ion for this compound. Three replicates were measured for each standard. Carvacrol was quantified by assessing their peak area ratio and the area of the peak corresponding to IS ([carvacrol]/[IS]).

HPLC-UV

Aqueous extracts of carvacrol were directly analyzed by HPLC-UV in a liquid chromatograph Agilent 1100 Series equipped with a C18 column (25 cm x 0.46 cm x 5 µm). The injection volume was 40 µL. The mobile phase consisted of a methanol:water (60:40) mixture with 0.1% of formic acid, at a flow rate of 1 mL min -1 . Detection was performed by UV spectroscopy at 274 nm. Calibration curves were obtained for carvacrol Three replicates were measured for each standard.

Results and Discussion

Un-weighted linear regression analysis was employed for the construction of the calibration curves. The calibration curve of carvacrol in cyclohexane by GC-MS as well as the calibration curve of carvacrol in methanol:water (50:50) by HPLC-UV were obtained and linear regressions with R 2 > 0.999 were determined, as could be observed in Table 1, where calibration parameters for both techniques are presented. The internal standard method was used to perform GC-MS calibration curve.

(Table 1) It was possible to use the calibration curve with cyclohexane to quantify carvacrol in olive oil samples because no interferences from the oil matrix were observed in the chromatograms. A statistical test was performed in order to determine if the results obtained with the two series of standards were different: series 1 corresponds to carvacrol directly dissolved in cyclohexane while series 2 corresponds to carvacrol dissolved in olive oil and then in cyclohexane (1:10). Series 2 represents the same treatment used in olive oil samples. Both series were plotted on the two axes of a regression graph (Figure 2). We found that there were not statistical differences between results obtained with both series of standards (at 95% probability) as r was close to +1, the confidence interval in the intercept, a, included zero, and the confidence interval for the slope, b, included 1 (Miller J. N. 1991).

In conclusion, the use of cyclohexane solutions for carvacrol determination was selected as Figure 3a shows the GC-MS chromatogram of carvacrol 2wt% migrated in olive oil at 25ºC after ten days of incubation. The mass spectrum of the peak at 11.2 minutes is shown in figure 3(b) and it is exactly the same than the spectrum obtained for the standard (figure 3(b)), so this peak was identified as carvacrol.

(Figure 3) Migration models based on Fick's second law have been extensively used for the assessment of migration of additives and contaminants from food packaging films (Reid et al. 1980, Sharma et al. 1990[START_REF] Begley | Methods and approaches used by FDA to evaluate the safety of food packaging materials[END_REF][START_REF] Chung | Simple models for assessing migration from food-packaging films[END_REF]. The main equation obtained from simplifications and considering the partition term negligible is as follows:

(1) where t f M , is the amount of migrant in the food at time t; o p M , is the initial amount of migrant in the packaging film; D is the diffusion coefficient (cm 2 s -1 ); δ is the thickness of the packaging film (cm). This equation could lead to reliable values of diffusion coefficients in the case of complete migration where partition between solutions and resistance to mass transfer are negligible [START_REF] Chung | Simple models for assessing migration from food-packaging films[END_REF]. Anyway, this model could result in considerable errors in the Nevertheless, these assumptions could be acceptable for comparison purposes, as the main goal of this work would be to assess the release of carvacrol to foodstuff at two different carvacrol concentrations. This model is simple and it is often used to provide estimations of the diffusion coefficient based on the available data, knowing that some errors must be assumed. It could be used for short migration times or when δ is large, as it was previously described [START_REF] Chung | Simple models for assessing migration from food-packaging films[END_REF].

The use of multiple data points and linear regression analysis, as used in this work, can provide a reliable estimation of D [START_REF] Chung | Simple models for assessing migration from food-packaging films[END_REF]. Figures 3 and4 show the fitting of migration data with equation 1 for carvacrol 1wt% and carvacrol 2wt%, respectively, during 30 days of migration for both food simulants at 25ºC and 40ºC. The fitting between experimental points (dots) and calculated values (lines) is reasonably good, in particular at the lower temperature and distilled water as simulant. Diffusion coefficients could be further calculated.

(Figure 4) (Figure 5)

The underestimation of the diffusion factor could be a problem when evaluating the Specific Migration Limits (SML) for a particular analyte in the food contact material. This is not critical in the case of carvacrol, as this compound has not SML recognized in the legislation, and the main goal of this study is the evaluation of the possible use of carvacrol in active packaging systems.

Another simple approach for the estimation of diffusion coefficients is based on the estimation of D correlated with the relative molar mass M r of the migrant and the absolute temperature T, based on empirical data. This is called the "worst case approach" and also defines a matrix specific parameter A p . This approach was proposed by Piringer and others [START_REF] Baner | The estimation of migration potential of additives from polymeric materials into foodstuffs[END_REF][START_REF] Baner | The application of a predictive migration model for evaluating the compliance of plastic materials with European food regulations[END_REF][START_REF] Brandsch | Modelling of additive diffusion coefficients in polyolefins[END_REF]Piringer, 1994;[START_REF] Piringer | Plastic packaging materials for food[END_REF] who developed this model to predict migration values. They proposed the following equation:

(2)

Where A p ' and τ are polymer specific parameters, M r is the molar mass of the migrant and T is the absolute temperature (K). Equation 2 was proposed for determination of additives in different polymers, including polyethylenes, polypropylenes and polyamides. For HDPE the value of A p ' (T<100ºC) is 14.5; τ is 1577. Some different variables should be considered in migration studies, in order to determine the actual release of chemicals to foodstuff. Tables 2 and3 show the amount of carvacrol migrated in olive oil and distilled water respectively for all the experimental conditions (different times and temperatures). A clear dependence on the release of the analyte from the material bulk with the initial concentration of migrant could be observed. The amount of carvacrol migrated in both simulants was higher in samples with carvacrol 2wt%. As (Table 2) (Table 3) Diffusion coefficients calculated by fitting the experimental data for carvacrol with those obtained after application of equation ( 1) can be observed in Table 4 while values calculated with the worst-case approach according to equation ( 2) are shown in Table 5. As was previously mentioned, D increased with incubation temperature in both simulants and for both carvacrol concentrations. Therefore, higher temperatures resulted in an increase in the release of the antioxidant due to the activation of both diffusion and desorption processes at higher temperatures (Haider and Karlsson, 2000).

