Alwyn R Fernandes

David Mortimer

Martin Gem

Pamela Dicks

Frankie Smith

Shaun White

Martin Rose

Brominated dioxins (PBDD/Fs) and PBDEs in Marine Shellfish in the UK

Keywords: BFRs, PBDDs, PBDFs, PBBs, dietary exposure, dioxins, shellfish

teaching and research institutions in France or abroad, or from public or private research centers.

ABSTRACT

The occurrence of brominated dioxins (PBDD/Fs) and polybrominated diphenyl ethers (PBDEs) was investigated in commonly consumed species of marine shellfish in the UK. Individual samples of Pacific oysters (Crassostrea gigas), native oysters (Ostrea edulis), mussels (Mytilus edulis), scallops (Pecten maximus), and cockles (Cerastoderma edule) were collected from different coastal regions between [2006][2007]. Samples of a particular species from each site were composited and 60 samples were analysed. Polybrominated dibenzofurans (PBDFs) occurred more frequently and generally at a higher level than polybrominated dibenzodioxins (PBDDs), except for 237-TriBDD which was the predominant PBDD/F congener in some of the species, notably oysters. This profile may reflect the environmental distribution of these compounds and the effects of removal mechanisms -degradation, selective uptake and metabolism. PBDEs were detected in all samples. The dominant congeners were BDEs 47, 49, 99 and 100 and, to a lesser extent, BDEs 66 and 154. The occurrence of BDE-209 was observed in most samples and appears to be species selective, with the highest values occurring almost exclusively in mussels and cockles. Among the species studied, oysters and mussels displayed relatively higher levels of both sets of contaminants; native oysters in particular, showed elevated levels of 237-TriBDD (up to 14.5 ng/kg). In general, contaminant levels appeared to be consistent with the extent of local industrialisation with lower levels observed in more remote areas such as the north of Scotland. Polybrominated biphenyls (PBBs) were also measured and PBBs 49, 52 and 77 were the most frequently detected, although levels were very low.

Dietary intakes, estimated for PBDD/Fs showed that 237-TriBDD from single portions of oysters constituted a high proportion of the total dietary intake of the congener, but otherwise, dietary intakes of PBDD/Fs from shellfish were relatively low.

INTRODUCTION

Fish and shellfish are generally recommended by nutritionists and the medical profession as healthy foods that provide a good source of protein. Many oily fish species contain nutrients such as omega-3 fatty acids, which may reduce the risk of heart disease and stroke, and it is suggested that the consumption of one to two fish meals per week is helpful in preventing heart disease. The Food Standards Agency advises that consumers should eat two portions of fish a week, one of which should be oily, as part of a balanced diet, and also recommends maximum amounts of fish that should be consumed (Food Standards Agency, 2004). Similarly, bivalve shellfish are an excellent source of protein and are high in essential minerals, and low in calories and fat. However, fish and shellfish can accumulate contaminants from the water in which they live or from the food they eat. Shellfish classed as bivalve molluscs feed by filtering plankton from the surrounding water that washes through their habitat.

This feeding mechanism leads to the bio-accumulation of pollutants of anthropogenic and biogenic origin from the surrounding waters even at a considerable distance from pollution sources.

Polybrominated diphenyl ethers (PBDEs), also known as polybrominated diphenyl oxides, and polybrominated biphenyls (PBBs) are mass-produced additive brominated flame retardants (BFRs) used in a variety of plastics, textiles, surface coatings, foams and manmade fibres. Their use has undoubtedly resulted in a reduction in human injury and fatality as a result of fires (Spiegelstein, 2001[START_REF] Emsley | International fire statistics and the potential benefits of fire counter-measures[END_REF].

However, the unrestricted application of these materials in the past has allowed diffusion of the contaminants into the environment during manufacture, use and disposal and continues to do so. This release is evident from the occurrence of PBDEs in environmental compartments such as water, sediments and biota (D 'Silva et al 2004) and accompanies an increasing amount of evidence on potential detrimental human health effects (D 'Silva et al 2004[START_REF] Darnerud | Toxic effects of brominated flame retardants in man and in wildlife[END_REF], Hakk and Letcher 2003). Emerging toxicological data shows that PBDEs can cause liver and neurodevelopmental toxicity and affect thyroid hormone levels. In recent years the EU has carried out a comprehensive risk assessment under the Existing Substances Regulation (793/93/EEC) of commercial PBDE products. The outcome was a ban on One of the principal sources of PBDD/Fs is widely accepted as being combustion processes such as incineration. However they can also occur as trace contaminants in brominated flame retardants such as PBDEs, and studies on formation chemistry show that the incineration of products containing BFRs as well as thermolysis of BFR material such as PBDEs is an important source of PBDD/F emissions. The substantial usage of BFRs will ensure that they remain a significant source of these contaminants.

It has also been demonstrated that PBDD/Fs can be formed during ultra-violet irradiation of decabromodiphenyl ether [START_REF] Olsman | Formation of dioxin-like compounds as photoproducts of DeBDE during UV-irradiation[END_REF]. Recently, there have been reports that some lower brominated PBDD congeners (tri-tetra-) may be produced through biogenic formation via precursor pollutants in the marine environment and bio-accumulate in some marine species such as shellfish (Malmvern et al 2005, Haglund et al 2007). Toxicologically, studies that have been carried out on PBDD/Fs show biological responses that are comparable to the chlorinated dioxins.

Structure activity relationship studies on PBDD/Fs suggest a similar mode of biological activity to the chlorinated analogues, which is likely to be initiated by Ah receptor binding [START_REF] Mason G Zacharewski | PBDDs and related compounds: quantitative in-vivo and in-vitro structure activity relationships[END_REF][START_REF] Birnbaum | Health Effects of PBDDs and PBDFs Environment International[END_REF]. There are of course likely to be subtle biochemical differences even between laterally-substituted compounds within the same homologue group. This would be due not only to the difference in substitution pattern as observed for the chlorinated dioxins but also to the differences in size and reactivity of the bromine and chlorine atoms and the difference in bond strengths for the organic carbon-bromine (weaker) and carbon-chlorine (stronger) bonds. Additionally, the larger size of the bromine atom and the degree of halogenation is also likely to alter the elicitation of biological effects [START_REF] Birnbaum | Health Effects of PBDDs and PBDFs Environment International[END_REF].

The occurrence of PBDEs in shellfish has been documented in a number of publications, some of which have used shellfish as indicators of marine and aquatic pollution rather than as food. Reports on PBDD/Fs are far less frequent and may be a reflection of the greater difficulty in measuring PBDD/Fs reliably at comparable concentrations to the chlorinated dioxins. More recently, structured studies that are directed towards the estimation of human dietary exposure to these compounds have investigated occurrence in different foods including fish and shellfish. Data for the sum of PBDE congeners in different foods in North America from a market basket survey showed that the maximum whole weight median concentrations (1725 ng/kg) occurred in fish [START_REF] Schecter | PBDE contamination of United States food[END_REF]. A recent survey of fish in Canada showed whole weight mean PBDE values ranging from 100 ng/kg for shrimp and tilapia to 2200 ng/kg for farmed salmon (Health Canada 2005). Similarly a study on different foods collected in the Catalonian region of Spain showed that the highest whole weight concentrations of PBDEs (1010 ng/kg) were found in blue fish. A recent Japanese Total Diet Study (TDS) found the highest average PBDE concentrations (1259 ng/kg) in the fish group [START_REF] Ashizuka | Determination of PBDEs and PBDD/Fs in marine products[END_REF], and a similar TDS study in the UK [START_REF] Fernandes | Brominated flame retardants and brominated dioxins in 2003 Total Diet samples[END_REF] also found the highest average PBDE concentrations (500 ng/kg) in the fish group (not including . Some recent studies have also shown evidence of the occurrence of these contaminants in shellfish. A study on a range of fish and shellfish species showed the occurrence of PBDEs and brominated dioxins in a number of the species, but particularly shellfish (Food Standards Agency 2006). Certain PBDEs were present in most or all of the shellfish analysed, the most abundant congeners being PBDEs 28,47,49,66,99,100,153 and 154 and among the PBDD/F congeners more PBDFs were detected than PBDDs.

This study aimed to investigate commonly consumed shellfish species such as oysters, mussels, scallops and cockles collected from different coastal regions around the UK.

The occurrence levels of these brominated contaminants were determined and will be discussed in the context of currently available information. The human exposure arising from the consumption of these foods will also be discussed. these samples were divided into separate adductor and gonad portions prior to pooling. The composite samples were freeze-dried and ground and the resulting powders were thoroughly mixed before taking sub-samples for analysis.

