Ruifen ; Huo

Domingo Salazar

Kieran Hyder

Xiangming Xu
email: xiangming.xu@emr.ac.uk.

East

J D Salazar

X.-M Xu

Modelling non-systemic pesticides residues in fruits with initial deposit variability and weather effects

Keywords: deterministic modelling, Risk assessment -modelling Additives/Contaminants: Pesticide residues Deterministic models, predicting, contact pesticides, residue dissipation

A flexible and generic model was developed to predict the decline of residues of a nonsystemic pesticide for both single and multi-spray situations as well as for different tree canopy zones. The model predicts not only the average residue levels but also the confidence interval of the residues through either a deterministic or stochastic approach. This generic model includes several key aspects of residue fates in the environment: initial deposit, physical loss and growth dilution. The model considers tree canopy into three distinct zones for which initial deposition of pesticides may differ. In addition to predicting the average residue within each zone, it also estimates the 95% and 99% confidence intervals of residues on individual fruit within each zone. For the purpose of evaluation, this model was parameterised specifically for captan, one of the most important non-systemic fungicides used to control diseases in horticultural crops. The observed average initial deposit for each zone was used in the evaluation. The overall correlation between predicted average residues and those observed on apple fruit in two applications was 0.93. Confidence intervals were also predicted accurately.

Introduction

Pesticides are widely used to control pests and diseases of many plant species. Their residues in food crops are strictly regulated and monitored [START_REF] Hamilton | Pesticide residues in food -acute dietary exposure[END_REF][START_REF] Hyder | Maximum residue levels: Fact or fiction?[END_REF]. For this purpose, mathematical models have been developed to optimise pesticide input and monitor residues on fruit, e.g. [START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF], [START_REF] Quest | Statistical interpretation and graphical representation of the degradational behaviour of pesticide residues. IV. Physically based models[END_REF] and [START_REF] Holland | Modelling of pesticide Residues on Fruit II: Persimmon[END_REF].

Many research studies have revealed that the non-systemic pesticide residues per fruit weight unit are dependent on three processes, i.e. variation in initial deposit [START_REF] Walklate | A generic method of pesticide dose expression: application to broadcast spraying of apple trees[END_REF][START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF], physical decay due to weather factors [START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF] and growth dilution [START_REF] Lakso | An expolinear model of the growth pattern of the apple fruit[END_REF]. The degradation of pesticide residues concentration due to the three processes has been approximately described by a first-order reaction model [START_REF] Holland | Modelling of pesticide Residues on Fruit II: Persimmon[END_REF][START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF]. This first-order reaction model, however, can not provide the confidence limit of their predictions as the variation in initial deposit is unknown. Moreover, because three processes in tandem drive the degradation of pesticides, the predictions by first-order reaction models can be very poor when the initial deposit variation is big and the weather effect is large. One of the most important factors influencing pesticide residues is the canopy structure [START_REF] Franz | Aerial spray deposit relations with plant canopy and weather parameters[END_REF][START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF]), however, this factor is often not explicitly incorporated into residue models.

In this paper, we describe a flexible model developed to predict the decline of residues of a contact (i.e., non-systemic) pesticide, primarily for tree crops. First, the general model structure is described. Then, we present model parameters calibrated specifically for captan, a widely used contact fungicide [START_REF] Elzemaity | Degradation behavior of captan and folpet on greenhouse tomatoes[END_REF][START_REF] Gilvydis | Residues of captan and folpet in strawberries and grapes[END_REF][START_REF] Northover | Dissipation of captan residues from cherry and peach fruits[END_REF]). Finally we evaluate the model predictions against field data of captan residues on apple.

Model overview

This model estimates residues for both single and multi-spray situations. Furthermore, it predicts not only the average residues but also the confidence interval of the residues through either a deterministic or stochastic approach. It consists of three main components: (1) initial deposits, (2) physical decay due to weather variables, and (3) host growth model. The model estimates true pesticide loss by removing the growth dilution effect. It is coded in Delphi (version 5) for Windows.

Model for Initial deposits

Accurate estimation of initial deposits is critically important for any model. [START_REF] Holland | Modelling of pesticide Residues on Fruit II: Persimmon[END_REF] assumed that the amounts of initial deposit on fruit were linearly related to the application rate and the surface area of fruits at the time of application. However, in many practical situations, the initial deposit is strongly influenced by variability in canopy structure, weather conditions and spray technology [START_REF] Franz | Aerial spray deposit relations with plant canopy and weather parameters[END_REF][START_REF] Walklate | Comparison of different spray volume deposition models using LIDAR measurements of apple orchards[END_REF][START_REF] Walklate | Relationship between orchard tree crop structure and performance characteristics of an axial fan sprayer[END_REF][START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF]) but less dependent on the fruit area.

Further, plant tissue surface structure can also greatly affect the initial deposit concentration [START_REF] Hall | Changes in spray retension by apple leaves during a growing season[END_REF][START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF]. Thus, the initial deposit concentration varies greatly with crop growth stages. For example, due to the increasing wax deposition on apple surface with increasing fruit maturity, the deposits do not cover the surface evenly. Instead, most of them accumulate on the stalk and/or the calyx ends depending on the fruit orientation. Thus, it is unlikely that the amount of deposit is proportional to the area of fruit surface. Furthermore, many studies showed the importance of canopy structure in affecting initial deposit concentration; usually fruits Assuming a constant retention factor per unit of area or weight (µg/cm 2 or µg/g), the average initial deposit (L) per fruit (here we called it 'load') for each spray application at the full manufacturer's recommended rate is estimated as,

RX L = (1)
where X is the average surface area (cm 2 ) or fruit weight (g) at the time of the application. Another option to estimate average initial deposit per fruit is to assume a constant retention factor per fruit (µg/fruit), i.e. L=1R ('1' indicates one fruit).

In both methods, the model assumes that the input parameter of the retention factor (R) for each zone is for cases when the pesticide is applied at the full manufacturer's recommended rate. Then R is assumed to vary linearly with the rate of application as the percentage (F) of the full recommended rate. The mean of initial deposits due to the ith application in each single zone is then given by

i i LF L = , 0 (2)
where F i is the rate of the ith application rate as percentage of the full recommended rate. Estimating variability in initial deposits. To model the variability in initial deposit, the model needs a further parameter (V) describing the variation in the initial deposit. This can be either variability factors (FAO/WHO 1997) at 5% (V 5 ) and 95% (V 95 ) percentiles or standard deviation of the initial deposit. The model also assumes that V is not dependent on the application rate. For each zone, the model uses parameter V to estimate the 95% confidence interval for initial deposits:

F

     = = i i i i L V L L V L , 0 5 5 , 0 , 0 95 95 , 0 (3)
where we have taken L 0,i as a proxy for the mean of the initial distribution for a specific zone.

Alternatively, we estimate the 95% confidence interval of the initial deposit by assuming that it follows a lognormal distribution as shown by many researchers [START_REF] Cross | Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 1. Effects of spray liquid flow rate[END_REF][START_REF] Hamey | A practical application of probabilistic modelling in assessment of dietary exposure of fruit consumers to pesticide residues[END_REF][START_REF] Hill | Residue variability and sampling -practical problems and consequences for residues monitoring[END_REF]. The mean (m) and variance (S 2 ) of a lognormal distribution for a random variable X are related to the original mean (µ) and variance σ 2 of the X [START_REF] Evans | Statistical Distributions[END_REF]:

                 +         =         + = 1 ln ln 2 2 2 2 µ σ σ µ µ S m (4).
In the present context, µ is equivalent to L 0,i . Thus the 95% confidence interval for initial deposits can be estimated as In order to take into account the growth dilution effect and hence estimate the true pesticide loss, only the total pesticide residues (µg) per fruit are used in the subsequent decay model, instead of the concentration (µg/cm 2 or µg/g).

Physical decay model with the weather effects

The model considers two situations for pesticide loss from fruit surface: loss under dry and under rainy conditions. All studies indicated that rain can wash a large proportion of pesticides off the plant tissue surface although the precise relationship between the amount of rainfall and the extent of pesticide wash-off varies greatly even for same pesticides between studies or between fields in the same studies [START_REF] Frank | Disappearance of captan from field-grown and greenhouse-grown tomato fruit in relationship to time of harvest and amount of rainfall[END_REF][START_REF] Frank | Persistence of captan on apples, grapes, and pears in Ontario, Canada 1981-1983[END_REF][START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF]. The daily loss of pesticides under dry conditions is generally much less than that due to rain wash-off. Similarly, the loss under dry conditions varies greatly between studies and to a large extent this variability is due to growth dilution effects.

The model assumes a constant daily loss (k 1 < 1.0) of pesticide residues on fruits.

Several factors, such as temperature and relative humidity, may affect k 1 although we have insufficient information to implement such relationships in the model. However, the model is so structured that these relationships can be incorporated easily into the model in future.

Rain wash-off is modelled by several alternative models where the rate of loss (k 2 < 1) due to rain is either linearly or exponentially related to the amount of rainfall [START_REF] Bruhn | Weathering of chlorothalonil residues in a potato canopy[END_REF][START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF]: where Y is the daily total amount of rainfall (mm), and a and b are parameters. The linear relationship must be bound by 1 for the interval of acceptable rainfalls. Another model is also implemented to estimate the effect of rainfall on residue wash-off. The rationale for this model is that the first rainfall after an application may result in the largest losses in residues but subsequent rainfalls are likely to result in far less loss due to the tenacity of remaining residues on the surface. We used a step function to model this kind of rain wash-off:

   + - + = ) ip relationsh l exponentia ( ) exp( 1 ) ip relationsh linear ( 2 bY a bY a k (6)
     > ≤ = max 3 max 3 max 2 if rainfalls
Y Y k Y Y k Y Y equation as k (7)
where Y max is the threshold of rainfall such that further increase over this threshold will not lead to increased wash-off of residues, and k 3 (< 1) is the maximum loss due to the rain other than the first after an application.

