Ron Laurentius Hoogenboom

Jan Van Eijkeren
email: jan.van.eijkeren@rivm.nl

Marco Zeilmaker

Wim Kees Kan

Ron ; Hoogenboom

A

Keywords: Dioxins, PCBs Dioxins, PCBs, chicken, feed, soil, PBPK-model 45

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

Controlled exposure studies under laboratory conditions have been performed to examine 47 carry-over of dioxins and dioxin-like PCBs to eggs from feed mixed with contaminated oil 48 and from clean feed mixed with contaminated soil (Hoogenboom et al., 2005). The 49 experiments with contaminated feed not only served the determination of steady state 50 carry-over, but the study of the kinetics of carry-over as well. The work also aimed to 51 developm a computer model to describe carry-over. Only a few authors have until now 52 described a model to relate contamination levels of eggs to the contamination of ingested 53 soil or feed. [START_REF] Schuler | The transfer of polychlorinated dibenzo-p-309 dioxins and dibenzofurans from soil into eggs of foraging chicken[END_REF] presented the calculation of total TEQ contamination of 54 eggs from ingestion of soil, based on soil contamination levels, congener specific transfer 55 efficiencies and background feed contamination levels. Their model did not contain time as 56 a variable and implicitly assumed steady state contamination of the eggs. However, egg 57 contamination caused by uptake from contaminated soil or feed is a kinetic process and the 58 apparent contamination levels may not represent steady state. [START_REF] Huygebaert | The deposition 306 profile of oxy-carotinoids, fat and PCBs in egg yolks[END_REF] 59 developed a kinetic model for the excretion of PCBs in egg yolks from contaminated feed. 60

Their model assumption was that excretion is governed by the hydraulics of, and 61 absorption in the animal intestinal tract. However, not only absorption of contaminants 62 over the intestinal wall is an important aspect for the kinetics of egg contamination, but 63 also the distribution of the contaminant over body tissues, its metabolism and the 64 characteristics of excretion, i.e. laying efficiency and, for highly lipophilic contaminants 65 like dioxins, egg lipid content. 66

This study concerned the modelling of transfer of dioxins and dioxin-like PCBs from 67 contaminated feed to eggs and their accumulation in fat tissue. A kinetic model was 68 developed that considers the uptake of the contaminant, i.e. its absorption over the 69 intestinal wall after oral intake, the distribution of the contaminant over the body and its 70 carry-over from feed to eggs provided by Hoogenboom et al. (2005) were used to calibrate 72 the model, relating residues in eggs to exposure through feed at different contamination 73 levels. Data obtained from [START_REF] Hoogenboom | Residues of dioxins and PCBs in eggs following short-term exposure of 304 laying hens to feed from the Belgian crisis[END_REF], obtained from a study with much 74 higher dioxin and PCB levels, served model verification. The calibrated and validated 75 model has been applied to estimate uptake from another matrix, i.e., from contaminated 76 soil added to clean feed. Moreover, the model was applied for comparing the European 77 Maximum Residue Limits for dioxin residues (expressed as TEQ values) in feed and eggs. 78 All data for calibrating the model were obtained from the experiment described in the 82 accompanying paper by Hoogenboom et al. (2005). Verification data were obtained from 83 the study by [START_REF] Hoogenboom | Residues of dioxins and PCBs in eggs following short-term exposure of 304 laying hens to feed from the Belgian crisis[END_REF]. 84 85

Modelling approach 86

The underlying physiologically based pharmacokinetic (PBPK-)model describing the 87 kinetics of carry-over of contaminants from feed to eggs is depicted in figure 1

c abs c c f f f c c f f dA F D q y k A q A dt dA q A q A dt ε = - + + + = - ( 1) 92
Here, the reduced parameters expressed in terms of the unknown PBPK-model parameters 93 , , , , and

y f y f c f c c f f c c c c P W Q Q CL q q y k PV P V PV PV = = = =
( 2) 94 are the rate constants of mass transfer from the central to the fat compartment and vice 95 versa, excretion with egg yolk fat and hepatic clearance, respectively. 96

The time course of the concentration in egg yolk, expressed as pg TEQ/g yolk fat 97

and in abdominal fat (pg TEQ / g fat) is observed. If the latter is representative of the 98 residue levels in the fat compartment, than the models for these observations, can be 99 derived from equation ( 1) to be 100

F o r P e e r R e v i e w O n l y ( ) ( ) 1 2 2 1 , , 2 1 ( ) 1 t t abs y f y f r e r e F D y C t W r λ λ λ λ λ λ     + - + = ⋅ -         -    
( 3) 101 for the concentration in egg yolk fat, and 102 (  )

1 2 2 1 2 1 1 ( ) 1 t t c abs f f f q F D e e C t W q r λ λ λ λ λ λ     - = ⋅ -       -     (
2 1 2 2 1 4 2 1 4 2 c f c f f c f c f f
q r q q r q q r q r q q r q q r λ λ

  = - + + - + + -       = - + + + + + -    
( 5) 106 are the exponential rates of the slow, long lasting, terminal phase (λ 1 ≈ q f r/(q c + q r + r) and 107 the fast, short lasting, initial phase λ 2 ≈ q c + q r + r. 108

Note, that the experimental data necessitate the introduction of a few additional 109 parameters with respect to the model in equation (1), such as the amount of fat in egg yolk, 110 which is known, and the weight of the fat compartment, which is unknown. As a result, the 111 model contains the four unknown kinetic parameters for transport and elimination, q c , q f, k 112 and y, the unknown absorbed fraction F abs and the unknown fat compartment weight, W f . 113

Straightforward analysis shows that the inter compartment transfer rate parameters 114 q c and q f can be identified unconditionally from the data. Therefore, these parameters can 115 be freely varied to fit the data. However, the elimination rate parameters y and k, the 116 fraction absorbed and the fat compartment weight W f can only be determined in relation to 117 each other, i.e., the parameters 118 Without introducing these new parameters of equation ( 6) into the computer model, this 125 problem can be resolved as follows. The fraction absorbed is 1 at maximum, while the 126 metabolism rate is 0 at minimum. If the model is fit to the data while the metabolism rate is 127 kept constant, k = 0, then the maximum value for the excretion rate y max = c 1 can be 128 determined from the data, as well as the minimum fraction absorbed, F abs, min , and the 129 minimum weight of the fat compartment W y,min . This would represent the case that 130 elimination by metabolism is negligible compared to elimination through eggs. Once these 131 parameters are identified under this restrictive condition, then for F abs, max = 1, i.e., 132 representing the case that the amount ingested is totally absorbed over the gut wall, the 133 minimum value for the excretion rate is found to be the product y min = y max . F abs,min of the 134 values found for the case k = 0. From this minimum, the maximum value for the 135 metabolism rate k max = y max -y min can be found. The maximum value of the fat compartment 136 weight follows from W f,max = W f,min / F abs,min . 137

It is assumed that after ovulation, the yolk is not any more contaminated during egg 138 white formation and deposition. Therefore, one should provide for a one-day delay 139 between ovulation of the yolk and laying of the corresponding egg. Besides, while in the 140 long range, the daily loss by excretion is the product of laying efficiency and excretion per 141 day, i.e. εy, the amount to be found in an excreted egg is proportional to y only. Moreover, 142 one should provide for the time, τ, between the start of feeding contaminated feed and the 143 first yolk ovulation thereafter, which is apparent as an off-set in the time course of egg 144 contamination. This latter parameter is estimated from the data too. 145 because of the extreme lipophilicity of the compounds, and by its relatively poor blood 151 flow that may even be further limited by intra-tissue diffusion. Therefore, equilibrium 152 between the concentration in this compartment and the concentration in blood is likely to 153 be reached much slower, typically in the order of a few days, than equilibrium between the 154 concentration in blood and other tissues. 155

The central compartment comprises all the other tissues, which will reach 156 equilibrium with blood in the order of hours rather than in the order of days. After 157 absorption, the contaminants enter the systemic blood circulation via the vena porta, i.e., 158 into the central compartment ( abs F D ). Biotransformation of the contaminants is assumed to 159 take place in the liver, i.e. the central compartment, which is characterized by the 160 parameter CL (for clearance). Excretion by egg yolk is from the blood, i.e., from the 161 central compartment into yolk. The concentration in the growing yolks is assumed to be 162 proportional to the concentration in blood (partition coefficient , y f P ). A model, based on 163 the work of Donoghue and co-workers [START_REF] Donoghue | Modelling 284 residue uptake by eggs: 1. Similar drug residue patterns in developing yolks following 285 injection with ampicillin or oxytetracycline[END_REF](Donoghue et al., , 1997a(Donoghue et al., , 1997b;;Donoghue 164 and Meyers, 2000;[START_REF] Donoghue | Mechanisms regulating drug and pesticide residue uptake by egg 295 yolks: development of predictive models[END_REF] that incorporated also yolk growth as determinant of 165 the concentration had to be abandoned, because it systematically under-estimated yolk 166 contamination levels during the initial phase. After ovulation, the yolk is assumed to be 167 excreted from the central system, because the lipophilic contaminants are not likely to 168 interact between system and yolk during the phase of white formation. This is in contrast 169 Eggs in the trial weighed about 60 grams in the mean, of which according to Gilbert (1971) 172 32% was assumed to be yolk weight) and 30% of yolk weight to be egg yolk fat weight, 173 amounting to 5.8 g of yolk fat per egg. Laying performance during the experiment was 174 about 90%. Data on total TEQs, i.e., the sum TEQs of dioxins, furans, mono-and non-175 ortho PCBs, as presented in the accompanying article (Hoogenboom et al. 2005), were 176 used for further calibrating the model. This consisted of fitting the two transfer rate 177 constants, q c and q f , the egg yolk excretion rate y, the fraction absorbed F abs and the fat 178 compartment weight W f , while keeping the elimination constant k = 0. The model was 179 fitted to both the data on egg yolk and abdominal fat levels of all the different feed 180 contamination levels simultaneously. Therefore, all the available data served model fitting 181 in only one optimization run, optimizing the log likelihood of the fitting parameter set. As 182 yolks ovulate the day before the eggs are sampled, eggs yolk levels analyzed from time t 183 (day), were compared to computed levels at time t -1 (day). Moreover, as ovulation after 184 the start of applying contaminated feed, considered to be t = 0, takes place at an unknown 185 time, another parameter, τ < 1 (day), was fitted to the data. Data fitting was performed with 186 ACSL Optimize, optimizing the log likelihood of the parameter values. The fact that the transfer parameter from the central to the fat compartment q c is higher 199 than q f implies that the distribution volume (P f V f ) of the fat compartment, i.e., its 200 physiological volume corrected for its storage capacity as compared to the same volume of 201 blood, is greater than the distribution volume (P c V c ) of the central compartment. 202

