Hamborg Bodil

Annette Jensen

Tue Petersen

Christensen

Keywords: Exposure assessment, Probabalistic modelling Additives/Contaminants: Pesticides -organophosphorous Food Types: Cereals, Fruit, Vegetables Organophosphorus, Carbamates, Probabilistic, Cumulative, Acute exposure assessment, Relative Potency Factor, Food

Probabilistic assessment of the cumulative dietary acute

exposure of the population of Denmark to organophosphorus and carbamate pesticides

Introduction

The acute and chronic exposure assessment of pesticides has usually been performed on a single pesticide basis. However, consumers can be exposed to more than one pesticide during a day as they eat various kinds of fruits, vegetables and cereals. Furthermore, the exposure to pesticides can arise from pesticides that have a common toxic effect because they have a common mode of action. The organophosphorus and carbamate pesticides are examples of pesticides, which have the same mode of action since they are both acetyl cholinesterase inhibiting (AChE) compounds. They act on the central nervous system by inhibiting the enzyme responsible for the metabolism of acetylcholine, which transmits signals between nerve cells and between nerve and muscle cells. So it is of interest to estimate the cumulative exposure to these pesticides. More generally, Regulation (EC) No 396/2005 on maximum residue levels (MRLs) emphasizes the importance of developing a methodology that takes into account the cumulative and synergistic effects of pesticides (EU Commission. Regulation No 396/2005). A cumulative acute exposure assessment for the population of Denmark to organophoshorus and carbamate pesticides has been performed previously using the deterministic estimate approach [START_REF] Jensen | Cumulative risk assessment of the intake of organophosphorus and carbamate pesticides in the Danish diet[END_REF]. However, this approach did not take into account the fact that acute exposure to a pesticide can arise from more than one crop at the same time. Moreover, the deterministic approach is not suitable for estimating the exposure for the total population, which is also described in the "Opinion [START_REF] Food | Opinion of the Scientific Panel on Plant protection products and their Residues on acute dietary intake assessment of pesticide residues in fruit and vegetables[END_REF]. As described in the "Opinion on a request from EFSA to evaluate the suitability of existing methodologies" (EFSA 2008) and by [START_REF] Boon | RIKILT -Institute of Food Safety[END_REF], probabilistic modelling addressing the total population is needed in cases where you need to address several crops simultaneously, which is very likely when addressing a group of compounds and trying to assess the actual exposure. In the present study, we used the probabilistic approach to estimate the actual cumulative acute exposure to organophosphorus and carbamate pesticides in the population of Denmark. We are aware that this might be an overestimation because organophosphorus and carbamate pesticides do not have quite the same mechanism of action, as described by Van Raaij et al. (2005). One of the differences is that organophosphates inhibit the acethylcholine esterase nearly irreversibly, while the enzyme inhibition by carbamates is reversible. In a study performed by Boon et al. (2008) they therefore made a separate cumulative risk assessment for each of the two groups of compounds. We used the Relative Potency Factor (RPF) approach as described by [START_REF] Wilkinson | Assessing the risks of exposures to multiple chemicals with a common mechanism of toxicity: How to cumulate[END_REF] to estimate the acute cumulative dietary exposure. In this approach, RPFs which express the relative toxic potency of each compound compared to an index compound (IC) are used to normalize the residue levels for all compounds in the group. We used the two index compounds, chlorpyrifos and methamidophos, in this study. Information on RPFs for the two compounds was obtained from the literature. Danish food consumption and residue data were used in the calculations.

Methods

Food consumption data

Intake estimates were based on consumption data obtained from the nationwide dietary survey performed in 2000-2002 by the Department of Nutrition, Technical University of Denmark [START_REF] Andersen | The Danes dietary habits 2000-2002. Main results[END_REF]. The survey included 4068 people aged 4-75 years (the general population). Among these, the child population aged 4-6 years accounted for 231 people. The survey used a seven-day prospective food record with a pre-coded (semi-closed) questionnaire that included answering categories for the most commonly eaten foods and dishes in the Danish diet. The survey was distributed equally over the seven days of the week over the whole year to take into account possible seasonal variation in dietary habits. The questionnaire was organised according to a normal daily meal pattern. The body weight of each person was noted and used in the calculations.

Pesticide residue data

Residue data were obtained from the Danish pesticide monitoring programme during the period 2004-2007 including 6704 samples of fruits, vegetables and cereals. The samples were taken randomly throughout the year between commodities while the samples were not taken randomly between all commodities. Commodities with a high intake are taken every year. The samples were analysed for the content of about 190 different pesticides and metabolites and isomers of these compounds, including 48 organophoshorus pesticides and 9 carbamate pesticides. In total 22 organophosphates and three carbamates were found. For the assessment of the cumulative intake, RPFs could be found for 25 compounds when addressing chlorpyrifos while RPFs for only 18 compounds could be found when methamidophos was used as IC. Only commodities with at least one detectable residue above the limit of reporting (LOR) were included in the calculations. The selected/relevant pesticides were found in 43 commodities. No distinction was made between imported commodities and those of Danish origin.

Relative Potency Factor (RPF)

Since it was not the aim of this study to derive Relative Potency Factors for chlorpyrifos and methamidophos, these values were obtained from the literature. An index compound should be a compound with an extensive toxicological database and one of the best studied within the group [START_REF] Wilkinson | Assessing the risks of exposures to multiple chemicals with a common mechanism of toxicity: How to cumulate[END_REF], EPA, 2001[START_REF] Boon | Cumulative Exposure to Acetylcholinesterase Inhibiting Compounds in the Dutch Population and Young Children[END_REF]. RPFs for 25 organophosphorus and carbamate pesticides were included in this study and they were obtained from a Dutch and a Brazilian study (Boon andvan Klaveren 2003, Caldas, 2006). The RPFs were derived from either using benchmark dose at 10% acetylcholinesterase inhibition (BMD 10 ) in rat brain or the nonobserved adverse effect level (NOAEL) with inhibition of acetylcholinesterase in brain or red blood cells. The RPFs used here are shown in table 1. The residue concentration for a given pesticide is multiplied by the RPF value for this substance to give an equivalent content of the index compound. The cumulative acute exposure of organophosphorus and carbamate pesticides adjusted to an index compound can then be summed and compared to the ARfD for the index compound in the risk assessment.

[Insert table 1]

Processing factors

The use of processing factors is an important issue when assessing exposure to pesticides. In this study we used processing factors for commodities which were peeled. For citrus fruits, melon, banana and pineapples a processing factor of 0.1 was applied as provided from the report of the Danish monitoring programme [START_REF] Poulsen | Pesticide Food Monitoring Program 1998-2003[END_REF]). For rye, wheat grain and flour, the analytical results were also converted to estimates of residues in bread by multiplying the analytical results for wheat by 0.77 and by 0.65 for rye as provided by [START_REF] Poulsen | Pesticide Food Monitoring Program 1998-2003[END_REF]).

In the previous study performed by [START_REF] Jensen | Cumulative risk assessment of the intake of organophosphorus and carbamate pesticides in the Danish diet[END_REF], processing factors were also applied for rinsing and washing/cooking. However in the EU, there is no common agreement on using processing factors for rinsing and washing and they have therefore not been included in this study. Fore reasons of comparison, we also performed the cumulative intake calculations without processing.

Levels below the limit of reporting (LOR)

Levels below the LOR can be handled in various ways. Samples could contain residues below the LOR but higher than zero. So the question is which value is the most correct to use in the calculation, e.g. LOR, ½ LOR or zero. For comparison, both ½ LOR and zero were used in the cumulative exposure calculations for chlorpyrifos.

Probabilistic acute intake calculations and uncertainty analysis

The cumulative acute exposure was calculated using the Monte Carlo Risk Assessment program (MCRA 6.1), an internet-based program developed by Biometris and RIKILT -Institute of Food Safety, Wageningen UR, the Netherlands [START_REF] Boer | MCRA, Release 6.A web-based program for Monte Carlo Risk Assessment[END_REF]. The calculations were performed as follows. A person was randomly selected out of the consumption database. The consumption of each commodity that person ate in one day was multiplied by randomly selected normalised residue concentrations in the residue database. The intake was summed over foods giving an empirical estimate of the acute cumulative intake distribution. This calculation was performed 100,000 times for both the children and the general population giving a probability distribution for the pesticide intake. Processing factors as described above were applied in the calculations. No variability factors were used in the calculations. All estimates of possible intakes were adjusted for the individual body weights of each consumer. The exposures were specified at percentiles P50, P90, P95, P99, P99.9 and P99.99 and compared to the acute reference dose (ARfD) for chlorpyrifos and metamidophos, respectively. The percentile 99.9 was used as reference point. The uncertainty in the exposure calculations at each percentile expressed as a 95% confidence interval was estimated using the Bootstrap technique (Manly, B.F.J.1998[START_REF] Vose | Risk Analysis: a Quantitative Guide[END_REF][START_REF] Caldas | Probabilistic assessment of the cumulative acute exposure to organophosphorus and carbamate insecticides in the Brazilian diet[END_REF]).

Results

Food consumption and residue data

Table II shows a summary of the food consumption data from the nationwide survey. White bread, followed by wine (only general population), rye bread and apple, had the highest mean consumption levels for all consumers (total population, both consumers and non-consumers) of the foods that contained at least one positive level of the pesticides addressed. Wine (only general population), followed by melon, apple, pear, white bread and rye bread, had the highest mean consumption on consumption days only (included only days with consumption) for both children and the general population. The highest percentage of consumption days was seen for white bread, rye bread, onion and apple for both children and the general population.

