Angeles Mª

García

M A García-Esparza

E Capri

P Pirzadeh

M Trevisan

COPPER CONTENT OF GRAPE AND WINE FROM ITALIAN FARMS 1 2

Keywords: Copper residue, wine, grape, pest management strategy 29 30 31

come

Copper content of grape and wine from Italian farms

Fungal diseases represent a large threat to wine growers, causing significant damage and 1 severely reducing product quality. In European viticulture, downy mildew (Plasmopara 2 viticola) is one of the most dangerous diseases affecting grapes [START_REF] Pearson | Compendium of Grape Diseases[END_REF]. [START_REF] Sauvage | Trace metal studies of selected 4 white wines: an alternative approach[END_REF] Other fungal diseases include oidium (Uncinula necator), botritis (Botrytis cinerea) and 4 black-rot [START_REF] Boubals | Il rame nella protezione sanitaria della vite. Evoluzione e prospettive[END_REF][START_REF] Martelli | Meccanismo d'azione degli antiparassitari a base di rame[END_REF]. Besides organic pesticides (for example, 5

Mancozeb, Zineb, Dimetomorf, Metalaxyl), copper either alone, or formulated with other 6 agrochemicals, is an important measure in the prevention of the outbreak of fungal diseases. 7

However, cupric products are readily removed from treated crops by rainfall and only 8 protect organs sprayed with the product. Therefore, in the case of high disease pressure, fast 9 growing vines and/or rains, it may become necessary to spray more frequently. Copper has 10 traditionally been used to protect vines against fungal disease since the end of the eighteenth 11 century [START_REF] Lafforgue | La Bouillie Bordelaise. 1 er Congrés International de la Vigne et du Vin[END_REF]. Above all, European organic viticulture is predominantly based 12 on copper and sulphur treatments for the control of downy mildew, in line with European 13 Regulation (EC) nº 2092/91 amended by the regulation (EC) nº 473/2002. The 14 aforementioned regulation limits the use of copper to 8 kg/ha/year until 31 December 2005, 15 and up to 6 kg/ha/year from 1 January 2006. 16

The intensive and long-term use of copper has raised concerns regarding negative effects on 17 the environment, through toxicity to aquatic and soil organisms (Beltrami andCapri 1999, 18 Capri et al. 1999), and impacts on human health [START_REF] Araya | Copper 3 exposure and potential biomarkers of copper metabolism[END_REF][START_REF] Turnlund | Long-term high copper intake: effects on indexes of 11 copper status, antioxidant status, and immune function in young men[END_REF]. 19 High levels of copper can also damage vine plants, through phytotoxicity (Fleming and 20 Trevors 1989), and ruin the quality and processing of wine [START_REF] Gennaro | Undesirable and harmful 21 metals in wines-determination and removal[END_REF]). 21

Concentrations above 1 mg/l in white wine may cause copper casse, resulting in a turbid 22 wine (Ribereau-Gayon 2000). For these and other reasons the European Regulation (EC) nº 23 2092/91 has fixed a maximum permitted field dose of copper at 8 kg/ha/year, and a MRL in 24 grape and wine of 20 mg/kg and 1mg/L respectively(reference?). [START_REF] Martelli | Meccanismo d'azione degli antiparassitari a base di rame[END_REF] Copper applied in the field in the form of the bivalent ion (Cu ++) does not dissipate. 26 Following application to the vine plants, the sole route for the decrease of concentrations on 27 the grapes is by wash-off. By this means, copper enters the soil where it is accumulated as a 28 result of strong binding to soil particles [START_REF] Flores-Vélez | Study of the distribution 16 of copper in an acid sandy vineyard soil by three different methods[END_REF][START_REF] Müller | Agrochemicals: Composition, Production, Toxicology, Applications[END_REF]. As 29 transport of copper from the roots appears to be of minor importance in determining 30 concentrations in the aerial parts of the plant [START_REF] Brun | Evaluation of copper availability to 9 plants in copper-contaminated vineyard soils[END_REF]), the variables that determine 31 the concentration of copper in grape at the time of harvest are presumably the total amount 32 The concentration of copper in wine may be decreased compared to that in the grape due to 3 the elimination of copper during fermentation. Copper is reduced, forming insoluble 4 sulphides, which are removed through sedimentation together with yeasts and lees (Tromp 5 andKlerk 1988, Ribéreau-Gayon et al. 2000). Furthermore, copper content may differ 6 between red and white wine, as a result of differences in the winemaking process. In a study 7 performed by [START_REF] Amati | Il rame e l'enologia[END_REF] the concentration of copper was higher in red must than in white 8 must. This was explained by the fact that, during the initial winemaking process for red 9 must, the skin of the grapes is not removed. An understanding of the overall fate of copper 10 in the field is necessary in order to assess the key factors influencing the concentration of 11 copper on grape for human risk assessment, and to enable the proposal of measures to 12 minimize residue levels in grape and wine. In this study we have considered the influence of 13

(1) the number of copper applications, (2) the period of time between the last application 14 and harvest and (3) the total mass of copper applied, on copper residue levels in grape and 15

wine. 16

Materials and methods 17 18

Data collection 19

A total of 38 grape and 34 wine samples were collected from sixteen farms dedicated to 20 grape cultivation for winemaking, during the harvest period of 2003. All samples were 21 obtained by random sampling techniques in the cultivated crop area. Different pest 22 management strategies were used on the farms (conventional (n=5), integrated (n=8) and 23 organic (n=3)). The farms sampled were located in Lombardia (LO), Trentino-Alto Adige 24 (TN), Emilia-Romagna (ER), Friuli-Venezia-Giulia (FV) and Lazio Regions (LZ) in Italy. [START_REF] Martelli | Meccanismo d'azione degli antiparassitari a base di rame[END_REF] The use of pesticides differs according to the viticulture production system. In the 26 conventional pest management strategy, the application of active ingredients does not follow 27 rules established by the regional authority. In the integrated pest strategy, the use of 28 pesticides is restricted and their application is allowed only when pests and diseases exceed 29 defined limits. In organic viticulture, diseases can be managed using a combination of 30 cultural strategies and acceptable, mineral-based fungicides, such as sulphur and copper. 31 Usually, in conventional and integrated pest management strategies, copper is formulated 1 with organic pesticides to improve the efficacy of cupric products. Copper is included in 2 fungicidal formulations as oxychloride, sulphate or hydroxide. The advantage of these 3 formulated products is their adhesiveness to guarantee an adequate deposit on the treated 4 vegetable. In our study, the different formulations used by farmers (excluding organic 5 farms) included copper with Dimetomorf, Fosetyl-Al, Iprovalicarb, Cimoxanil and 6

Metalaxyl. These favour the penetration of the active ingredient into the plant, and this may 7 increase the time for which the compound remains in the vineyard. 8

It is important to know the level of disease and damage in order to use the most suitable 9 active ingredients. Disease pressure may be especially high during wet years, and under 10 such conditions grapes could receive increased fungicide applications. In the year of our 11 study (2003), climatic conditions were unusual (dry and slightly rainy). In 2003, rainfall 12 ranged between 700 and 890 mm in Trentino-Alto Adige (TN) and Friuli-Venezia-Giulia 13 (FV) regions. In Lombardia (LO), Emilia-Romagna (ER), and Lazio Regions (LZ) rainfall 14 ranged between 250 and 594 mm. [START_REF] Flores-Vélez | Study of the distribution 16 of copper in an acid sandy vineyard soil by three different methods[END_REF] In the present study, the collaboration of farmers was fundamental. Besides the samples of 16 grape and wine, they provided important information regarding the pest management 17 strategy in place, the number of copper applications made during the vegetative period, the 18 time elapsed between the last application (DLA) of copper and harvest, and the dose of 19 copper (kg/ha) used in each application. [START_REF] Gennaro | Undesirable and harmful 21 metals in wines-determination and removal[END_REF] One of the most important factors in the study of pesticide residues, is the number of 21 applications during the vegetative period. In our study, this factor ranged from one to 22 eleven, and the number of applications made did not follow established rules for the 23 different pest management strategies in place. The statutory period of time between the last 24 application (DLA) of copper and harvest is 21 days. All of the selected farms respected this 25 legal condition, and the DLA ranged from 21 to 112 days. The quantity of copper used in 26 vine crops is limited to 8 kg/ha/year until December 2005 (EC 2092/91). The total mass of 27 copper applied for all farms considered in this study was between 0.525 kg/ha and 7.8 kg/ha. 28

The highest application rates of copper correspond to farms located in the north of Italy (TN 29 and FV) where problems with downy mildew are greater. 30 Table 1 summarises the farms and their location, the number of samples of grape and wine 1 collected from each farm, the pest management strategy, the number of applications, the 2 time between last application and sampling, the total mass of copper (kg/ha) applied and 3 rainfall (mm/year) during 2003. Total copper is calculated by multiplying its fraction in the 4 formulation by the applied dose. 5

