The estimation of exposure to food packaging materials. 1

Introduction

At present, the assessment of exposure to migrants from food contact materials in the EU is based on a crude method. This crude and conservative method assumes that 1kg of food packaged in 6 dm 2 of the material of interest is consumed per individual each day over a lifetime. It also assumes that migration occurs at the maximum permitted level. More refined methods are available for the estimation of exposure to other food chemicals such as the international estimated daily intake (IEDI) for pesticide residues [START_REF] Who | Guidelines for predicting dietary intake of pesticide residues. Prepared by the Global Environment Monitoring System-Food Contamination Monitoring and Assessment Programme (GEMS/Food) in collaboration with Codex Committee on Pesticide Residues[END_REF] or the estimated daily intake (EDI) for food additives (FAO/WHO ). In addition to differences in exposure assessment methods, the regulation of food packaging materials is different to that of other food chemicals. For example, the use of a food additive is permitted only in specific foods and the maximum level of use in each food is governed by legislation. In contrast, once a chemical is permitted for use in plastic packaging, it can be used in any plastic packaging, which in turn can be used for any food once safety and migration limits are not exceeded.

It is recognized that more refined exposure estimates are needed in the EU for migrants from food packaging material (ILSI 1996[START_REF] Svensson | Exposure from food contact materials[END_REF][START_REF] Gibney | Realistic estimates of migrants from food packaging materials[END_REF]).

One of the key elements needed to refine the exposure assessment of food packaging material migrants is the capacity to link the use of a specific food packaging type to the consumption of a specific food item. This information permits the likelihood that a chemical migrant is present in a food to be assessed. One of the best ways to gather comprehensive packaging data on foods consumed by the population is to collect packaging information as part of a national food consumption survey. This aspect of data collection has been an objective of national food surveys carried out in Ireland over the past number of years. To provide accurate information on the types of packaging used for foods, an Irish Food Packaging Database (IFPD) was constructed in conjunction with the National Children's Food Survey (NCFS), which was conducted between [2003][2004]. This food survey collected detailed consumption information that is, by and large, consistent with the level of detail recently proposed by the European Food Safety Authority (EFSA) for food chemical exposure assessment (EFSA 2005). The aim of the food packaging database was to store information on type of packaging used for different foods so that data from this database could be combined with food consumption data to determine consumption factors for the different types of packaging used. The collection of food packaging information is continuing in Ireland with the collection of packaging materials as part of the National Teen Food Survey, which commenced in 2005 and which will gather such data on 13-17 year olds (www.iuna.net).

The overall aim of this data collection is to enhance the estimation of exposure to food packaging migrants in the population, however this process has a number of different phases. The first is the collection of information on the exact packaging type (e.g. rigid plastic bottle) and contact layers used for foods. The second is to link this information with actual consumption of these foods. The third is the estimation of the level of chemical migrants, from the food packaging, in the food. The present paper describes the first phase of this process, that is, the collection of information on the packaging used for different foods. Future work will describe the consumption level of packaged food and the application of this data to complete exposure assessments to packaging migrants.

Methodology

Data collection for the Irish Food Packaging Database

Food packaging information was collected as part of the NCFS conducted in Ireland.

The NCFS collected information on 594 Irish children aged between 5-12 years. This study was a 7-day food intake survey that collected quantitative food intake data, physical activity measurements, lifestyle information, attitudes, and packaging information (e.g. unpackaged or the type of packaging used). The subjects recorded the type (e.g. strawberry yoghurt), brand (e.g. Mr. X strawberry yoghurt) and amount of food consumed and also the type of packaging material used and the unit weight of the food product. This data collection included packaging information on ingredients used in homemade recipes. All of the packaging collected in the course of the study was forwarded to the coordinating centre for analysis. The correct contact layer i.e. plastic, glass, metal and alloys, paper and board or a combination of these was identified for entry into the packaging database. Where packaging information was incomplete in the survey diaries or where the packaging material was not collected, data were sought from supermarket audits, online shopping facilities and, where necessary, purchase of the food. The latter was undertaken only when (a) the brand was consumed at least nine times in the survey and (b) there were no data on the packaging material used.

Construction of the Irish Food Packaging Database

The For each food packaging item entered into the IFPD, information was recorded on a number of different parameters that described the product type and the packaging type (Table 1). There were two grouping systems used in the database to classify entries.

The first, the food packaging group, was used to group similar food types that had similar packaging types, for example, "bread (brittle clear plastic)", "bread (paper)" and "bread (soft clear plastic)". This grouping scheme was designed to aid identification of the contact layer and to ensure representative samples were sent for further analysis for exact contact layer identification. These groups were also used at the start of data collection to distinguish and describe the packaging material prior to more precise identification. Packaging samples were assigned to packaging groups based on visual inspection. The second grouping system, food group, was used to group foods of similar type with similar storage conditions, for example, "processed foods, chilled" or "processed foods, frozen".

[Insert Table 1 about here]

Reference library

A digital photograph was taken of all the packaging samples collected. These were filed according to their appropriate packaging group. A 3-4cm 2 sample was cut from each packaging sample and stored for future reference before the remainder of the packaging was discarded. Microbially contaminated or otherwise unsuitable samples (e.g. duplicate samples or samples that were badly torn) were not stored.

Contact layer identification

Although identification of the crude packaging type (i.e. paper and board, plastic, metal and glass) was documented in the IFPD, information on the exact material used for the contact layer was also collected. A number of different methods were used to collect information on the exact contact layer of the packaging that could not be identified by visual inspection. In cases where the type of polymer used to make the plastic packaging was identified on the package, it was recorded in the database. The type of polymer is often identified on packaging for recycling purposes and this information is located on the food label or embossed into the plastic. If the plastic packaging type was not identified on the pack, information from manufacturers was sought (ii) or further analyses were completed (iii).

(ii) Manufacturers information

Some 34 food companies were contacted and requested to forward data on the contact layer material used for specific foods. Of these 17 responded and these included some very large food companies covering many individual brands.

To encourage a reasonable response, all companies were offered a nondisclosure agreement by the investigator. In addition to food companies, one packaging manufacturer was contacted to provide information on the exact contact layer used for their packaging.

(iii) Infa-red analysis

The third method used to identify contact layers was Attenuated Total Reflectance Fourier Transform Infa-Red (ATR-FTIR) techniques. These analyses were conducted at the Polymer Development Centre, Trinity College Dublin, Ireland, using a Nicolet NEXUS Model 470/670/870 bench with a zinc selinide crystal. Where necessary, samples were analysed on a Nicolet Continuum Microscope (Spectra Tech Continuum Model No. 912A0429) using a germanium crystal. The objective of the analysis was to identify the inner surface of the packaging sample. This process provided a spectrum of the uppermost two microns of the surface of the sample polymer. In this way, the food contact layer was analysed without any interference being present from subsequent polymer layers that may be present in the packaging film.

Although multilayer plastic films may have been used for the food packaging and migration may occur from these other layers, due to resource constraints, only the contact layer/coating of the plastic packaging was identified using ATR-FTIR. Samples were sent for ATR-FTIR analyses if no contact layer information was available on the packaging itself or from manufactures.

Samples were chosen from the different "food packaging groups" to ensure a identified. Some food packaging samples were identified by both ATR-FTIR and the food manufacturer. In the majority (>80%) of these cases both sources identified the same polymer as being in contact with the food, however a small number of co-polymers (n=8) caused some discrepancy.

Results

A total of 3672 branded food items and their packaging types were entered into the database. Of these entries, 1904 (52%) were food packaging items directly collected in the course of the survey while 1768 (48%) of them were entered after collecting information on the packaging type from the supermarket shelf audits, online shopping facilities or by purchasing the foods consumed in the NCFS.

Plastic packaging was the most common contact layer used with 2787 (75.9%) of all entries having plastic as the sole contact layer (Table 2). When packaging was used in combination with other contact layers, the percentage of entries with a plastic contact layer increased marginally to 2874 (78.3%). The next most popular contact layers were paper and board (n=270, 7.4%) and metal and alloys (n= 261, 7.1%). As can be seen from Table 2, there were a number of packaging formats used for foods where a combination of different materials were in contact with the food (n=350, 9.3%). In addition there were 173 (4.7%) entries that had more that one type of polymer in contact with the food (results not shown). Multimaterial multilayers, as determined by visual inspection, were commonly used for food packaging (n=498, 13.6%). Of these multimaterial multilayers 459 (92%) had plastic contact layers.

Of the 2874 food packaging items that had one or more plastic contact layers, data on polymers used were available for 2211 (77%) items. Of these 2211 food packaging items, 54 had 2 plastic contact layers that were identified during the analysis.

