Martine M Ros

Saskia S Sterk

Hans Verhagen

Anton Fh Stalenhoef

Dr Nynke De Jong
email: nynke.de.jong@rivm.nl

Phytosterol consumption and the anabolic steroid boldenone in humans: a hypothesis piloted

Keywords: phytosterol enriched margarine, boldenone, human intervention, anabolic steroid, postlaunch monitoring, side effect

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Abstract

The presence of the anabolic steroid boldenone in animals has become a research topic as its occurrence is proposed to be a marker for illegal hormone administration.

However, boldenone can also be formed from β-sitosterol, a phytosterol present in animal feed, as well as from endogenous sources. The observations in animals together with the increased consumption of phytosterol-enriched foods in the western population led us to the hypothesis that consumption of phytosterol-enriched foods might possibly lead to increased boldenone levels in humans. We performed a pilot study among female volunteers (n=10) to investigate whether boldenone concentrations in urine were detectable after consumption of 25 g/day of phytosterolenriched margarines for one week. Urine samples were collected at day 0, day 3 or 4 and day 7. Urine of a sitosterolemia (a rare autosomal recessively inherited lipid metabolic disorder) patient was collected as a positive control case. No traces of boldenone were detected in either the volunteers or in the patient. In conclusion, there is no evidence of formation of boldenone in women after consumption of the recommended amount of phytosterol-enriched margarines.

Introduction

For a number of years, the presence of the anabolic steroid boldenone (1dehydrotestosterone or androsta-1,4-diene-17β-ol-3-one) in various animal species has become a topic of research as the occurrence of this hormone or its metabolites in biological samples is proposed to be a marker for illegal hormone administration (De Brabander and others 2004). Especially urine samples of cattle and veal calves have been subject of research in various EU countries. The investigation into the origin of the boldenone in bovine urine is still ongoing. Boldenone can be formed from βsitosterol (a phytosterol naturally occurring in plants) present in animal feed, but also endogenous production can still be one of the sources (Poelmans and others 2005).

Phytosterols are a normal constituent of the human diet. Normal levels of consumption are approximately 200-400 mg/day, which may increase to 700-800 mg/day in those who consume a large amount of soy-based foods (Andersson and others 2004). The recently introduced phytosterol-enriched foods deliver 2-3 g of phytosterols per day if the recommended amount is consumed. The intestinal absorption of phytosterols is estimated between 0.4-3.5% (Hallikainen and others 2000).

The observations in animals together with the increased consumption of phytosterolenriched foods in the western population to lower (slightly) elevated serum cholesterol levels lead us to the hypothesis that in humans phytosterol-enriched food may possibly lead to an unwanted amount of the anabolic steroid boldenone as in animals. This would then identify a new potential health hazard in humans.

Phytosterol-enriched margarines are on the market for some years now, and other plant sterol-enriched food products including dairy products have also been introduced. The Within the framework of Postlaunch Monitoring (PLM) of functional foods in which positive but also potential side effects are investigated (de Jong and others 2004;2005a;2005b;Wolfs and others 2006) we studied the hypothesis postulated above. To our knowledge there were no data of biotransformation of the phytosterol β-sitosterol into boldenone in humans. We performed a pilot study in 10 women to investigate whether boldenone concentrations in urine were detectable after consumption of the recommended daily amounts of phytosterol-enriched margarines. In addition, a urine sample of a sitosterolemia patient was analysed as a positive control case. Patients with sitosterolemia are characterized by a > 50-fold elevation in plasma phytosterol levels, which results from an increased absorption and decreased hepatic removal of phytosterols that leads to accumulation of phytosterols in blood and tissues (Ketomaki and others 2005). Because these patients have very high levels of plasma phytosterols we anticipated that especially in these patients the presence of boldenone might be demonstrated. For our pilot 10 healthy female volunteers (aged 22 to 51) were recruited. In daily life, these women did not use phytosterol-enriched margarines. We included only women because they excrete less to none anabolic hormones by nature. During our one week trial we provided phytosterol-enriched margarines. The participants were asked to consume 25 grams per day (i.e. 2 grams of phytosterols or ~1 gram of β-sitosterol) of the phytosterol-enriched margarines, which corresponds to the recommended intake by the manufacturer. At the beginning of the study 7 portions of 25 grams were distributed to the volunteers. Spot urine samples were collected on day 0, on day 3 or 4, and on day 7 at around lunchtime. In this way we collected one voidance from a day at about the same time. Within 30 minutes after collection the urine samples were frozen at minus 20 o C. As a normal food product was supplied in physiological quantities and the collection of spot urine samples was not invasive, no authorization from the medical ethical committee was necessary (personal communication with the Medical Committee of Ethics of the University Medical Centre Utrecht, April 2006). The volunteers received a little incentive after participation.

Materials and methods

Study design

In the blind urine samples, ß-sitosterol, campesterol, stigmasterol, ß-boldenone, αboldenone, α-testosterone and ß-testosterone were analysed according to ARO-SOP 507 (ARO-SOP 507 RIVM: Bilthoven) a detailed description of which follows below.

In addition, one spot urine sample of our positive control case, i.e. a patient with sitosterolemia, was collected and analysed.

Method of analysis

Chemicals

Extraction procedure Isolation

A sample portion of 5 ml of urine was transferred to a 10 ml glass tube. The samples were spiked with 10 ng ß-boldenone-D3 , 10 ng testosterone-D2 and 25 cholesterol-D6. To the samples 1 ml of phosphate buffer (pH 7.4) and 50 µl of ß-glucuronidase was added. The mixture was vortexed and hydrolysed for 3 hours at 52°C.

A SPE C 18 column (3 ml) was preconditioned with 3 ml of methanol and 3 ml of water.

The centrifuged sample was passed through the column. The SPE C 18 column was washed with 3 ml of water and 3 ml 30:70 v/v-% acetonitrile/water. The anabolic steroids were eluted with 4 ml of 80:20 v/v-% methanol/water. The eluate was evaporated at 55°C under a gentle stream of nitrogen until dry and further processed as described in Preparative HPLC. The sterols were eluted with 3 ml of iso-octane, the eluate was collected and evaporated at 55°C under a gentle nitrogen until dryness and derivatised as described under derivatisation.

Preparative HPLC The dried extracts were reconstituted in 120 µl of 65:35 v/v-% methanol/water, from which 100 µl was injected. One fraction was collected from 4.9 to 20 minutes. The collected fraction was dried under a stream of nitrogen. The dried extracts were reconstituted in 300 µl ethanol, transferred to a 2 ml vial and evaporated at 55°C under a gentle stream of nitrogen to dryness.

Derivatisation

The dried extracts containing cholesterol, coprostanol, campesterol, sitosterol and cholesterol-D6 were reconstituted in 20 µl of derivatisation reagent (MSTFA++) and incubated for one hour at 60°C. After 1 hour the derivatisation reagent was evaporated.

The dried residue was reconstituted in 75 µl of iso-octane.

The dried extracts containing α/ß-boldenone, α/ß-testosterone, α/ß-androsterone, etiocholanolon, testosterone-D2 and ß-boldenone-D3 were reconstituted in 50 µl of derivatisation reagent (HFBA/aceton) and incubated for one hour at 60°C. After 1 hour the derivatisation reagent was evaporated. The dried residue was reconstituted in 50 µl of iso-octane.

Detection

Mass spectrometric detection was performed in selected ion monitoring (SIM) mode, see Table 1 and 2 for an overview of the m/z monitored and typical retention times of the analytes. In Table 3 the results of our pilot study are presented. Unexpectedly and in contrast with our hypothesis no traces of boldenone in the urine of any of the volunteers could be detected. The levels of ß-sitosterol, campesterol and stigmasterol were almost the same over the 7 days of consumption of phytosterol enriched margarines. Also the patient with sitosterolemia showed no traces of urinary boldenone. As expected, the patient levels of ß-sitosterol, campesterol and stigmasterol were elevated compared to the mean levels of the female volunteers. Table 4 presents prelimary data on urinary levels of α-testosterone and ß-testosterone, which showed a slight increase in testosterone compared to baseline levels, after a few days of phytosterol-enriched margarine consumption.

Results

F

Discussion

From our pilot we conclude that there is no evidence of formation of the anabolic steroid boldenone in women volunteers after consumption of the recommended amount (i.e. 25 grams per day) of phytosterol-enriched margarines. In addition, although the urinary sitosterol and stigmasterol levels in our sitosterolemia patient are elevated as expected, also no urinary boldenone in this positive control case could be detected.

Therefore, within the PLM perspective of this study there seems no health hazard for women forming boldenone associated with this type of functional food.

