Richard John Fussell
email: r.fussell@csl.gov.uk

Michael T Hetmanski

Alison Colyer

Marianne ; Caldow

Frankie Smith

Dawn Findlay

Assessment of the Stability of Pesticides During the Cryogenic Processing of Fruits and Vegetables

Keywords: apples, grapes, lettuce, oranges, pesticides, cryogenic sample processing, stability

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Introduction

Laboratories routinely monitor fruit and vegetables for pesticide residues to check for compliance with statutory Maximum Residue Limits (MRLs) and to provide the results for consumer exposure assessment. Fruit and vegetable samples for official monitoring programmes are usually taken according to CODEX Sampling Guidelines (Codex 1993), thus samples received at the laboratory should comprise a minimum of 5 or 10 units (individual fruit or vegetable units) with a minimum total weight of 2 or 1 kg respectively. The laboratory sample is then comminuted (chopped or blended) to produce a homogeneous sample from which representative sub-samples, typically 10-50g, are taken for subsequent laboratory analysis. The majority of laboratories continue to comminute samples at ambient temperature even though losses for a number of pesticides including chlorothalonil, folpet and tolylfluanid have been reported to occur during this procedure, [START_REF] Hill | Effects of Sample Processing on Pesticide residues in Fruit and Vegetables, in Principles and Practices of Method Validation[END_REF][START_REF] Lyn | Measurement Uncertainty from physical sample preparation: estimation including systematic error[END_REF][START_REF] El-Bidaoui | Testing the Effect of sample Processing and Storage on the Stability of Residues, in Principles and Practices of Method Validation[END_REF]. Losses of pesticides during sample processing will result in an underestimation of residue concentrations with implications for both MRL compliance monitoring and consumer risk assessments. In the UK, these losses resulted in public concern regarding the accuracy of the national monitoring programme [START_REF] Hill | Effects of Sample Processing on Pesticide residues in Fruit and Vegetables, in Principles and Practices of Method Validation[END_REF]. There is evidence to suggest that processing samples at low temperatures (cryogenic milling) can minimise the extent of these reported losses and thus produce more reliable results, [START_REF] Hill | Effects of Sample Processing on Pesticide residues in Fruit and Vegetables, in Principles and Practices of Method Validation[END_REF][START_REF] Fussell | Assessment of the stability of pesticides during cryogenic sample processing. 1. Apples[END_REF].

Cryogenic milling requires the sample to be frozen, usually at -20 ºC, before being disintegrated into a fine, friable powder in the presence of dry ice (solid CO 2 ) or liquid nitrogen. By reducing the temperature at which the samples are comminuted, the potential reactions between any pesticide residues present in the samples and chemicals/enzymes released when plant cells are disrupted, can be slowed and hence the losses of pesticides minimised. Chlorothalonil, which is not approved for use on protected Winter lettuce in the UK, is particularly prone to losses during sample processing at ambient temperature [START_REF] Hill | Effects of Sample Processing on Pesticide residues in Fruit and Vegetables, in Principles and Practices of Method Validation[END_REF].

Since 1996, samples of lettuce analysed as part of the UK pesticide surveillance and enforcement programmes have been comminuted in the presence of dry ice to minimise the loss of chlorothalonil and thus avoid an underestimation of residue concentrations. Before the use of cryogenic sample processing was extended to other commodities, it was considered necessary to check that the cryogenic milling process did not have adverse effects on stability of pesticide residues.

In 2002, results from the assessment of the stability of 106 pesticides and related compounds during the cryogenic processing of apples were reported [START_REF] Fussell | Assessment of the stability of pesticides during cryogenic sample processing. 1. Apples[END_REF]. The results from this initial study on apples were considered as indicative rather than definitive, as a full statistical analysis had not been carried out. The majority (94 out of 106) of the pesticides were stable (losses of <10 %), and only 3 out of 106 showed losses >20 %. The most significant findings were that losses of several pesticides (bitertanol, heptenophos, isofenphos and tolylfluanid) reported to occur during ambient processing of apples did not occur during cryogenic processing. Furthermore losses of dichlofluanid, chlozolinate, and etridiazole; also reported to occur during ambient processing of apples, were reduced to much lower levels (10 %, 17 % and 14 %, respectively) by cryogenic processing.

The aim of the work reported here was to assess the stability of a greater number of pesticides in representative commodities; lettuce, grapes and oranges. This work also included a statistical assessment of results reported previously for apples [START_REF] Fussell | Assessment of the stability of pesticides during cryogenic sample processing. 1. Apples[END_REF]. The proposed protocol did take into account the practice of freezing samples prior to comminution but did not follow normal procedures of comminuting 1-2 kg samples. Rather, the protocol was designed to assess the 'proof of principle' that pesticides are more stable when comminuted at low temperatures. The procedure, previously evaluated for apples [START_REF] Fussell | Assessment of the stability of pesticides during cryogenic sample processing. 1. Apples[END_REF], was based on the analysis of single crop units of fruit/vegetables. This simplified the spiking procedure and also enabled the analysis of the entire comminuted sample to minimise sub-sampling errors. In addition, analysis of single crop units was relatively cost-effective and allowed sufficient replication (duplicate analysis on 7 separate occasions), to provide conclusive evidence of relative stability or instability.

Experimental

Overview of Protocol

A detailed explanation of the development of the experimental protocol was reported in the original study on apples [START_REF] Fussell | Assessment of the stability of pesticides during cryogenic sample processing. 1. Apples[END_REF]. Most importantly, spiking was undertaken prior to freezing of whole apples, and the dry ice was allowed to evaporate at -20 °C after processing and prior to extraction of the comminuted sample. The stability of each pesticide during processing was assessed by comparing the mean recovery for the batch (method) recovery samples (spiked after sample processing and immediately before solvent extraction; 'postprocessing') with the mean 'survival recovery' of the pesticides spiked before cryogenic processing i.e. 'pre-processing'. Pesticide method recovery and survival recovery results were 'corrected' using chlorpyrifos-methyl (CPM) or chlorpyrifos (CP) as an internal deposition standard. Chlorpyrifos-methyl and chlorpyrifos were selected for use as internal deposition standards (to correct for physical losses of pesticides and volumetric errors during sample processing and analysis procedures), because they are known to be stable and to yield good recovery using the analytical procedures employed in this study. CPM or CP deposited on the surface of the crop unit sample was subjected to the same conditions as the pesticides being assessed. As the pesticides evaluated had a wide range of physico-chemical properties, a number of different extraction, clean-up and determination procedures were required to provide satisfactory recovery and precision for all analytes tested. Individual whole units or halved units (oranges and lettuce) weighing approximately 100 g were spiked at a concentration of approximately 0.05 -0.1 mg/kg with mixed pesticide solutions.

The spiked 'units' were frozen individually in a freezer (-20 °C), stored, and then milled in the presence of dry ice on each day of the experiment. The comminuted samples were immediately placed in a freezer at -20 °C to allow carbon dioxide to dissipate before extraction.

The samples spiked pre-processing, blank extract and the batch recovery extracts were analysed in discrete batches using GC-MSD, GC-FPD or LC-MS/MS. The whole procedure, was repeated on seven different days for each commodity-method combination. The individual results were corrected using the internal deposition standard, CPM or CP.

In these studies, the determination of pesticide concentrations in the mill washes (rinsing of the component parts of the mill with solvent) and filter papers (if used for spiking) was also carried out. These results were used to calculate the overall mass balance (recoveries) of pesticides spiked pre-processing. The mass balance for each pesticide was calculated as the combined survival recovery of the pesticide in the sample, filter papers (if used) and mill washes, uncorrected for IDS, and was approximately 70 -80 % for most experiments.

Samples

Apples (variety Royal Gala), oranges (varieties Valencia or Navel) and red grapes (variety unknown) were labelled as organically produced. Seedless green grapes and round lettuce (varieties not known) were not organically produced. All samples were purchased from local retail outlets and were used to prepare bulk blank samples of each commodity (for batch (Basingstoke, UK).

Equipment

Nalgene centrifuge tubes (part number CF615-45) and polypropylene microtubes (part number QP/509N) were purchased from Camlab (Cambridge, UK).

Preparation of standard solutions

A mixed standard solution (containing 20 or 40 Pg/ml of each pesticide) and chlorpyrifosmethyl or chlorpyrifos (internal deposition standard at 20 or 40 Pg/ml) was prepared in ethyl acetate. Solutions of TPE or TPP (10 µg/ml) were also prepared in ethyl acetate for use as volumetric internal standards. Whole apples were wiped with hexane to remove wax, placed on a filter paper contained in a glass Petri dish and the pesticide solution carefully applied, dropwise, onto the surface of the fruit. A sufficient number of individual grapes (total 100 g) were stacked (stalk end down) on a filter paper contained in a glass Petri dish and spiked. Oranges were wiped with hexane to remove wax, cut in half horizontally, placed cut side down onto a glass Petri dish and spiked.