[Insert table II]

Figure 1 shows the ten most frequently found organophoshorus and carbamate pesticides in the Danish monitoring programme. Only commodities with at least one detectable residue were included in the assessment. Chlorpyrifos with more than 500 detects was clearly the most frequently found organophosphate pesticide in the monitoring program.

[Insert figure 1]

Table III shows the frequencies, mean concentrations in samples with positive content, and the main pesticides found in the 43 different commodities of fruit, vegetables and cereals. Exotic fruits, including passion fruit, mango, guava, carambola, kaki, rambutan and kumquat, had the highest detection frequency (13.9%), followed by cherry (12.5%), mandarin (9.9%), lemon (8.6%), asparagus (7.7%), peach/nectarines (7.6%), rice (7.3%), grapefruit (6.9%) and pineapple (6.9%). Commodities with the highest mean concentrations were basil followed by lemon, grapefruit, dates, oranges and mandarin.

[Insert table III]

Exposure and risk assessment using chlorpyrifos and methamidophos as index compounds

Table IV shows the cumulative acute exposure estimates using both chlorpyrifos and methamidophos as index compounds. Processing factors were applied and levels below the LOR were set to zero. The exposure calculated in µg/kg bw/day was compared to the ARfD of 100 and 3 µg/kg bw/day for chlorpyrifos and methamidophos, respectively, (EU Com., doc. 3010, 2008).The exposure calculation is shown for both children and general population for both index compounds.

[Insert table IV]

When we used chlorpyrifos as index compound, the cumulative exposure was calculated to 1.8% and 0.8% of the ARfD of 100 µg/kg bw/day at the percentile 99.9 for children and the general population, respectively, while when we used methamidophos, the cumulative exposure was calculated to 31.3% and 13.8% of the ARfD of 3 µg/kg bw/day for children and the general population, respectively.

Commodities and pesticides that contributed most to the intake

Figure 2 shows the commodities that contribute most to the cumulative intake in relation to the index compound used in the calculation. It can be seen that the six commodities, apple, carrot, table grapes, pear, white bread and lettuce, contribute most to the intake whether chlorpyrifos or methamidophos was used as index compound. When we used methamidophos as index compound, wine contributed with 27.1% to the total intake, while when we used chlorpyrifos, wine was not among the commodities that contributed most. In both cases, apples contributed by far the most to the total intake.

[Insert figure 2]

Effect of processing Figure 3 shows the nine commodities that contributed most to the cumulative acute intake using chlorpyrifos as index compound. The exposure is shown for the general population, and with and without processing factors being applied in the calculations.

[Insert figure 3]

It can be seen from the figure that apples contributed most to the intake in both cases. Due to a high detection frequency in citrus fruits, the commodities mandarin, oranges and grapefruit were among the 9 commodities that contributed most to the intake when no processing factors were applied. Banana and melon which are also peeled contributed also most to the intake when no processing factors were applied. When a processing factor of 0.1 was applied to the commodities which are peeled their contribution decreased in a very great extent and instead apples, carrot, table grapes, pear, white and rye bread, lettuce and peach/nectarine contributed most to the intake. When we used processing factors in the cumulative intake calculation, the exposure was calculated to 0.82% of the ARfD of 100 µg/kg bw/day at the 99.9 percentile for the general population.

Effect of using ½ LOR

Figure 4 shows the estimated cumulative acute intake with processing factors applied compared to processing factors applied and ½ LOR used in the calculations. The exposure calculation was performed with chlorpyrifos as index compound. The calculated intake was compared to the ARfD of 100 µg/kg bw/day for chlopyrifos and is shown for the general population. When ½ LOR is used in the calculation, the cumulative exposure amounts to 1.0% of the ARfD at the 99.9 percentile.

[Insert figure 4]

Discussion

Probabilistic approach

When the deterministic approach is used, the acute exposure is addressed from one compound and one crop at a time. The exposure is calculated for the consumers only. But if we want to estimate the cumulative exposure, non-consumers should also be included in the calculations, because a consumer of one commodity can be a nonconsumer of other commodities. In the EFSA Opinion from 2007 [START_REF] Food | Opinion of the Scientific Panel on Plant protection products and their Residues on acute dietary intake assessment of pesticide residues in fruit and vegetables[END_REF], it was also stated that the deterministic approach is not suitable for estimating the exposure for total populations (consumers and non-consumers).To address the exposure to multiple compounds simultaneously present in more than one food, we used the probabilistic approach. Other advantages in using the probabilistic approach include that the variation in the population's eating habits is taken into account and compared with individual bodyweights. The whole distribution of residues is used in the calculations and the uncertainties at different percentiles can be quantified.

Percentile used as threshold of concern

The percentile that should be used as the threshold of concern in the probabilistic exposure calculation depends on what risk the risk manager is willing to accept. The US EPA (EPA 2000) used the percentile 99.9 as reference point. This percentile was also used in this study to evaluate the risk for the consumer. As seen in Table III, the confidence interval is broader at the higher percentile of 99.99 due to higher sensitivity to uncertainties in data collection. This confirms that the calculated intake at this percentile should be treated with caution.

Exposure and risk assessment using chlorpyrifos and methamidophos as index compounds

The output from using the two different index compounds chlorpyrifos and methamidophos in the calculations is significantly different. When we used chlorpyrifos, the cumulative exposure was estimated to 0.8% and 1.8% of the ARfD of chlorpyrifos for the general population and the child population respectively; when we used methamidophos, the intake was calculated to 14% and 31% of the ARfD for the general and the child population respectively. At the percentile 99.99, the ARfD is exceeded by 14% with a 95% confidence interval of 74-196% of the ARfD using methamidophos as index compound. This indicates that a few consumers might exceed the ARfD, but as noted earlier the uncertainty at the percentile 99.99 is very high. So values at this percentile should be treated with caution.

When we addressed the cumulative intake for methamidophos, RPFs could only be found for 18 compounds compared with 25 compounds for chlorpyrifos. The cumulative intake would therefore have been even higher if RFPs could have been found for all the compounds found in the monitoring programme. The estimated intake using methamidophos as index compound can therefore only be used as an indication of what the cumulative intake is.

The RPFs derived for chlorpyrifos were all derived from NOAEL, while the majority of the RPFs for methamidophos were derived using BMD modelling . The results therefore seem to indicate that using RPFs that are derived from BMD modelling yields a higher estimated exposure level compared with using RPFs derived from NOAEL. In a study performed by [START_REF] Caldas | Probabilistic assessment of the cumulative acute exposure to organophosphorus and carbamate insecticides in the Brazilian diet[END_REF], they compared the output using both methamidophos and acephate where the majority of the RPFs were derived from BMD modelling for both compounds. They calculated the cumulative exposure to 34% and 70% of the ARfD for methamidophos and acephate respectively for the total population. The ARfD for methamidophos used in their study was 10 µg/kg bw/day, whereas we used 3 µg/kg bw/day as derived by EFSA. If an ARfD of 3 µg/kg bw/day had been used in their study, the ARfD would have been exceeded by more than 100%.

In the study performed by Boon et al. (2008), they estimated the exposure for the organophosphates and carbamates separately with the argument that the mechanism of action is not quite the same for the organophosphorus and carbamate pesticides because of the difference in binding and binding time to the enzyme. In their study, levels below the LOR were assigned zero and the majority of the RPFs were derived from BMD modelling.They showed that if acephate is used as IC for the organophosphorus compounds, there might be a risk for the child population since the ARfD was exceeded by 14%.

Another thing that makes a comparison with results obtained in other studies difficult is how the levels below the LOR are handled in the calculations. The LOR used to be rather higher for methamidophos for example, so using ½ LOR in the calculation may result in a higher overestimation of the real exposure. Despite the uncertainties in the calculation, the results in our study showed that the cumulative acute exposure is below 100% of the ARfD for both index compounds chlorpyrifos and methamidophos at the 99.9% percentile, which show that there is no cumulative acute risk from AChE compounds for consumers in Denmark.

Commodities and pesticides that contributed most to the intake

Apples contributed most to the cumulative intake when the two different index compounds chlorpyrifos (35%) and methamidophos (45.5%) were used. Carrots, table grapes, pears, white bread and lettuce were among the nine commodities that contributed most to the total intake in both cases. Wine contributed with 27.1% to the total intake when methamidophos was used as index compound, but only 2.2% when chlorpyrifos was used as index compound. Carbaryl was the only pesticide found in wine and the big difference in the size of contribution to the total intake can be explained by the difference in the relative toxicity of carbaryl compared with chlorpyrifos and methamidophos. As seen in Table I, carbaryl has a relative potency factor which is 0.07 of that for chlorpyrifos and a toxicity of 0.3 compared to methamidophos. This means that the relative toxicity for carbaryl compared to methamidophos is much higher than the relative toxicity for carbaryl compared to chlorpyrifos. That apple contributed most to the intake was caused by the many detects from nine different pesticides (azinphos-methyl, carbaryl, chlorpyrifos, dimethoat, omethoate, malathion, parathion-methyl, phosmet and pirimicarb) and because of a high consumption. When we used methamidophos as IC, RPFs for only six of these compounds could be found. That means that the contribution from apples would be even higher if RPFs for all nine compounds could have been used in the calculation. However in future it is most likely that the exposure from organophosphates and carbamates will be less than calculated in this study, because many of the organosphos phosphates and carbamates are not included in Annex 1 to Directive 91/414 EEC and the authorizations will be or have been withdrawn for a number of these substances. For example, four of the pesticides found in apples (azinphos-methyl, carbaryl, omethoate, malathion) are not included in Annex 1 to Directive 91/414 EEC.

