Santiago Rodellar
email: srodella@aspb.cat

; M Fontcuberta

; J F Arqués

; J Calderon

L L Serra-Majem

; L Ribas Barba

Mercury and methylmercury intake estimation due to seafood products for the Catalonian population (Spain)

Keywords: Exposure, Metals analysis Additives/Contaminants: Heavy metals -mercury mercury, methylmercury, dietary intake, fish, fishery products, food safety, Spain

This study estimates mercury and methylmercury levels in fish and fishery products commercialized in the city of Barcelona from 2001Barcelona from to 2007. . Combining data of mercury levels in food with the consumption data of 2158 people (as the median of two 24-hour-recall), the total Mercury intake of the Catalonian population was calculated.

Mercury was detected in 32.8% of analyzed samples. The general population average weekly intake of total mercury in Catalonian population was 0.783 µg/kg of body weight. This value is clearly lower than the Joint FAO/WHO Expert Committee on Food Additives (JECFA) provisional tolerable weekly intakes (PTWI) of 5 µg/kg of body weight.

The fish group was the main contributor to this value, mainly due to predatory species.

Introduction

Heavy metals like mercury (Hg) and specially its organic form (methylmercury, MeHg) are environmental contaminants of special concern due to a wide distribution in the environment and likely adverse effects for human health. [START_REF] Silva-Pereira | Cytotoxicity and genotoxicity of low doses of mercury chloride and methylmercury chloride on human lymphocytes in vitro[END_REF][START_REF] Toimela | Mitochondrial viability and apoptosis induced by aluminum, mercuric mercury and methylmercury in cell lines of neural origin[END_REF].

For the general population (non-occupationally exposed population) the main route of exposure to mercury is via the diet, mainly through fish and seafood consumption (World Health Organisation (WHO) 1990). Other food sources than fish and seafood products can contain traces of mercury but are considered of lower concern. The methyl mercury content in fish and shellfish varies, but it is generally assumed that it is over 90% (European Commission 2004). In marine sediments, elemental or inorganic Hg can be easily methylated by microbiological activity and it can be accumulated in animal tissues and increase in concentration through the food chain.

Following human ingestion, methylmercury, unlike elemental or inorganic forms, is almost completely absorbed from the gastrointestinal tract and distributed into all tissues, and readily crosses the hemato-encephalic barrier and the placenta.

Central nervous system is considered to be the target organ for methylmercury.

Prenatal exposition to methylmercury may cause disorders on the brain development. (EFSA, 2004). Therefore, the European Commission has made some consumption recommendations for protecting vulnerable groups of the population, such as infants, children, pregnant women and breast-feeding mothers. (European Commission 2004). European legislation set a maximum level for total mercury in fish and fishery products of 0.5 mg/kg wet weight, except for some predatory species where the limit was 1 mg/kg wet weight. (European Commission 2006).

On the other hand, the joint FAO/WHO Expert Committee in Food Additives (JEFCA) established a provisional tolerable weekly intake (PTWI) for total mercury of 5 µg/kg body weight (bw) and also for methylmercury at 1.6 µg/kg bw. Exposure to mercury depends on the diet habits and geographical location. Spain is the first fish market in the EU, with an average per capita consumption of 40kg/person-year [START_REF] Failler | Future prospects for fish and fishery products: Fish consumption in the European Union in 2015 and 2030[END_REF].

The main purpose of this study is to calculate mercury levels in fish and fishery products commercialized in the city of Barcelona and combine this data with seafood products consumption data, to estimate if there may be population groups in Catalonia with a mercury or methylmercury intake that exceed the PTWI recommended by the WHO.

Materials and Methods

Sampling:

Food samples were collected as part of the IQSA program (Food Health Quality Research Program) [START_REF] Arqués | Bases metodològiques del programa d'investigació de la qualitat sanitària dels aliments (IQSA)[END_REF]. This surveillance program developed by the city public health services started in 1984 with the aim of monitoring additives and pollutants, both chemical or microbiological, in the food sold in the city.

Foodstuffs taken for the IQSA program were obtained in order to reflect food consumed in the city, to monitor levels of food hygiene and safety and compliance with existing regulations; thus sampling covers both diverse production origins (local, national and EU, imported from other countries…), and different marketing channels (municipal markets, supermarkets, and other retail stores).

From 2001 to 2007, 688 samples of 45 different fish and seafood products were obtained from the retail and wholesale commerce of the city. They were classified in six groups: fresh fish (93 samples of 21 species), canned fish (36 of 4 species), bivalves (185 of 7 species), cephalopods (185 of 3 species), crustaceans (175 of 9 species) and gastropod (14 of 1 species).

Mercury determination:

The method used for total mercury determination in food was developed in the Barcelona Public Health Agency Laboratory and has been fully validated evaluating parameters such as trueness precision and linearity, along the For the determination of total mercury, an Advanced Mercury Analyser AMA-254 (LECO) was used. This is an elemental analyzer based on an atomic absorption spectrophotometer that analyses the mercury levels in triturated and homogenized samples without any previous chemical pre-treatment.

An aliquot of 0,1 g of the homogenized sample is weighted into a nickel boat which is introduced into a sealed combustion/catalyzed tube where the decomposition process at 750 C by catalytic oxidation takes place. Products from the decomposition are carried out to the amalgamator using an oxygen flow where only mercury vapors are trapped onto the gold-plated ceramic tube.

Once all byproducts have passed through the measuring cell, the amalgamator is flash heated at about 300 C and the mercury is dosed to the measuring cell, using an atomic absorption spectrophotometer at 253,7 nm for each determination. The absorbance obtained for the sample is compared with the standards calibration, which is formed by the following standards, injecting 200µgHg/L: 10µg/L, 25µg/L, 50µg/L, 75µg/L, 100 µg/L,150µg/L, 200µg/L, 250µg/L, 300µg/L, 400µg/L, 500µg/L, 750µg/L, 1000µg/L, 1250µg/L and 1500 µg/L. There are two ranges of calibration; the low one corresponds to the standards 10 -20 and the high one 200-1500.

Different seafood products were used to validated the method. The validated concentration level for total mercury were 0.10 mg/Kg, 1 mg/Kg and 3 mg/Kg, and 12 replicates were analysed for each level. Mean recovery of the process was 99,2%, precision was 11% and uncertainty 22% The limit of quantification validated was 0,10 mg/Kg.

Complementarily, the laboratory participates regularly (5-10 times a year) in proficiency testing for Hg in fish and seafood products as a tool to ensure the quality of generated data, obtaining always satisfactory results (z-score< 2.0). The analytical method for methylmercury determination was not validated in the Barcelona Public Health Agency Laboratory when this study was developed. So, total mercury was determined in fish samples, and the results were multiplied for a factor of 0,90. (European Commission, 2004). However, in 2007 some preliminary results were obtained in the Barcelona Public Health Agency Laboratory showing that methylmercury was 89% of the total mercury. This analytical method for MeHg is based in a extraction of 1g of sample with clorhidric acid (HCl 5M) and CH2Cl2 (dichlormethane). Inorganic mercury elutes with the acid phase, which is analyzed with CV-AAS. MeHg is calculated extracting the inorganic Hg from total Hg. Recovery for this method are 90-110 %.