Thus, if the residue on day t is L t , then the residue on day t+1 (L t+1 , µg/fruit) is given by

) 1 ( 2 1 1 k k L L t t - - ⋅ = + (8)
where k 2 is zero on dry days or calculated as in equations [7][8] in rainy days depending on the user's choice. If day t+1 is an application day, the estimated initial deposits for the three points (mean and 95% confidence intervals) for each zone from the application are then added to L t+1 .

Host growth

This model assumes that host tissue (fruit or leave) growth does not result in actual loss or degradation of pesticide residues but only affects residue concentrations over time due to host expansion. Hence the growth model does not affect residue losses as Fruit growth pattern can be represented by a number of functions such as the expolinear [START_REF] Lakso | An expolinear model of the growth pattern of the apple fruit[END_REF], and Gompertz and logistic [START_REF] Thornley | Plant and crop modelling[END_REF] equations. We have implemented these functions in the model. For example, a logistic model for fruit expansion is given as

) exp( 1 max t B A W W t ⋅ - ⋅ + = (9)
where W t is the fruit weight (g) on day t (t = 0 on the day of full bloom), W max is the maximum weight (g) to which fruit can reach, A and B are parameters. Once the fruit weight is obtained, residue concentration (µg/g) on day t can then be estimated.

Stochastic effects

To evaluate the uncertainty propagation in the deterministic model, we have incorporate sub-models dealing with two specific aspects of stochastic variation. First, the model can sample an initial deposit within each zone from a lognormal distribution. Second, on any given day, parameters k 1 , k 2 and k 3 for the physical decay sub-model are sampled from normal distributions. These two aspects of stochasticity can be invoked simultaneously or separately to investigate their joint effects on the dynamics of residues over time. Details on the implementation are not presented here since this paper deals with validation of the deterministic model. Then, the model loops through days between applications at a daily step. Depending on weather conditions, the model uses equations [6][7][8] to estimate daily loss of residues and hence to calculate residues for the three points within each zone. On the day of an application other than the first, the estimated initial deposits for the three points (mean and 95% confidence intervals) for each zone are added to the respective predicted residues on the day as a result of previous application(s). At the same time, daily average fruit weight is estimated using the fruit growth model. Finally the model presents predicted residues in the form of both total residues per fruit and residue concentrations (µg/g).

In addition to the model parameters described in section 2.1-2.3, the model also needs the following input: date and rate (as a percentage of the full recommended rate)

of each application and daily average temperature and humidity as well as daily total rainfall (mm).

Model evaluation

We evaluate the model predictions against a specific contact fungicide, Captan (N-

[(trichloromethyl)thio]-4-cyclohexene-1,2-dicarboximide).
Captan is one of the most important fungicides used to control apple scab, particularly when apple mildew is not severe, and is also widely used to control diseases in many other crops [START_REF] Elzemaity | Degradation behavior of captan and folpet on greenhouse tomatoes[END_REF][START_REF] Gilvydis | Residues of captan and folpet in strawberries and grapes[END_REF][START_REF] Northover | Dissipation of captan residues from cherry and peach fruits[END_REF] [START_REF] Elzemaity | Degradation behavior of captan and folpet on greenhouse tomatoes[END_REF][START_REF] Frank | Disappearance of captan from field-grown and greenhouse-grown tomato fruit in relationship to time of harvest and amount of rainfall[END_REF][START_REF] Frank | Persistence of captan on apples, grapes, and pears in Ontario, Canada 1981-1983[END_REF][START_REF] Kiigemagi | Residues of benomyl (determined as carbendazim) and captan in postharvest-treated pears in cold-storage[END_REF][START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF].

Model parameters for captan

Model parameters were obtained from previously published as well as from unpublished studies at East Malling Research. The most difficult parameters to obtain are those related to initial deposition of captan. Spray retention of captan on leaves varies greatly within a growing season due to the change in canopy leaf density [START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF]. Retention of a Zn tracer on apple fruit also varied greatly between years and between orchards [START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF]). Because of these uncertainties, we decided to use the actual average captan deposit for the inner zone from the orchard experiments as an input, which is 10 µg per fruit (i.e. the retention rate -R). Then, based on previous work on a Zn tracer [START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF], we assumed that the average captan deposit in the top and outside zones is 25% more than the inner zone, i.e. 12.5 µg per fruit. Furthermore, the variability factors (V 5 and V 95 ) are assumed to be equal among the three zones (V 5 = 0.42 and V 95 = 2.2) based on the same Zn data though these values were not given in the previous paper [START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF]; this is equivalent to a 45% coefficient of variation.

Several previous studies reported that there was no appreciable loss of captan on tomato fruit [START_REF] Frank | Disappearance of captan from field-grown and greenhouse-grown tomato fruit in relationship to time of harvest and amount of rainfall[END_REF], apple and grape [START_REF] Frank | Persistence of captan on apples, grapes, and pears in Ontario, Canada 1981-1983[END_REF], and cherry and peach [START_REF] Northover | Dissipation of captan residues from cherry and peach fruits[END_REF]) under dry conditions. However, large loss of captan was shown under dry conditions in another field study [START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF] (Xu, unpublished), and showed that daily loss of captan on fruit and leaves was very small. From these data, k 1 was estimated to be 0.01 irrespective of temperature and humidity, and hence that value is used in the model.

As reported previously [START_REF] Frank | Disappearance of captan from field-grown and greenhouse-grown tomato fruit in relationship to time of harvest and amount of rainfall[END_REF][START_REF] Frank | Persistence of captan on apples, grapes, and pears in Ontario, Canada 1981-1983[END_REF][START_REF] Northover | Dissipation of captan residues from cherry and peach fruits[END_REF][START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF], there was significant loss of captan due to wash-off by rain.

The relationships between the loss and the amount of rainfall varied greatly between experiments and fields. In two recent experiments conducted at East Malling Research (Xu, unpublished), we demonstrated that even rainfall as little as 1 mm after an application led to about 50% loss of captan on leaves of rootstock plants independently of the amount of rainfall. However, the loss due to subsequent rainfall was very small.

Thus, we have used a step function to estimate the rain wash-off of captan. The first rainfall less than or equal to 1 mm is not treated as a rainfall event for the following reason. More rain is needed to penetrate tree canopy than rootstock plants to wash captan from leaves and fruit inside the canopy. This is supported by field observations that 1 mm rainfall resulted in negligible loss of captan even on leaves [START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF].

The loss of captan on the upper surface of leaves due to the first rainfall after an application is estimated to be 65% (Xu, unpublished) although the average loss is circa 50% over the upper and lower surface. Because of the nature of fruit shape (round) and surface (waxier than leaves), we set k 2 to 0.65 instead of 0.50. From the same experiment, we estimated k 3 to be 0.15 and Y max to be 12 mm (since the maximum rainfall in the experiment was 12 mm).

In this evaluation, we did not activate the fruit growth sub-model. As explained earlier, the growth model does not affect the prediction of captan residues. Therefore, An electronic data logger (Skye, Wales) was installed in each of the three zones in a selected tree in each orchard.

F
For each application, a number of fruit were sampled from each of the three canopy zones on six occasions: 0 (the same day), 5, 8, 13, 19 and 26 days after the May application, and 0, 5, 9, 15, 21 and 28 days after the August application. For the day 0 (initial deposit) and the last sampling day, there were 26 samples (either single-fruit samples or two-fruit composite samples) from five trees for each zone. For all other sampling days, there were 16 samples (either single-fruit samples or two-fruit composite samples) from four trees for each zone. Throughout the sampling periods, the same four trees were sampled for each application and the same fifth tree was used for the day 0 and the last day sampling as well. The total weight of the fruit in each sample was determined before captan extraction.

Each sample (one or more fruit) was transferred to a 150 mm x 25 mm test tube and 5 ml ethyl acetate (containing 50 mg kg -1 captafol as internal standard) was then added.

The tube was then whirl-mixed for 15 sec, ensuring that all surfaces of the fruit were completely wetted by the solvent. When fruit was too large for the tube, each sample 14 was transferred to a small polythene bag and then 5 ml extraction solvent was added to the bag. The mouth of the bag was closed by twisting, and the bag was then shaken manually for about 15 sec. A portion of the extract was transferred to a 2 ml GC vial;

the vial was then capped with a Teflon-lined crimp-cap lid and stored at -18°C until analysed. Preliminary studies showed that the recovery rate of captan by this method was > 80%.

Analysis of captan and captafol was performed on a Hewlett-Packard HP6890 gas chromatograph equipped with an HP-5 capillary column (30 m x 0.32 mm, film thickness 0.25 mm), and an ECD detector. The carrier gas was helium (3 ml min -1 ) and the make-up gas was nitrogen (60 ml min -1 ). Injector temperature was 200ºC, and detector temperature was 300ºC. Following sample injection, the initial temperature of the oven was maintained at 100 °C for 1 min, then increased at a rate of 50°C min -1 to 275 °C, and maintained at 275°C for 5 min. Retention times for captan and captafol were ca 6.5 min and 8.0 min, respectively. For each batch of samples, four standards (200, 100, 50 and 25 mg kg -1 captan in the extraction solvent with 50 mg kg -1 captafol)

were quantified first to obtain a calibration curve. The instrument was programmed first to quantify the 200 mg kg -1 captan standards twice before the four standards to condition the instrument. In addition, the instrument was instructed to quantify the 50 mg kg -1 captan standard after every 10th experimental sample to adjust for the potential drift in the detector.

Validation results

Figures 2 and 3 show the predicted levels of residues against the observed for each zone over the two orchards. The predicted average residues and 95% confidence interval for each zone are shown in Fig. 2. Overall, the predicted residues were close to those 15 observed for both sprays. For the May application, the level of residues actually increased for the last two sampling days, especially for the outer and inside zones (Fig. 2bc). For the August application, there appeared to be no systematic prediction errors in the average residues. The Pearson's correlation coefficients between predicted zone averages and the observed were 0.92 and 0.94 for the May and August application, respectively; the overall correlation was 0.93.