Therefore 2265 (2211 + 54) polymer contact layers were identified in total. These data are given in Table 2. Polyethylene (PE) was the most frequently used contact layer (n=941, 41.6%) while polypropylene (PP) was a close second with (n=809, 35.7%) entries having a contact layer made from this polymer. Other polymers identified as contact layers included polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), polyvinyldiene chloride and polyamide. To gain further insight into the distribution of contact layers used for different food products, an analysis was completed on the 26 food groups listed in Table 3. Plastic was used in all food groups. Some of the groups that had a majority of entries as plastic contact layers included "chocolate confectionery", "sugar confectionary", "crisps, nut and snacks", "processed meats", "cereal and cereal products" and "liquid beverages non-alcoholic". Some groups that differed from this trend included "Jams and spreads", which were more commonly packaged in glass containers with metal lids (n=82, 78%) or glass jars with plastic lids (n=22, 21%). Also "Processed foods and meals stored at ambient temperatures" were commonly packaged in cans (n=138, 56%) or glass jars with metal lids (n=68, 28%). This group included processed and canned fruit and vegetables, savoury sauces and canned soups. Items in the "Fruit (fresh & dried)" and "Vegetables (fresh)" groups had a high percentage of entries with plastic as the contact layer at 71 (90%) and 76 (87%), respectively. However this percentage is only for packaged fruit and vegetables and does not account for fruit and vegetables that are bought not packaged or loose.

[Insert Table 3 about here]

To investigate the type of polymers used for different food groups, analysis was completed on the frequency of use of polymers as contact layers for the 26 food groups (Table 4). No polymers were identified for food groups "jams and spreads" or "bakery products". For "Chocolate confectionary" PP was the only polymer identified while for "Fish & seafood (fresh & frozen & processed)" PE was the only polymer identified. PS was a popular contact layer for the "Dairy products" group due to PS being used for yoghurt pots (n=93, 46%). PET (n= 307, 51%) and PE (n=289, 48%) had a high frequency in the "Liquid beverages non-alcoholic" group. This was due to the high frequency of fruit juice and carbonated drinks in the database (this group also contained water and milk). Fruit juices were commonly packaged in a

[Insert Table 4 about here]

A number of methods were used to identify the exact polymer used as the contact layer. The majority of polymer contact layers were identified from information provided by the food companies (n=687, 30.5%) (Table 5). Information on the packaging was also a valuable source of polymer identification (n=661, 29.4%). Food groups that had the polymer type identified on their packaging included carbonated soft drinks and squashes, fat spreads, yogurts and confectionary. Packaging samples that were not actually analysed by ATR-FTIR but whose contact layer could be identified based on visual inspection of the material by experts and on the basis of similar entries being identified by the polymer development centre, were coded separately in the database. The total contact layers identified by this combined method of the polymer development centre and expert opinion based on information provided the polymer development centre was 618 (26.7%).

[Insert Table 5 about here] Discussion:

The IFPD is unique in Europe in terms of the level and quality of food packaging information it contains. The development of this database as an add-on to the NCFS (incremental cost of approximately 10%) has proved successful and is the first packaging database created to directly link with a food consumption survey to gain insight into the distribution and types of food packaging used. The information recorded in this database provides valuable details on the types of foods that are packaged and the packaging types that are used. This information is vital so that more realistic estimates of exposure to food packaging migrants can be developed as part of the ever-developing discipline of exposure assessment.

The NCFS of which the present study was a component meets all of the requirements of food consumption surveys as recently proposed by EFSA (EFSA 2005), including their use for the estimation of nutrient intakes and the intake of food chemicals including food contact materials. Although this paper focuses on the types of packaging used for foods, information was recorded in the packaging database on the EU food category (i.e. aqueous, acidic, alcoholic and fatty) and migration test conditions of the foods. This ensures that the combination of the food survey and the packaging database will aid the further development of exposure modelling of food packaging migrants.

Other databases have been developed to store information on the type of packaging used for foods. Maurice Palmer Associates (MPA 1995) developed a database from the results of a number of studies on the use of food packaging material mainly focused on the UK and Italy. This database was discussed at a workshop of ILSI Europe Packaging Material Task Force (ILSI 1996). Although information on food type, package size, packaging material and contact coating was collected as part of this study, these data were not related to actual use of packaging ascertained through a national food intake survey. The data collected from the UK and Italy were extrapolated to the then 15 EU Member States based on a number of assumptions.

Because of these assumptions, the confidence in the data produced was estimated to vary from a low of about 60% for Ireland, Finland and Sweden to a high of 90% for the UK market (ILSI 1996). Other databases that store information on packaging used for foods include the Dutch Grootverbuik Product Informatie database established in 1995 by a number of wholesalers who provide food to the catering, hospital and restaurant industries (www.gpi.nl). Although this database contains packaging descriptions for over 12 000 articles and is continuously being updated it is for foods sold at wholesale level and not retail level. Therefore it lacks information on foods bought and consumed by the public. The Dutch EAN DAS is a not-for-profit datapool for Fast Moving Consumer Goods (FMCG) established in 2000 and collects information on packaging formats used for products. They have packaging information on over 63 000 FMCG of which approximately 35% are food products (www.eandas.nl). The quality of packaging information recorded in this database ranges from crude (e.g. box) to more specific (e.g. polypropylene bag). Another source of food packaging information is commercial food and consumer databases that monitor trends in the consumer product market. The Mintel Global New Products Database (www.gnpd.com) monitors worldwide consumer packaged goods markets and covers the food, beverage and non-food sectors. The Innova Food and Beverage 10 database also collects information on ingredients, packaging and formulation of foods and beverages (www.innova-food.com). Again the level of packaging information in these databases ranges from crude to more specific. The German Association for Packaging Market Research (GVM) also collate data on the packaging types used for foods (R Franz, personal communication, 2005). This data is collected at retail level and not at food consumption level. Overall these databases are a useful resource of general packaging information. However information is not routinely recorded on the exact contact layer used for the food, the polymer type if the packaging is plastic or if the packaging is a multilayer. It is this level of detail that is needed to refine exposure assessments to chemical migrants from packaging materials. Also, these databases were not created to directly link with food consumption data and therefore give an overview only of the types of packaging used for foods available on the market but not the packaging used for foods that are actually purchased and consumed by individuals.

Few difficulties arose during the development of the IFPD. However, collecting information on the polymers used as contact layers was the most time consuming aspect. Identification of these contact layers used for the packaging was sought from a number of different sources. Food manufacturers proved to be a very useful source of information, once followed up with emails and phone calls. Although a large number of polymer types used for the food contact layer were identified from the packaging, from food manufacturers and from a packaging manufacturer, there were a large number of food packaging items that needed further identification. Only some of these were identified by ATR-FTIR due to the cost involved to obtain a complete data set. One limitation in using ATR-FTIR alone to identify the polymer is with the case of co-polymers. If the signal from one of the co-polymers is saturated or swamped by that of the second co-polymer a co-polymer will not be identified, as only the dominant spectrum will be analysed. Difficulties may arise with identification of packaging samples due to the history of that individual sample, such as grease being present on the surface contact layer, or perhaps the inner layer being removed or worn away before it was collected and tested. Also the ATR-FTIR scanned only the first two micrometers of the surface contact layer and not the whole plastic film. This point should be borne in mind when determining the probability of presence of a migrant in When the frequency of contact layers used was established the polyolefins, PE and PP were the main polymers used in contact with foodstuffs. These results are similar to those found in a Dutch study completed in 1997[START_REF] Van Lierop | Enforcement of food packaging legislation[END_REF][START_REF] Bouma | Domestic use of food packaging materials in the Netherlands[END_REF]. For example, PE and PP were the main materials in contact with foods in both studies. Also, PP was the main material used for confectionary in both studies. The Dutch study was designed to gain information on which materials were used in contact with foods and to collect information on the size of the packaging used for the food. It was not designed to estimate consumption of foods packaged in these materials. The popularity of PE and PP for food packaging applications is due to their relatively low cost, their range of versatile properties and the ease in which they can be processed into different packaging forms [START_REF] Tice | Packaging Materials: 3. Polypropylene as a packaging materials for foods and beverages[END_REF][START_REF] Tice | Packaging Materials 4. Polyethylene for food packaging applications[END_REF]. There are a number of types of PE polymer. The different types include low density polyethylene (LDPE), liner low density polyethylene (LLDPE) and high density polyethylene (HDPE). For ease of analysis, all these types were classified as PE. However, details on the exact type of PE are documented in the IFPD.

Every effort was made to collect comprehensive information on the packaging used for foods consumed by children in Ireland. However, it should be noted that most of the packaging collected by the children in the NCFS was packaging for products sold at retail level. Information was not collected on the packaging used in catering establishments. Therefore the IFPD does not contain a complete history of all packaging that may have been in contact with the food or food ingredients, though packaging information on homemade recipes and takeaway foods was collected.

However to put the number of eating occasions by children in catering establishment in perspective, in a study completed on the NCFS it was found that only 2% of eating occasions were in catering establishments which contributed to only 3.2% of total energy intake [START_REF] Burke | A study of food consumption patterns of children and adults in Ireland: Implications for the development of food based dietary guidelines[END_REF]. Information was collected on most food groups available on the Irish market with the exception of alcoholic products as the NCFS collected information on children aged between 5-12 years. Also it was not within the remit of this project to collect information on the surface area of the packaging in contact with the food. Despite these limitations the database provides a huge amount of information regarding the frequency of use and trends of packaging used for food groups on the Irish market. This work reduces the uncertainty regarding the types of packaging used for foods and therefore when completing an exposure assessment the overall uncertainty of the assessment is reduced. However other areas of uncertainty that may need to be reduced in the exposure assessment are the probability that the migrant is present in the food and the level at which it is present.