In contrast to the clear results on the absence of urinary boldenone in both our human volunteers and in our sitosterolemia patient, the results on urinary testosterone are rather uncertain. At day 3 or 4 the ratio of β-testosterone and α-testosterone seems to be high, but this value was influenced by one low concentration of α-testosterone near the detection limit. The variation in these data is large and it should be demonstrated in future larger trials whether any shift in concentration is caused by true differences in human metabolic systems or by measurement errors.

Several (mechanistic) issues remain to be clarified in the future, especially in cattle consuming a large amount of phytosterol -rich feed. There is still debate about the implications of the detection of boldenone in cattle and veal calves' urine. Questions that are raised focus among others on whether boldenone is endogenously synthesized or indeed illegally administered. Also, detection methods might have been improved over time. Another theory that has been proposed is that due to bacteria in feed or a different feed composition (more sterols), the side chain at the c17 position in the Dring can be broken and the sterols can be formed into anabolic steroids such as To our knowledge, this study is the first to investigate the association between phytosterol (β-sitosterol) consumption and urinary boldenone concentration in humans.

And although our pilot study only aimed at hypothesis generation, we very well realize that there are limitations to our pilot study. We had a limited number of participants who only consumed the recommended dose of physterol-enriched margarine, during a limited amount of time. But as the metabolic effects of physiological amounts of phytosterol-enriched margarines are normally observed within one day (Ellegard and others 2005) we are of the opinion that the duration of our pilot had been sufficiently long. Within the scope of the hypothetical aim of this pilot, it was not necessary to collect representative 24h urine samples to take into account variations in urine volume. Therefore, spot urine samples were appropriate to investigate whether boldenone is detectable in urine. Although no levels of boldenone were found, we are of the opinion that in theory it would have been possible to detect sterols and boldenone in human urine samples. Given a mean urine excretion of 1,5 l/day, a phytosterol intake of 2 g/day, and a mean intestinal absorption of 2% (see introduction section), the theoretical maximal concentration of sterols in urine is a factor 300 higher (2% of 2 g sterols = 40 mg/1,5 l = ~30mg/l) than the detection limit of 0.1 µg/l. We did not have any compliance measure of the phytosterol-enriched margarine consumption, but we do not have any indications that among our 10 female volunteers who were contacted regularly during the study period there were any non-compliant subjects. 1.1 ± 0.6

F

1.1 (0.5-2.4) 0.9 ± 0.5 0.8 (0.5-1.9)

1.2 ± 0.4

1.1 (0.6-1.9)

13.5

1 In this patient levels of boldenone and phytosterols were measured once 2 nd: not detected below limit of detection: i.e. for campesterol, stigmasterol and βsitosterol and boldenone < 0.1µg/l

  to displace cholesterol from mixed micelles in the intestine and thereby reduce intestinal cholesterol absorption; the exact mechanism, however, remains still unknown (Katan and others 2003). In animals boldenone works as an anabolic agent and is misused in cattle fattening. It is commonly used to enhance athletic performance and muscular development in humans. Hypertension, heart attacks, strokes, liver and other types of internal organ cancers have been associated with anabolic steroid use (De Brabander and others 2004; Sullivan and others 1998).

  All chemicals and reagents were of high purity quality. α/ß-boldenone, α/ßtestosterone, androsterone, etiocholanolon, α-androsterone, testosterone-D2 and ßboldenone-D3 were obtained from the RIVM-CRL, Bank of Reference Standards.Cholesterol, coprostanol, campesterol, ß-sitosterol, stigmasterol were obtained from Matreya. Cholesterol-D6 was obtained from Isotel. ß-glucuronidase from E.Coli K12 (Roche). Derivatization reagent for coprostanol, cholesterol, cholesterol-D6, ) / ammoniumiodide/dithiothreitol (1000:2:4, v/w/w) (Alltech), for α/ß-boldenone, α/ß-testosterone, androsterone, etiocholanolon, αandrosteron, testosterone-D2 and ß-boldenone-D3 the derivatization reagent consisted of 10 µl heptafluorobutyric Acid Anhydride (HFAA) (Pierce) and 40 µl of dried acetone. Phosphate buffer pH 7.4 was prepared by dissolving 2.278 g of disodiumhydrogenphosphate and 0.416 g of potassium-dihydrogenphosphate in 800 ml of water, the pH was adjusted to 7.4 ± 0.1 and water was added to a final volume of 1000 ml.ApparatusLiquid Chromatography (LC): Waters Chromatography autosampler, two Waters pumps, Pharmacia controller, ThermoQuest multi-channel UV-detector. HPLC-column used was a Superspher RP-18 (L 125 mm, 4 mm ID, 4 µm) with pre-column (Waters).Column temperature 40°C. Fraction-collector (Foxy Jr). Datasystem, PC1000ThermoQuest. The LC mobile phase consists of solution A: 65:35 v/v-% methanol/water, solution B: 100% methanol. Gradient starts at 0% B, after 8 min the percentage B is increased in 8 min to 100% and remains at 100% till 25 min. The gradient then returns in 0.1 min to the initial condition. The flow rate was 0.7 ml/min.

  coupled to a mass-spectrometer (GC-MS) analysis was carried out on an Agilent 5973 MSD. GC capillary column, 30 meter VF-17MS (Varian) i.d. 0.25 mm, 0.15 µm film thickness, constant flow of 1.1 ml helium/minute. Injection, splitless mode at 250°C, injection volume 2 µl. The oven temperature was kept constant at 80°C for 1 min and was increased, 20°C per min, to 340°C and was kept constant at this temperature for 4 min.

  . One should bear in mind that in human food we do not expect this type of bacteria, but the implications for humans in the light of beef consumption needs more research.

Figure 1 .

 1 Figure 1. Structures of a.testosterone, b. ß-sitosterol, c. campesterol, d. stigmasterol

Table 1 .

 1 Our main focus was the detection of urinary boldenone. Despite the limitations described above we conclude that within our setting consumption of the recommended amounts of phytosterol -enriched margarines, does not lead to detectable levels of the anabolic steroid boldenone in women. Further research on boldenone formation is dependent on the observations in cattle and veal calves in relation to beef and veal consumption. Also, the differences in the exact metabolic mechanisms between humans and cattle, and among humans themselves with regard to testosterone are topics of further research. Overview of the m/z of the different TMS-compounds measured.

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

Table 2 .

 2 Overview of the m/z of the different (HFB) compounds measured.

	Compound	m/z	Retention time
			(min.)
	ß-Boldenone	678	12.97
	Boldenone-D3	681	12.95
	α-Boldenone	678	13.30
	α-testosterone	680	12.81
	ß-testosterone	680	13.29
	androsterone	486	15.02
	etiocholanolone	486	15.11
	α-androsteron	486	15.83
	testosterone-D2	682	13.28

Table 3 .

 3 Urine levels of boldenone and phytosterols in 10 healthy human volunteers and a sitosterolemia patient

			Volunteers		Patient with
					sitosterolemia 1
		Baseline	Day 3-4	Day 7
	β-boldenone (µg/l)	nd	nd	nd	nd 2
	α-boldenone (µg/l)	nd	nd	nd	nd
	Campesterol (µg/l)
	mean ± sd	0.8 ± 0.
	median (min-max)

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

Acknowledgements

The authors would like to thank the volunteers as well as the sitosterolemia patient for their willingness to participate. We also would like to thank H.J. van Rossum and M.H. Blokland for their technical assistance. We acknowledge the Netherlands Organisation for Health Research and Development (ZonMW) for supporting the project on PostLaunch Monitoring (Grant Number 014-12-010).