EXPERIMENTAL

The composite samples were analysed for the following compounds: 3,2,3,2,3,7,8-Br substituted PBDD/Fs: tetra -hexa brominated congeners (note that this includes only one hexa-Bromosubstituted furan as no standards were available for the other three congeners).

Polybrominated diphenylethers (PBDEs) -IUPAC numbers 17, 28, 47, 49,66,71,77,85,99,100,119,126,138,153,154,183 and 209. PBB congeners: IUPAC numbers 15,49,52,77,80,101,126,169,153 and 209. (Those for which 13 Carbon labeled standards were used as internal or sensitivity standards are shown in bold type and these also included six PBDD/F congeners). Additionally, chlorinated dioxins and PCBs and, in some cases, tetrabromobisphenol-A and hexabromocyclododecane were also determined but these are reported elsewhere (Driffield et al 2008[START_REF] Fernandes | Brominated and chlorinated contaminants in shellfish. Report to the Food Standards Agency[END_REF][START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF].

The analytical standards used, native as well as 13 Carbon labeled for the individual analyte groups, were obtained as nonane solutions either from Cambridge Isotope Labs, Mass. USA or from Wellington Laboratories Inc. Ontario, Canada.

The methodology used for the analysis has been robustly validated and accredited to the ISO17025 standard. A full description of the procedures has been reported elsewhere [START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF], Fernandes et al 2004). In brief, samples were fortified with 13 C-labelled analogues of target compounds and exhaustively extracted using mixed organic solvents. PBDEs were chromatographically fractionated from the brominated dioxins and furans on activated carbon. The two fractions were further purified using adsorption chromatography on alumina. Analytical measurement was carried out using high resolution gas chromatography coupled to high resolution mass spectrometry (HRGC-HRMS, Waters Autospec Ultima instrument fitted with a Hewlett Packard 6890N gas chromatograph). Additional control was provided by the inclusion of methods blanks and a reference material. Where reliable inter-calibration schemes exist for food matrices (PBDEs), the methodology has been further validated by successful participation in these studies (e.g. Dioxins in Food, Quasimeme) over The Z-scores achieved for fish and fish based matrices in these studies, ranged from -0.61 to 0.81.

RESULTS AND DISCUSSION

A full version of the detailed results for all of the analytes described in this work is presented elsewhere [START_REF] Fernandes | Brominated and Chlorinated contaminants and PAHs in shellfish flesh from Scottish Innshore and Offshore harvesting areas[END_REF][START_REF] Fernandes | Brominated and chlorinated contaminants in shellfish. Report to the Food Standards Agency[END_REF] and contains in addition to the PBDD/Fs and PBDEs described in this paper, data on PBBs chlorinated dioxins/PCBs and for some samples, speciated HBCD and TBBPA. Due to the large volume of PBDD/F and PBDE data, only a summarised version of the results are presented in this paper. These are shown in Table 1. Data for the sum of the 17 PBDEs measured and for the PBDD/F TEQ are reported as upper-bound, i.e.

where congeners were not detected they were assumed to be present at the level of the limit of detection. All concentrations are expressed on a whole weight basis to allow comparison with other data. The key statistics for this data are summarised in Table 2.

PBDEs are presented as the sum of 17 different congeners but, due to the high level of current interest in BDE-209, data for this congener is presented separately, as is data for BDE-47, BDE-99 and BDE-100. PBDD/F data is presented as toxic equivalents (TEQs) based on the use of the analogous chlorinated dioxin and furan (PCDD/F) toxic equivalent factors (TEFs) (Van den Berg et al 1998), an approach that has been used in other studies [START_REF] Ohta | Levels of PBDEs, TBBPA, TBPs, PCDD/Fs PxDD/Fs and PBDD/Fs in human milk of nursing women and dairy milk products in Japan[END_REF][START_REF] Colles | Fourth WHO-coordinated survey of human milk for persistent organic pollutants: Belgian results[END_REF][START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF], Food Standards Agency 2006) [START_REF] Birnbaum | Health Effects of PBDDs and PBDFs Environment International[END_REF]. The approach however provides data to allow comparisons with other studies that have provided similar data [START_REF] Ohta | Levels of PBDEs, TBBPA, TBPs, PCDD/Fs PxDD/Fs and PBDD/Fs in human milk of nursing women and dairy milk products in Japan[END_REF][START_REF] Fernandes | Brominated flame retardants and brominated dioxins in 2003 Total Diet samples[END_REF][START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF], Food Standards Agency 2006).

PBDEs were detected in all the shellfish species and, although the relative distributions of the lower-to mid-brominated congeners (BDE-47, 99, 100) measured in this study were very similar across the species, the relative distributions of the higher brominated congeners (BDE-183, BDE-209) varied. This was evident for BDE-209 in mussels and, particularly, in cockles (Table 1). The two groupings of congeners may be associated with commercial mixtures such as "Penta" which contains BDE-47, 99 &100, and "Deca" which is predominantly BDE-209 ( BDE-183 is an expected breakdown product of "Deca"). Analysis of the congener distribution also shows that the levels of BDE-47 correlate well (R 2 > 0.98) with the total PBDE level for all species except cockles. As observed elsewhere (D 'Silva et al 2004), BDE-47 is the major component of "Penta". On the other hand, BDE-209 does not correlate well with total PBDE in oysters or scallops, but shows strong correlation in cockles (R 2 = 0.95). These distributions of BDE congeners within the species may be indicative of the differences in bio-accumulative potential between species, with mussels showing the potential to bio-accumulate both Penta and Deca; oysters show very low levels for Deca and cockles the highest. It is unlikely that spatial distribution influences the correlation between BDE-209 and mussels through the selective loading of this contaminant at particular locations, because different species collected in the same area do not show relatively similar amounts of this congener. It is pertinent at this stage to mention the potential difficulties with the measurement of BDE-209 (De Boer 2004[START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF]. Laboratory blanks invariably show the presence of this congener, so blank subtraction is necessary, but removal mechanisms such as degradation during sample extraction/purification due to UV exposure [START_REF] Olsman | Formation of dioxin-like compounds as photoproducts of DeBDE during UV-irradiation[END_REF], and other processes such as adsorption, are also known to occur. Hence the monitoring of the measurement process with the use of the commercially available 13 C labelled BDE-209 as used in this work is strongly recommended in order to provide better measurement control [START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF].

In terms of absolute concentrations, higher levels of PBDEs were observed in mussels compared to the other species, with the lowest levels being observed in cockles. The observed predominance of BDE-47, 49, 99 and 100 and to a lesser extent BDE-66 and BDE-154 is similar to that observed in other studies [START_REF] Ashizuka | Determination of PBDEs and PBDD/Fs in marine products[END_REF][START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF], Food Standards Agency 2006[START_REF] Van Leeuven | Brominated flame retardants in fish and shellfish-levels and contribution of fish consumption to dietary exposure of Dutch citizens to HBCD[END_REF] and demonstrates the persistence of these contaminants, as the use of the commercial formulations from which these congeners derive has been banned for some years now.

There are very few reports on the occurrence of brominated dioxins and furans in food or biota. Recently however, PBDDs have been reported as occurring in marine biota, including shellfish taken from the Baltic Sea (Malmvern et al 2005, Haglund et al 2007). These studies targeted the brominated dibenzo-p-dioxin rather than dibenzofuran molecules and the occurrence of specific di and tri-brominated dioxins was recorded, with lower levels of tetrabrominated congeners. The authors suggest that this specific distribution originates from marine biogenic processes. The formation of individual brominated dioxin congeners is thought to proceed through condensation reactions of naturally occurring bromophenols mediated by the action of bromoperoxidases in the presence of bromide (Haglund et al 2007). This formation pathway is likely to be governed by kinetic processes and would explain the relative abundances of the individual congeners. However, the shellfish samples analysed in this work showed not only the congener profiles discussed above (for PBDDs); they also showed the presence of significant levels of PBDFs (Figure 1). The chromatographic profiles for the furans (tri-to penta-brominated) show similarities to those observed for environmental samples, in that a wide range of furan congeners are observed. This observed occurrence of a range of congeners rather than specific compounds suggest an origin from thermodynamic processes and it is likely that the observed profile and levels are due to combustion processes such as incineration. The predominance of PBDFs over PBDDs has also been observed in other studies involving combustion. Thus it is likely that the PBDD/Fs observed in these samples reflect both types of sources, biogenically mediated (Haglund et al 2007) as well as anthropogenic.