Exposure assessments are completed as part of the safety assessment of substances. In terms of regulatory compliance most materials and articles are not covered by specific legislation but by the framework regulation (EC) No 1935/2004. Article three of this regulation specifies that materials and articles should not transfer substances into food in concentrations that may pose a risk to human health. Except for plastics, regenerated cellulose, lead and cadmium in ceramics, bisphenol A diglycidyl ether (BADGE), nitrosamies and nitrosable substances in rubber teats, no legal limits are set to indicate the concentration that does not pose a risk to human health. It is the responsibility of the producer to complete the risk assessment for the material or article. The risk assessment process is composed of four elements-hazard identification (i.e. identification of permitted monomers and additives; identification of non intentionally added substances-impurities, reaction intermediates and decomposition products; identification of additives not covered by the positive list such as colourants and polymerisation production aids), hazard characterisiation (toxicology data) and exposure assessment. These three elements combined enable risk characterisation to be completed and hence complete the risk assessment process.

If the producer has toxicology data and concentration data this is a first indication of the safety of the substance. However the potential for the substance to be a safety hazard in the human diet is determined based on the amount of the substance consumed. If the food packaging applications of this substance are known then the data in this study can help determine what foods the substance may be present in and consumption data of these foods can then be used to refine the exposure assessment.

If future plastic regulations are broaden to cover multimaterial multilayers that contain a plastic food contact layer it means that the specific migration level (SML) or the overall migration level (OML) will have to take account of any migration from nonplastic components of the multilayer, in addition to the migration from plastics [START_REF] Heckman | Food packaging regulation in the United States and the European Union[END_REF]). In the present study there were 498 entries (13.6%) that were multimaterial mulitlayers with 459 of these with plastic contact layers (92%). This figure demonstrates the prominence of multimaterial multilayers that have plastic as the food contact layer and needs to be borne in mind when considering the impact for compliance testing if such regulations are implemented.

In conclusion, the collection of food packaging materials as part of a national food survey has enabled the development of a food packaging database, which is unique in Europe. The methodology used to construct this database has proved efficient and will allow further analysis of the packaging types used for foods consumed by children and the development of food packaging consumption factors. Data entry into the database will continue as more packaging material is collected in the National Teen's Food Survey and will therefore be an up-to-date source of packaging information for the foreseeable future. This food group classification was developed to allow similar types of foods with similar storage conditions to be grouped together. Twenty-six of these food groups were created in total (see Table 3).

Biscuit

Contact layer information

The exact material or polymer in contact with the food was recorded if identified. If there were two or more contact layers used for a food item both were recorded.

Polypropylene Identifier of contact layer

The method by which the contact layer was identified was recorded.

Polymer Development Centre (ATR-FTIR) Date of contact layer identification

The date on which the contact layer was identified was recorded. This is to ensure the packaging database remains up-to date as the packaging used for different brand foods main change in the future.

20-08-2004

Presence of multimaterial multilayer

In the present study the term multimaterial multilayer refers to packaging made with more than one layer and the layers are made from different materials (e.g. an outer plastic layer, a middle paper and board layer and an inner plastic layer). This was determined by visual inspection. It was not used to record multilayers where each layer was made from plastic.

No EU food category

The food was classified into one of the EU food categories-acidic, alcoholic, aqueous or fatty (Council Directive 85/572/EEC).

D (Fatty) Migration test conditions

The appropriate conditions of migration testing were assigned to each food (Council Directive 82/711/EEC).

10 days at 40 0 C Sample Present Not all entries in the packaging database had their sample stored (e.g. samples that were microbially contaminated were not stored). If the packaging sample for a food item was retained and stored this was documented in the database.

Yes, sample present

  IFPD database was created in Microsoft Access ® , Microsoft Office 2000 SR-1 Professional for Windows (Microsoft Corporation, Redmond, WA, USA). A number of tables were created in the database to store information on brand foods and their packaging type, the EU classification (i.e. aqueous, acidic, alcoholic or fatty) according to Council Directive 85/572/EEC and the suitable test conditions (e.g. 10 days at 40 0 C) according to Council Directive 82/711/EEC.

4

  Data entry into the Irish Food Packaging Database

  packaging materials used for different foods were

  which had a PE contact layer and carbonated beverages were routinely packaged in PET bottles.

  also the level at which it is present, as migration may occur from the layers beyond the polymer surface layer.

Page 12 of 24 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
	13

Table 1 . List of parameters and their description recorded in the Irish Food Packaging Database

 1 the packaging database was assigned a brand code. Of the 5551 brand codes in the National Children's Food Survey, 3441 had corresponding entries in the packaging database. A unique brand code was assigned to a food packaging item if it was a new brand or a new flavour of the brand food. As different unit weights and packaging formats of the same brand food may exist, these items were also entered into the database. Therefore there were a total of 3672 entries in the packaging database. the weight of the food product as packaged. If the food product was part of a multipack and packaged individually then the unit weight of the individual item was recorded.This was a general description of the packaging material based on visual inspection. It also included a description of secondary packaging if present (e.g. outer cardboard box).

	Parameter	Description

Table 2 . Frequency of packaging formats used as contact layers in the Irish Food Packaging Database

 2 All polymers used for plastics in the database were not identified. Therefore the "total polymers identified" represents only those plastics where the polymer contact layer was identified.

	Packaging Formats	Frequency (n)	Percent (%)
	Single contact layer formats
	Metal and alloys	261	7.1
	Paper and board	270	7.4
	Plastic	2787	75.9
	Wax	1	0.0
	Subtotal	3319	90.4
	Combination of contact layer formats
	Glass and metal and alloys	163	4.4
	Glass and plastic	40	1.1
	Metal and alloys and plastic	8	0.2
	Metal and alloys and paper and board	100	2.7
	Paper and board and plastic	39	1.1
	Subtotal	350	9.3
	Missing information	3	0.1
	Total	3672	100.0
	*Plastics
	Polyethylene (PE)	941	41.6
	Polypropylene (PP)	809	35.7
	Polyethylene terephthalate (PET)	358	15.8
	Polystyrene (PS)	126	5.6
	Polyvinyl chloride (PVC) and polyvinlydiene	19	0.8
	Others (e.g. Polyamide, polyvinylacetate)	12	0.5

*

Total polymers identified 2265 100 Page 19 of 24 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

	Food Group						Glass
					Plastic		&
				Metal &	&		Metal &
	N	Plastic	Paper & Board	Alloys	Paper	Wax	Alloys
	F o r
	P
	e
	e r
	R
	e
	v i e
		w
		O n l
			y

Table 5 . Source of information of contact layer identification for polymers used in the Irish Food Packaging Database Source of information Frequency Percent

 5 Food packaging items entered into the IFPD were grouped (food packaging groups) to allow efficient sampling of food packaging types for analyses. Packaging samples that were not actually analysed by ATR-FTIR but whose contact layer could be identified based on visual inspection of the material by experts and on the basis of similar entries being identified by the polymer development centre were coded separately in the database (Similar identified by Polymer Development Centre).

	Polymer Development Centre	289	11.8
	Similar identified by Polymer Development Centre*	329	14.9
	Subtotal for Polymer Development Centre	618	26.7
	Food Manufacturer	687	30.5
	Packaging	661	29.4
	Packaging Manufacturer	299	13.5
	Total	2265	100.0

*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

Metal &Alloys &

Metal &

* N represents the number of food items in each food group that had one or more of their plastic food contact layers identified. † Some of the food items in these groups had more than one plastic contact layer identified hence the sum of individual polymers identified (n) is greater than the number of food items in this group (N).


Keywords: Risk assessment -modelling, Statistical analysis artificial neural networks, pesticides, foodstuffs, physico-chemical properties, crop type

The relationship between physico-chemical properties and maximum residue levels and tolerances of crop protection products for crops set by the US, EU and Codex

Pernille Thorbek, Kieran

The relationship between physico-chemical properties and maximum residue levels and tolerances of crop protection products for crops set by the US, EU and Codex

Introduction

Residues of crop protection products on foodstuffs are highly regulated. Thus, maximum residue levels (MRLs) and tolerances (hereafter collectively termed residue limits), are based on carefully conducted supervised field trials, and represent the highest residue that would normally be expected when observing good agricultural practice (EC 1997b[START_REF] Epa | Residue Chemistry Test Guidelines[END_REF], FAO/WHO 2002).