Other important parameters to be considered in the migration of a compound through a polymer matrix could be the intrinsic properties of the migrant itself, such as polarity and solubility. Both parameters influenced the migration rate due to interactions between polymer, migrant and food simulant (Helmroth et al. 2002). The nature (oil or aqueous) of food simulants in contact with the polymer had also a large influence in antioxidants migration (Sharma et al. 1990, Miltz et al. 1998). The amount of carvacrol that migrated to olive oil was clearly higher because of the high solubility of this antioxidant in oil. It is also necessary to consider that sorption phenomena could occur in oil environment, with some simulant penetrating in the polymer matrix and increasing the possibility of movement of polymer additives and consequently favouring their migration (Haider and Karlsson 2000).

As it is known the migration rate depends on the size and shape of the migrant and on the number of voids and gaps in the polymer structure (Helmroth et al. 2002). The absorption of certain amount of simulant to the polymer matrix could cause an increase in the gap size and consequently an increase in migration could be expected.

Carvacrol also migrated in distilled water, but in a lower amount than in olive oil (Tables 2 and3). As carvacrol is a lipophilic molecule [START_REF] Nostro | Effects of oregano, carvacrol and thymol on Staphylococcus aureus and Staphylococcus epidermidi biofilms[END_REF]) the diffusion and further migration of carvacrol through the polymer in contact with olive oil is a natural consequence of the analyte characteristics. Nevertheless, we also observed some migration of carvacrol to water and this could be mainly attributed to the small size of the molecule (molar mass, 150 g mol -1 ) that could move easily through the polymer structure. However, carvacrol has been reported to possess a relative hydrophilicity (Griffin et al. 1999) and this factor could also contribute to the slight migration of this analyte in water.

On the other hand, if we think in carvacrol as polymer stabilizer, we should be aware of the high migration tendency of this compound, which could result in some loss in stability of the own polymer. Some published work (Linssen et al. 1998) demonstrated that antioxidants added to a polymer could lose their protective effect in long-term basis by a phenomenon called "physical loss", which is controlled by diffusion of the additive through the polymer.

Conclusions

Carvacrol is present in a natural extract and its main source (oregano) is used as flavoring spice, and is totally harmless to the human health. In addition, their phenolic chemical structure makes it a very promising natural additive for HDPE used in food packaging. The addition of carvacrol to HDPE could be interesting in terms of active packaging, as the release of this compound from the polymer matrix can be controlled. Results showed that carvacrol migrated in oil and aqueous simulants. Therefore we could think about this compound as an additive for active packaging and a wide range of foodstuff, not only as an antioxidant but also by its antimicrobial properties. The use of this packaging system for any particular food will depend on the compatibility between foodstuff and the antioxidant, suitability of the polymer matrix and the foreseen shelf-life. Higher migration in oil simulant is also a positive feature for the protection of foodstuff with high fat content, since they need a higher protection from oxidation. In addition, it was observed that higher temperatures lead to an increase of migration. Concentrations of carvacrol found in this study are high enough to ensure the antioxidant activity of the material. Some authors (Vardar-Ünlü et al. 2003) claimed that antioxidant activity of pure carvacrol was adequate to protect foodstuff at concentrations around 5 µL mL -1 .

A main drawback for the use of essential oils in food packaging applications is their organoleptical alteration of the foodstuff due to the release of high odor chemicals. A sensory evaluation of oregano essential oils (including carvacrol) suggested that concentrations lower than 20 µg g -1 would be acceptable to consumers [START_REF] Chi | Application of chitosan films enriched with oregano essential oil on Bologna -Active compounds and sensory attributes[END_REF].

Anyway, a specific sensorial evaluation for these samples will be carried out in the future. Ladrón de Guevara, R.G., González, M., García-Meseguer, M.J., Nieto, J.M., Amo, M.,

  terms active and intelligent packaging were first introduced by Regulation 2004/1935/EC of the European Parliament and Council (Regulation (EC) No 1935/2004). This regulation covers the definition of active packaging and creates a legal framework for further development of active packaging legislation. This regulation is also related to Directive 89/107/EC concerning food additives which classifies additives in different categories such as flavorings, colorants, stabilizers, depending on their effects on food (Council Directive 89/107/EEC). Natural extracts, such as essential oils and their constituents, such as carvacrol, are categorized as flavorings by European Decision 2002/113/EC (Commission Decision 2002/113/EC). Essential oils and their constituents are categorized as 'Generally Recognized as Safe' (GRAS) by the U.S. Food and Drug Administration (Lopez et al.

  ) as a function of time by determination of the concentration of this compound in two simulants and two different temperatures. The final goal is to be to control the release of carvacrol to prepare an active packaging system.

  Specific migration tests were performed according to the European Standard EN 13130 part release of carvacrol from the polymer was studied in two simulants: distilled water and virgin olive oil at two different temperatures: 25 and 40ºC. Incubation was always carried out in a water bath at controlled temperature (± 0.1ºC) (Memmert WB 22, 22 litres).

  known amounts of this compound in methanol:water (50:50) solutions.

  more favorable in practice.

  coefficients in the case of significant partitioning as it underestimates the value of diffusion coefficient. When partitioning occurs the model is no longer the same, and it yields different diffusion coefficients for the same migration rate.

  of the analyte was higher at 40ºC since migration is directly dependent on temperature according to the Arrhenius equation(Koros and Hopfenberg 1979, Linssen et al. 1992).

  the samples preparation and testing and Dr. M. Carmen Garrigós for useful discussion. Also authors thank Repsol-YPF for supplying the material. Mercedes Peltzer is granted by Alban Programme (European Union) No (E04D031633AR).