Lettuces were cut in half longitudinally, placed cut side down onto an aluminium foil covered plastic tray and spiked.

Spiking was carried out using a standard solution of pesticides in ethyl acetate. The mixed standard solution was applied dropwise using a micro-syringe to the whole, undamaged surfaces of the crop unit(s); taking care not to damage the fruit and to minimise 'run off'. The spiking solution also contained chlorpyrifos methyl (CPM) and/or chlorpyrifos (CP) to act as an internal deposition standard. The spiked crop unit samples were stored in a freezer (-20 °C), for a minimum of 24 hours prior to cryogenic sample processing. Unspiked samples of each of the above commodities were prepared and stored in the same wayprior to use as blank control samples.

Methodology

Cryogenic processing

Each individual frozen crop sample unit (approximately 100 g) was placed in a Tecator Mill (Model 1094, Tecator AB, Hoganas, Sweden) and comminuted for one minute in the presence of dry ice (approximately 200 g). As much as possible of the comminuted sample was recovered and immediately placed in a pre-weighed Schott bottle. The bottle was immediately transferred to a freezer (-20 °C) for 24 hours to allow the dry ice to dissipate. Two spiked crop unit samples and at least two blank samples were processed on each day of the determinations. The pH of cryogenically processed samples was 3.7 and 3.9 for apples, 3.4 (Valencia oranges), 3.1 (Navel oranges), 3.2 (grapes), 5.5 and 5.8 (lettuce).

Extraction of samples

Ethyl acetate based methods, Method Codes 1-2

Each comminuted crop unit sample was homogenised with ethyl acetate (200 ml) in the presence of anhydrous sodium sulfate (120 g) and sodium hydrogen carbonate (17 g) at 30 °C ± 3 °C. The resulting supernatant extracts were filtered through solvent washed cotton wool.

For analysis an aliquot (10 ml) of ethyl acetate extract was cleaned-up using a 500 mg ENVI-Carb™ solid phase extraction cartridge. The cartridge was eluted with ethyl acetate (Method Code 1a) or acetonitrile/toluene (3:1) (Method Code 1b). The cleaned-up extracts were concentrated and internal standard (TPP or TPE) added prior to quantification by GC-MSD.

For polar organophosphorus pesticides, an aliquot (5 ml) of the ethyl acetate extract was concentrated and a volumetric internal standard (sulprofos or triphenyl phosphate) was added before making up to volume (1 ml) with ethyl acetate. Quantification was by GC-FPD (Method Code 2a) Alternatively an aliquot (1 ml) of the ethyl acetate extract was taken, without further clean up or concentration, and an aliquot of internal standard (TPP) was added prior to analysis by GC-MSD (Method Code 2b).

Acetonitrile based methods, Method Codes 3-4

Each comminuted crop unit sample was shaken with acetonitrile (100 ml) and then sonicated for 1 minute. Anhydrous magnesium sulfate (40 g) and sodium chloride (10 g) were added and the mixture was shaken immediately. Then 0.5 ml of a 20 Pg/ml solution of TPP in acetonitrile was added and the mixture was shaken for a further 30 seconds. PSA sorbent (25 mg) and anhydrous magnesium sulfate (150 mg) were added to a 1.5 ml micro-centrifuge vial, followed by a 1 ml aliquot of the acetonitrile extract. The mixture was shaken for 30 seconds and then centrifuged at 6000 rpm for 1 minute. The supernatant extract was transferred to vials for GC-FPD analysis (Method Code 3a) or GC-MSD analysis (Method Code 3b). Alternatively a 500 µl aliquot was taken and 10 µl of 10 % acetic acid in acetonitrile was added prior to addition of PSA and anhydrous magnesium sulphate. The supernatant extract was transferred to vials for GC-MSD analysis (Method Code 3c).

For quantification using LC-MS/MS, an aliquot (1 ml) of the initial acetonitrile extract was dried using anhydrous magnesium sulphate (150 mg) (Method Code 4).

Measurement of pesticide concentrations in extracts Typical conditions for GC-MSD determination

Determinations were made using an Agilent 6890 gas chromatograph connected to an Agilent 5973 Mass Selective Detector (MSD) operated in SIM mode. Injection (3 µl) was splitless at 250 ºC and the detector temperature was set at 280 ºC. Chromatographic separation was performed using a DB-5 MS capillary column (30 m x 0.25 mm i.d. x 0.25 µm film thickness) with the carrier gas (helium) at a flow rate of 0.9 ml/min in constant flow mode. The oven temperature was programmed as follows: initial temperature 100 ºC, held for 1 min; programmed to 150 ºC, at 30 ºC/min; then programmed to 220 ºC at 3 ºC/min, held for 1 min, and then finally programmed to 280 ºC at 2 ºC/min with a final hold time of 3 min.

Typical conditions for GC-FPD determination

Determinations were made using an Agilent 6890 gas chromatograph fitted with a flame photometric detector (FPD). Injection (1 µl) was pulsed splitless at 210 ºC and the detector temperature was set at 250 ºC. Chromatographic separation was performed using a DB1701 megabore column (30 m x 0.53 mm i.d. x 1.0 µm film thickness) with the carrier gas (helium) at a flow rate of 4.0 ml/min in constant flow mode. The oven temperature was programmed as follows: initial temperature 100 ºC, held for 0.5 min; programmed to 200 ºC, at 20 ºC/min, held Jones Genesis C18 analytical column (150 x 2.1 mm i.d.) fitted with a Phenomenex Security Guard C18 cartridge (4 x 2 mm i.d.). Mobile phase A was 10 mM aqueous ammonium acetate, mobile phase B was methanol. Gradient elution started at 10 % B increasing linearly over 10 min to 90 % B, held at 90 % B for a further 12 min before returning to the initial conditions.

The total injection cycle time was 28 min, the flow rate was 0.2 ml/min and the injection volume was 5 Pl.

Analytical Quality Control

Validation of the analytical methods

Prior to the analysis of the samples, the extraction and clean-up methods described in the experimental section were validated for the pesticides of interest by analysis of seven 100 g replicate samples spiked at 0.05 -0.1 mg/kg, using the procedures described.

Post-processing batch recoveries were obtained from processed blank samples spiked at 0.05 -0.1 mg/kg, and analysed at least individually, or more commonly in duplicate. These were analysed together with the pre-processing survival recoveries on each occasion. All determinations (including method validations) were calculated using multi-point, matrixmatched standards, which bracketed the samples. TPP or TPE was used as an internal volumetric standard. The CPM/CP corrected mean batch extraction or post-processing recoveries for each pesticide were calculated on the same basis.

Statistical analysis

The stability of pesticides during sample processing (comminution) was assessed by comparing the mean concentration of pesticides in samples spiked before comminution (mean survival or pre-processing recovery) with the mean concentration of pesticides in batch recovery samples spiked immediately after comminution (mean method post-processing recovery).

Prior to the statistical analysis, the results were 'corrected' using the response of the internal deposition standards CPM or CP. Statistical analysis of the collated data was performed, using (REML) due to the 'unbalanced design' of the data (e.g. not all pesticides were measured for all commodities). Variation due to the analytical system, day, experiment, repetition value and commodity, where appropriate, were taken into account when calculating the error estimates of the main effects of interest.

The mean differences between recoveries from pre-and post-sample processing were then calculated by commodity for each of the pesticides analysed, together with 95% confidence intervals. This was performed to investigate if the effect of sample processing for each pesticide varied with the type of commodity.

Results and Discussion

Method Validation

Validation results for nearly all pesticide/commodity combinations were satisfactory. The mean recoveries (not corrected for IDS) were generally within the range 70-110 % with coefficients of variation (% CVs) below 10 %. A definitive and quantitative assessment of pesticide stability during sample processing as well as an accurate determination of residue concentrations; can only be carried out on the basis of good quality analytical data. Thus any protocol devised for this type of study is dependent on the availability of reliable and robust analytical methods for the full range of pesticides evaluated.