Klaus Schneider

Inga Ollroge

Martin Clauberg

Ulrike Schuhmacher-Wolz

Erlenkämper, B., Freidank, N M Wilhelm

management processes in the derivation of maximum levels for environmental contaminants in food

Keywords: Health significance, Regulations, Risk assessment Additives/Contaminants: Dioxins, Heavy metals, PCBs Food Derivation of maximum levels, environmental contaminants, food, 2 risk assessment, risk management

authorities (USA, Germany). Both the risk assessment process (derivation of 13 tolerable intake values and intake assessment by scientific bodies) and the risk 14 management process (derivation of maximum levels by risk management 15 bodies) are discussed. The different organisations show similar principal 16 approaches and similar numerical values for those maximum levels that are 17 available for the same contaminants in the same food items. For the risk 18 management decision-making, a noticeable lack of transparency is observed in 19 all the investigated systems. Recommendations for harmonisation and 20 improvement are provided for the exposure assessment, for the communication 21 between risk assessment and risk management processes and for 22 improvements for better documentation and transparency of the risk 23 management decision-making processes. 24 Significant steps towards harmonisation have been achieved in the European 7 Union (EU) for the regulation of environmental contaminants in food. Common 8 maximum levels have been enacted for heavy metals (lead, cadmium, 9 mercury), polychlorinated dibenzodioxins and furans (PCDD/F), and 10 benzo(a)pyrene as a reference substance for polycyclic aromatic hydrocarbons . 11 Additional maximum levels are in preparation, e.g. for non-dioxin-like 12 polychlorinated biphenyls (PCBs). But for other contaminants, maximum levels 13 only exist on different national scales, e.g. arsenic maximum allowable 14 concentrations in the UK for food in general, while in Spain only for fruits, 15 vegetables and seaweed. 16 17 At the same time, the internationally agreed upon maximum levels from the 18 Codex Alimentarius (CA) are growing in their importance as a means to secure 19 the safety of food worldwide and to avoid trade barriers for food products 20 between countries. CA maximum levels are referred to by the World Trade 21 Organization (WTO) as suitable hygienic standards in the context of the WTO 22 "Agreement on Sanitary and Phytosanitary Measures" (SPS Agreement). 23 In recent years, it has been emphasized that food safety and transparency of 2 the food-related regulatory process is of pivotal importance to enhance 3 consumer's confidence in food in Europe [START_REF] Horton | Risk analysis and the law: international law, the World 21 Trade Organization Codex Alimentarius and national legislation[END_REF]. Taking into 4 consideration the above mentioned parallel existing standards for environmental 5 contaminants in food, the German Federal Ministry for the Environment, Nature 6 Conservation and Nuclear Safety initiated a research project, which was 7 overseen by the German Federal Institute for Risk Assessment. This project 8 aimed to provide an analysis of the regulatory processes leading to the 9 derivation of maximum levels for environmental contaminants in food. It focused 10 on the methods for standard-setting in the EU and at the Codex Alimentarius, 11 as well as on the national values existing in Germany and -for comparison -in 12 the USA and takes into account both risk assessment and risk management 13 The analysis of the derivation of the maximum levels in food can be 11 differentiated between the tasks of the risk management bodies and the tasks of 12 the risk assessment bodies. The latter evaluate health effects and the intake of 13 a contaminant (including derivation of values for a tolerable intake, e.g. tolerable 14 daily intakes (TDI)), whereas risk managers, by setting maximum levels, 15 consider the outcome of the risk assessment process, the concentration of the 16 contaminant in food, socio-economic arguments, the technical feasibility of 17 derived values and other arguments. As the setting of maximum levels by the 18 CAC as well as in the European Union requires an agreement of independent During the project it was noticed that documentation of the regulatory processes 20 for all organisations is generally scarce, at least for some aspects. [START_REF] Cordle | Risk assessment in a federal regulatory 22 agency: an assessment of risk associated with the human consumption of 23[END_REF] Consequently, the project approach was broadened. When expanding the 22 source material to beyond the documents provided by the regulatory bodies as 23 food safety. Furthermore, food safety experts were contacted bilaterally via e-6 mail or face-to-face interviews for in-depth analysis of specific topics. This 7 expanded analysis was presented to and supported by an expert panel 8 accompanying the project during two meetings. 9 10

Results

11

Synopsis: Derivation of maximum levels 12

The evaluation showed that maximum levels or comparable limit values exist for 13 only a few environmental contaminants (Table I). These values partially differ in 14 The general objectives of the standards are principally the same: they aim at 11 ensuring the food to be safe for consumer's health and reducing the burden of 12 contaminants in food to a level as low as possible. In addition, the standards of 13 the Codex Alimentarius as well as the EU standards intend to facilitate the free 14 trade of food products. 15 16 With respect to the data and methods used to derive the values, distinct 17 differences could be discerned (Table II German SHmV) only limited or no information on the exposure data (data on 1 food contamination as well as on food consumption) underlying the derivation of 2 the values is available. Therefore, a satisfactory transparency is not possible 3 here. For the remaining maximum levels, the underlying exposure data are 4 more or less well documented. 5

6

A review and update is intended for all maximum levels, if the apparent data 7 situation warrants it, with the exception of the German SHmV. For the US 8 values, however, no updates have been undertaken in the last 20 years after 9 initial amendments. In all regulations examined, the availability of suitable 10 analytical procedures for monitoring purposes is considered important. Only for 11 the maximum levels of the US FDA are the substance-specific derivations well 12 documented. For the EU values, no documents are available that provide 13 information on the rationale for the individual values. The short background 14 information given in the German SHmV also does not provide sufficient 15 information to understand nor to retrace the numerical derivation of the values. 16

In spite of access to the protocols of the sessions of the Codex Alimentarius 17 The methodological approach used by JECFA and WHO committees has been 4

described in two Environmental Health Criteria documents (WHO, 1987; 1994) 5 and continuous efforts for methodological improvements and harmonisation is 6 undertaken by WHO in the framework of its "IPCS harmonization programme" 7 (http://www.who.int/ipcs/methods/harmonization/en/) and the "IPCS project to 8 update the principles and methods for the assessment of chemicals in food" 9 (http://www.who.int/ipcs/food/principles/en/index.html). 10 11 The principal approach for deriving tolerable intakes used by EU scientific 12 committees is comparable to that of JECFA, applying safety factors to NOAELs 13 (no-observed-adverse-effect-levels). Evaluation of polychlorinated biphenyls in 14 the USA by FDA used both threshold and non-threshold concepts. This 15 probably reflects the state-of-knowledge on this group of substances back in the 16 1980s, and should not be taken as proof of diverging methods. Only for the 17

Codex and EU maximum levels could continuous follow-up activities be 18 identified for the underlying toxicity and exposure assessments. 19

Proposals for improvement or harmonisation with respect to exposure 20 assessment 21

Exposure assessment for environmental contaminants in food requires data for 22 the concentration of the contaminants in various food types, as well as data on 23 the food consumption. Information on contaminant concentrations may come 1 from total diet studies (also called market basket surveys), from duplicate 2 studies or from other data on individual dietary items. To calculate contaminant 3 intake from data on occurrence of contaminants in various food items and from 4 food consumption data food categorisation systems are of special importance 5 (Ireland and Moeller, 2000). Categorisation systems allow to bring both types of 6 data together and to compare the food items investigated. These systems 7 aggregate various food items into groups, which may be hierarchically 8 structured or not, e.g. in a hierarchical system oranges may belong to the group 9 of citrus fruits, which belongs to the fruits group. 10 11 Various categorisation systems are in use, e.g. two different systems have been 12 used recently by the Codex Committee on Food Additives and Contaminants for 13 food additives and contaminants (CAC, 2001a; CAC, 1997). In EU "Scientific 14 cooperation" (SCOOP) task reports, which compile exposure data for various 15 contaminants in food, different approaches have also been used to collect data 16 from Member States. The SCOOP report on heavy metals and arsenic used the 17

Codex categorization system for food additives (SCOOP, 2003). The SCOOP 18 reports on dioxins didn't make use of a specific categorization system. Rather, 19 member states were asked to provide consumption data for any food item for 20 which occurrence data were available [START_REF] Scoop | 19 Assessment of dietary intake of dioxins and related PCBs by the population 20 of EU Member States[END_REF]. Recognizing that different purposes may require different systems and no single 1 system may serve all purposes, there is still a need and room for improvement 2 for the harmonisation of categorisation systems. Considerable effort has already 3 been spent in the past to harmonise categorisation systems for food or to find a 4 common approach for the exchange of data (e.g. EU research projects DAFNE, 5 EFCOSUM, see e.g. Ireland and Moeller, 2000; EFCOSUM, 2001). One 6 promising approach is the Euro Food Groups Proposal, which combines food 7 items in 33 basic groups (Table III). In a recent opinion of the EFSA Scientific 8

Committee on exposure-related questions it was proposed to aggregate the 9 Euro Food Groups further into only 16 groups for the development of an EU 10 concise food consumption database (EFSA, 2005a). 11

[Insert Table III about here] 13

Additional difficulties arise when intake of contaminants is to be assessed from 14 insufficient data on the occurrence in food. Uncertainty in the assessment refers 15 to, among other things, insufficient or unknown analytical quality standards, 16 availability of data for only unprocessed crops, and different ways to deal with 17 concentrations below the limit of detection [START_REF] Kroes | Assessment 4 of intake from the diet[END_REF]. The poorer the 18 data quality situation, the more important it is to indicate how data quality and 19 reliability is assessed and how data gaps are dealt with. Table IV Probabilistic exposure assessment methods allow for consideration of (some of) 2 the variability and uncertainty connected to the variables influencing the 3 exposure assessment [START_REF] Kroes | Assessment 4 of intake from the diet[END_REF]. Probabilistic methods are especially 4 useful to describe the variability in intake levels in the human population. Intake 5 assessment should not only focus on the average exposure but consider also 6 intakes for special risk groups (e.g. children) and the higher end of the 7 distribution (population groups with higher than average intakes, e.g. 8 populations with high fish consumption with respect to contaminants in fish). 9