  and articles in contact with foodstuffs Plastics substances subject to limitation Part 1: Guide to test methods for the specific migration of substances from plastics to foods and food simulants and the determination of substances in plastics and the selection of conditions of exposure to food simulants. Garde, J., Catalá, R., Gavara, R. 1998. Global and specific migration of antioxidants from polypropylene films into food simulants. J. Food Protection. 61(8), 1000. Griffin S.G, Wyllie, S.G., Markham, J.L., Leach, D. 1999. The role of structure and molecular properties of terpenoids in determining their antimicrobial activity. Flavour. Frag. J. 14: 322-332. Haider, N., Karlsson, S., 2000. Kinetics of migration of antioxidants from polyolefins in natural environments as a basis for bioconversion studies. Biomacromolecules. 1(3): 481-487. Helmroth, E., Rijk, R., Dekker, M., Jongen, W. 2002. Predictive modelling of migration from packaging materials into food products for regulatory purposes. Trends in Food Sci. & Technol. 2002, 13(3), 102-109. Koros, W.J., Hopfenberg, H.B. 1979. Scientific aspects of migration of indirect additives from plastics to food.Food Technol. 1979, 33(4): 56-60.

  . 2002. Effect of adding natural antioxidants on color stability of paprika. J. Sci. Food Agric. 82(9): 1061-1069. Lagouri, V. and Boskou, D. 1995. Screening for antioxidant activity of essential oils obtained from spices. In Food Flavors: Generation, Analysis and Process In¯uence, ed. G. Charalambous, pp. 869±879. Elsevier, Amsterdam. Linssen, J.P.H., Reitsma, J.C.E., Roozen, J.P. 1992. Polystyrene sheet composition and temperature as parameters for migration of styrene monomer into corn oil. Packag. Technol. Sci., 5(4): 217-222. Linssen, J.P.H., Reitsma, J.C.E., Cozijnsen, J.L. 1998. Research note -Migration of antioxidants from polyolefins into ethanolic simulants. Packag. Technol. Sci. 11(5): 241-245. Lopez, P. Sánchez, C., Battle, R., Nerin, C. 2007. Development of flexible antimicrobial films using essential oils as active agents. J. Agric. Food Chem., Vol. 55, No. 21: 8814-8824. Mallegol, J., Carlsson, D.J, Deschenes, L. 2001. Antioxidant effectiveness of vitamin E in HDPE and tetradecane at 32ºC. Polym. Degrad. Stabil., 73(2): 269-280. Miltz, J., Hoojjat, P., Han, J.K., Giacin, J.R., Harte, B.R., Gray, I.J. 1988. Food and transport, quality assurance and legislation. Weinheim and New York: Wiley-VCH. Piringer O. 1994. Evaluation of plastics for food packaging. Food Additives and Contaminants 11:221-230. Regulation (EC) No 1935/2004 of the European Parliament and of the Council of 27 October 2004, on materials and articles intended to come into contact with food and repealing Directives 80/509/EEC and 89/109/EEC. Reid, R.C., Sidman, K.R., Schwope, A.D., Till, D.E. 1980. Loss of adjuvants from polymer films to foods or food simulants. Ind. & Eng. Chem. Prod. Res. Dev. 1980, 19(4): 580-587. Schwope, A.D. Till, D.E., Ehntholt, D.J., Sidman, K.R. Whelan, R.H., Schwartz, P.S., Reid, R.C. 1987. Migration of Irganox 1010 from ethylene-vinyl acetate films to foods and food-simulating liquids. Food and Chem. Toxicol., 25 (4): 317-326. Sendón-García, R., Sanches-Silva, A., Cooper, I., Franz, R., Paseiro-Losada, P. 2006. Revision of analytical strategies to evaluate different migrants from food packaging materials. Trends in Food Sci. & Technol. 17 (7), 354-366. Sharma, G.K., Madhura, C.V., Arya, S.S., 1990. Interaction of plastic films with foods. II. Effect of polyethylene and polypropylene films on the stability of vegetable oils. J. Food ., Albertsson, A.C. 2006. Improvement of α-tocopherols long-term efficiency by modeling its heterogeneous natural environment in polyethylene. J. Polym. Sci., Part A: Polymer chemistry, 44:1660-1666. Suffield, R.M., Dillman, S.H., Haworth, J.E. 2004. Evaluation of antioxidant performance of a natural product in polyolefins. J. Vinyl & Additive Technology, 10(1): 52-56. Tawfik, M.S., Huyghebaert, A. 1999. Interaction of packaging materials and vegetable oils: Oil stability. Food Chem. 64: 451-459. Till, D.E., Enholt, D.J., Reid, R.C., Schwartz, P.S., Sidman, K.R., Schwope, A.D., Whelan, R.H. 1982. Migration of BHT antioxidant from high density polyethylene to foods and food simulants. Ind. Eng. Chem. Prod. Res. Dev. 21(1): 106-113. Vardar-Ünlü, G., Candan, F., Sökmen, A., Daferera, D., Polissiou, M., Sökmen, M., Dönmez, E. and Tepe, B. 2003. Antimicrobial and Antioxidant Activity of the Essential Oil and Methanol Extracts of Thymus. J. Agric. Food Chem., 51 (1): 63-67.

Figure Captions Figure 1 Figure 2 -

 Captions12 Figure Captions

Figure 3

 3 Figure 3 -a) Chromatogram of carvacrol 2wt% migrated in olive oil at 25ºC (10 days of

Figure 4 -

 4 Figure 4 -Fitting of migration data by equation (1) for carvacrol 1wt% materials.

Figure 5 -

 5 Figure 5 -Fitting of migration data by equation (1) for carvacrol 2wt% materials

Table 1 -

 1 Carvacrol calibration parameters a .

	Method	b (slope)	SDb b	a (intercept)	SDa b	r 2
	HPLC-UV	30.5	0.4	-50.0	29.7	0.999
	GC-MS	1.7	0.1	0.0	0.1	0.999

a Calibration curve: y=bx+a; b SD: Standard Deviations. Page 22 of 33 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Table 2 -