Stability

Data were collated for 134 pesticides over the four different commodities, from 21 sets of experiments. Each of the experiments had measurements over 6-7 days. Results for some pesticides were obtained using two or more sets of experiments and/or two different analytical systems. The mass balance for each pesticide was calculated as the combined survival recovery of the pesticide in the sample, filter papers (if used) and mill washes, uncorrected for IDS, and was approximately 70 -80 % for most experiments. It is difficult to explain these losses of 20 -30% of the pesticides. As the whole crop unit sample was comminuted and extracted, these losses could not be explained by gravimetric error caused by condensation. [START_REF] Schmidt | Evaluation of losses during cryogenic sample homogenization with liquid nitrogen and dry ice[END_REF] conducted experiments using radiolabelled pesticides and reported that losses up to 20 % occurred during the spiking and freezing process but no significant losses were observed during the cryogenic homogenisation step. The use of internal deposition standards (CPM or CP) which are known to be stable under cryogenic processing conditions, to correct for recovery, removed the effect of physical losses of pesticide from the results, thus enabling a more accurate assessment of stability to be made.Therefore all further assessments were based on the relative stability of the pesticide to chlorpyrifos or chlorpyrifos-methyl.

[Insert Figures 1234

here]

The relative stability results (difference between pre-and post-processing recoveries) for all of the pesticide/commodity combinations are summarized in Figures 1234. The results obtained from GC-MSD and GC-FPD measurements are presented in Figures 123, results derived from LC-MS measurements are shown in Figure 4. The data in Figures 1234show a plot of mean differences between pre-and post-processing recoveries with 95 % confidence limits for all of the individual pesticides. They are also coded by commodity. Pesticides which have 95 % confidence intervals not containing zero, i.e. 95 % confidence limits entirely <0 % indicate that the mean difference between pre-and post-sample processing results are statistically significantly different. However for practical analytical purposes, a mean difference of <-20 % is regarded as evidence of a significant level of instability or loss during sample processing. The results for some of the LC-MS pesticides (Figure 4) showed apparent survival (preprocessing recoveries) of >100 %. These analytes are generally less volatile and more polar than CP, therefore CP is not the optimum internal deposition standard for these pesticides.

Stable isotope analogues could be used, however these are expensive and only available for a limited number of pesticides.

The data in Figures 1234show that 120 pesticides were stable (mean difference between pre-and post-processing recoveries was >-20 %) during cryogenic sample processing. Fourteen of the pesticides showed some instability or loss during processing, and results for these compounds are listed in detail in Table 1.

[Insert Table 1 here] The pesticides showing processing losses were dichlorvos, etradiazole (all commodities), biphenyl (apples, grapes, oranges), tecnazene (grapes, lettuce and oranges), captan and ethoxyquin (apples, oranges), malaoxon (apples), chlorothalonil and tebuconazole (grapes), cadusafos, disulfoton, heptenophos, phorate and trifluralin (oranges). The 95 % confidence limits for these mean results are also listed in Table 1. Figures 1234show that there were also a further 4 pesticides with results for mean difference >-20 %, but where the upper and lower 95 % confidence limits were <0 % and <-20 % respectively. These compounds were deltamethrin and dicofol (grapes), aldicarb and dichlofluanid (oranges); however as the mean difference between pre-and post-processing recoveries was >-20 %, for practical analytical purposes they were regarded as stable. Cadusafos, captan, chlorothalonil, heptenophos, malaoxon, tebuconazole, tecnazene and trifluralin all showed losses for at least one commodity, but not in other commodities tested.

Although oranges were the individual commodity most frequently showing losses, it was not possible to make a definitive comparison as (i) only 8 pesticides showed apparent commodityspecific losses and (ii) not all pesticides were evaluated in all commodities.

Overall, the data showed most (120 out of 134) pesticides were stable during cryogenic sample processing. This is especially significant as the pesticides evaluated included those suspected to be unstable during processing. For apples, lettuce and oranges, some comparison can be made with previously reported data for ambient processing, although ambient processing data was acquired using different experimental protocols and thus an exact comparison is not possible. The ambient processing results were also based on the simple difference between mean results from pre-and post-processing spiked samples, with no further statistical treatment of the data.

In the case of apples, losses (>20 %) of a number of pesticides (bitertanol, chlorothalonil, dichlofluanid, heptenophos, isofenphos, oxadixyl, prochloraz, tebuconazole and tolylfluanid) previously reported to occur during ambient processing [START_REF] Hill | Effects of Sample Processing on Pesticide residues in Fruit and Vegetables, in Principles and Practices of Method Validation[END_REF], were minimised using cryogenic processing. These finding are consistent with those of [START_REF] Poulsen | Cryogenic homogenisation of fruits[END_REF], who reported higher recoveries for some pesticides in apples after cryogenic processing compared to processing apples at ambient temperature.

A comparison of lettuce showed a similar pattern. Losses of pesticides (captan, chlozolinate, dichlofluanid, dicofol, iprodione, metalaxyl and tolylfluanid; all >20 %) previously reported to occur during ambient processing of lettuce [START_REF] Hill | Effects of Sample Processing on Pesticide residues in Fruit and Vegetables, in Principles and Practices of Method Validation[END_REF], were not observed using cryogenic processing. 17 Processing oranges at ambient temperature also resulted in losses (>20 %) for several pesticides by comparison with cryogenic processing. Losses observed for dichlofluanid, dicloran, and tolylfluanid during ambient sample processing [START_REF] Fussell | Measurement uncertainty associated with sample processing of Oranges and Tomatoes for pesticide residue analysis[END_REF], were not found in this evaluation of oranges.

There is limited data on the stability of pesticides during the ambient milling of grapes and therefore a relevant comparison of the stability of pesticides during cryogenic and ambient processing is not possible. However, losses of biphenyl, dichlorvos, tecnazene, ethoxyquin and etridiazole were found in all commodities for which these pesticides were tested. The losses of the volatile pesticides, biphenyl, dichlorvos and tecnazene; may be due to the large amount of carbon dioxide gas released during comminution, as these pesticides may be volatilised, despite the low temperatures. The reason for the losses of ethoxyquin and etridiazole is not known. As previous data on ambient processing also showed large losses, this indicates that an accurate determination of residue concentrations for the volatile pesticides may not be possible, until the problem of volatilisation loss is solved.

Conclusion

Overall, the data showed most (120 out of 134) pesticides were stable during cryogenic sample processing. This is especially significant as the pesticides evaluated included those previously suspected to be unstable during processing at ambient temperature. Cryogenic milling reduces the likelihood of underestimating residue concentrations (and thus possible MRL noncompliances) of some widely used pesticides. The improved stability of pesticide residues together with improved homogeneity of sub-samples supports the increasing use of cryogenic sample processing in the UK pesticides monitoring programme. It is recommended that

  , for individual batch blanks and for use in the preparation of individual spiked unit samples. Reagents Ethyl acetate, acetonitrile (both HPLC grade), anhydrous sodium sulfate and anhydrous sodium hydrogen carbonate (both Analytical Reagent grade) were purchased from Fisher Scientific (Loughborough, UK). Certified reference pesticides were purchased from QMx Laboratories Ltd (Saffron Walden, UK), Sigma-Aldrich Chemical Co. (Poole, UK) or Greyhound Chemicals (Birkenhead, UK). Tetraphenylethylene (TPE, 98 % purity) and triphenyl phosphate (TPP, 99.5 % purity) were obtained from Sigma-Aldrich Chemical Co. (Poole, UK). ENVI TM -Carb solid phase extraction cartridges were purchased from Supelco Ltd. Magnesium sulphate (anhydrous) was obtained from York Glassware Services Ltd. (York, UK). PSA solid phase extraction material (particle size 40 Pm, part number 12213023) was purchased from Varian Ltd. (Walton on Thames, UK). Sodium chloride was obtained from ICN Pharmaceuticals Ltd.

  (or one) processed blank samples were spiked with the same mix of pesticides immediately prior to extraction, for use as post-processing method recovery

  ; then programmed to 240 ºC at 5.0 ºC/min, held for 2 min, and then finally programmed to 280 ºC at 5 ºC/min with a final hold time of 12 min.Typical conditions for LC-MS/MS determinationLC-MS/MS determinations were achieved using an Applied Biosystems Sciex API 2000 triple quadrupole mass spectrometer interfaced with an Agilent 1100 HPLC system. The mass spectrometer was used in positive electrospray mode. Two selected reaction monitoring (SRM) transitions were acquired per compound using a single time window. Chromatography used a

  internal deposition standardThe CPM or CP corrected mean survival or pre-processing recovery (%) in the cryogenically processed samples is the average of the individual corrected survival results expressed as percent for each of the cryogenically milled samples.Individual % uncorrected survival or pre-processing recovery results (SU) were calculated for each pesticide as follows: µg pesticide in the sample x 100 = SU µg pesticide added Individual CPM/CP % survival or pre-processing recovery correction factors (IS) were calculated as follows: µg CPM (or CP) in the sample x 100 = IS µg CPM (or CP) added Therefore individual corrected % survival or pre-processing recovery results (SC) were calculated as follows: SC = SU/IS x 100

  package Genstat8, by Restricted Maximum Likelihood Analysis

  include a description of the sample processing procedure employed to avoid misinterpretation of pesticide residue results.