Figure 2 shows the residue data and the computed concentration-time curves for the 203 total TEQ levels in both egg yolk and abdominal fat based on the fitted parameter values. 204

Figure 3 shows similar curve fits for the total TEQ, but also the TEQ levels derived from 205 the dioxins and furans, the non-ortho and the mono-ortho PCBs. Notice the clear bi-phasic 206 time course of egg residue levels, while abdominal fat levels seem to be mono-exponential. 207

The (short-lasting) fast first phase has a half-life of about 2.5 days. The (long-lasting) slow 208 terminal phase appeared to have a terminal half-life of about 50 days. Fat storage behaves 209 like a large capacitor, with slowly reacting kinetics, while egg levels, that are proportional 210 to blood levels, reflect the small capacitance, fast reaction kinetics of blood levels. This 211 phenomenon appears not only during the period of feeding contaminated feed, but also 212 during the stage thereafter. The apparent difference in the kinetics of residues in eggs and 213 abdominal fat is also depicted in Figure 4 for one of the soils and 60% for the other, as compared to about 90% (estimated range: 80 241 to 100%) for the oil contaminated feed. 242

In the current study, indicator PCBs (PCBs 28,53,101,118,138,153 and 180) 243 were also spiked to the oil used for preparing the feed. Figure 6 shows the data for total 244 indicator PCB levels in eggs and abdominal fat together with a curve fit. Parameters had to 245 be slightly modified, since without modification there was a clear overestimation of the 246 egg levels (Table 1). The reduced value of notably f q shows that the distribution volume 247 of the fat compartment for these indicator PCBs is greater than for dioxins and dioxin-like 248 PCBs, resulting in a greater storage capacity and reduction of residue levels. 249

Corresponding half-lives of the initial and terminal phase are 2.8 days and 55 days, 250 respectively, ten percent increased as compared to the values of 2. 13 fast response after contamination in the order of days followed by a much slower 265 response in the order of months 266

• In contrast, residues in abdominal fat did not show bi-phasic kinetics: the short-267 lasting fast response phase lacks almost completely. Differences in residue levels in 268 eggs and abdominal fat, expressed as pg TEQ/ g fat can be attributed to differences 269 in the kinetics of the corresponding compartments. At steady state, after prolonged 270 exposure to contaminated feed, residue levels are expected to be about the same. 271

• The model was applied to carry-over of dioxins from two soils incorporated in feed 272 to eggs. The range of absorbed fractions from the two soils was still high but lower 273 than the absorption of dioxins from oil incorporated into feed. 274 egg yolk fat that is produced with weight W y,f per day with a laying efficiency of ε. These 326 processes can also described by the set of reduced parameters (B) derived from the PBPK 327 model parameters (see Materials and Methods), with k for hepatic clearance, y for 328 excretion through the yolk and q c and q f being the compartment transfer parameters from 329 central to fat compartment and vice versa.

• From

Lelystad

  .H. van Eijkeren 1* , M.J. Zeilmaker 1 , C.A. Kan 2 , W.A. Traag 3 & L.A.PInstitute of Public Health and the Environment, P.O.Box 1, 3720 BA, Bilthoven, The 17 Netherlands 18 2 Animal Sciences Group of Wageningen UR, Division Nutrition and Food, P.O. Box 65, 8200 AB 19

  metabolism and excretion by egg yolk fat. Experimental data on 71

  (upper 88 panel). The corresponding compartment model in figure1(lower panel) can be formulated 89 mathematically as a set of two mass balances, representing the changes in absolute 90 amounts of contaminants in respectively the central and the fat compartment. 91 ( )

  in abdominal fat. Here, r = εy + k represents the total elimination of 104 the compounds and 105 ( )

  be varied freely to fit the data, but not the parameters y, k, F abs , and W y separately. 120Note that 1 c denotes the total elimination from the system of the amount that is absorbed 121 wall, 2 c denotes the elimination through eggs of the amount that is ingested by 122 the hens and 3 c relates the experimntally determined abdominal fat concentrations to the 123 unknow fraction of the contaminant that absorbed. 124

  underlying physiologically based pharmacokinetic (PBPK-)model describing the 147 kinetics of carry-over of contaminants from feed to eggs is depicted in figure 1. The fat 148 compartment comprises abdominal fat, subcutaneous fat, fat fraction of the skin, bone 149 marrow and intermuscular fat. This compartment is characterized by its storage capacity, 150

  lipophilic drugs. Yolk fat of weight , y f W is produced with an efficiency of ε per 170 day. Laying efficiency is determined experimentally and thus is a known model parameter. 171

  first line shows unconditional results, while the resulting interval estimates in 192 the second and third line are mutually dependent. Taking e.g. the minimum possible value 193 for metabolism, k = 0, the corresponding value for excretion is maximal and corresponding 194 values for absorption and fat compartment weight are minimal. The value for the mean 195 ovulation time τ for the population was 0.19 day after the start of feeding contaminated 196 feed, being about four and a half hours. Clearly, considering the values for y and k, the 197 main route of elimination of dioxins from the body is by egg yolk fat and not metabolism. 198

  for the 1.85 ng TEQ/kg feed contamination 214 group. Note that during the contamination period, abdominal fat residue levels are lower 215 then egg residue levels, and vice versa during the depletion period on clean feed. After 216

•

  A model was developed for the kinetics of total sum TEQ carry-over of dioxins and 255 dioxin-like PCBs from feed to eggs. The model could not only predict total sum 256 TEQ, but also the results for sum TEQs of separate groups (dioxins and furans, 257 mono-ortho PCBs and non-ortho PCBs). The model was not tested for individual 258 congeners. The model could successfully be applied for data on the kinetics of total 259 and group sum TEQs of contaminated feed with a much higher level of 260 contamination and a quite different contaminant composition 261• Absorption of the sum TEQ of the dioxin-like compounds from feed prepared with 262 contaminated oil is quite efficient, being around 90%; the main elimination route is 263 by yolk fat excretion. Residues in eggs show clearly bi-phasic kinetics, indicating a 264

Figure 1 .

 1 Figure 1. Two compartment PBPK model (A) for the disposition of lipophilic compounds 321

330 331Figure 2 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .

 33023456 Figure 2. Measured levels (symbols) and computed concentration-time curves based on 332

Table 1 .

 1 model calculations, it is evident that EC MRL values for dioxin-derived TEQ 275 levels in laying hens feed and in eggs are not in accordance/compliance. At least a 276 fourfold reduction is required to guarantee egg levels below the MRL. This should 277 Optimized parameters for the model for dioxin-like compounds and indicator 312PCBs. The parameters q c and q f represent the transfer from the central compartment to the 313 fat compartment and vice verse, y and k the elimination via yolk fat and clearance, F abs the 314 absorbed fraction, and W f the weight of the fat compartment.

	278	be verified experimentally.
	279

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

Acknowledgements 280

This study was sponsored by the Dutch Ministry of Agriculture, Nature Management and 281 Food Quality. 282


Richard Mark Weightman

Chris Dyer

John Buxton

Derek Farrington

Keywords: lettuce, endive, nitrate, light, shading, variability, sampling directive, Lactuca sativa, Cichorium endiva

Effects of light level, time of harvest and position within field on variability of tissue nitrate concentration in commercial crops of lettuce (Lactuca sativa) and endive (Cichorium endiva)

Introduction

Nitrate is naturally present in all vegetables. Green, leafy vegetables for example lettuce and spinach, contain the highest concentration, and are the major source of nitrate in the diet. The European Commission has set maximum limits for levels of nitrate in lettuce and spinach (Anon. 2005). The limits for tissue nitrate concentration (TNC) in fresh lettuce are as follows (in each case values are presented for crops grown either under cover, or in the open air respectively, on a fresh weight basis): 4500 and 4000 mgNO 3 -kg -1 for non-Iceberg type lettuces harvested from 1 October to 31

March; 3500 and 2500 mgNO 3 -kg -1 for non-Iceberg type lettuces harvested from 1 April to 30 September; 2500 and 2000 mgNO 3 -kg -1 for Iceberg type lettuces (harvested at any time of the year).

Currently, nitrate analysis for monitoring purposes is based on a determination made on a bulked (composite) sample of typically 10 lettuce plants (Anon. 2005). This choice of sample size is based on values found in the sampling Directive 2002/63/EC (Anon.