Processing factors

In the present study, many detects of organophosphates and carbamates were found in commodities which are peeled before consumption. Since the analyses are performed on the raw commodities, it would have a great impact on the calculated exposure if processing factors were used in the intake calculation. Without processing factors, the cumulative exposures were estimated to be 1.2% and 2.9% of the ARfD when chlorpyrifos was used as IC for the general population and children, respectively. This means that the inclusion of processing factors reduces the calculated exposure by 33% and 48%, respectively (40.6% on average), which shows that this is a very important issue to address if we want the most realistic picture of the exposure estimate. Effect of using ½ LOR Due to the many non detects, the intake was increased by about 20% when levels below the LOR were set to ½ LOR as opposed to zero. As explained by [START_REF] Raaij | Cumulative exposure to cholinesterase inhibiting compounds: a review of the current issues and implications for policy (320508001/2005[END_REF], it is probably closer to reality to assume that all non-detects are true zeros than to set all non-detects at ½ LOR. However, if we use ½ LOR in the calculation, we can get a worst case picture of the intake.

Variability

When the deterministic point estimate approach described by FAO/WHO ( 2001) is used in acute intake calculations, variability factors are applied in the NESTI calculations. Variability factors take into account that the residue concentration found in the composite sample can derive from only one of the units in the composite sample. In this study, no variability factors were used in the calculation of the cumulative exposure. In a cumulative exposure assessment where several commodities are assessed at the same time, it also seems unlikely that one would eat the crop in the composite sample with the highest residue level every time. Besides, there are as yet no guidelines on how to take variability into account in probabilistic exposure calculations (Boon et al., 2000). In the study performed by Boon et al. (2008), they developed an approach which simulates the variability factor by defining it as a model parameter. In the deterministic approach, the exposure level becomes higher when variability factors are used in the calculation, but this is not always the case when incorporating variability as done by Boon et al (2008). Since there is no agreed approach on how to incorporate variability in probabilistic modelling, this is an issue for further development and discussion in the future.

Relative Potency Factor

When RPFs were used, the two index compounds chlorpyrifos and methamidophos in the cumulative acute exposure calculations showed very different results in the level of exposure. If RPFs for all compounds for methamidophos could have been found, the differences would have been even higher. The RPFs for chlorpyrifos were derived from NOAEL while the majority of the RPFs for methamidophos were derived from BMD modelling. The results seem to indicate that the exposure is higher when the RPFs are derived from BMD modelling than when the RPFs are derived from NOAEL. So the toxicological data from which the RPFs are derived constitute another very important issue to address, and the difference between the RPFs derived from BMD modelling and NOAEL should be investigated. A thing that needs to be established is whether using BMD or NOAEL-derived RPFs gives the most realistic level of exposure.

Conclusion

We assessed the acute cumulative intake of organophosphorus and carbamate pesticides for consumers in Denmark using the probabilistic approach. The assessment was performed using two different index compounds. We evaluated the risk to be much lower when chlorpyrifos was used as the index compound than when methamidophos was used. The different results show how important the choice of index compound is. It also seems that the exposure is higher when BMD-derived RPFs are used than when RPFs derived from NOAEL are used in the cumulative exposure Many other uncertainties, such as food consumption data, residue data, the method used to perform the intake calculation, the use of processing factors and variability factors in the calculation, and the treatment of levels below the LOR, will have an effect on the calculation. So guidelines on how to incorporate variability factors, for example, and how to address values below LOR in the calculations should be developed. Despite the limitations and the uncertainties in the calculation of the dietary cumulative intake of the organophosphates and carbamates in the present study, the results show that the cumulative intake is below 100% of the ARfD for both chlorpyrifos and methamidophos at the percentile 99.9 for consumers in Denmark. Table III. Summary of residue data of actylcholinesterase inhibiting compounds on samples analyzed in the Danish monitoring programme during 2004-2007.

Table IV. The cumulative acute exposure using the two index compounds chlorpyriphos and methamidopos as index compounds. Data are shown for both children (4-6 years) and the general population (4-74 years) at different percentiles. Levels < LOR = 0, processing factors applied.

  assessment" from the European Safety Authority

  It should therefore be investigated which data are the most reliable for deriving the RPF values for various index compounds.

  Figure 1

Figure 1 .

 1 Figure 1. Ten most frequently found organophosphate and carbamate pesticides in the 2004-2007 Danish monitoring programme.

Figure 2 .Figure 3 .

 23 Figure 2. Contribution (% per product to the total distribution) for top nine commodities to the acute exposure of organophosphates and carbamates for the general population (4-75 years) using chlorpyriphos as index compound. Calculations are shown with and without processing factors included in the calculations. Levels < LOR = 0

Figure 4 .

 4 Figure4. Contribution (% per product to the total distribution) for the top nine commodities to the acute exposure of organophosphates and carbamates for the general population (4-75 years) using both chlorpyriphos and methamidophos as index compound. Levels < LOR = 0, processing factors applied.

Table I .

 I Relative potency factors used in the cumulative assessment.

	Figure 4			Deleted: 3
				Deleted: 4
	Figur 3			Deleted: 2
	4.0%
	3.5%	Processing factors used
	Without processing 3.0% Processing factors used, ½	Methamidophos With processing
	Table grapes 9% Others 18% LOD F o r Others Mandarin 2% Rye Peach 3% bread 2% Table grapes 4% Grape fruit 4% 20% Apple 19% F o r 0.0% 0.5% 1.0% 2.0% 2.5% % of ARfD 1.5% F o r Pear P Lettuce 3% Cabbage Carrots Wine 27% White bread 1% 2% 4% Pear Table grapes 2% Onion 2% Lettuce 3% Peach 3% Rye bread 2% Mandarins 2% Others 18% 5% e e r Oranges 9% Carrots 8% White bread 4% 4% Mandarins 10% Water-melon 18% White bread 6% P 50% 90% 95% 99% Percentiles P Table grapes Pear 7% e e 9% e r e r	Others 8% 99.9%	Apple 46% Apple 36% 99.99% Carrots 14%
			R R R
			e e e
			v i e v i e v i e
				w w w
				O n l O n l O n l
				y y y

Table II .

 II Summary of food consumption data from Danish national dietary survey2000-2002.

  % of non-zero consumption days for consumers. c Children age 4-6 years d General population age 4-74 years