When a crop protection product is used on a crop, residues of the active ingredient (a.i.) can be found on food and feed commodities at harvest. The level of residues depends on a large number of factors. Factors relating to effect of use pattern and growth conditions on residue levels have been extensively investigated. For example, application rate [START_REF] Fletcher | Literature review and evaluation of the EPA food-chain (Kenaga) nomogram, an instrument for estimating pesticide residues on plants[END_REF][START_REF] Hoerger | Pesticide residues on plants: correlation of representative data as a basis for estimation of their magnitude in the environment[END_REF], OECD 2002b[START_REF] Pfleeger | Field Evaluation of the EPA (Kenaga) nomogram, a method for estimating wildlife exposure to pesticide residues on plants[END_REF][START_REF] Sadlo | Quantitative relationship of application rate and pesticide residues in greenhouse tomatoes[END_REF], pre-harvest intervals (PHI) [START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF], number of applications [START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF]) crop characteristics [START_REF] Fletcher | Literature review and evaluation of the EPA food-chain (Kenaga) nomogram, an instrument for estimating pesticide residues on plants[END_REF][START_REF] Hoerger | Pesticide residues on plants: correlation of representative data as a basis for estimation of their magnitude in the environment[END_REF][START_REF] Pfleeger | Field Evaluation of the EPA (Kenaga) nomogram, a method for estimating wildlife exposure to pesticide residues on plants[END_REF][START_REF] Santier | Reassessment of the role of cuticular waxes in the transfer of organic molecules through plant cuticles[END_REF], climate [START_REF] Elliot | Temporal Dynamics of Chlorothalonil Residues on Peanut Foliage and the influence of Weather Factors and Plant Growth[END_REF][START_REF] Galera | A study of fenpropathrin residues in tomatoes and green beans grown in greenhouses in Spain[END_REF][START_REF] Nigg | Weather and pesticide residues[END_REF], OECD 2002b), and growth dilution [START_REF] Holland | Modelling of pesticide Residues on Fruit II: Persimmon[END_REF] all affect residues. The influence of the properties of the a.i. itself on residues is less well understood. However, there is some evidence that physico-chemical properties of formulations and a.i.s affect residues [START_REF] Briggs | Influence of physicochemical properties of uptake and loss of pesticides and adjuvants from the leaf surface[END_REF][START_REF] Holloway | Tank-mix adjuvants and pesticide residues: some regulatory and quantitative aspects[END_REF][START_REF] Spynu | Predicting pesticide residues to reduce crop contamination[END_REF]).

Factors indirectly affecting residue levels include rain-fastness, foliar uptake, uptake through roots, volatilisation, photostability, degradation rate and persistence on the plant surfaces, all of which are linked to the physico- 4 chemical properties of the a.i.s [START_REF] Bentson | Fate of Xenobiotics in Foliar Pesticide Deposits[END_REF], Bromilow & Chamberlain 1991, Bromilow & Chamberlain 1995[START_REF] Holloway | Tank-mix adjuvants and pesticide residues: some regulatory and quantitative aspects[END_REF][START_REF] Trapp | Plant uptake and transport models for neutral and ionic chemicals[END_REF]). The physico-chemical properties may interact, and the relationship with residues is not necessarily linear [START_REF] Baker | Physicochemical properties of agrochemicals: their effects on foliar penetration[END_REF]. For example, water solubility and lipophilicity affect uptake as well as persistence on the plant surfaces (Bromilow & Chamberlain 1995[START_REF] Tice | Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?[END_REF], Willis & McDowell 1987).

Furthermore, there may be interactions among use patterns, crops, climate and physico-chemical properties of the a.i.s [START_REF] Bentson | Fate of Xenobiotics in Foliar Pesticide Deposits[END_REF][START_REF] Galera | A study of fenpropathrin residues in tomatoes and green beans grown in greenhouses in Spain[END_REF][START_REF] Schonherr | Modelling foliar penetration: its role in optimising pesticide delivery[END_REF]. Consequently, data that cover a wide range of crops, use patterns and climatic conditions are necessary in order to draw generally applicable conclusions about the relationship between physicochemical properties of a.i.s and residues in foodstuffs. Since the trials used to set residue limits are normally conducted under varying local and climatic conditions, residue limits often provide a more substantial basis for modelling residues than results from single experiments (EC 1997b[START_REF] Epa | Residue Chemistry Test Guidelines[END_REF], FAO/WHO 2002[START_REF] Hoerger | Pesticide residues on plants: correlation of representative data as a basis for estimation of their magnitude in the environment[END_REF].

Here, the relationship between physico-chemical properties of crop protection products and residue limits in foodstuffs are explored. This was done for fungicides, herbicides and insecticides registered for use by the EU, Codex and US. Furthermore, residue limits in different regions and for different crop types were compared. Only a.i.s that had information on both physico-chemical properties and residue limits were included in the analyses.

Methods

Data collation and datasets

Physical and chemical properties of active ingredients. The dataset containing the physico-chemical properties of a wide range of a.i.s ( datasets, the EU MRLs included some a.i.s which were not registered for use in the EU; for those a.i.s the MRL was not set on the basis of residue trials but at the level of quantification (LOQ). Thus, in the EU dataset, it was not possible to distinguish between the LOQs that represented genuinely low residues and those which were set at LOQ because the a.i. was not registered for use. Consequently, the analyses of the EU MRL dataset were performed without MRLs set at LOQ.

The datasets represented a wide range of a.i.s and crops (Table II, Table III).

There was some but not a complete overlap between the a.i.s and crops in the three datasets. Residues have been shown to differ between crops [START_REF] Fletcher | Literature review and evaluation of the EPA food-chain (Kenaga) nomogram, an instrument for estimating pesticide residues on plants[END_REF][START_REF] Galera | A study of fenpropathrin residues in tomatoes and green beans grown in greenhouses in Spain[END_REF][START_REF] Hoerger | Pesticide residues on plants: correlation of representative data as a basis for estimation of their magnitude in the environment[END_REF][START_REF] Pfleeger | Field Evaluation of the EPA (Kenaga) nomogram, a method for estimating wildlife exposure to pesticide residues on plants[END_REF], so the crops were grouped into seven categories in order to allow comparisons (Table II). The grouping was made on the basis of surface area to weight ratio, direct exposure to spray, and consumers (humans or animals).

For each a.i., the mean residue limit was taken for all the crops belonging to a crop type, and this value was used in the data analyses. Thus, the models predict residue limits for a crop type rather than an individual commodity.

Animal feed and 'others' were excluded from the analyses because residues in foodstuffs of plant origin intended for human consumption are the focus of this study.

Data analysis

Summary statistics for legal regions, crop type and main uses were calculated. Artificial neural networks (three layer perceptrons) were used to analyse the relationship between residue limits and the physico-chemical properties of the a.i.s. The three layer perception was chosen because it is one of the most commonly used neural network architectures, and it is a powerful tool for analysing data for prediction and classification [START_REF] Bishop | Neural Networks for Pattern Recognition[END_REF].

Furthermore, the three layer perceptron is capable of modelling complex linear and nonlinear relationships and is good at handling large numbers of interdependent variables, which often complicates more traditional approaches to statistical modelling [START_REF] Bishop | Neural Networks for Pattern Recognition[END_REF]. The datasets were also analysed with general linear models (anovas, multiple regression and combinations thereof), but as neural networks performed much better, results from these more traditional statistical methods are not presented. The residue limits were log-transformed prior to all analyses.

Variable selection and training of neural networks. In order to train the neural networks each of the datasets of residue limits from the three legal regions were combined with the dataset containing physico-chemical properties of the a.i.s. Neural networks require large datasets to be properly trained. As the number of variables increases the more observations are required to train neural networks [START_REF] Bishop | Neural Networks for Pattern Recognition[END_REF]. Consequently, variable selection and reduction is paramount for successful training. The following procedure for variable selection was used. Pair-wise correlations between residue limits and all the physico-chemical variables were performed. Then feature selection was applied with forwards, backwards and genetic selection. On the basis of these two approaches, variables were chosen that appeared to be important.

Results

General trends in residue limits

An initial inspection of the summary statistics of the three raw datasets of residue limits revealed some general trends relating to main use, crop types and legal region. Of the three main uses, fungicides had the highest residue limits, insecticide residue limits were intermediate and herbicide residue limits were lowest (Figure 1A). For food commodities, leafy vegetables had the highest residue limits, whereas more 'compact' commodities such as fruits (e.g. apples) and compact vegetables (e.g. cucumbers) were intermediate (Figure 1B). The lowest residue limits were found for root and bulb vegetables The residue limits varied among legal regions. Thus, US tolerances tended to be higher than Codex MRLs and EU MRLs were lowest (Figure 1). However, interpretation of the differences among the legal regions should be made with caution, because even though there is some overlap between the three datasets with respect to a.i.s and crops, there are also some substantial differences (Table III). Nonetheless, the trend remained the same for fungicides and insecticides when the a.i.s found in all three datasets were compared (there were not enough herbicides in the Codex MRL dataset for this comparison).

Prediction of fungicides

Of the three main uses, neural networks generally performed best on fungicide residue limits. Thus, a higher proportion of the variation was explained than for the other main uses (Table IV, Table V, Table VI). The general trends in residue limits were captured for EU MRLs, Codex MRLs and US tolerances (Table IV, Figure 2). On the basis of crop type and 2-4 physicochemical variables it was possible to explain 40-50% of the variation in residue limits (Table IV).