2002a) for the official control of pesticide residues in, and on, products of plant or animal origin. For the purposes of monitoring contaminants other than nitrate, this sample size would only apply where the unit weight is less than 250 g. This weight covers all lettuce varieties except Iceberg types, which have a higher marketable head weight (Anon. 2001) and where normally five plants or 2 kg of fresh plant tissue would be sampled (Anon. 2002a). However, the regulations pertaining to nitrate stipulate that the minimum number of units per laboratory sample is 10 for Iceberg lettuce types (Anon. 2005). One of the aims of the present study was to determine whether the present method of bulking 10 plants is adequate to provide a representative sample of lettuce, and was part of a larger study designed to provide guidance on sampling lettuce and spinach for nitrate analysis (Weightman et al. 2005). Before an optimum sample size can be estimated, the variance of the population must be known. However, at the outset of the study, there were no data available regarding the typical variability in TNC for commercially grown crops of lettuce, particularly outdoor crops. Moreover in designing sampling plans, it is important to understand the sources of any variability present.

It is known that there is considerable variation in TNC in lettuce and spinach, with typically reported ranges for protected crops (i.e. crops grown under cover) of 2000 -5000 mgNO 3 -kg -1 (mean 3300 mgNO 3 -kg -1 ) for lettuce, and 2600 -4900 mgNO 3 -kg -1

(mean 3900 mgNO 1940s and 1950s (Lessells 19591940s and 1950s (Lessells , 1973;;Boyd and Simpson 1956). Moreover much of the data on which the earlier studies are based are not publicly available, and where documents are available, relate principally to soil rather than plant sampling.

It has been demonstrated that winter-sown crops have generally higher TNC levels than summer crops in the same environment (Byrne et al. 2004), and that northern European crops have higher TNC levels than corresponding southern European crops (Anon. 2005). It is recognised that these differences can be due to both higher irradiance in summer, which tends to reduce nitrate, and also to higher growth rates which coincide with periods of high irradiance and warmer temperatures (Kanaan and Economakis 1992). Burns (2000) suggested that shading in the 10-14 days prior to harvest would increase TNC in glasshouse lettuce, even when best agricultural practice is being followed. Therefore, current guidelines in UK crop assurance protocols suggest that growers avoid sampling lettuce during dull weather conditions (Anon. 2002b). However, the importance of short timescale variations in irradiance (e.g. 24 -48 h) and how much these contribute to variability in TNC at sampling, is less clear.

Before nitrate can be incorporated into amino acids, it must be reduced by the enzyme nitrate reductase (NRD) in the plant, using light generated energy directly. There is therefore a tendency for nitrate levels to decline in light, causing a diurnal rhythm as reduction takes place (Carrasco and Burrage 1992). However, in contrast, Hardgrave (1994) showed that for three glasshouse lettuce experiments (in winter, spring and summer), time of day (am or pm) had no effect on plant nitrate residues. Moreover, in two studies with lettuce (one winter and one summer), Byrne et al. (2004) showed that time of day (08.00, 12.00 and 16.00 h) had no significant effects on TNC. In a further growth room study by the same authors, sampling on 9 occasions through the day, there was no significant temporal trend in TNC.

The present work reports the results of three separate studies to address sources of variability in TNC. In the first, two fields of lettuce were sampled, and TNC determined for individual lettuce heads for a relatively large sample size (60 plants). These data therefore allowed the testing of whether 10 plants would give an unbiased estimate of the mean, and also allowed assessment of the utility of various sampling patterns in the field. In the second study, the effect of short-term fluctuations in light level on TNC was investigated. The third study was carried out in order to examine the levels of TNC in lettuce through the day and to estimate their significance, in four different field situations. In each of these studies, determination of TNC of individual plants enabled further statistical analysis to be carried out. Based on analysis of variance (ANOVA) the significance of the effects of shading and time of harvest could be estimated, as well as the residual error, enabling estimates to be made of the standard error.

Materials and methods

Locations of crops

Seven commercial crops of lettuce (Lactuca sativa L.) and one crop of endive Further analysis was carried out on the data, by retrospectively selecting 10 data points from the field layout as if they had been sampled by conventional W or X shaped sampling patterns. This selection was repeated twice for each sampling pattern at each site.

For each site, individual TNC values for 10 plants, selected from both W shaped and X The aim of this experiment was to assess the effect of short term (24 h) periods of shading on TNC in lettuce and endive crops. Differential treatments were achieved by placing shade cloth over the plants in the experimental area using the design described below. Prior to starting the experiment, the amount of light transmitted through the shade material was determined using a 'sunfleck' ceptometer (Decagon) placed above and below the shading material. The ceptometer gave an instantaneous measurement of photosynthetically active radiation (PAR), and observations of 10 repeated measurements showed that the material absorbed 85% (SD=3.1) of the incident PAR.

This level of shading would be representative of incident light on a dull day, with full cloud cover.

The experiment was repeated four times in separate commercial crops (Table I). The plants in shading treatment 1 were shaded for 24 hours, then left unshaded for the remainder of the experiment (24 -48 h), while treatment 2 remained unshaded throughout the experiment (total duration 48 h). Ten plants were taken from each shading treatment at each of two harvests, either (a) after 24 h, or (b) after 48 h.

At each site, there were four replicates of each treatment, arranged within the commercial crop, and 40 plants were harvested in total (10 plants for each shading x harvest treatment combination). Plants were weighed and then frozen prior to nitrate analysis.

Analysis of variance (ANOVA) was used to estimate the significance of the various treatment effects and to partition the total variance, thereby allowing assessment of the magnitude of the residual variation (or residual error). The treatments were studied in a fully factorial design and the results subjected to ANOVA, where the main treatment effects (shading and harvest timing each with 1 df) and their interactions (each with 1 df) were compared to the residual error (df=36).

Experiment 3 -magnitude of diurnal variation in TNC

Plants from commercially grown crops of lettuce were harvested from two sites each (Table I). Samples were harvested over a 24 h period on six occasions, at 3 h intervals (times; 07.00, 10.00, 13.00, 16.00, 19.00 and 22.00 h). On the day prior to sampling, 60 plants were numbered in the field, and randomly sampled the following day. At each sampling occasion, 10 plants were harvested, plant fresh weights were recorded, and individual plants bagged and frozen, prior to analysis.

Each data set was analysed separately by ANOVA in order to assess the significance of time of harvest during the day, assuming that each timing had equal variance. The main treatment effect (harvest timing with 5 df) was tested for significance against the residual error (variation between plants within harvests; 54 df).

Analytical methodology

Results and discussion

Experiment 1

Appreciable variability in lettuce head weights was measured within a site. Reinink and Eenink (1988) examined diverse genotypes of lettuce and found significant differences in TNC, although they noted that these were confounded with differences in plant weight between varieties. Smaller lettuce plants tend to have higher TNC levels.

However, in the present study, sorting data on the basis of the plant weight showed no significant differences in mean TNC for plants above, or below the mean plant weight in each case at p=0.05: Site A; 933 (SE 93.8, n=27) vs 760 (SE 68.3, n=33) mgNO 3 -kg -1

for plants <170g and >170g respectively, and site B; 1486 (SE 128.9, n=29) vs 1226 (SE 91.4, n=31) mgNO 3 -kg -1 for plants <300g and >300g respectively. Since plant weights had relatively little effect on TNC within a site and plant fresh weights were distributed evenly around the mean, the effect of head weight was not considered further.

The distribution of individual plant TNC within the two sites was not uniform, and areas of more than six continuous sampling positions were identified, which had individual TNC lower than the site mean, at both sites. Table II shows the grand mean TNC (n=60) for each site, and the mean TNC's based on groups of 10 plants. All analyses described here were based on arithmetic means. The grand means differed significantly between the two sites, with site B being highest.

[Insert Table II about here]

Having a detailed field plan allowed assessment of different options for estimating TNC within the field, based on standard sample numbers (n=10). In the first example, plants were grouped by plot order (Table II). This would be representative of a sampling pattern whereby 10 plants were sampled contiguously along a row. There was appreciable within site variation, with plots 41-50 at Site A having significantly higher mean TNC levels than plots 11-20, 21-30 and 31-40 at p=0.05. More importantly, certain groups of 10 plants, gave mean TNC values which were significantly different to that of the grand mean. For example, at site A, plots 21-30 had significantly lower mean TNC than the grand mean, whereas plots 41-50 had mean significantly higher mean TNC than the grand mean at p=0.05. Similarly for site B, the mean TNC for plots 11-20 was significantly lower than that of plots 21-30, and also of the grand mean at p=0.05. This spatial variability in plant TNC probably reflects underlying variation in soil mineral nitrogen within the field, but also differences in individual plant growth in response to localised areas of soil compaction, or variability in soil pH.

For plants sampled along the row, the coefficient of variation had an approximate twofold range between plant groups within a site (31-56% for Site A and 20-67% for Site B).

Next, rather than using plot order, individual plant TNC data was allocated to 6 groups of 10 plants chosen at random. Each of these randomisations was repeated 10 times at each site (data not presented here for the sake of brevity). In all cases, the mean and the median were very similar (838 vs 840 for Site A and 1352 vs 1333 mgNO 3 -kg -1 for Site B). There was some evidence from these randomised data that the SE increased as TNC levels increased within a site. However, it should be noted that coefficients of variation tended to be lower at Site B, with the higher mean TNC.

The estimates of the TNC for bulked/composite samples gave values which in three out of eight cases, appeared to be outside the confidence limits of their respective grand means. However, considering the greatest deviation of a pooled value from the grand 10.5% of the mean, a deviation of this magnitude can be considered acceptable. The estimates of TNC based on arithmetic means calculated here using data from either X or W sampling patterns, gave values which were not significantly different from one another, or significantly different from the grand means for TNC, at their respective sites at p=0.05 based on their confidence intervals (Table III).