	Page 21 of 23			Food Additives and Contaminants
	Table I Table II Rye bread Strawberry Rice Table IV	57 2.3	61 2.9 137	83 3.2 10 (7.3%) 76 4.9	68 71 0.0081 Malathion 80 58 Pirimiphos-
	TEF, acute Chlorpy-riphos 1 Metha-midophos 2 Mean consumption (g/day of total survey days) a 4.9 7.9 1.7 2.0 334 859 0.15 0.24 75 92 417 4 IC Chlorpyriphos % consumption 36 47 3.6 3.4 8 (2.4%) 9 (1.0%) days b 1.5 2.5 83 85 3 (0.7%) 3 (75%) IC Methamidophos Mean 13 16 46 0.0099 methyl 0.0488 Chlorpyriphos 60 consumption (g/day, consump-9.4 9.8 90 108 0.0540 Methamidophos 0.0822 Methamidophos 95 % confidence 95 % confidence tion days only) Children c General popu-General popu-General 0.0 63 0.0 19 0.0 345 Samples analyzed Samples detected * Mean positive concentration † Pirimiphos-% of ARfD interval % of ARfD interval Main pesticide 868 20 (2.3%) 0.0163 methyl Children 4-6 years Pesticide Organophos Crop Sweet pepper Watermelon Rye bread Table III Strawberry Wheat bran White bread Sweet pepper Watermelon Wine Commodity Percentile White bread a Included zero and non-zero consumption days. popu-(mg/kg) Wine 81 7 (8.6%) 0.0021 Carbaryl 95 0.04 (0.03-0.05) 0.13 (0.10-0.2) lation d Children lation Children lation 99 0.39 (0.28-0.5) 1.5 (1.0-2.2) phates Acephate 2 Apple 50 55 37 35 136 159 Apple 3867 163 (4.2%) 99.9 1.8 (1.2-2.4) 31 (20-60) 0.0667 Chlorpyriphos 0.08 b Azinphos-methyl 2 Apricot 0.14 0.1 0.2 0.4 55 29 Apricot 136 6 (4.4%) 99.99 5.8 (2.3-8.8) 115 (75-196) 0.0259 Azinphos-methyl 0.09 b Chlorfenvinphos 2 Chlorpyrifos 1 0.05 b Diazinon 5 0.01 b Dichlorvos 2 0.03 b Dimethoate 0.5 0.32 b F o r Asparagus 0.04 0.60 0.3 1.4 12 37 Banana 25 26 27 20 93 128 Bean with pod 0.09 0.1 5.4 4.8 1.6 2.5 Cabbage, head 1.7 4.0 6.1 10 27 40 Carrot 30 32 44 43 69 76 Cauliflower 0.8 2.3 6.2 10 12 22 F o r F o r Asparagus 26 2 (7.7%) 0.0895 Chlorpyriphos Banana 440 20 (4.5%) 0.0227 Chlorpyriphos Basil 9 1 (11%) 0.9090 Parathion-methyl Bean with pod 508 17 (3.3%) 0.1426 Dimethoat Cabbage, head 38 1 (2.6%) 0.0850 Dimethoat Carrot 819 18 (2.2%) 0.0895 Chlorphenvinphos F o r General population 4-75 years 95 0.02 (0.01-0.03) 0.13 (0.06-0.23) F 99 0.16 (0.12-0.2) 1.1 (0.65-2.3) 99.9 0.83 (0.57-1.1) 14 (6.0-23) o r 99.99 2.6 (0.97-5.8) 46 (18-86)	Formatted Table Formatted: Superscript Formatted: Superscript Deleted: III Deleted: II Deleted: † Deleted: * Deleted: * Deleted: † Formatted: Font: 11 pt Formatted: Font: Italic Formatted: Left Deleted: ¶ Formatted: Font: 11 pt
	Ethion Fenitrothion Fenthion Malathion Methamidophos Methidathion Mevinphos Omethoate Celery Cherry Cucumber Currants Dates, dried Dill Egg plant Fruit exotic Cauliflower Celery Cherry Cucumber Currants Dates, dried Egg plant Fruit exotic	2 0.02 1.43 0.02 3.3 5 4 4 0.2 0.44 36 0.02 0.01 0.01 0.15 0.37	1.1 a 0.03 a 0.33 b 0.0003 b 1.0 b 0.32 b P e e r 0.5 0.5 0.8 0.3 0.4 0.4 22 59 52 0.02 87 82 0.13 0.1 0.3 0.02 0.8 1.8 0.43 0.9 1.9 0.41 89 8.7 P e e r P e e r 73 2 (2.7%) 64 4 (6.3%) 24 3 (13%) P P 375 2 (0.5%) 138 4 (2.9%) e e 23 1 (4.3%) 160 3 (1.9%) 346 48 (14%) e r e r	38 101 60 0.02 16 1 17 0.4 0.0027 Dimethoat 62 77 42 0.02 46 1.3 23 0.5 0.0143 Diazinon 0.0260 Dimethoat 0.0460 Methamidophos 0.0116 Phosmet 0.2273 Parathion-methyl 0.0253 Carbaryl 0.0513 Carbaryl
	Parathion-methyl Phosalone Phosmet Pirimiphos-methyl Profenofos Tolclos-methyl Triazophos Carbamates Carbaryl Carbofuran Pirimicarb 1 RPFs were derived from acute NOAEL with inhibition of acetylcholinesterase in red blood 9.1 0.12 b 0.5 0.22 0.5 0.04 b 2 0.004 b 0.02 0.83 5.58 a 0.07 0.3 a 0.45 1.5 a 0.06 cells and brain as critical effects. From Boon and Van Klaveren (2003) 2 RPFs were either derived form acute NOAEL with inhibition of acetylcholinesterase in red blood cells and brain as critical effects, marked with a or from benchmark dose at 10 % acetylcholinestase inhibition (BMD 10 ), marked with b . From Caldas et al (2006) a RPF derived from NOAEL b RPF derived from BMD 10 Rice 2.3 3.3 16 15 15 Raspberry 56 1 (1.8%) 0.0049 Carbaryl 20 R e v i e w O n l y Grapefruit 1.6 1.61 13 11 12 14 Grape, table 3.8 5.2 4.9 5.7 79 92 Kiwifruit 2.3 2.1 3.8 2.4 62 89 Leek 1.0 2.1 6.4 11 15 20 Lemon 0.34 1.0 2.6 4.3 14 21 Lettuce 3.0 8.8 21 34 14 27 Mandarin 5.7 5.7 13 11 43 51 Melon 5.8 7.2 3.6 3.6 163 213 Mushrooms 1.0 3.1 8.3 11 12 29 Oranges, sweet 10 10 13 11 42 92 Oats, rolled 8.8 6.8 29 23 30 31 Onion, bulb 5.9 11 53 61 11 R e v i e w R e v i e w Grapefruit 1476 102 (6.9%) 0.2431 Chlorpyriphos Grape, table 744 33 (4.4%) 0.1369 Carbaryl Kiwifruit 713 15 (2.1%) R R 0.0390 Diazinon Leek 66 2 (3.0%) 0.0185 Acephate Lemon 988 85 (8.6%) e e 0.2992 Chlorpyriphos Lettuce 1196 33 (2.8%) 0.0758 Dimethoat Mandarin 2616 259 (9.9%) 0.1485 Chlorpyriphos Melon 745 9 (1.2%) 0.1060 Chlorpyriphos Mushrooms 47 1 (2.1%) 0.0018 Carbaryl Oranges, sweet 3319 159 (4.8%) v i e v i e 0.1564 Chlorpyriphos Oats, rolled 63 2 (3.2%) 0.0178 Pirimiphos-methyl w w 19 Pasta products 7.1 7.2 30 26 24 28 Pea, dry 0.03 0.04 5.4 4.8 0.6 0.8 Peach, nectarines 2.4 3.5 3.3 3.8 74 95 Pear 14 15 11 9.4 127 159 Pineapple 0.65 1.1 1.1 1.6 58 69 Plum 1.3 1.4 3.3 2.3 38 O n l O n l Onion, bulb 115 4 (3.5%) 0.0019 Carbaryl Pasta products 25 1 (4.0%) 0.0145 Pirimiphos-methyl Pea, dry 16 1 (6.3%) 0.0220 Dimethoat Peach, nectarines 900 68 (7.6%) 0.0440 Azinphos-methyl Pear 1831 56 (3.1%) 0.0738 Azinphos-methyl O n l O n l 65 Raspberry 0.14 0.2 1 1.6 15 11 y y Pineapple 144 10 (6.9%) 0.0100 Carbaryl Plum 1479 23 (1.6%) 0.0474 Chlorpyriphos y y	Formatted: Font: Italic Formatted: Font: 11 pt Formatted: Font: 11 pt Formatted: Font: 11 pt Formatted: Font: 11 pt Formatted: Not Superscript/ Formatted: Font: 11 pt Subscript

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Mario ; Schirra
email: mario.schirra@ispa.cnr.it

Salvatore D'aquino

Paolo Cabras

Alberto Angioni

Influence of post-harvest application rates, of cyprodinil, treatment time, and temperature on residue levels and efficacy to control green mold on 'Valencia' oranges

Keywords: Citrus, Penicillium decay, hot water dip, cyprodinil, residues

Influence of postharvest application rates of cyprodinil, treatment time, and temperature on residue levels and efficacy to control green mold on 'Valencia' oranges

Introduction

Cyprodinil, a fungicide belonging to the anilinopyrimidine class that includes pyrimethanil and mepanipyrin, was developed in the mid-1990s to control a broad range of fungal diseases of fruit, vegetables and ornamental crops [START_REF] Heye | CGA 219417 -a novel broad spectrum fungicide[END_REF][START_REF] Tomlin | The Pesticide Manual[END_REF]).

Cyprodinil inhibits excretion of hydrolytic enzymes and methionine biosynthesis in fungal cells [START_REF] Masner | Possible methionine biosynthesis inhibition by pyrimidinamine fungicides[END_REF][START_REF] Gullino | Use and challenges of novel compounds for plant disease control[END_REF][START_REF] Leroux | Modes d'action des produits phytosanitaires sur les organismes pathogèns des plantes[END_REF]. In viticulture cyprodinil is recommended for preventive use to reduce the risk of resistance development in Botrytis cinerea Pers. populations [START_REF] Rosslenbroich | Botrytis cinerea -history of chemical control and novel fungicides for its management[END_REF].

Cyprodinil was also found to be effective on apples, against isolates of P. expansum resistant to thiabendazole [START_REF] Errampalli | Control of blue mold caused by Penicillium expansum on apples 'Empire' with fludioxonil and cyprodinil[END_REF]. Studies on Empire and McIntosh apples have shown that decay reduction was positively correlated to cyprodinil concentration: at 40 mgl -1 it provided complete control of blue mold caused by both thiabendazole-sensitive and -resistant isolates of P. expansum during 30 days of storage at 4°C and subsequent 6 days of shelf life at 20°C (Errampalli and Brubacher 2006). [START_REF] Zhou | Interactions between Pseudomonas syringae MA-4 and cyprodinil in the control of blue mold and gray mold of apple[END_REF] reported studies on integrated control of postharvest blue mold in apple with Pseudomonas syringae and cyprodinil, showing that a reduced concentration of fungicide was needed to control both isolates of P. expansum. However, no papers we found in literature on residue levels and efficacy cyprodinil in inhibiting green mold caused by Penicillium digitatum (Pers.) Sacc., one of most important postharvest decay of citrus fruit [START_REF] Eckert | Postharvest disorders and diseases of citrus fruits[END_REF].In this study we investigated the possible relationships between residues and efficacy of cyprodinil to control green mold decay caused by Penicillium digitatum in 'Valencia' oranges, as a function of treatment time, temperature, and active ingredient (a.i.) concentration.

Materials and Methods

Fruit samples

Commercially mature 'Valencia' oranges [Citrus sinensis (L.) Osbek] were handharvested in an experimental orchard of the Institute of Sciences of Food Production in central-western Sardinia, Italy, managed under standard horticultural practices. Fruits were placed in plastic trays and delivered to the laboratory immediately after harvest.

Then, medium size fruits, free from rind defects were selected, returned to each box and left overnight at 20°C.

Treatment device

A tub fitted with 3.96 kW/h heating elements and an electronic recirculation pump ( 22L/min water flow) was used for treatments. Two hundred litres of water or fungicide mixtures were used for the treatments, dipping one box of fruit per run. Bath temperature was constantly maintained within ± 0.5°C of the set temperature by an electronic thermostat (OEM/HT, Carel, France) and probe (PTC 40,Carel,France).

Influence of treatments and storage conditions on cyprodinil residues

'Valencia' oranges were grouped into 24 treatment groups and subjected to dipping in water mixtures containing cyprodinil at: a) 150, 300, or 600 mgl -1 and 20°C for 30, 90, or and 55°C for 30 s; d) 50, 100, or 150 mgl -1 and 60°C for 15 s. Fruits were then stored at 20°C and 90% relative humidity (RH) (simulated shelf-life conditions). Analyses of cyprodinil were performed at time 0 (immediately after treatment) and after 20 days.