For each legal region there was more than one neural network that performed equally well with respect to precision of predictions. The neural networks differed both with respect to the variables included and network architecture (number of neurons in different layers). Even so, there were some general trends with respect to variable selections. Crop type was an important variable and was included in most neural networks. Variables describing non-hydrogen atom characteristics (aromatic proportion, non-carbon proportion and halogen proportion), also appeared in many neural networks, as did water solubility.

The relationship between the physico-chemical variables and residue limits was not straightforward. One of the simpler neural networks was used to explain this point, namely the best Codex network, where three variables (crop type, molecular weight and aromatic proportion) explained 50% of the variation in Codex MRLs. Analysed separately, there was no obvious relationship between molecular weight or aromatic proportion and residue limits (Figure 3). However, when molecular weight and aromatic proportion were combined a pattern emerged; at low aromatic proportions molecular weight only had minor effect, but at higher aromatic proportions there was a negative relationship between residue limits and molecular weight. Thus, the highest residue limits were found for compounds, which had a high aromatic proportion and low molecular weight (Figure 3). Although molecular weight was not included in the neural networks for the EU MRL and US tolerance datasets, the same pattern was still apparent (Figure 4). The a.i.s where high residue limits were linked to low molecular weight and high aromatic proportion, were a mixture of compounds with CAS dates before 1965 and newer compounds with CAS date after 1965. Some of the other neural networks performed as well as the one just described (Table IV), but they included more variables and the relationships between variables and residue limits were more complex.

Prediction of herbicides

The neural networks were able to capture general trends in US tolerances for herbicides on the basis of 4-5 physico-chemical variables. The neural networks explained 40% of the variation in US tolerances (Table V), but underestimated tolerances in the higher end (Figure 5B). For EU MRLs for herbicides, the neural networks also captured the general trends in the lower end, but the tendency to underestimate residue limits in the higher end was even more pronounced than for US tolerances (Table V, Figure 5A). There

were not enough herbicides in the Codex dataset for analysis (Table III).

In contrast to fungicides and insecticides, crop type did not appear to be important for herbicide residue limits. As in the analyses of fungicides, variables describing non-hydrogen atom characteristics (aromatic proportion, non-carbon proportion and halogen proportion) tended to be influential. In contrast to both fungicides and insecticides, acidity and basicity were also important. There was a tendency for a downward trend in residue limits at high hydrogen-bond acidity (Figure 5C), but other than that it was difficult to discern the shape of the relationship between the physico-chemical variables and residue limits (e.g. aromatic proportion, Figure 5D). VI, Figure 6). The prediction of EU MRLs was best with 52% of the variation explained. The neural networks were able to explain 38% of the variation in Codex MRLs but only 28% of the variation in US tolerances for insecticides.

Prediction of insecticides

In the same way as for the fungicides, crop type was important for insecticide residue limits as were physico-chemical variables describing non-hydrogen atom characteristics (aromatic proportion, non-carbon proportion and halogen proportion) (Table VI). Again, the relationships between residue limits and the physico-chemical variables were not straightforward. As an example the simplest neural network for EU MRLs for insecticides was chosen. There was a negative relationship between halogen proportion and residue limits, but the effect of non-carbon proportion on residue limits could only be observed when combined with halogen proportion (Figure 7). Thus, a combination of high halogen proportion and low non-carbon proportion gave the lowest residue limits. One of the neural networks for Codex MRLs also included non-carbon proportion and halogen proportion. However, the compounds with high halogen proportion were compounds with CAS dates from before 1965, and some of them also had use as fumigants. The combination of halogen proportion and non-carbon proportion was not included in the neural networks for US tolerances (Table VI). The summary statistics of the residue limits showed substantial variation among and within main uses, crop types and legal regions. Of the three main uses, herbicides had the lowest residue limits, fungicides the highest and insecticides were intermediate. Herbicide residues were expected to be low because they are often applied with long pre-harvest intervals (PHI).

Discussion

Fungicides tend to have higher application rates and shorter PHI than insecticides, which might explain why they had higher residue limits.

Our results indicate that residue limits are affected by commodity surface to weight ratio and exposure to direct spray. These factors along with application rate have been shown to cause variation in initial residues [START_REF] Fletcher | Literature review and evaluation of the EPA food-chain (Kenaga) nomogram, an instrument for estimating pesticide residues on plants[END_REF][START_REF] Hoerger | Pesticide residues on plants: correlation of representative data as a basis for estimation of their magnitude in the environment[END_REF], OECD 2002a[START_REF] Pfleeger | Field Evaluation of the EPA (Kenaga) nomogram, a method for estimating wildlife exposure to pesticide residues on plants[END_REF] and subsequent decline of residues [START_REF] Galera | A study of fenpropathrin residues in tomatoes and green beans grown in greenhouses in Spain[END_REF].

In general, US tolerances were higher than Codex MRLs, which were higher than EU MRLs. Many factors might have contributed to these differences among legal regions. Firstly, there were differences with regards to which a.i.s

were registered for use in the three legal regions. However, the trend remained the same even when only a.i.s and crop types that occurred in all three legal regions were included. Secondly, differences in pest and disease prevalence, use patterns and climatic conditions may contribute to the variation (Bruhn & Fry 1982[START_REF] Galera | A study of fenpropathrin residues in tomatoes and green beans grown in greenhouses in Spain[END_REF], OECD 2002a, Willis & McDowell 1987). Thirdly, different calculation methods for residue limits may How good were the predictions?

Neural networks explained 30 to 50% of the variation in residue limits and although there was some noise, the general trends in residue limits were captured on the basis of crop type and 3-5 physico-chemical variables. A prediction of 40-50% of the variation could be considered fairly good considering that information on use pattern had not been taken directly into account. However, use pattern alone does not explain all variation in residue limits either. Thus, even if only initial deposit is considered, application rate only explains 20% of the variation for some crop types, but up to 90% for others [START_REF] Pfleeger | Field Evaluation of the EPA (Kenaga) nomogram, a method for estimating wildlife exposure to pesticide residues on plants[END_REF]. Nonetheless, many factors that have an effect on residue limits were not included in our analyses (e.g. application rate, number of applications, PHI and formulation) [START_REF] Briggs | Influence of physicochemical properties of uptake and loss of pesticides and adjuvants from the leaf surface[END_REF][START_REF] Frehse | The behaviour of pesticide residues in fruits and vegetables: evaluation by decliine curves[END_REF][START_REF] Hoerger | Pesticide residues on plants: correlation of representative data as a basis for estimation of their magnitude in the environment[END_REF][START_REF] Holloway | Tank-mix adjuvants and pesticide residues: some regulatory and quantitative aspects[END_REF][START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF]. Our results indicated that crop type and physicochemical properties of the a.i.s controlled important aspects of the processes leading to a residue limit, irrespective of the use pattern.

Which variables were important?

Physico-chemical properties are likely to be important for rainfastness [START_REF] Mulrooney | Rainfastening of bifenthrin to cotton leaves with selected adjuvants[END_REF], photo degradation [START_REF] Bentson | Fate of Xenobiotics in Foliar Pesticide Deposits[END_REF]), volatilisation [START_REF] Bentson | Fate of Xenobiotics in Foliar Pesticide Deposits[END_REF], metabolism [START_REF] Bates | The prediction of pesticide residues in crops by the optimum use of existing data[END_REF]), retention on plant surface (Willis et al. 1994), degradation rate [START_REF] Timme | Statistical interpretation and graphic representation of the degradational behaviour of pesticide residues I[END_REF], uptake into plants and transport in plants (Bromilow & Chamberlain 1991, Bromilow et al. 1991[START_REF] Schonherr | Modelling penetration of plant cuticles by crop protection agents and effects of adjuvants on their rates of penetration[END_REF][START_REF] Schonherr | Modelling foliar penetration: its role in optimising pesticide delivery[END_REF][START_REF] Trapp | Plant uptake and transport models for neutral and ionic chemicals[END_REF]. Often more than one neural network gave similar predicted outcomes. This indicated that there were several variables affecting the dissipation processes, and/or that variables were interdependent and therefore interchangeable. The relationships between non-hydrogen atoms characteristics and residue limits were not straightforward. There were interactions between variables as well as non-linear relationships involved. These complex relationships might have been caused by non-hydrogen atom characteristics affecting different aspects of the dissipation processes simultaneously. For instance, non-

How do main uses and legal regions vary with respect to variable selection and precision of predictions?

Neural networks performed best on fungicides followed by insecticides and had the poorest performance on herbicides. This reflected the general trends in residue limits among main uses. Thus, not surprisingly, it was easiest to get good predictions for the main uses with detectable residues, whereas it was This may explain the differences among the main uses with respect to which variables appeared in neural networks.