Table Legend Table 1 .

 Legend1 Difference in pre-and post-spiking methods with approximate 95% confidence interval, where the difference is less than -20 (i.e more than 20 % loss) for at least one commodity per pesticide.

	Page 23 of 29		Food Additives and Contaminants
	Pesticide Pesticide Figure Captions [Table 1]	Commodity Commodity	Pre Pre	Post Post	Mean Mean	LCI* UCI** LCI* UCI**	Method Method
	Pesticide 60 60	Commodity	Pre Mean Mean	Post Mean Mean	Mean Diff Diff	LCI* UCI**	Method Code Code
	40 40	apple apple	Mean 50.1 99.3	Mean 129.4 -79.3 -96.63 -61.94 Diff 100.9 -1.5 -6.19 3.16	Code 2b 1a
	F o r P e e r R e v i e w Figure 1. Difference Between Pre and Post Spiking Concentration with Approximate 95% Confidence Limits, Part 1 Figure 2. Difference Between Pre and Post Spiking Concentration with Approximate 95% Confidence Limits, Part 2 Figure 3. Difference Between Pre and Post Spiking Concentration with Approximate 95% Confidence Limits, Part 3 Figure 4. Difference Between Pre and Post Spiking Concentration with Approximate 95% Confidence Limits, for Pesticides Tested Using LCMS F o r P e e r R e v i e w apple 48.8 86.0 -37.2 -47.98 -26.43 1a, 2b grapes 40.2 80.2 -40.0 -53.00 -26.91 1b Biphenyl orange 18.5 98.3 -79.8 -96.65 -62.96 1a, 3c apple 96.4 100.2 -3.9 -10.59 2.82 1a grapes 88.4 99.9 -11.5 -15.10 -7.94 1a, 1b lettuce 93.6 101.3 -7.7 -10.62 -4.78 1a, 3a, 3b Cadusafos orange 76.2 99.7 -23.5 -26.41 -20.57 1a, 3a, 3c apple 22.5 80.7 -58.2 -115.60 -0.82 grapes 39.6 79.4 -39.8 -71.48 -8.13 1a, 1b lettuce 114.1 123.3 -9.2 -42.13 23.81 1a Captan orange 76.7 81.9 -5.2 -38.14 27.80 1a apple 84.7 85.6 -0.9 -12.47 10.72 2b F o r P e e r R e v i e w Ethoxyquin orange 41.9 98.6 -56.6 -69.75 -43.54 3c apple 55.1 87.2 -32.1 -40.32 -23.91 1a, 2b grapes 50.9 78.8 -27.9 -34.83 -20.93 1a, 1b lettuce 60.3 99.6 -39.4 -48.84 -29.89 3b Etridiazole orange 34.4 90.5 -56.1 -63.33 -48.85 1a, 3c apple 91.6 96.0 -4.4 -8.89 0.08 1a grapes 89.0 99.2 -10.2 -13.82 -6.57 1a lettuce 87.6 95.8 -8.2 -11.74 -4.75 1a Heptenophos orange 52.8 104.5 -51.7 -57.03 -46.35 3c apple 79.3 99.4 -20.1 -26.25 -14.01 1a grapes 101.3 102.5 -1.1 -6.13 3.87 1a lettuce 107.4 102.3 5.1 0.51 9.76 1a Malaoxon orange 101.2 101.7 -0.5 -5.08 4.17 1a Phorate orange 67.1 104.1 -37.0 -46.35 -27.61 4 F o r P e e r R e v i e w grapes 89.5 99.0 -9.5 -13.03 -5.96 1a lettuce 92.9 99.7 -6.8 -10.35 -3.28 1a Trifluralin orange 67.9 102.0 -34.0 -37.57 -30.50 1a, 3c * LCI -lower confidence interval ** UCI -upper confidence interval F o r P e e r R e v i e w Pesticide Acephate Azinphos_Methyl Azoxystrobin Bendiocarb Bifenthrin Biphenyl Bitertanol Bromopropylate Bupirimate Buprofezin Cadusafos Captan Carbaryl Carbofuran Chlorfenvinphos Chlorothalonil Chlozolinate Cyfluthrin Cypermethrin DDD_pp DDE_pp DDT_op DDT_pp Deltamethrin Diazinon Dichlofluanid Dicloran Dichlorvos Dicofol Dicofol_bd Dimethoate Diphenylamine EPN Endosulfan_I Endosulfan_II Endosulfan_Sulphate Ethion Ethofumesate Mean Difference in Concentration -120 -100 -80 -60 -40 -20 0 20 Apple Grape Lettuce Orange F o r P e e r R e v i e w Pesticide Ethoprophos Ethoxyquin Etridiazole Etrimfos Fenitrothion Fenpropathrin Fenpropidin Fenpropimorph Fenvalerate Flurochloridone Flusilazole Fonophos Furalaxyl HCH_alpha HCH_beta HCH_gamma Heptenophos Imazalil Iprodione Isazophos Isofenphos Kresoxim_Methyl Lambda_Cyhalothrin Malaoxon Malathion Mecarbam Mephospholan Metalaxyl Methamidophos Methidathion Methiocarb Monocrotophos Myclobutanil Napropamide Ofurace Omethoate Oxadixyl Paclobutrazol Mean Difference in Concentration Apple Grape Lettuce w Orange e e r R e e i Pesticide v Parathion_Ethyl Parathion_Methyl Penconazole Pendimethalin Permethrin Phenthoate Phosalone Phosmet Phosphamidon Pirimicarb Pirimiphos_Ethyl Pirimiphos_Methyl Prochloraz Procymidone Profenofos Prometryn Propanil Propargite Propiconazole Propoxur Propyzamide Prothiofos Pyridaphenthion Pyrimethanil Quinalphos Simazine Tebuconazole Tebufenpyrad Tecnazene Tetrachlorvinphos Tetradifon Tolclofos_Methyl Tolyfluanid Triazophos Trifloxystrobin Trifluralin Vinclozolin -120 -100 -80 -60 -40 -20 0 20 F o r P Mean Difference in Concentration
	Chlorothalonil [Figure 1] [Figure 2] Tebuconazole	grapes orange apple grapes lettuce	O n l y O n l y -31.7 -41.96 -21.51 -9.9 -19.38 -0.45 1a, 2b, 1b 3c O n l y 3.5 -2.67 9.75 1a, 2b 117.1 -20.6 -25.86 -15.31 1a, 1b 80.5 25.0 90.9 106.9 115.3 48.7 15.1 94.4 96.5 -8.4 -13.31 -3.54 1a, 3a O n l O n l O n l O n l y y y y
		apple orange	48.4 97.0	82.5 102.0	-34.1 -41.17 -27.07 -5.0 -11.91 1.91	3a 1a
	Dichlorvos	grapes apple	51.8 84.0	93.2 93.1	-41.3 -51.76 -30.87 -9.1 -16.79 -1.45	3a 1a
		lettuce grapes	63.6 63.3	96.1 85.9	-32.5 -42.99 -22.10 -22.6 -26.86 -18.35	3a 1a
	Tecnazene
		orange lettuce	-3.0 60.0	94.9 94.8	-97.9 -108.33 -87.44 -34.8 -38.90 -30.70 1a, 3a 3a
	Disulphoton	orange orange	77.6 60.6	107.0 -29.4 -39.29 -19.48 94.8 -34.2 -38.27 -30.07 1a, 3c 4
				22 1 2 3 1 1 1

http://mc.manuscriptcentral.com/tfac Email: tfac@csl.gov.uk

http://mc.manuscriptcentral.com/tfac Email: tfac@csl.gov.ukFood Additives and Contaminants

Acknowledgements

We gratefully acknowledge Defra (Department for Environment Food and Rural Affairs) for funding.