 2 Migration of carvacrol in olive oil at 25ºC and 40ºC.

	Olive oil	Carvacrol 1%	Carvacrol 2%
	T (ºC)	Time	mg kg -1	mg dm -2	mg kg -1	mg dm -2
	25	2 days	2.8 ± 0.3	0.3 ± 0.1	8.3 ± 0.1	0.72 ± 0.01
		5 days	6.9 ± 2.5	0.6 ± 0.2	17 ± 1	1.4 ± 0.1
		10 days	13 ± 1	0.6 ± 0.8	35 ± 6	3.0 ± 0.5

Table 3 -

 3 Migration of carvacrol in distilled water at 25ºC and 40ºC.

	Distilled water	Carvacrol 1%	Carvacrol 2%
	T (ºC)	Time	mg kg -1	mg dm -2	mg kg -1	mg dm -2
	25	2 days	0.9 ± 0.3 0.08 ± 0.03	2.0 ± 0.2	0.2 ± 0.0
		5 days	1.2 ± 0.1	0.1 ± 0.0	5.3 ± 0.7	0.5 ± 0.1

Table 4 -

 4 Diffusion coefficient calculated by using Equation (1).

	Sample	Temp (ºC)	Simulant	D (cm 2 s -1 )
	Carvacrol 1wt%	40	Olive oil	(2.03 ± 0.30) 10 -10
	Carvacrol 1wt%	25	Olive oil	(6.36 ± 0.86) 10 -12

Table 5 -

 5 Diffusion coefficient by Equation (2).

	Sample	Temp (ºC)	D (cm 2 s -1 )
	Carvacrol	40	1.39 10 -9

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

Acknowledgements

The authors wish to thank the Department of Civil and Environmental Engineering, University of Perugia in Terni (Prof. José M. Kenny and Dr. Antonio Iannoni), the Department of Materials Science and Engineering (Polytechnic University of Valencia) for


M Rivas Casado
email: m.rivas-casado@cranfield.ac.uk

D J Parsons

R M Weightman

N Magan

S Origgi

mycotoxin concentration in bulk cereals

Keywords: Geostatistical analysis, deoxynivalenol, ochratoxin A, bulk, cereals. 35

Deoxynivalenol (DON) and ochratoxin A (OTA) in agricultural commodities present hazards 15

to human and animal health. Bulk lots are routinely sampled for their presence, but it is 16 widely acknowledged that designing sampling plans is particularly problematical because of 17 the heterogeneous distribution of the mycotoxins. Previous studies have not explicitly looked 18 at the interactions between the spatial distribution of the mycotoxin and the strategy used to 19 take samples from bulk. Sampling plans are therefore designed on the assumption of random 20 distributions. The objective of this study was to analyse the spatial distribution of DON and 21 OTA in bulk commodities with geostatistics. This study was the first application of 22 geostatistical analysis to data on mycotoxins contamination of bulk commodities. Data sets 23 for DON and OTA in bulk storage were collected from the literature and personal 24 communications, of which only one contained data suitable for geostatistical analysis. This 25 data set represented a 26 t truck of wheat with total of 100 sampled points. The mean 26 concentrations of DON and OTA were 1342 µg kg -1 and 0.59 µg kg -1 , respectively. The 27 results showed that DON presented spatial structure whilst OTA was randomly distributed in 28 space. This difference between DON and OTA probably reflected the fact that DON is 29 produced in the field, whereas OTA is produced in storage. The presence of spatial structure 30 for DON implies that sampling plans need to consider the location of sample points in 31 addition to the number of points sampled in order to obtain reliable estimates of quantities 32 such as the mean contamination.

Designing sampling plans for mycotoxins is particularly problematic because of the 38 heterogeneous distribution of these contaminants in bulk lots of different commodities 39 [START_REF] Stroka | Novel sampling methods 384 for the analysis of mycotoxins and the combination with spectroscopic methods for 385 the rapid evaluation of deoxynivalenol contamination[END_REF], Schatzki 1995aand 1995b[START_REF] Jewers | aflatoxin distribution studies on a 4 tonne batch 348 of maize[END_REF]. For example, 40 contamination within a lot may result from a very small proportion of grains or kernels 41 containing high concentrations of the mycotoxin. Because of the heterogeneous nature of 42 mycotoxins, the problems lead to uncertainty over both how many samples to take and how 43 to decide on the sampling positions. Normal practice is to aggregate incremental samples 44 taken from a bulk commodity and test a sub-sample to estimate the mean concentration of the 45 mycotoxin. As a result, information on the spatial variability and distribution of the 46 mycotoxin is lost. 47

Extensive work on mycotoxin sampling has been carried out by the group of Whitaker 48 [START_REF] Whitaker | Theoretical investigations into the accuracy of sampling 393 shelled peanuts for aflatoxin[END_REF][START_REF] Whitaker | Variability associated 395 with sampling, sample preparation and chemical testing for aflatoxin in farmers stocks 396 of peanuts[END_REF][START_REF] Whitaker | Variability associated with testing corn for fumonisin[END_REF]Whitaker et al., 49 2000;[START_REF] Whitaker | standardisation of mycotoxin sampling procedures: an urgent necessity[END_REF][START_REF] Whitaker | Mycotoxins in food: detection and control[END_REF][START_REF] Whitaker | Sampling foods for mycotoxins[END_REF]. These studies concentrate on the 50 relative magnitude of errors relating to sampling and the processing of samples but they 51 provide little or no information on the interaction between the spatial distribution of the 52 mycotoxin and the strategy used to take samples from bulk. It is usually assumed that the 53 samples are independent, but this may not be the case if spatial structure is present. 54