PBDD/Fs were detected in most of the samples at varying concentrations depending on the species. The calculated TEQs were higher in scallop gonad than in mussels (0.083 ng/kg compared to 0.055 ng/kg whole weight respectively), although mussels showed a more complete range of detectable congeners, particularly the PBDFs (Figure 1). Relatively lower TEQ levels were detected in the scallop adductor muscle and this may relate to the lower lipid content of the adductor. The contribution to TEQ was derived mainly from 2,3,7,8-TBDF, 2,3,4,7,8-PBDF and 2,3,7,8-TBDD [START_REF] Fernandes | Brominated and chlorinated contaminants in shellfish. Report to the Food Standards Agency[END_REF]. This composition is similar to the TEQ distribution of PCDD/Fs where, for fish and shellfish, these congeners, along with 1,2,3,7,8-PCDD, are often the main contributors. Most samples in this study showed the presence of tribromo dioxins and furans. In particular, 2,3,7-tribromo dioxin was the predominant congener in oysters, native oysters (Ostrea edulis) showing relatively elevated levels (up to 14.5 ng/kg whole weight). The toxicology of the tri-brominated dioxins and furans is the subject of continuing international study (Hornung et al 1997[START_REF] Birnbaum | Health Effects of PBDDs and PBDFs Environment International[END_REF][START_REF] Behnisch | Brominated dioxin-like compounds: in vitro assessment in comparison to classical dioxin-like compounds and other polyaromatic compounds[END_REF] with available toxicological data indicating that these compounds elicit more potent biological responses than their chlorinated analogues [START_REF] Behnisch | Brominated dioxin-like compounds: in vitro assessment in comparison to classical dioxin-like compounds and other polyaromatic compounds[END_REF]. It should particularly be noted that the concentrations of tribromo substituted PBDD/F congeners reported here have not been included in the summed TEQs, as there are no recognised analogous TEF values for tri-chloro substituted PCDD/Fs. Moreover, although the levels of tri-and tetra-BDD/BDF congeners are elevated in comparison to the other 2,3,7,8-Br substituted compounds, they occur at a relatively low level in comparison to other non-laterally substituted triand tetra-congeners that were present in the samples. The greater abundance of these congeners (some are orders of magnitude higher in occurrence), merits further research, both in terms of toxicology -none is currently available -as well as occurrence in other marine and aquatic species, particularly those used as food.

Although PBDD/Fs exposure in humans has been reported (Ohta et al, Colles et al), data on the tri-brominated compounds was not available. However, apart from the levels reported in this work, tri-brominated compounds have also been detected in other foods (Food Standards Agency, 2006c), which makes it likely that humans are exposed to these compounds. Apart from 2,3,7-tribromoBDD, the occurrence of PBDFs generally predominates that of PBDDs, reflecting profiles observed for environmental samples which in turn mirror the formation chemistry of these molecules through thermodynamic pathways that are a feature of combustion processes.

Insert figure 1 here PBDE commercial mixtures are reported to be one of the major sources of PBDD/Fs (Hayakawa 2004et al 2004[START_REF] Silva | Brominated Organic Micropollutants -Igniting the flame retardant issue[END_REF], Hagberg 2008), which occur as contaminants in the mixtures. Hayakawa et al report a positive correlation between these two groups in atmospheric deposition. There is very little information on pathways for PBDD/Fs or the occurrence of these compounds in sediments or soils, but given the similarities in physical and chemical properties to the chlorinated dioxins, it is not unreasonable to expect similar transfer mechanisms, although the PBDD/Fs are more likely to suffer degradation due to the higher susceptibility of the C-Br bond. In these samples, apart from the set of cockles (which should be interpreted with care because of the low number of samples) there is poor correlation between concentrations of PBDD/F and PBDEs. This is perhaps unsurprising given the various degradation/absorption/metabolic/bio-accumulation processes that define the occurrence of both these sets of contaminants in shellfish.

Although the data for PBBs is reported elsewhere [START_REF] Fernandes | Brominated and chlorinated contaminants in shellfish. Report to the Food Standards Agency[END_REF][START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF], they were measured in all the samples and generally showed relatively low levels of occurrence. The most frequently detected congeners were PBB 49 and PBB 52 and these occurred typically in the range 0.2 -1.0 ng/kg. The non-ortho substituted PBBs (congeners PBB77, PBB126 and PBB169) were also measured and of these PBB 77 was the most frequently detected, typically in the range of 0.01 -0.05 ng/kg.

The relative concentrations of the flame retardants -PBBs (low levels) and PBDEs (higher levels) is consistent with the greater and more recent usage of PBDEs in the UK. The low levels of PBBs observed are likely to arise from long range marine and aerial transport as observed in the detection of this contaminant in tissue from Arctic polar bear (D 'Silva et al 2006).

Geographically, the spatial distribution of the sampling sites covers most of the shellfish harvesting areas around the UK coastline. The different levels of contaminants observed appear to be consistent with the levels of industrial activity in these regions. In general, mussels from locations in Scotland, particularly the north and north west (the Orkneys, Western Isles and Northern Highlands) showed, on average, lower levels of PBDEs and broadly similar levels of PBDD/Fs to locations in the rest of Britain and Northern Ireland. For Pacific oysters the situation was reversed, with higher levels observed in Scottish samples. The concentrations of the tri-brominated dioxins, however, appear to be species dependent with average concentrations over an order of magnitude higher in samples of native oysters compared with Pacific oysters. This observation is valid even for samples of the two species harvested in the same location and may be related to the individual uptake and metabolism processes of the different species. The detection of most contaminants, albeit at low levels in all samples, some taken from the more remote areas confirms the long-range transport of these compounds, which is a defining characteristic of persistent organic pollutants.

Dietary Intakes

Dietary intakes from the shellfish alone were estimated from the average concentrations found in the different types of shellfish, assuming a portion size of 70 g. Dietary intakes from the whole diet excluding fish and shellfish were estimated from the results of a survey for brominated chemicals in samples from a Total Diet Study (TDS) (Food Standards Agency, 2006c). The TDS is a model of the typical UK diet. Foods were purchased from 24 randomly selected locations in the United Kingdom, and these were combined into 20 food groups. Foods that are consumed in large quantities such as bread, milk and potatoes, and foods that are susceptible to contamination such as fish (which includes shellfish) and offals, are kept separate.

Table 3 shows the estimated dietary intakes by 60 kg adults, of PBDD/Fs and dioxinlike PBBs, from single 70 g portions on their own, and from one and two weekly 70 g portions in combination with the rest of the diet, from shellfish.. In the case of oysters, especially native oysters, the estimated dietary intake of 2,3,7-triBDD by those who consume the species exceeds the average dietary intake from the rest of the diet by the UK population by a large margin. Otherwise, the estimated dietary intakes of brominated chemicals from shellfish alone are a low proportion of those from the rest of the diet. The difference between upper and lower bound values is likely due to the absence of PBDDs (apart from tri-and tetra-brominated dioxins) which have higher TEF values and would therefore have contributed to ~ two-thirds of the TEQ. This contrasts with PCDD/Fs in shellfish where most PCDDs and PCDFs are usually present. The results of a previous survey for brominated chemicals in farmed and wild fish and shellfish (Food Standards Agency, 2006a) were assessed by the independent expert Committee on Toxicty of Chemicals in Food, Consumer Products and the Environment (COT). The COT concluded that consumption of the shellfish was unlikely to pose a hazard to human health (Food Standards Agency, 2006b). The current results were similar to those for shellfish tested in the previous survey.

CONCLUSION

The data presented here confirms the widespread occurrence of PBDEs and PBDD/Fs in shellfish. The differences in levels for some congeners, notably 2,3,7-triBDD may suggest differences in bioaccumulation potential among the species. The dioxin-like toxicity arising from the occurrence of PBDD/F TEQ levels are lower than those reported for chlorinated dioxins [START_REF] Fernandes | Brominated and Chlorinated Dioxins and Brominated Flame Retardants in Scottish Shellfish: Methodology, Occurrence and Human Dietary Exposure[END_REF], and the assessment of the average dietary intake of these contaminants in the UK as a result of shellfish consumption shows that it is unlikely that toxicity arising from these contaminants alone is of concern. However, the TEQ from PBDD/Fs is an important contribution to the overall burden of human exposure to dioxin-like TEQ that results from PCDD/Fs, PCBs, PCNs and other similar halogenated aromatic compounds, and may be particularly significant for high level shellfish consumers. The exact magnitude of the contribution will remain indicative until toxicological studies provide a specific and comprehensive set of toxicity factors for PBDD/Fs including the contribution from the tri-brominated compounds.

The universal occurrence of PBDEs in these samples, characterised by the dominance of BDE-47, 99, 100 (and to a lesser extent BDE 209) in the congener profiles, demonstrates the persistence of these contaminants, as the use of the commercial formulations from which these congeners derive have seen a reduction in usage or have been banned for a few years. With the exception of 2,3,7-triBDD from oysters, estimated dietary intakes of brominated chemicals from shellfish were low compared to those from the rest of the diet. No change to the Food Standards Agency's advice on fish and shellfish consumption was needed on the basis of these results.

  mixtures such as Penta-and Octa-BDE since 2004, whilst deca-BDE remains under review until additional toxicological and occurrence data are finalised.