Mercury intake estimation:

In order to estimate the health risk associated to fish and seafood consumption, the weekly dietary mercury intake for the Catalan population was estimated as the product between the mean mercury concentration for each species (IQSA data) and its weekly consumption. As the number of samples with nonquantifiable mercury residues was large (67,2%), it was decided not to assign a value of one half the limit of quantification (0.050 mg/kg) to these samples in order to avoid overestimation of the amount of mercury ingested. As it is shown below, the mercury weekly intake for each 2158 individuals was calculated considering their body weight and their daily personal intake of 43 different species: 24 fresh fishes, 4 canned fishes; 6 crustaceans; 5 bivalve mollusks; 3 cephalopods; 1 gastropods. For 13 species out of 43, a mercury concentration was not available in the IQSA program. Therefore, a mean value calculated for species of the same food group was used. Deleted: Mercury concentrations under limit of quantification were assumed to be 0 mg/kg. The weekly intake (WI) for each individual was calculated as the formula below: WI =Σ (Is*Cs) * 7days/ Kg body weight (Is= daily intake of fish and seafood products species; Cs = total mercury content in these species (µg/Kg) Once the weekly intake of 2158 individuals was calculated, a general population mean weekly intake was also estimated and also by sex and age (5 age groups were considered: 10 -17, 18-24, 25-44, 45-64, >65). In order to estimate methylmercury intakes, a factor of 0.90 was applied to mercury results (European Commission, 2004).

Statistical procedures

All statistics were performed using SPSS 13.0 software. Mercury weekly intakes were calculated for each individual, and its median intake in general population as well as for sex and age group. Statistically significance were analysed between this mercury intake and studied variables by U-Mann-Whitney and Kruskal-Wallis test. Chi-square (χ 2 ) statistics tested differences between subgroups to evaluate percentages of interviewed people above the PTWI.

Results

Mercury levels

Mercury was detected in 32.8% of analyzed samples in the surveillance program (226 of 688). By food groups (Table 1), mean concentration levels were: fish (0.285 mg/kg) [non-predators=0.104 mg/kg; predators=0.750 mg/kg]), canned fish (0.289 mg/kg), crustacean (0.082 mg/kg), gastropods (0.071 mg/kg), cephalopods (0.047 mg/kg) and bivalves (0.005 mg/kg).

The highest mercury concentrations corresponded to two predatory species: In both cases, the mercury levels exceeded the European limit of 1 mg/kg: a porbeagle (Lamna nasus) and a little tuny (Euthynnus quadripunctatus) with values of 7.6 mg/kg and 1.4 mg/kg respectively. Mercury concentration range in the rest of fish samples was between 0 and 0.98 mg/kg. Any bivalves or gastropods were above the maximum level of 0.5 mg/kg. Two crustaceans (lobster: 0.6 mg/kg and prawn: 0.9 mg/kg) and one cephalopod (octopus: 0.8 mg/kg) exceed the limit.

Weekly intake of mercury and methylmercury

The general population average weekly intake of total mercury in Catalonian population (Table 1) was 0.783 µg/kg of body weight. Figure 1 and Table 1 show that the highest contribution to this value is provided by fish (0.440 µg/kg body weight; 56.2% of the total intake) followed by canned fish (0.284µg/kg body weight; 36.3%), crustaceans (0.032µg/kg body weight), cephalopods (0.022µg/kg body weight), gastropods (0.004µg/kg body weight) and bivalves (0.001µg/kg body weight). In terms of species (Table 3), the most important contributors to the total mercury intake were canned tuna fish (0.27 µg/kg body weight and week) and young hake (0.11mg/kg). It has been no possible to calculate the contribution of porbeagle and little tuny because of consumption data were not available in the ENCAT Survey.

For methylmercury, the general population average weekly intake was 0.705 µgMeHg/kg body weight (Table 1). No differences between sex (p=0.733) were found for mercury or methylmercury intake, but differences between age groups existed in both cases (p< 0.026), being the youngest and the oldest those with lowest levels of mercury and methylmercury intake.

Overall, 31 individuals (1.4% of the population) could have intakes above the PTWI for total mercury (Table 2a), and no differences were observed between sex (p=0.180) or age group (p=0.084). On the other hand, 187 women (16.1% of the population) would have methylmercury intakes above the PTWI for methylmercury (Table 2b). No difference was observed between age groups (p=0.151), although a slight significant difference was observed between sex (p=0.045), being women the ones with a higher percentage. The general population average weekly intake of total mercury for Catalan population was 0.783 µg/kg of body weight. This value is clearly below the recommended PTWI (5 µg/kg bw).

Discussion

The main contribution to this intake was provided by fish, because of its higher consumption and higher mercury concentration, mainly through predator fish: fresh tuna is the most polluted, but its consumption is relatively low. However the contribution of some species like young hake is due mainly to its high consumption. Only considering tuna (canned and fresh), which represents 16% of fish intake, it can be explained more than 42% of total mercury intake.

Other studies corroborate this higher contribution provided by fish and similar levels are found [START_REF] Sahuquillo | Methylmercury determination in fish and seafood products and estimated daily intake for the Spanish population[END_REF][START_REF] Scoop | Assessment of dietary exposure to arsenic, cadmium, lead and mercury of the population of the EU Member States[END_REF].

In recent years there has been published several studies of mercury intake estimation. Nevertheless, methodological divergences, that a lot of times are not specified, make comparisons tremendously difficult: foodstuffs included in each food group, number of samples analyzed, different analytical methods and quantification limits, the different statistical treatment given to negative results, the different information sources used to estimate the consumption habits of the geographical area being investigated, the average weights used to work out, the amount of food ingested per Kg of body weight, etc.

Always bearing in mind the caution required due to the different methodologies used, we can observe that the present results are similar to the ones obtained in other studies carried out in the same geographical area. In the study "Total Diet" conducted by the Catalan Government in 2002-2003, an average weekly ingestion of mercury of 0.892 µg/kg body weight was estimated. (Departament de Salut. Agència Catalana de Seguretat Alimentaria. 2005). Other studies carried out in the same geographical area (catalan population) describe mercury ingestions for the standard adult male (70 Kg) that range between 5.3 µg/day and 9.89 µg/day (weekly ingestion per Kg of body weight = 0.53 -0.989). [START_REF] Bocio | Human exposure to metals through the diet in Tarragona: temporal trend[END_REF][START_REF] Falco | Daily intake of arsenic, cadmium, mercury, and lead by consumption of edible marine species[END_REF][START_REF] Llobet | Concentrations of arsenic, cadmium, mercury, and lead in common foods and estimated daily intake by children, adolescents, adults, and seniors of Catalonia, Spain[END_REF]. Following what appears to be a general trend, these studies give the negative results half their limit of quantification, considering the possibility of mercury traces in the samples with lower values than the laboratory limit. Nevertheless, this system can lead to an overestimation of the amount of mercury ingested and, if the foodstuffs considered in the study include some consumed by a high percentage of the population, it could identify them as important contributors to the metal ingestion even if all their analytical results were negative.

On the other hand, unlike the current study, which uses the individual foodstuffs consumption data and individual body weights, most of the studies considered are carried out with aggregated consumption data and "standard body weights".

Recalculating the weekly mercury ingestion taking average consumption data for a standard 70 kg individual, we would obtain a weekly ingestion of 0.239 µg Hg per kg. Thus, if we calculate the ingestion using population standard values of the variables "body weight" (70Kg) and "consumption", the result we obtain is much lower, which would indicate an underestimation of the ingestion in this type of studies. At European level [START_REF] Scoop | Assessment of dietary exposure to arsenic, cadmium, lead and mercury of the population of the EU Member States[END_REF], a weekly mercury ingestion of 29.2 µg is estimated through fish and fish products, for an average 70 Kg body weight individual. This equals 0.41 µg/kg body weight per week. We must bear in mind however, that this result is the average of data belonging to 13 countries, where in most of them (with the exception of Norway) the consumption of fish products (between 10 and 53 g/day, depending on the country) is a lot lower than in Catalonia (62.1 g/day).