If spatial autocorrelation is present, it is necessary to design sampling programmes 55 that take the spatial distribution into account, to reduce the probability of falsely classifying a 56 batch below or above the acceptance level [START_REF] Macarthur | Statistical modelling as an aid to 352 the design of retail sampling plans for mycotoxins in food[END_REF]. For example, hot spots of 57 deoxynivalenol (DON) found in wheat may differ from other mycotoxins in wheat or other 58 commodities because it is mainly generated in the field rather than in storage, which may 59 have an effect on its distribution in subsequent storage and transport the mycotoxin is 60 distributed on a loaded truck. Sampling methods must be both representative and practicable 61 [START_REF] Stroka | Novel sampling methods 384 for the analysis of mycotoxins and the combination with spectroscopic methods for 385 the rapid evaluation of deoxynivalenol contamination[END_REF]) so the error associated with the sampling protocol selected is reduced 62 [START_REF] Whitaker | Sampling foods for mycotoxins[END_REF]. tests using Moran's I, which was one of the first measures of spatial autocorrelation to be 74 developed. Slight non-randomness and autocorrelation were seen at some scales in some 75 cases. [START_REF] Schmale | Spatial patterns of viable spore deposition of 376 Gibberella zeae in wheat fields[END_REF], studying spore deposition, had insufficient data points to use lag 76 correlation measures, so applied nonparameteric methods (SADIE statistics and Mantel tests), 77 while noting that the data sets were small even for these methods, and found that spore counts 78 above the canopy were usually random, but deposition counts were often aggregated. 79 However, these approaches give only limited consideration, if any, to the relationship 80 between distance and autocorrelation, and have all considered field data, rather than bulk 81

commodities. 82

Geostatistics is a branch of statistical science that deals with the spatial structure of 83 the variables under study and has the advantage over other methodologies of accounting for 84 the spatial autocorrelation of the sampled variable, in this case mycotoxin concentration. In 85 geostatistics the spatial variation is considered random and is modelled through a stochastic 86 process. Geostatistics is based on the variogram calculation, a plot that relates the distance 87 between any two sampled points with their semivariance. Effective and efficient sampling 88 strategies can be designed through the characterisation of the variogram parameters: the 89 range, the sill and the nugget. Examples of studies where the variogram was used to analyse 90 the spatial pattern of diseases are provided by [START_REF] Orum | Using predictions based on 358 geostatistics to monitor trends in Aspergillus flavus strain composition[END_REF] and [START_REF] Rekah | Spatial distribution and temporal development of 365 fusarium crown and root rot of tomato and pathogen dissemination in field soil[END_REF]. 91

The former used the variogram to determine the trends and distribution of the aflatoxigenic 92 species Aspergillus flavus in soil. The latter used it to study the spatial pattern of fusarium 93 crown and root rot in tomatoes. 94

Geostatistics has also been used as a tool to investigate the spatial dynamics of plant 95 disease propagation. [START_REF] Chellemi | Analysis of the spatial pattern of 343 plant-pathogens and diseased plants using geostatistics[END_REF] used geostatistics to examine the spatial pattern of 96 a population of plant pathogens and diseased plants. [START_REF] Stein | A 381 geostatistical analysis of the spatio-temporal development of downy mildew 382 epidemics in cabbage[END_REF] applied geostatistics 97 for the analysis of the spatio-temporal pattern of downy mildew pathogen (Peronospora 98 parasitica) in cabbage to predict the disease at any point in time, to develop optimal sampling 99 patterns for future assessments, to calculate the expansion rate of the disease and to determine 100 the source of the initial inoculum in space and time. [START_REF] Gottwald | Spatial and temporal 346 analyses of citrus tristeza virus in eastern spain[END_REF] analysed the 101 spatial, temporal and spatio-temporal dynamics of citrus tristeza virus (CTV) in Valencia 102 (Spain) to determine the likely rates of disease increase and spread. 103 As part of a project for the UK Food Standards Agency, six different sources of data 104 were obtained through the literature review and personal contact [START_REF] Parsons | Development of 361 representative sampling plans for mycotoxins in foods using distribution modelling[END_REF]. Table 105 1 shows a description of each of the data sets considered for analysis. Each set of data was 106 first assessed for its suitability for geostatistical analysis based on the number of points 107 available for the analysis. [START_REF] Webster | Sample adequately to estimate variograms of soil properties[END_REF] suggest that variograms computed with 108 fewer than 50 data points are of little value and that at least 100 points are needed for a soil 109 variable to be analysed. The only reference on the number of points required for the 110 variogram calculation of mycotoxins is provided by [START_REF] Stein | A 381 geostatistical analysis of the spatio-temporal development of downy mildew 382 epidemics in cabbage[END_REF] who determined that 111 49 observations were necessary to estimate the spatial variogram of downy mildew pathogen 112 in cabbage. Similar requirements would be expected for DON. Those data sets with fewer 113 than 50 points were not considered for analysis. Of the six data sources, only the one 114 collected by [START_REF] Biselli | Investigation of variability associated 339 with testing lots of wheat kernels for deoxynivalenol and ochratoxin A (case study 340 truck)[END_REF] was for mycotoxins in grain and contained sufficient data 115 points with their coordinates. 116

This study focuses on using a geostatistical approach to characterise the spatial 117 distribution of DON and OTA in the data collected by [START_REF] Biselli | Investigation of variability associated 339 with testing lots of wheat kernels for deoxynivalenol and ochratoxin A (case study 340 truck)[END_REF]. 118 119

Materials and Methods 120

Data set 121

The data set contained DON and OTA results obtained from a 26 t lot of wheat on a truck in 122 Germany. The truck was 2.5 m wide and 10 m long. The data were recorded from 100 points 123 at 20 x 5 grid positions at 0.5 m spacing in the horizontal plane through the truck, using a 5 124 aperture probe sampler. The probe was vertically inserted into the load in the centre of each 125 grid cell to take a single incremental sample containing grain from 5 depths. Each 126 incremental sample was mixed and sub-sampled before the DON and OTA concentrations 127 were measured [START_REF] Biselli | Investigation of variability associated 339 with testing lots of wheat kernels for deoxynivalenol and ochratoxin A (case study 340 truck)[END_REF]. Thus the final sets of values described the DON and 128 OTA concentrations in a two dimensional horizontal plane aggregated over the depth of the 129 lorry. 130 131