  Individual sub-samples of mussels, oysters and scallops were collected from coastal locations around Scotland between January and March 2006. Further samples of oysters, mussels and cockles from England, Wales and Northern Ireland (NI) were collected during the latter half of 2006 to early 2007. The individual samples were shucked and the flesh obtained from this process was representatively pooled and homogenised to yield 60 analytical samples -17 mussels, 19 oysters, 4 cockles and 10+10 scallops. As scallops are sometimes retailed as the adductor muscle alone,

  (Quasimeme 2004, Norwegian Institute of Public Health 2005, 2007).

  scheme. It should be recognised that this is an interim measure until a more rigorously evaluated set of TEF values specifically for the brominated dioxin/furan congeners become available(Food Standards Agency 2006B). The detection of brominated dioxins in foods, particularly fish and shellfish(Fernandes et al 2006B, Fernandes et al 2008) makes this a very necessary requirement, particularly as an obvious limitation of the approach used here is the exclusion of toxicity relating to the tribrominated congeners which have been shown to exhibit a similar mode of Ah receptor activity as the 2378-substituted dioxins and furans[START_REF] Mason G Zacharewski | PBDDs and related compounds: quantitative in-vivo and in-vitro structure activity relationships[END_REF]

Figure 1 :

 1 Figure 1: HRMS ion chromatograms for lower brominated PBDD/Fs in shellfish (Most co-eluting peaks fulfil acceptance criteria for positive identification as PBDD/Fs)

Table 1 :

 1 Occurrence levels of PBDEs and PBDD/Fs in shellfish (fresh weight basis) The sum of the 17, PBDEs and PBDD/F TEQ are presented as upper bound levels

						Sum of 17			PBDD/F
	Sample ID.	BDE-47 BDE-99 BDE-100 BDE-209	PBDEs 237-T3BDD 238-T3BDF	TEQ
		ng/g	ng/g	ng/g	ng/g	ng/g	pg/g	pg/g	pg/g
	Pacific Oysters (Crassostrea gigas)
	Shetland	0.25	0.32	0.12	0.02	0.89	0.11	0.23	0.035
	South Shian Bay	0.08	0.04	0.03	0.01	0.22	0.28	0.16	0.037
	Loch nan Ceol	0.05	0.04	0.02	0.01	0.17	0.13	0.11	0.031
	Loch Fyne	0.23	0.09	0.08	0.01	0.58	0.32	0.51	0.121
	Craigens	0.11	0.05	0.04	0.01	0.33	0.63	0.32	0.042
	Norfolk	0.79	0.29	0.25	< 0.03	2.13	0.74	0.34	0.028
	Northern Ireland	0.12	0.05	0.05	0.02	0.32	0.26	0.10	0.016
	Anglesey	0.21	0.09	0.10	0.05	0.72	0.52	0.14	0.025
	Exeter	0.29	0.10	0.08	< 0.03	0.68	0.36	0.07	0.014
	Dorset	0.04	0.02	0.01	0.01	0.12	0.09	0.01	0.011
	Northumberland	0.12	0.05	0.05	< 0.01	0.34	0.57	0.05	0.025
	Northern Ireland	0.12	0.07	0.04	0.07	0.39	0.10	0.17	0.016
	Cornwall	0.14	0.06	0.06	< 0.02	0.38	0.30	0.07	0.015
	Cornwall	0.45	0.18	0.15	< 0.03	1.02	0.23	0.11	0.013
	Native Oysters (Ostrea edulis)
	Dorset	0.06	0.03	0.02	0.02	0.17	6.59	0.01	0.047
	Portsmouth	0.32	0.11	0.13	0.01	0.78	14.53	0.05	0.065
	Cornwall	0.10	0.05	0.04	0.03	0.34	0.73	0.02	0.014
	Hampshire	0.04	0.02	0.02	< 0.01	0.12	5.30	0.03	0.033
	Kent	0.38	0.10	0.15	0.01	0.94	3.51	0.05	0.030
	Mussels (Mytilus edulis)
	Linga	0.37	0.21	0.11	0.04	0.84	0.13	0.08	0.028
	Troustan	1.22	0.53	0.33	0.09	2.74	0.04	0.40	0.064
	Loch Etive	0.11	0.09	0.05	0.04	0.37	0.11	0.26	0.118
	Whalwick	0.07	0.04	0.03	0.04	0.23	< 0.03	0.08	0.037
	Glenvig Bay	0.07	0.04	0.03	0.09	0.27	0.03	0.07	0.035
	Catfirth	0.06	0.03	0.02	0.04	0.18	< 0.04	0.06	0.037
	Wadbister	0.06	0.03	0.02	0.03	0.20	0.04	0.07	0.038
	Loch Leven	0.24	0.21	0.10	0.07	0.75	0.06	0.26	0.097
	Lismore	0.10	0.07	0.04	0.03	0.32	0.06	0.21	0.063
	Mussel composite	0.24	0.21	0.08	0.08	0.72	0.02	0.11	0.061
	Northern Ireland	0.17	0.07	0.06	0.03	0.44	0.10	0.07	0.013
	Anglesey	0.14	0.06	0.07	0.07	0.53	0.21	0.06	0.032
	Dorset	0.06	0.03	0.02	< 0.01	0.17	0.15	0.02	0.027
	Cumbria	0.87	0.55	0.14	0.23	2.13	0.02	0.26	0.030
	Cornwall	0.16	0.06	0.06	0.02	0.44	0.10	0.09	0.018
	Devon	0.12	0.04	0.04	0.02	0.32	0.04	0.09	0.025
	Belfast Lough	1.34	0.46	0.34	0.53	3.23	0.08	0.36	0.205

*

Table 1 (

 1 cont'd): Occurrence levels of PBDEs and PBDD/Fs in shellfish (fresh weight basis)

						Sum of 17			PBDD/F
	Sample ID.	BDE-47 BDE-99 BDE-100 BDE-209	PBDEs 237-T3BDD 238-T3BDF	TEQ
		ng/g	ng/g	ng/g	ng/g	ng/g	pg/g	pg/g	pg/g
	Scallops (Pecten maximus) Gonad
	Clyde 8	0.23	0.15	0.07	0.02	0.70	0.09	0.33	0.054
	South Minch 10/11	0.07	0.04	0.02	0.02	0.24	0.10	0.25	0.055
	Jura	0.08	0.06	0.03	0.02	0.31	0.15	0.35	0.233
	East Coast Scotland	0.23	0.14	0.11	0.03	0.73	0.12	0.35	0.178
	South Minch 1/2	0.06	0.04	0.02	0.02	0.24	0.14	0.25	0.056
	Orkney	0.03	0.02	0.01	0.01	0.13	0.09	0.10	0.047
	Clyde 5	0.39	0.32	0.11	0.04	1.25	0.11	0.44	0.061
	Outer Hebrides	0.03	0.02	0.01	0.01	0.12	0.28	0.11	0.040
	South Minch 3	0.08	0.06	0.03	0.03	0.31	0.11	0.23	0.040
	Moray Firth	0.05	0.03	0.02	0.03	0.20	0.10	0.12	0.068
	Scallops (

Pecten maximus) Adductor muscle

	F o r
		P
		e
			e r
	East Coast Scotland South Minch 1/2 South Minch 10/11 South Minch 3 Outer Hebrides Jura Orkney Moray Firth Clyde 5 Clyde 8	0.03 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.04 0.02	0.02 0.01 0.01 0.02 0.01 0.01 0.01 < 0.02 0.04 0.02	0.01 < 0.01 < 0.01 R 0.01 < 0.01 e < 0.01 < 0.01 0.01 < 0.03 0.02 0.02 0.01 0.02 0.02 0.01 0.01 0.01 < 0.03 v i e 0.01 < 0.02 w 0.11 0.07 0.06 0.08 0.05 0.06 0.04 0.08 0.16 0.10	< 0.01 0.01 0.02 0.01 0.04 0.02 < 0.01 0.02 0.01 0.01	0.03 0.02 0.03 0.03 0.02 0.03 0.01 0.02 0.05 0.03	0.041 0.039 0.033 0.033 0.035 0.032 0.040 0.024 0.024 0.022
	Cockles (Cerastoderma edule) Dorset 0.01 Cornwall 0.02 Kent 0.07 Swansea 0.04	0.01 0.02 0.06 0.04	< 0.01 0.01 0.04 0.02	0.02 < 0.02 0.13 0.22	0.06 0.09 0.40 0.37 O n 0.01 0.01 0.04 0.01 l	< 0.01 0.01 0.02 0.01	0.013 0.010 0.026 0.025
	* The sum of the 17, PBDEs and PBDD/F TEQ are presented as upper bound levels y

Table 2 :

 2 Summary of statistical parameters describing the occurrence of PBDEs and PBDD/Fs in shellfish TEQ calculated using analogous PCDD/F WHO-TEFs + Scallop gonad and (adductor) tissue analysed separately.