Focusing in the percentage of population whose PTWI is bigger than that fixed by the WHO (5 µg total Hg/kg body weight), we observe that the 1.4% calculated in the Catalan population is slightly bigger than, for instance, that of the Basque Country [START_REF] Sanzo | Estimation and validation of mercury intake associated with fish consumption in an EPIC cohort of Spain[END_REF], where a 0.1% of individuals have an intake of over 75% of the PTWI. In a recent study in France (Agence Francaise de Sécurité Sanitaire dels Aliments 2007), 35% of the individuals studied had an intake of metylmercury that was above the PTWI (1.6 µg metylmercury/kg body weight).

In the present study 14.6% of the Catalan population is over the PTWI for metylmercury, with an average weekly ingestion of 0.705 µgMeHg/kg body weight.

From a health protection point of view, fertile women and children (consumption data no available for this group) are considered especially vulnerable to the toxic effect of contaminants such as mercury. For that reason, most consumption recommendations are addressed to them and they are the special targets in toxic intake studies. According to [START_REF] Guallar | Mercury, Fish oils, and the risk of myocardial infarction[END_REF] this advice should be extended to the general adult population. However, great care must be taken when providing health guidance related to fish consumption. While some studies [START_REF] Mozaffarian | Fish Intake, contaminants, and human health: evaluating the risks and the benefits[END_REF]) conclude that "among adults... the benefits of fish intake exceed the potential risks" , others [START_REF] Stern | Public health guidance on cardiovascular benefits and risks related to fish consumption[END_REF]) conclude that there are easily available fish that offer both high polyunsaturated fatty acids and low MeHg by choosing among the available fish, in order to maximize the nutritional benefits, as for instance polyunsaturated fatty acids, and to decrease the risks.

In this way, it is important to stress that the population group with the highest percentage of individuals that exceed the PTWI is the group of women aged between 18 and 44: in the case of total mercury, 14 out of the 20 women (70%) whose PTWI is higher than the recommended level are within this age group, although this difference with the rest of them is not significant (p=0.11); in the case of metylmercury, 107 out of the 187 women whose weekly intake is higher than the PTWI belong to this age group (57%), a difference that is statistically significant (p<0.02). Another vulnerable sector of the population is that with an extremely high intake of fish.

Conclusions

Total mercury was detected in 32.8% of 688 analyzed samples. The concentration range oscillates between 0 and 0.98 mg/kg, with the exception of two predator fish samples with extreme values (1.4 and 7.6 mg/kg) The weekly median intake of total mercury and methylmercury by general population in Catalonia were estimated as 0.783 µg/kg and 0.705 µg/kg of body weight, respectively. These values were similar to another studies set in the same population, and fish group was the main contributor to these values. Women 18-44 years old (fertile women group), had a higher percentage of individuals with an intake above the PTWI for total mercury and methylmercury, which could cause adverse effects to the developing foetuses in case of pregnancy.

O n l y

Table 1. Mean concentration of total mercury (mg/Kg) by food groups, daily intake of food groups (g/day) and mean weekly intake of total mercury and methylmercury (µg/kg/week), for the general population and by sex.

  This method is accredited by the National Accreditation Body (ENAC [Entidad Nacional de Acreditacion]) since 2000.

  and other individual data (weight, sex and age) was taken from the latest Catalan Nutrition Survey (ENCAT, 2002-03) (Serra-Majem et al. 2007) conducted by the Catalan Government Department of Health. Dietary intake was assessed by means of two 24-hour recalls on nonconsecutive days. A probabilistic sample of 2158 individuals from 83 cities (995 males and 1163 females) aged 10-80 years was interviewed in 2002-03.

Table 2a .

 2a Individuals with a total mercury weekly intake higher than the Tolerable weekly Intake (5 µg Hg/kg/week), by age and sex

						Food Additives and Contaminants			Page 14 of 18
			General population		Men				Women
	Age groups		>PTWI		>PTWI				>PTWI
	(years )	n	n (%)	n	n (%)			n	n (%)
	10-17		203	4 (2,0%)	114	2 (1,8%)			89	2 (2,2%)
	18-24		309	8 (2,6%)	127	2 (1,6%)			182	6 (3,3%)
	25-44		702	10 (1,4%)	326	2 (0,6%)			376	8 (2,1%)
	Fresh fish Canned fish 45-64 ≥65	93 36 F o 0,285 (0-7,60) 0,289 (0-0,88) r 602 9 (1,5%) 342 0 (0%) F o r	265 163	40,8 7,1 5 (1,9%) 0 (0%)	41,1 7,8	337 179	40,6 6,5 4 (1,2%) 0,440 0,284 0 (0%)	0,408 0,280	0,467 0,287	0,396 0,256	0,367 0,252	0,420 0,259
	Bivalves Total	185	2158	0,005 (0-0,20) P 31 (1,4%) P	995	2,3 11 (1,1%)	2,1	1163	2,5 20 (1,7%) 0,001	0,001	0,001	0,001	0,001	0,001
	Cephalopods	185		0,047 (0-0,81) e e		6,9	8,1			5,9	0,022	0,025	0,019	0,019	0,022	0,017
	Crustaceans	175		0,082 (0-0,90) e r e r	4,5	4,3			4,7	0,032	0,027	0,038	0,029	0,024	0,034
	Gastropods Total	14 688		0,071 (0-0,30)	0,5 62,1 R R	0,6 63,9		0,5 60,6	0,004 0,783	0,004 0,745	0,004 0,815	0,004 0,705	0,004 0,671	0,003 0,734
					e e
						v i e v i e
						w w
									O n l O n l
										y y

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Table 2b .

 2b Individuals with a methylmercury weekly intake above the PTWI (1,6 µg Hg/kg/week), by age groups and sex.

		General population		Men		Women
	Age groups		>PTWI		>PTWI		>PTWI
	(years)	n	n (%)	n	n (%)	n	n (%)
	F o r
		P
		e
			e r
				R
				e
				v i e
					w
					O n l
							y

Table 3 .

 3 Consumption, mercury concentration and mercury mean intake (µg/kg body weight/week) due to fresh fish and canned fish species and its contribution to the total mercury Figure 1: Seafood group percentage of contribution to the mercury weekly intake of the Catalan general population.

	intake.
						Mean mercury weekly
		n	Mean consumption (g/day)	Mean mercury concentration (mg/Kg)	intake (µg/kg body weight/week) and contribution to the total mercury intake due to fresh fish (%)	Contribution to the total mercury intake (%)
	Young hake	4	10.82		0.11	0.113 (26%)	14.5 %
	Common sole	4	4.91		0.05	0.025 (6%)	3.2 %
	Cod	1	4.58		0.00	0.000 (0%)	0.0%
	Hake	6	3.79		0.18	0.069 (16%)	8.8%
	Angler fish	3	3.34		0.24	0.080 (18%)	10.2%
	Sardine	14	2.76		0.11	0.033 (8%)	4.6%
	Salmon	9	2.32		0.03	0.008 (2%)	1.0%
	European anchovy	2	1.97		0.00	0.00 (0%)	0.0%
	Tuna	20	1.27		0.46	0.061 (14%)	7.8%
	Mackerel	6	1.15		0.11	0.013 (3%)	1.6%
	Gilthead seabream	3	0.81		0.11	0.010 (2%)	1.3%
	European seabass	1	0.79		0.00	0.00 (0%)	0.0%
	Megrim	2	0.42		0.04	0.002 (0%)	0.2%
	Trout	1	0.31		0.00	0.00 (0%)	0.0%
	Other species		1.54			0.021 (5%)	2.7%
	Total fish		40.8			0.440
						Mean mercury weekly
		n	Mean consumption (g/day)	Mean mercury concentration (mg/Kg)	intake (µg/kg body weight/week) and percentage of contribution to the total mercury intake	Contribution to the total mercury intake
						due to canned fish	(%)
	Canned Tuna	13	6.51		0.395	0.2713 (95.5%)	34.7%
	Canned Sardine	13	0.52		0.199	0.0100 (4.6%)	1.7%
	Canned Bonito	6	0.06		0.338	0.0025 (0.9%)	0.3%
	Canned Mackerel	4	0.02		0.168	0.0003 (0.1%)	0.0%
	Total Canned Fish		7.11			0.2841

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Food Additives and Contaminants

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I Blanco-Penedo

M López-Alonso
email: marta.lopez.alonso@usc.es.