Considering the increased efforts for a probabilistic assessment, a tiered 10 approach might be useful, starting with simple point estimates of intake. The 11 second step is the description of intake distributions, taking into account 12 variability in the human population. The most sophisticated step would consist 13 in probabilistic modelling, considering major sources of uncertainty and 14 variability [START_REF] Hart | Application of uncertainty 19 analysis in assessing dietary exposure[END_REF][START_REF] Leclercq | Development of database for use in validation 7 studies of probabilistic models of dietary exposure to food chemicals and 8 nutrients[END_REF]. EFSA also recognized the 15 significance of a clear methodological approach to exposure assessment, 16 especially with respect to associated uncertainties (EFSA, 2005a). 17 The focus of maximum levels is to regulate exposure from food. But for certain 19 substances exposure from other sources may significantly contribute to the 20 overall body burden. E.g. infants may take up substantial amounts of lead from 21 drinking water used for the preparation of baby formulas (Wilhelm et al., 2004). 22

Other potential sources are inhalation or dermal/oral contact to consumer 23 products, house dust, soil, etc. [START_REF] Olin | Workshop Summary Report on "Aggregate Exposure 12 Assessment[END_REF]. Significant contributions from these 24 pathways to the overall intake of a contaminant should be considered in the 1 exposure assessment, taking into account the pathway-specific absorption 2 efficiency, as absorption from water, food and air may vary considerably 3 [START_REF] Sunderman | 5 Nickel absorption and kinetics in human volunteers[END_REF]WHO, 1994). 4

Proposals for improvement with respect to risk management 5 Risk management decisions make use of the outcome of the risk assessment 6 process but have to take into account additional arguments. These include the 7 considerations to avoid trade impediments, to address social and economic 8 factors, and to reduce concentrations of contaminants in food to levels as low 9 as possible, while taking into account the technical feasibility in applying best 10 practice (conformity to the values with justifiable methods, analytical 11 verifiability). 12 13 Both in the Codex system and in the EU, the risk assessment and the risk 14 management processes are separated and dealt with by different organisational 15 bodies. This guarantees that scientific opinions are not influenced by the above 16 mentioned risk management arguments. At the same time, information flow 17 must be secured [START_REF] Lützow | Harmonization of exposure assessment for food chemicals. 10 The international perspective[END_REF]. For optimal information of risk management 18 committees, it is proposed here that the risk assessment body which carries out 19 the exposure assessment describe in detail what proportion of the tolerable 20 daily intake is consumed by different levels of the contaminant in food. This 21 information is most important for risk managers when contemplating lines of 22 argumentation for higher or lower maximum levels. After having proposed 23 specific maximum levels, risk managers should also inform the public about the 1 level of contaminant intake associated with these maximum levels compared to 2 the tolerable daily intake (Figure 1). Risk management decisions require that various arguments and interests be 6

weighed and balanced against one another, which might be difficult especially 7 on an international scale. For reasons of transparency, major lines of argument 8 leading to the derivation of specific maximum levels should be summarized. 9

The outcome of the decision-making process should also be interpreted in 10 terms of the health protection level achieved, in terms of the economic and 11 social balances, and in terms of the technical feasibility. Publication of 12 substance-specific reasons for a maximum level in a concise document would 13 be an important step towards transparency and would help to increase 14 confidence in food safety. agreement. To the extent disclosed in the available documents, the principles 3 for deriving maximum levels are similar with all institutions. The prime objectives 4 are to protect human health, while avoiding trade barriers. 5 6 To provide better information concerning the risk management process, it is 7 proposed herein that for various contaminant levels in food, the degree to which 8 the tolerable daily intake values are exhausted at these levels, should be 9 described in the risk assessment reports. This exercise should be done at least 10 for those food items which contribute most to the intake of the population. This 11 would inform the risk managers directly about the health consequences of 12 various risk management options and would make time-consuming additional 13 consultations with the risk assessment bodies unnecessary. 14 15 The synopsis shows that differences exist between the risk assessment and the 16 risk management portion of the derivation of maximum levels for environmental 17 arguments, but has also to achieve agreement of involved states. Nevertheless, 17 all these arguments are important for the attained numerical values. For the 18 sake of transparency of the overall process of deriving maximum levels, 19 documentation of the methodological approach as well as the substance-20 specific discussion and results is urgently needed for all parts of the process. 21

4 1

 4 was performed for existing maximum levels for 17 environmental contaminants in the EU Regulations 466/2001, 221/2002, 18 2375/2001, 684/2004, and the Codex Alimentarius Standards CAC/GL 39-2001, 19 CAC/GL 7-1991, and Codex Stan 230-2001, the German Schadstoff-20 Höchstmengenverordnung (SHmV, "Pollutant Maximum Level Directive") and 21 the action levels and tolerances of the US Food and Drug Administration for 22 methyl mercury and polychlorinated biphenyls. 23Environmental contaminants as defined herein are substances that originate 2 from diffuse sources and may appear in foods based on their ubiquitous 3 presence in the environment. Only non-carcinogenic substances or carcinogens 4 with a presumed threshold-related mode-of-action (e.g. PCDD/F) are included 5 in this synopsis.

  the maximum levels, several key elements for deriving maximum 1 levels were identified and compared with the actual procedures used. To this 2 end, publications in the scientific literature were evaluated as well as 3 background documents (e.g.CAC, 1997; CAC, 2004; WHO, 2000; WHO, 4 2003), research reports (e.g. EFCOSUM, 2001), and other sources related to 5

19 (

 19 their legal status. The maximum levels of the European Union are legally 15 binding to all member states. By definition, the Codex Alimentarius values only 16 represent recommendations, yet they attain a prescriptive character at the same 17 time due to their acceptance by the World Trade Organization (WTO) as 18 international hygienic standards. Action levels and tolerances of the US FDA Food and Drug Administration) differ in their degree of legal responsibility and In cases where values from different procedures are available for the same 1 contaminant within the same food commodity (e.g. heavy metals in EU and CA 2 regulations) and, thus, a comparison is possible, the numeric values are 3 identical for the most part. For example, values for lead in milk, fruits and other 4 food groups are numerically identical in the respective EU Directive 5 466/2001/EEC and Codex Standard 230-2001. For cadmium the EU set 6 maximum levels for a lot of individual types of vegetables. This was done by the 7 CAC only recently in a similar way when Codex Standard 248-2005 came into 8 force. 9 10

Commission 2 3 6 7

 26 and the Codex Committee on Food Additives and Contaminants 18 (CCFAC), it remains unclear which considerations were finally responsible for19 setting the values of the Codex Alimentarius.20 21 As a result of the incomplete documentation, it cannot be ascertained for 22 individual values whether additional criteria (e.g. best available practice, socio-23 factors) besides safety for human health were important when 1 deriving maximum levels.In summary, the documentation of the derivation of maximum levels is 4 insufficient for a transparent standard-setting. A methodological document only 5 exists for the limit values of the Codex Alimentarius procedure.Synopsis: Derivation of tolerable intakes 8The toxicological evaluations that form the basis for the maximum levels are 9 better documented. The Joint FAO/WHO Expert Committee on Food Additives 10 (JECFA) is responsible for the evaluation and derivation of TDIs for 11 contaminants upon request of CAC. JECFA is presenting its evaluations in 12 comprehensive monographs. The JECFA evaluations regarding heavy metals 13 also formed the basis for the derivation of maximum levels in the context of 14 Directive (EC) 466/2001. The Scientific Committee for Food (SCF), which was 15 responsible for the toxicological evaluation of PCDD/F in 2001 in the EU, 16 formulated its evaluation as opinion reports. Responsibility for the evaluation of 17 contaminants in the EU has recently been reassigned to the newly formed 18European Food Safety Authority (EFSA) and EFSA's "Panel on contaminants in 19 the food chain" (CONTAM) continued the SCF tradition to document its 20 evaluations by publishing opinion reports (e.g.[START_REF] Efsa | Opinion of the Scientific Panel 17 on Contaminants in the Food Chain on a request from the Commission 18 related to mercury and methylmercury in food[END_REF]. EFSA acts as an 21 independent scientific body that responds to requests by the Health and 22 General of the European Commission on the 1 basis of defined "Terms of Reference". 2 3