[insert table 1] 6 7

Extraction method and apparatus 8

Each unwashed grape sample (sin raspon) consisted of 1 kg fruit, which was minced to a 9 homogenous paste in a stomacher (Stomacher® 400, Seward Ltd). Approximately 10 g of 10 the paste was dried in an oven for about 10 hours at 105ºC, after which 1 g was weighed into 11 a glass test tube. The wine samples were collected from 1 l bottles. A volume equivalent to 12 1 g was transferred to a test tube. 5 ml of HNO 3 and 2ml of HClO 4 were added to the tubes 13 to facilitate the mineralization of the samples, which was performed using a digestion 14 apparatus (Model: Büchi 425). The digestion tubes are heated consistently from all sides. 15 This guarantees optimum boiling characteristics without boiling delay. The temperature is 16 easily adjustable up to approx. 250°C. Due to the temperature controls, samples tending to 17 foam can be digested without any problems. The digestion unit is suitable for four samples 18 with a tight suction module and with a continuously adjustable IR-heating. The metal 19 components of the equipment are made of acid-resistant steel. The digestion times are 20 typically 20 to 60 minutes depending on quantity and type of sample. 21 Following mineralization, samples were washed with 10-15 ml of Mili-Q water and were 22 filtered through 150 Ø mm filters (Schleicher & Schuell). Blank samples were prepared as 23 controls for the procedure. All reagents and chemicals were of analytical grade. High-purity 24 water from a Mili-Q apparatus was used to prepare standard solutions. A THGA graphite 25 furnace mounted on a Perkin-Elmer (mod. AA600) atomic absorption spectrophotometer 26 (AAS) was used with an auto-sampler for the determination of copper concentration at 324. 8 27 nm. Argon was the inert gas used, and a copper cathode lamp was used for the 28 measurements of copper. For calibration purposes, standard solutions were prepared by 29 diluting reference standard solutions of copper for the atomic absorption spectrophotometer 30 ). An average of 1 three readings of absorbance were taken for all samples(including controls). The calibration 2 curve was based on 4 concentrations, giving a correlation coefficient of 0.996 and a 3 percentage residue standard deviation of 10.19 % from the analytical means of different 4 samples. The average value for blank samples was 0.011 ± 0.007 mg/l . 5 6

Statistical analysis 7

Analysis of variance (ANOVA) and non parametric Kruskal-Wallis (with SPSS version 8 11.5 for PC) were carried out to asses differences in copper content for the number of 9 applications, the total dose applied and the interval between the last application and harvest. 10

Multiple regression analysis was also performed for these determining factors and levels of 11 copper residue, in order to assess the contribution of each factor to the regression. The effect 12 of pest management strategies on copper levels and differences between white and red grape 13 and wine were also tested with ANOVA. To fulfil the assumptions of the ANOVA, the data 14 was log-transformed. [START_REF] Flores-Vélez | Study of the distribution 16 of copper in an acid sandy vineyard soil by three different methods[END_REF] 16

Results and discussion 17

The analysis found that the average copper concentration for red grape (n= 22) and white 18 grape (n=16) was 11.32 ± 8.61 mg/kg and 7.54 ± 7.50 mg/kg respectively. These 19 concentration are lower than those obtained by [START_REF] Fregoni | La dotazione in rame dei vigneti italiani[END_REF] in which the total 20 copper residue concentration in grape from several italian regions ranged from 10 to 56 ppm. 21

Out of the 38 grape samples analysed, 13 % exceeded the established MRL (20 mg/kg). No 22 significant difference in copper content (p>0.05) was found between red and white grape. 23

24

The mean copper content found in red and white wine (n=20; n= 14) was 0.71 ± 0.52 mg/L 25 and 1.01 ± 0.95 mg/L respectively. Of the total number of wine samples analysed, 18% 26 exceeded the MRL (1 mg/L). No significant difference (p>0.05) was found between red and 27 white wine. The results found here were similar those obtained by Marengo and Aceto 28 (2003) who analysed 68 red wine samples from the Piedmont region and found copper 29 concentrations that ranged from 0.001 mg/l to 1.34 mg/l. Our results also agree with another 30 study where the copper content of white wines ranged from 0 to 1.8 mg/l (Sauvage et al. 31 2002). The values in the present work show that there may be a risk of turbidity and quality 32 The number of grape and wine samples exceeding the relevant MRL are relatively high 3 considering the small number of samples analysed. The recommended limit for total daily 4 intake of copper for adults is 10-12 mg/day (WHO 1996). Considering that the dietary intake 5 of copper can be increased from the regular consumption of certain foods, such as shellfish, 6 organ meats (e.g., liver and kidney), legumes, nuts and drinking water, the total daily intake 7 of copper could represent a health risk. The number of copper applications made at the 16 8 farms studied ranged from 1 to 11 events. Most commonly, between 2 and 6 applications 9 were made. The correlation between the number of applications and copper residues in grape 10 is very weak (r 2 = 0.16). Upon grouping the data by number of applications (1-2 (n=6), 3 11 (n=14), 4 (n=8) and 5-11 (n=10) applications) a statistical difference was found (Kruskal-12 Wallis; p= 0.006) between the group in which 4 applications were made and those groups 13 with lower and higher numbers of applications, further confirming the weak correlation. Of 14 the five data-points that exceeded the MRL, two were samples from farms where 4 15 applications were made, and three from farms where 5-11 applications were made (figure 1 a 16 & b). Perhaps, rather than the number of applications, the total dose applied and the time at 17 which the applications are made may better describe copper residue levels on grape. If, for 18 example, five applications with a high total dose are made at the beginning of the growth 19 period, the likelihood of the copper being washed off in the period between application and 20 harvest is greater than for three applications at a lower dose made at the end of the growing 21 season, close to harvest. Copper residue level in wine did not appear to be affected by the 22 number of applications. In a similar study, no positive correlation was found between copper 23 residues in wine and the number of applications made (Taccheo Barbina 1992). 24

[insert Figure 1]

25 A further factor that can affect copper content in grape is the total dose applied during the 26 vegetative period. For our data set, correlating total applied dose and copper residue level in 27 grape gave a correlation coefficient, r 2 , of 0.26. Upon grouping the same data into six groups 28 based on application rate (0-2000 g/ha (n=6), 2000-3000 g/ha (n=9), 3000-4000 g/ha (n=8), 29 4000-5000 g/ha (n=9), 5000-8000 g/ha (n=5)) a significant difference was observed between 30 the 3000-4000 g/ha and 4000-5000 g/ha groups (Kruskal-Wallis; p= 0.000). One of the data-31 points exceeding the MRL was from the 2000-3000g/ha group, while two were in the 4000-32 5000g/ha group and two in the 5000-8000 g/ha group (figure 2 a & b). For wine samples, no 1 relationship was observed. The average concentration of copper in wine samples does not 2 depend on the quantity applied in the vineyard. During the wine making process, copper 3 concentrations decrease by between 84% and 98% due to yeast consumption (Tromp and 4 Klerk 1988) or precipitation as copper-sulphides [START_REF] Amati | Il rame e l'enologia[END_REF]). Similar results were 5 obtained by Rodríguez et al. (1999). The authors show a clear influence of the fermentation 6 processes on metallic cation content. The copper concentration fell by 96% during the 7 winemaking process. 8

[insert Figure 2]

9

The importance of time in reducing levels of plant protection products on crops is firmly 10 established. To reflect this, safety times after which application is not permitted are defined 11 for each plant protection product, and are often based on the degradability of the compound. 12 This limit is intended to guarantee a safe level of plant protection products in crops. The 13 safety time for copper and copper-products is 20 days. Since copper does not degrade like 14 organic plant protection compounds, this safety time is considered sufficient for copper 15 concentrations to decrease to an acceptable level by wash-off. Correlation between the time 16 interval between the last application and harvest and the copper residue level in grape gave a 17 r 2 value of 0.27. Most samples had a safety time limit of 20-60 days, and a small group of 18 samples had a 112 day safety limit (n = 8). The data were grouped into four categories, 19 based on the safety time limit (20-30 days (n = 8), 31-40 days (n = 9), 41-60 days (n = 13) 20 and 112 days (n = 8)). A significant difference was observed between the 112 day and the 21 20-30 and 31-40 day safety time limit groups (Kruskal-Wallis; p = 0,000). No difference 22 was observed between the wine samples. Of the three grape samples exceeding the MRL, 23 one each was from the 20-30 day, 31-40 day and 41-60 day safety time limit groups (figure 24 3 a & b). The average values (average ± stdev) for the concentrations of the groups are, 18.6 25 ± 10.7; 10.5 ± 6.3; 7.8 ± 5.6; 3.2 ± 1.4 mg/kg for the 20-30 day, 31-40 day, 41-60 day and 26 112 day groups respectively. The average residue concentration for the 20-30 day safety 27 limit group is close to the MRL of 20 mg/kg, with an upper 95% confidence limit as high as 28 29.3 mg/kg. By contrast, the 31-40 day group has a lower residue level with an upper 95% 29 confidence limit of 16.8 mg/kg (i.e., below the MRL). This dataset indicated that a period of 30 20 days may not be a sufficient safety time for copper. Rather, the data suggest that a safety 31 time of at least double the current recommendation is needed, recalling that only residues in 32 However, it is acknowledged that the grape samples in this study were collected in 2003 2 which was a particularly dry year. Precipitation was lower than the annual average, 3 especially in the northern regions. Had the study had been performed in a more normal year 4 with regards to precipitation, then the copper residue levels may have been lower, and may 5 have been below the MRL in the 20 day group. In light of the unusual climatic conditions in 6 the study year, the findings of this study may be considered a worst case scenario. Thus, a 7 safety limit of 40 or 50 days could be adequate, even for very dry years. 8