M Miranda

J Hernández

J L Benedito

R F Shore

M López

Non-essential and essential trace element concentrations in meat in cattle 1 reared under organic, intensive or conventional production systems 2

Keywords: Toxic elements, Trace elements beef cattle, farming systems, non-essential elements, essential trace 30 elements, muscle, nutrition

We evaluated if differences in non-essential and essential trace element 11 accumulation in beef-cattle reared under different systems (including organic, 12 conventional and intensive management) were reflected in the meat derived from 13 these animals. Diaphragm muscle from 166 calves from nine farms were analysed.

14

Muscle cadmium concentrations were low (<10 µg/kg wet wt.) and muscle arsenic, 15 mercury and lead concentrations were below limits of detection (<12, 2 and 3 µg/kg 16 respectively) in most (77-97%) samples; there were no significant differences 17 between farms. Essential trace element concentrations in muscle were generally 18 within adequate physiological ranges and, although they varied significantly 19 between farms, this was not apparently related to management practices. There 20 were no significant correlations in element concentrations between muscle and 21 liver or kidney (organ concentrations that better reflect exposure), except for cobalt 22

other countries (for review see Jorhem et al. 1989;[START_REF] López-Alonso | Toxic and trace elements in liver, kidney and meat from cattle slaughtered in 305 Galicia (NW Spain)[END_REF]. In contrast, Se concentrations in cattle from all farms in the current study were above 173 the normal range (0.070-0.150 mg/kg wet weight; Puls 1994) and were higher than 174 concentrations reported in cattle elsewhere (Jorhem et al. 1989). Indeed, most 175 animals in the present study had muscle Se concentrations within the high (0.250-176 0.500 mg/kg) or toxic (0.500-1.500 mg/kg) range. However, the ranges for 177 essential element concentrations proposed by [START_REF] Puls | Mineral levels in animal health[END_REF] are not comprehensive 3). There was also a significant negative association between Zn 210 concentrations in the muscle and those in the liver and kidney (Table 3). Taking

  various food scares over the past decade that has involved meat 36 products. These include Salmonella in chicken, antibiotics in pork, Escherichia coli were determined for these elements using samples 125 spiked at a concentration that gave absorbance values some 2-10 times greater 126 than the normal levels in muscle. Mean recoveries were 89% and 96% 127 respectively. The precision of the analytical method, calculated as the relative 128 standard deviation (RSD) of Co and Mo concentrations in 10 digests of the same 129 sample, were between 5.8 and 9.3 %. 130 Statistical analysis 131 All statistical analyses were done using the program SPSS for Windows (v.15.0). 132 Non-detectable concentrations were assigned a value of half the detection limit 133 when calculating mean element concentrations in muscle. Data were tested using 134 a Kolmogorov-Smirnov test and were generally not normally distributed. They were 135 therefore log-transformed before analysis, and average concentrations are 136 therefore given as geometric means. 137 One-way Analysis of Variance followed by Tukey´s honest significant difference 138 (HSD) post-hoc tests were used to test for differences in non-essential and 139 essential trace element concentrations between farms. The significance of 140 correlations between the levels of non-essential and essential trace elements in 141 muscle and liver and kidney in each farm were calculated using Spearman rank 142 correlation analysis. In all cases statistical significance was taken to be indicated 143 by p<0.05. element concentrations in muscle from cattle from different farms 146 are presented in

178

  and do not take into account possible differences between different types of 179 muscle. Comparison of results from separate studies superficially suggest that 180 essential trace element concentrations in muscle vary 2-3 fold between cattle from 181 different countries (Jorhem et al. 1989; López-Alonso et al. 2000) even when 182 essential trace element concentrations in the liver and kidneys are comparable and 183 within the adequate physiological range. However, differences in muscle element 184 concentrations more likely reflected differences in the type of muscle analysed. For 185 example, cattle diaphragm has been found to contain nearly twice the Cu and Se 186 concentration of pectoral muscle (López-Alonso et al. 2000; López-Alonso 187 unpublished data), perhaps reflecting greater metabolic activity in the diaphragm. 188 There were statistically significant differences between farms for most essential 189 trace element concentrations in muscle (Figure 1). As with inter-farm differences in 190 concentrations (Blanco-Penedo et al. 2009), there was no 191 evidence of any specific pattern of differences between regions or different types of 192 farm. Cattle from the organic farm in Montederramo (MO) had the lowest mean Co, 193 Cu, Ni and Se concentrations found in our study. This was the only farm where a 194 high proportion of animals had hepatic Co and Se concentrations that were within 195 the marginal or deficient range, and (together with the organic farm in Baralla 196 (BO)), had calves with hepatic Cu concentrations that were below the range 197 considered adequate (Blanco-Penedo et al. 2009). Calves from the MO farm also 198 had significantly higher Zn muscle concentrations than cattle from other farms 199 although, surprisingly, calves from the MO farm had the lowest mean Zn hepatic 200 and renal concentrations that we recorded (Blanco-Penedo et al. 2009). The 201 highest mean concentrations in muscle of most of the essential trace elements 202 (Cu, Fe, Mn, Mo and Se) were in cattle from the intensive farm in Vilalba (VI), 203 although it did not correspond with a higher intake of these elements in the diet or 204 higher concentrations in the liver or kidney (Blanco-Penedo et al. 2009). 205 When analysing the relationship between essential trace element concentrations in 206 muscle and those in the liver or kidney (data from Blanco-Penedo et al. 2009), the 207 only significant positive association was for Co; muscle Co concentrations were 208 positively associated with Co concentrations in both liver and kidney (Figure 2; 209 Table

  211into account that essential trace element concentrations in the liver, and to a lesser

  trace element concentration in muscle is generally not indicative of 215 mineral status. This is presumably because muscle, unlike the liver and kidney, 216 does not have essential trace element storage capacity. At adequate dietary 217 intakes, mineral concentrations in muscle may be most closely related to protein 218 synthesis and the predominant type of metabolism in the muscle (Schricker et al. 219 1982). 220 Although our results did suggest that muscle Co might correlate with Co status, this 221 has not been found elsewhere. Van Ryssen et al. (1987) compared tissue Co 222 concentrations in cattle given little or no dietary Co supplementation (Co 223 requirement in cattle is 0.1 mg/kg DM; NRC 2001) with those in cattle given large 224 Co supplements (10 and 40 mg/kg DM). The authors found that although liver and 225 pancreatic Co concentrations significantly increased with Co supplementation 226 (increase from 7.60 to 11.15 mg/kg dry weight in liver, and from 7.88 to 10.69 227 mg/kg dry weight in the pancreas), there was no concomitant increase in muscle 228 Co (8.36 vs 8.56 mg/kg dry weight). It is possible that the low dietary levels of Co in 229 cattle in our study were below those necessary to maximise metabolic activity 230 (although normal or adequate Co concentrations have not been established for 231 muscle; Puls 1994) and so Co concentrations in the muscle of cattle from farms 232 where Co nutrition was better may have significantly increased alongside 233 concentrations in the liver and kidney.

F

Figure captions 368 Figure 1 .Figure 3 .

 36813 Figure captions

  Figure 1.

Table 2 .