[Insert Table III about here] Spatial variability in NO 3 -is appreciable in most UK soils and this means that sampling protocols need to be designed robustly in order to obtain a reliable estimate of the mean. The data confirm that the choice of 10 plants, sampled randomly using an X or W shaped sampling pattern, provided acceptable measures of the overall mean, for the two fields of lettuce sampled in this study. For vegetable crops harvested from narrow beds, it is assumed that a number of beds being harvested on the same day could be pooled to make one block, and also harvested using similar shaped sampling patterns.

Experiment 2

In the short term shading experiment the main treatment effects on TNC were small.

Averaged across both times of harvest, shading had no significant influence on plant TNC (Table IV). However, in two of the four trials (experiments E and F), the overall effect of time of harvest on plant TNC was significant (means averaged across the two shading treatments).

[Insert Table IV In addition to the main treatment effects, there were significant interactions noted between shading and harvest treatments in experiments C and F (p<0.01 and p<0.05 respectively; Table IV). In both these experiments, shading in the 0-24 h immediately prior to harvest (timing a) increased TNC. However, there was no significant difference in TNC between shaded and unshaded treatments 24 h after the shading treatments had been removed (timing b). These interactions were only significant in two out of four cases. Given that the shading treatment employed here was relatively severe compared to average light levels experienced on a dull day under normal conditions, it is unlikely that these short term effects on shading will be of importance in commercial practice. These results support the conclusions of Byrne et al. (2001) who found that extending daylength with supplementary light for four days prior to harvesting lettuce had no significant effect on plant nitrate levels.

Experiment 3

There were no significant effects of time of harvest on plant fresh weight (data not presented), but a significant effect on TNC (p<0.05 at both sites; Table V). At both sites, harvest at 07:00 and 13:00 h gave the lowest TNC levels and at 16:00 h gave the highest TNC levels. The sampling at 16:00 h gave TNC values significantly higher than those at the previous sampling at 13:00 h, based on the SED.

[Insert Table V about here]

Although the time of day the plants were harvested was shown to have a significant effect on variability in TNC values in lettuce, there were no consistent temporal trends.

It should also be noted that the differences recorded between sampling times would not solely be due to environmental effects, but also included some variability associated with sampling (e.g. operator, speed of transfer of samples to cold storage etc). However, studies on lettuce by Reinink (1991), Hardgrave (1994) and Byrne et al.

(2004) all failed to detect a significant relationship between TNC and time of harvest within the day. It is most likely that any true diurnal variation due to nitrate uptake and cycling of NRD, is masked by variation due to transient wilting in high light and temperature conditions (which would tend to increase TNC during the day) and differences in overall growth rate of the plant.

Summary of data from experiments 1-3 and effect of increasing sample size on error estimates

The data from these experiments are summarised in Table VI as mean, SE and CV.

Burns (2000) showed that CVs for hydroponic and soil-grown glasshouse in winter were low in the range 8-17% and higher in summer soil-grown lettuce (44%). Based on the data in Table VI, a similar range was seen in the present study and the average CV was c. 35%. Based on a population with a mean TNC of 1000 mgNO 3 -kg -1 , variation of this magnitude would imply that increasing the sample size from 10 to 40 plants would decrease the SE from 12 to 6% of the mean. However, within the estimates of residual variation, is also contained the analytical, or measurement error, which has been estimated at 10.5%. Therefore, however large the sample size becomes, there is a point beyond which the standard error cannot decrease any further, and it is likely that the SE would not fall below c. 12% of the mean without any reduction in the analytical error.

[Insert The studies were designed to quantify the relative importance of short-term effects of irradiance and diurnal variation, and to gain an estimate of the residual variation in TNC in commercial lettuce crops.

It is concluded that short-term differences in light levels on mean TNC are relatively small, compared to the differences in mean TNC seen between harvest at different times of the year, or shading 10-14 days prior to harvest (Burns 2000). Although the time of day at which plants are harvested appeared to influence TNC levels, there was no trend in TNC with time which could be used as a basis for improving advice regarding time of sampling during the day.

Since a sample of 10 lettuce plants, assuming they are harvested using an appropriate random (e.g. W or X shaped) sampling pattern gives a reasonable estimate of the grand mean, it would appear that 10 is an acceptable sample size for surveillance purposes. However, similar data have not been collected at other points in the supply chain e.g. from packhouses or warehouses, where such sampling patterns may not be appropriate and where the possibility of non-compliance can result in serious financial penalties to the grower.

F

  Effects of light level, time of harvest and position within field on variability of tissue nitrate concentration in commercial crops of lettuce (Lactuca sativa) and endive (Cichorium endiva)

  shaped patterns were used to calculate means, by two different methods: a). An estimate of the TNC which would have been measured on a pooled/composite sample of 10 lettuce heads, using a weighted mean, calculated as follows;TNC of composite sample = (TN 1 +TN 2 …TN 10 )/(PW 1 +PW 2 ….PW 10 ), where, TNn = amount of nitrate-N (mg) in an individual lettuce head, TNn= TNCn*PWn/1000 for n = 1….10, and TNC is tissue nitrate concentration (mg kg -1 ); PW is plant fresh weight (g) and n = 1 to 10. b). From the individual TNC values measured, the arithmetic mean, and associated standard error (SE) and coefficient of variation (CV) were calculated. short term effects of shading on TNC

  to 16.5% of the grand mean, and the analytical error represents c.

  Table VI about here]

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Table I .

 I BYRNE, C., HENNERTY, M. J. and MAHER, M. J. (2001). Can the nitrate content of Irish protected lettuce be reduced ? University College Dublin, Faculty of Site, variety details and harvest dates for lettuce and endive crops sampled in 2003.

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	ANON. (1991). Soil sampling -it has to be right. Unpublished Agricultural Development HARDGRAVE, M. (1994). Glasshouse lettuce: Reduction of nitrate residues. Final NEEDHAM, P. & HARROD, M. F. (1973). Sampling Plants for Analysis. Unpublished
	and Advisory Service paper, SS/91/6 3pp. report on project PC88 to the Horticultural Development Council, East Malling, Agricultural Development and Advisory Service paper, SS/Phys/29. Closed
	Site code	Site type	Species	Variety	Date harvested
	Kent. conference of soil scientists plant physiology and horticultural committees, Experiment 1 A Field Lettuce Goodison 16 th September ANON. (2001). Commission regulation (EC) No 1543/2001 of 27 th July 2001, laying Agriculture Research Report, 2000-2001. November 1972. B Field Lettuce Iceberg 16 th September
	down the marketing standard for lettuces and curled-leaved and broad-leaved KANAAN, S. S. and ECONOMAKIS, C. D. (1992). Effect of climatic conditions and time Experiment 2
	(Batavian) endives. Official Journal of the European Communities, L203/9, BYRNE, C., MAHER, M. J., HENNERTY, M. J., MAHON, M. J. and WALSHE, P. A. of harvest on growth and tissue nitrate content of lettuce in nutrient film culture. REININK, K. (1991). Genotype x Environment interaction for nitrate concentration in C Tunnel Endive Glory 15 th April D Glasshouse Lettuce M'lady 16 th April
	o r P e e r 28.7.2001. ANON. (2002a). Commission directive 2002/63/EC of 11 th July 2002. Methods of sampling products of plant and animal origin for the determination of pesticide residues for checking compliance with MRLs. Official Journal of the European Communities, L 187, 16.7.2002. F o r P e e r (2004). Reducing nitrate content of protected lettuce. Teagasc research report, Teagasc, Kinsealy Research Centre, Malahaide Rd, Dublin 17, Ireland. Available from: http://www.teagasc.ie/research/reports/horticulture/4561/eopr-4561.htm [Accessed 2005 Sept 20]. CARRASCO, G. A. and BURRAGE, S. W. (1992). Diurnal fluctuations in nitrate F o r P e e r Acta Horticulturae, 323 Soil and Soilless Media under Protected Cultivation; 75-80. LESSELLS, W. J. (1959). Report on the investigation into the technique of soil F o r P e lettuce. Plant Breeding, 107; 39-49. REININK, K. and EENINK, A. H. (1988). Genotypical differences in nitrate F o r P E Field Lettuce Little Gem 29 th May F Field Lettuce Lobjoits 30 th May Experiment 3 G Field Lettuce Pinnochio 4 th July H Field Lettuce Pinnochio F o r 17 th July P accumulation in shoots and roots of lettuce. Scientia Horticulturae, 37; 13-24. e e sampling, 1957-58. Grassland. Unpublished Agricultural Development and Advisory Service paper, SS/C/296, 19pp. Duplicated report WJL/9/10/59. e r e r e r
	R e v i e w ANON. (2002b). Cultural advice to growers. In Crop Specific Protocol -Protected lettuce 302. Assured Produce Ltd. Issue No 1/2002. p13. ANON. (2005). Commission regulation (EC) No 1822/2005 of 8 th November 2005, amending Regulation (EC) No 466/2001 as regards nitrate in certain R e v i e w accumulation and reductase activity in lettuce (Lactuca sativa L.) grown using nutrient film technique. Acta Horticulturae, 323 Soil and Soilless Media under Protected Cultivation; 51-59. R e v i e R R R LESSELLS, W. J. (1973). Soil sampling investigations, 1947-58 -a summary. e e e Unpublished Agricultural Development and Advisory Service paper, SS/SAF/7, Rothamsted Experimental Station. v i e v i e v i e DAMPNEY, P. M. R., GOODLASS, G., RIDING A. E. and FROMENT, M. A. (1997). The spatial variation of soil mineral nitrogen in two arable fields in England, and w MAFF (1979). Sampling of soils, soilless growing media, crop plants and miscellaneous w Acta Horticulturae, 145, The Use of Fertiliser in Protected Vegetable w w
	O n l vegetables. Official Journal of the European Communities, L293/11, 9.11.2005. BOYD, D. A. and SIMPSON, E. P. (1956). Report on the investigation into the O n l the implications for decision processes on nitrogen fertiliser use. Proceedings, Vol III of the 11 th World Fertiliser Congress, Fertilisation for Sustainable Plant Production and Soil Fertility, Gent, September 7-13, 1997. pp155-162. O n l substances for chemical analysis. Booklet 2082. Ministry of Agriculture Fisheries and Food (Publications), Tolcarne Drive, Pinner, Middlesex HA5 2DT. Crown copyright, 1979. O n l Production; 251-256. WEIGHTMAN, R. M., BUXTON, J., DYER, C. and FARRINGTON, D. (2005). O n l O n l
	y technique of soil sampling 1954-55. Unpublished Agricultural Development and y y y Investigation of sampling and sources of variation in nitrate levels in lettuce and y y
	Advisory Service paper, SC/C/203, 19pp. Duplicated report EPS/18/5/56. FARRINGTON, D. S. (2001). A comparison of the accuracy of extraction methods used MAFF (1983). Sampling for soil analysis. Leaflet 655, amended 1983. Ministry of spinach. Final report on Project CO3030 to the UK Food Standards Agency,
	in nitrate residues monitoring programme. Final report on Project C03016 to the Agriculture, Fisheries and Food (Publications), Lion House, Willowburn Estate, June 2005.
	BURNS, I. (2000). Development of a decision support system for nitrogen fertiliser UK Food Standards Agency. Alnwick, Northuumberland, NE66 2PF. Crown copyright, 1979.
	application in glasshouse lettuce (LINK). Final report on project PC88a (LINK
	project LK 0438) to the Horticultural Development Council, East Malling, Kent.