18

 18

3

 3

4 [

 4 Insert Figure1about here] 5

  contaminants, specifically in respect to documentation and transparency. 18 Scientific bodies like JECFA and the CONTAM panel of EFSA are publishing 19 their substance-specific risk assessments in comprehensive monographs or Outcomes of the decision-making process within risk management bodies like 1 CCFAC and SCFCAH, are not documented transparently nor presented in 2 satisfactory detail. A recent example is the proposal of maximum levels for non-3 dioxin-like PCB by the European Commission (BMU, 2006). Proposed 4 maximum levels are not accompanied by any documentation of the arguments 5 leading to specific numerical values. Recently, the EFSA CONTAM panel 6 evaluated the non-dioxin-like PCB. The panel did not derive a tolerable intake 7 because parallel exposure of non-dioxin-like and dioxin-like PCB impedes the 8 differentiation between them. The panel emphasised, however, that efforts to 9 lower the levels in food should be continued (EFSA, 2005b). 10 11 Protocols of CCFAC meetings are also not particularly helpful in this regard, as 12 the arguments leading to specific values are not documented. A reason might 13 be that socio-economic and technical arguments cannot be communicated as 14 easily as the scientific (e.g. toxicological) arguments. Ultimately, setting 15 maximum levels is a political decision, which takes into account scientific 16

  Recommendation on the division of tasks between risk assessment (Steps 1 and 2) and risk management in the derivation of maximum levels (TI:  tolerable intake ) 168x241mm (300 x 300 DPI)Page 24 of 24 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

  Table I gives an overview of the evaluated maximum levels 6

	7	and the associated legal documents.
	8
	9	[Insert Table I about here]
	10

Page 12 of 24 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk Food Additives and Contaminants

		13
	1	[Insert Table IV about here]
		F o r
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	20	lists some
	21	important issues which should be addressed within an exposure assessment for
	22	food contaminants.

Table V

 V and non-dioxin-like PCB. As far as comparisons are possible, the numerical 1 values, e.g. from EU directives and from Codex standards, are in close 2

		F o r
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	15 16	lists recommendations for improving v i e transparency of the risk management process. w
	17 18	[Insert Table V about here]	O n l
	19		y
	20	Discussion
	21	At present, only a few maximum levels for environmental contaminants are
	22	provided by the EU and the Codex Alimentarius. But numbers are expected to
	23	rise in the near future, substituting still existing national values, e.g. for arsenic

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


Erecius Mette

Hanne Kyhnau Poulsen

Jens Jørgen Hansen

Hanne Bjerre Sloth

Jens Hinge Christensen

Andersen

Keywords: Chromatographic analysis, Exposure assessment, Metals analysis -ICP/MS Additives/Contaminants: Copper, Pesticide residues pesticide residues, copper, monitoring programme, table grapes, processing factors, rinsing, intake calculations, Hazard Quotient

come

Introduction

Although pesticide residues in foodstuffs seldom exceed the maximum residue limits set by the Food authorities, consumer awareness of health issues due to pesticide residues as food contaminants is increasing. Food and health authorities around the world are continuously monitoring pesticide residues in fruit and vegetables, as well as many other agricultural products, to ensure that consumers are not exposed to any health risks (U. S. Food and Drug Administration

2005, The European Commission 2005).

In Denmark consumers are very concerned about the levels of pesticide residues in food, which often lead the NGO's to give the public advice on how to reduce pesticide intake, e.g. by buying fruit and vegetables from a specific country. These recommendations are often based on very small sets of data collected for other purposes and may therefore not be valid for the conclusions drawn.

It is well known that food processing may affect the level of pesticide residues [START_REF] Zabik | Pesticides and other industrial chemicals[END_REF].

Processing has been defined as 'any action performed on a food product from the point of harvest to consumption' [START_REF] Ritchey | Effect of processing on pesticide residues in foods[END_REF]. Typical household processing includes rinsing, boiling, peeling and juicing of fruits and vegetables. Effects of processing on pesticide residues in foods have been compiled in reviews by [START_REF] Liska | Effects of processing on pesticides in foods[END_REF], [START_REF] Geisman | Reduction of pesticide residues in food crops by processing[END_REF], [START_REF] Ritchey | Effect of processing on pesticide residues in foods[END_REF], [START_REF] Zabik | Pesticides and other industrial chemicals[END_REF] and [START_REF] Holland | Effects of storage and processing on pesticide residues on plant products[END_REF] and cover a wide range of processing practices. The effect of processing on residues has been seen to vary with both crop and pesticides (Burchat et al. 1998), and may be correlated with the physico-chemical parameters of a pesticide. In general, when investigating the effect of processing, it is important to use field-sprayed samples, since absorption, translocation and weathering of the pesticide might influence the effect of a processing practice [START_REF] Krol | Reduction of pesticide residues on produce by rinsing[END_REF].

The processes acting on pesticide residues in the field such as volatilisation, hydrolysis, oxidation, metabolism and enzymatic transformation are relevant for reduction of pesticide residues during processing [START_REF] Holland | Effects of storage and processing on pesticide residues on plant products[END_REF].

Several studies have investigated the effect of washing (rinsing) on pesticide residue content, and found that washing (rinsing) had none or only limited effect on the residue content (Burchat et al. 1998[START_REF] Rasmussen | Distribution of multiple pesticide residues in apple segments after home processing[END_REF][START_REF] Boulaid | Effect of Household Processing and Unit-to-Unit Variability of Pyrifenox, Pyridaben, and Tralomethrin Residues in Tomatoes[END_REF], Anderson et al. 1998, Christensen et al. 2003a).

However, no studies could be found on rinsing of table grapes. The only type of processing investigated for grapes were the processing to wine (Cabras et al. 2000[START_REF] Navarro | [END_REF].

Prediction of exposure to pesticide residues through the diet is vital for approving the use of pesticides and for gaining official acceptance on pesticides residue levels, which occur in food commodities. Over the years more specific and detailed models have been developed to give an accurate risk assessment. When performing dietary risk assessment the changes of pesticide residue content during industrial and household processing should be taken into account, as it will produce more accurate estimates of the actual consumer exposures from fruit and vegetables (FAO/ WHO, 2001) and consequently give a more accurate foundation for consumer guidance.

In this study, pesticide residues and the essential element copper (Cu), have been monitored in grapes from the Danish market from 1998-2003. Copper was included, since copper sulfate is a vital ingredient of the Bordeaux and Burgundy mixture and has an estimated agricultural usage of approx. 140.000 tonnes per year worldwide (The Copper Development Association, 2007). The present study evaluates the levels of pesticides residues and copper in table grapes sampled and The results obtained here were used to evaluate potential differences in pesticide residues pattern between the countries of origin of the grapes. Additionally, sub-samples from 1999 and 2001 were rinsed with tap water in order to estimate processing factors from rinsing for the various pesticides.

Grapes were chosen, as a representative sample type for the group of vegetables and fruits, as they are eaten without any other processing than gentle rinsing. Table grapes have a large surface/volume ratio and may therefore be difficult to rinse thoroughly. The intake is calculated using the deterministic approach, as this method is widely used for calculation based on commodity-by-commodity basis. Present study involves the intake calculation both before and after processing. In order to evaluate multiple findings and the health hazard related to these, it is possible to perform a cumulative risk assessment (Wilkinson et al. 2001[START_REF] Jensen | Cumulative Risk Assessment of the intake of Organophosphorus and Carbamate Pesticides through the Danish Diet[END_REF].. In present study the cumulative risk assessment is performed in order to evaluate and compare the findings of pesticide residues in table grapes from different countries.

Materials and methods

Sampling

The samples were collected as part of the Danish Monitoring Programme on pesticide residues [START_REF] Poulsen | Results from the Monitoring of Pesticide Residues in Fruit and Vegetables on the Danish Market 2000-01[END_REF] programme for grapes were extended with 100 samples in order to elucidate the differences in residues between the main producers of table grapes and to perform additional processing studies.

For evaluation of differences between pesticide residues in samples from different countries al the 563 samples were used. For calculation of processing factors by rinsing 120 of the 563 samples were used. These samples were collected in 1999 and 2001

Sample preparation.