Cyprodinil analysis

Eight oranges per replication were weighed and their peel was removed. The peel was weighed and frozen at -18°C until cyprodinil analysis. Five g of minced and homogenized peel were weighed in a 40 ml screw-cap flask and 10 ml of acetone/hexane mixture (1/1, v/v) and 4 g of NaCl were added. For recovery assay samples of untreated orange peel were fortified with appropriate amount of standard stock solutions of cyprodinil to reach the concentrations of 0.1, 1.0, 5.0 mgkg -1 . The recovery ranged from 78% to 102%, with a maximum 9% coefficient of variation (CV). Good linearity was achieved in the 0.5-9.72 mgkg -1 range, with correlation coefficients between 0.9991 and 0.9995.

Apparatus. A TQ Trace gas chromatograph, coupled with a NPD 80 detector, a split/splitless injector, and an AS200 autosampler (TermoQuest, Milan, Italy) was used.

The column was a fused silica capillary DB 5 MS (30 m x 0.25 mm i.d.; 0.25 µm, J & W Scientific, Folsom, CA). The injector and detector temperatures were set at 200 and 300°C, respectively. The sample (2 µl) was injected in splitless mode (30 s). The oven temperature was programmed as follows: 110°C (1 min), raised to 310°C (at 20°Cmin -1 ).

Helium was the carrier gas and N 2 the make up at 1.1 and 15 mlmin -1 , respectively. NPD conditions were as follow: source current, 2.8; polarization voltage, 3.5. Oxygen and H 2 flows were at 60 and 2.3 mlmin -1 , respectively.

Fungal strains and growth conditions

The used isolate of P. digitatum was collected from decayed fruit from the Institute's experimental orchard, where occasionally only copper fungicides were used, and its conidia were not able to germinate in potato dextrose agar containing either TBZ or IMZ. The isolate was maintained in culture on potato dextrose agar (PDA), in Petri dishes at 4°C. Conidial suspension grown at 20°C for 7 days and adjusted at 10 6 conidia ml -1 was used as inoculum.

Assays with wounded and artificially inoculated fruit

Fruit were grouped into 30 treatment groups, disinfected by immersion for 30 s in 1% household bleach, rinsed with water and left to dry at ambient temperature. Each fruit was then wounded by 2 slits equatorially, at the two opposite sides with a 2-mm-wide and 2-mm-long stainless steel rod and inoculated by dipping fruits for one min in a 72-l, high density-polyethylene tank containing 50-l conidial suspension of P. digitatum (final concentration 1•10 5 conidia ml -1 ). After inoculation, fruit were incubated at 20°C and 90% RH for 20 h before treatment to simulate infection occurring at harvest either in the field or during transit. The same rod was used to wound the fruit that were not inoculated.

The experimental plan for assays with inoculated fruit included the 24 treatment regimes used for sound oranges to determine residue levels, and treatments with water alone at: a) 20 or 50°C for 30, 90 or 180 s; b) 55°C for 30 s; c) 60°C for 15 s. Fruit were then left to dry at room temperature and subsequently stored at 20°C at ca 90% RH. Fruit were evaluated for incidence of decay caused by green mold (percentage of fruit with decay lesions of any size compared to the total, within each replicate) after 7, 12, and 18 days. Each treatment was applied to four replicates of 40 fruit each.

Statistical analysis

Statistical analysis was performed by Statgraphics software (Statgraphics Centurion, version XV). Analysis of variance (one-way ANOVA) was carried out using a unifactorial complete randomized block design. Percentages were transformed in arcsin√x before ANOVA. Mean comparisons of the effects of treatments were calculated by Fisher's least significant difference test at P ≤ 0.05.

Results

Treatments with cyprodinil at 150 or 300 mgl -1 and 20°C were not affected by treatment time whereas cyprodinil at 600 mgl -1 for 90 or 180 s resulted in significantly higher residues than fruits treated for 30 s. (Table 1). When treatment was performed at 50°C, cyprodinil residues consistently increased by increasing treatment time. Treatment for 30 s did not produce significant differences due to application rate. Treatment with cyprodinil at 50 or 100 mg -1 for 90 s resulted in similar residues; significant increases in residues were recorded when the application rate of cyprodinil was increased to 150 mgl - 1 . When treatment time was extended to 180 s, the amount of residues was closely related to fungicide concentration.

After application at 55°C for 30 s, cyprodinil residues did not change significantly when fungicide concentration was increased from 50 to 100 mgl -1 , although residues increased when the application rate was 150 mgl -1 . Following treatment with cyprodinil at 50 mgl -1 and 60°C, residue concentration was 1.33 mgkg -1 . A double fungicide concentration (100 mgl -1 ) produced a significant increase in residue level compared to the treatment at 50 mgl -1 ; no significant increase in cyprodinil residues was recorded when the application rate was increased from 100 to 150 mgl -1 .

In comparison to treatments performed at 20°C, the application with heated cyprodinil mixture resulted in notably higher residues, their extent depending on temperature and treatment time (at 50°C). After treatment with cyprodinil at 150 mgl -1 and 50, 55, for 30 s or at 60°C for 15 s, residues were 4-5-fold higher than those left by treatments at 20°C. When cyprodinil at 50°C was applied for 90 or 180 min, residues were 7-8-and 12-fold higher. Residues of cyprodinil showed high persistence during 20 days' storage at 20°C. All treatments with cyprodinil reduced decay development, the extent depending on treatment time, temperature and fungicide dose (Table 2). After 7 days' incubation, green mold incidence was reduced from 53.8 to 6.7% by 30 s treatment with cyprodinil at 150 mgl -1 and 20°C. Higher fungicide dose or longer treatment time were equally effective in controlling green mold in most samples. After 18 days, treatment with cyprodinil at 150-300 mgl -1 for 30 s similarly reduced green mold, but was less effective than treatment with 600 mgl -1 , which reduced green mold from 100% (control) to 15.4%. When dip time was extended to 90 or 180 s, fungicide effectiveness increased gradually at increasing fungicide concentration. While green mold incidence was unaffected by 30-90 s, treatments with 50°C water, it was significantly reduced when dip time was extended to 180 s. When water at 50°C was combined with cyprodinil, treatment efficacy consistently increased, being generally unaffected by fungicide dose or treatment time. After 7 and 18 days, treatments with water at 55°C for 30 s were as effective as cyprodinil at 50-100 mgl -1 but less effective than at 150 mgl -1 . After 7 days, treatments at 60°C did not reveal any significant decay difference between control and cyprodinil treatments; after 18 days, treatment with cyprodinil at 150 mgl -1 was consistently more effective than at 50-100 mgl -1 and hot water alone.

Discussion

Previous investigations of citrus fruit have shown that post-treatment imazalil residue levels significantly increased by increasing treatment time [START_REF] Brown | Uptake of imazalil by citrus fruit after postharvest application and the effect of residue distribution on sporulation of Penicillium digitatum[END_REF][START_REF] Smilanick | Improved control of green mold of citrus fruit with imazalil with warm water compared with its use in wax[END_REF][START_REF] Cabras | Factors affecting imazalil and thiabendazole uptake and persistence in oranges following dip treatments[END_REF], whereas the residues of thiabendazole were not affected by length of treatment [START_REF] Cabras | Factors affecting imazalil and thiabendazole uptake and persistence in oranges following dip treatments[END_REF]. After dip treatment with fludioxonil at 20°C, residue levels in nectarines did not change significantly when dip treatments were increased from 1 to 3 min but almost doubled after 6 min. When fludioxonil was applied at 48°C, residues in nectarines increased significantly at increasing dip times (D 'Aquino et al. 2007). Results of this study show that when cyprodinil was applied at 600 mgl -1 and 20°C, residues were significantly affected by treatment time but were not when fruits were treated with 150-300 mgl -1 . When treatment was performed at 50°C, residues consistently increased at increasing treatment time. Differences in residue levels as a function of treatment time may thus be dependent on fungicide dose, temperature and the physico-chemical characteristics of the a.i. and formulation types. Cyprodinil had a great persistence, with residue levels in fruit after 21 days' storage being similar to those recorded just after treatment.

Cyprodinil is classified as reduced risk fungicide by the United States Environmental Protection Agency (USEPA, 1998) and was recently introduced for decay control of various horticultural crops. The maximum U.S. residue limit (MRL) depends on the commodity (MRL database 2008). For example, the MRL for strawberry, table grape, apple and pear is 5, 2, 0.1 and 0.1 mgkg -1 and 0.6 for citrus fruit.