RICHARDSON, S. J. and HARDGRAVE, M. (1992)

. Effect of temperature, carbon dioxide enrichment, nitrogen form and rate of nitrogen fertiliser on the yield and nitrate content of two varieties of glasshouse lettuce. Journal of the Science of Food and Agriculture, 59; 345-349.

VAN EYSINGA, J. P. N. L. R.

(1984)

. Nitrate in vegetables under protected cultivation.

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Table II .

 II Mean TNC levels, with associated standard errors and coefficients of variation, for either Cos or Iceberg type lettuce plants sampled in experiment 1, each based on either 60 plants, or 10 plants chosen in successive plot order.

	Grouping		Mean TNC		SE	CV
			(mgNO 3 -kg -1 )	(%)
	Grand means				Site A (Cos)
	Plots 1-60		838		57.1	52.8
					Site B (Iceberg)
	Plots 1-60 Ranked by plot order Plot 1-10 F o r	1352 771		79.3 Site A (Cos) 135.2	45.4 55.5
	Plot 11-20 Plot 21-30 Plot 31-40 Plot 41-50 Plot 51-60 Plot 1-10	684 575 681 1329 987 1448 e P e r		99.8 57.1 107.0 182.0 97.7 Site B (Iceberg) 46.1 31.4 49.7 43.3 31.3 243.1 53.1
	Plot 11-20 Plot 21-30 Plot 31-40 Plot 41-50		832 1775 1537 R 1001 e	180.0 206.4 168.3 64.5	68.5 36.8 34.6 20.4
	Plot 51-60		1348	107.8 i v e	25.3
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Table III .

 III TNC estimated for a pooled (composite) sample of 10 heads, and the corresponding arithmetic mean from TNC values of individual lettuce heads, with its associated standard errors and coefficients of variation, for lettuce plants sampled in experiment 1. Each estimate is based on 10 plants selected using a W or X shaped sampling pattern, compared to the grand mean for each site.

	Sampling pattern	N *	Pooled TNC † (mgNO 3 -kg -1 )	Mean TNC (mgNO 3 -kg -1 )	SE	LCL ‡	UCL ‡ CV (%)
	W1 W2 X1 X2	10 10 10 10 F o r	969 733 886 976		1016 716 929 952	Site A 212.3 114.9 168.0 105.3	592 486 593 741	1140 66.1 945 50.8 1265 57.2 1163 34.9
	Grand Mean W1 W2 X1	60 10 10 10	P e 1440 1400 1192 e r	838 1402 1421 1204	57.1 Site B 198.5 215.9 216.1	723 1005 989 772	952 52.8 1799 44.8 1853 48.0 1636 56.8
	X2 Grand Mean	10 60		1388	1359 1352 e R	249.2 79.3	860 1193	1857 58.0 1511 45.4
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Table IV .

 IV Tissue nitrate concentrations in early or late season lettuce grown with or without shading, at two harvest timings either (a), during shading or (b) 24 hours after removal of shading material.

			Food Additives and Contaminants
	Site	Time of	Shading treatment		Tissue nitrate
	code	season				concentration (mg NO 3 -kg -1 )
						Harvest timing
						(a)	(b)
	C	Early	Unshaded			1505	2390
		Shaded Harvest*Shade SED Harvest timing means Harvest SED o F r		2158 235.6 1831 166.6	1939 (**) † 2164 (ns)
	D	Early	P Unshaded Shaded e Harvest*Shade SED Harvest timing means e r		(a) 1680 1915 245.7 1797	(b) 1747 1871 (ns) 1809
			Harvest SED	R		173.7	(ns)
	E	Late	Unshaded Shaded Harvest*Shade SED e Harvest timing means v i e Harvest SED w	(a) 886 1259 208.8 1072 147.6	(b) 818 714 (ns) 766 (*)
	F	Late	Unshaded Shaded Harvest*Shade SED	(a) 562 815 106.7 O n l	(b) 535 457 (*)
			Harvest timing means Harvest SED		688 75.4 y 496 (*)
	† Significance of effects (df = 36); **, p<0.01; *, p<0.05; ns, not significant.

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Table V .

 V Diurnal variation in tissue nitrate concentration for lettuce at two sites.

			Food Additives and Contaminants
	Site code			Tissue nitrate concentration (mg NO 3 -kg -1 )
				Time (h)
			7:00	10:00	13:00	16:00	19:00	22:00
	G	Mean	1983 2173	1812	2511	2138	2155
		SED	197.4 (*) †
	H	Mean SED F o r 2591 2896 179.7 (*)	2627	3111	2950	2701
	† Significance of effects (df = 54); *, p<0.05. P
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Table VI .

 VI Summary of mean TNC, with associated standard errors and coefficients of variation from Experiments 1-3.

	Site	Tissue nitrate concentration	SE	CV (%)
	code	Grand mean
		(mgNO 3 -kg -1 )
	A	838	57.1	52.8
	B	1352	79.3	45.4
	C	1998	166.6	26.3
	D	1803	173.7	30.5
	E	919	147.6	50.8
	F	592	75.4	40.3
	G	2129	139.6	20.7
	H	2813	127.0	14.3

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Acknowledgements

The authors would like to thank the commercial growers for generously allowing harvesting of their crops during the course of this study, and Ms Jo James from ADAS Rosemaund for carrying out sampling. The financial support of the UK Food Standards Agency is gratefully acknowledged.


María Luisa Fernández-Cruz

Susana Grimalt

Mercedes Villarroya

Francisco José López

Susana Llanos

José María García-Baudín

Residue levels of captan and trichlorfon in field-treated kaki fruits

Keywords: residue, captan, folpet, dichlorvos, trichlorfon, metabolites, variability 16 factor, processing, kaki, vegetable and minor crop 17 18

Introduction

Harmonization of maximum residue limits (MRLs), especially in minor crops, and the risk assessment of these residues for the consumer are currently main objectives in Europe. Kaki (Diospyros kaki L.f.) is an important minor crop in Spain, with the 70% of the production exported mainly to the European market. Kaki fruits have been found to be excellent source of pro-vitamin A, mainly beta-carotene, with anti-oxidative properties.

Captan (3a,4,7,7a-tetrahydro-2-[(tricloromethyl)thio]-1H-isoindole-1,3(2H)-dione) is a fungicide of the phthalimide group. Captan is registered in Spain for treatment of fruit trees for the control of anthracnose, brown rot and scab. The European residue definition of captan is the sum of captan and folpet. Folpet (2-[(trichloromethyl)thio]-1H-isoindole-1,3(2H)-dione) is also a fungicide of the phthalimide group. Trichlorfon (dimethyl 2,2,2-trichloro-1-hydroxyethylphosphonate) is a non-systemic organophosphate insecticide for the control of different categories of insects on many crops. In Spain, trichlorfon is registered in kaki for control of ceratitis and carpocapsa, two important pests for this crop.

Determination of captan/folpet and trichlorfon in fruits has been reported in the literature using mainly gas chromatographic methods [START_REF] Mattern | Determination of several pesticides with a chemical ionization ion trap detector[END_REF][START_REF] Mattern | GC/MS ad LC/MS determination of 20 pesticides for which dietary oncogenic risk has been estimated[END_REF][START_REF] Niessner | Multiresidue screening methods for the determination of pesticides in plant materials[END_REF][START_REF] Angioni | GC-ITMS determination and degradation of captan during wine making[END_REF]. The major problem of trichlorfon is its thermal instability, particularly in the injection port. In order to avoid this dehydrohalogenation to dichlorvos (2,2-dichlorovinyl dimethyl phosphate) derivative has been performed for analysis [START_REF] Conrad | Determination of trichlorfon in biological media and technical products following derivatization with acetic-anhydride by gas chromatography[END_REF]. Other authors prevent the Niemczyk 1991). On-column injection methods permit the analysis of trichlorfon in agricultural foods without derivatization [START_REF] Brito | Determination of pesticide residues in coconut water by liquid-liquid extraction and gas chromatography with electron-capture plus thermionic detection and solid-phase extraction and high-performance liquid chromatography with ultraviolet detection[END_REF].