Similarly, the model predicted the residue variability reasonably well and the prediction error in the variability shows no systematic trend. Table 1 shows the observed coefficient of variation (CV) of total captan per fruit for each zone on any sampling day. The average CV over all sampling days for the May application was greater than the model parameter (45%), but was close to that value for the August application. CVs fluctuated greatly between sampling days but with no obvious pattern.

When the predicted zone averages were regressed on to the corresponding observed values, the fitted linear model did not differ significantly between the May and August applications, but it did differ between the three zones. However, this zone difference only accounted for about 2.5% of the total variation, with coefficients of determination R 2 increasing from 0.851 to 0.876. The constant term (i.e., the intercept) was not significantly different from 0 but the regression slope (0.82 ± 0.03) was significantly less than 1.0, thus indicating that the captan model systematically underestimated residues by 18%.

Discussion

We have developed a generic model to predict the dissipation of contact pesticides over time. This generic model included several key aspects of residue fates in the environment including the initial deposit, physical loss and growth dilution. This shown to be able to predict average residues together with the confidence intervals accurately. The overall correlation between predicted average residues and the observed in two applications was as high as 0.93, which is surprisingly accurate given the fact that the model was developed from independent data from published or unpublished sources.

When parameterising the model for captan, we encountered difficulties in estimating average initial deposits for each canopy zone. In the end, we resorted to the observed average residues for the inside zone to initiate the model; this is the only model parameter that was obtained from the evaluation data. Spray retention of captan on leaves varies greatly within a growing season due to the change in canopy leaf density [START_REF] Smith | The retention and redistribution of captan on apple foliage[END_REF] and surface structure [START_REF] Hall | Changes in spray retension by apple leaves during a growing season[END_REF]. Retention of a Zn tracer on apple fruit also varied greatly between years and between orchards [START_REF] Xu | Variability of initial spray deposit in apple trees in space and time[END_REF]. Variability in initial deposit has been observed for many other pesticides in relation to plant architecture [START_REF] Bennett | Temporal and spatial distribution of azinphos-methyl applied to alfalfa[END_REF][START_REF] Bruhn | A mathematical model of the spatial and temporal dynamics of chlorothalonil residues on potato foliage[END_REF][START_REF] Cross | Quantification of spray deposits and their variability on apple trees[END_REF] and to spray technology [START_REF] Ade | The influence of some application variables on spray deposition in field-grown tomatoes[END_REF][START_REF] Brenneman | Deposition and retention of chlorothalonil applied to peanut foliage: effects of application methods, fungicide formulations and oil additives[END_REF][START_REF] Cross | Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 1. Effects of spray liquid flow rate[END_REF]. In modelling initial deposit on kiwi fruit, a constant retention factor per unit of surface fruit area was assumed [START_REF] Holland | Modelling of pesticide Residues on Fruit II: Persimmon[END_REF]) and the resulting model generated reasonable predictions for several chemicals. We believe that the accurate prediction of initial spray deposits is one of the bottlenecks for accurate prediction of residues on food stuff. Accurate prediction of residues depends on many factors as aforementioned, including canopy structure, tissue surface structure and spray technology.

The large differences in the average observed residues between different zones indicated the importance of including the canopy in the captan model. The overall consistent correlation between predicted and observed average residues for each zone 17 further supported our assumption on the effect of canopy. Namely, the canopy mainly affects the residues by influencing the initial deposition process but it does not exert any significant differential effect on the loss of captan. It reinforces the previous point that estimating initial deposits is critically important, whereas predicting subsequent declines is relatively straightforward, at least for captan on apple.

The model generally predicted the overall trend in residue decline over time for each canopy zone. However, there was an apparent and statistically significant increase in captan residues from June 8 th to 14 th fruit samples. The most likely explanation is the phenomenon of June-drop. That normally takes place from mid-June to early July, but may have occurred earlier in 2005 as the trees blossomed earlier than usual that year.

Therefore, most fruit retained on the tree and sampled on the last two dates for the May application may have been from cohorts bigger than the average at the time of application. Hence the stalk and calyx end of these fruit would also have been bigger at the time of application. The average size of fruits was small at the time of application (about 1 g in weight) and the smallest of these may be most susceptible to June-drop.

Our model differed from several previous models in several aspects. The model developed by [START_REF] Holland | Modelling of pesticide Residues on Fruit II: Persimmon[END_REF] did not consider the spatial variability of initial deposits due to canopy structure. To take into account some reports that decline of residues sometimes was much faster shortly after spray applications than the later stage, they used decay constants to describe the two decay phases. However, it is difficult to decide which of the two decay constants should be adopted in which circumstance. The two different decay rates may be due to the variation in wash-off between first rain and the subsequent rains, where the former causes considerably more loss of residues than the latter. Thus, our model explicitly assigned a decay constant depending on weather conditions. Timme and his co-workers [START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF][START_REF] Timme | Statistical interpretation and graphical representation of the degradational behaviour of pesticide residues[END_REF]) did 18 not consider initial deposits in their model and used a series of reaction kinetics, labelled as 1 st , 1.5th, etc., order equations to model the degradation behaviour. Later, they [START_REF] Quest | Statistical interpretation and graphical representation of the degradational behaviour of pesticide residues. IV. Physically based models[END_REF]) proposed a two-zone model where the degradation of residues in the outer zone and the degradations in the inner zone are assumed to follow different rules, which was used to accommodate dramatic losses of residues observed occasionally soon after applications. This two-zone model, however, does not consider weather effects explicitly. In addition, our model assumed that the canopy structure affects the initial deposition process only.

It is well known that variability in pesticide residues whether in field or shelf samples is very large [START_REF] Ambrus | Within and between field variability of residue data and sampling implications[END_REF][START_REF] Harris | How the variability issue was uncovered: the history of the UK residue variability findings[END_REF][START_REF] Harris | Unit to unit variation of pesticide residues in celery, plums and kiwi fruit[END_REF][START_REF] Hill | Residue variability and sampling -practical problems and consequences for residues monitoring[END_REF][START_REF] Hill | Unit-to-unit variability of pesticide residues in fruit and vegetables[END_REF]. Accurate prediction of acute dietary risks of pesticide residues in food also depends critically on our understanding of this variability. Our current model assumed a CV for residues within each canopy zone. This is an over-simplification but a necessary one since as yet we do not understood residue variability sufficiently to predict it. This is illustrated by the large yet erratic variability in captan residues observed in the present field trials. There are many factors that may have contributed to the observed variability by influencing the dissipation process, including canopy structure, spray technology, sampling and weather condition [START_REF] Dubus | Sources of uncertainty in pesticide fate modelling[END_REF]. In addition, instrumentation used in quantifying residues can also contribute substantially to the observed variability [START_REF] Ambrus | Reliability of measurements of pesticide residues in food[END_REF]. It remains a challenge to predict this variability accurately. One way forward is to implement a stochastic version of the current deterministic model. There are two stochastic processes that should be investigated: initial deposition and weathering effects (i.e., uncertainty in the model parameters k 1 , k 2 , k 3 ). This is our current research focus. In addition, there is another factor that can affect residue variability, though not residue loss, i.e. plant growth. This growth process can also be studied stochastically.

In summary, we have developed a generic framework for predicting dissipation of contact pesticides. We did not conduct sensitivity analyses for the captan model because it is obvious that the model would be most sensitive to the two parameters (k 2 and k 3 ), describing rain wash-off effects, once an accurate estimation of initial deposit is given.

Evaluation of the generic model specifically parameterised for captan against independent field data has showed promising results. The model not only predicted very accurately the average residues but also estimated the residue variability reasonably well. However, one of the remaining key challenges for the prediction of residues is the accurate estimation of initial deposit concentrations. Extra loss occurs due to weather effects, i.e., rain events.

  and outside regions of the canopy are likely to receive more deposits than those inside the canopy.Estimating average initial deposits. The model considers the canopy effect by dividing it into three distinct zones: top, outside and inside. Then, it estimates the initial deposit within each zone via two methods: constant retention factor (R) per unit of area/weight or per fruit.

  physical decay component but only the presentation of predicted residues to the end users. This may be important since the dietary risk assessment is often expressed on the basis of residue concentrations.

Figure 1

 1 Figure 1 presents the model flow diagram. The model executes by first looping through

  parameterised specifically for captan of evaluation purposes and is

Figure 1 .

 1 Figure 1. A flow diagram of the model. Initial deposits are for the top, outer and inner

Figure 2 .

 2 Figure 2. Predicted captan residues on apple plotted against the observed values

Figure 3 .

 3 Figure 3. Predicted average captan residues on apple for each zone against the observed

  , which may have resulted from heavy dew. Several papers have stated that the half-life of captan on vegetation is between 3-17 days, but no detailed data were provided (de Cock

	conducted several experiments to investigate the loss of captan under dry conditions on
	both leaves and fruit
	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y
	12

et al. 1998[START_REF] Decision | Reregistration Eligibility Decision (RED): Captan[END_REF]

. Without rainfall, such a short half-life time is most unlikely and may result from not taking growth dilution effects into consideration. We recently

Table I

 I The observed coefficient of variation (CV, %) of total captan residues per apple fruit within each zone on each sampling date over two orchards. The model used a single CV value to estimate the 95% confidence intervals for the initial deposit for each zone.

	Sampling	May application		Sampling	August application
	day a	Inner	Outer	Top	day	Inner	Outer	Top
	0

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

Acknowledgements

This research was funded jointly by the Biotechnology and Biological Sciences Research Council and Syngenta.