Lara La Pera
email: laralapera20@libero.it

Giacomo Dugo

Rossana Rando

Giuseppa Di

Roberta Maisano

Francesco Salvo

Statistical study of the influence of fungicide treatments (mancozeb, zoxamide 1 and copper oxychloride) on heavy metals concentrations in Sicilian red wine 2 3 4

Keywords: fungicides, Heavy metals Food Types: Grape, Wine fungicide treatment, grape, grape-stalk, heavy metals, marc, statistical analysis, wine. 35 36

In recent years, great importance has been directed towards the monitoring of food products for 43 heavy metals contamination, and the assessment of the source of food contamination. The presence 44 of trace elements in wines, depends on many factors such as the type of soil, wine processing 45 equipment, vinification methods [START_REF] Hsia | Influence of Must Processing on Iron and Copper 401 contents of Experimental Wines[END_REF]Golimowsky et al, 1979 b;Karadjova et al., 46 2002). Nowadays, a number of different metallic compounds are used in agricultural chemicals and 47 can, as a result, contaminate crops. Fungicides are considered to be indispensable aids in agriculture 48 and are extensively used in vine-growing; many studies report the toxic effect of these chemicals on 49 human health but there are few data about the influence of the use of pesticides on wine 50 composition and particularly on metal concentrations. Trace metals in wine, including Cu, Mn and 51

Zn are essential for human, animals, and plants and particularly Zn at low concentrations plays an 52 important role in wine fermentation. On the other hand, Cu and Mn at high concentrations 53 participate significantly in the destabilization of wines and in their oxidative evolution (Benìtez et 54 al., 2002). At the same time the analysis for certain elements, such as Pb and Cd, in wines is of 55 special interest due to their toxicity in case of excessive intake [START_REF] Ribérau-Gayon | Precipitation dans le Vins[END_REF]56 Gennaro et al, 1986;[START_REF] Mc Kinnon | Size Fractionation of Metals in Wine Using Ultrafiltration[END_REF][START_REF] Pereira | The Importance of Metallic Elements in Wine[END_REF]. A recent study has 57 provided evidence that the use of some organic and inorganic fungicides -quinoxyfen, dinocap, 58 penconazole, azoxystrobin and powder of sulphur-in wine-growing, may moderately influence the 59 concentration of trace elements (Cd, Cu, Pb and Zn) in red and white wines [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF]. 60 61

The present study has been focussed on the possible correlation between the presence of Mn (II), Zn 62 (II), Cu (II), Cd, (II), and Pb (II), in samples of Sicilian red wines and mancozeb-zoxamide and 63 copper oxychloride treatments that occurred during viticulture; in order to better investigate the 64 effects of these fungicide treatments, heavy metals analysis were also performed on grapes, marcs, 65 and grape stalks; furthermore data concerning heavy metal levels in soil near the plots were 66 2004. Ethylenebisdithiocarbamate of manganese and zinc (mancozeb)-[C 4 H 6 N 2 S 4 Mn] x (Zn) y -68 belongs to a wider group of ethylenebisdithiocarbamate fungicides and is widely used in 69 agriculture, mainly on fruits, ornamental plants and vegetables [START_REF] Maroni | Biological monitoring of pesticides exposure: a 411 review[END_REF]. It is 70 characterized by low acute toxicity and short environmental persistence, for this reason the total 71 amount of mancozeb used is increasing worldwide. Zoxamide [3, 5-dichloro-N-(3-chloro-1-ethyl-1-72 methyl-2-oxopropyl)-4-methylbenzamide] is a new fungicide for foliar use on potatoes, grapevines 73 and vegetables to control Oomycete fungi [START_REF] Egan A R | RH 7281: a novel fungicide 388 for control of downy mildew and late blight[END_REF]. Despite the production of a wide 74 variety of synthetic organic fungicides, copper oxychloride-CuCl 2 •3Cu(OH) 2 -still predominates 75 the field of fungicidal plants disease control and its wide use is also due to its general low toxicity 76 for humans [START_REF] Helling | Effect of fungicide oxychloride on the growth 399 and reproduction of Eisenia fetida (Oligochaeta). Ecotox[END_REF]. 77 78The determination of Mn, Zn, Cu, Cd and Pb in grapes, grape stalks, marcs and wines was carried 79 out by using derivative stripping chronopotentiometry (dSCP), an electro-analytical technique 80 widely employed for trace and ultra-trace metals analysis in food and environmental matrices 81 [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF][START_REF] Pera | Simultaneous determination of Cd (II), Cu (II) 406 Pb (II) and Zn (II) in citrus essential oils by derivative potentiometric stripping analysis[END_REF]Dugo et al., 2004;Dugo et al., 2005a;Dugo et al., 2005b;82 Dugo et al., 2005c;[START_REF] Palmieri | Simultaneous determination of Cd 413 (II), Cu (II), Pb (II) and Zn (II) by stripping chronopotentiometry in Pittosporum tobira leaves: a 414 measurement of local atmospheric pollution in Messina (Sicily, Italy)[END_REF]. The analytical techniques conventionally used 83 for metal determinations are both emission and absorption spectrophotometry, as well as 84 voltammetry [START_REF] Balduini | Determination of Transition Metals in Wine by IC, 368 DPASV-DPCSV and ZGFAAS Coupled with UV Photolisys[END_REF]. These methods require sample pre-treatment which enable the 85 complete destruction of the organic matrix such as wet digestion, dry ashing and microwave oven 86 dissolution [START_REF] Reid | Investigation of Decomposition Products of 418 Microwave Digestion of Food Sample[END_REF]. Derivative stripping chronopotentiometry allows the direct 87 determination of heavy metals in wines without the need of a laborious sample preparation 88 procedure but acid adjustment, as the glassy (GCMFE) is inert towards organic molecules 89 interferences during the signal collection phase (Town and[START_REF] Town | Fundamental features of metal ion determination by stripping 428 chronopotentiometry[END_REF]2002).

Materials and methods 92

Apparatus 93 Metals analysis were carried out by using a PSA ION 3 potentiometric stripping analyzer 94 (Steroglass, S. Martino in Campo, Perugia, Italy). The analyzer was equipped with a conventional 95 three-electrodes cell. The plating process with mercury of the glassy carbon working electrode, the 96 reference and the auxiliary electrodes, have been described in previous papers [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF]97 La Pera, et al. 2003;Dugo et al, 2005 a, b, c;Matarese Palmieri et. al 2005;Dugo et al., 2004 b) 98

Reagents 99

All glassware was rinsed with 10% (v/v) nitric acid. Ultra-pure water (18.2 Mohm cm -1 ) was 100 obtained from a Pure Lab RO and a Pure Lab UV system (USF, Ransbach-Baumbach, Germany). 101 Ultra pure hydrochloric acid (34-37%), Hg (II) (1000 mgL -1 , 1M in hydrochloric acid) and Cd (II), 102 Cu (II), Pb (II), Zn (II), and Mn (II) (1000 mg L -1 , 0.5 N in nitric acid) standard solutions were 103 purchased from Panreac Quimica (Barcelona, Spain). Glucose, fructose, gallic acid, tartaric acid 104 and potassium tartrate used to prepare the model wine solution (14%v/v aqueous ethanol, 2 gL -1 105 fructose, 2 gL -1 glucose, 1 gL -1 gallic acid, saturated with potassium hydrogen tartrate and adjusted 106 to pH 3.5 with tartaric acid), were purchased from Sigma Aldrich (Milwakee, WI, USA). Supel-107 ENVI carbon columns were obtained from Supelco (Bellefonte, PA, USA) 108

Wines characteristics and pesticides treatments 109

Trials were conducted in five plots located in the Sicilian wine-producing zone of Etna; grapevines 110 were from the Nerello Mascalese variety. Plots were investigated over two crop years 2003 and 111 2004. Plots B-C were characterized by the use of mancozeb and zoxamide and they differed for the 112 time elapsed from the last fungicide treatment and the grape harvesting (∆t) ; plots D-E by the use 113 of copper oxychloride and they also differed for the ∆t, and plot A was characterized by the use of 114 water and represented the control plot (Table 1). All the studied plots measured between 0.25-0.5 115 applied by the wine farmers with a manual sprayer. In all the considered crop years (2003 and 117 2004), the grapes were harvested in the period from Aug. 25 to Sept.10. Newly cropped Vitis 118 vinifera were crushed and de-stemmed, then spiked with SO 2 (5 g/hL), selected yeasts (30 g/hL) 119 and let to ferment for 10 days at 28°C, making three fullings a day. The wine was drawn from the 120 vat and the marcs were crushed by a hydraulic press. After 4 weeks the lees were removed and the 121 wine was spiked with SO 2 (2 g/hL). Subsequently the wine was spiked again with SO 2 (2 g/hL) and 122 bottled in dark bottles at 4°C for the duration of the experimentation. Each sample was separately 123 fermented and bottled, following the protocol mentioned above. From each investigated plot, both 124 in 2003 and 2004, representative samples of grapes (about 10 kg), grape-stalks (about 1.4 kg), 125 marcs (about 0.5 Kg), and wines (7.5 L) were delivered to the laboratory and stored at -10°C. 126