Sample preparation prior to pesticide analysis was performed as described in [START_REF] Andersen | Results from the Monitoring of Pesticide Residues in Fruit and Vegetables on the Danish Market 1998-99[END_REF]. Samples for estimating processing factors were prepared as follows: Immediately after the samples arrived at the laboratory, the samples were divided into three sub-samples of approximately 1 kg each. Each bunch of grapes was divided into an upper, middle and lower section of approximately equal size and the 3 sub-samples were made up of an equal number of the three different sections and labeled A, B and C. The sub-samples C were rinsed thoroughly in running cold tap water for 30 seconds and dried gentle with paper towels. Before the samples were homogenised, representative test sample of 100g each were taken from all sub-sample for immediate analysis of dithiocarbamates. All samples and sub-samples were then homogenized using a food processor until peel and pulp were cut to pieces smaller that 5 mm and put into sealed polyethylene bags and kept frozen at -20 degree Celsius until analysis.

Chemical analysis. The Danish Institute for Food and Veterinary Research performed the analysis for Cu content. The pesticide analyses were carried out at the two Regional Food Control Laboratory in Copenhagen and Odense. All laboratories were accredited in accordance with ISO17025 by the Danish body of accreditation, DANAK. The grapes were analysed for approximately 150 pesticides and degradation products using 1) GC multi-method with EC-, NP-and MS-detection (140 pesticides), 2) HPLC method with UVdetection (6 fungicides), 3) spectrophotometric method for the sum of dithiocarbamates and 4) inductively coupled plasma optical emission spectrometry (ICP-OES) method for the determination of copper.

GC multi-residue method. The analytical procedure is detailed in [START_REF] Poulsen | Validation of a multiresidue method for analysis of pesticides in fruit, vegetables and cereals by GC/MS iontrap system. In Principle and Practices of Method Validation[END_REF]. An analytical portion (25 g) was extracted by homogenisation with acetone, cyclohexan, ethyl acetate and anhydrous sodium sulfate. The extract was cleaned by gel-permeation chromatography. Approximately 140 pesticides (including isomers and metabolites) could be identified by retention time and quantified by capillary gas chromatography using 2 columns with different phase polarities and detection by ECD, NPD or MS.

HPLC multi method. The analytical procedure is described in detailed in [START_REF] Juhler | Pesticide residues in selected food commodities: Results from the Danish pesticide monitoring program 1995-1996[END_REF]. An analytical portion (25 g) was extracted by homogenisation with ethyl acetate in alkaline solution, cyclohexane and anhydrous sodium sulphate. Benomyl and thiophanat-methyl were converted into carbendazim by heating with ethanolic hydrochloric acid. The extract was fractionated on a silica cartridge. Residues of carbendazim and thiabendazole were determined by HPLC with UV detection (280 nm) using a C8 column and isocratic elution with methanol and triethylamin/phosphate buffer adjusted to pH 4.3 or 8.6 with phosphoric acid. Detected residues were determined using two different columns or two eluents with different pH.

Dithiocarbamate method. An analytical portion of 100 g was boiled with hydrochloric stannous chloride to convert dithiocarbamates to carbon disulphide, which subsequently was distilled, cleaned and determined by spectrophotometry at 372 and 430 nm as described in [START_REF] Juhler | Pesticide residues in selected food commodities: Results from the Danish pesticide monitoring program 1995-1996[END_REF]. The method cannot distinguish between the different dithiocarbamates.

Copper method. Determination of copper was performed by wet-ashing followed by detection ICP-OES. Details of the instrument and operating parameters are summarized in Table 2.

Analytical portions of approximately 2.5 g were weighed into PTFE vessels of pressure digestion system, and 3 ml of ultrapure, subboiled nitric acid was added. The vessels were then heated to 160 degrees Celsius for 4 hours. After cooling, the resulting digests were diluted to a final volume of 40 ml with laboratory water (18 MΩ) and stored in polyethylene vials until analysis.

[insert Table 2 here]

Quality Assurance

The quality assurance for the pesticide residues method are described in detail by [START_REF] Poulsen | Results from the Monitoring of Pesticide Residues in Fruit and Vegetables on the Danish Market 2000-01[END_REF]. The analytical methods for pesticides were quality controlled by analysing two spiked samples as well as randomly chosen replicate samples and blank determinations in each analytical series of approximately 16 samples. Quality control for the analysis of copper included the analysis of minimum one analytical blank, one certified reference material (NBS 1572 Citrus leaves) (National Bureau of Standards, Washington DC, USA), and one double determination for each batch of 10 unknown samples. All quality control samples were evaluated for accuracy and precision, and in case of deviation from the quality criteria the analysis for the entire batch of samples was repeated. The uncertainties for all analytical methods were estimated according to the recommendations from the ISO 'Guide to the expression of uncertainty in analytical measurements (International Organization for Standardisation 1995) and the EURACHEM/CITEC Guide to quantifying the uncertainty in analytical measurements [START_REF] Eurachem/Citac | Guide CG 4. Quantifying uncertainty in analytical measurements[END_REF]. A 4-step procedure, using data from the in-house validation (top-down approach) was used in the quantification of the uncertainty (Christensen et al. 2003b). Limits of determination were calculated as 6 times the standard deviation of 7 spiked samples. The samples were spiked at concentration levels close to the detection levels. The limits of determination and uncertainty estimates, for each individual identified residue, are presented in Table 3.

[insert Table 3 here]

Results and discussion

Pesticide residues

The 563 samples of table grapes analysed in the Danish Pesticide Monitoring Project from 1998-2003 have been imported from 17 different countries. Italy and South Africa were the major exporters to the Danish market, with approximately one third of the samples from Italy, one third from South Africa and the last third from 15 other countries worldwide. Table 4 shows the number of grape samples analyzed in the period 1998-2003 and the countries of origin.

[insert Table 4 Differences between the main exporters to the Danish market, Italy and South Africa.

Since a low number of samples were collected for most of the countries, it has only been possible to compare results for the Italian and South African samples with regards to differences in number of pesticide residues and risk assessment.

Percent of samples with pesticides residues. During the year 2000 the laboratory structure at the regional level changed and the two regional pesticide residues laboratories were merged.

Unfortunately, a lower number of samples were analyzed this year and the results seem to differ from the other years. However, apart from 2000 no major differences were seen between the Italian and South African samples with respect to the number of samples with residues, where residues were found in 54-78% of the samples (data from 2000 not included). The residue level of the Italian and South African samples is shown in Figure 1. A one-way analysis of variance shows no differences for the whole period. However, from 2001-2003, the period where the majority of samples were collected, a significant difference between the two countries (p=0.04) was found showing that a higher percentage of the Italian samples contained pesticide residues compared to South African samples.

[insert Figure 1 here] Number of residue per sample. Grapes from Italy had on average 1.65 residues per samples and the frequencies increased significantly through the period. In 2003, the number of residues per sample was increased to 2.75 and one specific sample contained 11 different residues (See Figure 1).

Grapes from South Africa samples had 0.98 residues per sample, also slightly increasing during the period. A one-way analysis of variance showed no differences between the countries over the whole period, but significant differences (p=0.01) from 2001-2003.

Excedances of MRL and ARfD: Only samples from Italy exceeded the MRL. Five samples (2.6%) had residues above the EU-MRL. Two of these (phosalone and fenitrothion) were not significantly exceeding the MRL considering the analytical uncertainty. Three samples with bromopropylate were significantly exceeding the MRL (2-9 times). Two samples from South Africa had residues of prothiofos (0.131 and 0.150 mg/kg), which were estimated to exceed the Acute Reference Dose, ARfD. No international accepted ARfD for prothiofos is yet establish. The estimations are therefore based on the manufacturer (personal communication, 2003) proposal at 0.00125 mg/kg bw. A consumption of 225 g of grapes for an adult, would lead to exceedances of ARfD at 164-188%.

Corresponding, a consumption of 153 g for a child (19 kg) would lead to exceedances of ARfD at 422-483%.

Number of different pesticide residues. Samples of Italian table grapes contained in total 34

different pesticide residues, whereas 21 were found in South African samples. Table 5 shows the list of pesticide residues found in the grapes for the two countries together with their respective values for acceptable daily intake (ADI). For further details on the listed ADI see Poulsen et el. (2005) [insert Table 5

Processing factors

To investigate any effect of processing it is important to have as identical sub-samples of each sample as possible, where one of the sub-samples is subjected to the processing. In this study the effect of rinsing with tap water was investigated. Prior to the experiment the grape samples in this investigation were carefully divided in 3 sub-samples (A, B and C) as described in Methods and Materials.

Sub-samples A and B were not rinsed in water and the results from the analyses were used to test whether the sub-samples were comparable or not. The two sets of data for each pesticide group were tested by a Paired Student's t-Test [START_REF] Miller | Statistics for Analytical Chemistry[END_REF] and the results showed no difference between the concentrations in sub-samples A and B. Consequently, it was concluded that the division of a sample into three identical sub-samples was successful.