G Gizzi

U Vincent

Christoph Von Holst
email: christoph.von-holst@ec.europa.eu

J De Jong

C Genouel

Validation of an analytical method for the determination of carbadox and olaquindox in feedingstuffs by liquid chromatography coupled to UV and/or diode array detector

Keywords: Chromatography -HPLC, Inter-laboratory validation HPLC-UV, HPLC-Diode Array Detector, validation study, growth promoters, feedingstuffs, olaquindox, carbadox

    Validation of an analytical method for the determination of Carbadox and Olaquindox in feedingstuffs by Liquid Chromatography coupled to UV and/or DAD Christoph von Holst

Introduction

Following the European ban of some antibiotics (European Communities 1998a) and growth promoters (European Communities 1998b) as feed additives, the European funded research project SIMBAG-FEED [START_REF] De Jong | establishing Community methods of analysis for the determination of amino acids, crude oils and fats, and olaquindox in feedingstuffs and amending Directive 71/393/EEC[END_REF] was launched with the main objective of developing and validating analytical methods for the official control of five antibiotics (spiramycin, tylosin, virginiamycin, avoparcin, zinc bacitracin) and two growth promoters (carbadox and olaquindox). For the simultaneous detection of the two banned growth promoters carbadox and olaquindox a method based on high performance liquid chromatography coupled to spectrophotometric ultraviolet or diode array detection (HPLC-UV or HPLC-Diode Array Detector (DAD)) was developed and subjected to validation by conducting a collaborative study. The minimum target concentrations to be included in the study were set at 4 mg kg -1 for carbadox and 3 mg kg -1 for olaquindox, which were 5 times lower than the lowest -3 -contents formerly fixed by European legislation. A successful validation of this method was envisaged as improving the capability of official feed laboratories to enforce the ban of the target growth parameters, since the former European official methods are only single analyte methods and have not been validated at low concentration levels as included in the present study (European Communities, 1998c;European Communities, 1999) In this paper, we report on the results of the collaborative study to validate a HPLC method for the detection of carbadox and olaquindox in feed. The purpose of the study was two-fold:a) to test the suitability of the method as a quantitative method, indicating the concentration of these substances in the samples. The lower range of the study was set at 4 mg kg -1 for carbadox and 3 mg kg -1 for olaquindox., b) to test the suitability of the method for confirmatory analysis by using information of the full UV spectrum when the measurement has been performed with a diode array detection, especially in cases with limited availability of mass spectrometry instrumentation.

Methods and materials

Design of the study

The laboratories followed a step-by-step approach involving first a training period on the method through audiovisual material, namely a CD-ROM with the method description and a presentation of the method by the means of media show. In addition the laboratories had to analyse known and blind test samples, reporting the results on an electronic spreadsheet with a feedback questionnaire. This was a specific exercise for the candidate laboratories designed to get them familiar with the method protocol, to evaluate their effective performances on a limited number of samples and to check their rigorous application of the protocol. In total 22 laboratories were selected from the training period to participate in the collaborative study. Out of these 22 laboratories 21 delivered results. In addition, the information from the participants to the training period was utilised to adjust the method protocol in order to facilitate the correct application of the method.

A detailed and revised method protocol, 20 samples to be analysed and electronic forms to report the results of the analyses, the DAD data and to specify details of the execution of the method were sent to -4 -each participant to the collaborative study. In this study the emphasis was placed on two aspects, which were a) quantitative analysis, in which the laboratories were asked to report on the content of the carbadox and olaquindox by expressing the response in mg kg -1 and b) the identification of the analytes based on the DAD data.

Statistics

The design of the study and the target performance characteristics for the evaluation of the LC-UV method were selected according to internationally accepted guidelines for method validation [START_REF] Horwitz | Protocol for the design, conduct and interpretation of method-performance studies[END_REF]. The quantitative results submitted by the laboratories were used to estimate average concentrations and standard deviations under repeatability and reproducibility conditions. Extreme values were not included in the statistical evaluation for different reasons, especially when they were related to obvious experimental deviations from the protocol. Furthermore, as indicated by the IUPAC protocol [START_REF] Horwitz | Protocol for the design, conduct and interpretation of method-performance studies[END_REF] the initial reported valid data must be purged of all outliers flagged by the harmonised outlier removal procedure. This procedure basically consists of the sequential application of the Cochran and Grubbs tests (at 2.5% probability level, 1 tail for Cochran and 2 tails for Grubbs) until no further outliers are flagged or until a drop of 22.2% (= 2/9) in the original number of laboratories providing valid data would occur.

The precision data obtained from the collaborative study were then compared with predicted acceptable levels of precision. These levels, as estimated by the Horwitz equation [START_REF] Horwitz | Quality assurance in the analysis of foods and trace constituents[END_REF], provide an indication as to whether the method is sufficiently precise for the concentration level of the analyte being measured. This indication is expressed by the HORRAT value [START_REF] Horwitz | The Horwitz ratio (HorRat): A useful index of method performance with respect to precision[END_REF]. For the between-laboratories reproducibility, the HORRAT value is a comparison of the measured reproducibility precision with the precision calculated by the Horwitz equation for a method measuring at that particular concentration level of analyte: where C is the mass concentration expressed in power of 10 (i.e. 1 mg/g = 10 -3 ). The target value for the HORRAT value is set at 1, the acceptable limit is 1.5 and the rejection level is 2.

Test materials

In this study 10 different materials were utilised as shown in Table 1. The table also indicates the presence and concentration of more banned feed additives that were not measured with the method of this study but with other methods developed within the project SIMBAG-FEED.

(please insert Table 1 here)

The test materials were compound feedingstuffs for cattle, pig, poultry and calf containing typical ingredients using a realistic recipe. The banned feed additives were then mixed to the feedingstuffs in two steps. First, the target analytes were added individually to soybean meal to obtain different premixtures each of them containing one of the target analytes at a relatively high concentration, viz 360 mg/kg for carbadox and olaquindox. In a second step the pre-mixtures fortified with the target analytes were mixed with the other ingredients to obtain the final test materials [START_REF] Kramer | Production of 4 blank and 10 spiked compound feeds for SCA SIMBAG-FEED[END_REF]). The final feedingstuff samples were checked for sufficient homogeneity applying the procedure described elsewhere (Thompson and Wood, 1993). An additional criterion was that if a test material was proven homogeneous for one compound, it would also be for the other compounds. This was possible since the preparation of the samples was identical for all substances and since the suitability of the preparation procedure was confirmed by conducting a pre-study in which two test material were prepared and analysed for all compounds (Vincent et al. 2007). In the same study also suitable storage conditions of the test samples were established by conducting stability experiments of the target analytes. The objective of this study was to assure that the concentration of these analytes was stable within the time -6 -frame of the period of the validation study. For the final homogeneity study 10 randomly selected samples from each material were analysed in duplicates for specific analytes (Table 2) and the results were subjected to analysis of variance (ANOVA), in order to assess the "between-bottle" variation and the "within-bottle" variation. Significant differences of both values, indicating possible heterogeneity were evaluated by using the F-test. More details regarding the homogeneity and the stability study are given by Vincent et al. (2007).

The results of the homogeneity study are shown in Table 2. The materials 1, 2, 3, 5, 6, 7, 8, 10 were considered homogeneous, since the calculated F-value was below the critical F-value (F critic), which was in this exercise 3.02. In contrast, the F-values of materials 4 and 9 were above F critic. However, also these materials passed the test of sufficient homogeneity since the sampling error representing possible heterogeneity and which was calculated from the between and within-bottle variation was for both materials below 50% of the target standard deviation of the analytical methods as defined by the Horwitz equation (Thompson and Wood, 1993). This means that possible heterogeneity of the materials 4 and 9 as indicated by the F-test did not interfere with the purpose of the collaborative study.

(Please insert

Method

The two growth promotors are extracted from the feedingstuff sample with a mixture of methanol and water (1:1 v/v). The extract is or filtered, then purified through a short open glass aluminium oxide column or by an alumina Solid Phase Extraction (SPE). The final extract is filtered on membrane syringe filter and analysed by C8 reversed phase HPLC with UV detection at a wavelength of 375 nm.

Alternatively, for confirmation purposes, a DAD, applying a sample wavelength of 375 nm and a spectrum range of 220 nm to 400 nm can be used.

Applying the selected isocratic liquid chromatography conditions allows for a simultaneous determination of olaquindox and carbadox eluting at around 4-6 min and 18-22 min, respectively. A detailed method description was published by [START_REF] Genouel | Final method description. Animal feedingstuffs -Simultaneous determination of carbadox and olaquindox content -Method using HPLC[END_REF]. The presence of furazolidon can interfere with the determination of carbadox. When using a DAD, many other veterinary drugs or feed additives can be detected such as ronidazol, meticlorpindol, nitrofurazon, dimetridazol, furaltadon, and sulphonamides, without interfering with the determination of the target analytes of this study.

Results and discussion

Training phase

In the training phase the participants were asked to determine the growth promoters carbadox and olaquindox in the training samples, including blind samples of MAT 2 and MAT 7. 22 out of 28 invited laboratories in the training phase reported results. For carbadox, no particular problem was highlighted.

For olaquindox, four laboratories were identified as presenting diverging results and were asked for further clarification. The information obtained from the laboratories revealed that they deviated at various places from the protocol. The method protocol was therefore amended in order to clarify the identified potential sources of misinterpretation. In general, no severe problems regarding the implementation and application of the method protocol were reported to the organiser of the study. All laboratories that delivered results were selected to participate in the collaborative study.

Collaborative study

In total 21 laboratories delivered results. In detail, carbadox was measured in 7 materials and olaquindox in 6 materials as shown in Table 1. However, after checking the rigorous application of the protocol in the collaborative study, only 19 were considered valid as two laboratories deviated from the protocol. Indeed, two laboratories used gradient elution which was not foreseen in the method protocol.

The results from another laboratory were excluded because of evident mismatch of samples. 11 out of the remaining 18 laboratories utilised a DAD, whereas 7 laboratories applied a UV detector. Figure 1 shows the chromatogram and the corresponding DAD spectra submitted by one of the participating laboratories.

Other results were further excluded as outliers after Cochran and Grubbs tests as highlighted in Table 3 and Table 4. For both target analytes not more than 22.2 % of the laboratories were identified as outliers. The reported results and the indication of the outliers are presented in Table 3 andTable 4. 3 and Table 4 here)

The values for the relative standard deviation for repeatability (RDS r ) and relative standard deviation for reproducibility (RDS R ) per test material and per compound are outlined for carbadox and olaquindox in Table 5 andTable 6, respectively. (Please insert Table 5 andTable 6 here).

The RDS r varied between 1.1 % and 5.5 % for carbadox and between 2.5 % and 6.2 % for olaquindox.