										Page 22 of 24
							Sum of 17			PBDD/F
			BDE-47 BDE-99 BDE-100 BDE-209	PBDEs 237-T3BDD 238-T3BDF	TEQ*
			ng/g	ng/g	ng/g	ng/g	ng/g	pg/g	pg/g	pg/g
		Mean	0.21	0.10	0.08	0.02	0.59	0.33	0.17	0.031
	Pacific	Median	0.13	0.07	0.06	0.02	0.39	0.29	0.13	0.025
	Oysters	Min	0.04	0.02	0.01	0.01	0.12	0.09	0.01	0.011
	n=14	Max	0.79	0.32	0.25	0.07	2.13	0.74	0.51	0.121
		Mean	0.18	0.06	0.07	0.01	0.47	6.13	0.03	0.038
	Native	Median	0.10	0.05	0.04	0.01	0.34	5.30	0.03	0.033
	oysters	Min	0.04	0.02	0.02	<0.01	0.12	0.73	0.01	0.014
	n=5	Max	0.38	0.11	0.15	0.03	0.94	14.53	0.05	0.065
		Mean	0.32	0.16	0.09	0.09	0.82	0.07	0.15	0.055
	Mussels	Median Min	0.14 0.06	0.07 0.03	0.06 0.02	0.04 0.01	0.44 0.17	0.06 0.02	0.09 0.02	0.037 0.013
	n=17	Max	1.34	0.55	0.34	0.53	3.23	0.21	0.40	0.205
	Scallops +	Mean	0.12 (0.02)	0.09 (0.02)	0.05 (0.01)	0.02 (0.02)	0.42 (0.08)	0.13 (0.02)	0.25 (0.03)	0.083 (0.032)
	Gonad (adductor)	Median	0.07 (0.01)	0.05 (0.01)	0.03 (0.01)	0.02 (0.02)	0.27 (0.07)	0.11 (0.01)	0.25 (0.03)	0.056 (0.033)
	n=10	Min	0.03 (0.01)	0.02 (0.01)	0.01 (<0.01)	0.01 (0.01)	0.12 (0.04)	0.09 (0.01)	0.10 (0.01)	0.04 (0.022)
		Max	0.39 (0.04)	0.32 (0.04)	0.11 (0.01)	0.04 (0.03)	1.25 (0.16)	0.28 (0.04)	0.44 (0.05)	0.233 (0.041)
		Mean	0.04	0.03	0.02	0.10	0.23	0.02	0.01	0.018
	Cockles Median	0.03	0.03	0.02	0.07	0.23	0.01	0.01	0.019
	n=4	Min	0.01	0.01	<0.01	0.02	0.06	0.01	<0.01	0.010
		Max	0.07	0.06	0.04	0.22	0.40	0.04	0.02	0.026

* http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk Food Additives and Contaminants F o r P e e r R e v i e w O n l y

Table 3 Estimated intakes of PBDD/Fs and dioxin-like PBBs

 3

	Brominated dioxins + dioxin-like PBBs,
	pg	5 TEQ/kg BW/day

Minutes

Page 24 of 24 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Food Additives and Contaminants

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

Consumer Products and the Environment. Statement on organic chlorinated and brominated contaminants in shellfish, farmed and wild fish, available at http://www.food.gov.uk/multimedia/pdfs/cotstatementfishsurveys.pdf .

Food


Annalisa De Girolamo
email: annalisa.degirolamo@ispa.cnr.it

Vincenzo Lippolis

Erik Nordkvist

& A Visconti

by Fourier-Transform Infrared (FT-NIR) spectroscopy

Keywords: FT-NIR spectroscopy, deoxynivalenol, wheat, PLS regression, discriminant analysis

come

Rapid and non invasive analysis of deoxynivalenol in durum and common wheat by Fourier-Transform Infrared (FT-NIR) spectroscopy

Introduction

Deoxynivalenol (DON) is a type B trichothecene mycotoxin produced by fungi of the Fusarium genus, in particular Fusarium graminearum (Gibberella zea) and Fusarium culmorum. It is frequently associated with contaminated cereal crops such as wheat, maize, barley, oats, rye and less often with rice, sorghum and triticale [START_REF] Canady | Deoxynivalenol. In: Safety evaluation of certain mycotoxins in food. Fifty-sixth Meeting of the Joint FAO/WHO Expert Committee on Food Additives (JECFA)[END_REF][START_REF] Schothorst | Report from Scoop task 3.2.10. Collection of occurrence data of Fusarium toxins in food and assessment of dietary intake by the population of EU members states. Subtask: trichothecenes[END_REF]. DON inhibits the synthesis of DNA, RNA and proteins, and has a hemolytic effect on erythrocytes. In animals, acute/subacute oral toxicity of DON is characterized by vomiting, feed refusal, weight loss and diarrhea [START_REF] Schlatter | Toxicity data relevant for hazard characterization[END_REF]).

Based on the temporary tolerable daily intake (TDI) of 1 µg/kg body weight established by the EU Scientific Committee on Food (SCF) (SCF 2002), the European Commission has set maximum permitted levels for DON in a variety of cereal based foods (European Commission 2007). In particular, 1750 µg kg -1 and 1250 µg kg -1 were set as maximum levels for unprocessed durum and common wheat, respectively; lower levels were set for wheat flour, bran, germ and pasta (750 µg kg -1 ), for wheat and wheat products for direct human consumption (750 µg kg -1 and 500 µg kg -1 ), and for wheat-based infants and baby food (200 µg kg -1 ). Rapid and sensitive methods for the quantification of DON in wheat and wheat-based food products are strongly demanded by private and public quality control laboratories, as well as in surveys aiming to provide data for risk assessments. Several methods based on competitive enzyme-linked immunosorbent assays (ELISA) have been developed for the rapid screening and quantification of DON in cereals. ELISA methods are sensitive, rapid and easy-to-use, but they often show strong cross-reactivity against DON acetyl derivatives, require long incubation times for complete antigen-antibody reaction and commercial kits are still rather expensive. A fluorescence polarization (FP) immunoassay has been recently developed for the rapid and quantitative analysis of DON in durum wheat, semolina, and pasta at levels below the EU maximum permitted levels [START_REF] Lippolis | Optimization of a fluorescence polarization immunoassay for rapid quantification of deoxynivalenol in durum wheat-based products[END_REF].

Although these methods can be used for screening purposes, they are destructive and require preparation and extraction steps. In the past few decades, many researchers have focused on the potential use of infrared spectroscopy in the analysis of food and feed, both in the mid-and near-infrared regions. Infrared spectroscopy (IR) is a rapid analytical technique requiring very little labour, once a calibration is obtained, and no sample extraction and, therefore, does not create chemical waste. Petterson and Aberg (2003) used near infrared (NIR) transmittance for the determination of DON in whole wheat kernel samples at levels higher than 500 µg kg -1 , whereas NIR reflectance has been used for detection of scab and estimation of DON and ergosterol in single kernels of highly infected wheat [START_REF] Dowell | Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy[END_REF]. A method for the analysis of Fusarium fungi on corn using Fourier transform-mid infrared (FT-MIR) spectroscopy with attenuated total reflection (ATR) was developed which enabled the segregation of DON contaminated corn samples from non contaminated (blank) samples (Kos et al. 2003[START_REF] Kos | A comparative study of mid-infrared diffuse reflection (DR) and attenuated total reflection (ATR) spectroscopy for the detection of fungal infection on RWA2-corn[END_REF][START_REF] Kos | Optimisation of a sample preparation procedure for the screening of fungal infection and assessment of deoxynivalenol content in maize using mid-infrared attenuated total reflection spectroscopy[END_REF][START_REF] Delwiche | Detection of scab-damaged hard red spring wheat kernels by near-infrared reflectance[END_REF].