Only data which fulfilling the following criteria were included in the calculation: 1) For pesticide residues the processing factors were calculated for different classes of pesticides and a minimum number of results were needed to obtain statistical significant results. In order to include as many results as possible the limit were set at n ≥5. Therefore only results from the pesticide classes with more than 5 samples entered the calculation. 2) Only result from samples where residues were found in both sub-samples A and B were included in the calculations. 3) Result below the detection limits in sub-samples C were replaced with values of half of the detection limits in order to facilitate the calculations. 4) Since copper occurs naturally in grapes only samples with a mean concentration of at least 10 times the detection limit were used for the calculation of the processing factor for copper. Following these criteria, 116 of 142 pesticide residues found in the 120 samples were analyzed and used for the calculation of the processing factors (see Table 6). The processing factor of rinsing was calculated for 8 pesticide classes; copper, benzilates, dicarboximides, dithiocarbamates, organophosphorus pesticides (OPs), phenylamides, pyrethroids and triazoles. However, since the dicarboximides iprodione and procymidone were frequently found, the processing factor for these two pesticides was calculated individually.

[insert Table 6 here] Calculating of processing factor. The processing factor for each pesticide or pesticide class were calculated by the following equation: For copper, iprodione, procymidone and dithiocarbamates a significant effect of rinsing was found (See Table 7). For copper, iprodione and dithiocarbamates almost half of the residues were rinsed of with a significance level at 99% (p<0.01). For procymidone the residue were reduced by one fifth at the significance level 95% (p<0.05)

[insert Table 7 here]

No effect of rinsing with tap water was found for OPs, pyrethroids, benzilates, phenylamides and triazoles. However, this may be due to a high RSD value for the analytical method in combination with only few samples. For that reason, the remaining results were subjected to statistical Power Analysis [START_REF] Zar | Biostatistcal analysis 2[END_REF]) in order to calculate the number of samples required to detect an effect, taking the RSD of the analytical method into consideration. For OPs and pyrethroids the Power Analysis demonstrated that the number of samples in the study was sufficient to conclude that rinsing had no effect on these pesticides, see Table 8. However, the number of samples with residues of benzilates, phenylamides and triazoles were too low to demonstrate whether rinsing with tap water had an effect.

[insert Table 8

Intake and risk assessment

The intake for residues of pesticides and cobber is calculated separately due to the big differences in intake. Furthermore, copper is also a naturally occurring element, which differ it from the other pesticide residues.

The average daily intake of pesticides was calculated for The consumption was calculated for 6 different populations and results are shown in Table 9. The calculations of the intake of pesticides from the grapes after rinsing, was done in the same manner.

For the pesticides showing potential for reduction by rinsing, the concentration was reduced by the processing factor shown in Table 7. The results for pesticides show that the intake in µg/day from Italian grapes was 34% higher than from South African grapes. Additionally, it would be possible to reduce a higher percentage of the pesticide residues from the South African grapes by rinsing, 27%

versus 10% from the Italian. After rinsing the intake from South African grapes was almost half of the intake from rinsed Italian grapes. The results are strictly correlated with the consumption and because women on average eat more grapes than men, their intake was higher. Equally, persons with high consumption of fruit and vegetables, grapes included, also have a higher intake of pesticides. The obtained data for copper clearly indicate, that the majority of the samples from Italy were exposed to the use of copper formulation, whereas only few indications of the use of copper formulation were seen in the rest of the samples including samples from South Africa. Therefore, the intake from Italian grapes was more the three times higher than from South African. The data also show that it was possible to reduce the intake of copper from rinsed Italian grapes to a larger extend, 42% versus 27% from the South African.

[insert Usually, risk assessments of the exposures are performed for the individual pesticides. A total risk assessment of all the residues is strictly not possible, because the pesticides have different toxicological end points. However, to provide an indication on the differences in exposure between

Italian and the South African grapes, the daily intakes have been normalised by the ADI values. The method has been described in details in [START_REF] Poulsen | Pesticides. Food Monitoring[END_REF]. Briefly, a so-called Hazard Quotient (HQ) is calculated for all individual pesticide by dividing the intake with the relevant ADI. Then the HQs are summed up (ΣHQ) which to give a Hazard Index. For copper the exposure is calculated based on the Provisional Maximum Tolerable Daily Intake (PMTDI) at 0.5 mg/kg body weight (WHO/FAO 1982).

[insert Table 10 here] The results presented in Table 10 show that although the intake in µg/day from Italian grapes was higher than from South African grapes, the ΣHQ from the South African grapes was more than double the ΣHQ compared to the Italian grapes. This indicates that the pesticides used in South Africa were more toxicological problematic. Analogous to the intake in µg/day the ΣHQ correlates to the consumption. However, since women have, not only, a higher consumption but also a lower bodyweight than men, the difference between males and females was even more conspicuous for the ΣHQ than for the intake. Because the pesticides with the lowest ADI values were not between the pesticides that can be reduced by rinsing it was only possible to reduce the ΣHQ for Italian grapes with approximately 5% of the ΣHQ and the South African with only 2%. For copper the 'ΣHQ' was lower than for pesticides due to the relatively high PMTDI value.

Conclusions

The main exporters of grapes to Denmark 1998-2003 were Italy and South Africa. The Italian grapes contained higher concentrations of pesticide residues and copper, higher number of pesticides residues per sample and more different pesticides compared to South African grapes. For copper, iprodione, procymidone and dithiocarbamates, a significant effect of rinsing was found,

where up to half of the residues were removed by rinsing in tap water. No significant effect was found for OPs and pyrethroids, whereas for benzilates, phenylamides and triazoles the number of samples in the study was insufficient to demonstrate any significant effects. Because more Italian samples contained pesticides and copper with a potential for reduction by rinsing with tap water, the intake from Italian samples were reduced the most by rinsing. The exposure in relation to ADI for pesticides were higher for the South African samples, due to use of pesticides with lower ADIs.

F

  period 1998-2003 of an on-going fruit and vegetable monitoring programme conducted by The Danish Veterinary and Food Administration.

  and the sampling procedure conformed to the EU directive 2002/63/EC (EU Commission 2002) on sampling for official control of pesticide residues.Sampling was performed year round by authorised personnel from food control units. A total of 563 samples of table grapes were collected from1998 until 2003 . From 2001 -2003 the sampling the sampling

  percentage equal to the reduction by rinsing for each pesticide or pesticide class conc. AB : the mean concentration of pesticide residues in sub-sample A and B: conc. C: the concentration of pesticide residues in sub-sample CThe results for the processing factors are shown in Table7 and Table8. All the results were evaluated by performing a Paired Student's t-Test where the adopted null hypothesis was 'concentration in sub-sample A = concentration in sub-sample C'.

  Italian and South African grapes based on consumption data from the Danish National Survey of Dietary Habits 2000-2002 (Danish Veterinary and Food Administration 2002). The survey was based on a combination of personal interviews about e.g. physical activities and a 7-day diary record. The consumption data were given for each individual as an average of the 7 days for each eaten food item together with information about gender, age and body weight. In total approximately 4000 persons from 4 to 75 years of age participated in the survey.The intake of pesticides from grapes before rinsing was calculated by summing up the contribution from all the pesticides detected in the Italian or South African grapes. The contribution from each pesticide was calculated by multiplying the average concentration in Italian or South African grapes by the consumption of grapes. The average concentration for the individual pesticide residue were calculated by the following equation: Average concentration of the individual pesticides including correction by 50% of the Reporting Limit in the samples where the pesticide was not detected.C agv,pos :Average concentration of the pesticide in the samples where it has been detected.

  Number of samples the analysed for the individual pesticide.N pos :Number of samples where the individual pesticide is detected.

Table 9

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Table 1 . ICP-OES Instrumentation and operating conditions for the determination of copper

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Table 2 .

 2 Limits of determination and uncertainty estimates for copper and the pesticide residues detected in the samples.

		Limit of
		determination,	Uncertainty
	Compound	mg/kg ww	estimate, %
	Bifenthrin	0.012	18
	Bromopropylate	0.012	18
	Captafol	0.004	16
	Carbendazim	0.05	25
	Chlorpyrifos	0.021	16
	Chlorpyrifos-	0.009	16
	methyl
	Copper	0.2	2.5
	Cypermethrin	0.006	25
	Deltamethrin	0.004	10
	Diazinon	0.009	6
	Dichlofluanid	0.003	8
	Dimethoate	0.002	10
	Dithiocarbamate	0.04	25
	Endosulfan	0.004	11
	Esfenvalerat	0.008	24
	Fenarimol	0.007	19
	Fenitrothion	0.01	19
	Iprodione	0.004	10
	Metalaxyl	0.004	13
	Myclobutanil	0.005	19
	Omethoate	0.004	28
	Parathion	0.039	25
	Phosalone	0.053	28
	Phosmet	0.043	28
	Procymidone	0.004	16
	Pyrazophos	0.002	10
	Tebuconazole	0.007	30
	Tetradifon	0.052	23
	Triadimenol	0.003	9
	Vinclozolin	0.03	18

Table 3 . Number of grape samples collected and analyzed from 1998-2003 and countries of origin.