The study reported here had two different major objectives. The main objective was to study the fate of captan, folpet and of trichlorfon after kaki field application of captan and trichlorfon (as commercial formulations), respectively, in order to establish and propose MRLs and to obtain dissipation curves to predict the pre-harvest interval (PHI).

Residue analysis was performed in our Laboratory of Pesticide Residue Analysis (LARP) of the University Jaume I following the Principles of Good Laboratory Practices (GLP), which are mandatory for these kinds of studies. We have developed and validated an analytical method based on gas chromatography-mass spectrometry (GC-MS) in negative chemical ionisation mode for the simultaneous determination of captan and folpet, after extraction with acetone and clean-up by solid phase extraction (Barreda et al. 2005). In the same way, an analytical method based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive ionisation mode by electrospray after extraction with acetonitrile, has also been developed and in-house validated for trichlorfon residue analysis. The validation of the methods (specificity, precision, recovery, and limit of determination) were performed according to European requirements and guidelines (European Communities 1996, European Commission 2000a) at two concentration levels (0.05 and 0.5 mg/kg).

Another objective of this study was to establish the behaviour of the residues after the processing of the vegetable products as well as the variability of the residues between WHO 1997[START_REF] Hamilton | Pesticide residues in foodacute dietary exposure[END_REF][START_REF] Efsa | Opinion of the Scientific Panel on Plant health, Plant protection products and their Residues on a request from Commission related to the appropriate variability factor(s) to be used for acute dietary exposure assessment of pesticide residues in fruit and vegetables[END_REF]. This part of the study (including the determination of levels of the organophosphate insecticide dichlorvos (2,2-dichlorovinyl dimethyl phosphate), the major decomposition product of trichlorfon), was carried out in our Food and Agrarian National Research and Technological Institute (INIA) laboratory. For these purposes, an analytical method for the simultaneous determination of captan and folpet, based on gas chromatography with electron capture detector (GC-ECD) after extraction with ethyl acetate, was developed and validated, as well as an analytical method based on on-column GC-ECD for the simultaneous determination of trichlorfon and dichlorvos.

Materials and methods

Field trials

Kaki plots were treated in October Captan GC-MS determination. Ten ml of 0.1M zinc acetate solution were added to 20 g of homogenised and triturated kaki flesh, or 10 g in case of kaki peel, and then were fortified with 1 ml of the mixture 500 ng/ml acetone of internal standards captan D 6 and folpet D 4 . At ambient temperature, 60 ml of acetone were added and at high speed in a mixer-extractor Ultraturrax the samples were extracted. The homogenate of peel was filtered through pleat paper filters by gravity whereas the homogenate of flesh was centrifuged in a Consul centrifuge at 3000 rpm during 5 min and filtered in a Büchner funnel with vacuum. Final volume was adjusted to 100 ml with acetone in a volumetric

flask. An aliquot of 20 ml was adjusted to 100 ml with HPLC-grade water. This extract was passed through a Envi-Chrom solid phase extraction tube, previously conditioned with 4 ml of acetone and 4 ml of HPLC water, applying a vacuum of 10 4 Pa approximately. The tube was dried by nitrogen or air during 1 hour. Then, it was eluted with 8 ml of dichloromethane and this eluted was evaporated to dryness with nitrogen in a Multibloc bath at 40ºC. The residue was dissolved in 1 ml of hexane and 2 µl were injected in the Agilent GC/MS system. The GC/MS system was fitted with a 30 m x were prepared ranging from 0.05 to 2.5 µg ml -1 in acetone, containing 0.1 µg ml -1 of the mixture of the deuterium compounds. The mixed standards were used for the preparation of matrix-matched standard and for the fortification of blank extracts.

Trichlorfon LC-MS/MS determination. The extraction of 20 g of kaki flesh, or 10 g in the case of kaki peel, with 60 ml of acetonitrile at high speed in a mixer-extractor

Ultraturrax was performed at ambient temperature during 2 min. The homogenate was filtered in a Büchner funnel with vacuum through filter paper. Final volume was adjusted to 100 ml with acetonitrile in a volumetric flask. An aliquot of 2.5 ml of the extract was adjusted to 10 ml with HPLC-grade water, 20 µl of this extract dilution was injected in the HPLC-MS/MS system that was fitted with a Discovery C 18 5 µm, 50 x 2 mm column (Supelco, Bellefonte, USA). The mobile phase flow rate was adjusted to 0.3 ml/min and the gradient elution was 5% methanol:95% water from 0 to 7 min, increasing to 50% methanol at 12 min and decreasing to 5% methanol at 13 min. The Captan GC-ECD determination. Whole raw, flesh and cooked kaki (25 g) homogenized in a blender were mixed in a rotary stirrer (5 min) with 2 portions of 50 ml ethyl acetate and 20 g of sodium sulphate. For the analysis of peel, 5g thinly cut were blended with 2 portions of ethyl acetate (25 ml) and sodium sulphate (4 g). The homogenate was centrifuged in a Sorvall centrifuge at 4000 rpm (2430 G), ambient temperature during 15 min. After shaking and centrifugation, the organic phases collected from the 2 extractions were pooled and dried in a rotary evaporator at 45ºC. The residue of kaki samples were dissolved in 20 ml of a solution of cyclohexane/ethyl acetate (9.5:0.5), 2.0 µl of this solution was injected in the GC/ECD system under the following conditions:

split injection mode with a ratio of 30; injector temperature, 240ºC; detector temperature, 300ºC; carrier gas, helium and make-up nitrogen. The GC was operated in

Results and discussion

Raw kaki residue levels

The determination of captan and folpet and trichlorfon residues in the samples was conducted by applying the GC-MS method or the LC-MS/MS methods described, respectively. Both methods were validated according to GLP protocol of the laboratory and performed considering accuracy (recovery experiments), precision, selectivity and limit of quantification (LOQ) according to European requirements and guidelines (European Communities 1996, European Commission 2000a). Accuracy and precision were studied using fortified samples at two concentration levels (0.05 and 0.5 mg/kg, n=5) which were run obtaining satisfactory recoveries between 93 to 107 % for captan and folpet and between 97 to 101 % for trichlorfon with relative standard deviations (RSDs) lower than 7% (see "Table II"). The limit of quantification, defined as the lowest concentration tested, at which an acceptable mean recovery is obtained (70 to 110%) with satisfactory precision (relative standard deviation lower than 20%) (European Communities, 1996), were estimated to be 0.05 mg/kg for the three compounds. No interfering peaks appeared at the retention times of the compounds, thus demonstrating their selectivity.

"[Insert Table II about here]"

Residue levels of captan and folpet and trichlorfon were determined in the peel and pulp of raw kaki from 8 plots (4 plots in two consecutive years) at different times, to study the dissipation of the residue levels in the whole fruit with time and propose maximum residue limits in the established PHI (7 days for captan and 10 days for trichlorfon). The Although the European residue definition for captan is the sum of captan and folpet, folpet could be detected not even at trace amounts. This was also observed in a study with captan applied to cauliflowers (Fernández-Cruz et al. 2005). Residue levels of captan and trichlorfon found in kaki fruits are reported in "Table III". No interfering peaks were observed in control plots and in samples harvested before treatment.

It could be observed that residue levels of captan in kaki remained in the same order during the study period with a mean (RSD) of 0.63 mg/kg (38 %), 0.88 mg/kg (83 %) and 0.78 mg/kg (78 %), 3 hours, 3 days and 7 days after treatment, respectively ("see Table III"). The same trend was observed with results found in 3 plots by GC-ECD analysis. No dissipation of residues was observed 7 days after treatment with a mean (RSD) of 0.28 mg/kg (47 %) and 0.20 mg/kg (25 %), 3 hours and 7 days after treatment, respectively ("see Table VI").

"[Insert Table III about here]"

To study the dissipation of the residue, mean values at each time ("see Table III") of the four plots designed for dissipation studies (kaki plots 1,4,5,7) have been analysed by a non-linear least squares regression with the program STATGRAPHICS Plus 4.1

Version and fitting data to a first-order kinetic with the following equation:

C t = C 0 e -kt
Where C t is the estimated concentration at a time t, Half-life of the residue was calculated with the equation:

t ½ = ln 2/k
Dissipation curves of captan and trichlorfon in kaki are reported in "Figure 1". The halflife of dissipation of captan in kaki could not be calculated from these data, as a diminution in residue levels was not observed 7 days after treatment. Dissipation of trichlorfon in kaki fitted adequately to a first-order kinetic (C = 4.0088*e -0.0981t ) with a coefficient of correlation R 2 of 0.86. Dissipation of the residue was slow with a t ½ of 7.1 days. A study of residues of trichlorfon in olives after olive tree treatments with trichlorfon (Dipterex 40 soluble concentrate (SL), 200 g/ha) shows a higher elimination of trichlorfon in 10 days from 0.981 to 0.103 mg/kg [START_REF] Cavanna | Residues of fenthion and trichlorofon in olives and olive oil after olive tree treatments[END_REF].