Pedoclimatic conditions 127

The vineyards lay on calcareous soil (300 m asl) that contains on average 37% of sand, 36 % of 128 silt, 27% of clay and 4.5 % of organic matter; water contents at 10-20 cm depths were 30.1% by 129 volume. Heavy metal concentrations of soil and groundwater samples collected in different points 130 of each plot before the first pesticides application both in 2003 and 2004, determined in an external 131 laboratory by ICP-AES, were available as background data (Table 2). In the studied area the climate 132 is arid and the low rainfall results in a lack of water except in the winter months. The environmental 133 conditions were recorded by an AD-2 automatic weather station from April to September; during 134 the experiments, the minimum and maximum average temperatures were 12-35 °C in 2003 and 135 12.6-35.5 °C in 2004. The average precipitation rate over the studied area was 665 mm/year in 2003 136 and 600 mm/year in 2004; in particular the rainfall was 0.7 mm in spring and 0.1 mm in summer on 137 1 rainy day in 2003 and 0.55 mm in spring and 0.0 mm in summer on 1 rainy day in 2004. 138 Wine samples were directly analyzed after acidification to pH 2 with 5 M HCl and filtration trough 140 a Carbon column as described in previous studies [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF][START_REF] Dugo | Determination of some 382 inorganic anions and heavy metals in D.O.C. Golden and Amber Marsala wines: statistical study of 383 the influence of the ageing period, color and sugar content[END_REF]. Grapes 141 from each plot were beaten by a blender equipped with titanium blades to avoid metal 142 contamination, until a homogeneous juice was obtained. An aliquot of 2 mL of the juice was placed 143 into a teflon beaker with 4.0 mL of 37% HCl and 6.0 mL of ultrapure water and extracted as 144 described in a previous paper (Dugo et al., 2005 a;Dugo et al., 2004 b). Marcs and grape-stalks 145 from each plot were separately dried at 110 C° to constant weight in order to measure their moisture 146

Sample preparation 139

contents. An aliquot of 1 g of dried samples was dissolved in 10.0 mL of 35% HCl and extracted at 147 80 °C as discussed in previous studies concerning metal analysis in vegetable matrices (Dugo et al, 148 2005c, Matarese Palmieri et. al 2005). 149

Derivative chronopotentiometric stripping analysis 150

Cd (II), Cu (II), Pb (II) and Zn (II) were determined simultaneously in wine, grapes, grape-stalks 151 and marcs as described in a previous papers [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF], Dugo et al, 2005 b, c;Matarese 152 Palmieri et. al 2005); Mn (II) was determined separately by placing 500 µL of acidified wine (or 153 acid extract of grapes, marc, grape-stalks) in the electrochemical cell together with 1 mL of 0.5 M 154 ammonia-ammonium chloride buffer (pH 9.5), 10.0 mL of ultrapure water and 1.0 mL of 1000 µg 155 mL -1 Hg (II) standard solution as oxidant agent, and de-oxygenating for 5 min by N 2 purging. The 156 best electrochemical conditions were optimized by fixing the electrolysis potential at -1700 mV, the 157 final acquisition potential and the cleaning potential respectively at -500 mV and -1500 mV, and 158 varying the electrolysis time as afterwards described; the solution was stirred at 1000 rpm; the 159 manganese peak was registered around -1460 mV (Dugo et al., 2005 c). The quantitative analysis 160 of metals was done by the multiple-point standard additions method. Optimum precision and 161 accuracy were obtained with two additions of appropriate aliquots of standard solution of Mn (II) in 162 order to double the sample peak area, and performing the measurements five times for each addition 163 [START_REF] Pera | Simultaneous determination of Cd (II), Cu (II) 406 Pb (II) and Zn (II) in citrus essential oils by derivative potentiometric stripping analysis[END_REF]Dugo et al, 2005

Method performance: sensitivity, accuracy and repeatability 168

Method performances for Cu, Cd, Mn, Pb and Zn chronopotentiometric analysis in wines and 169 vegetables have been reported in previous papers [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF][START_REF] Pera | Simultaneous determination of Cd (II), Cu (II) 406 Pb (II) and Zn (II) in citrus essential oils by derivative potentiometric stripping analysis[END_REF]Dugo et 170 al, 2005 a, b, c;Matarese Palmieri et. al 2005;Dugo, et al.2004). 171

Statistical methods 172

The statistical elaboration of the data was performed by two softwares: SPSS for Windows (version 173 12.0; 2003) and STATISTICA package for Windows (version 6.0, 2000). The non parametric test 174 of Kruskal-Wallis and Linear Discriminant Analysis (LDA) were performed grouping wines, 175 grapes, marcs and grape stalks according to the pesticide treatments applied; data expressing 176 concentrations of Mn, Zn, Cu, Pb and Cd in wines, marcs, grapes and grape-stalks samples from 177 2003 and 2004 vintages were processed. In order to define statistically the difference between 178 samples submitted to a specific fungicide treatment (A, B, C, D, E), the data were elaborated using 179 a multivariate non-parametric Analysis of Variance and subsequently a Linear Discriminant 180 Analysis in order to distinguish samples coming from one of the five treatment groups according to 181 a simple statistical model [START_REF] Dugo | Determination of some 382 inorganic anions and heavy metals in D.O.C. Golden and Amber Marsala wines: statistical study of 383 the influence of the ageing period, color and sugar content[END_REF]. The choice to perform a non-parametric analysis 182 was related to the fact that the distribution of Mn, Zn, Pb, Cd and Cu levels did not seem to meet 183 the necessary conditions required to apply a simple Analysis of Variance (ANOVA), because there 184 was not a homogeneity of variance between samples coming from different groups. Moreover 185 samples belonging to different treatments did not show a normal distribution over the same group. 186 Therefore the five variables (Mn, Zn, Pb, Cd and Cu concentrations expressed in mg/L), were 187 submitted to the non-parametric test of Kruskal-Wallis to verify the significances of differences 188 between the control and treated samples. This procedure tested the hypothesis that n independent 189 samples came from populations having the same distribution.

Derivative stripping chronopotentiometry for the analysis of metals in enological samples 193

Growing concern about heavy metals food contamination has led to increasing needs to develop 194 sensitive analytical techniques capable to detect trace and ultra-trace concentrations of metals in a 195 variety of alimentary matrices. Derivative stripping chronopotentiometry has been recognised as a 196 powerful tool for measuring trace metals in food samples. Its remarkable sensitivity is attributed to 197 the concentration step during which the metals are accumulated onto the working electrode. Under 198 optimized analytical conditions, detection limits lower than 1 ng g -1 have been obtained for the 199 studied metals in different food matrices; the analytical performances of SCP tested on certified 200 reference materials are reported in the cited literature [START_REF] Pera | Simultaneous determination of Cd (II), Cu (II) 406 Pb (II) and Zn (II) in citrus essential oils by derivative potentiometric stripping analysis[END_REF]Dugo et al, 2005 a, b, c;201 Matarese Palmieri et. al 2005;[START_REF] Renman | Asymmetric distribution of results in calibration curve and standard 420 addition evaluation[END_REF]. Sample preparation played an important 202 role in ensuring the accuracy of determination, because metals were present in very low 203 concentrations in complex matrices such as wine, grape, marcs and stalks. Interferences from 204 organic molecules are minimized in SCP, hence a simple and rapid sample pre-treatment based on 205 concentrated hydrochloric acid extraction at 80°C was required which enabled the extraction of the 206 metals in less than 1 h. Wine may be analysed after acidification to pH 2, followed by filtration 207 through a carbon column, without the need for sample pre-treatment. A speciation study performed 208 on a certified reference wine has shown that at pH<3 heavy metals are present in the soluble form 209 [START_REF] Azenha | Assessment of the Pb and Cu in vitro availability 366 in wines by means of speciation procedures[END_REF][START_REF] Karadjova | Fractionation and speciation of Cu, Zn and Fe in wine samples 404 by atomic absorption spectrometry_Spectrochimica[END_REF]. A recovery test carried out on a model 210 wine spiked at different levels with Cu (II), Mn (II), Zn (II), Cd (II) and Pb (II) standard solutions, 211 acidified to pH 2 with HCl and filtered trough a carbon column. The obtained recovery factors (95-212 99%) provided evidence that metal loss did not occur during sample pre-treatment. Comparative 213 analysis between filtered and unfiltered red wine discussed in a previous paper, demonstrated that 214 metal quantification was not affected by the filtration procedure and a higher analytical response 215 was achieved by passing the wine sample trough the carbon column prior to the 216 As Table 3 reports, Mn and Zn levels in 2003 wines from zoxamide-mancozeb treatments (plot B 242 and C) were about 3 fold and 2 fold higher than those observed in the control samples, respectively; 243 the ∆t parameter did not greatly influence the concentration of these elements. Mn levels in 2004 244 wines from zoxamide-mancozeb treatments (plot B and C) were about 3-fold higher than those 245 observed in the control samples; the ∆t parameter seemed to exert a significant influence on Zn 246 level as the metal concentration was 3.5 fold higher than that observed in the control in samples 247 harvested 14 days after the last treatment (plot B) and 6.5 fold higher in samples harvested 28 days 248 after the last treatment (plot C). Moreover the studied treatments lead to a significant increase of Pb 249 and Cd concentrations (an average of 150% over the control) in wines from 2003, meanwhile only a 250 slight enhancement in Pb levels in samples from 2004 was observed. As given in Table 4, Mn levels 251 in 2003 grape-stalks from plots B and C were from 2.4 to 6.5-fold higher than those observed in the 252 control and from 5.6 to 6.8-fold higher in 2004 grape-stalks; in particular, samples harvested 28 253 days after the last treatment showed the highest values. Zn levels were from 1.5 to 2.6-fold higher 254 with respect to the control in grape-stalks from 2003 and from 4 to 6 higher in 2004 samples; in 255 particular, samples harvested 14 days after the last treatment showed the highest values. Pb and Cd 256 concentrations in 2003 grape-stalks from zoxamide-mancozeb treatments were from 5 to 7 fold 257 higher than those observed in the blanc, whereas samples from 2004 showed a slight increase of Pb 258 concentration as compared to the control and a great enhancement of Cd levels (7 to 11 fold higher 259 with respect to the blanc). As Table 5 reports, the ∆t parameter seemed to exert a significant 260 influence on Mn level in grapes from both the crop years: samples from 2003 harvested 14 days 261 after the last treatment (plot B) presented Mn concentrations 11 fold higher with respect to the 262 control, whereas samples harvested 28 days after the last treatment (plot C) 4-fold higher than the 263 control; on the contrary samples from 2004 harvested in the plot B showed Mn levels 2-fold higher 264 with respect to the blanc, whereas samples harvested in the plot C presented Mn levels 4-fold higher 265 than the blanc. A similar profile was observed for Zn levels, particularly for grapes from 2004. 266 to 4-fold higher than those observed in the controls; treated samples from 2004 showed an increase 269 of Pb levels with respect to the control, whereas no significant influence of the treatments on Cd 270 levels in marcs was noticed (Table 6). 271 272