[insert Figure 3]

9 Multiple regression analysis of the three factors considered revealed that the combination of 10 time between last application and harvest, and total dose, best explain the residual level of 11 copper in grape with a correlation coefficient (r 2 ) of 0.44. Including the number of 12 applications in the regression does not increase correlation coefficient. The low correlation 13 illustrates that predicting copper residue levels on grape is complicated. Many additional 14 factors not considered here may be influential, for example, meteorological conditions and 15 application practices. 16

In the study presented here, we have observed that the number of applications, the total dose 17 and time between last treatment and harvest to some extent influence the final copper 18 concentration in grape. These aspects all form part of the management regime implemented 19 by the farmer, and may differ depending upon the pest management strategy used. In Italy, 20 integrated, conventional and organic farms differ from each other with regards to regulations 21 and guidelines which, among other things, govern the use of pesticides. Organic farms only 22 use sulphur and copper, while integrated farms follow a list of recommended pesticides 23 proposed by the authorities. Conventional farms can follow the minimal requirements posed 24 by law. One could perhaps hypothesise that in organic farms larger quantities of copper are 25 used as copper is the only permitted control for fungal disease, and this may result in higher 26 residual levels of copper. The mean copper residue values and standard deviation in grape 27 and wine for conventional farms were 10.62 ± 12.44 mg/kg (n=7) and 1.17 ± 1.34 mg/L 28 (n=7). The values for integrated farms were 11.84 ± 7.36 mg/kg (n=21) for grape and 0.76 ± 29 0.39 mg/L (n = 19) for wine. Grape and wine from organic farms had copper residue levels 30 difference in copper levels for grape and wine were observed between the different pest 1 management strategies (figure 4). It is important to note that in our study there exists an 2 imbalance in the number of samples analysed for each pest management type and the number 3 of farms that utilise each pest management strategy. For this reason, a larger study focusing 4 on this aspect could better clarify the impact of pest management strategies on copper residue 5 levels on grape. However, considering that the limit for copper application (8 kg/ha/year) is 6 general, regardless of the pest management strategy, and considering that the total copper 7 applied varied widely between strategies (table 1), it is not unreasonable to expect that the 8 residual levels would not differ between farm management types. 9 In the work presented here, the influence of the number of applications, total applied dose 12 and time between the last application and harvest on copper residue levels in grape and wine 13 were studied. The two most influential factors were the total amount of copper applied and 14 the time between the last application and harvest which together explain 44% of the 15 regression. This is a weak correlation, and additional factors not investigated here, such as 16 meteorology and application methods, may also influence residue levels. In order to 17 decrease the probability of copper residue levels being close to the MRL under 18 meteorological conditions similar to those prevalent during the experiment, (i.e., dry 19 conditions representing a worst case scenario), the recommended time between the last 20 application and harvest is 40-50 days. Furthermore, the copper content in grape and wine did 21 not appear to be dependent on the pest management strategy of the farm. Standard 22 deviations have high values suggesting heterogeneity among the practices considered.. It is 23 therefore important to investigate alternative methods to decrease the application of cupric 24 products. Perhaps the most important factor influencing the amount of copper used and, 25 ultimately, the level of residue is, that applications are made depending upon the prediction 26 of a disease outbreak rather than the pest management strategy in place. With regards to the 27 risk to human health from exposure to copper, 13% of the grape samples and 18% (i.e., one 28 in five) of the wine bottles sampled exceeded the MRL. It is therefore important to regularly 29 monitor copper residue levels in grape for wine-making as they may also be consumed as 30 grape juice or unprocessed grape.

  applied, the number of applications made and the amount precipitation in the 1 period between the last application and harvest. 2

6 (

 6 AAS), certified by the manufacturers (Aldrich Chemical Company, Icn.

  wine as a result of copper casse due to copper concentrations in excess of 1 1 mg/L in almost one of every five bottles. 2

  group were statistically lower than those in the 20-30 day and 31-40 day groups. 1

of 4 .

 4 70 ± 4.34 mg/kg (n= 10) and 0.70 ± 0.55 mg/L (n= 8) respectively. No significant 31

4 DLA: 5 Page

 45 Figure 1 1 2

Figure 1 3 7 Figure 2 8Figure 3 13Figure 4 18

 17234 Figure 1 3 Copper residue level in grape (mg/kg) for different number of treatments, A) presented with 4 the linear regression equation and correlation coefficient (R 2 ) and B) in groups. The line at 20 5 mg/kg indicates the MRL. 6 7 Figure 2 8 Copper residue level in grape (mg/kg) for different total applied doses, A) presented with the 9 linear regression equation and correlation coefficient (R 2 ) and B) in groups. The line at 20 10 mg/kg indicates the MRL. 11 12 Figure 3 13 Copper residue level in grape (mg/kg) for different safety intervals between last application 14 and harvest, A) presented with the linear regression equation and correlation coefficient (R 2 ) 15 and B) in groups. The line at 20 mg/kg indicates the MRL. 16 17 Figure 4 18 Average copper concentration in grape (mg/kg) and wine (mg/l) for different pest strategies. 19 Error bars signify Standard Deviation. Maximum Residual Level, MRL a (limit by European 20 Community) and MRL b (used by Italian authorities), for grape and wine respectively. 21

Table 1 . 1 2

 11 Locations

	31

Table 1 1

 1 Main characteristics of the studied data-set. 2

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

Acknowledgements

This study was supported financially by the Marie Curie Programme within the project 2 "Field test of pesticide indicator for wine and grape farms" under the Fifth Framework 3

Programme CONTRACT Nº QLK5- CT-2002-51605. 4 The authors thank the farmers for supplying the study with grape and wine samples and 5 information on performed practices.


Ochratoxin A and citrinin loads in stored wheat grains: impact of grain dust and possible prediction using ergosterol measurement

Keywords: Mycotoxins, Mycotoxins -other, Mycotoxins -ochratoxin

published or not. The documents may come

Ochratoxin A and citrinin loads in stored wheat grains: impact of grain dust and possible prediction using ergosterol measurement

Introduction

Grains entering store carry a wide range of microorganisms, including bacteria, yeasts, and filamentous fungi, the population structure being dependent on field climatic conditions and harvesting processes [START_REF] Magan | Post-harvest ecology: impact of fungal growth and mycotoxin accumulation in stored grain[END_REF]. If harvesting is performed at the right time, crop storage should be done under hygienic conditions for safe products. Poor post-harvest management can lead to rapid deterioration in grain quality, severely decreasing germinability and nutritional values of stored grains. This is why an efficient preservation system is important for the sustainable use of food resources. However, it was observed that the storage is always performed under some environmental contamination risks (dusts, insects, birds or rodents). Dusts generated during the previous cereal-storage can be colonized by microorganisms like toxigenic fungi, which in conductive environmental conditions, may proliferate and produce mycotoxins. Ochratoxin A (OTA) has been found on a large number of agricultural products after storage [START_REF] Smith | Mycotoxins in human nutrition and health[END_REF][START_REF] Chandelier | Mycotoxins survey in Belgium and toxigenic Fusarium in Belgian wheat[END_REF]. OTA, in combination either with sterigmatocystin or citrinin (CIT), has often been found in stored wheat, barley, oat and corn [START_REF] Abramson | Mycotoxin formation in hullness barley during granary storage at 15 % and 19 % moisture content[END_REF]. In Belgium, analyses of different commodities including raw foodstuffs and processed food performed during 2001-2002 have shown that of the 184 samples investigated, OTA contamination was above the limit of quantification in 73 samples (40%) and that cereal grain samples were contaminated up to 25.4 µg kg -1 [START_REF] Chandelier | Mycotoxins survey in Belgium and toxigenic Fusarium in Belgian wheat[END_REF]). Note that this contamination exceeded the current maximum level of 5 µg kg -1 (EC 2002). Nowadays in Europe, cereals are frequently stored at the farm for economic reasons (low prices, use as feed). Under such 5 conditions, the mycotoxin risk increases because grains are not always harvested at a sufficiently low water activity to guarantee the absence of fungal growth during the storage period. In addition, the farmers' storage facilities (barns, lofts, attics, silos) are more difficult to clean up thoroughly in comparison with the more sophisticated commercial storage facilities. There can also be more important moisture sources in the farmers' storages (leaks in the roof, humidity from the walls, etc) and, hence an increased risk of mycotoxin contamination at the farm. So, [START_REF] Pussemier | Chemical safety of conventionally and organically produced foodstuffs: a tentative comparison under Belgian conditions[END_REF] have reviewed data from different countries and concluded that atypical severe contaminations of cereals by OTA (up to 1000 µg kg -1 as observed in Poland, for example) could be linked to bad storage conditions in individual farms from either the organic or the conventional side. Based on these findings, mycotoxins can be a serious contamination problem in poorly stored grains intended for human consumption. It can also be a problem for animal health because cereals can be used to prepare animal feed and also because grain dusts collected in large grain storage facilities can be used, as a source of fiber and/or cellulose, for incorporation into feed.