 2 Overall, concentrations were low, and with the exception 147 of Cd, most samples (96.6 % for As and Hg, 77.3% for Pb) had levels which were 148 not detectable. The remainder of samples had concentrations close to the 149 detection limit. Cd concentrations were all below 10 µg/kg wet weight. In general, 150 non-essential element residues in meat in this study were similar to those 151 described in previous studies in cattle in NW Spain (López-Alonso et al. 2000; 152 2004) and were within the range of those described in other countries (for review 153 see López-Alonso et al. 2000). None of the samples analysed in the current study 154 exceeded the maximum admissible levels for Cd and Pb (0.050 and 0.01 mg/kg

	156
	157	European Commission has not established statutory limits for As and Hg but Hg
	158	residues, when detected, were 1000-fold lower than those allowed in fish
		(European Commission 2001).

155 fresh weight respectively) established by the European Commission (2001). The 159 No

significant differences were found between farms in muscle Cd concentrations

	169	are presented in Figure 1. In general, and with the exception of Se, essential trace
	170	element concentrations in cattle muscle were within the ranges described by Puls
	171	(1994) as adequate and were similar to those reported for cattle muscle in most
		F o
		r
		P
		e e r
		R e v i e w
	160 161	(mean values ranged from 3.16 to 4.07 µg/kg wet weight) nor in the proportion of O n
	162 163	samples with detectable residues for the other non-essential elements (Table 2). There was no significant association between Cd concentrations in muscle and l y
	164	those in the liver or kidney (Table 3). This is broadly consistent with findings
	165	elsewhere that suggest that muscle concentrations of non-essential elements,
	166	except perhaps for As, are not closely related to the level of exposure, (Vreman et
	167	al. 1988).

Table 1 .

 1 Results of the analytical quality control programme used in the

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

Table 2 .

 2 Non-essential (As, Cd, Hg and Pb) element concentrations in muscle 357 (µg/kg wet weight) in cattle in our study. Abbreviations for farms are as follows B:

	358
	359	Baralla, M: Montederramo, V: Vilalba, C: Conventional, I: Intensive, O: Organic
		Farm				As		Cd		Hg		Pb
		BC	N (<ld)		13 (13)		13	13 (13)		13 (11)
			Geometric mean		ND	3.73	ND		1.81
			Range		ND-ND	2.10-6.13	ND-ND		ND-15.6
		BI	N (<ld)		14 (13)		14	14 (14)		14 (12)
			Geometric mean		6.83	3.48	ND		1.75
			Range		ND-28.1	1.82-5.62	ND-ND		ND-11.7
		BO	N (<ld)		14 (14)		14	14 (13)		14 (11)
			Geometric mean		ND	3.62	1.06		2.15
			Range		ND-ND	2.28-6.60	ND-5.56		ND-18.9
		MC	N (<ld)		14 (14)		14	14 (13)		14 (11)
			Geometric mean		ND	3.16	1.03		2.13
			Range		ND-ND	1.87-4.86	ND-3.30		ND-18.0
		MI	N (<ld)		13 (11)		13	13 (13)		13 (9)
			Geometric mean		6.95	3.57	ND		2.42
			Range		ND-14.5	2.16-7.05	ND-ND		ND-5.83
		MO	N (<ld)		8 (7)		8	8(8)		8 (7)
			Geometric mean		6.82	3.20	ND		1.93
			Range		ND-14.5	2.02-5.81	ND-ND		ND-10.5
		VC	N (<ld)		14 (14)		14	14 (14)		14 (11)
			Geometric mean		ND	4.07	ND		2.16
			Range		ND-ND	2.76-7.21	ND-ND		ND-8.91
		VI	N (<ld)		14 (14)		13	14 (13)		14 (10)
			Geometric mean		ND	3.59	1.07		2.25
			Range		ND-ND	1.72-5.67	ND-5.78		ND-15.7
		VO	N (<ld)		15 (15)		15	15 (14)		15 (11)
			Geometric mean		ND	4.02	1.05		2.17
			Range		ND-ND	1.76-8.93	ND-5.22		ND-7.20
	360
	361	that	were	below	the	limit	of	detection	(N<LoD);	ND:	non	detected

N is the number of samples analysed and numbers in parenthesis indicates the number of samples

Table 3 .

 3 Rank correlations between mean non-essential and essential trace 362 element concentrations in muscle and liver and kidney in each farm in cattle in our 363 study. Results are expressed as Spearman rank correlations coefficient and 364 probability (*p<0.05, * p<0.01, *** p<0.001). Correlations have been not calculated 365 for As, Hg and Pb because most samples had not detectable concentrations

	366
	Element (N)	Muscle vs. liver	Muscle vs. kidney
	Cd (118)	-0.247	-0.165
	Co (119)	0.875 **	0.913 ***
	Cr (119)	-0.089	-0.367
	Cu (164)	0.120	0.594
	Fe (166)	-0.074	-0.137
	Ni (119)	0.224	0.535
	Mn (165)	0.608	0.325
	Mo (165)	0.133	-0.587
	Se (166)	0.256	-0.104
	Zn (166)	-0.837 **	-0.704 *

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

Acknowledgements 264 This study was supported by the Xunta de Galicia (Spain)


Monica Rivas Casado

David J Parsons

Richard Mark Weightman

Naresh Magan

Simona Origgi

David ; Parsons

M Rivas Casado
email: m.rivas-casado@cranfield.ac.uk

D J Parsons

Modelling a

Keywords: Geostatistical modelling, deoxynivalenol, ochratoxin A, bulk, cereals, sample 36 size, sampling strategy. 37

Figure1.emf Figure2.emf Figure3.emf Figure4.emf design effective and efficient sampling plans. There has been considerable emphasis on 16 identifying the different sources of uncertainty associated with mycotoxin concentration 17 estimations, but much less on identifying the effect of the spatial location of the sampling 18 points. This study used a two-dimensional statistical modelling approach to produce detailed 19 information on appropriate sampling strategies for surveillance of mycotoxins in raw food 20 commodities. The emphasis was on deoxynivalenol (DON) and ochratoxin A (OTA) in large 21 lots of grain in storage or bulk transport. The aim was to simulate a range of plausible 22 distributions of mycotoxins in grain from a set of parameters characterising the distributions. 23

For this purpose, a model was developed to generate data sets which were repeatedly sampled 24 to investigate the effect that sampling strategy and the number of incremental samples has on 25 determining the statistical properties of mycotoxin concentration. Results showed that for 26 most sample sizes, a regular grid proved to be more consistent and accurate in the estimation 27 of the mean concentration of DON, which suggests that regular sampling strategies should be 28 preferred to random sampling, where possible. For both strategies, the accuracy of the 29 estimation of the mean concentration increased significantly up to sample sizes of 40-60 30 (depending on the simulation). The effect of sample size was small when it exceeded 60 31 points, which suggests that the maximum sample size required is of this order. Similar 32 For consumer health to be effectively protected it is important that consumer exposure to 40 natural toxic contaminants in food, such as mycotoxins, is minimised. The ability to obtain a 41 representative sample for analysis, from a raw material or processed product, is critical as 42 part of a prevention strategy. Previous studies have proved that designing sampling plans for 43 mycotoxins is particularly problematical because of the heterogeneous distribution of these 44 contaminants in bulk lots of different commodities [START_REF] Stroka | Novel sampling methods 428 for the analysis of mycotoxins and the combination with spectroscopic methods for the 429 rapid evaluation of deoxynivalenol contamination[END_REF], Schatzki, 1995aand 45 1995band Jewers et al., 1988). Because mycotoxin sampling is time-consuming and 46 expensive, a limited number of samples are taken to obtain an estimate of the mean 47 concentration. Normal practice is to take several small quantities of the commodity, known as 48 incremental samples, from different locations (European Commission, 2005). These are 49 mixed together to form the aggregate sample, from which a portion is extracted for analysis. 50

In this paper, the process of collecting several incremental samples is referred to as sampling, 51 the set of discrete incremental samples is called the sample, and the number of incremental 52 samples is the sample size. 53