Crop type occurred in most fungicide and insecticide neural networks but not in herbicides. This, was expected as herbicides are targeted at weeds rather than the crop. Water solubility appeared in most fungicide models but less frequently in insecticides. This may be explained by insecticides having more uniform water solubility than fungicides.

Aromatic proportion in combination with a measure of molecular size (molecular weight or volume) and water solubility appeared to be important in determining the residues of fungicides. In general, the highest residue limits were found for compounds which had a high aromatic proportion and low molecular weight. Newer a.i.s tend to have higher molecular weights and lower application rates than old a.i.s [START_REF] Tomlin | The Pesticide Manual, 12th Edition[END_REF], and such correlations may also have been picked up by the neural networks. However, the compounds were a mixture of compounds with old and new CAS dates, and came from different pesticide classes, so the pattern appeared to have general Halogen proportion appeared to be important for controlling the residues of insecticides and there was a negative relationship between halogens and residue limits. A closer inspection of the data showed that negative correlations were to some extent driven by a few compounds which had high halogen proportion and low residue limits. These were all compounds with CAS date from before 1965, and some were also used as fumigants. It is therefore questionable whether this trend has general applicability.

Nonetheless, as halogens are added to insecticides to make them more stable with respect to insect metabolism (Clarke & Delaney 2003), a.i.s with high halogen proportion may be more efficacious and persistent, consequently requiring lower application rates.

The variables, which were included in herbicide neural networks, were different from those for fungicides and insecticides. For instance, acidity often appeared in the herbicide neural networks. There tended to be a negative relationship between acidity and herbicide residue limits. Acid groups are more common in herbicides than in fungicides and insecticides, possibly because acid groups tend to have phytotoxic effects [START_REF] Tice | Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?[END_REF], Clarke & Delaney 2003). Furthermore, acidity is linked to transport of chemicals within plants [START_REF] Trapp | Plant uptake and transport models for neutral and ionic chemicals[END_REF]. Additionally, Abraham's A is linked to hydrogen-bond & Delaney 2003, Platt et al. 1999). Thus, acidic herbicides may have lower residue limits because they are more efficacious and less stable.

Overall, the best predictions were achieved for EU MRLs, and Codex MRLs could also be predicted fairly well, whereas models predicting US tolerances had the poorest performance. The variables that were used by the neural networks varied among the EU, Codex and US, even for the same main use.

This could be caused by differences in which a.i.s were included in the different datasets. Alternatively, if climate and physico-chemical properties interact [START_REF] Galera | A study of fenpropathrin residues in tomatoes and green beans grown in greenhouses in Spain[END_REF], Willis & McDowell 1987), this may also cause differences between which variables were included in the different neural networks. For instance, foliar uptake is affected by molecular weight, but the relationship between foliar uptake and molecular weight changes with temperature [START_REF] Schonherr | Modelling foliar penetration: its role in optimising pesticide delivery[END_REF]). Moreover, photodegradation not only depends on photo-stability but also on uptake into leaves where compounds are more stable [START_REF] Bentson | Fate of Xenobiotics in Foliar Pesticide Deposits[END_REF]).

Importance of use patterns

Use patterns were not included directly in the analyses, but we suspect that the differences among main uses, crop types and legal regions are linked to differences in use pattern. As main uses and legal regions were analysed separately and crop type was included in most models, use pattern was indirectly included in the analyses.

  Four datasets were collated for analyses: one of physico-chemical properties of a.i.s (Clarke & Delaney 2003) and three on residue limits for different commodities from the EU (Liaison Database [Internet]), Codex (Liaison Database [Internet]) and US (US Environmetal Protection Agency [Internet]).

  both representing commodities that have a low direct exposure to spray deposits compared to other crops.

  able to capture the general trends in EU MRLs and Codex MRLs on the basis of crop type and 3-4 physico-chemical variables (Table

  Crop type was included in the majority of neural networks. Non-hydrogen atom characteristics of the pesticides were also important, and at least one of aromatic proportion, non-carbon proportion and halogen proportion were included in all neural networks. The non-hydrogen atom characteristics affect water solubility, lipophilicity and polarisability, which are all factors that are thought to be important for uptake into and transport around plants(Bromilow et al. 1991, Clarke & Delaney 2003[START_REF] Trapp | Plant uptake and transport models for neutral and ionic chemicals[END_REF]). Nonetheless, non-hydrogen atom characteristics were more important for prediction of residue limits than water solubility, polarisability and lipophilicity themselves. This could have been caused by the non-hydrogen atom characteristics also affecting other properties of the a.i.s, such as stability, volatilisation, toxicity and metabolism(Clarke & Delaney 2003), or because uptake into plants and transport around plants does not always control the final residues.

  affect both water solubility and lipophilicity, which affect rainfastness as well as uptake into plants(Bromilow & Chamberlain 1995[START_REF] Mulrooney | Rainfastening of bifenthrin to cotton leaves with selected adjuvants[END_REF][START_REF] Trapp | Plant uptake and transport models for neutral and ionic chemicals[END_REF]). Non-hydrogen atom characteristics also affect the stability of the a.i.s, causing differences in degradation and metabolism rate(Clarke & Delaney 2003). Non-carbon proportion affects the solubility of the compound, its ability to permeate membranes and how well it sticks to a waxy surface(Bromilow & Chamberlain 1995[START_REF] Tice | Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?[END_REF][START_REF] Trapp | Plant uptake and transport models for neutral and ionic chemicals[END_REF]). However, the effect will depend on whether groups containing nitrogen and oxygen or halogens are added. This could be the reason for non-carbon proportion and halogen proportion often appearing together in the neural networks. Aromatic proportion was included in many neural networks. Aromatic proportion is likely to affect both stability and ability to permeate membranes, and possibly also toxicity(Clarke & Delaney 2003[START_REF] Tice | Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?[END_REF], which may affect efficacy and hence application rate. To complicate things further, plants have different surface properties and morphology, which may be another reason why crop type was included in most networks.

  herbicides where residues were low and showed less variation.The three main uses differ in physico-chemical profiles and in systemicity(Clarke & Delaney 2003[START_REF] Tice | Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?[END_REF][START_REF] Tice | Selection the right compounds for screening: use of surface -area parameters[END_REF]. Thus, most herbicides tend to be systemic, whereas the majority of insecticides have contact action, and fungicides are somewhere in between(Clarke & Delaney 2003[START_REF] Tice | Selecting the right compounds for screening: does Lipinski's Rule of 5 for pharmaceuticals apply to agrochemicals?[END_REF]).

  explanation may stem from the relationship between molecular weight and aromatic proportion, where each addition of an aromatic ring leads to a rather high increase in molecular weight leading to a complex relationship.

  are thought to have a negative correlation with stability (Clarke

Figure 3 .

 3 Figure 3. Relationship between physico-chemical variables and Codex MRLs for

Figure 4 .Figure 5 .

 45 Figure 4. Relationship between fungicide residues and molecular weight (MWT) and

Figure 6 .

 6 Figure 6. Predictions of insecticide residue limits by neural networks. (A) EU MRL;

Figure 7 .

 7 Figure 7. Relationship between physico-chemical variables and EU MRLs for

  Figure 3.

  Figure 5.

  Figure 6.

Table I .

 I toxicity for the target organisms may lead to lower application rates, which will lead to lower residues. It would be interesting to include information on use pattern as a future development of the models. Physico-chemical variables used to predict residue limits. For details of sources and calculation methods see Clarke & Delaney 2003.