Today, microbiological analyses and, more particularly, the detection of fungal genes involved in mycotoxin biosynthesis, are more and more performed in order to detect mycotoxigenic fungi. It is often proposed to use such screening tools in order to forecast mycotoxin risks in food or feed commodities [START_REF] Nicholson | Rapid detection of mycotoxingenic fungi in plants[END_REF]. Such approach has been successful for some mycotoxins and the links between mycotoxigenic fungi and mycotoxin production are not always obvious [START_REF] Huwig | Mycotoxin detoxication of animal feed by different adsorbents[END_REF][START_REF] Nicholson | Rapid detection of mycotoxingenic fungi in plants[END_REF]). The lack of relationship between mycotoxin-producing fungi and the measured mycotoxins may be due to the degradation of the fungi after the production of toxin or to the competitive growth of non-toxin producing species [START_REF] Halstensen | Ochratoxin A in grain dust: estimated exposure and relations to agricultural practices in grain production[END_REF] or also to the high genetical diversity [START_REF] Frisvad | Ochratoxin A producing Penicillium verrucosum isolates from cereals reveal large AFLP fingerprinting variability[END_REF]. Moreover, many fungi are not able to produce mycotoxins or produce them in different amounts, depending on the substrate on which they grow [START_REF] Huwig | Mycotoxin detoxication of animal feed by different adsorbents[END_REF]. Therefore, the present investigation was not only initiated to explore the possibilities offered by the chemical analyses of grain dusts derived from cereal storages but also to combine it with incubation assays with wheat grain in order to develop a new experimental tool for the assessment of the mycotoxigenic potential in wheat storages. This study also tried to ascertain whether measurement of ergosterol concentration in stored wheat grains could be useful in forecasting CIT and OTA loads.

Materials and methods

Reagents

Pure crystalline form of ergosterol (ERG), OTA and CIT were obtained from Sigma Chemical Co. (St Louis, MO, USA). Ergosterol solution was prepared in n-hexane, while the stock solutions of the mycotoxin standards were prepared in methanol, ethanol or acetonitrile. The concentrations were measured by spectrophotometry. Further dilutions were made in corresponding solvent and multi-mycotoxin standards were made in 50 % methanol and stored at -20°C until use. Acetonitrile,methanol,ethanol,ethyl acetate,isopropanol,amyl alcohol,hydrochloric acid (37 %), phosphoric acid, were obtained from J.T. Baker (Deventer, The Netherlands), Riedel-de Haën (Seelze, Germany) or Biosolve (Valkenswaard, The Water of HPLC-quality was obtained from a Milli-Q system of Millipore (Molsheim, France).

Grain dusts

Four settled-grain dusts were collected from storage-companies (2) and private farmers (2) established in the main agricultural region of Belgium (loamy region in central Belgium).

In each case, composite dusts (with grain debris if any) were collected from cereal (wheat and/or winter barley) storages, which were performed between 2001 and 2003. On delivery, dust samples were sieved to remove particles larger than 2.0 mm. The sifted dusts were stored at +4°C in polypropylene cans. Before use, they were mixed to ensure homogeneity.

Experimental set-up

All the incubation experiments (preliminary assessment and extended incubation trials) were conducted with natural wheat grains (Triticum aestivum) Decan cultivar. Wheat grains [60g, 10.4 % moisture content (m.c.)]were dispersed into 300 ml-Erlenmeyer flask. For the preliminary assessment, sterilized (autoclaved at 121°C for 20 min) and non-sterilized batches were incubated with grain-dust (1 %, w/w) at 25°C in the dark for 28 days with two occasional shakings per week. A control treatment without dust addition to the grains was carried out to determine the normal fungal activity patterns. At the end of the incubation period, the products were autoclaved, ground and stored at -20°C until analysis.

All these preparations were performed in triplicate for sterilized and non-sterilized trials.

For the extended incubation trials, a case-control study design was adopted, using non-sterilized grains only. Wheat grains (60 g per Erlenmeyer flask, 10.4 % m.c.) were adjusted to 14, 17 and 20 % m.c. with the addition of the appropriate volume of sterile distilled water dispersed into the Erlenmeyer. Moistened samples were roughly shaken and mixed with grain dust powder (1 % w/w). They were then statically incubated in a thermostatically control room at 25°C for 4 weeks. Samples were daily shaken during the first 5 days and thereafter once a week basis till the end of the trials. Storage environmental conditions were kept at target moisture content (14, 17 and 20 %) and temperature (25°C) conditions. Sample weights were weekly monitored and additional humidification treatment was necessary in order to avoid changing final target water availability. At the end of the incubation period, samples were autoclaved at 121°C for 20 min and were thereafter dried at 103°C for 18 hours, ground and stored at -20°C until analysis.

A control treatment without dust but with water addition to reach the target moisture content of the wheat grains was carried out to determine the normal fungal activity patterns.

All these preparations were run in triplicate at each moisture content for the different dust samples.

Ergosterol determination in cereals and grain dusts by Liquid chromatography

Ergosterol analysis in wheat and grain-dusts was carried out as described by [START_REF] Bakan | Toxigenic potential of Fusarium culmorum strains isolated from French wheat[END_REF] and [START_REF] Pietri | Occurrence of mycotoxins and ergosterol in maize harvested over 5 years in Northern Italy[END_REF]. Briefly, a sample (5 g) of wheat-flour or dust thoroughly mixed was transferred into a 250-ml centrifuge container. Methanol (150 ml), ethanol (50 ml) and potassium hydroxide (10 g) were added and roughly mixed. The mixture was refluxed for 30 min at 80°C, cooled to 20°C with water (20 ml) and filtered through a folded filter paper in a separator funnel. Additional methanol (25 ml) was added to rinse the flask and cake. The supernatant was isolated and filtered through the filter paper.

Saponified mixture was then intensively shacken for 1 min with n-hexane (50 ml) and let rest for 10 min to allow complete separation of layers. Upper layer (n-hexane) was then isolated and dried over anhydrous sodium sulphate (Na 2 SO 4 ) into 250 ml-round bottom flask. A second extraction was similarly performed and the organic extracts were pooled and concentrated (5-20 ml) at the vacuum rotation evaporator (Büchi) at 40°C. Quantitation was performed by HPLC. The chromatographic system consisted of a Waters 510 pump (Milford, MA, USA) equipped with an automatic WISP model 712 injector (Waters, Milford, MA, USA), a Chromspher Si (100 x 3.0 mm i.d., 3 µm particles) column, a Diode array detector (Thermo Finnigan Spectra System UV 6000LP) set at 282 nm. The method is derived from a multi-mycotoxin analysis method based on the method described by [START_REF] Pierard | Multi-mycotoxin determination methodology[END_REF] for 6 mycotoxins and adapted to the larger set of 15 mycotoxins including OTA and CIT. This method has been described in detail elsewhere [START_REF] Tangni | Ergosterol and mycotoxin in grain dusts released from cereal storages in Belgium: A preliminary screening survey[END_REF]. Briefly, a 10 g-portion of flour was transferred into a centrifuge container and mixed with 50 ml of acetonitrile-water (90:10) acidified with phosphoric acid (pH = 1.5). The mixture was triturated for 3 min with the Ultra-Turrax device CAT x 620 (Staufen, Germany) fitted with a T17-V shaft at 13500 rpm to produce slurry. The supernatant was then isolated by centrifugation at 1660g during 10 min. A second extraction was identically performed with the remaining cake with 50 ml of acetonitrile-water (90:10, pH = 1.5). The combined supernatants were then concentrated and further processed for clean up on a SPE column Chromabond ® XTR (Macherey-Nagel).

OTA and CIT determination in wheat and grain dusts by Liquid chromatography

The chromatographic system consisted of a system of two Waters 510 pumps (Milford, MA, USA) equipped with an automatic WISP model 712 injector (Waters, Milford, MA, USA), an Alltima (25.0 x 4.6 mm i.d., 3 µm particles) C18 column and a Waters 474 scanning fluorescence detector (Milford, MA, USA).

Recovery experiments performed with wheat flour samples spiked at 40 ng CIT g -1 and 20 ng OTA g -1 gave averages of 74.0 ± 8.9 % and 92.0 ± 11.0 %, respectively. The analytical method as implemented had detection limits (LOD) of 0.4 and 0.1 ng g -1 , respectively, whereas the respective quantification limits o(LOQ) were 1.4 and 0.3 ng g -1 . The calibration curves were calculated using the least-squared method. Experimental results are reported as the mean ± standard deviation (SD). The increase of ergosterol and mycotoxins (Net production) derived from the incubation experiment was computed by subtracting the amount provided by wheat (control) and dusts from the total amount recovered from the analysis of wheat incubated with dust as follows:

Data handling and statistical analyses

Net production = Total Amount (dust inoculated wheat) -Amount (wheat) -Amount (dust) (1)

The statistical significance of data was tested applying non-parametric procedures in order to eliminate difficulties caused by heterogeneous data distribution. Mann-Whitney Utest was used to compare the mean ergosterol level between sterilized and non-sterilized batches and to compare the mycotoxin levels between the control and the treated batches according to humidity level and selected dusts. Spearman correlation analysis was used to study the consistency of the association between the net mycotoxin production and the levels of ergosterol or mycotoxins in dust as well as between the ergosterol level, CIT and the sum of the OTA and CIT concentrations in the incubated samples. As data had skewed distributions, they were plotted on a log-scale. Effects with p < 0.05 were deemed as statistically significant. Statistical analyses were performed by the statistical program SPSS (Release 10.0.5. 1999).