Geostatistical approach 132

Geostatistics describes the spatial autocorrelation among sampled points based on the semi-133 variogram, a plot that relates the distance between any two points in the space with their 134 semivariance (Figure 1). The semi-variogram shows how similar any two points separated by 135 a distance h are: if the semi-variance is small, the points are closely correlated. The distance h 136 is known as the lag distance or lag. Each variogram is characterised by three parameters 137 known as the range, the sill and the nugget (Figure 1). The range is the distance at which the 138 spatial autocorrelation between any two points is lost. The sill is the semi-variance value that 139 corresponds to the range. The nugget is the intrinsic variance of the data (e.g. measurement 140 and sampling error). 141

The geostatistical analysis included three phases: (i) data preparation, (ii) variogram 142 calculation and (iii) analysis of the spatial structure. Appendix 1 provides a more detailed 143 explanation of the methodology. 144

The selected data set was prepared for geostatistical analysis. The methods require 145 that the data are normally distributed and stationary, that is they do not exhibit spatial trends. 146

For this purpose, the data were analysed for linear and quadratic trends in the coordinate 147 variables. The data set was considered to present a trend if the model explained more than 148 20% of the variance. Normality was tested visually by plotting the histogram, box plot 149 (showing median, quartiles, extremes and outliers), and Q-Q plot (quantile-quantile plot, 150 which should be a straight line for a normal distribution). The skewness and kurtosis (Kenney 151 and Keeping, 1961) were calculated. The normal distribution has a skewness of 0 and a 152 kurtosis of 3. If the data do not have an approximately normal distribution, they may be 153 transformed using one of a range of standard transformations to give a close approximation to 154 normality [START_REF] Webster | Meso-and microscale patterns of Fusarium Head Blight in 391 spring wheat field in Minesota[END_REF][START_REF] Sokal | Biometry: the principles and practice of statistics in biological 378 research[END_REF]. 155

The empirical variogram was calculated in 4 directions defined by azimuth 0°, 90°, 156 45° and 315° to detect anisotropy (differences in the variogram depending on direction). 157

Azimuth 0° was along the axis of the truck facing forward and angles were measured 158 clockwise in the horizontal plane. The lag distance, azimuth tolerance and maximum distance 159 of analysis were selected according to the results obtained from a sensitivity analysis on these 160 variables. The exponential and spherical models [START_REF] Webster | Meso-and microscale patterns of Fusarium Head Blight in 391 spring wheat field in Minesota[END_REF] The OTA data were strongly skewed with mode 0 and therefore could not be transformed to a 167 normal distribution. They were fitted by an exponential distribution with mean 0.57 µg kg -1 168 and standard deviation of 1.13 (Figure 2). (present/absent) variable. The results showed a pure nugget variogram: that is, there was no 171 evidence of spatial structure. Figure 3a shows the distribution of OTA in space: OTA 172 presented a random spatial distribution of foci of contamination which is consistent with the 173 variogram results obtained. 174

The DON data were log transformed (natural log) to meet the normality requirement; 175 Table 2 summarises the descriptive statistics of the original and transformed data. The 176 skewness and kurtosis for the transformed data were close to 0 and 3 respectively, which 177 indicated that the assumption of normality could be accepted. Figure 3c shows the spatial 178 distribution of the log transformed values of DON concentration. In the trend analysis, the 179 linear model accounted for 2.2% of the variance and the quadratic model accounted for 180 18.9%, which were both below the level at which the data would be considered to present a 181

trend. 182

The minimum distance between points in the data set was 0.5 m, due to the sampling 183 scheme. The sensitivity analysis on the lag distance showed that a lag distance of 0.7 m 184 provided a good compromise between the number of pairs of points for each lag distance and 185 the number of points in the variogram. 186

The anisotropy analysis showed that the variogram calculated with azimuth tolerance 187 60° was significantly different along (azimuth 0°) and across the truck (azimuth 90°). A 188 second analysis was carried out to determine if the anisotropy was due to differences in the 189 spatial pattern of DON or lack of points to compute the variogram across the sampled area. 190

The results showed that the number of pairs of data points per lag distance was significantly 191 smaller when calculating the variogram for azimuth 90°: the apparent anisotropy was an 192 artefact of the smaller number of points across the truck. Therefore, the omnidirectional 193 variogram was used. 194 The maximum number of pairs of data points for each lag distance was reached at 195 about 4 m for the majority of the azimuths when analysing the longitudinal direction. This 196 indicated that the maximum distance of analysis for the variogram should be around 4 m. The 197 final omnidirectional variogram was calculated with lag distance step 0.7 m and maximum 198 distance 4 m. The spherical model was found to fit better than the exponential. 199

The results obtained (Figure 4) showed that there was spatial correlation for DON 200 concentration in the selected batch. The variogram was defined by a 4.35 m range, a 0.07 sill 201 and a 0.013 nugget. The non-zero nugget indicates that there was a small amount of variation 202 that either was not captured by the sampling strategy applied or was intrinsic to the 203 mycotoxins. Most of these ignore the spatial component. Geostatistics provides an explicit 214 characterisation of spatial autocorrelation, so is capable of quantifying effects that would not 215 be detected by other methods. Geostatistics also allows prediction of the value of the variable 216 under study at non measured locations using interpolation techniques such as kriging. 217

The data set came from a truck load of wheat that was selected for sampling because a 218 high level of contamination was found. The results for this need, therefore, to be treated with 219 caution when attempting to generalise to other situations. 220

The data set showed clear evidence of spatial structure for DON, but none for OTA. 221 This difference may reflect the fact that DON is mainly produced in the field by a widespread 222 organism, whereas OTA is typically produced in localised 'hot spots' in storage, but this 223 needs to be tested in other data sets, including other commodities. [START_REF] Biselli | Investigation of variability associated 339 with testing lots of wheat kernels for deoxynivalenol and ochratoxin A (case study 340 truck)[END_REF], using 224 classical statistics, also found that the OTA was present in hot spots and had higher 225 variability than DON. 226 The analysis of the DON data set showed that there was a significant spatial 227 correlation to the distribution of DON mycotoxins in stored grain. The presence of spatial 228 structure implies that samples cannot always be assumed to be independent and that sampling 229 plans need to consider the location of sample points in addition to the number of points 230 sampled in order to obtain reliable estimates of quantities such as the mean contamination 231 and the variance. 232