Previous studies have looked at the complexities associated with the estimation of the mean 54 mycotoxin concentration in bulk commodities. For example, [START_REF] Johansson | 392 Predicting aflatoxin and fumonisin in shelled corn lots using poor-quality grade 393 components[END_REF] looked 55 at the distribution of fumonisin and aflatoxin concentration in maize. They divided test maize 56 samples into damaged kernels, whole kernels and other materials and found that toxins were 57 concentrated in the poor quality components. As a result, they suggested that analyses in 58 smaller samples of poor quality components of the sample provided a better prediction of the 59 level of these mycotoxins in the bulk lot. [START_REF] Whitaker | Standardisation of mycotoxin sampling procedures: an urgent necessity[END_REF] investigated the sources of error in 60 the mycotoxin test procedure for aflatoxin in raw shelled peanuts and described it as the 61 combination of number of errors including at sampling, sample preparation method, and 62 analytical method stages. In his study, the sampling step was identified as the largest source 63 of error. Other studies have looked at the coefficients of variation in relation to sampling, 64 sample preparation and analysis for different aflatoxins [START_REF] Whitaker | Mycotoxins in food: detection and control[END_REF][START_REF] Whitaker | Sampling foods for mycotoxins[END_REF] [START_REF] Coker | 376 Design of sampling plan for mycotoxins in foods and feeds[END_REF] reviewed the complexities 67 associated with the design of sampling plans for different commodity types, sample 68 composition, sample preparation method, analytical sample and batch acceptance level. Their 69 review concluded that approximately 100 incremental samples are required to obtain a 10 kg 70 representative aggregate sample in commodities composed of large particles. For oilseed 71 cakes and meal, fifty incremental samples are sufficient to obtain a representative 5 kg 72 aggregate sample. [START_REF] Miraglia | The role of sampling in 400 mycotoxin contamination: an holistic view[END_REF] identified another source of error associated with the 73 sampling steps. They divided the sampling procedure into primary and secondary sampling. 74

The primary sampling focuses on determining where, why and when to take samples along 75 the food chain. The secondary sampling aims at determining and establishing how samples 76 should actually be collected, and more importantly how many samples to take to obtain a 77 representative sample. Other studies have identified the difficulties of sampling the spatial 78 aggregation of plant disease and have proposed different methodologies to address this 79 limitation [START_REF] Maanen | Modelling plant disease epidemics[END_REF]. [START_REF] Macarthur | Statistical modelling as an aid to 397 the design of retail sampling plans for mycotoxins in food[END_REF] identified the need to design 80 adequate sampling programmes that take into account the heterogeneity in the spatial 81 distribution of mycotoxins so that decisions on acceptance/rejection of contaminated lots are 82 more accurate. [START_REF] Whitaker | Sampling foods for mycotoxins[END_REF] discussed methods to reduce sampling error and stated that 83 these methods should focus on defining the number of samples required to obtain a specific 84 level of confidence on the mean mycotoxin concentration and on determining the location of 85 the samples to be taken so the likelihood of detecting the mycotoxins is maximised. 86

There has been considerable emphasis on identifying the different sources of 87 uncertainty associated with mycotoxin concentration estimations, but much less on 88 developing the statistical basis for robust sampling plans that account for the spatial 89 distribution of the mycotoxins in bulk commodities. Recent studies (Rivas Casado et al., 90 2009 and[START_REF] Parsons | Development of 405 representative sampling plans for mycotoxins in foods using distribution modelling[END_REF]) have looked at the potential of geostatistics to characterise the 91 spatial distribution of deoxynivalenol (DON) and ochratoxin A (OTA). Results showed that 92 while no spatial structure could be identified for OTA due to its random occurrence, 93 geostatistics was a useful tool to describe the spatial distribution of DON. Some studies have 94 successfully looked at the application of geostatistics to characterise the spatial structure of 95 Apergillus flavus in soil [START_REF] Orum | Using predictions based on 402 geostatistics to monitor trends in Aspergillus flavus strain composition[END_REF], fusarium crown and root rot in tomatoes (Rekah 96 et al., 1999), plant pathogens in diseased plants [START_REF] Chellemi | Analysis of the spatial pattern of 372 plant-pathogens and diseased plants using geostatistics[END_REF], the downy mildew 97 pathogen (Peronospora parasitica) in cabbage [START_REF] Stein | A 425 geostatistical analysis of the spatio-temporal development of downy mildew epidemics 426 in cabbage[END_REF] and citrus tristeza virus 98 [START_REF] Gottwald | Spatial and temporal 386 analyses of citrus tristeza virus in eastern Spain[END_REF]. 99 This study used a two-dimensional statistical modelling approach to produce detailed 100 information on sampling strategies for surveillance of mycotoxins in raw food commodities. 101

The emphasis was on DON and OTA in large lots of grain. 102 103

Materials and Methods 104

The model was developed using the R statistical programming language (R Development 105

Core Team, 2007) to investigate the effect that sample size and strategy have on determining 106 the mean concentration of DON and OTA in bulk commodities. The method was divided in 107 four sequential steps: generation of mycotoxin concentration data, spatial distribution of the 108 generated data, repeated generation of samples and assessment of the performance of each 109 sample size and strategy combination. The model was then run for a range of scenarios. 110 111

Data generation 112

Rivas [START_REF] Rivas Casado | submitted 415 Geostatistical analysis of the spatial distribution of mycotoxin concentration in bulk 416 cereals[END_REF] analysed the spatial structure of DON and OTA in a 26 t 113 truck of wheat with a total of 100 sampled points, from a data set collected by Biselli et al. 114 (2005). The results showed that the distribution of DON was best described by a log-normal 115 distribution of mean 1342 µg kg -1 and standard deviation 340, and presented spatial structure. 116

In contrast, OTA was best described by an exponential distribution with mean 0.57 µg kg -1 117 and standard deviation 1.13, and was randomly distributed in space. Rivas Casado et al. 118 (2009) used geostatistical techniques to investigate the spatial distribution of DON in the 119 truck in more detail. 120

Geostatistics analyses the spatial structure of a variable (in this case mycotoxin 121 concentration) using the variogram, a graph that relates the distance between any two points, 122 known as the lag distance, with their semivariance (Figure 1). Generally, points close 123 together have more similar values than those farther apart, giving low semivariance, or 124 conversely high autocorrelation, at short lag distances. Each variogram can be described by a 125 set of parameters: the sill, the range and the nugget. The sill is the a priori variance, σ 2 , of the 126 process and is generally assumed to be equal to the variance of the population [START_REF] Barnes | The variogram sill and the sample variance[END_REF]. 127

The range is the point at which the semivariance approaches the sill, so the autocorrelation 128 between pairs of points becomes 0, marking the limit of spatial dependence: points farther 129 apart than this lag distance are spatially independent. The nugget is the semivariance at a lag 130 distance of 0, and identifies the measurement error and the variations that occur over lag 131 distances less than the shortest sampling interval [START_REF] Webster | Geostatistics for environmental scientists[END_REF]. Geostatistical 132 The geostatistical procedure generated simulations of normal random fields for the 153 given variogram parameters. This was implemented using the geoR and RandomFields 154 packages from the R programming language (R Development Core Team, 2007). This 155 procedure required values for the sill, the range and the mean of the DON population to be 156 generated. It was assumed that the data represented values that had been transformed to give 157 a normal distribution, for example by a log transform, so the mean and sill should be those of 158 the transformed data. The results were transformed back to the original units for 159 interpretation. The procedure could not fully control the properties of the simulated 160 population due to processes for random generation of values, so several were generated for 161 each set of parameters, then the variogram for each was displayed and compared to the target 162 variogram to select the one that best represented the target spatial distribution. 163