Results

Ergosterol and mycotoxins in grain-dusts: screening of collected dusts

The concentrations of ergosterol and mycotoxins in four grain-dusts are presented in table I. The ergosterol content of the four dust samples selected for this study ranged from 39 to 823 µg g -1 of dust. High CIT and OTA contaminations were recorded for all dust samples (ranges: 137 -343 ng CIT g -1 and 17.3 -318.0 ng OTA g -1 ). One must note that for these two toxins, there was a high degree of variability in the concentrations found in the 4 dust samples and that the highest concentrations of 344 ng CIT g -1 and 318 ng OTA g - 1 were found in dust B which was associated with mouldy grains and grain debris. Based on this result, the dust sample with the low ergosterol content was used for a preliminary trial aimed at assessing the influence of the intrinsic fungal population in wheat whereas, the 3 others were chosen for the extended incubation trials with grains.

"[Insert

Preliminary trial on sterilized and non-sterilized wheat samples

Table II presents a comparison of the mean ergosterol contents under intrinsic moisture content in sterilized and non-sterilized wheat grains after incubation at 25°C for 28 days.

"[Insert table II about here]"

Comparing the ergosterol production in sterilized and non-sterilized batches, the Mann-Whitney test did not reveal any statistically significant difference (p = 0.667) between ergosterol production in these two batches, showing thus that similar fungal activity occurred whether the grains are sterilized or not. So, under 10.4 % m.c. (drier conditions) the addition of grain dust increased the net production of ergosterol as a result of fungal biomass increase. From this preliminary experiment it may be concluded that : i) the increase of fungal biomass as measured by the ergosterol concentrations is a consequence of the loading with dust and is not due to the development of the intrinsic fungal microflora naturally present in wheat (no difference between sterilized and non- ii) even at low moisture content there is evidence that some fungal biomass growth is observable on grains when they are contaminated with grain dust.

Changes in ergosterol contents in grains inoculated with dusts upon incubation under various moisture contents

The concentrations of ergosterol provided by wheat, dusts and incubation process are depicted in figure 1, for three different dust samples and three moisture contents.

"[Insert figure 1 here]"
In general, for all control and dust inoculated samples the synthesis of ergosterol increases in wheat at high water activity. There is already a net production of ergosterol even at the lower moisture levels and moistening clearly increases this fungal activity (1.9, 2.2, 3.0 µg ergosterol g -1 at 14, 17 and 20 % m.c., respectively).

The Kruskal-Wallis test performed for different types of dusts indicated a significant moisture content effect (p = 0.027) in ergosterol accumulation in stored grains inoculated with dust. This significant effect was observed for all the tested dusts with various patterns. This means that water availability and the nature of the grain dust materials are two major inductive factors with various patterns for fungal proliferation in wheat. So, the fungal growth potential of the inoculum was also grain dust specific.

Moreover, upon incubation at 20 % m.c., fungal activity, as measured by the ergosterol assay, was clearly higher after the inoculation with the dust that contained the highest initial

Citrinin and ochratoxin A contaminations by grain dusts

The accumulations of CIT and OTA in stored wheat resulting from the experimental trials performed with the incorporation of three different grain dusts loaded at the rate of 1 % (w/w) in wheat grain batches are depicted in figure 2.

"[Insert Figure 2 about here]"
Incorporation of grain dust contributes to the increases of CIT and OTA levels in stored grain. First, contaminated dusts B, C and D provided 1.5, 0.9, and 0.6 ng CIT g -1 of grain. Moreover, storage at various water availabilities generated an additional contribution up to 10 ng CIT g -1 of grain with the dust B under 20 % m.c.. Indeed, control batch content of 0.6 ng CIT g -1 moved up to 12.1 ng CIT g -1 by adding 1 % of grain dust B. The same CIT production pattern also occurred in the case of inoculation with dusts C and D.

Generation of OTA was also observed in wheat with tremendously increases from 2.1 ng OTA g -1 up to 39.0 and 52.2 ng OTA g -1 of grain in the cases of dusts D and C under 20 % m.c.. One must note that OTA synthesis did not occur at a low level of water availability. In contrast, the highest OTA contaminated dust B could not significantly generate such higher additional OTA accumulation within the tested moisture contents (Figure 2). In the present study, no significant relationship was observed between these two toxins. So, CIT and OTA accumulation in wheat occurred at various concentration ratios according to the moisture in wheat and the dust material samples. 3B). These relations are modelled as outlined by the "equations ( 2)-( 3)", showing thus that ergosterol is a good quality parameter in assessing CIT and OTA in stored wheat.

"[Insert Figure 3 about here]" log(OTA + CIT, ng g -1 ) = (1.298 ± 0.207)*log(ergosterol, µg g -1 ) + (-0.254 ± 0.179) (r = +0.748, p < 0.0001, n = 33)

(2) log(CIT, ng g -1 ) = (1.102 ± 0.182)*log(ergosterol, µg g -1 ) + (-0.376 ± 0.159) (r = +0.742, p < 0.0001, n = 33)

(3)

Note that these equations may be used to roughly forecast CIT + OTA as well as CIT concentrations within one order of magnitude.

Discussion

This study describes the presence of the CIT and OTA mycotoxins in dusts collected in grain storages (either from farmers or from commercial companies). It also reports the potential mycotoxigenicity of the grain dusts when they are incorporated in stored wheat.

Assessment of fungal invasion potential

First, an evaluation of fungal development was performed in all the collected dusts. This evaluation can be carried out mainly either with mycological methods that lead to the estimation of viable fungal population or with the chemical quantification of a specific stable fungal component, evaluating both dead and alive fungal biomass (Gourama and In the present study, an assessment of mould growth was done by using ergosterol determination, which gives a measure of effective control of total fungal growth and activity in grains. Ergosterol is considered as the principal sterol of fungi and may play an essential role as a component of the cell membrane and other cell constituents. Another reason that ergosterol is a useful indicator of fungal growth is that harsh physical treatments do not affect existing ergosterol concentrations [START_REF] Bailly | Evaluation of a fluorodensitometric method for analysis of ergosterol as a fungal marker in compound feeds[END_REF]. Total ergosterol was measured as a more valid estimation than free ergosterol of fungal biomass and mycotoxin production in cereal grains, since both esterified ergosterol and free ergosterol are reported [START_REF] Abramson | Mycotoxin formation in hullness barley during granary storage at 15 % and 19 % moisture content[END_REF]. The ergosterol content of the four dust samples selected for this study ranged from 39 to 823 µg g -1 of dust. As reported by [START_REF] Pietri | Occurrence of mycotoxins and ergosterol in maize harvested over 5 years in Northern Italy[END_REF], cereal of ordinary quality should contain less than 3 µg ergosterol g -1 while it is considered to be of a scarce quality over 8 µg ergosterol g -1 . Moreover, it is well known that in general, the levels of ergosterol in mouldy grain are in the range of 8-15 µg g -1 of dry matter [START_REF] Srzednicki | Determination of ergosterol in paddy rice using solid phase extraction[END_REF]).

Therefore, it can be postulated that grain dusts merit special attention, as they can be inoculums for fungal growth in new grain deliveries in existing storage facilities. The addition of dust to wheat grain may increase the mycotoxin contents in grain as a contaminant and/or as an inoculum allowing the fungal proliferation with mycotoxin production during storage. In the present study, these two patterns were explored in mycotoxin accumulation in grain during storage. ng CIT g -1 and 318 ng OTA g -1 ). This means that grain dusts can not only act as an inoculum but also as a contaminant source. Considering the authorized maximum level of OTA in cereals of 5 ng g -1 , the presence of 1.6 % (w/w) of dust B in grains can indeed be considered as a threat to exceed the legal limits. For many mycotoxins, the grain dusts may thus represent an additional route of contamination, with the subsequent result that stored grains can be contaminated with toxic substances. In addition, the use of low-grade of grain dusts in animal feeding may increase animal exposure to mycotoxins, which may affect breeding performance as well as milk and meat quality values. Moreover, as these grains are processed during milling, dust particles may be dispersed in the environment and thus increase the exposure to mycotoxins by inhalation among grain workers since aerial contamination by these particles was observed in Italian grain mill (Dacarro et al. 2005).

Natural occurrence of mycotoxins in grain dusts: chemical assessment of their contaminant potential

One can note that storage work generated, on average, more than 5 times the dust obtained through threshing, so expectedly, storage work was found to be an important exposure determinant [START_REF] Halstensen | Ochratoxin A in grain dust: estimated exposure and relations to agricultural practices in grain production[END_REF].