	F o r (eds P.J. Holloway, R.T. Rees & D. Stock), pp. 1-26. Springer-Verlag, Berlin. Bromilow, R.H. & Chamberlain, K. (1991). Pathways and mechanisms of transport of herbicides in plants. In Target sites for herbicide action (ed R.C. Kirkwood), pp. 245-284. Plenum Press, New York. Bromilow, R.H. & Chamberlain, K. (1995). Principles governing uptake and US Environmetal Protection Agency Tolerances Database [Internet]. Accessed September 2003. Available from http://www.epa.gov Willis, G.H. & McDowell, L.L. (1987) Pesticide persistence on foliage. Review of Environmental Contamination and Toxicology, 100, 23-73. Willis, G.H., McDowell, L.L., Southwick, L.M., & Smith, S. (1994) Azinphosmethyl and fenvalerate washoff from from cotton plants as a TABLES Physiochemical property Description ClogP Log of the partition coefficient between octanol and water. Values from clogP v4© software MWT Molecular weight N_Rot_bond Number of single non-terminal bonds instance, high F o r transport of chemicals. In Plant contamination. Modelling and simulation of organic chemical processes (eds S. Trapp & J.C. McFarlane), pp. 38-68. CRC Press, Inc., Boca Raton, Florida. F o r F o r F o r F o r N_Donor Number of hydrogen-bond donors function of time between application and initial rainfall. Archives of Environmental Contamination and Toxicology. F o r N_acceptor Number of hydrogen-bond acceptors Aromatic proportion* Number of non-hydrogen atoms in aromatic systems divided by total number of non-hydrogen atoms Non-carbon proportion* Number of non-carbon non-hydrogen atoms divided by total number of non-hydrogen atoms F o r
	P e e r P e e r Bromilow, R.H., Chamberlain, K., & Evans, A.A. (1991). Molecular structure and properties of xenobiotics in relation to phloem translocation. In Recent advances in phloem transport and assimilate compartmentalisation.Fourth International Conference on Phloem Transport and Assimilate Compartmentation, Charente, France, 1990. P e e r P e e r P e e r P e e r P e e r Halogen proportion* Number of halogen atoms divided by the total number of non-hydrogen atoms N_Cation Number of positive charges N_Anion P Number of negative charges Charge e Formal charge at pH 7 A Abraham's A (a measure of hydrogen-bond acidity). Values from Absolv v1.4 © B Abraham's B (a measure of hydrogen-bond basicity). Values from Absolv v1.4 © S Polarisability. Values from Absolv v1.4© e r
	R e v i e w O n l y R e v i e w O n l y R R R R R Excess molar refraction. Values from Absolv v1.4© Length of molecule. Values from Absolv v1.4© R Ouest Editions, Presses Academique, Nantes, France. Bruhn, J.A. & Fry, W.E. (1982) A mathematical model of the spatial and temporal dynamics of chlorothalonil residues on potato foliage. Phytopathology, 72, 1306-1312. e v i e w e v i e w e v i e w e v i e w e v i e w McGowan volume. Values from Absolv v1.4© DOH E L V e Difference between log of the partition coefficient between octanol and water and log of the partition coefficient between hexadecane and water Water solubility Log of the measure of water solubility from Absolv v1.4© ESOL Log of ESOL, a measure of water solubility (Delaney, 2004) v i e * collectively termed non-hydrogen atom characteristics; which equate to heavy atom characteristics in Clarke & Delaney 2003. w Clarke, E.D. & Delaney, J.S. (2003) Physical and Molecular Properties of Agrochemicals: An analysis of Screen Inputs, Hits, Leads and Products. Chimia, 57, 731-734. O n l O n l O n l O n l O n l O n l Delaney, J.S. (2004) ESOL : Estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer y y y y y y
	Sciences, 44, 1000-1005.
	EC (1997a). Calculation of maximum residue levels and safety intervals, Rep.
	No. 7039/VI/95 EN. Commission of the European Communities.
	EC (1997b). Working Document: Guidelines for the generation of data
	concerning residues as provided in Annex II part A, section 6 and
	20 21 25

Furthermore, if the physico-chemical properties are linked to toxicity and stability this may affect use pattern. For (eds J.L. Bonnemain, S. Delrots, W.J. Lucas & J. Dainty), pp. 332-340.

Table II .

 II Number of crops with residue limits in the three legal regions and grouping of crops.

	Crop type	EU	Number of crops Codex	US	Example of typical crops
	Leafy vegetables	20	24	190	Leafy salad, spinach, leafy brassica, herbs
	Compact vegetables	29	28	54	Head cabbages, head salad, tomatoes, squash
	Root and bulb vegetables	21	22	90	Onions, potatoes, radish, carrots
	Fruits	44	54	102	Apples, melons, berries, grapes
	Seeds	45	79	254	Cereals, corn, nuts, oil seeds, legumes
	Animal feed* Other* * excluded from data analyses --F o r	36 64	577 575	Hay, alfalfa, fodder beet, forage crops Animal produce, milk, tea, processed commodities
		P
		e
		e r
			R
			e
				v i e
					w
					O n l
					y

Table III .

 III Number of active ingredients (a.i.s) and observations included in the datasets.

	Main use	EU MRLs	EU MRLs	Codex	US	a.i.s shared by
		including	excluding	MRLs	tolerances	all datasets
		LOQ	LOQ
	Fungicides Number of a.i.s Number of cases*	62 260	37 176	45 196	47 173	22
	F o r
	P
	e
		e r
		R
		e
			v i e
				w
				O n l
					y
						29

Table IV .

 IV Performance of neural networks used to analyse the relationships between residue limits of fungicides and crop types and the physico-chemical properties of the active ingredients. R 2 indicate the proportion of the variation in residue levels that was explaind by the neural network. Over-learning occurs when a neural network fits the model to the noise rather than real patterns.

	Dataset	Variables included in neural	R 2	Graphical inspection of predicted	Check for over-
		network		plotted against observed residues	learning
	EU MRLs	ClogP, aromatic proportion,	0.48	Good over all fit. Captures high, but	No over-learning
		non-carbon proportion,		overestimates low residues
		halogen proportion, ESOL Crop type, non-carbon proportion, B, ESOL Crop type, non-carbon proportion, halogen F o r	0.46 0.52	Captures high values, but overestimates most lower and lower intermediate values Captures high values, but overestimates most lower and lower	No over-learning Moderate over-learning
	Codex MRLs	proportion, B, ESOL Crop type, aromatic proportion, halogen proportion, V P Crop type, MWT, aromatic proportion e Crop type, V, ESOL e 0.44 0.50 0.41 r	intermediate values Good fit Good fit Good fit	Moderate over-learning Moderate over-learning Moderate over-
					learning
	US tolerances	Non-carbon proportion, halogen proportion, water solubility Non-carbon proportion, halogen proportion, ESOL Non-carbon proportion, halogen proportion, aromatic proportion	0.41 R Good fit in mid-high range, does not capture low residues 0.42 Good fit in mid-high range, does not capture low residues e 0.41 Good fit in mid-high range, does not capture low residues v i e	Minor over-learning Minor over-learning Minor over-learning
				w
				O n l
				y
					30

Table V .

 V Performance of neural networks used to analyse the relationships between residue limits of herbicides and crop types and the physico-chemical properties of the active ingredients. R 2 and over-learning as in TableIV.

	Dataset	Variables included in neural	R 2	Graphical inspection of	Check for over-
		network		predicted plotted against	learning
				observed residues
	EU MRLs	Aromatic proportion, non-		Fit only reasonable in mid-rage,	Moderate over learning.
		carbon proportion, ESOL		i.e. underestimate high values,	Several models with
		or		and overestimate low residues	similar fits
		Non-carbon proportion, A,	0.30
	Codex MRLs	ESOL or Aromatic proportion, A,B F o r	Too few observations for analysis
	US tolerances	Aromatic proportion, halogen proportion, S, ESOL, DOH Aromatic proportion, A, L, ESOL P e e 0.41 0.41 r	Poor fit Capture general trend but underestimated tolerances in high end	Minor over-learning Moderate over-learning
			R
			e
			v i e
				w
				O n l
				y
					31

Table VI .

 VI Performance of neural networks used to analyse the relationships between residue limits of insecticides and crop types and the physico-chemical properties of the active ingredients. R 2 and over-learning as in TableIV. General trends of residue limits relating to target organisms (main uses), crop type and legal region. Means of residue limits for (A) main uses, and (B) crop types (for details on grouping see TableII). Error bars show 95% confidence limits.

	FIGURE LEGENDS
	Dataset EU MRLs Figure 1. Figure 2. Predictions of fungicide residue limits by neural networks. (A) EU MRLs; Variables included in neural network R 2 Graphical inspection of predicted plotted against observed residues Check for over-learning Crop type, clogP* aromatic proportion, halogen proportion 0.52 Good fit No over-learning. Crop type, non-carbon proportion, halogen proportion 0.49 Good fit (but larger spread at Moderate over-learning high values than model with 4 variables) Codex MRLs # Crop type, non-carbon proportion, halogen proportion, DOH, water solubility 0.38 Reasonable to good fit in all range Moderate over-learning. US tolerances Crop type, clogP, aromatic 0.26 Poor fit No over-learning F variables used by neural network: crop type, non-carbon proportion, halogen o r proportion, B, ESOL. (B) Codex MRLs; variables used by neural network: crop type,
	proportion, ESOL Crop type, aromatic proportion, S *ClogP may be substituted by ESOL molecular weight, aromatic proportion. (C) US tolerances; variables used by neural 0.28 Poor fit Moderate over-learning P e network: non-carbon proportion, halogen proportion, aromatic proportion. Black
	# One observation excluded because MRL was very high e r
	R
	e
	v i e
	w
	O n l
	y

circles: predictions. Dotted lines indicate perfect prediction, thus circles above line are over-predictions and circles below line are under-predictions.

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

Acknowledgements

We thank John Delaney and Eric Clarke for providing data on the physicochemical properties of crop protection products. We are indebted to Anne Taylor for help on collating the datasets on residue limits. We are grateful for comments on earlier drafts of this report from Kim Travis, John Delaney, Geoff le Patourel, Mike Skidmore, Leslie Bray and Nina Heard.