Results from plots treated with copper-oxychloride 273

Cu levels in wines from 2003 were very much lower with respect to those observed in the following 274 crop year; moreover Cu concentrations in 2003 wines from all the plots were very similar, whereas 275 a significant influence of copper-oxychloride treatments was noticed in wines from 2004 (plots D 276 and E) that showed a Cu amount increased by 50% with respect to the control; moreover copper-277 oxychloride applications lead to a moderate increase of Pb and Cd concentrations in wines from 278 both the crop years (Table 3). Copper-oxychloride treatments greatly influenced Cu levels in grape-279 stalks from 2003 and 2004 that presented concentrations from 4 to 50-fold higher than those 280 detected in the control; samples harvested 28 days (∆t) after the last treatment (plot E) showed the 281 highest Cu amounts. In particular, copper-oxychloride applications seemed to exert a remarkable 282 effect on Pb levels in grape-stalks which are 24-fold higher than that of the control in samples from 283 2003, nevertheless this effect was not observed in samples from the following crop year (Table 4). 284

As was already observed for wines, grapes samples from 2003 presented lower Cu concentrations 285 than those detected in samples from 2004; the influence of copper-oxychloride treatments over Cu 286 concentrations in grapes from both the vintages was particularly evident since treated samples 287 presented values 3-4 fold higher than those of the controls (Table 5). A significant influence of Cu 288 oxychloride treatments over Cu levels in marcs from both the crop years was noticed since samples 289 from plots D and E had concentrations 3-5 fold higher than the control; moreover treated samples 290 from 2004 showed an increase of Pb levels with respect to the control, whereas no significant 291 influence of the treatments on Cd levels in marcs was noticed (

Statistical analysis 294

As shown in Tables 3456, all the four types of enological matrices (wines, marcs, grapes and grape-295 stalks) presented a significant difference through the five treatments. This is explained by the 296 calculated significance levels (p) from Kruskal-Wallis test, that resulted ≤.001 for all of the five 297 variables considered independently over the two years. This result suggests that the studied 298 fungicide treatments greatly influenced the concentrations of the Mn, Zn, Pb, Cd and Cu in the 299 wines, marcs, grapes and grape-stalks samples from both the vintages. The concentrations of Mn, 300 Zn, Pb, Cd and Cu have been further studied using Linear Discriminant Analysis (LDA) in order to 301 create a model that, through some classification functions, could separate samples in relation to the 302 particular treatment used. LDA was performed separately on wines, marcs, grapes and grape-stalks 303 samples from the two crop years. The results have shown a very good degree of classification 304 (100%) of the samples according to the functions created in every analysis. Scatterplots (Figures 1-305 4) of discriminant functions, that explained on average the 93% of the total variance over the 306 samples, have shown a good separation of the five groups representing various fungicide treatments 307 applied to grapevines. Generally it can be observed (overall for the wine samples) a little proximity 308 between samples belonging to treatments "B" with those from the treatment "C"; and between 309 samples from treatments "D" and "E". This means that these groups of treatments influence in a 310 common way the concentrations of metals in the samples. Moreover any classification function can 311 be represented as a linear combination of the five variables, providing a good statistical model to 312 characterize the treatments. For example, the two classification functions regarding wines from 313 2003 can be described by the following equations: 314 315 Root1 477Zn +53,871Cd +10,013;316 Root2 = 2,379Zn +472,649Pb +190,525 where Root1 is strongly negatively correlated with the Zn level and Root2 is positively correlated 319 with the Cu level. 320 321 On the whole the statistical analysis of the data has evidenced that wines, grapes, grape-stalks and 322 marcs from both the crop years treated with zoxamide-mancozeb and with copper oxychloride 323 showed a significant increase of Mn (II), Zn (II) and Cu (II) concentrations with respect to the 324 control; the effect of time elapsed between the last application of fungicides and the harvesting (∆t) 325 on the concentration of Mn, Zn, Pb, Cd and Cu in wines, grapes, grape-stalks and marcs has not 326 been yet elucidated as heavy metal levels in treated samples irregularly increased in a ∆t-dependent 327 manner. Furthermore Mancozab-zoxamide and copper-oxychloride treatments lead to a moderate 328 increase of Pb (II) levels in treated wines, grape-stalks, grapes and marcs with respect to the control. 329

As shown in a previous paper [START_REF] Salvo | Influence of mineral and organic 424 pesticides treatments on Cd (II), Cu (II) Pb (II) and Zn (II) content determined by Derivative 425 Potentiometric Stripping Analysis in Italian red and white wines[END_REF], the increase of Cd and Pb levels in treated 330 samples could be the result of the presence of trace concentrations of these contaminants in the 331 chemicals used for the treatments. 332 333

Inter-seasonal variations 334

Pesticide treatments were the variables that mostly influenced metal levels in the enological 335 matrices from 2003 and 2004 vintages . Therefore, in order to estimate inter-seasonal variations due 336 to the environmental conditions, only the control plots have been considered. Tables 3456give 337 evidence that Cu and Pb levels in 2004 wines, grapes, and marcs were much higher than those 338 found in 2003, whereas Mn and particularly Zn levels in 2003 wines, grapes, grape-stalks and marcs 339 were higher than those found in 2004. As Table 2 reports, these results are related to metal levels in 340 the soil/groundwater sampled in the control plots in both the crop years. The relationship between 341 metal levels in the enological matrices and in soil/groundwater system may explain the observed 342 inter-seasonal variations. It must be added that it is very difficult to asses all the factors including, 343 Administration; for a 70 kg adult they were 18 mg Zn/day, 5 mg Mn/day, 3 mg Cu/day (La Pera and 352 Dugo, 2005). Considering the concentration ranges of Zn (0.4-3 mgL -1 ), Mn (0.5-1.6 mgL -1 ) and Cu 353 (0.05-1 mgL -1 ) found in this study and a 0.25 L daily wine consumption, the RDA % were 354 respectively of 0.5-4 %, 2-8 % and 0.3-8 %. Moreover Cu and Zn levels were always below the 355 legal limits established by the Italian Republic, 1.0 and 5.0 mgL -1 respectively (Italian Republic, 356 1986). Toxic elements such as Cd and Pb, present in foodstuff mainly as a result of a contamination 357 process, were found in the studied treated and untreated wines in concentrations well below the 358 legal limits established by the European Community (0.2 mg Pb L -1 and 0.1 mg Cd L -1 ) (European 359 Community, 2001). The nutritional evaluation of the obtained data has shown that a moderate daily 360 consumption of treated wines from both the crop years, provides a Cu, Mn and Zn intake below the 361 RDA and that Pb and Cd levels are within the legal limits; therefore a correct use of these 362 fungicides in wine-growing does not represent a risk for human health.