Effect of grain dusts on fungal development and mycotoxin production: inoculum effect

Intrinsic cereal microflora has limited effect on ergosterol production (sterilized versus non-sterilized wheat) and fungal growth, as measured with ergosterol index, shows that fungal development caused by the dust inoculum is possible even at low water activity levels. Extended trials at different moisture contents have shown that fungal growth in grains is not only linked to the water availability but also to the nature of the dust material and particularly to its initial ergosterol content. In addition, it appears that, for the main 18 relevant storage mycotoxins (OTA and CIT), an increase of the concentrations is observed as a direct consequence of the inoculum effect (net production). For OTA, this is especially true when the moisture level is very high (20 %) whilst for CIT net production is observed whatever the moisture status of the grains. Results suggest that within the tested moisture levels, a positive relationship could be established between OTA + CIT concentrations and ergosterol content in incubated or stored grains. This could be explained by the fact that many fungal species and chemotypes are present at various levels; some of them favoring preferentially OTA production while other fungi produce CIT according to the moisture conditions. The measurement of both mycotoxins and the summing up of measured concentrations could help to provide some leveling of these effects. Our results agree with a recent study performed by [START_REF] Abramson | Mycotoxins, ergosterol and odor volatile in durum wheat during granary storage at 16 % and 20 % moisture content[END_REF] upon the farm storage of durum wheat during 20 weeks at 20 % of m.c.. Indeed, OTA and CIT were found to be the main contaminants in stored grains at high concentrations (6.5 and 11.6 µg g -1 , respectively).

Nevertheless, these mycotoxins were absent at 16 % m.c.. Here, the incorporation of dust allows the contamination by CIT at low water availability. The concentrations of CIT and OTA found in this study may also increase according to the storage time.

The usefulness of chemical analysis as a tool to predict the mycotoxigenic potential of grain dust.

OTA and CIT production show a very versatile image when considering the amounts of toxins produced according to the nature of grain dust and the water activity in grain maintained during incubation. For CIT, there is a steady increase in mycotoxin with moisture and this, for the three different dusts selected in this study whilst for OTA, there is This seems to corroborate the idea that many species and chemotypes act together and that their expressions depend on local conditions (nature of dust, humidity level). It seems thus obvious that a predictive tool based on identification of the microbial species/chemotype will be of limited use under such conditions.

An alternative solution could be the development of a rather robust tool based on the chemical analysis of grain dust and the quite easy determination of its ergosterol content as a general marker of fungal biomass. Present results suggest that this predictive tool could be valid for predicting the contamination level of OTA and CIT taken together within the tested moisture content (14 -20 %). It clearly appears that, at higher water activity, rather unpredictable situations will dominate with hot spots of OTA. Considering that OTA and CIT can act synergistically and that high moisture levels such as 20 % will not be representative of the normal storage conditions of wheat in bulk, it is worth recommending to further study the relevance of such a tool in order to assess the mycotoxigenic potential of grain dust and the associated risks for contamination of wheat storages.

Implication for safe management of cereal storage

The results gathered in this study as well as in other studies show that mycotoxin production can occur either at high moisture level and/or at high (ergosterol rich) dust loads. In the latter case, a high m.c. is not necessary for mycotoxin production as depicted in figure 4. This could have important consequences for the management of cereal storages, especially in traditional farms that are not specifically equipped to keep grains under ideal storage conditions. In fact, two steps could be identified as critical for the contamination of stored cereals. In a first step, a primary mycotoxigenic inoculum could be produced in spots with high humidity (e.g., along the walls made of concrete or bricks in silo's or lofts) by allowing the grains to turn mouldy (ergosterol content higher than 8 µg g -1 ) and to produce ergosterol rich dust (as high as 800 µg g -1 as measured in this study).

In a second step, this mycotoxigenic dust could act as a secondary inoculum for the bulk of stored cereals (especially when new deliveries are loaded in the storage facility). It is important to note that this second step in the general process of mycotoxin production in storages does not necessarily require a high moisture level. Therefore, it seems worth recommending to not only control the moisture level of the bulk of the stored cereals (which is generally already performed) but also to pay attention to any other possible way to contaminate the grains with ambient dust. It seems particularly important to very carefully discard mouldy grains, grain debris and dust from the storage facility before a new arrival of harvested grains. So, clearing the remains of previous harvests and cleaning the stores before loading in the new harvests might be some basic sanitary measures against storage deterioration.

Conclusions

Grain dusts released from cereal storages were found to be mycotoxigenic. Incorporated in wheat grain storage, they act as a contaminant and/or as an inoculum. The subsequent mycotoxins production varied widely among dust materials and water activity in grain. Mycotoxin contaminated grain (> 3 µg ergosterol g -1 )

F

New grain entry (< 3 µg ergosterol g -1 ) New grain entry (< 3 µg ergosterol g -1 )

Release of dust (up to several hundred µg ergosterol g -1 ) Grain shipment Release of dust (possibly less than 100 µg ergosterol g -1 )

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Food Additives and Contaminants

  therefore, recommend taking precautionary measures not only by controlling and maintaining moisture at a reasonable level during storage of the raw materials but also by paying more attention to the cleaning of the stores before loading in the new harvests Key words: Cereals, Storage, Grain dusts, Fungal growth, Ergosterol, Mycotoxins

  They were either of HPLC grade or of analytical grade. SPE columns Chromabond ® XTR (70 ml, 14500 mg) were ontained from Macherey-Nagel (Düren, Germany). Microfilters type Millex  -HV, SLHV 013NL (0.45µm) and Durapore  membrane filters (type 0.22 µm GV, GVWP04700) were purchased at Millipore Corporation (Bedford, MA, USA).Potassium hydroxide pellets were obtained from VWR International whereas sodium sulphate (exsiccated) was obtained from Riedel-De Haën AG (Seelze Hannover).

  of the experiments, fungal growth was assessed by ergosterol determination as an indicator of total fungal contamination in wheat .

  This also holds for the incubation at lower m.c., but in a lesser extent than observed at 20 % m.c.

  available data, it appeared that when considering CIT and the cumulative levels of CIT and OTA, positive relationships exist with the ergosterol content in the samples incubated with different dusts under14, 17 and 20 % m.c. (Figures 3A and

  increase in toxin production and this, only at the highest level of moisture.

Figure 1 .

 1 Figure 1. Levels of ergosterol in wheat grains inoculated with three dusts (1 %, w/w) after

Figure 2 :

 2 Figure 2 : Levels of CIT and OTA provided by wheat and dust together with the resulted

Figure 3 :

 3 Figure 3 : Correlation between the total ergosterol content, CIT + OTA (A) and CIT (B)

Figure 4 :Figure 1 .Figure 2 :Figure 3 :Figure 4 :

 41234 Figure 4 : Hypothetical pathway for mycotoxin contamination in bulk stored grains under dry

Table I .

 I Major storage mycotoxins are CIT and OTA that may be forecasted by the levels of ergosterol in stored cereals. On the basis of the present study, it may be stressed that care should be taken during storage preparation in order to reduce possible production of such mycotoxins by grain dusts. So far, these results have been obtained at the labscale and owing that similar situation can be obtained at a large storage scale, field trials (farm or industrial storage conditions) would be essential to confirm whether similar results can be obtained. So, the cereal stocks would benefit from the safe management with drastic cleaning and drying procedures as a challenge for storing and processing chains. Ergosterol, OTA and CIT occurrence in the grain dusts TableII. Ergosterol contents in batches of sterilized and non-sterilized wheat inoculated with dust A * (1 %) after storage for 28 days at 25°C and 10.4 % m.c.

	Page 25 of 33	Food Additives and Contaminants
		List of tables
	o F o F o F o F o F o
	r r r r r r
	P P P P P P
	e e r e e r e e r e e r e e r e e r
		R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w R e v i e w
		O n O n O n O n O n O n
		l y l y l y l y l y l y
		21

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Table I .

 I Ergosterol, OTA and CIT occurrence in the grain dusts.

		Ergosterol	CIT	OTA
	Dusts (origin)	µg g -1	ng g -1	ng g -1
	A (farm)	38.9	301.3	19.2
	B (farm*) C (storage-company) D (storage-company) F o	823.3 110.5 152.3	343.9 191.8 137.0	318.0 59.0 17.3
	r
	P
	e e r
	R e v i e w
			O n
				l y

Table II .

 II Ergosterol contents in batches of sterilized and non-sterilized wheat inoculated with dust A * (1 %) after storage for 28 days at 25°C and 10.4 % m.c.. Ergosterol and mycotoxins contents of this graindust are presented in table 1 † : the results were obtained with the control batches

	List of figures
	F o F o r r Non-sterilized wheat Sterilized wheat P inoculated wheat Ergosterol (µg g -1 of wheat) derived from wheat † dust A net production 3.259 1.534 0.389 1.336 ± 0.15 3.278 1.383 0.389 P 1.506 ± 0.13 e e r e e r
	R e v i e w R e v i e w
	O n O n
	l y l y

* :

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Acknowledgments

The financial support of the Belgian Federal Public Planning Service (Science Policy) is acknowledged here. Thanks are due to farmers and to the storage-companies for their kind participation in collecting grain dust samples.

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Food Additives and Contaminants


M Miraglia
email: miraglia@iss.it

B De Santis

V Minardi

F Debegnach

C Brera

The role of sampling in mycotoxin contamination: a holistic view

Keywords: sampling, mycotoxins, surveillance, monitoring

The need of obtaining a representative sample deserves particular consideration since a wrong sampling plan can greatly affect the reliability of the measured levels of mycotoxins. This can even result in legal disputes and barriers to trade.