The application of cyprodinil at 20°C effectively controlled decay and produced a long lasting effect. Given the tested temperature and a.i. rates, our data indicate that the best results were recorded at 600 mgl -1 and 20°C for 90 s. The extent of green mold incidence after 18 days was 5.13%; a.i. residues during fruit storage remained unchanged (1.04 vs 0.99 mgkg -1 ) and were higher than the U.S. MRL. Similar efficacy and residue levels were found when cyprodinil was applied at 600 mgl -1 at 20°C for 180 s. Decay was also efficiently controlled in tests carried out at 50°C with cyprodinil at 150 mgl -1 for 30 s or 90 s and at 50 mgl -1 for 180 s. Our findings indicate that in order to obtain samples with a.i. residues below the US MRL we should operate at 300 mgl -1 a.i. and 20°C for 90 s (0.45 mgkg -1 ), or 180 s (0.36 mgkg -1 ), conditions in which green mold should stay under 12.8%. On mouse the oral DL 50 for cyprodinil is 2000 mgkg -1 , the acceptable dose intake (ADI) is 0.03 mgkg -1 and the recommended food daily intake (FDI) for fruit approximately 0.5 kgday -1 . This means that the tolerable level (ADI/FDI) should be 0.06 mgkg -1 , or 4.2 mg for a person weighing 70 kg. Proper effectiveness requires the use of 150 mgl -1 cyprodinil at 50°C for 30 s. This would yield a residue level of about 1.5 mgkg - 1 , which is notably under the toxicological limit. Given the MRLs on other crops, the MRL for citrus should be increased according to the toxicological level.

  b) 50, 100, or 150 mgl -1 and 50°C for 30, 90 or 180 s; c) 50, 100, or 150 mgl -1

  active ingredient; 0 = water. y Each value is the mean of 3 5 replicates (40 fruit per replicate, 2 wounds per fruit). Percentages were transformed in 6 arcsin√x before statistical analysis. Means in columns with unlike letters are significantly 7 different by Fisher's least significant difference (LSD) procedure, P ≤ 0.05. 8

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  US Environmental Protection Agency (USEPA), 1998. General overview: reduced-risk pesticide program. Environ. Prot. Agency of Pestic. Programs, Staff Background. pp.

	2-4.
	http//www.mrldatabase.com/results.cfm (03 December 2008).
	F o r P e e r R e v i e w O n l y F o r P e e r R e v i e w O n l y F o r F o r P e P e e r e r R e R v e i e v i e w w O n l y O n l
	y
	1

Table 1 .

 1 Influence of postharvest treatments on cyprodinil residues (mgkg -1 , on a 1 whole fruit basis), in 'Valencia' oranges after one-min dip treatment (time 0), 2 and after 20 days of storage at 20°C and 90% relative humidity. 3

		Cyprodinil	Treatment time	Cyprodinil residues (mgkg -1 ) y
		conc. (mgl -1 ) x	and temperature	Storage duration (days)
				0	20
		150	30 s at 20°C	0.45 il	0.42 no
		300		0.56 il	0.41 no
		600		0.72 i	0.59 mn
		150	90 s at 20°C	0.38 l	0.35o
		300		0.59 il	0.45 no
		600		1.04 h	0.99 hi
		150	180 s at 20°C	0.41 l	0.35 o
		300		0.57 il	0.36 o
		600		1.28 gh	0.79 l
		50	30 s at 50°C	1.36 g	0.70 lm
		100		1.31 gh	0.85 il
		150		1.58 fg	1.24 g
		50	90 s at 50°C	2.63 d	1.89 e
		100		2.81 d	2.29 d
		150		3.47 c	2.11 d
		50	180 s at 50°C	3.18 c	2.83 c
		100		5.10 b	3.83 a
		150		6.00 a	3.12 d
		50	30 sec at 55°C	1.53 g	1.03 hi
		100		1.41 g	1.24 g
		150		1.87 ef	1.30 g
		50	15 s at 60°C	1.33 gh	1.17 gh
		100		2.04 e	1.56 f
		150		1.98 e	1.57 f
	4	x Concentrations refer to active ingredient. y Means in columns followed by unlike
	5	letters differ significantly by Fisher's least significant difference (LSD)
	6	procedure, P ≤ 0.05.

Table 2 .

 2 Influence of postharvest dip treatments to control green mold on 'Valencia' 1 oranges inoculated with (10 5 conidia ml -1 ) Penicillium digitatum. The percentage of lesions 2 caused by green mold was recorded after selected periods of incubation at 20°C and 90% 3

	4	relative humidity.
		Cyprodinil	Treatment time	Green mold incidence (%)
		conc. (mgl -1 ) x	and temperature	Storage duration (days)
				7	12	18
		0	30 s at 20°C	53.8 a	87.2 a	100 a
		150		6.7 c-f	20.0 c-f	30.0 d-h
		300		7.7 c-g	28.2 c	48.7 cd
		600		5.6 f-g	10.3 c-i	15.4 f-m
		150	90 s at 20°C	3.3 d-g	13.3 c-h	23.3 e-i
		300		5.1 e-g	7.7 f-m	12.8 h-n
		600		2.6 f-g	2.6 i-m	5.13 m-o
		150	180 s at 20°C	5.0 c-g	13.3 c-h	21.7 e-l
		300		2.6 f-g	5.1 h-m	12.8 h-n
		600		0.0 g	2.6 i-m	5.12 i-o
		0	30 s at 50°C	56.4 a	74.4 ab	84.6 ab
		50		20.1 cd	25.6 c-e	30.8 d-h
		100		0.0 g	10.3 e-l	15.4 h-n
		150		0.0 g	5.1 g-m	5.1 m-o
		0	90 s at 50°C	33.3 ab	56.4 b	64.1 bc
		50		7.7 c-f	15.4 c-h	30.6 d-h
		100		2.6 fg	23.1 c-e	33.3 d-g
		150		0.0 g	2.6 i-m	7.7 i-o
		0	180 s at 50°C	15.4 bc	28.2 c	35.9 d-f
		50		0.0 g	0.0 m	2.6 o
		100		5.1 c-g	10.3 d-l	10.3 l-o
		150		7.7 c-f	12.8 c-h	12.8 h-n
		0	30 s at 55°C	6.7 c-f	18.3 c-g	21.

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Acknowledgements

The authors gratefully acknowledge Mr. Mario Mura for technical assistance.

Research partially supported by MiPAF. Project: 'Ricerche e Sperimentazioni Avanzate in Agrumicoltura e loro Applicazioni" (RAVAGRU), Paper n. 23.


Cristina Nerin
email: cnerin@unizar.es8

Elena Canellas

Margarita Aznar

Paul Silcock

C Nerín

ANALYTICAL METHODS FOR SCREENING OF POTENTIAL VOLATILE MIGRANTS FROM ACRYLIC-BASE ADHESIVES USED IN FOOD CONTACT MATERIALS

Keywords: Chromatographic analysis, Chromatography -GC/MS, Clean-up -SPME, Headspace Additives/Contaminants: Additives general, Food contact materials, Migration, Packaging screening, adhesives, food packaging, GC-TOF-MS, SPME, laminates, 31 analysis, migration, acrylic 32

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

Introduction 34

Most food packages and food contact materials are multilayer materials manufactured 35 using different substrates and adhesives. Although most of the substrates have to fulfil 36 the legislation for being in contact with food (Directives 2002/72 EU, 2007/ The adhesive industry uses a large variety of raw materials in food packaging, both 41 natural and synthetic [START_REF] Booth | Industrial Packaging Adhesives[END_REF]. Apart from the polymer, an adhesive formulation 42 may contain the carrier , plasticizers, tackifiers, thickeners, fillers, surfactants, biocides 43 and fungicides, emulsifiers, waxes and antioxidants [START_REF] Ashley | Adhesives in packaging[END_REF]. 44 45The study of adhesives is a difficult task for many reasons: a) There are many different 46 formulations and standardization is not possible; b) There are a wide variety of 47 substrates, such as plastics, paper and board, aluminium foil, cork or wood; c) The 48 number of compounds involved in the adhesives formulation is very high and no 49 information is provided about them by the companies; d) Migration behaviour is 50 unknown and has never been tested. Adhesives are not as yet regulated mainly due to 51 these difficulties. Only in extremely limited cases such as the polyurethane based 52 adhesive layers, a clear legal restriction appears, specifically for aromatic amines, which 53 migration restriction is 'non-detectable' at 0.010 mg/kg (Commission Directive 54 2002/72/EC and Directive 19/2007/EC). 55 56 All these facts highlight a strong need to develop a solution for this problem, and for 57 this reason, the European Commission decided to finance the EU Project Migresives, in 58 which frame the work reported here has been carried out [www.migresives.eu]. 59 60

The analysis of adhesives is the first step to find out which substances could diffuse 61 throughout the different layers and migrate to the food in contact with them (Gruner and 62 Piringer et al. 1999). Nowadays it is well accepted that molecules of molecular mass 63 lower than 1000 daltons can migrate (Figge 1996[START_REF] Jickells | Possible control measures for paper and board. Work shop on paper 370 and board in food contact applications[END_REF], both in direct and 64 indirect contact with the food, as the diffusion rates are high enough to cross the barrier 65 of the materials. Diffusion rates are higher for volatile compounds, for this reason, the 66 volatiles are the priority migrants to be studied. 67 68 Several analytical procedures can be used for screening the potential volatile migrants 69 coming from the adhesives. The determination of potential migrants in a food contact 70 material usually involves an extraction step followed by the analysis using a 71 chromatographic method. However, sample handling is often time-consuming for fast 72 screening procedures. The methods based on the direct analysis of the headspace by gas 73 chromatography-mass spectrometry (GC-MS) permit the fast identification of migrants 74 with a minimum sample handling but they usually does not provide enough sensitivity 75 [START_REF] Nerín | The simplest sample treatment 398 techniques to assess the quality and safety of food packaging materials[END_REF]. Advanced analytical techniques such as GC-TOF-MS (time of 76 flight) provide an accurate mass measurement of ions in mass spectra [START_REF] Mamyrin | Time of flight mass spectrometry (concepts, achievements and 378 prospects)[END_REF] 77 and for this reason has been widely used to identify target and non-target compounds 78 (Marsman et al.2008, Marsman et al. 2007[START_REF] Setkova | Rapid headspace solid-phase 424 microextraction-gas chromatographic-time-of-flight mass spectrometric method for 425 qualitative profiling of ice wine volatile fraction -I. Method development and 426 optimization[END_REF], Bianchi et al.2007, Hao 79 et al. 2005, Meruva et al.2004). This technique combined with ChromaLynx software, 80 able to deconvolve complex mass spectra, allows a reliable identification of the 81 compounds and it was used in this work in order to identify the compounds present in 82 the pure adhesives. 83 84 An alternative for screening purposes is the analysis of the headspace by solid phase 85 microextraction (HS-SPME) coupled to GC-MS. HS-SPME offers several advantages 86 versus other methods [START_REF] Pawliszyn | Solid Phase Microextraction: Theory and Practice[END_REF][START_REF] Pawliszyn | Application of solid phase Microextraction[END_REF][START_REF] Wercinski | Solid Phase Microextraction: A practical Guide[END_REF], mainly a 87 considerably improvement of the sensitivity (Nerín et al, 2008), for this reason, it has 88 been shown as a powerful tool to analyse different solid and liquid samples (Yang et 89 al.2008[START_REF] Da Silva | Exploratory analysis of the volatile profile of 343 beers by HS-SPME-GC[END_REF][START_REF] Pena | Headspace solid-phase 412 microextraction gas chromatography-mass spectrometry analysis of volatiles in orujo 413 spirits from a defined geographical origin[END_REF], Batlle et al, 2001;[START_REF] Nerín | Determination of bisphenol-type 401 contaminants from food packaging materials in aqueous foods by solid-phase 402 microextraction-high-performance liquid chromatography[END_REF]Lopez et al. 90 2006, Domeno et al. 2005[START_REF] Salafranca | Use of solid-phase microextraction for the 420 analysis of bisphenol A and bisphenol A diglycidyl ether in food simulants[END_REF]and Batlle et al. 1999). In this work, 91 HS-SPME-GC-MS was used in the screening of laminates, since they contained a low 92 quantity of adhesive and a high sensitivity was necessary. 93 94