Papers on the application of FT-IR spectroscopy for a determination of DON content in grain mostly exploit the range below 4000 cm -1 . In particular, [START_REF] Mossoba | Analysis of trichothecene mycotoxins in contaminated grains by gas chromatography/matrix isolation/Fourier transform infrared spectroscopy and gas chromatography/mass spectrometry[END_REF] reported DON characteristic absorption bands between 1244 and 1750 cm -1 . Some additional bands at ca. 3500 and close to 3650 cm -1 [START_REF] Young | Analysis of Fusarium mycotoxins by gas chromatography-Fourier transform infrared spectroscopy[END_REF] and not far from 1700 cm -1 (Abramovic et al. 2007) have been reported. The most recent theoretical calculations of IR bands cover the MIR range below 4000 cm -1 [START_REF] Turker | A theoretical study on vomitoxin and its tautomers[END_REF], also used by [START_REF] Kos | Optimisation of a sample preparation procedure for the screening of fungal infection and assessment of deoxynivalenol content in maize using mid-infrared attenuated total reflection spectroscopy[END_REF] for corn analysis. Therefore the identification of any spectral features of DON within the NIR range is highly desirable. FT-IR spectroscopy allows to perform rapid measurements with an excellent signal-to-noise ratio and high resolution.

The aim of the present work was to investigate the feasibility of using FT-NIR for the rapid and non-invasive determination of DON in durum and common wheat. Cyclotec 1093 (International PBI, Hoganas, Sweden) laboratory mill equipped with a 500 µm sieve, and were analyzed by HPLC and by FT-NIR spectroscopy. DON levels, as established by HPLC analysis, ranged from 50 to 2600 µg kg -1 in durum wheat and from 50 to 3000 µg kg -1 in common wheat. Samples containing less than 50 µg kg -1 DON (the quantification limit of the HPLC-method) were considered as DON-free. Mean recoveries of DON from durum and common wheat samples (n = 3) spiked at levels of 200, 800 and 1500 µg kg -1 were 75-93% with RSD r of 1.4-14.2% for durum wheat and 79-83% with RSD r of 1.1-12.2% for common wheat. To investigate the distribution of DON during sieving, five durum wheat samples were ground by a Bühler MLI 204 (Bühler S.p.A., Milan, Italy) to pass a 1-mm sieve and analyzed by HPLC for DON. A fraction of these samples was sieved to pass a 300 µm sieve, and the two fractions (below and above 300 µm) were analyzed by HPLC.

Materials and methods

Sample

HPLC quantitative analysis. HPLC quantitative analysis was performed to obtain the reference value of the DON content for all wheat samples tested herein. Wheat samples were analyzed according to the procedure described by Visconti et al. (2004) with minor modifications. Briefly, 25 g of ground wheat was extracted with 100 mL of phosphate buffer solution (PBS, 10 mM sodium phosphate, 0.85% sodium chloride, pH 7.4) by blending at high speed for 2 min with a Sorvall Omnimixer (Sorvall Instruments, Norwalk, Conn.). The extract was filtered through both filter paper (Whatman no. 4) and glass microfiber filter (Whatman GF/A) and 2 mL of the filtered extract were cleaned up by DONTest TM (Vicam, Watertown, MA, USA). After washing the column by passing 5 mL water through it, DON was eluted with 1.5 mL methanol. The eluate was dried under nitrogen stream at 50°C, re-dissolved in 200 µL of mobile phase (acetonitrile:water, 10:90, v/v) and 50 µL were injected onto an LC system with ultraviolet diode array detector set to 220 nm.

FT-NIR analysis. FT-NIR spectra were recorded using an Antaris II FT-NIR spectrophotometer (Thermo Electron Corporation, Madison, WI, USA) equipped with an interferometer (containing a fixed and a moving mirror, a beam-splitter), an integrating sphere working in diffuse reflection, and an indium and gallium arsenide (InGaAs) detector. A sample cup spinner allowed the automatic collection of several subsamples from each sample. These subsamples were averaged to obtain representative spectra of relatively heterogeneous samples. The integrating sphere's internal reference was also used to collect the background spectrum. The moving mirror speed was set at 1.63 scans/sec. Approximately 30 g of ground wheat samples were placed into the rotating sample cup spinner with a quartz cup. Spectroscopic data were recorded as absorbance between 10000-4000 cm -1 with 8 cm -1 resolution to give a total of 750 points per sample.

Each sample spectrum, consisting of an average of 128 consecutive interferometer scans, was collected in about 1 min 20 s and stored in a separate data file.

FT-NIR transmission spectra of DON were obtained using the Transmission module (Thermo Electron Corporation) consisting of a sample holder to allocate glass tubes.

Approximately 1000 µl of solution were transferred into glass culture tubes (6 x 50 mm). Spectroscopic data were recorded as absorbance between 10000-4000 cm -1 with 8 cm -1 Commercially available crystalline DON standard (Sigma, Milan, Italy) was dissolved in acetonitrile to prepare a 1 mg ml -1 standard solution. FT-NIR transmission spectrum of DON was determined after subtraction of the mean spectrum (n = 10) of acetonitrile from the mean spectrum (n = 10) of DON standard solution. Furthermore, two sub-samples of a DON-free durum wheat sample (< 50 µg kg -1 ) were spiked with acetonitrile and with the same volume of DON standard solution (spiking level 10000 µg kg -1 ), respectively.

The FT-NIR spectrum was obtained after subtraction of the spectrum of acetonitrile spiked wheat from that of DON spiked wheat. Similarly, another FT-NIR spectrum was obtained after subtraction of a DON-free durum wheat spectrum from that of a naturally contaminated one (13000 µg kg -1 DON).

Multivariate data analysis. Based on DON content (as determined by HPLC analysis)

wheat samples were distributed into classes each covering 100 µg kg -1 DON. Calibration and validation samples were randomly selected from each class by considering an approximate ratio of 2.5:1 between calibration and validation samples. Spectroscopic data were processed by TQ Analyst ® professional edition (Thermo Electron Corporation). The Spectrum Outlier diagnostic (TQ Analyst ® ) was run to seek sample spectra that were the most different from the other samples. In particular, it was calculated the mean spectrum for all calibration samples, measured the distance between the mean spectrum and the spectrum of each calibration sample. The Chauvenet Test estimated whether the difference between spectra was significant. Once the initial population was free of spectral outliers calibration models were developed and statistically elaborated for quantitative analysis using PLS regression with full cross validation (leave one out).

DON data obtained by FT-NIR were transformed using Standard Normal Variate (SNV) [START_REF] Barnes | Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra[END_REF], first derivative, mean centering and Savitzky-Golay smoothing (2 ndorder polynomial filtering operation performed on 3 points) [START_REF] Savitzky | Smoothing and differentiation of data by simplified least squares procedures[END_REF], and correlated with those obtained by HPLC.

The Partial Least Squares (PLS) models performance were evaluated by calculating the correlation coefficient (r) between the reference (HPLC data) and the predicted (FT-NIR data) DON levels, the root mean square error of calibration (RMSEC), the root mean square error of cross-validation (RMSECV) and the number of PLS factors. External data sets (validation sets) were also used to evaluate the performance of the models for prediction, and the root mean square error of prediction (RMSEP) values were calculated.

Validation sets were selected to cover the range of DON-content of interest. RMSEC, RMSECV and RMSEP were expressed by the same DON level units (µg kg -1 ) in wheat samples. In the development of all calibration models, 10 PLS factors were set up as a maximum number to work with. The optimum number of PLS factors was automatically calculated by the TQ Analyst ® software corresponding to the lowest number of factors giving the closest to minimum value of the prediction residual error sum of squares (PRESS). As the calculated number of PLS factors gave models that strictly dependent on calibration samples, an appropriate number was chosen giving comparable RMSEC, RMSECV and RMSEP values. The accuracy of each model was evaluated based on the coefficient of determination (r 2 ) for predicted versus reference DON levels in calibration and validation sets. It was assumed that with r 2 = 0.50-0.65 discrimination between high and low values was possible, r 2 = 0.66-0.81 allowed approximate quantitative prediction, r 2 = 0.82-0.90 allowed good prediction, and excellent calibration model were obtained with r 2 = 0.91 or higher (Williams 2003). The prediction accuracy of models was also evaluated based on the residual predictive deviation (RPD), defined as the ratio of the standard deviation of the reference DON values in the validation set to the RMSEP. In general, RPD values of 3.1-4.9 indicate NIR calibration models suitable for screening purposes, whereas values of 5.0-6.4 are considered adequate for quality control [START_REF] Fearn | Assessing calibrations: SEP, RPD, RER and R 2 . NIR news 13:12-14. Fusarium fungi on corn using mid-infrared spectroscopy with attenuated total reflection and chemometrics[END_REF]. However, a more qualitative interpretation of the RPD considers values lower than 1.5 insufficient for most applications and values greater than 2 as excellent (Williams 2001; [START_REF] Smyth | Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: possibilities and limits[END_REF]).