Reported here is a holistic view for an ideal sampling plan, which is based on two consecutive steps: 1) to establish "why, where and when" sampling has to be performed by assessing the purpose, the appropriate time and the site for collecting the samples; 2) to establish "how" to draw samples by assessing practical ad hoc guidelines, considering that, for bulk goods in particular, mycotoxins are not at all homogeneously distributed in a lot. So far, step 1 is not yet covered by specific guidelines while for step 2, European regulations establish the procedures for the sampling of bulk and retail products potentially contaminated by mycotoxins.

Introduction

The most prominent reason for collecting food samples for the investigation of contaminants such as mycotoxins, is to protect consumer health, mainly verifying the compliance of food and feed with acceptable safety standards.

Sampling is one of the most crucial, but underestimated parts of the multifaceted and complex bulk of activities aimed at addressing and managing food issues. In practice, the overall objective of good sampling is to provide reliable samples to be analysed that can represent the basis for "fit for purpose" investigations.

In most cases, meaningful sampling is a process comprising two very dissimilar steps: i) the first step (hereafter referred to as "primary sampling") consists in taking the decision on "why, where and when" to collect the samples. In other words, the process of "statistically" locating the sites (populations) from which food samples should be taken; ii) the second step (hereafter referred to as "secondary sampling") consists of establishing how samples should be collected in order to be representative of the lot under investigation. For both steps the quality and the consequent reliability of the data are strongly dependent on the available resources and on the skill of the people involved.

For this class of contaminants, the need for statistically based planning is particularly relevant for i) the multifaceted implications of mycotoxin contamination (health, trade, ethical issues related to developing countries' difficulties), ii) the largely inhomogeneous distribution of the toxins within food commodities, with the consequent need for careful secondary sampling. 3 Good primary sampling schemes have so far been developed for several classes of contaminants such as dioxins and pesticides [START_REF] South | U.S. Food and Drug Admintration's Dioxin Monitoring Program[END_REF], in contrast to the very few valid ones so far proposed for mycotoxins.

In contrast, a large number of papers have appeared, related to secondary sampling schemes for aflatoxin B 1 (particularly on its distribution in a lot and on related sampling plans) [START_REF] Whitaker | Variability of aflatoxins test results[END_REF][START_REF] Whitaker | Variability associated with testing cottonseed for aflatoxin[END_REF][START_REF] Whitaker | Variability associated with testing corn for aflatoxin[END_REF][START_REF] Whitaker | Variability associated with sampling, sample preparation, and chemically testing farmers stock peanuts for aflatoxin[END_REF], but only a few studies deal with some Fusarium toxins [START_REF] Whitaker | Variability associated with testing shelled corn for fumonisin[END_REF][START_REF] Hart | Variability of vomitoxin in truckloads of wheat in a wheat scab epidemic year[END_REF][START_REF] Whitaker | Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol[END_REF]. Conversely, specific studies focused on the distribution of OTAcontaminated units are not yet available, apart from the vague assumption that "representative sampling" for aflatoxins is more difficult than sampling for other known mycotoxins in food products. Sampling procedures recommended for aflatoxins should thus be adequate for other mycotoxins [START_REF] Dickens | Safety evaluation of certain mycotoxins in food WHO Food Additives Series 47 (Geneva: WHO)[END_REF]. Nevertheless, the European legislation dealing with sampling plans for OTA to be used for official control was only recently adopted (Commission Directive 2005/5/EC 2005).

Due to the lack of specific issues on the overall sampling for OTA, this paper is mainly devoted to illustrating a holistic view of sampling for mycotoxins in general, assuming the given considerations could in most cases be adapted to OTA.

Primary sampling schemes: "why, where and when" to collect samples for mycotoxin analysis

The above mentioned health and trade issues represent the overall reasons why samples have to be collected. Where and when to collect samples will depend on the practical purpose for which they are destined. The sampling methodology to be employed should be chosen on the basis of a rationale in accordance with a "fit-for- purpose" approach. Where to collect samples refers to the selection of the sites where sampling should be done (ship, dock, container, farm, stable and so on up to the table).

When to collect samples refers to the purpose of the collection, such as mandatory or targeted investigations.

"Purposes": monitoring, surveillance and targeted sampling

Collecting samples for mycotoxin contamination is mostly performed for monitoring, surveillance and targeted purposes.

According to the World Health Organization/Communicable Diseases Department/Communicable Diseases Surveillance & Response (WHO/CDS/CRS), monitoring is "the performance and analysis of routine measurements, aimed at detecting changes in the environment or health status of populations", while surveillance can be defined as "the ongoing systematic collection, analysis and interpretation of data, followed by the dissemination of information to all those involved so that directed actions may be taken" (WHO 2004[START_REF] Lo Fo Wong | Second FAO/WHO Global forum for food safety regulators[END_REF].

In other words, monitoring activities are somehow both preliminary and routinely performed activities, while surveillance is undertaken whenever data from monitoring reveals that standard/legal values have been exceeded, and it aims at providing a basis for centralized and qualified feed-back [START_REF] Noordhuizen | Monitoring and surveillance systems (MOSS), design and operazionalization[END_REF]. The amount of samples to be collected for monitoring should be proportional to the food consumption rate and take into account the amount of domestic production and the amount of import.

Targeted sampling is instead the action undertaken when there is a concrete suspicion that contaminants (mycotoxins) are present in excessive amounts as a consequence of previous findings. Targeted sampling focuses on sample populations, which are likely to be noncompliant (food and feedstuff) or more sensitive (groups of consumers). As for mycotoxins, suspected samples include goods produced or stored under bad conditions and food derived from animals showing clinical signs of intoxication.

As an example of sampling due to a suspicion of contamination, decisions were taken at the European level imposing special conditions on the import of certain products consigned from countries suspected of production of contaminated goods It should be considered that targeted sampling should not be used for the exposure assessment since it may lead to an overestimation of the exposure (WHO 1997).

"Secondary" sampling: how to collect samples

Mycotoxin-contaminated units are not homogeneously distributed throughout the lot and a few units (approximately 0.1% for a wide variety of agricultural products) are likely to be highly contaminated (mycotoxin clusters), while most of the grains are mycotoxin-free. However, after the lot is milled the contaminants are usually more homogeneously distributed throughout the bulk; the mean level in the end product could still be unacceptable from the health perspective [START_REF] Whitaker | Theoretical investigations into the accuracy of sampling shelled peanuts for aflatoxin[END_REF][START_REF] Johansson | Testing shelled corn for aflatoxin, Part II: Modeling the distribution of aflatoxin test results[END_REF].

Collecting samples for analysis only from the highly contaminated grains or from the mycotoxin-free ones will provide incorrect final results. Therefore, it is extremely relevant to collect and gather randomly many incremental samples from the grain in bulk in order for the analysis to be representative of the whole lot.

Without implementing a good secondary sampling plan, an associated error in the evaluation of the mycotoxin level of the lot could easily occur, generally leading to an underestimation; whenever the sampling is performed for monitoring/surveillance purposes, a poorly developed secondary sampling plan could produce false information for risk assessors/managers. For inspection purposes, incorrect secondary sampling can result in litigation problems.

In order to define an appropriate sampling plan, knowledge of the distribution of contaminated units within the bulk is essential. The matter has been investigated especially for aflatoxins and, to a lesser extent for deoxynivalenol and fumonisins [START_REF] Whitaker | Variability of aflatoxins test results[END_REF][START_REF] Whitaker | Variability associated with testing cottonseed for aflatoxin[END_REF][START_REF] Whitaker | Variability associated with testing corn for aflatoxin[END_REF][START_REF] Whitaker | Variability associated with sampling, sample preparation, and chemically testing farmers stock peanuts for aflatoxin[END_REF]1998;2000;[START_REF] Hart | Variability of vomitoxin in truckloads of wheat in a wheat scab epidemic year[END_REF].

Sampling steps

The steps usually employed in the evaluation of the mycotoxin level in a lot include: sampling (random collection of incremental samples throughout the bulk), sample preparation (gathering and grinding the incremental samples) and analysis (generally on the slurry aggregate samples). It is widely recognized that the sampling step is by far the largest contributor to the total error and the associated variability is largely dependent on the level of the toxin. It has been recognized since 1993 that the sampling plan is a function both of the employed testing procedures and of the acceptance/rejection limit (FAO 1993). An exhaustive and updated review on the overall issue of secondary sampling has been provided recently by [START_REF] Whitaker | Sampling for mycotoxins[END_REF], who in the last decade successfully studied the contribution of sampling error to the total variance during mycotoxin determination. Unfortunately, no similar study has so far been performed for OTA. Nevertheless pragmatic sampling plans are available (Commission Directive 2005/5/EC 2005), based on the assumption that OTA distribution is somehow less heterogeneous than for aflatoxins. The following considerations, mainly derived from the Whitaker paper referring to the issue of sampling for mycotoxins in general, are likely to be applicable to OTA.