Materials and methods 95

Materials and reagents 96

Three acrylic adhesives were selected for this study (adhesive 1, 2 and 3) and they were 97 supplied by three adhesive companies. They were representative of commonly used 98 confidential and cannot be explained here. The laminates studied in this work were 100 either supplied by the companies or prepared in the laboratory: Laminate 1 was 101 prepared with adhesive 1 (grammage 45 g m -2 ) between aluminum foil and polyethylene 102 (PE) (40 µm thickness); laminate 2 was made using the adhesive 2 (grammage 18 g m -2 ) 103 between polypropylene (PP) ( 25 µm thickness) and PE (40µm thickness); and laminate 104 3 was made using the adhesive 3 (grammage 12 g m -2 ) with polypropylene (17.5

Sample preparation 115

For the GC-TOF-MS analysis, 1 g of pure adhesive sample (non cured) was extracted 116 with 10 g of acetonitrile. Then, this solution was filtered using 0.22 µm pore size filters 117 in order to remove the acrylic polymer that had precipitated when the acetonitrile was 118 added. After this step, samples were diluted 1/100 with acetonitrile and directly injected 119 into the GC. 120 121 For the HS-GC-MS and HS-SPME-GC-MS analysis, 1cm x 1cm laminates prepared 122 from the pure materials and using about 20 g.m -2 of adhesive were cut and placed into 123 20 mL headspace vials. 124 125 For the migration studies a cell consisting of two plates of aluminium, among which a 126 constant pressure could be applied was used. Two films of PE (40µm) were placed at 127 each side of the laminateand this stack was placed between the plates and stored at 40ºC 128 for one hour.

GC-TOF-MS electron ionizaton and chemical ionization modes 150

The first step in the screening was to study the pure adhesive formulations in their liquid 151 state. Liquid extraction with acetonitrile was applied to the adhesive and GC-MS-TOF 152 was used for the analysis after removing the precipitated polymeric phase by filtration. 153 Electron ionization (EI) was used initially and chemical ionization was further applied 154 to confirm the compound assignments and to check the reliability of the system for 155 screening. The same procedure was applied to all the adhesive samples. Figure 1 shows 156 the chromatogram obtained from the pure adhesive 3 extract using GC-TOF-MS in 157 electron ionization mode. Table 3 lists the compounds identified by this method in the 3 158 adhesives. Deconvolution of the peaks and identification of the compounds were done 159 using Chromalynx combined with NIST 08 library. This software establishes a list of 160 the most feasible candidates according to the match factor obtained comparing the mass 161 spectrum of the unknown compound with those contained in the NIST database. The 162 match factor is a measure of the certainty of the library search result and ranges from 0 163 to 1000. Afterwards, for the 5 fragments the most abundant of the spectrum, it 164 calculates the mass difference (∆mDa) between the accurate masses of the detected 165 fragments and the accurate masses of the candidate fragments. With all these data the 166 software assigned or did not assign identification for the compound. 167 168 EI columns in table 3 show the molecular formula of the main fragment, its accurate 169 mass and the mass difference between the experimentally obtained mass fragment and 170 the theoretical mass value of the proposed compound. Even though table 3 shows the 171 data for the main fragment, mass difference was calculated for five fragments in each 172 compound. This technique enabled a reliable identification even for those compounds 173 with a low match. A total of 42 volatiles were detected by EI in the adhesives studied. 174

Most of the compounds found were esters, probably coming from the polymer involved 175 in the formulation; some alcohols, alkanes and alkoxy groups were also found. Four 176 halogenated compounds were found in adhesives 1 and 3. 177 178 To confirm the identification and complete as much as possible the screening, chemical 179 ionization (CI) GC-TOF-MS was applied to the acetonitrile extracts. Figure 2 shows the 180 chromatogram obtained from the pure adhesive 3 extract using GC-TOF-MS in CI 181 mode. In the CI mode pseudo molecular ions [M+H] + are detected, being the molecular 182 mass a good confirmation of the compound. Again the identification was based on the 183 comparison between the theoretical accurate mass of the proposed compound and the 184 experimental accurate mass, and the match factor from EI experiments. This technique 185 allowed us to identify 14 unknown compounds and in addition, it was possible to 186 confirm 17 compounds previously identified. A total of 56 compounds were detected, 187 even though the three adhesives were acrylic based, only 9 of the volatiles were found 188 in more than one sample. These results showed the variability of adhesive formulations 189 even though the three samples were acrylic-base and some similarities could be 190 expected. This gives an idea of the complexity of the adhesives and emphasizes even 191 more the importance of the study.Table 3 shows the compounds identified in the three 192 adhesives using CI mode. For some compounds CI provided a higher sensitivity than 193 EI. Some of these compounds, such as 1-1-hexanol-2-ethyl, n-butyric acid 2-ethylhexyl 194 ester or 3(2H)-isothiazolone-2-methyl were found in adhesives where they had not been 195 detected by EI. New compounds were also found by CI such as 4-heptanone, 3-methyl, 196 2-propanone, 1-bromo, acetic acid,2-ethylhexyl ester, guanidine, 2-propenoic acid,2-197 ethylhexyl ester, hexanoic acid, 2-ethyl-,2-methylpropyl ester, 2,5-pyrrolidinedione, 1-198 (benzoyloxy)-, ethanol2(2butoxyethoxy), ethanol,2(2-butoxyethoxy) acetate, 1-199 dodecanol, 2,4,7,9-tetramethyl-5-decyn-4,7-diol and phenol 2,4-bis(1,1-dimethylethyl). 200 Toxtree v 1.51 (Ideaconsult LTD) was used to estimate the toxicity according to Cramer 201 rules [START_REF] Cramer | Estimation of Toxic Hazard. A Decision Tree 340 Approach[END_REF]). Cramer rules classify the compounds in three levels of toxicity 202 depending on its chemical structure and propose a maximum daily intake for each 203 compound depending on its toxicity. The maximum intake is 3.0, 0.91 and 0.15 204 mg/bodyweight Kg/day for class I, II and III respectively. Some of the compounds 205 found in these adhesives, such as guanidine or 2,4,7,9-tetramethyl-5-decyn-4,7-diol and 206 phenol 2,4-bis(1,1-dimethylethyl) had a high toxicity (Class III, Cramer list). 2,4,7,9-207 Tetramethyl-5-decyn-4,7-diol, nevertheless, was considered as moderate toxic by EPA 208 that means a LOAEL (lowest observed adverse effect level) of 200mg/Kg/day. From 209 this study, it could be confirmed that GC-TOF-MS is a powerful tool for adhesives 210 screening. The use of 2 different ionization modes allowed a more reliable identification 211 of the compounds detected. However, once the adhesive has been applied to the 212 substrates and cured the extraction with acetonitrile will not be so exhaustive and 213 another screening methodology with higher sensitivity, especially for the more volatile 214 compounds, is necessary. For these reason, HS-SPME-GC-MS analysis were carried 215 adhesives. As all the samples studied were acrylic adhesives and had similar chemical 234 characteristics, the optimized method was applied to all of them. 235 Three SPME fibers were checked for the optimization, a polyacrylate fiber, a PDMS 236 fiber and a PDMS/DVB fiber. Finally, the polyacrylate fiber was selected for the study 237 since it showed the best sensitivity and the highest number of compounds detected 238 (Figure 3). 239 240The number of compounds detected with the polyacrylate fiber was higher than the 241 number of compounds detected by HS-GC-MS, and the intensity of the peaks was also 242 higher when using the SPME. This result confirmed that SPME was much more 243 sensitive as even compounds present at very low concentration in the laminate, where 244 the amount of adhesive was about 2 mg, were detected. Therefore this technique was 245 selected to study the adhesives composition. The compounds identified using this method are shown in Table 4. Comparing these 255 data with those obtained by GC-TOF-MS, a considerably reduced number of substances 256 were found in SPME. This could be expected, since the samples analysed by SPME 257 were laminates, with a low quantity of adhesive (about 2 mg), while pure adhesives 258 were analysed by GC-TOF-MS. Some of the compounds found such as 1-hexanol-2-259 ethyl and 2-ethylhexylacetate were probably impurities of the monomers used to form 260 the polymer and 2 propenoic acid, 2-ethylhexyl ester were residual monomers. Other 261 compounds found were common additives used in adhesive formulations, 1-dodecanol 262 is used combined with the monomer to form the polymer, and 2,4,7,9-tetramethyl-5-263 decyne-4,7-diol is a surfactant .. 264 265 Analytical features obtained for some compounds are shown in table 5. Good detection 266 limits were obtained, all of them below 0.8 ng/dm 2 and reproducibility was always 267 The presence of many compounds in the packaging material does not necessarily imply 273 that they will migrate. In fact, it is expected that most of them remain in the adhesive or 274 in the material layers at both sides of the adhesive. Therefore a migration test will be 275 necessary. Since some of the laminates were manufactured using paper in one side, 276 liquid simulants could not be used for the tests. For this reason, PE was used as receptor 277 for a quick test of the "migration potential" of the compounds identified in the 278 laminates, as it is well known that in general compounds diffuse very fast through it. 279