The range error ratio (RER), calculated by dividing the range of the reference DON values in the validation set by the RMSEP, was used as a useful indicator to assess the practical utility of the calibration as a predictive model. Models with RER of less than 3 had a little practical utility; between 3 and 10 had limited to good practical utility; and above 10 indicated a high practical utility value [START_REF] Fearn | Assessing calibrations: SEP, RPD, RER and R 2 . NIR news 13:12-14. Fusarium fungi on corn using mid-infrared spectroscopy with attenuated total reflection and chemometrics[END_REF]Williams 2003).

Qualitative analysis of different wheat cultivars. The ability of FT-NIR spectroscopy to discriminate between blank and naturally contaminated samples of different cultivars of both durum and common wheat was evaluated by Discriminant Analysis. Based on the DON content as determined by HPLC analysis, samples were split into two classes, one including samples with less than 300 µg kg -1 DON (assumed as "blank") and the other one including naturally contaminated (from 300 µg kg -1 up to about 3000 µg kg -1 DON) wheat samples. Validation samples were randomly selected to cover the range of interest (up to 2000 µg kg -1 ). Calibration and validation sets contained samples in the approximate ratio of 3:1. All spectroscopic data were transformed using variance scaling normalization, Standard Normal Variate (SNV) [START_REF] Barnes | Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra[END_REF], first derivative, mean centering and Savitzky-Golay smoothing (2 nd -order polynomial filtering operation performed on 5 points) [START_REF] Savitzky | Smoothing and differentiation of data by simplified least squares procedures[END_REF].

Discriminant Analysis was performed to create the distribution model by using the spectral information from all calibration samples. In particular, during calibration, the software computed an average spectrum and then generated a distribution model by estimating the variance at each frequency in the analysis range. When the calibrated method was used to analyze validation samples, the software performed a Principal Component Analysis (PCA) on the validation sample spectra and on the variance spectra to determine score values. The scores were used to produce Mahalanobis distance values, which in turn were used to rank the classes. Discriminant Analysis assigned the ranking class based on the level of similarity with the spectrum of the validation sample. The Mahalanobis distance between the validation sample and each class center was also recorded.

Results and discussion

Effect of sample preparation on DON analysis by FT-NIR analysis. Kos et al. (2003;2007) reported that the repeatability of spectral measurements performed on ground As an initial approach, ground wheat samples were sieved to pass a 300 µm sieve and analysed by FT-NIR spectroscopy. An alternative sample preparation was used to avoid the time-consuming sieving step (20-30 min if carried out after grinding). In particular, wheat samples were milled with a Tecator Cyclotec laboratory mill (Tecator Cyclotec) equipped with a 500 µm sieve and the obtained fraction was directly analysed by FT-NIR spectroscopy. Spectra for 50 repetitive measurements of a sieved sample were compared to those of an unsieved sample of the same wheat variety and with a comparable DONcontent.

In order to statistically evaluate spectra differences between wheat samples with particle size < 300 µm and < 500 µm, the analysis of variance (ANOVA, one way) was applied to the two groups of repetitive measurements. No significant difference was found within each group, while a significant difference (F = 29.62, F critic = 3.84) was observed between the mean spectra of the two groups due to the different distribution of particle sizes and relevant chemical composition. The PCA score/score plot revealed that the cluster containing samples with particle size < 300 µm was more scattered than the cluster containing samples with particle size < 500 µm, showing a more homogeneous distribution of particle size in the unsieved sample (Figure 1). Furthermore, the HPLC analysis of different fractions obtained after sieving at 300 µm of five different wheat samples showed a clear segregation of DON between fractions with respect to the original ground wheat (Table I). In particular, mean DON levels in the samples dropped by 84% in wheat fractions with particle size > 300 µm, and accumulated by 115% in fractions with particle size < 300 µm.

[Insert Table I about here]

Consequently, the sample preparation approach performed by using a Tecator Cyclotec milling at < 500 µm was preferred in order to avoid both the time-consuming sieving step, and the erroneous DON determination due to the different distribution in the sieved fractions.

DON absorption in the NIR region. The major FT-NIR absorption bands of DON dissolved in acetonitrile (Figure 2, line a) were identified in the range of 7400-7100 cm -1 with a peak at 7095 cm -1 , and between 5000 and 5500 cm -1 with a peak at 5251 cm -1 . The absorption band at 5251 cm -1 was much stronger than that at 7095 cm -1 . The spectrum obtained after subtraction of the spectrum of acetonitrile spiked wheat from that of DON spiked wheat showed absorption bands at about 7000 and 5250 cm -1 (Figure 2 [Insert Figure 2 about here] DON levels in wheat are generally several orders of magnitude lower than levels of the main wheat constituents (protein, starch, etc.), and spectra recorded for contaminated and uncontaminated wheat may differ one from another more because of difference in chemical composition (due to development of the fungus) than because of DON content. Thus, subtraction of the spectra can produce spurious features not necessarily related to DON content due to the relatively low DON levels commonly occurring in wheat.

However, the use of highly contaminated wheat (at levels higher than 10000 µg kg -1 ) produced subtractive spectra with appreciable FT-NIR spectral bands (particularly at 5251 cm -1 ) related to DON as shown in Figure 3. The characteristic absorption bands of DON could not be identified in wheat samples contaminated at lower levels, such as those used herein (up to 3000 µg kg -1 ) for the three PLS regression models. Nevertheless, a certain discrimination between low and high DON levels was obtained by considering the entire spectral range between 10000 and 4000 cm -1 (see below).

DON analysis by FT-NIR spectroscopy. Three PLS regression models were built by considering the spectral range between 10000 and 4000 cm -1 for durum, common and Comparing the mean DON levels (ranging from 439 to 887 µg kg -1 ) with median levels (from 188 to 656 µg kg -1 ) for durum or common wheat samples, a very skewed data distribution was observed, with many small values and fewer large ones. After combination of the two wheat species (durum+common wheat) a better distribution of DON level was obtained and the resulting mean (585-750 µg kg -1 ) and median (395-523 µg kg -1 ) DON levels were closer.

[Insert Table II about here] Validation sets of common and durum wheat were selected, both consisting of 30 samples. The two PLS models were validated up to about 2000 µg kg -1 DON to cover the range of regulatory levels set by the EU for unprocessed wheat and most wheat based products. The selection was performed based on DON content (measured by HPLC) in order to have as many samples as possible of the validation set within the range of DON content to be considered for the calibration (Table II).

Running the PLS analysis on the calibration sets a positive linear correlation (r > 0.90) was obtained in the measured range for both models, indicating a god fit between HPLC and FT-NIR data. According to the coefficients of determination (r 2 ) and slope values, both > 0.82, a good prediction of DON content by FT-NIR spectroscopy was achieved in both PLS calibration models (Figure 3a and3b). Furthermore, RMSECV values were 470 and 516 µg kg -1 respectively, both corresponding to 4 PLS factors (Table III). The performance of these models for DON prediction on the validation sets gave RMSEP values of 306 µg kg -1 for durum wheat and 348 µg kg -1 for common wheat. These values were not too far from RMSECV values obtained in cross-validation. The r 2 values of the validation sets (0.62-0.63) indicated that the PLS models were able to discriminate between low and high DON levels in durum and common wheat. An improvement of RMSECV and r 2 values was obtained by using the recommended number of PLS factors (5-6), however the obtained PLS models quantified the calibration samples with great accuracy but did not produce the same level of accuracy when applied to validation samples and gave RMSEP and RMSECV values that were too far from RMSEC. Based on these findings, it was decided to run the calibrations with a low number of factors in order to obtain comparable values of RMSEC, RMSEP and RMSECV.

[Insert Table III about here]

In addition to the RMSECV, r 2 and RMSEP, the PLS models were evaluated using the model to discriminate between high and low contaminated samples. However, it is evident that a further improvement of RPD values, and then more accurate PLS models, could be obtained by increasing the range and the distribution of DON content in the validation samples. On the other hand, RER values lie within 6 and 7, indicating a good practical utility of the models for DON prediction in wheat samples (Table III).

Results obtained in the present study were similar to those reported by Pettersson and Aberg (2003) in terms of correlation coefficient, slope and RMSEP. However, a remarkable improvement was obtained with the PLS regression models used herein for prediction of DON in durum and common wheat because calibrations were made on a larger number of calibration and validation samples. Furthermore DON contamination levels used herein were close to the DON maximum limits set by the European Union for unprocessed durum (1750 µg kg -1 ) and common (1250 µg kg -1 ) wheat, whereas Pettersson and Aberg (2003) calibrated the models at higher and less realistic levels (up to 10000-15000 µg kg -1 ).