The corresponding RDS R ranged from 6.4 % to 10.7 % for carbadox and from 12.7 % to 20 % for olaquindox. Figure 2 depicts the HORRAT values obtained for the precision data of both target analytes showing that they were always below 1.5 except for olaquindox in material 2 (1.53). Since this value was still very close to the first benchmark of 1.5 and far below the second benchmark of 2, the result for olaquindox in material 2 was considered acceptable. From these results it was concluded that the method is fit for the intended purpose.

(Please insert Figure 2 here) In addition, the results of the study were also utilised to assess the trueness of the method, which was possible, since the target values of the analytes in the test materials were known. Therefore we calculated the percentage ratio of the estimated average values obtained in the validation exercise (Table 5 andTable 6) to the target concentrations of carbadox and olaquindox ( Table 1) as indicator of the trueness of the method. As shown in Figure 3, for all materials the estimated trueness was comprised between the acceptance limits of 80% and 110% as proposed by Commission Decision 657/2002 (European Communities, 2002) for analyte concentrations above 0.01 mg kg -1 . This supports the conclusion that the method is fit for quantification purposes of the two growth promoters. The results from 11 laboratories which have applied a DAD were further evaluated to examine, whether the method can also be used for confirmatory purposes. Therefore appropriate criteria were established in a workshop with all participating laboratories against which the suitability of the DAD for confirmatory purposes was evaluated. This was achieved by calculating the correct identification rate of the target analyte by close examination of the obtained UV spectra.. Unfortunately, presently no general confirmation criteria are officially defined for the multi analyte determination of banned growth promoters in feedingstuffs by means of LC-DAD. Only information from the two official LC-UV single analyte methods (European Communities, 1998c;European Communities, 1999) for the determination of the target analytes in feed are available, however at higher levels of concentration, using also DAD for confirmatory aspects. Therefore, four confirmation criteria were defined and described in the method protocol, which were set by analogy with the ones described for specific food matrices (European Communities, 2002) and reported in single official methods for coccidiostats and/or growth promotors determination in feeds such as diclazuril (European Communities, 1999) or halofuginone (European Communities, 1993). In detail, the following criteria were applied:

(Please insert

a) The retention time of the target analytes in the test samples shall be equal within a range of ± 5 % of the retention time of the target analytes in the standard solution.

b) The purity of the peak of the target analytes in the test sample is evaluated by comparing baseline corrected UV spectra (difference spectra) measured at the apex, the up-slope and the down-slope inflection points of the peak. For those parts of the spectra with a relative absorption of at least 10 % related to the wavelength with the highest absorption, at each wavelength the relative absorption shall be equal for all spectra. Two different hypothetical criteria were applied which were (1) an acceptable relative deviation of ± 15% and (2) an acceptable relative deviation of ± 25%.

Sufficient agreement of the difference spectra of the target analytes in the test sample and the standard both measured at the apex of the corresponding peaks were evaluated by comparing those parts of the spectra with a relative absorption of at least 10 % related to the wavelength with the highest absorption.

Sufficient agreement between both spectra were confirmed when the following criteria were met: c) at each wavelength the relative absorption shall be equal for spectrum from the standard and the test sample. Two different hypothetical criteria were applied which were (1) an acceptable relative deviation of ± 15% and (2) an acceptable relative deviation of ± 25% d) the same maxima are present within a wavelength margin of 4 nm.

The criterion of 15 % regarding the acceptable deviation was taken from the corresponding single analyte official methods (European Communities, 1998c;European Communities, 1999).

Figure 4 and Figure 5 summarise the percentage rate of correct identification obtained for the above mentioned criteria. For the criteria b) and c) the results for both acceptability options (± 15% and ± 25%) are shown. The statistical treatment has been carried out with all matrices separately and no major influence of the matrix nature (poultry, cattle or pig) could be identified. Therefore, the results from all materials were pooled, regardless of the specific matrix or analyte concentration, but the statistical assessment was done individually for carbadox and olaquindox. However, as for another collaboratively studied method within the SIMBAG-FEED which is based on thin layer chromatography (Vincent et al. 2006), the calf feedingstuff has been identified as being a problematic matrix also in this case for the analysis of olaquindox. The results of percentage rate of correct identification obtained for olaquindox are therefore represented with and without inclusion of the calf feedingstuff data.

As can be seen from Figure 4, for the two criteria (a) and (d) the results obtained for the percentage rate of correct identification for carbadox and olaquindox were equal to or above the acceptance limit of 95 %. For the criterion (c) applying the ± 15% acceptable variation, the corresponding method performance characteristics were for carbadox about 87 % and for olaquindox 80%, when including the data from the calf feed or 85 % without the data from the calf feed. However, intrinsically, it is well known in UV absorbance measurements that a slight difference in solvent composition and/or matrix composition could lead to significant differences into the spectra obtained, e.g. a shift at the apex. The criterion (c) in particular could therefore be too strictly defined because not taking into account the specific characteristics of feed analysis (e.g. matrix effects) -11 -When the decision threshold was set at 25% (Figure 5) for criteria (b) and (c), the situation dramatically improves for carbadox hence ensuring percentage rate of correct identification far above the 95% acceptance limit. For olaquindox, the same method performance characteristic is also increased and is of the order of magnitude of 94% for both criteria b and c. Furthermore, there are no significant differences whether the calf feedingstuff data are included or not. These results demonstrate that the LC-DAD method is suitable for the confirmation of both the presence of carbadox and olaquindox in feedingstuffs. The confirmation criteria were hence refined in the validated method protocol [START_REF] Genouel | Refined method description. Animal feedingstuffs -Simultaneous determination of carbadox and olaquindox content -Method using HPLC, SIMBAG-FEED report[END_REF].

Conclusion

A HPLC method coupled to UV detection for the simultaneous determination of the concentration of the banned growth promoters olaquindox and carbadox has been validated via a collaborative study.

The results of the validation experiment confirmed fitness for purpose of the method as a quantitative method, since all HORRAT values were below or at the critical limit of 1.5 and the trueness was in all cases between the acceptance limits of 80 % and 110 %.

When utilising a DAD detector instead of measuring exclusively at one wavelength, an additional evaluation demonstrated that the method lends itself also for confirmatory purposes. Based on the result from this study, appropriate decision criteria for the evaluation of the UV spectrum have been proposed. Therefore it can be concluded that the analytical method validated in this study is suitable for official control purposes. 2. Fitness for purpose of the relative standard deviation for reproducibility indicated by the HORRAT value. The target value is set at 1, the acceptable limit is 1.5 and the rejection level is 2

Figure 3. Estimation of the trueness of the method: The average concentration for carbadox and olaquindox obtained in the study related to the target values ( Table 1). Error bars represent the expanded uncertainty which is two times the standard error.

  is the relative standard deviation of reproducibility obtained in the validation exercise and RSD R,Horwitz is the calculated standard deviation of reproducibility using the Horwitz equation:

  Figure 3 here) Confirmation criteria -Use of DAD

  Portugal); CentralControl and Test. Inst. Of Agric. (Bratislava, Slovakia); Laboratoria Arbitral Agro. MAPA (Aravaca -Madrid, Spain); Lab. Agroalimentari Gen. Catalunya (Vilassar de Mar -Barcelona, Spain); SVA (Uppsala, Sweden); RIKILT (Wageningen, The Netherlands).The SIMBAG-FEED project is funded by EC, DG RTD, 5 th Framework Programme, Competitive and Sustainable Growth (GROWTH) Programme, Measurements and Testing generic activity, Contract no. G6RD-CT-2000-00413. We are also very grateful to Gerard Kramer and Håkan Emteborg (both IRMM) for the test samples prepared within this project and utilised in the present study.

Figure 4 .

 4 Figure 4. Sensitivity calculated for each confirmation criteria of the LC-DAD method. For the criteria (b) and (c) the ± 15% limit was applied. Criterion (a): Correspondence of the retention times between standard and test sample. Criterion (b): purity of the UV-spectrum in the test sample. Criterion (c): correspondence of spectra between standard and test sample, comparing the absorbance characteristics. Criterion (d): correspondence of spectra between standard and test sample, comparing the wavelengths. Details are given in the text

Figure 1 :PageFigure 2 .Figure 3 .

 123 Figure 1: Chromatogram and DAD spectrum from laboratory 19 obtained on material 2

Figure 4 .Figure 5 .

 45 Figure 4. Percentage rate of correct identification calculated for each confirmation criteria of the LC-DAD method. For the criteria (b) and (c) the ± 15% limit was applied. Criterion (a): correspondence of the retention times between standard and test sample. Criterion (b): purity of the UV-spectrum in the test sample. Criterion (c): correspondence of spectra between standard and test sample, comparing the absorbance characteristics. Criterion (d): correspondence of spectra between standard and test sample, comparing the wavelengths. Details are given in the text

Table 2

 2

here)

Table 1 .

 1 Target concentrations of the analytes in the samples used in the this study (all concentrations are expressed on a gravimetric basis, in mg kg -1 )

		Mat 1 Mat 2 Mat 3 Mat 4 Mat 5 Mat 6	Mat 7 Mat 8 Mat 9 Mat 10
	Feed matrix	Pig	Pig	Poultry Poultry Calf	Cattle	Cattle Calf	Pig	Poultry
	Avoparcin		3.1	4.1		3.1	2.0		1.0	5.1
	Bacitracin	3.2		1.9	0.6	1.3	1.9	2.6	6.4
	Spiramycin	0.7	3.6				1.4	2.9	2.2	5.0	7.2
	Tylosin			1.1	4.4	3.3	2.2	5.5	11.0	7.7
	phoshate
	Virginiamycin 4.0	2.0		1.0			5.0	3.0	10.0	7.0
	Olaquindox	10.0	5.0		12.0	3.0		7.0	15.0
	Carbadox		8.0	4.0		6.0	16.0		12.0	10.0	20.0

Table 2 .