  data. The experimentation was carried out over two crop years, 2003 and 67

  were used at the doses recommended by the manufacturer and the treatments were 116

  a, b, c;Matarese Palmieri et. al 2005; Renman and Jagner, 164 Calibration curve with correlation coefficients > 99.5% were obtained. The linear 165 concentration range was 0-10000 µgL -1 . Under optimized electrochemical conditions, the analysis 166 lasted 25 minutes. 167

  noticed. Mn and Zn levels in 2003 and 2004 marcs from plots B and C were from 2 268

  emissions, harvesting and storage that that may influence metal levels in food 344 plants grown in the same zone in two different crop years. and manganese are essential micronutrients since they are co-factors of many 348 enzymes, a low dietary intake of these microelements may cause severe deficiency syndromes; on 349 the other hand at high concentrations these trace elements may have toxic effects. For this reason 350 Recommended Daily Allowances (RDAs) have been established by the Food and Drug 351

Figure 1 :Figure 2 :

 12 Linear Discriminant Analysis for wines from 2003 (a) and 2004 (bLinear Discriminant Analysis for grape stalks from 2003 (a) and 2004 (b

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 6

	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	O n l
	y
	13

Table 1 .

 1 Fungicide treatments applied for each plot. 1

	363
	364

Table 2 .

 2 Heavy metal concentrations (as well as pH) in soil (mgkg -1 d.w.) and groundwater (µgL -1 ) 5 samples collected in different points of the studied plots. Each result is the mean of three replicates. 6

		Plot	pH	Mn	Zn	Cu	Pb	Cd
						2003
		A s*	7.9	100.2±11.3 85.4±7.2	11.1±1.3	1.0±0.2	1.5±0.1
		A g*	7.3	198.2±15.6 67.1±8.8	5.9±0.5	0.1±0.0	0.2±0.0
		B s	8.0	95.5±19.1	73.2±8.4	10.5±1.1	1.5±0.5	1.0±0.3
		B g	7.4	177.5±25.9 58.3±5.5	5.0±0.5	0.2±0.0	<0.1
		C s	7.9	103.4±16.3 78.8±5.1	8.9±0.9	1.6±0.4	1.3±0.3
		C g	7.2	212.2±25.3 66.3±7.1	3.8±0.3	0.2±0.0	0.1±0.0
		D s	7.9	89.0±9.6	81.4±9.4	9.1±0.9	1.3±0.7	1.6±0.2
		D g	7.3	161.1±14.3 70.0±5.9	4.1±0.3	0.1±0.0	0.1±0.0
		E s	7.8	106.7±13.5 75.6±8.3	11.7±1.9	1.3±0.6	1.0±0.2
		E g	7.5	224.0±21.5 62.5±5.8	6.0±0.6	0.1±0.0	<0.1
						2004
		A s	8.0	80.2±10.0	74.3±7.2	13.2±1.0	1.7±0.3	1.1±0.1
		A g	7.5	68.5±6.1	49.9±5.1	7.3±0.6	0.2±0.0	<0.1
		B s	8.1	85.5±9.4	83.0±8.5	15.2±2.0	1.9±0.7	1.0±0.3
		B g	7.5	62.3±5.5	54.2±5.9	8.5±0.6	0.2±0.0	<0.1
		C s	7.8	103.0±11.2 94.2±7.3	10.5±1.5	2.0±0.2	1.9±0.3
		C g	7.3	70.0±6.9	58.6±4.8	7.0±0.5	0.2±0.0	0.1±0.0
		D s	7.9	101.7±8.5	82.8±8.8	8.5±0.9	2.1±0.5	1.4±0.2
		D g	7.4	69.8±6.3	55.6±6.1	7.0±0.6	0.2±0.0	<0.1
		E s	7.9	78.9±7.5	67.4±6.7	8.2±1.1	1.8±0.6	1.0±0.2
		E g	7.3	75.2±7.0	41.5±3.9	6.3±0.4	0.1±0.0	0.1
	7	*s=soil, g=groundwater

Table 3

 3 Metal concentrations (mean±st.dev) in wines subjected to different pesticide treatments, as 8 indicated in Table1; five samples from each plot were analysed in triplicate (mean±st.dev). 9 Significances of differences between blank and treated samples are also reported as chi-square (χ 2 ) 10

	11	and p values from Kruskal-Wallis test*
	12	*the treatments number was considered as the grouping variable; the freedom degrees were 4
	13
		year			2003
			Mn	Zn	Cu	Pb	Cd
		PLOT	(mgL -1 )	(mgL -1 )	(µ µ µ µgL -1 )	(µ µ µ µgL -1 )	(µ µ µ µgL -1 )
		A	0.5±0.1	1.4±0.2	43.5±2.0	10.4±10.1	5.5±1.0
		B	1.7±0.1	3.0±0.1	50.2±1.3	31.3±5.2	9.3±4.2
		C	1.2±0.2	2.6±0.2	47.3±3.4	18.4±1.5	13.5±3.9
		D	0.5±0.1	0.5±0.1	57.2±2.7	13.0±1.5	2.2±1.0
		E	0.6±0.1	0.5±0.0	44.0±2.1	19.3±1.4	6.3±1.0

Table 4 .

 4 Metal concentrations in grape-stalks subjected to different pesticide treatments; five 15 samples for each plot were analysed in triplicate (mean±st.dev). Significances of differences 16 between blank and treated samples are also reported as chi-square (χ 2 ) and p values from Kruskal-17

	18	Wallis test
	19
		year			2003		20
			Mn	Zn	Cu	Pb	Cd	21
		PLOT	(mgL -1 )	(mgL -1 )	(mgL -1 )	(µ µ µ µgL -1 )	22 µgL -1 ) (µ µ µ
		A	7.4±1.2	17.3±1.4	6.44±0.75	8.3±1.2	23 17.2±1.4
		B	18.8±1.0	47.2±2.0	8.84±1.47	55.1±1.4	24 86.1±2.5
		C	48.4±1.6	26.2±1.0	11.2±1.2	58.4±2.9	25 73.9±2.5
		D E	10.4±1.5 4.5±0.6	15.6±1.4 16.2±1.2	196±12 314±12	191.1±11.0 192.8±2.5	62.1±1.3 26 27 17.4±2.1
							28
							29
							30
							31
							32
							33
							34
							35
							36
							37
	38
	39
	40
	41
	42

Table 6 .

 6 Metal concentrations in marcs subjected to different pesticide treatments; five samples for 73 each plot were analysed in triplicate (mean±st.dev). Significances of differences between blank and 74 treated samples are also reported as chi-square (χ 2 ) and p values from Kruskal-Wallis test 75

	76
	77
	78
	79	year			2003
	80 81 82	PLOT A	Mn (mgL -1 ) 7.4±1.6	Zn (mgL -1 ) 2.5±0.3	Cu (mgL -1 ) 31.2±3.7	Pb (µ µ µ µgL -1 ) <0.1	Cd µgL -1 ) (µ µ µ <0.5
	83	B	19.3±2.8	7.2±0.7	56.1±6.9	<0.1	31.3±7.2
	84	C	33.2±4.0	9.1±1.5	51.4±2.9	<0.1	80.0±6.1
	85	D	3.9±0.7	8.7±0.5	161.7±10.1	<0.1	<0.5
	86 87	E χ 2	12.2±1.7 23.077	4.6±0.4 21.150	150.3±12.6 21.267	<0.1 23.622	<0.5 23.529
	88	p <	0.000	0.000	0.000	0.000	0.000
	89				2004
	90	A	5.6±0.92	3.0±0.5	32.2±4.8	<0.1	<0.5
	91	B	10.7±2.0	4.7±0.7	33.2±3.6	71.1±2.2	<0.5
	92 93 94 95 96	C D E χ 2 p <	12.4±1.2 5.5±0.9 2.8±0.8 21.186 0.000	10.9±1.4 0.6±0.1 9.2±0.9 22.457 0.000	31.7±2.9 86.1±7.4 93.5±3.7 17.935 0.001	42.3±6.4 46.8±7.1 <0.1 22.401 0.000	63.4±8.3 <0.5 <0.5 23.622 0.000
	97
	98
	99

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