Variance (V) in the evaluation of mycotoxins

The total error (TV ), associated with the evaluation of mycotoxins is obtained by summing up the error associated with the sampling of incremental samples from the lot ( SV = sampling variance), the error of the sample preparation ( SPV = sampling preparation variance) and the error of the analytical determination ( AV = analytical variance) (Figure 1 mill and quantifying by immunoassay, the contribution of SV , SPV and AV was 75.6, 15.9 and 8.5 % respectively [START_REF] Whitaker | Sampling for mycotoxins[END_REF]).

The sampling variability has been quantitatively studied for many agricultural products (peanuts, corn, soybean, cottonseed, pistachio, wheat, figs) mainly for aflatoxins, but also for deoxynivalenol in wheat and fumonisin in maize [START_REF] Whitaker | Variability associated with testing shelled corn for fumonisin[END_REF]2000;[START_REF] Hart | Variability of vomitoxin in truckloads of wheat in a wheat scab epidemic year[END_REF]. Equations linking the sampling error to the concentration of the toxin and to the size of the aggregate (gathered) sample have been derived, for aflatoxins, fumonisins and DON. In all cases SV increases with the lowering of the toxin concentration. (Figure 2a,b,c). The sampling variance pattern is similar for the three toxins, but the AFB 1 values are higher than for the other two.

Therefore, AFB 1 is probably less homogeneously distributed in the bulk.

The variability associated with the sample preparation has so far been described for aflatoxin in several crops, for fumonisins in corn and for deoxynivalenol in wheat.

In all cases, for a given size of sub sample, a decrease of the variance with a decrease of the particle size has been demonstrated.

The analytical variability will be discussed in a separate paper in this issue.

Uncertainty in the mycotoxin evaluation and OC curves

Due to the variance associated with the mycotoxin evaluation, a 100% level of certainty is far from being achievable. An overestimation is a risk for the seller/producer, that a good lot could be wrongly rejected, while an underestimation could result in a risk for the buyer/consumer that a bad lot could be wrongly accepted.

For a given sampling plan the Operating Characteristic (OC) curve (Figure 3) describes the probability of acceptance of a lot as a function of its actual quality (Codex ). For mycotoxins, an OC curve links the concentration of the toxin in the lot (x-axis) to the probability of accepting that lot (y-axis). For a prefixed limit of acceptance the OC curve evaluates the risks of the seller/producer and the buyer/consumer (marked areas in Figure 3). For every sampling plan those risks are, defined by sample size, preparation and size of the sub sample, number of analyses and methodology.

The aim of any good sampling plan is to reduce the above mentioned areas. The risks are reduced when both the aggregate sample and the sub sample size are increased or when the ground particle size is decreased.

Lowering the limit of acceptance increases the seller's risk, while an increase of that limit reduces the buyer/consumer risk.

OC curves have been drawn for several sampling plans for aflatoxins [START_REF] Dickens | Safety evaluation of certain mycotoxins in food WHO Food Additives Series 47 (Geneva: WHO)[END_REF], but there is a lack of information on the OC curves for OTA.

Sampling protocols

Due to the inhomogeneous distribution of contaminated kernels in a lot and the consequent relevance of gathering small incremental samples to form an aggregate sample, methodologies and equipment employed in collecting such incremental samples are crucial in reducing errors.

CODEX approach. According to FAO (FAO 2001b), the most accurate and precise procedure for taking random incremental samples for aflatoxins is to draw small samples while the lot is transferred, that is, during the loading or unloading of the product (dynamic sampling). For big lots this methodology is unfortunately rather time- Whenever dynamic sampling is not applicable, as in the case of static lots, sampling probes have to be used. This could be the case when large volumes of a product are stored in a single bin (buck, railcar) or in many small containers (bags or sacks). According to Codex the probes should be carefully selected on the basis of the type of container, since all the units should have the same chance of being selected.

European approach. Since 2001, the European Union put in force a package of Directives concerning sampling procedures for the most prominent mycotoxins, namely aflatoxins, patulin, Fusarium toxins and ochratoxin A.

For OTA, Directive 2005/5/CE on sampling methods, amending the previous Directive 2002/26/CE, has been very recently endorsed. This Directive deals with provisions both for bulk and packaged commodities such as cereals, dried vine fruits, roasted and soluble coffee, grape juice and wine.

For lots in bulk, the aggregate sample sizes depend on the commodity and lot size, ranging from 1 kg to 10 kg. For wine and grape juice, the minimum number of incremental samples to be taken ranges from 3 to 10 for juice and from 1 to 3 for wine. where SF is the sampling frequency (every n th sack or bag from which an incremental sample must be taken)

All weights have to be expressed in kilograms.

Monitoring and surveillance activities

The status of monitoring and surveillance activities for mycotoxins is widely different from country to country. Developed countries have developed for a number of years monitoring programmes for selected mycotoxins such as aflatoxins, ochratoxin A and more recently Fusarium toxins (MAFF 1994;CFIA 2002). The results have been usefully employed for advanced surveillance programmes on selected goods.

In the past decades, coordinated programmes have been launched by the EU aimed at the surveillance of the status of contamination by aflatoxins in spices, pistachios and baby food and by ochratoxin A in cocoa and coffee. Those activities were carried out before the recent European enlargement and efforts should be devoted to compare the contamination of those mycotoxins in the new European countries, in order to verify discrepancies in frequency and level of contamination for health and trade purposes. For relatively "new" mycotoxins such as Fusarium toxins, for which legislation has been recently enforced, although monitoring activities were somehow carried out, not many surveillance activities have so far been undertaken. As for OTA residues in food of animal origin, a particularly careful surveillance sampling is directed to pork tissues in Denmark: in 1978, Denmark established a guideline to control ochratoxin A levels in pork products in their slaughterhouses. In the case of macroscopic changes, pig kidneys are analysed for their ochratoxin A content. If the ochratoxin A level of pig kidney is higher than 25 µg/kg, the entire pork carcass is 12 rejected as the meat is also suspected to be highly contaminated as a consequence. If the level is between 10 and 25 µg/kg, edible offals are eliminated and if lower than 10 µg/kg, only pig kidneys are discarded [START_REF] Buchman | Analysis, occurrence and control of ochratoxin A residues in Danish pig kidneys[END_REF].

Work aimed at collecting and elaborating mycotoxin occurrence has been performed by the SCOOP tasks so far developed for aflatoxins, ochratoxin A (SCOOP Task 2002) patulin and Fusarium toxins (SCOOP Task 2003). Actually, the ultimate scope of the above tasks was to evaluate the exposure of those mycotoxins in the European population, but the work included the evaluation of information derived from the random or targeted sampling employed for each set of data.

Conclusion

An effective plan to evaluate statistically the level and impact of mycotoxin contamination in general and of OTA in particular, should consist of a "statistically" based identification of the sites where samples are to be collected and by the reliable evaluation of the status of contamination at that site.

As far as specifying the sites where collected samples are to be taken is concerned, any specific guideline on where and when to take samples depends on the reason why the samples are collected (health, trade, control). In this respect no specific guideline for monitoring and surveillance purposes is so far available for mycotoxins.

The reliable evaluation of the status of contamination at each site should be performed through sampling, sub sampling and analytical steps. Great efforts so far, have been devoted to improve the reliability of the analytical measurements. Sampling has also been extensively studied for several mycotoxins except for OTA. Generally, it should be noted that data derived from monitoring/surveillance activities should be updated and ameliorated. In this respect, there is a paucity of data from the new European countries.

In addition, the acquisition of new databases developed on soundly performed primary and secondary sampling should be encouraged.

  effective view of the sampling requirements for surveillance of mycotoxins has been given by the Joint FAO/WHO Expert Committee on Food Additives (FAO/WHO 2001a). This points out the relevance of generating meaningful data from surveys through the collection of representative samples. This should reflect the selection of the sites where to collect samples within the food chain and across the countries, taking into consideration also differences in the agro-climatic conditions.

  ):It has been shown that the SV is the biggest contributor to the total variance due to the large variability among the contaminated units. Using a 0.91 kg sample of shelled corn with a 20 µg/kg concentration of aflatoxin, grinding the test sample in a Romer

  the procedure implies drawing samples at regular intervals of time which may be all night and day, sometimes interrupting the procedure due to the forced closure of a hold, as in the case of rain. The deployment of automated sampling equipment, such as crosscut samplers, could greatly assist the process. (Codex Alimentarius 2004).

  As for lots traded in individual packages, the sampling frequency, both for aflatoxins (Commission Directive 98/53/EC 1998) and for OTA (Commission Directive 2002/26/EC 2002) is the following:

  EC; 2004/428/EC and 2005/85/EC). Actually in this case the border line between surveillance and targeted sampling is rather ambiguous, depending on the level of suspicion.

	(Commission Decisions 2002/80/EC; 2002/233/EC; 2002/679/EC; 2003/552/EC;
	2004/429/EC; 2002/79/EC; 2002/678/EC; 2003/550/EC; 2000/49/EC; 2003/580/EC;
	2003/423/

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Acknowledgements

The ILSI Europe Natural Toxin Task Force supported the workshop at which this work was presented. Industry members of this task force are Groupe Danone, Kraft Foods, Nestlé, Numico, RHM Technology, and Swiss Quality Testing Services (SQTS). For further information about ILSI Europe, call +32 2 771 00 14 or email info@ilsieurope.be. The opinions expressed herein are those of the authors and do not necessarily represent the views of ILSI Europe.