The spatial pattern was lost for lag distances greater than 4 m. If this is typical, it has 233 two different implications for sampling, depending on its purpose. If the intention were to Surveillance sampling always aggregates the incremental samples before analysis, so 240 all information about the distribution of contaminants is lost. In order to provide better data 241 for the design of sampling protocols and risk management, there is a need for more good 242 quality data sets in which the incremental samples are analysed and recorded separately. 243 Furthermore, in order to determine the importance of spatial structure in mycotoxins and its 244 potential effect on sampling, experimental sampling should be based on regular grids and 245 record the sample coordinates. On the basis of experience in soil sampling, data sets should 246 contain of the order of 100 points or more [START_REF] Webster | Meso-and microscale patterns of Fusarium Head Blight in 391 spring wheat field in Minesota[END_REF]Webster and Oliver, 247 1992). 248 The semi-variance is a measure of the similarity between points at a given lag 269 distance. The smaller the semi-variance, the more alike the points are. The graph of semi-270 variance against lag distance is the experimental semi-variogram (Figure 1). The semi-271 variogram shows how similar any two points separated by a lag distance h are. In general, the 272 closer any two points are, the more similar their value is. In the empirical semi-variogram the 273 semi-variance increases with lag distance up to a distance a, called the range, at which the 274 semi-variance remains constant. The range is the point at which the autocorrelation between 275 points becomes 0 and marks the limit of spatial dependence: points further apart are spatially 276 independent. The semi-variance value at the range is called the sill (c). The sill is the 277 maximum of the empirical semi-variogram and is the a priori variance, σ 2 , of the process.

∑ = + - = ) ( 1 2 )) ( ) ( ( ) ( 1 ) ( h m i i i h x z x z h m h γ (

278

The semi-variance at lag distance 0 is called the nugget (c 0 ) and identifies the measurement 279 error and the variations that occur over lag distances less than the shortest sampling interval 280 [START_REF] Webster | Meso-and microscale patterns of Fusarium Head Blight in 391 spring wheat field in Minesota[END_REF]. The sill, the range and the nugget are the three parameters that 281 characterise the semi-variogram. 282

The model fitted to the experimental semi-variogram is called the empirical semi-283 variogram (Figure 1) and is a simplification of the experimental variogram. The model is 284 fitted using one of the 'authorised' functions [START_REF] Webster | Meso-and microscale patterns of Fusarium Head Blight in 391 spring wheat field in Minesota[END_REF]. The spherical 285 (equation 2) or exponential (equation 3) functions are two of the most frequently used models 286 in geostatistics so these models were selected for the variogram fitting in this study. The 287 fitting of the model was done using ordinary least squares. 288 289

     > + ≤               - + = a h c c a h a h a h c c h for for 2 1 2 3 ) ( ˆ0 3 0 γ (2) 290 291 ( ) ( )    > + ≤ - + = a h r c a h r h c c h for for / exp 1 ) ( ˆ0 0 γ (3) 292 293
where c o is the nugget, c is the sill, a is the range, h is the lag distance and r is a distance 294 parameter that defines the spatial extent of the model. The exponential model approaches its 295 sill asymptotically and therefore, does not have a finite range. Generally, r or effective range 296 is assumed to be the lag distance at which the semi-variance equals 95% of the sill variance, 297 which is approximately 3r. 298 These definitions extend to the case of two-dimensional data by measuring the lag 299 distance h in all directions instead of along the transect. However, even if the data are 300 sampled using a regular grid, the distances between points are not all multiples of the grid 301 size, because all possible pairs of points are considered, not just those lying on the same row 302 or column. In this case, the semi-variance for a given lag distance is estimated by using all 303 the points separated by distances within a certain tolerance of the required lag distance. In 304 effect, the lag distance axis of the variogram is divided into a series of discrete intervals 305 whose width is the lag tolerance. 306

For the empirical semi-variogram model to be fitted accurately it is necessary to have 307 sufficient points in the experimental semi-variogram, but for the semi-variance value to be 308 accurate requires sufficient pairs of observations contributing to each point. Increasing the lag 309 tolerance will reduce the first of these, but increase the second. Therefore, the choice of 310 tolerance is a compromise between the accuracy of model fitting and the accuracy of the 311 estimation of the semi-variance. 312

It is also possible to consider the effect of direction within a two-dimensional data set, 313 by considering pairs of points separated from one another at a specific angle (for example at 314 45° to the grid rows). The angle relative to some reference direction is called the azimuth. If 315 the variograms differ with azimuth, the data are said to be anisotropic or to present 316 anisotropy. For example, the spatial structure of contamination in a wheat field might differ 317 parallel and perpendicular to the prevailing wind direction or the tramline direction. In a 318 similar way to lag tolerance, the points included for each azimuth are defined by the azimuth 319 tolerance. If the azimuth tolerance is 180°, all directions are included and the variogram is 320 said to be omnidirectional. 321

The number of points used to estimate each point in the variogram depends on the lag 322 tolerance, as described above, and similarly on the azimuth tolerance. It may also depend on 323 the azimuth itself, either because the sample space has different extents in each direction, or 324 because of the geometry of the sampling grid. Finally, there is a maximum distance beyond 325 which the number of pairs decreases significantly. A sensitivity analysis may be carried out 326 to quantify the effects of these four parameters. 327

For the geostatistical analysis to be effective the variable under study must be 328 normally distributed, second-order stationary and must present no trend. Data that are not 329 normally distributed may be transformed to achieve normality. A second-order stationary 330 process is characterised by a mean, variance and covariance that depend only on the 331 ab Measurements of Fusarium head blight were taken at five study sites. At each site 16 quadrants of 0.5 m x 0.5 m were randomly selected along a W-shape walk. [START_REF] Biselli | Investigation of variability associated 339 with testing lots of wheat kernels for deoxynivalenol and ochratoxin A (case study 340 truck)[END_REF].