The test was carried out at 40ºC simulating the conditions used in migration tests for 280 food contact materials at room temperature (Council Directive 82/711/EEC). PE was in 281 contact with the laminate for 1 hour since it was observed that this time was enough for 282 the compounds to reach the equilibrium. Nevertheless it should be taken into account 283 that due to the low polarity of PE, the migration of very polar compounds could be 284 underestimated with this test. The results from these migration studies are shown in 285 table 5. None of the detected compounds were found previously in the virgin PE films. 286

As it can be seen, the concentrations ranged from 0.04 to 1.6 µg/dm 2 (0.11 to 4.4 µg/g) 287 of PE in the PP side and from 0.27 to 28 µg/dm 2 (0.74 to 75 µg/g) in the paper side. The 288 concentration of the compounds was always higher in the paper side, as it was expected 289 due to the porosity of the paper. The most toxic compound detected in the screening, 290 2,4,7,9-tetramethyl-5-decyne-4, was not found in none of the sides. After applying a 6 291 dm2 to 1 kg food simulant conversion factor (Directive 82/711/EEC) the maximum 292 value found in the PP side, that will be the side in contact with the food, was 9.8 µg/Kg 293 of polyethylene, being 10 µg/Kg the maximum migration value which has been 294 accepted as of no concern (Directive 2007/19/EC) This study proved that the HS-295 SPME-GC-MS technique could be used for screening purposes as well as for 296 quantitative determination in migration studies from solid materials, even to quantify 297 levels of non concern. The determination of diffusion and partition coefficients, which 298 are important data for migration modelling, can be also determined by applying this 299

  -MS and HS-SPME-GC-MS 218 Once the pure adhesives were analysed, HS-GC-MS and HS-SPME-GC-MS techniques 219 were used for screening the volatile compounds released by the laminates prepared with 220 these adhesives. 221 222 Headspace GC-MS is the most commonly applied technique for the screening of 223 volatile compounds when concentrations are high, as only a portion of the vapour in 224 equilibrium is analysed, while the technique based on SPME is used at lower 225 concentration levels since it is more sensitive due to the pre-concentration step that 226 takes place before injection into GC-MS. Both techniques were checked in this case for 227 screening purposes and applied under the experimental conditions described above. 228 229 The HS-SPME-GC-MS technique is based on the sorption properties of the stationary 230 phase bonded to the microfiber, and therefore the selection of the appropriate microfiber 231 is a key point. The first step was the selection of the SPME fibres and the optimization 232 of the parameters for the HS-SPME analysis. This optimization was done for one of the 233

  6.0 (Umetrics AB) was used for the optimization of the 248 parameters for the HS-SPME and a Plackett Burman model was selected for this 249 purpose. Three variables were optimized: extraction time (5-30 min), extraction 250 temperature (40-80ºC) and desorption time (1-10 min).Finally, the optimum conditions 251 were as follows: extraction temperature 80ºC, extraction time 25 minutes and desorption 252 time 1 minute. 253 254

  an average value of around 6%. These results confirm that HS-SPME-268 GC-MS is a valuable tool for the screening of volatile compounds coming from 269 laminates, since it allows detecting compounds at very low concentration levels. 270 271 Migration studies and quantitative values 272

  Figge, K. 1996. Plastic Packages for Foodstuffs (Stuttgart:Wissenschaftliche) 360

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1.Chromatogram of adhesive 3 obtained by GC-TOF-MS and EI mode. Identification numbers explained in table 3.

Table 1 :

 1 Instrument parameters for the GC-TOF-MS analysis

	Instrumentation
	Ionization mode		EI	CI
	GC conditions	Gas flow (ml min -1 )	1	1
		Injector (ºC)	230,splitless	230,splitless
		Oven	60ºC, 5min; 5ºC min -1 ;	60ºC, 5min; 5ºC min -1 ;
			220ºC 6min	220ºC 6min
	MS conditions	Ionization mode	EI+	CI+
		Mass range	50-800	50-800
		Reaction gas	No	Methane

Table 2 .

 2 Instrument parameters for the HS-GC-MS and HS-SPME-GC-MS analysis

			Page 16 of 25
	Instrumentation
	CombiPal HS	Preincubation time (s)	120
		Incubation Temperature (ºC)	80
		Volume extracted (mL)	1
	CombiPal HS-SPME	Preincubation time (s)	120
		Incubation Temperature (ºC)	80
		Extraction time (s)	1500
		Desorption time (s)	60
		Postfiber condition time (s)	1200
	GC conditions	Column	DB-5(30 mx0.25 mm,0.25 um)
		Gas flow (ml min -1 )	1.5
		Injector (ºC)	250,splitless
		Oven	40ºC, 5min; 10ºC min -1 ; 300ºC
			1min
	MS conditions	Mass range	45-400
		Source (ºC)	230
		Quadrupole (ºC)	150
		Electron energy (eV)	70

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Table 3 :

 3 Compounds identified in adhesives 1, 2 and 3 by GC-TOF-MS in EI and CI modes, retention times (RT), identification number of the compounds in figures 1 and 2 (No), molecular formula, match factor; main fragment formula, its accurate measured mass and ∆mDa for the EI mode; accurate measured mass of MH and ∆mDa for the CI mode (∆mDa was calculated referred to the theoretical mass from NIST database)

	EI	CI

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Table 4 .

 4 Compounds identified by HS-SPME-GC-MS in adhesives 1, 2 and 3, retention times (RT) and identification number of the compounds in figure 3 (No).

	Page 21 of 25	Food Additives and Contaminants
				Deleted: 6
	RT	Compounds	Adhesive	No
	(min)
	9.2	benzaldehyde	2
	10.72 1-hexanol-2-ethyl	1/3	1
	11.9	undecane	2
	12.3	benzene ,1,3,5-triethyl	1
	12.8	acetic acid, 2-ethylhexyl ester	1/3	2
	13.4	2-ethylhexylacrylate	3	3
	13.6	1-butoxy-2-ethylhexane	1
	14.0	ethanol, 2-2(butoxyethoxy)	3	4
	14.1	dimethyl adipate	3	5
	15.3	n-butyric acid, 2-ethylhexyl ester	1
	16.0	ethanol, 2-2(butoxyethoxy) acetate	3	6
	16.1	propanoic acid, 2-methyl-,hexyl ester	2
	16.1	3-hydroxypropanoic acid 1-butyl ester	2
	16.7	2,4,7,9-tetramethyl-5-decyne-4,7-diol	3	7
	17.3	1-dodecanol	2
	22.97 butanoic acid, butyl ester	2
	23.25 hexanedioic acid, 3-methyl-,dibutyl ester	2

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Table 5 .

 5 Concentration of the compounds found in PE from migration experiments expressed as µg/dm2 of PE and as µg/g of PE, limits of detection (LOD) expressed as ng/dm2 of PE and RSD (%) of the analytical method.

		laminate PP side	laminate PP	laminate paper	laminate paper	LOD	RSD	Formatted
		(µ µ µ µg/dm2)	side (µ µ µ µg/g)	side (µg/dm2)	side (µg/g)	(ng/dm2)	(%)
	1-hexanol-2-ethyl	0.04	0.11	0.34	0.93	0.79	5.5
	2-ethylhexylacetate	0.05	0.13	1.2	3.3	0.39	6.2
	Ethanol-2(2	0.17	0.46	3.2	8.7	0.59	6.7
	butoxyethoxy)
	2-ethylhexylacrylate	0.04	0.11	0.58	1.5	0.39	5.3
	Dimethyladipate	0.07	0.18	0.27	0.74	0.79	5.4
	Ethanol 2(2-	1.6	4.4	28	75	0.39	8.3
	butoxyethoxyacetate)
	2,4,7,9-tetramethyl-5-	n.d.	n.d	n.d.	n.d.	0.59	6.0
	decyne-4
	n.d. non detected in the migration experiment

Table Formatted :

 Formatted Font: Not Bold

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Acknowledgements

This work has been supported by the European Union under the Collective Research 312 Programme Contract No. COLL-CT2006-030309 MIGRESIVES. The findings and 313 conclusions in this paper are the responsibility of the authors alone and should not be 314 taken to represent the opinion of the European Commission. Financial support has been 315 received also from Grupo Consolidado de Investigación T-10 from Gobierno de 316 Aragón, Spain. E. Canellas acknowledges the grant from Gobierno de Aragón.