 2 Summary of the homogeneity study of the samples used in the SIMBAG-Feed project (all concentrations are expressed on a gravimetric basis in mg kg -1 ). LC-UV: Liquid chromatography coupled to UV detector; LC-MS/MS: Liquid chromatography coupled to a triple quadrupole mass spectrometer. Critical F-value: 3.02, RSD (%): Relative standard deviation of the analytical error measured in the homogeneity exercise

	Material Feed	Compound	Analytical	F-value	Target	Measured	RSD (%)
			analysed	method		concentration	concentration
				applied		(mg kg -1 )	(mg kg -1 )
	1	Pig	Zinc	LC-MS/MS	1.15	3.2	3.7	4.9
			Bacitracin
	2	Pig	Spiramycin	LC-MS/MS	1.86	3.6	3.8	6.3
	3	Poultry	Carbadox	LC-UV	2.60	4.0	4.6	2.5
	4	Poultry Olaquindox	LC-UV	8.90	12.0	12.0	2.0
	5	Calf	Olaquindox	LC-UV	1.10	3.0	3.0	2.5
	6	Cattle	Spiramycin	LC-MS/MS	0.33	1.4	1.3	8.4
	7	Cattle	Spiramycin	LC-MS/MS	1.72	2.9	2.5	4.4
	8	Calf	Spiramycin	LC-MS/MS	0.22	2.2	1.8	2.4
	9	Pig	Carbadox	LC-UV	8.80	10.0	9.3	1.9
	10	Poultry	Carbadox	LC-UV	3.13	20.0	20.3	3.3

Table 3 .

 3 Single results form the collaborative study on the determination of carbadox in the test samples abbreviated as MAT. The concentrations are given in mg kg -1 and the composition of the materials is shown in

Table 1 .

 1 Results identified as outliers ( a due to the Cochran test and b due to the Grubbs test) are given with grey background and were not included in the statistical assessment of the method performance characteristics. n.r.: no result due to an interference from another peak

						Food Additives and Contaminants
	Lab	MAT 2	MAT 3	MAT 5	MAT 6	MAT 8	MAT 9	MAT 10
	No.	a	b	a	b	a	b	a	b	a	b	a	b	a	b
	1	8.3	8.4 4.5 b	4.5 b 10.6 b 10.3 b	0 b	0 b 16.1 b 15.4 b	9.6	9.5	19	18.9
	5	6.8	6.8	3.3	3.3	4.1	4	13.1	12.9	7.9	9.2	8.3	8.2	17.1	16.8
	8	7.5	7.2	3.5	3.5	5.6	5.6	14	14	11	11	8.7	8.6	18	18
	10	7.3	7.8	3.6	3.6	5.7	5.7 14.5 a	2.6 a	11.1	11.4	9.2	8.9	18.4	18.4
	12	0 a	6.8 a	3.4	3.4	4.5	5	13	13.1	9.5	7.3	8.1	8.1	16.1	16.6
	14	7.4	7.2	3.5	3.5	5.5	5.6	14.8	14.8	11.7	11.6	9.2	9	19.1	19.1
	16	6	6	3 a	3.2 a	4.7	4.9	12.4	13.3	9.3	9	8.2	7.2	14	15.7
	19	7.6	7.5	3.6	3.7	5.8	5.8	14.5	14.4	11	11	9.5	9.3	19.1	19.4
	20	7.8	7.7	3.8	3.8	6	6	15.2	15.2	11.3	11.6	10.1	9.8	20.1	20.6
	25	8	7.6	3.9	3.8	5.8	5.8	14.5	14	11.3	10.7	9.2	9.1	18.6	18.1
	28	7.3	7.4	3.6	3.5	6.1	5.8	15.1	13.8	10.7	11.5	8.4	8.7 17.6 a	0 a
	29	7.3	7	3.6	3.6	5.8	5.7	13.5	13.5	10.6	10.8	8.8	8.6	17.9	17.9
	30	8.3	7.8 4.6 b	4.2 b	7.1 a 11.7 a	15.4	15.3 17.1 b 17.2 b	8.4	10.3	19.6	17.5
	31	6.9	7.5	3.8	3.8	5.1	5.3	14	13	10	11	8.4	8	17	18
	32	7.4	8.1	3.7	3.7	5.6	5.8	14.5	14.5	10.9	11.4	9.2	8.9	18	18.6
	33	6.1	6	3	3	7 a	8.1 a	11.8	11.9	10.6	10.5	7.4	7.2	15.3	15.6
	37	7.8	7.9	3.4	3.5	5.6	5.4	15	16	12	11	9.4	9.6	20	20
	41	7.3	6.8	0 b	0 b n.r.	n.r.	18 a	15 a n.r.	n.r.	11	9.3	24	22

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Table 4 .

 4 Single results form the collaborative study on the determination of olaquindox in the test samples abbreviated as MAT. The concentrations are given in mg kg -1 and the composition of the materials is shown in Table1. Results identified as outliers ( a due to the Cochran test and b due to the Grubbs test) are given with grey background and were not included in the statistical assessment of the method performance characteristics.

	Lab	MAT 1	MAT 2		MAT 4		MAT 5		MAT 7	MAT 9
	No.	a	b	a	b	a	b	a	b	a	b	a	b
	1	10.1	10.2	5	5.2	12.5	12.6	3	2.9	7.1	7	15.3	14.9
	5	7.3	6.7	3.3	3.3	8.4	7.6	0 b	0 b	4.8	4.7	9.7	9.9
	8	9.7	9.6	4.5	4.5	11.7	12	2.3	2.3	6.6	6.6	14.9	14.8
	10	9.3	9.3	4.8	5	11.8	11.7	3	2.8	6.9	7	15.7	15.4
	12	8.8	9	4.1	5.1	10.4	9.9	1.9	2.3	7.1	6.9	13.6	13.5
	14	10.3	10.3	5	5.3	12.4	12.2	3.1	2.7	7.4	7.1	15.2	15.2
	16	7.5	8.3	3.7	3.7	9.5	9.2	2.5	2.6	6.2	6.3 13.2 a	11.1 a
	19	11.7	11.4	6.3	6.3	14.1	14	3.6	3.7	8.5	8.9	19.4	18.3
	20	9.3	9.5	4.6	4.4	11.6	11.4	2.2	2.5	6.4	6.5	13.9	14.7
	25	9.8	9.1	5.4	4.4	10.9	11.2	2.3	2.5	6.3	5.6	14.2	14.1
	28	9.5	8.8	4.8	4.4	10.2	11.7	2.5	3	0 a	6.3 a	13.9	14.2
	29	9.9	9.8	5	4.9	12	11.8	2.9	2.9	6.8	6.8	14.8	14.4
	30	10.2	10.2	5	5.1	12	12	2.3	2.4	14 b 14.9 b 14.5 a	17.4 a
	31	9.6	9.2	4.8	4.5	11	11	2.7	2.6	9 a	6.3 a	14	14
	32	9.5	9.1	4.2	4	11.2	10.6	2.2	2.1	5.9	6.5	13.8	13.3
	33	6.6	7.6	3.4	3.2	9.4	9.7	1.4	1.4	5.1	4.6	11.2	10.8
	37	13 a	16 a	7	6.8	16	15	3	2.9	7.9	8.3	17	18
	41	11	10	4.4	4.2	21 a	15 a	3.7 a	0 a	7.9	7.3	23 a	20 a

Table 5 .

 5 Results from the statistical evaluation for carbadox in feedingstuffs. S r : standard deviation for repeatability; RSD r (%): relative standard deviation for repeatability; S R : standard deviation for reproducibility; RSD R (%): relative standard deviation for reproducibility. "No. of laboratories" indicates the number of laboratories retained after removal of outliers. The number in brackets indicates the number of laboratories identified as outliers.

		MAT 2 MAT 3	MAT 5	MAT 6 MAT 8	MAT 9	MAT 10
	No. of laboratories	17 (1)	14 (4)	14 (4)	15 (3)	15 (3)	18 (0)	17 (1)
	Target concentration
	(mg/kg)	8	4	6	16	12	10	20
	Average (mg/kg)	7.3	3.6	5.4	14	11	9	18
	S r (mg/kg)	0.24	0.04	0.14	0.40	0.58	0.48	0.65
	RSD r (%)	3.3	1.1	2.5	2.9	5.5	5.4	3.6
	S R (mg/kg)	0.65	0.23	0.56	1.1	1.1	0.83	1.9
	RSD R (%)	8.9	6.4	10.3	7.7	10.6	9.4	10.7
	HORRAT value	0.7	0.5	0.8	0.7	0.9	0.8	1.0

Table 6 .

 6 Results from the statistical evaluation for olaquindox in feedingstuffs. S r : standard deviation for repeatability; RSD r (%): relative standard deviation for repeatability; S R : standard deviation for reproducibility; RSD R (%): relative standard deviation for reproducibility. No. of laboratories indicates the number of laboratories retained after removal of outliers. The number in brackets indicates the number of laboratories identified as outliers.