Spatial distribution 165

The sampled area was always represented by a 1 unit x 1 unit square, which could be 166 scaled to the required dimensions. The data values were generated on a regular grid of 2500 167 points (50 x 50) in this area according to the chosen spatial distribution. For OTA, the 168 generated values were randomly assigned to the grid points. For DON, the values were 169 distributed in the space according to the specified sill and range values. The grf function from 170 the RandomFields package in R was used for this purpose. This function generates 171 simulations of normal random fields and distributes them in a unit square accordingly to a 172 given a set of variogram parameters. The generated data set was treated as the true 173 representation of DON or OTA concentration in a bulk commodity from which samples were 174 to be taken. 175

For each set of parameters for either the geostatistical or classical method, M data sets 176 were generated, where M was usually set at 30. The variability of the realisations generated 177 was derived from these M simulations (Figure 2). 178 179

Repeated generation of samples 180

The simulated distribution was sampled using different sample sizes and two 181 sampling strategies, regular square grid and random, to derive statistics to allow them to be 182 compared. Each sample size between 4 and 100 was then selected R times per simulation, 183

where R was fixed at 30. For random sampling, the required number of values was drawn 184 from randomly selected points, so all sample sizes between 4 and 100 were possible, and 185 repetition used new random samples. For regular grids, the only sample sizes considered 186 were those that formed squares, that is the nine regular grids with 2-10 points in each 187 direction giving 4, 9, 16, 25, 36, 49, 64, 81 and 100 points. In this case, repeated samples 188 from the same distribution were taken by changing the origin of the grid. There were thus a 189 total of 87300 (30 x 97 x 30) and 8100 (30 x 9 x 30) automated results for the random and 190 regular grid sampling strategies respectively (Figure 2). The performance of each of the combinations of sample size and sampling strategy 207 was assessed using the Root Mean Squared Error (Equation 5). This statistic combines 208 accuracy and precision. The smaller the RMSE, the better the estimation of the sample mean. 209

The RMSE has the advantage of having the same units as the variable under study (µg kg -1 ). 210 normal when more than 30 repetitions are calculated [START_REF] Cochran | Sampling Techniques[END_REF] and therefore, 217 confidence intervals can be calculated. The size of the confidence interval for each sample 218 size and strategy was calculated by multiplying the standard error of the 30 RMSE values by 219 the standard normal value c for a specified probability level. To model the effect that the 220 sample size had on the RMSE, a locally weighted regression smoothing (LOESS) function 221 [START_REF] Higgins | Introduction to modern nonparametric statistics[END_REF] was fitted to the RMSE confidence interval with the sample size as the 222 independent variable. 223

224

Level of contamination 225

The analysis was repeated using three different means, for both DON and OTA, to 226 have a representative result for a range of concentrations. The selected concentrations were 227 (European Commission, 2006). For DON the model was run 229 with log-normal DON data sets with mean 6.43, 7.14 and 7.82, that is log e 625 µg kg -1 , 230 log e 1261 µg kg -1 and log e 2490 µg kg -1 , respectively. For OTA, the model was run with 231 concentrations 0.6 µg kg -1 (found in the data from [START_REF] Biselli | Investigation of variability associated 368 with testing lots of wheat kernels for deoxynivalenol and ochratoxin A (case study 369 truck)[END_REF], 5 µg kg -1 and 232 10 µg kg -1 . The spatial structure parameters found in the data from Biselli et al. were used as 233 reference values to generate DON data for any of the three mean concentrations considered. 234

The variogram parameters used were sill 0.07, range 4 m, and nugget 0. 235 A total of 30 simulations (M=30) of log-normal DON data sets with mean 7.14 and 30 236 simulations of exponential OTA data sets with mean 0.6 µg kg -1 were generated to assess the 237 reliability of the methods for data generation. The results showed that there was little 238 variation between simulation, so subsequently only one was run for each set of parameters. 239 240

Results

242

For DON, the results for the assessment of the reliability of the geostatistical method 243 for data generation showed that the approximations to the requested population mean and 244 standard deviation were close to the target values: for the 30 simulations the average of the 245 population mean was 7.15, which was close to the 7.14 target mean. The maximum and 246 minimum population mean obtained were 7.26 and 7.00, respectively. The standard deviation 247 (σ = 0.27) obtained was also very close to the target value (σ = 0.23), this being determined 248 by the sill of the spatial structure. 249

The simulated DON spatial structure showed that the nugget and the sill were close to 250 the required values. The mean sill was 0.069 with a standard deviation of 0.0068 which was 251 very close to the 0.07 target. This was consistent with the results obtained for the standard 252 deviation of the population. The mean nugget was 0.0010 with a standard deviation of 0.0037 253 which was also very close to the 0 target nugget. The values of the range did not accurately 254 adjust to the target value. The mean range obtained for the 30 repetitions was 0.165 units with 255 a standard deviation of 0.056, which differed considerably from the target value of 0.4 units. 256

For OTA and a target mean of 0.6 µg kg -1 , the thirty simulations had a mean value of 257 0.602 µg kg -1 and a standard deviation of 0.012. This showed that there was little variation 258 between generated data sets for the same target value. 259 For both cases, the statistics derived by repeated sampling from different simulations 260 with the same parameters differed little between simulations. As noted above, it was 261 concluded that it was sufficient to generate one simulated population for each set of 262

parameters. 263

The RMSE changed in proportion to the mean concentration. For example, for the 264 simulation of DON with mean concentration of 625 µg kg -1 and for different samples, the 265 RMSE was 90-300 µg kg -1 (14-48% of the mean) and for the simulation with mean 266 concentration of 2490 µg kg -1 it was 300-1100 µg kg -1 (12-44%). Similar results were seen 267 for OTA. 268

The general pattern observed for the majority of the LOESS equations fitted showed a 269 decrease in the slope of the curve at 40-60 samples (Figure 3), beyond which the RMSE did 270 not decrease as significantly for each increment in the sample size. The RMSE is made up of 271 the sample standard deviation and the bias. The bias should tend to 0 when increasing the 272 sample size and therefore the main contribution to the RMSE for large samples is the 273 standard deviation of the sample. For sample sizes close to the population size, the RMSE 274 would be very close to the standard deviation of the population and no further gain in 275 accuracy could be obtained. This point was reached at 40-60 samples. 276

For DON, the graphs of RMSE + 95% confidence interval (Figure 4a) showed that the 277 results of regular grid sampling were more consistent than random sampling. For a given 278 sample size, the RMSE for random sampling was sometimes as low as or lower than that for 279 regular grid sampling, but in most cases it was higher. This is probably the result of bias 280 introduced by clusters of relatively closely spaced, and therefore correlated, points. This 281 suggests that it is better to use regular grid sampling strategies when spatial structure is 282 expected for the mycotoxin concentration. For OTA (Figure 4b), the difference between the 283 strategies was small. The scatter-plots for the RMSE + 95% confidence interval showed 284 overlapping results for the two methods. In general, for the OTA data sets the difference 285 between the two sampling strategies was negligible. 286 The model was reliable at generating data sets with the target DON and OTA mean 292 concentration. However, in some cases the model failed to reproduce the target spatial 293 structure for DON. When the maximum range value generated was below the target range, a 294 less smooth spatial distribution than intended was generated. In general this would be 295 expected to reduce the differences between random and regular sampling, and to increase the 296 bias. Despite this, clear differences were found between the two sampling methods. If the 297 intention in future was to simulate a particular observed distribution, this could be done by 298 increasing the target value of the range until the result from the simulated data matched the 299 observation. In this case the aim was to examine a range of possible distributions. Consistent 300 results were obtained across all mean concentrations, and contrasts were found between 301 random and regular sampling. 302