Another study from four different sites obtained each in greenhouse or field-treated bananas with trichlorfon 80 % SP, 320 g a.s/Hl [START_REF] Otazo | Degradation studies of commonly used pesticides in banana plantations in the Canary Islands[END_REF] With respect to the definition of the variability factor a new statistical has been adopted, being the residue in the 97.5 th percentile unit divided by the mean residue for the lot [START_REF] Hamilton | Pesticide residues in foodacute dietary exposure[END_REF][START_REF] Efsa | Opinion of the Scientific Panel on Plant health, Plant protection products and their Residues on a request from Commission related to the appropriate variability factor(s) to be used for acute dietary exposure assessment of pesticide residues in fruit and vegetables[END_REF]. If concentration values for all the items in a population are placed in sequence, the 97.5 th percentile value may be conceptually visualized as the one that divides the top 2.5% of the values from the remainder.

At the beginning of our study the samples units that should be analysed were not indicated nor proposed. Currently, [START_REF] Hamilton | Pesticide residues in foodacute dietary exposure[END_REF] recommend studies with 100 items whereas the PPR Panel consider studies with at least 50 items. In our design, we choose to collect in each plot the same number of units that forms a composite sample as MRLs are fixed in base to these samples. In our case (18 units) the 97.5 th percentile value coincide to the highest level of residue in the individual size samples.

The variability of residue levels of captan and trichlorfon between individual fruits ( 6individual fruits from 3 plots) 3 hours and 7 or 10 days after treatment were analysed as well as composite samples of another 6 fruits collected from the same plots. Residue levels were analysed with the GC-ECD methods described. The methods have been single-laboratory validated for the determination of captan and folpet and trichlorfon and its metabolite dichlorvos in whole kaki fruits. No interfering peaks appeared at the retention times of the compounds. Quantification was carried out using calibration curves of 5-6 calibrations points of mixed standard solutions ranging from 0.01 to 2 µg/ml for captan and folpet and from 0.025 to 0.50 µg/ml for trichlorfon and dichlorvos.

The linearity in the two methods was very good (R 2 >0.99). Satisfactory recoveries of The residues of trichlorfon in each fruit ranged from 0.79 to 5.01 mg/kg, 3 hours posttreatment and from 0.20 to 2.06 mg/kg, 10 days post-treatment ("see Table V"). The average concentrations in composite samples were 3.02 mg/kg and RSD 11% and 0.85 mg/kg and RSD 26%, 3 hours and 10 days post-treatment, respectively ("see Table V").

Variability factors (ν) were 1.6 and 2.4, 3-hours and 10-days post-application, respectively. Ten-days after treatment, only 3 individual fruits contained higher residues than the average value (0.85 mg/kg).

In our study the default variability factor of 3 is adequate for the risk assessment of captan and trichlorfon in kaki fruits.

Effect of household processing on residue levels

The major factors influencing the stability of the residue in typical processes are temperature, pH, water content and chemical nature of the residues. The effect of household processing on residue levels of captan and trichlorfon in kaki fruits has been studied. Kaki fruits are eaten raw normally without its peel. The fruit is also used in the elaboration of marmalades and in pastry. The distribution of residue levels in kaki flesh and peel and the effect of temperature on levels of these compounds have been assayed.

The presence of dichlorvos and folpet has also been studied.

Residue levels have been determined by the described GC-ECD analytical methods.

Recovery assays of captan and folpet at 0.04 and 0.4 mg/kg in kaki matrices (peel and whole boiled kaki, n=3) ranged between 79 and 106% with RSD<15% (see " Captan and trichlorfon are classified as non-systemic. Results in peel and flesh indicated that captan residues practically remained in peel. Processing factors are used to characterise the transfer of residue from fresh crop into processed fraction .The average processing factor is calculated dividing concentration in processed commodity by concentration in raw agricultural commodity. For captan this factor is 1 in peel and 0 in flesh.

On the contrary trichlorfon could penetrate into the flesh. The percentage weight of peel and flesh in kaki fruits is 11 and 89 %, respectively. The distribution of trichlorfon between flesh versus peel 3-hours and 10-days after treatment was 20 and 70%, respectively, showing a fast penetration of the residue. The processing factor for trichlorfon in peel and in flesh is 0.2 and 0.7, respectively. This result is in contradiction with other non-systemic pesticides, fenitrothion in kaki [START_REF] Fernández-Cruz | Field incurred fenitrothion residues in kakis: comparison of individual fruits, composite samples, peeled and cooked fruits[END_REF], chlorpropham in potatoes (Lentza-Rizos and Balokas 2001), cypermethrin, diazinon and parathion in carrots [START_REF] Burchat | The distribution of nine pesticides between the juice and pulp of carrots and tomatoes after home processing[END_REF], where the amount of residue removed by peeling was higher than 90%. Trichlorfon is highly soluble in water (120g/l), whereas Temperature did result in an evident decrease of residue levels of captan after cooking kaki fruits without water during 20 minutes ("see Table VI"). Trace amounts of captan could only be detected. The transfer factor is 0. Previous studies [START_REF] Alary | Laboratory simulation of captan residues degradation during apple processing[END_REF], Fernández-Cruz et al. 2005) reported a complete degradation of captan by temperature.

Cooking kaki fruits, bubbling 20 min, results in a mean loose of weight of 77%. To compare the residue levels of trichlorfon in raw and cooked material, the residue levels should be corrected for the concentration (see "Table VI"). It is described that trichlorfon is thermo-labile and decomposes to dichlorvos at high temperatures. As it could be expected dichlorvos could be detected in cooked samples. The decrease of residue levels of trichlorfon at PHI was of 27%, with an average processing factor of 0.8.

In conclusion, field trials permitted the proposal of maximum residue levels in kaki of 3 and 5 mg/kg for captan and trichlorfon, respectively. A variability factor between units could be set in 3 for the acute risk assessment. No dissipation of captan residue levels was observed 7 days after treatment. Dissipation of trichlorfon was slow with a t ½ of 7.1 days. Levels of captan decreased more than 90 % after peeling whereas trichlorfon penetrated into the flesh in a proportion of 70 % of the residue, 10 days after treatment. The processing factors in peel for captan and trichlorfon were 1 and 0.

  to dichlorvos by adding hydrochloric acid (Spieszalski and

  . These data are necessary to refine the acute and long-term dietary risk assessment (

0

  .25 mm I.D., 0.25 µm HP-5MS column (J&W Scientific). Injection conditions were as follows: pulsed split-less injection mode (initial pressure 172.4 KPa (1.5 min), pressure 60.3 KPa); injection volume, 2.0 µl; injector temperature program: 40ºC, increase to /s and hold 0.5 min. The column temperature program used was 1.0 min at 90ºC, increase to 200ºC at 30ºC/min, and finally increase to 250ºC at 4ºC/min (4.83 min). The temperature of the transfer line was set at 250 ºC. The ions used for quantification were m/z 150 (captan), m/z 156 (captan D 6 ), m/z 146 (folpet) and m/z 150 (folpet D 4 ). Quantification of captan and folpet is based in the method of the internal standard (relate to captan D 6 and folpet D 4 ) and calibration curve in blank matrix extract. Individual stock standard solutions were prepared in acetone for captan, folpet, captan D 6 , and folpet D 4 , then mixed standard solutions of captan and folpet

  total run time was 15 min. The mass spectrometer in positive ionisation mode by electrospray (ES+) was optimized for trichlorfon obtaining 257 and 109 m/z as respectively 3.5 kV, 30 V and 15 eV for the selected mass transition. The dwell time for each transition was 0.5 s. The desolvation and source temperature were 350ºC and 120ºC, respectively. Nitrogen was the desolvation gas with a flow of 600L/h. Argon C-45 was the collision gas with a pressure setting of 0.3 Pa. Quantification of trichlorfon is based in the method of the external matrix matched standard calibration curve. Individual stock standard solutions were prepared in methanol. The final standard solutions for calibration curves (9 points) ranged from 0.5 to 250 ng/ml.

  constant flow mode at 1 ml/min and temperature program applied was: hold 1.0 min at 70ºC, increase to 240ºC at 20ºC/min, and finally increase to 265ºC at 20ºC/min hold 5 min. A Chrompack capillary column CP-Sil 8 CB Low Bleed/MS, 30 m x 0.25 mm ID meter length fused silica tubing uncoated 0.25mm ID purchased from Varian Iberia (Madrid, Spain) were used. Compounds were identified according to their retention times and quantified with the external standard calibration curve. Mixed standard solutions of captan and folpet were prepared from the stock solutions dissolved in cyclohexane/ethyl acetate (9.5/0.5) at concentrations ranging from 0.01 to 2 µg/ml.Trichlorfon GC-ECD determination. Twenty-five grams of whole raw, flesh and cooked kaki, homogenized in a blender were mixed in a rotary stirrer (5 min) with 50 ml ethyl acetate and 20 g of sodium sulphate. For the analysis of peel, 2.5 g thinly cut were blended with 25 ml ethyl acetate and 2 g of sodium sulphate. The homogenate was centrifuged in a Sorvall centrifuge at 4000 rpm (2430 G), 4ºC, during 15 min. After shaking and centrifugation, the organic phase was adjusted to 50 or 25 ml with ethyl acetate, respectively, 2.0 µl of this extract was injected in the GC-ECD system. Overall extraction procedure was performed under cold conditions. Chromatographic conditions were as follows: on column injection mode; injector temperature, 100ºC, hold 0.10 min, increase to 200ºC at 150ºC/min, hold 5.0 min; detector temperature, 250ºC; carrier gas, helium and make-up nitrogen. The GC operated in constant flow mode at 2.5 ml/min and temperature program was: hold 2.0 min at 60ºC, increase to 160ºC at 15ºC/min, and hold 8.0 min. A Chrompack capillary column CP-Sil 24 CB, 15 m x 0.32 mm ID purchased from Varian Iberia (Madrid, Spain) was used. Compounds were quantified with the external standard calibration curve. Mixed standard solutions of trichlorfon and dichlorvos were prepared from the stock solutions dissolved in ethyl acetate at concentrations ranging from 0.025 to 0.50 µg/ml.

  the estimated maximum initial concentration at time 0, K is the slope of dissipation.