		MAT 1	MAT 2	MAT 4	MAT 5	MAT 7	MAT 9
	No. of laboratories	17 (1)	18 (0)	17 (1)	16 (2)	15 (3)	15 (3)
	Target concentration
	(mg/kg)	10	5	12	3	7	15
	Average (mg/kg)	9.4	4.7	11.4	2.6	6.7	14.4
	S r (mg/kg)	0.36	0.27	0.38	0.16	0.26	0.36
	RSD r (%)	3.9	5.7	3.4	6.2	3.8	2.5
	S R (mg/kg)	1.2	0.9	1.8	0.5	1.1	2.2
	RSD R (%)	12.7	19.4	15.4	20.0	16.0	15.2
	HORRAT value	1.1	1.5	1.4	1.4	1.3	1.4

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

Acknowledgements

The authors would like to thank the different institutes participating in this ring trial, more specifically: Agric. Research Centre (Gembloux, Belgium); COVAX-VESELY (Praha, Czech Republic); KTTK(Vantaa, Finland); DGCCRF-L35 (Rennes, France); Sächsische Landesanstalt für Landwirtschaft (Leipzig, Germany); Bayerisches Landesamt für Gesundheid und Lebensmittelsicherheit (Oberschleissheim, Germany); LUFA-ITL(Kiel, Germany); LUFA Dep. Microbiology (Speyer, Germany); Nat. Inst. Agric. Quality Control -Central Lab.(Budapest, Hungary); State Laboratory (Dublin, Ireland); Istit. Zoo.Sper. Lombardia E. Romagna r. Bromat. Feeds Lab. (Brescia, Italy); RMIZS (Roma, Italy); AGES (Wien, Österreich); EZN INIAP (Vale de Santarem, Portugal); DIN (Sta. Comba Dao, Portugal); LNIV Food Chemistry and Toxicology Head Unit


L González-Osnaya

Jose Miguel Soriano
email: jose.soriano@uv.es

Juan Carlos Moltó

Jordi Mañez

J Mañes

EXPOSURE ASSESSMENT TO PATULIN FROM THE CONSUMPTION OF APPLE-BASED PRODUCTS

Keywords: apple products, estimated daily intake, patulin

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Patulin is a mycotoxin produced by certain species of Penicillium, Aspergillus, and Byssochylamys; these fungi generally grow in the post-harvest period of apples and pears [START_REF] Piemontese | Occurrence of patulin in conventional and organic fruit products in Italy and subsequent exposure assessment[END_REF]. The optimal conditions for mycotoxin production by P. expansum are: pH=6 and 25ºC in pears and 17ºC in apples. However, patulin may be also formed in a temperature range of 0-25ºC; mycotoxin production is inhibited when the fungi are exposed to an atmosphere containing 3% CO 2 and 2% O 2 at 25ºC [START_REF] Doores | The microbiology of apples and apple products[END_REF][START_REF] Paster | Production of patulin by different strains of Penicillium expansum in pear and apple cultivars stored at different temperatures and modified atmospheres[END_REF][START_REF] Podgorska | Effect of Penicillium expansum culture conditions on patulin production[END_REF]).

The effects caused by this mycotoxin are based on several studies made during the past fifty years; these involve acute, chronic and cellular level effects. Patulin may be neurotoxic, immunotoxic, immunosuppressive, genotoxic, teratogenic and carcinogenic.

The Ministry of Agriculture, Fisheries and Food from the United Kingdom has classified patulin as mutagenic (MAFF 1993). A study performed by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) concludes that this mycotoxin does not have either teratogenic or reproductive effects, but it shows embriotoxicity along with maternal toxicity (JECFA 2002). At relatively high doses, patulin has immunotoxic properties (WHO 1995). However, there is not adequate has not yet been tested in humans; therefore, the International Agency for Research on Cancer (IARC) classified patulin in group 3 or as "not carcinogenic to humans" (IARC 1986).

The natural presence of patulin in foodstuffs, mostly in apple-based products, has raised certain international attention. Nowadays, the Codex Alimentarius recommends residual patulin concentration to be less than 50 µg kg -1 in apple-based food destined to human consumption (Codex 2003); in some countries, e.g. Lithuania, Poland and Slovakia, the maximum content in baby food and food destined for infants was set at 20 µg kg -1 and 30 µg kg -1 , respectively (FAO 2004). The European Union (EU) established a maximum level of 50 µg kg -1 in fruit juices and fruit nectars; and 10.0 µg kg -1 in the case of food for infants, young children and other baby food (European Commission 2006).

Patulin, 4-hydroxy-4H-furo[3,2-c]pyran-2(6H)-one, is an unsaturated heterocyclic lactone. It is a colourless, crystalline compound with a melting point of 110°C and with a maximum UV absorption at 276 nm. Earlier methods for the determination of patulin in apple juice utilized thin layer chromatography (TLC), like the AOAC original official method (method number 974.18); this TLC method involved ethyl acetate extraction and clean up on a silica gel column [START_REF] Scott | Collaborative study of a chromatographic method for determination of patulin apple juice[END_REF]. LC coupled to UV detection has been extensively used given the polar nature of patulin, its characteristic UV absorption spectrum and the requirement to accurately determine levels in a very low concentration range [START_REF] Sydenham | Reduction of patulin in apple juice samples-influence of initial processing[END_REF]. Nevertheless, interfering compounds, like 5hydroxymethylfurfural (HMF), can simultaneously elute with patulin from a reverse phase column; therefore, an alternative approach based on the use of gas chromatography-mass spectrometry (GC-MS) has been used to confirm the presence of patulin in apple juice as its trimethylsilyl (TMS) derivative or as underivatized patulin using on column injection [START_REF] Lezi | Patulin production, purification, degradation and determination by gas chromatography[END_REF][START_REF] Llovera | Analysis of underivatizated patulin by a GC-MS technique[END_REF][START_REF] Rupp | Confirmation of patulin and 5-hydroxymethylfurfural in apple juice by gas chromatography/mass spectrometry[END_REF][START_REF] Roach | HPLC detection of patulin in apple juice with GC/MS confirmation of patulin identity. Mycotoxins and food safety[END_REF][START_REF] Tabata | A quantification and confirmation method of patulin in apple juice by GC/MS[END_REF].

Patulin is soluble in water and polar organic solvents, it is not destroyed by heat, and it is stable in acid medium but destroyed by alkaline pH or fermentation. Several methods for extraction and clean up of samples, mainly fruit juices such as apple and pear juice, have been established in recent years. In the AOAC official method 995.10, patulin is extracted with ethyl acetate and isolated by extraction with a sodium carbonate solution [START_REF] Brause | Determination of patulin in apple juice by liquid chromatography: Collaborative study[END_REF]. Extraction with polar solvents such as ethyl acetate or acetone is widespread. The following clean up can be performed with column chromatography using silica gel 60, florisil or celite [START_REF] Gokmen | Simultaneous determination of 5-hydroxymethylfurfural and patulin in apple juice by reversed-phase liquid chromatography[END_REF][START_REF] Leggot | Patulin in South African commercial apple products[END_REF]). However, the described methodologies use considerable solvent volumes (c.a. 25 mL) and perform back to back extractions, therefore are time consuming.

Given the adverse effects that this mycotoxin may cause and the necessity to control its presence in foodstuffs; the objective of this work was to monitor patulin levels in applebased products in order to determine the risk in its consumption by the Spanish population. purchased from Sigma (St. Louis, MO, USA). Patulin standard solution was prepared in ethyl acetate at 500 µg mL -1 ; it was kept wrapped in aluminum foil at -20ºC. Patulin working solutions were prepared daily by evaporating to dryness a known volume of the standard solution and redisolving in mobile phase. LC grade acetonitrile was obtained from Merck (Darmstadt, Germany). Deionised water (0.125 µS) was obtained using a Milli-Q water purification system (Millipore, Bedford, MA, USA).

Materials and methods

F

Samples

A total of fifty-three apple-based products were purchased in different stores and supermarkets from Valencia, Spain; 17 apple juices and beverages, 18 apple purees and compotes, 6 apple-containing dairy products, 3 ciders, 6 apple-containing breakfast cereals and snacks and 3 apples.

Extraction procedure and clean up

Recovery of the extraction method was determined by sample fortification. When solid samples were to be analyzed, 3 g of sample were placed in a centrifuge tube and fortified one hour before extraction with a patulin working solution at 0.05 µg mL -1 ; samples were extracted with 5 mL of acetonitrile and vortexed for 1 minute. Then, the mixture was centrifuged at 16ºC, 4000 rpm during 20 min. The supernatant was evaporated to dryness with N 2 at 40ºC. Once cooled, it was reconstituted in 500 µL of mobile phase. In the case of clear, cloudy apple juices and nectars, the same procedure was followed using 3 mL of sample.

Liquid Chromatography-UV (LC-UV)

A Merck Hitachi (Kyoto, Japan) L-7100 HPLC quaternary pump, a Rheodyne Model 7125 injector (20 µL loop) and an L-7400 UV-detector were used. Chromatographic conditions were: LC Phenomenex column Luna 5 µm C18 100 A (150 x 4.60 mm i.d.)

with a mobile phase consisting of water:acetonitrile (95:5 v/v) at a flow rate of 0.5 mL min -1 . Detection of patulin was carried out using a wavelength of 276 nm.

Confirmation by gas chromatography/mass spectrometry (GC/MS)

Analysis was carried out using a Trace GC (ThermoElectron Corporation, San Jose, CA, USA) gas chromatograph with a ThermoFinnigan AS 2000 automatic injector and the Xcalibur 1.2 software. Mass spectrometric data was collected in full-scan and SIM modes. Full scan data was used for the preliminary selection of the best target m/z ions and qualifiers. The SIM mode was used to maximize sensitivity and selectivity.

The column utilized was a fused silica column DB-5ms (J&W Scientific, Folsom CA, USA) of 30 m x 0.25 mm i.d. column, d.f. = 0.25 µm. The carrier gas was helium at a constant flow rate of 1.5 mL min -1 . The oven temperature was programmed at 140 °C and was ramped at 5°C min -1 up to 190°C, after one min temperature further ramped at 30°C min -1 up to 260 °C, and then was held for 5 min. The GC-MS transfer line was held at 298°C and the ion source was fixed at 284ºC. The MS was used under EI

Results and discussion

Extraction and clean up

Under the established chromatographic conditions, it is possible to analyze clear apple juice by direct injection, prior filtration, into the system given that at the retention time of patulin (15.82 min) there are no interfering peaks; however in order to have a lower detection limit and not cause accumulation of substrate in the column, the described extraction and clean up methodology should be utilized. Including acetonitrile in the mobile phase mixture at 5 % (v/v) resulted in good separation by faster elution.