For both strategies and both mycotoxins, the accuracy of the estimation of the mean 303 concentration increased significantly up to sample sizes of 40-60 (depending on the 304 simulation). The effect of sample size was small when it exceeded 60 points, which suggests 305 that the maximum sample size required is of this order. European Commission regulations 306 specify 60 incremental samples from grain lots of 10-20 t and 100 incremental samples from 307 lots over 20 t (European Commission, 2005). The model results show that these would be 308 adequate in the cases simulated. On this basis, there is no evidence to recommend either a 309 reduction or an increase in the number of incremental samples. These results are also 310 consistent with the findings of [START_REF] Coker | 376 Design of sampling plan for mycotoxins in foods and feeds[END_REF] that 50-100 samples are required for 311 different aggregate sample masses. 312

The model was designed to simulate lots of the order of 10-100 t. There is no 313 evidence on the spatial structure at smaller or larger scales so the results should not be 314 extended at these scales without further data collection and analysis. 315

The samples used in the simulation are idealised: each is the exact value at a single 316 point, and the mean is calculated precisely. In practice each incremental sample takes a small 317 mass of grain, these are mixed and the concentration is measured in a subsample. Thus there 318 are two additional sources of error: the difference between finite incremental samples and 319 point measurements, and the difference between the mean of the idealised samples and the 320 concentration measured from the aggregated sample. The effects of mixing and subsampling 321 have been investigated by [START_REF] Whitaker | Mycotoxins in food: detection and control[END_REF][START_REF] Whitaker | Sampling foods for mycotoxins[END_REF], and should be considered when examining 322 the total uncertainty. The mass of an incremental sample is normally 0.1-1.0 kg, which 323 implies that the dimensions are of the order of 0.1 m. This is small compared to the scale at 324 The random sampling method in the model is truly random: all points are equally 327 likely to be sampled, independent of which others have been selected. In practice, when 328 someone samples from a bed of grain, they are unlikely to take two samples very close 329 together, so real sampling behaviour is likely to be intermediate between random sampling 330 and regular grid sampling. 331

The model was based on a single set of data for DON and OTA. Further research 332 would be needed before drawing more general conclusions. 333

The model does not consider the variation in mycotoxin concentration with depth, but 334 only in two horizontal dimensions, because no data were available. Indeed, sampling using a 335 conventional sample spear aggregates over the depth from one point on the surface. The data 336 used to develop the model came from this type of sampling, so it is intended to be an 337 empirically based representation of normal practice. Recent works on geostatistical science 338 provide a methodology to develop 3D geostatistical models [START_REF] Culshaw | From concept toward reality: developing the attributed 3D geological 378 model of the shallow subsurface[END_REF]339 Stavropoulou et al., 2007) The model focused on the effect that the sampling protocol has on determining the 345 statistical properties of mycotoxin concentration. The conclusions drawn in this study must 346 be understood as preliminary outcomes of the model. The assumptions made in the 347 development of the model and its limitations must be considered when interpreting the 348

conclusions. 349

This study showed that the accuracy of the estimation of the mean concentration 350 increased significantly up to sample sizes of 40-60, which is consistent with the number of 351 incremental samples taken from bulk lots under EU regulations. This applied to both spatially 352 structured and randomly distributed data across the range of mean concentrations used. 353

A regular grid proved to be more consistent and accurate in the estimation of the 354 mean concentration where there was spatial structure (DON). The difference between the two 355 sampling strategies was negligible for randomly distributed data (OTA). This suggests that 356 regular sampling strategies should be preferred to random sampling, where possible. 357 ) from sample size 4-100 obtained for the random and regular grid sampling strategies. The model was run to simulate a bulk commodity with (a) a mean DON concentration of 7.14 (log e 1260 µg kg -1 ) and a spatial distribution described by a variogram with range 4 m, sill 0.07 and nugget 0; and (b) a mean OTA concentration of 0.6 µg kg -1 and random spatial distribution. The final simulated values were mean 6.5, range 3.66, sill 0.067 and nugget 0.0091 for DON and mean 0.577 for OTA.

  sample size apply to OTA, although the difference between regular and Introduction 39

  effective when the variable has a normal (Gaussian) distribution. For this 133 purpose, the data were log transformed (natural log) to meet the normality requirement. The 134 results showed that the spatial structure of DON could be described by a variogram with 135 range 4.35 m, sill 0.07 and nugget 0.013. The spatial structure defined by these parameters 136 was assumed to be representative of DON in bulk commodities, in the absence of other data, 137 and was therefore used as a reference condition for data generation. 138 Two different procedures for data generation were used based on the findings 139 described by Rivas Casado et al. (2009): classical statistics for OTA and geostatistics for 140 generation of DON concentration. The classical statistics procedure allowed the generation of 141 random deviates of an exponential distribution with a density function: the concentration of the mycotoxin and λ (λ > 0) is the rate parameter estimated the sample mean concentration. This only required the mean concentration of the 151 mycotoxin as an input parameter for the generation of data. 152

191 192

 191 Assessment of the performance of sample size-strategy combinations 193Two statistics must be considered for the quantification of sample and population 194 differences: accuracy and precision. Accuracy is measured by the bias b, which is the 195 difference between the sample mean concentration µ ˆ and the population mean concentration 196 Equation3). If the mean of the sample is equal to the mean of the population, then µ ˆ is 197 said to be unbiased, otherwise it is said to be biased degree of mutual agreement between all the points in the sample and 202 is estimated by the standard deviation of the sample σˆ:

  RMSE inr where i is the simulation number (1 ≤ i ≤ 30), n is the sample size 214 (4 ≤ n ≤ 100) and r is the repetition (1 ≤ r ≤ 30) was obtained by repeatedly calculating 215 RMSE values for a specific sample size. The distribution of RMSE values is approximately 216

  and above the maximum level of DON (1250 µg kg -1 ) and OTA (5 µg kg -1 ) set 228 by the European Commission

  in the model and its limitations, particularly the distributions 290 used, must be considered when interpreting the results and should be verified in practice. 291

  is intended to operate. It is therefore reasonable to treat the incremental 325 samples as point measurements. 326

Figure 1 .

 1 Figure 1. Example of a variogram function showing the sill, range and nugget parameters.

Figure 2 .

 2 Figure 2. The iterative process followed by the model.

Figure 3 .

 3 Figure3. Example of the changes in the value of RMSE (µg kg -1 ) from sample size 4-100 observed for DON for random (a) and regular (b) sampling strategies. The model was run to simulate a bulk commodity with mean DON concentration 7.14 (log e 1260 µg kg -1 ) and a spatial distribution described by a variogram with range 4 m, sill 0.07 and nugget 0. The final simulated values were mean 6.5, range 3.66, sill 0.067 and nugget 0.0091.

Figure 4 .

 4 Figure 4. Example of the changes in the value of the RMSE plus the upper 95% confidence interval (µg kg -1 ) from sample size 4-100 obtained for the random and regular grid sampling strategies. The model was run to simulate a bulk commodity with (a) a mean DON concentration of 7.14 (log e 1260 µg kg -1 ) and a spatial distribution described by a variogram with range 4 m, sill 0.07 and nugget 0; and (b) a mean OTA concentration of 0.6 µg kg -1 and random spatial distribution. The final simulated values were mean 6.5, range 3.66, sill 0.067 and nugget 0.0091 for DON and mean 0.577 for OTA.

  and could be applied to mycotoxin concentration, if suitable data 340

	341	sets were collected.
	342
	343	Conclusions
	344

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

maize. Int Biodeterior. 24(4-5): 393-398.

Acknowledgements 360

This research was funded by the Food Standards Agency Project CO3055. We wish to 361 thank Dr. Scarlett Bisselli (EUROFINS, Hamburg, Germany) for giving permission to use the 362 data on which the model is based. 363