  Residue variability among individual fruitsRecently, the possible risk associated with the presence of higher residues on individual food items than in the composite samples, from which MRLs are based, has been considered for an acute exposure(WHO 1997). Originally, in the absence of data, default values for variability factors (ν) (ratio of highest level of residue in the individual size samples to the corresponding residue levels in the composite samples) have been set in 5 for large crops (commodities weights > 250 g) and in 10 (latter in 7) for medium crops (commodities weights between 25 and 250 g). Under current European Union (EU) procedures, variability factors are considered for conducting consumer acute exposure assessments before granting authorization of uses of plant protection products and setting of MRLs[START_REF] Efsa | Opinion of the Scientific Panel on Plant health, Plant protection products and their Residues on a request from Commission related to the appropriate variability factor(s) to be used for acute dietary exposure assessment of pesticide residues in fruit and vegetables[END_REF]. These default values have been refined consecutively with the new data available.[START_REF] Hamilton | Pesticide residues in foodacute dietary exposure[END_REF] proposed to adopt a default variability factor of 3 for all commodities with unit weight exceeding 25 g. The Scientific Panel on Plant Health, Plant Protection Products and their Residues of the European Food Safety Authority (EFSA-PPR)[START_REF] Efsa | Opinion of the Scientific Panel on Plant health, Plant protection products and their Residues on a request from Commission related to the appropriate variability factor(s) to be used for acute dietary exposure assessment of pesticide residues in fruit and vegetables[END_REF] has studied this proposal. Their conclusions have been that the variability factors for supervised trials will exceed the proposed default value of 3 in 34% of cases, whereas the previous default value of 7 for medium-sized food items will be exceeded in 0.2% of cases. This should be considered when choosing an appropriate default value for use in dietary exposure assessments. To assist in this the EFSA-PPR Panel provides tables presenting a range of statistics. However they consider that the available data are not enough and their recommendations are that it would be very desirable to obtain and analyse additional supervised trials to more fully represent the different regions of the EU[START_REF] Efsa | Opinion of the Scientific Panel on Plant health, Plant protection products and their Residues on a request from Commission related to the appropriate variability factor(s) to be used for acute dietary exposure assessment of pesticide residues in fruit and vegetables[END_REF].

  of cypermethrin, captan, parathion, fenitrothion, diazinon and chlorpropham range from 0.01 to 89 mg/l. This fact could explain the ease of trichlorfon to penetrate across the kaki peel.

Figure 1 .

 1 Figure 1. Dissipation curves of captan and trichlorfon in kaki fruits

  Table IV").

	whole boiled kaki (n=3) ranged between 65 and 111% for dichlorvos and 76 and 103 %
	for trichlorfon with RSD<12% (see "Table IV").
	Folpet could not be detected in any of the samples analysed. Dichlorvos was detected in
	cooked samples. Residue levels of captan and trichlorfon obtained after household
	processing are reported in "Table VI". F r o "[Insert Table VI about here]"
	P
	e
	e r
	R
	e
	v i e
	w
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	y
	Recoveries of trichlorfon and dichlorvos at 0.05 and 0.5 mg/kg in kaki peel and in
	19

Table I .

 I Meteorological data collected in agro meteorological stations during the

	respectively and, 0 and 0.7 in pulp, respectively. Cooking kaki fruits resulted in a
	decrease of 27% of the residue levels of trichlorfon and a complete degradation of treatment and sampling periods
	captan indicating that processing factors should be fixed in 0.8 and 0, respectively. Plot 1 2 3 4 5 6 7	8
				Captan
	Period of	15/10/02	14/10/02	07/10/02	08/10/02	06/10/03	02/10/03	20/10/03	04/11/03
	study Medium Temp(ºC)	F o r F o r F o r F o r F o r F o r 22/10/02 21/10/02 17.4 18.1 F o r	14/10/02 18.9	15/10/02 18.9	13/10/03 18.7	09/10/03 19.3	27/10/03 13.6	11/11/03 13.7
	Daily/(Total)	0.0	P P P P P P 0.0-0.4 P	0.0-3.0	0.0-2.2		0.0-0.3	0.0-15.4	0.0-13.8	0.0-7.1
	rainfall(mm)	(0.0)	e e r e e r e e r e e r e e r e e r (0.4) (5.4) e Trichlorfon (4.2) e r		(1.0)	(18.0)		(27.0)	(7.3)
	Period of	15/10/02	14/10/02	R R R R R R 07/10/02 08/10/02 R	06/10/03	02/10/03	20/10/03	04/11/03
	study	25/10/02	24/10/02	e e e e e e 17/10/02 18/10/02 e	16/10/03	13/10/03	30/10/03	14/11/03
	Medium Temp (ºC)	17.2	18.3	19.1	v i e v i e v i e v i e v i e v i e 18.6 18.2 v i e	19.5		14.2	13.5
	Daily/(Total)	0.0	0.0-0.4	0.0-3.0	0.0-2.2	w w w w w w 0.0-19.8 w	0.0-15.4	0.0-13.8	0.0-7.1
	rainfall(mm)	(0.0)	(0.4)	(5.8)	(6.4)		O n l O n l O n l O n l O n l O n l (31.3) (18.4) O n l	(27.2)	(7.3)
								y y y y y y y
										2,
					21

Table II .

 II Data of the analytical captan/folpet GC/MS method and trichlorfon LC-MS/MS method

		Fortification	Flesh Recovery (%)	Peel Recovery (%)
	Compound
		(mg/kg)	(RSD %) a	(RSD %) a
			GC/MS
		0.05	98 (3)	100 (4)
	Captan
		0.50	94 (1)	97 (1)
		0.05	107 (4)	97 (2)
	Folpet
		0.50	105 (2)	93 (7)
			LC-MS/MS
	Trichlorfon	0.05	98 (7)	98 (4)
		0.50	97 (3)	101 (6)

a Mean of 5 replicates Page 28 of 32 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.

uk Food Additives and Contaminants F o r P e e r R e v i e w O n l y 28Table III .

 III Kaki fruit residue levels (mg/kg) of captan determined by GC/MS and trichlorfon determined by LC-MS/MS

	Page 29 of 32					Food Additives and Contaminants
	Time	Plot 1		Plot 2	Plot 3	Plot 4	Plot 5	Plot 6	Plot 7	Plot 8	mean	RSD (%)
							Captan
	3 h	0.83		0.49	0.63	0.45	0.97	0.90	0.38	0.39	0.63	38
	3 d	0.95				0.31	1.89		0.37		0.88	83
	7 d 3 h	0.76 3.55 F o r	0.40 2.33	0.34 5.11	0.31 3.05	1.60 Trichlorfon 5.84	1.84 7.29	0.71 2.16	0.29 4.51	0.78 4.23	78 42
	2 d	3.62 P		2.09	7.67		1.84		3.81	71
	5 d 7 d	1.95 2.21	e e r	1.60 1.04	5.19 1.52		1.52 1.40		2.57 1.54	69 32
	10 d	1.99		0.32	1.71 R	1.15	2.44	1.85	0.98	1.79	1.53	44
					e
					v i e
						w
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							y
					29

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Table IV .

 IV Analytical data of captan/folpet and trichlorfon GC/ECD

	methods
	Fortification	Recovery (%) (RSD %)
	Compound
	(mg/kg)	Fruit	Peel	Boiled
	0.04	97 (15) a	79 (15) b	86 (13) b
	Captan
	0.40	102 (11) a	106 (10) b	106 (7) b
	0.04	89 (7) a	102 (12) b	84 (12) b
	Folpet
	0.40	101 (6) a

Table VI .

 VI Mean values for captan, triclorfon and dichlorvos residues (mg/kg) and RSD (%) for composite and processed samples of kaki fruits from three plots (1, 3 and 4 for kaki) determined by GC-ECD

		Food Additives and Contaminants
	Time a	Whole Kaki	Peel	Flesh	Boiled
	Captan 3 h 7 d F o 0.28 (47) 0.20 (25) r	3.80 (30) 2.70 (29)	<0.04 <0.04	<0.04 <0.04
	Trichlorfon	P
	3 h 10 d	3.02 (11) e 0.85 (26) e r 16.25 (45) 1.76 (10)	0.50 (64) 0.50 (18)	2.50 (17) [1.92] b 0.80 (24) [0.62] b
	Dichlorvos 3 h	nd	nd R e	nd	0.43 (10) [0.33] b
	10 d	nd	nd v i e	nd	0.44 (23) [0.34] b
			w
				O n l
					y
			32

a Time after application; [] b residue level corrected for the concentration; nd: non detected Page 32 of 32 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

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Spieszalski W, Niemczyk HD. 1991. Improved gas chromatographic method for analysis of trichlorfon insecticide in soil and turfgrass thatch. Journal of Environmental Science and Health Part B 26: 575-588. WHO (World Health Organization). 1997. Food Consumption and exposure assessment of Chemicals. Report of the FAO/WHO Consultation. Geneva: World Health Organization. P 69.