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Table 4 Pesticide residues found in Italian and South African table grapes and their ADI.

 4

		Italian		S. Africa n
		samples 1		samples 1
						ADI
						Mg/
			Mean		Mean	kg
		Number of	concentration	Number of	concentration	body
	Pesticide	residues	mg/kg	residues	mg/kg	weight
	Total number of residuessample analyzed	315		198
	Azoxystrobin 2	3	0.065			0.1
	Bifenthrin	4	0.031			0.02
	Bromopropylate	19	0.437			0.03
	Captan+folpet			7	0.307	0.1
	Carbendazim (benomyl group)	2	0.135			0.03
	Chlorpyrifos	11	0.052	2	0.035	0.01
	Chlorpyrifos-methyl	30	0.039			0.01
	Cyfluthrin	11	0.025			0.02
	Cypermethrin	10	0.044	5	0.035	0.05
	Cyprodinil 3	14	0.240	4	0.029	0.03
	Deltamethrin	6	0.014			0.1
	Dichlofluanid	4	0.079			0.3
	Dimethoate	1	0.035	8	0.039	0.002*
	Endosulfan	2	0.020	1	0.021	0.006
	Esfenvalerat	1	0.031			0.02
	Fenarimol	1	0.045	1	0.007	0.01
	Fenitrothion	12	0.120			0.005
	Fenson	1	0.430
	Flucythrinate	1	0.047			0.02
	Fludioxonil 3	12	0.115			0.33
	Iprodione	5	0.504	77	0.457	0.06
	Maneb-group	29	0.459	23	0.253	0.017
	Metalaxyl	31	0.044	1	0.008	0.08
	Myclobutanil	5	0.042	1	0.014	0.03
	Omethoate			4	0.024	0.002*
	Parathion	2	0.015			0.0006
	Penconazole	9	0.029	1	0.009	0.03
	Permethrin	1	0.042	2	0.103	0.05
	Phosalone	4	0.357
	Phosmet			1	0.008	0.01
	Procymidone	60	0.269	37	0.405	0.1
	Prothiofos			4	0.175	0.0001
	Pyrimethanil 3	10	0.120	1	0.037	0.2
	Tebuconazole	3	0.050	6	0.036	0.03
	Tetradifon	1	0.070			0.02
	Triadimefon+triadimenol	8	0.054			0.03
	Trichlorfon	1	0.016			0.02
	Trifloxystrobin 2	1	0.009	2	0.009	0.1
	Vinclozolin			10	0.078	0.01

1 191 Italian samples and 203 South African samples are analysed for the majority of the pesticide found. 2 Only analysed in 2002 and 2003 in 78 Italian samples and 95 South African samples

Table 5 Number of residues included in calculations of processing factors per pesticides and class as well as for copper.

 5

				Number of
			Number of	residues
			residues	includes in
			includes in	calculations per
	Class	Pesticide	calculations	pesticide class
	Benzilate	Bromopropylate	7	7
	Dicarboximide	Iprodione	19	19
		Procymidone	21	21
	Dithiocarbamate	Dithiocarbamate	19	19
	OP-pesticides	Chlorpyrifos	6
		Chlorpyrifos-methyl	5
		Diazinon	1
		Dimethoate	1
		Phosalone	2
		Phosmet	1	16
	Phenylamide	Metalaxyl	11	11
	Pyrethroid	Bifenthrin	3
		Cypermetrin	5
		Deltamethrin	1
		Esfenvalerat	1
		Fenitrothion	7	17
	Triazole	Tebuconazole	4
		Triadimenol	2	6
	Pesticide residues In total		116	116
	Inorganic	Copper content	120	27

Table 6 The concentration range, RSD for the analytical method, number of samples, probability for reduction and the reduction in % for pesticide residues and copper by rinsing with tap water. Significant reduction are marked by single asterisks (p < 0.05) and double asterisks (p < 0.01).

 6

			RSD for the
			analytical	Measured
	Pesticide class	Conc. range	method %	samples, n Probability Reduction %
	Copper	0.2-179	8	12	<0.001	47**
	Dicarboximide -iprodione	0.01-0.55	13	19	<0.001	49**
	Dicarboximide -procymidone	0.01-0.95	13	21	0.017	22*
	Dithiocarbamates	0.011-0.62	20	19	<0.001	41**

Table 7 . The concentration range, RSD for the analytical method, number of samples, observed effect, probability for reduction and the required sample size.

 7 Number of samples in the study was insufficient to demonstrate a significant effect3 The negative reduction represents an increase in pesticide. However, the effect is not significant

	Pesticide class OP-pesticides Pyrethroids Benzilates Phenylamides Triazoles 1 Significant no-effect 2 Page 30 of 34 Conc. range RSD for the analytical method % Measured samples, n Observed Effect % Probability Required sample size, n 0.01-1.13 20 16 20 0.313 16 1 0.01-0.63 17 17 16 0.119 17 1 0.19-0.79 16 7 -23 3 0.084 9 2 0.01-0.15 28 11 11 0.193 87 2 0.02-0.085 28 6 14 0.472 55 2 Food Additives and Contaminants

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Table 8 Intake of pesticides and copper in µg/day from Italian and South African grape before and after rinsing. Copper results are shown in brackets. The calculations are performed for 6 populations

 8

	Page 31 of 34		Food Additives and Contaminants
	Intake, µg/day			Italy	South Africa
			Consumption	Before rinsing	After rinsing	Before rinsing	After rinsing
			g/day	Intake, µg/day	Intake, µg/day	Intake, µg/day	Intake, µg/day
	All, 4-70 years	5.2	3.9 (21)	3.5 (12)	2.6 (5.7)	1.9 (4.2)
	Male, 15-75 years	3.5	2.6 (14)	2.3 (8.1)	1.8 (3.9)	1.3 (2.8
	Female, 15-75 years	7.6	5.7 (31)	5.2 (17)	3.8 (8.4)	2.8 (6.1)
	Children, 4-14 years	3.6	2.7 (15)	2.5 (8.3)	1.8 (4.0)	1.3 (2.9)
	High F&V, Male	, 15-75 years 1)	13	9.8 (53)	8.9 (30)	6.5 (14)	4.8 (10)
	High F&V, Female , 15-75 years 1)	23	17 (93)	16 (53)	11 (25)	8.4 (19)
	1) High consumer of fruit and vegetables

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Table 9 The total exposure of pesticides expressed as

 9

	Σ Σ Σ ΣHQ for

Italian and South African grape before and after rinsing calculated for 6 different populations. The calculated exposure of copper (in brackets) was based on the PMTDI.

		Food Additives and Contaminants		Page 32 of 34
			Italy	South Africa
		Body	Before rinsing	After rinsing	Before rinsing	After rinsing
		weight, kg	ΣHQ,%	ΣHQ,%	ΣHQ,%	ΣHQ%
	All, 4-70 years	66.4	0.50 (0.06) 0.47 (0.04) 1.14 (0.02) 1.11 (0.01)
	Male, 15-75 years Female, 15-75 years Children, 4-14 years High F&V, Male , 15-75 years High F&V, Female , 15-75 years F o r 82.4 67.3 35.1 82.4 66.9	0.27 (0.03) 0.26 (0.02) 0.62 (0.01) 0.60 (0.01) 0.59 (0.09) 0.56 (0.05) 1.64 (0.02) 1.61 (0.02) 0.28 (0.08) 0.26 (0.05) 1.49 (0.02) 1.46 (0.02) 1.00 (0.13) 0.95 (0.07) 2.30 (0.03) 2.24 (0.03) 1.77 (0.28) 1.69 (0.16) 5.01 (0.08) 4.89 (0.06)
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Figure 1 Percent of samples with residues and number of residues per sample found in Italian and South African grapes. Numbers above the columns are number of samples analyzed.

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Food Additives and Contaminants

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Acknowledgements

We thank the persons who contributed to this work: Trine Sørensen, Merete Eis Lund and their coworkers at the regional food laboratories in Copenhagen and Odense.