An additional PLS calibration model was built by combining durum and common wheat samples to evaluate the possibility to include different wheat species belonging to different varieties in the same model. Also in this case a good fit of the PLS model (r = 0.84) was obtained (Figure 3, Table III). The r 2 values indicated that the model approximated quantitative predictions of DON in the calibration set (r 2 = 0.71) and allowed for discrimination between high and low DON levels in the validation set (r 2 = 0.58) (Table III). These results were also confirmed by the RER value (6.3), while the RPD value (1.0) failed to attain the minimum recommended for quantitative analysis. Although results obtained in the present study are not fully satisfying for quantitative or semi-quantitative analysis, they provide a good evidence of the feasibility of using FT-NIR spectroscopy as a screening tool for determination of DON in wheat samples. Furthermore, considering that different wheat species (durum and common) and wheat varieties contain different amount of chemical constituents, such as protein, lipids and moisture with remarkable spectral differences [START_REF] Cocchi | Durum wheat adulteration detection by NIR spectroscopy multivariate calibration[END_REF][START_REF] Morris | Kernel texture differences among US soft wheat cultivars[END_REF], our findings indicate that this variability does not affect the applicability of FT-NIR for DON analysis FT-NIR qualitative analysis of different wheat cultivars. The ability of FT-NIR spectroscopy to discriminate between blank and naturally contaminated durum and common wheat samples of different cultivars was evaluated by performing a discriminant analysis on 197 calibration samples. In particular, depending on DON content calculated by HPLC analysis, samples were split into two classes, one including 84 "blank" samples (up to 300 µg kg -1 DON) and the other one including 113 "contaminated" samples (> 300 µg kg -1 DON). The samples were classified as members of each group based on the lowest corresponding Mahalanobis distance, i.e. the multi-dimensional space whose boundaries determine the range of variation.

[Insert Figure 4 about here] Figure 4 shows graphically the Mahalanobis distance between each sample and the "blank" and "contaminated" classes that were selected for the X-and Y-axis of the plot, respectively. The boundaries, displayed as perpendicular lines in the plot, show the 95% confidence intervals for the two classes. All calibration and validation samples relevant to the "blank" class are expected to be clustered in the upper left corner of the plot, while samples falling in the lower right corner define the "contaminated" class. As observed in Figure 4, a few calibration samples of the two classes fall in the uncertainty area (lower left corner, 40 out of 197). Most of them were "blank" (16%) or contained DON levels between 300 and 500 µg kg -1 (30%), indicating that the model had a limited degree of specificity for wheat samples contaminated with DON levels close to the cut-off limit.

When the obtained model was applied to a set of 65 validation samples, it correctly classified 69% of samples as "blank" or "contaminated" wheat. The remaining 20 samples were incorrectly classified as false positive or negative sample. Also in this case, most of misclassified samples were "blank" (55%) or contained DON levels ranging from 300 to 400 µg kg -1 (25%), confirming that the goodness of the discrimination model was greatly influenced by the low DON cut-off limit fixed to distinguish the two classes.

Lower cut-off levels, i.e. 50 µg kg -1 (the quantification limit of the HPLC method), 100 and 200 µg kg -1 , led to lesser precise models, while higher cut-off levels gave poorly reliable models due to problems associated with the relatively limited number of samples with high DON levels. However, these results still recommend the use of FT-NIR as a sorting tool for screening purpose, since the legal limit at present is well over 300 µg kg -1 for most commodities (with the important exception of baby food where the limit is 200 µg kg -1 ). An improvement to this qualitative model could be obtained by increasing the number of samples, and in particular, by increasing the number of high DON contaminated samples.

  preparation. A total of 262 wheat (143 durum and 119 common) samples, belonging to 32 different varieties, were obtained from wheat naturally infected by Fusarium graminearum and Fusarium culmorum in experimental fields in Northern Italy from 2002 to 2006. Wheat samples were ground with a Tecator

  sample spectrum consisted of an average of 16 consecutive interferometer scans.

  size < 1 mm) was not good enough for attempting classification by MIR-ATR of DON contaminated maize, and that a sieving procedure (selecting particles with diameter between 250 and 100 µm) improved the repeatability of spectral measurements from 20% to 4.4%.

  , line b) corresponding to those of DON spectrum in acetonitrile. The increased intensity of the cm -1 was due to an overlapping of DON and water absorption bands. The same absorption bands were observed in the FT-NIR spectrum obtained after subtraction of a DON-free wheat spectrum from that of a naturally contaminated one (Figure2, line c).

  , respectively. TableIIsummarizes sample statistics for the calibration and validation set of durum, common and durum+common wheat.

  RER and RPD values. Both the RER and RPD standardize the RMSEP value of the model against the range and the standard deviation of the reference data in the validation set. RPD values obtained for durum and common wheat (1.6-2.0) show the ability of the

Figure 1 .

 1 Figure1. Score/score plot of spectral data (PC1 vs PC2) from 50 repetitive measurements of two wheat samples with different particle sizes. The two clusters represent wheat samples with particle size < 500 µm (left, unsieved) and < 300 µm (right, sieved).

Figure 2 .

 2 Figure2. FT-NIR subtractive spectra of deoxynivalenol in acetonitrile spectrum from that of acetonitrile (line a), of acetonitrile spiked wheat spectrum from that of DON spiked (10000 µg kg -1 ) wheat (line b) and of deoxynivalenol-free wheat spectrum from that of naturally contaminated (13000 µg kg -1 ) wheat.

Figure 3 .

 3 Figure 3. PLS regression models of deoxynivalenol content determined by FT-NIR and the reference method (HPLC) of durum (3 a), common (3 b) and durum+common (3 c) wheat samples.

Figure 4 .

 4 Figure 4. Pairwise distance plot reporting the Mahalanobis distance between each sample and the "Blank" and "Contaminated" classes.

  Figure 4. 1

Table I .

 I Deoxynivalenol content in ground durum wheat prior and after sieving (300 µm sieve).

			Sieved,		Sieved,
	Wheat sample	Unsieved		Recovery	Recovery
		< 300 µm		> 300 µm
	(#)	(µg/kg)		(%) *		(%) *
			(µg/kg)		(µg/kg)
	#1 #2 #3 F o 813 1466 819 r	995 1619 886	122 110 108	577 1226 696	71 84 85
	#4	776 P	846	109	751	97
	#5	1277 e	1588	124	1085	85
	Mean ± SD	e r	115 ± 8		84 ± 9
			R
			e
			v i e
				w
				O n l
					y

*Percentage of deoxynivalenol in the sieved fraction relative to the original (unsieved) wheat sample.

Table II .

 II Statistical summary of deoxynivalenol content by HPLC in the calibration and validation sets of durum, common and durum+common wheat with particle size < 500 µm.

	Page 31 of 33			Food Additives and Contaminants
		Durum Common Durum+common Durum Common Durum+common
				Calibration set			Validation set
	N. of samples Range (µg/kg) F 0-2600 76 o r	77 0-3000		149 0-3000	30 60-1990 0-2290 30	48 0-2400
		P
	Median (µg/kg)	188	656 e		523	245	530	395
	Mean (µg/kg)	439		887 e r	750	459	658	585
	SD* (µg/kg)	590		720	722 R e	470	681	533
	*SD = standard deviation				v i e
							w
							O n l
								y

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Table III .

 III Calibration and validation results for deoxynivalenol detection by FT-NIR in durum, common and durum+common wheat with particle size < 500 µm.

	Page 33 of 33
	Page 1: [1] Deleted		DeGirolamo	11/27/2008 6:46:00 PM
	RMSEC (µg/kg)		240		303	386
	Parameters*	Durum	Common	Durum+common
	Slope calibration	0.83		0.82	0.71
	r calibration	0.91		0.91	0.84
	r 2 Slope validation calibration r validation F o r 0.83 0.79 0.80 r 2 0.62 validation RMSEP (µg/kg) 306 P		0.82 0.62 0.79 0.63 348	0.71 0.71 0.76 0.58 379
	RMSECV (µg/kg) PLS factors	470 4 e e r	516 4	555 5
	RER	6.3		6.6	6.3
	RPD	1.6	2.0 R e v i e w 1.0	Deleted: RMSEC, root mean square error of calibration set;
					O n
					l y

* r 2 determination coefficient; RMSECV, root mean square error of cross-validation; RMSEP, root mean square error of prediction set; PLS, partial least squares; RER, ratio error range; RPD, residual predictive deviation.

Deleted: RMSEC (µg/kg) http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Food Additives and Contaminants

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

Acknowledgments

We are grateful to Dr. Tom Fearn, Department of Statistical Science, University College London, UK, for his advices on statistical elaboration of FT-NIR data. This work was supported in part by the National Research Council, Italy, the Short-Term Mobility Program 2007.