Most methodologies for patulin use liquid-liquid extraction with ethyl acetate as extraction solvent, the quantity used is considerably higher than that of acetonitrile used with the proposed method. Other methodologies utilize clean up cartridges or enzymes to clarify the extract [START_REF] Gokmen | Simultaneous determination of 5-hydroxymethylfurfural and patulin in apple juice by reversed-phase liquid chromatography[END_REF][START_REF] Leggot | Patulin in South African commercial apple products[END_REF], the proposed methodology allows avoiding the use of these elements either by centrifugation or [Insert Figure 1 about here] It was decided to perform a confirmatory procedure in case of finding naturally contaminated samples, although LC methods for the determination of patulin have mostly been preferred, a number of GC methods have been developed [START_REF] Tarter | Determination of patulin by capillary gas chromatography of the heptafluorobutyrate derivative[END_REF], Ehman & Gaucher 1997[START_REF] Roach | Capillary gas chromatography/mass spectrometry with chemical ionisation and negative ion detection for confirmation of identity of patulin in apple juice[END_REF][START_REF] Llovera | Parallel synthesis: A new approach for developing analytical internal standards. Application to the analysis of patulin by gas chromatography-mass spectrometry[END_REF]) since mycotoxins from the group of trichothecenes and patulin are readily derivatized to their TMSderivatives and can be separated by 30m fused-silica capillary columns like the one used in this study. This separation can be appreciated in Figure 1c.

Method performance

Mean recovery of fortified apple juice and puree samples (n=5) at a level of 10 ng mL -1 of patulin was 90.6 % with a relative standard deviation of 6.2 %, recovery remains almost unchanged when working at levels of 5 and 15 ng mL -1 ; in these cases recovery values were 87.8 ± 5.4 % and 89.1 ± 6.1 %, respectively. Intra-day (n=5) and inter-day (5 different days) variation values at a fortification level of 10 ng mL -1 were 4.4 and 6.3 %, respectively. These values do not exceed 15 %, which is the maximum variation for certification exercises for several mycotoxins [START_REF] Hald | Ochratoxin-A in wheat-an intercomparison of procedures[END_REF]). To estimate the limit of detection (LOD), apple juices and puree samples spiked at concentration levels of 10.0, 3.0, and 1.0 ng mL -1 were extracted and analyzed using the proposed procedure, the LOD and limit of quantification (LOQ) values were calculated by applying the 3σ This method offers several advantages, e.g. the quantity of solvents used is lessened and it does not require a large sample amount. Therefore, it is a good alternative that allows performing analysis with good precision and accuracy.

Incidence of patulin in apple-based products

Fifty-three apple-based products were analyzed. As it can be seen in Table 1, patulin was detected in 26.4 % of the samples, mainly in apple juices and purees; six of these samples had a patulin concentration over the maximum recommended limit in the EU.

The high incidence of patulin found in this study may be indicative of the poor quality of fruit used in production, given that patulin producing fungi may grow in the postharvest period of pears and apples even at low temperatures. However, growth of the blue mould (a distinctive characteristic of P. expansum) and subsequent production of patulin is normally associated in those cases where the surface tissue of fruit has been damaged, although the presence of patulin in otherwise visibly healthy fruit can not be discounted. Fruit cleansing procedures and removal of damaged tissue, immediately before pressing, do not necessarily eliminate all patulin present since patulin migration to healthy tissue may be up to 1 cm [START_REF] Taniwaki | Migration of patulin in apples[END_REF]). The problems that carry patulin intake and all mycotoxins in general may get worst if organic agriculture becomes more popular in the future. A patulin concentration of 244-3993 µg L -1 has been reported in conventional apple juice (made from apples where pre and post-harvest synthetic fungicides have been used); meanwhile in apple-based juices of organic production, patulin levels up to 45000 µg L -1 have been found. Patulin has also been found in legumes, moist cereals, feeds, bread with levels ranging from 20-300 µg kg -1 and in beet from 12-3700 µg kg -1 [START_REF] Lovejoy | Are organic foods safer? Austin, TX[END_REF][START_REF] Trewavas | Urban myths of organic farming[END_REF]). Fermentation processes destroy patulin, this is probably why this mycotoxin is not often found in products like cider, unless apple juice was used after fermentation with the purpose of homogenize alcohol concentration in the batch.

[Insert Table 1 about here] The results on patulin contamination found in this study are similar to those reported in other countries; in Italy, a survey on the occurrence of patulin was conducted on commercial puree apple juices and mixed apple juices. Patulin could be quantified in 34.8 % of the samples ranging from 1.58 to 55.41 µg kg -1 ( Spadaro et al. 2007). [START_REF] Burda | Incidence of patulin in apple, pear and mixed fruit products marketed in New South Wales[END_REF] found patulin in 23 % of the fruit product samples from the United Kingdom and 22 % of these samples contained patulin levels between 51 and 1130 µg L -1 . Among 100 apple juice samples surveyed for incidence of patulin in Spain, 82 samples were contaminated with a mean of 13.8 µg L -1 and seven of these samples had patulin levels above 50 µg L -1 [START_REF] Prieta | Survey of patulin in apple juice and children's apple food by the biphasic dialysis membrane procedure[END_REF]. In Iran, 33 % of the apple juice samples had patulin levels higher than 50 µg L -1 with a maximum level of 285 µg L -1 and 56 % of the apple juice concentrates with a patulin level higher than 50 µg L -1 and a maximum level of µg L -1 [START_REF] Majid Cheraghali | Incidence of patulin contamination in apple juice produced in Iran[END_REF]. Based on the results from this study on patulin contamination of apple juice and data on juice consumption by the Spanish population, patulin daily intake was estimated to be 0.42 ng kg -1 body weight per day (29 ng per person). This value was calculated considering the body weight for the Spanish population (68 kg) used in the "Assessment of dietary intake of Patulin by the population of EU Member States" (JECFA 2002); and it is in agreement with the data presented in the forementioned report (12.06-54.27 ng per person). Patulin intake does not represent a risk for the adult consumer given that the JECFA changed the provisional tolerable weekly intake (PTWI) of 7 µg kg -1 body weight per week to a provisional maximum tolerable daily intake (PMTDI) of 0.4 µg kg -1 body weight per day, based on a non observable effect level (NOEL) of 43 µg kg -1 body weight per day and a safety factor of 100 (WHO 1995). Table 2 presents patulin intake values based on patulin contamination data from this work and apple-based product consumption data found in literature.

Estimated daily intake of patulin

Based on the data exposed in this paper; by comparing patulin intake reported in Table 2 against the PMTDI, it could be stated that patulin present in apple based products contributes from 0.1-21.6 % to the level considered at risk for human health caused by patulin exposure in Spain alone; therefore it does not represent high risk for the consumer of these products; however there is a higher risk for those groups more vulnerable, like babies and children, where the consume of these products is superior regarding to their body weight.

[Insert Table 2 These intake data is for orientation purposes only, given that in Spain there is not official specific data on apple juice consumption. Despite of this, it is important to take notice on the products that exceeded the maximum recommended level for patulin, especially in products destined to infants and babies; therefore, measures to reduce patulin level in this kind of products must be taken.

In conclusion, results indicate good recovery and reproducibility of the method proposed in this work. It is a good alternative that allows performing analysis with good precision and accuracy given that with this methodology; the volume of solvent used for extraction is lessened being friendlier with the environment and cost effective. Patulin was found in 26.4 % of the samples analyzed, however the estimated patulin daily intake does not represent a serious risk for the adult consumer since it is far below the PMTDI; but attention must be paid in groups with higher risk of exposure like infants.

  has a carcinogenic effect over experimental animals and this effect

  N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) were

  ) mode (-70 eV), the ions selected for patulin identification were as follows: m/z 136, 183 and 226.Derivatization was made based on previous work by[START_REF] Melchert | Reliable identification and quantification of trichothecenes and other mycotoxins by electron impact and chemical ionization-gas chromatographymass spectrometry, using an ion-trap system in the multiple mass spectrometry mode. Candidate reference method for complex matrices[END_REF] with slight modifications; 100 µL of patulin working solution were derivatizated with 100 µL of MSTFA and kept 20 min at 60ºC in a heating block; the mixture was allowed to cool to room temperature for 15-20 min, then it was evaporated to dryness under a gentle stream of nitrogen and reconstituted in 300 µL of acetonitrile. A 1 µL aliquot was then analyzed by GC-MS. All positive extracts were analyzed with this protocol.

  . In Figure1b, the chromatogram of a naturally contaminated apple juice sample is shown; patulin concentration in this sample is 1.5 µg L -1 . No interfering peaks can be observed at the time patulin is retained.

  et al. 2000). The limits of detection and quantification for the optimized method were 0.1 and 0.3 ng mL -1 , respectively.Results indicate good recovery and reproducibility of the method proposed in this work.

  Figure caption

Figure 1

 1 Figure 1. a) Patulin working solution at 1 µg/mL, b) Naturally contaminated apple juice sample with a patulin content of 1.5 µg/L, c) Gas chromatogram of TMS-patulin from an apple juice sample. 190x254mm (72 x 72 DPI)

Table 1 . Incidence of patulin in apple-based products.

 1

			Range
	Product	Incidence
			(µg L -1 or µg kg -1 )
	Apple juice and apple-containing beverages	5/17	1.5-50.9
	Apple compote and purees	6/18	7.7-28.4
	Apple-containing dairy products	3/6	4.2-15.2
	Cider	0/3	nd
	Apples	0/3	nd
	Apple-containing breakfast cereals and snacks	0/6	nd
	Total	14/53	1.5-50.9
	nd: not detected, under the LOQ

Table 2 . Estimated patulin intake from apple-based products

 2 Apple based products consume by some Spanish population sectors reported at Reports on tasks for scientific cooperation 2002,Gimeno et al. 2003, Legarda &

		Individual
			Body	Patulin	Patulin daily
		daily
	Product		weight	content (b)	intake
		consume (a)
			(kg)	(µg L -1 or µg kg -1 )	(ng kg -1 body weight)
		(mL)
	Fruit juice	6	68	4.8	0.4
	Apple juice	50	20	4.8	12.0
	Apple juice	61	60	4.8	4.9
	Apple juice	216	12	4.8	86.4
	Apple juice	200	64	4.8	15.0
	(a) Burdaspal 2005.

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

Acknowledgements

This work has been supported by the Spanish Ministry of Education and Science (AGL-2006-04438/ALI). L. González-Osnaya thanks the grant from CONACyT of México (Consejo Nacional de Ciencia y Tecnología).