The data were recorded from 20 x 5 grid positions at 0.5 m spacing in the horizontal plane through a truck containing wheat, using a 5 chamber probe sampler. After aggregation and subsampling, DON and OTA concentrations were measured. [START_REF] Oerke | Spatial distribution of Fusarium spp. causing head 355 blight in wheat fields[END_REF].

a Measurements of 8 Fusarium species in wheat were recorded at 5 x 6 grid points on a skewed grid with spacings of 12 x 18 m. [START_REF] Schaafsma | Effect of previous crop, tillage, field 368 size, adjacent crop, and sampling direction on airborne propagules of Gibberella 369 zeae/Fusarium graminearum, Fusarium head blight severity, and deoxynivalenol 370 accumulation in winter wheat[END_REF].

a A total of 68 wheat fields were sampled for Fusarium. In each field a transect was selected. DON content was measured from samples of wheat heads randomly hand-harvested from nine traps equidistantly distributed along the transect. Wilhelm and Jones (2005).

c Fusarium head blight data in wheat field were collected at four different sampling resolutions: mesoscale, full-field, microscale and adjacent-scale. For mesoscale study 100 points were collected at sixty fields. For the fullfield scale nine fields were analyzed with a total of 45 points per field. For the microscale analysis three different grid resolutions were analysed. For the adjacent-head scale a total of 60 consecutive wheat heads were sampled in each of the each of the eight plots. Prof. P. Battilani, Catholic Univ. of Italy, Piacenza, Personal Communication b A total of 10 measurements of F. verticillioides and fumonisins were taken in a 10 m x 10 m area along the diagonals of each of three maize storehouses. Two different sub-samples were measured at each point at two different depths. The samples were aggregated and only the average for the three storehouses were reported. 418

Table 2

  Figure1_TFAC_2008_241_R.emf Figure2_TFAC_2008_241_R.wmf Figure3_TFAC_2008_241_R.emf Figure4_TFAC_2008_241_R.emf

  63A few studies have looked at the effect that sample size has on determining the mean 64 concentration of DON and ochratoxin A (OTA) in a bulk commodity based on individual the distance between samples. Similarly, Wilhelm and Jones (2005) 70 compared frequency distributions of Fusarium head blight incidence in fields with binomial 71 and β-binomial distributions at scales from 1 m to mesoscale (several counties). Agreement 72 with the β-binomial would indicate greater aggregation. The also performed lag correlation 73

  were used for 161 the fitting of the variogram. The spatial structure of the mycotoxin was assessed according to 162 the sill, range and nugget values obtained for each variogram.

  concentration. The variogram sill is generally assumed to be equal to 204 the variance of the population[START_REF] Barnes | The variogram sill and the sample variance[END_REF]. The sill was consistent with the variance 205 (0.055) obtained from the standard deviation in Table2the first application of geostatistical analysis to data on mycotoxin 210 contamination of bulk agricultural/food commodities. Previous studies have used statistics 211 such as the Moran's I, SADIE and Mantel's tests and the dispersion index[START_REF] Webster | Meso-and microscale patterns of Fusarium Head Blight in 391 spring wheat field in Minesota[END_REF] 212 Jones, 2005 and[START_REF] Schmale | Spatial patterns of viable spore deposition of 376 Gibberella zeae in wheat fields[END_REF] to try to detect non-random distributions of fungi or 213

  account for the spatial structure of the mycotoxin when determining 238 levels of contamination to minimise these errors. 239

  on the variogram model which relates the distance between any two 258 points in the space with their semivariance. Consider a transect along which observations of a 259 variable Z are taken at regular intervals. The positions are denoted as x i and the value of the 260 observation is z(x i ), i= 1,2…n. The variance of the differences between all the pairs of points 261 at a lag distance h apart can be calculated as follows: 262 263

  h) is the number of pairs of data points separated by lag distance h and γ(h) is the 266 average variance of all pairs of data points separated by lag distance h. The per-observation 267 variance or semi-variance )

  and not on their absolute positions. A systematic component in the 332 spatial variation is an indication of a trend. The trend must be removed from the data when 333 identified so the geostatistical analysis is only carried out for the residuals after subtracting

  Figures 420 421 Figure 1. Diagram showing the variogram parameters (i.e. range, sill and nugget). The 422 diagram shows both the experimental (dots) and the empirical (line) variograms. 423 424 Figure 2. Ochratoxin A concentration: histogram of observations and fitted exponential 425 distribution. 426 427 Figure 3. Spatial distribution of (a) OTA, (b) DON and (c) log e transformed DON. All 428 distances are in metres. Data collected are represented by a cross and each cell has been 429 coloured according to the measured value. 430 431 Figure 4. Spherical variogram for the log e transformed DON data (omnidirectional 432 variogram) calculated with lag distance 0.7 m and maximum distance 4 m. 433

  Table 2 summarises the descriptive statistics 169 obtained for OTA. The variogram was calculated by treating OTA as an indicator 170

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y

Table 1 . Description of data sources collected for analysis and criteria for rejection 415 ( a insufficient measurements available; b lack of coordinates identifying the location of 416 each measurement, c the original data were not available)

 1

	414	Tables
	417
		Source	Characteristics
		Xu et al. (2008).

. Descriptive statistics for the data analysed.

		F o r
		P
		e
			e r
			R
			e
			v i e
	419	Statistic	OTA µg kg -1 DON µg kg -1 DON transformed w
		Number of points Minimum, Maximum 1 st Quartile 3 rd Quartile Median Mean	100.0 0.05 8.6 0.05 0.52 0.2 0.59	100.0 830.0 2655.0 1121 1508 1272 1342	100.0 6.72 7.88 7.02 n O l 7.31 7.14 7.17 y
		Standard deviation	1.13	339	0.23
		Skewness	4.36	1.19	0.48
		Kurtosis	30.58	7.77	3.2

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

This research was funded by the Food Standards Agency Project CO3055. We wish to thank Dr.252Scarlett Bisselli (EUROFINS, Hamburg, Germany) for giving permission to use the data analysed

Acknowledgements 250