Georg Häubl

Franz Berthiller

Justyna Rechthaler

Günther Jaunecker

Eva Maria Binder

Rudolf Krska
email: rudolf.krska@boku.ac.at

Rainer Schuhmacher

Characterisation and application of isotope substituted (13C15)-DON

Keywords: deoxynivalenol, mass spectrometry, internal standard, maize .manuscriptcentral.com

come   L'archive ouverte pluridisciplinaire

Introduction

The plant pathogenic moulds of the genus Fusarium are growing on agricultural commodities in the field and produce several toxic secondary metabolites (mycotoxins) (Bennet 2003[START_REF] Jelinek | Worldwide occurrence of mycotoxins in foods and feeds -an update[END_REF][START_REF] Megalla | Production of deoxynivalenol and zearalenone by isolates of Fusarium graminearum Schw[END_REF][START_REF] Pathre | Analysis of Deoxynivalenol from Cultures of Fusarium Species[END_REF][START_REF] Sydenham | Production of mycotoxins by selected Fusarium graminearum and F. crookwellense isolates[END_REF]. They grow parasitically on living plants and lead to lower crop yields and poor seedling quality. Fusarium species are ubiquitous fungi occurring on a broad variety of hosts including barley, maize and wheat [START_REF] Krska | The state-of-the-art in the analysis of type-A and -B trichothecene mycotoxins in cereals[END_REF][START_REF] Luo | Comparative Study on the Natural Occurrence of Fusarium Mycotoxins (Trichothecenes and Zearalenone) in Corn and Wheat from High-and Low-Risk Areas for Human Esophageal Cancer in Cina[END_REF][START_REF] Tanaka | Worldwide Contamination of Cereals by the Fusarium Mycotoxins Nivalenol, Deoxynivalenol and Zearalenone. 1. Survey of 19 Countries[END_REF].

Contamination of cereals with mycotoxins causes food-and feed borne intoxications in man and animals. One of the most prevalent Fusarium toxins is deoxynivalenol, ,7,, also known as vomitoxin (figure 1).

[insert figure 1]

Maximum levels for DON in food were regulated by the European Commission and will be put in force from 1st July, 2006(Commission Regulation EC 856/2005). These levels range from 200 µg/kg for processed cereal-based food for infants, young children and babies up to 1750 µg/kg for unprocessed durum wheat, oats and maize.

No other technique in the area of chemical analysis developed so rapidly during the past ten years than liquid chromatography-mass spectrometry (LC-MS). Analytical methods for the determination of trichothecenes have been extensively reviewed (Langseth and Rundbergert 1998), [START_REF] Lawrence | In: Sample handling and trace analysis of pollutants: techniques, applications and quality assurance[END_REF], [START_REF] Yoshizawa | Mycotoxin Protocols[END_REF], [START_REF] Krska | The state-of-the-art in the analysis of type-A and -B trichothecene mycotoxins in cereals[END_REF], [START_REF] Lombaert | Mycotoxins and Food Safety[END_REF], [START_REF] Koch | State of the art of trichothecenes analysis[END_REF].

Mass spectrometric methods for DON determination utilizes both GC-MS [START_REF] Onji | Direct analysis of several Fusarium mycotoxins in cereals by capillary gas chromatographymass spectrometry[END_REF], [START_REF] Tanaka | Simultaneous determination of trichothecene mxcotoxins and zearalenone in cereals by gas chromatography-mass spectrometry[END_REF], [START_REF] Lombaert | Mycotoxins in infant cereal foods from the canadian retail market[END_REF] and HPLC-MS [START_REF] Kostiainen | Identification of trichothecenes by thermospray, plasmaspray and dynamic fast-atom bombardment liquid chromatography-mass spectrometry[END_REF], [START_REF] Huopalahti | Supercritical fluid extraction of mycotoxins from feeds with analysis by LC/UV and LC/MS[END_REF], [START_REF] Berger | Quantitative determination and structure elucidation of type A-and B-trichothecenes by HPLC/ion trap multiple mass spectrometry[END_REF][START_REF] Razzazi-Fazeli | Determination of nivalenol and deoxynivalenol in wheat using liquid chromatography-mass spectrometry with negative ion atmospheric pressare chemical ionisation[END_REF], [START_REF] Razzazi-Fazeli | Simultaneous determination of major B-trichothecenes and the de-epoxy-metabolite of deoxynivalenol in pig urine and maize using high-performance liquid chromatography-mass spectrometry[END_REF], [START_REF] Laganà | Liquid chromatography/tandem mass spectrometry for the identification and determination of trichothecenes in maize[END_REF]), (Dall´Asta et al. 2004), [START_REF] Royer | Quantitative analysis of Fusarium mycotoxins in maize using accelerated solvent extraction before liquid 13 chromatography/atmospheric pressure chemical ionization tandem mass spectrometry[END_REF]), [START_REF] Berthiller | Rapid simultaneous determination of major type A-and B-trichothecenes as well as zearalenone in maize by high-performance liquid chromatography-tandem mass spectrometry[END_REF], [START_REF] Biselli | Development of a multicomponent method for Fusarium toxins using LC-MS/MS and its application during a survey for the content of T-2 toxin and deoxynivalenol in various feed and food samples[END_REF], [START_REF] Cavaliere | Development of a multiresidue method for analysis of major Fusarium mycotoxins in corn meal using liquid chromatography/tandem mass spectrometry[END_REF], [START_REF] Biancardi | A rapid multiresidual determination of type A and type B trichothecenes in wheat flour by HPLC-ESI-MS[END_REF]. None of these methods employs isotope labelled internal standards. However, in order to achieve a reliable quantification, the use of a suitable internal standard substance (IS) is highly recommended since differences in ionization efficiencies and hence in signal intensities between calibrants and analytes in matrix limit LC/MS methods. Only recently, the successful use of a (13C15)-DON as IS for the determination of DON in maize and wheat without clean-up was demonstrated for the first time [START_REF] Häubl | Short communication: Suitability of a 13 C isotope labeled internal standard for the determination of the mycotoxin deoxynivalenol by LC-MS/MS without clean-up[END_REF] In the present study ( 13 C 15 )-DON was characterised with respect to its purity, identity and isotope distribution. Furthermore, ( 13 C 15 )-DON was successfully used as IS for the quantification subsequent to the application of a well established routine in-house LC-grade methanol and acetonitrile was obtained from J.T. Baker (Deventer, The Netherlands). Water, which was used for the preparation of extraction solvents, sample dilution and as LC mobile phase was purified successively by reverse osmosis and a Milli-Q plus system from Millipore (Molsheim, France).

Purity check of 13 C 15 -DON with HPLC-UV:

To determine the purity of the IS, HPLC-UV chromatograms were recorded at 200nm, 218nm and 230nm using a diode array detector. The separation was performed on a Phenomenex Luna 5µ C18(2) column (250mm x 3mm) after injecting 100µL of a 80mg/L solution of ( 13 C 15 )DON. Flow rate: 0.5mL/min, temperature 25°C. The following linear solvent gradient was applied: After 5min hold time with acetonitrile/water 10+90 (v+v) the gradient reached 100% acetonitrile after 25min. After another 5min hold time the gradient was switched back to acetonitrile/water 10+90 in 1min, before the column finally was re-equilibrated for 10min. 5µg DON and 5µg ( 13 C 15 )-DON respectively were dissolved in 1mL of water/methanol (80+20, v+v). Additionally, the pure solvent mixture was taken for blank measurements. Each of the solutions was introduced into the mass spectrometer using a syringe pump at a flow rate of 350µL/h (n=7 measurements per solution).

TOF-MS

The precision of the determination of the 12 C / 13 C isotope ratios were evaluated for

HPLC-MS/MS:

A 1100 Series LC system from Agilent Technologies (Waldbronn, Germany) was used for liquid chromatography. The QTrap equipment described above was used for MS measurements. Chromatographic separation was achieved on an Aquasil RP-18 column (3µm, 50mm x 2.1mm) from Thermo Electron (Woburn, MA, USA). Injection volume: 10µL, flow rate: 0.3mL/min, temperature: 25°C. The duration of a HPLC run was 15min, starting with methanol/water (15+85, v+v) as mobile phase. After an initial hold time of 2.5min, a linear gradient was used to reach 100% methanol in 1.5min (hold time: 3.5min), and back to methanol/water 15+85 (v+v) in 1.5min (hold time: 6.0min). In order to minimize contamination of the MS ion source with matrix components the HPLC eluate was allowed to enter the MS interface only between 2min and 4min after LC injection. For the stable isotope dilution, the method of IS calibration was applied. According to this method the calibration line was established by plotting the ratio of the DON detector signal / IS signal against the DON concentrations of the calibration solutions.

Recoveries were estimated from the slope of the regression curve according to both, the method of external standards and the method of IS calibration. Linear regression data were calculated according to the DIN standard 32645 using the validation software program ValiData © [START_REF] Wegscheider | Validata[END_REF].

Results and Discussion

To control the purity of the ( 13 C 15 )-DON standard, a concentrated solution (80mg/L) was analysed by HPLC/DAD and chromatograms at different wavelengths were recorded. Figure 2 shows a typical chromatogram at the maximum absorbance for DON in acetonitrile (218nm). The relative peak area of the peak corresponding to ( 13 C 15 )-DON compared to the sum of all peak areas in the respective chromatogram was 99.5% or higher, depending on the UV wavelength chosen for data evaluation Therefore, it was concluded that, based on the UV measurements, the impurities were ≤ 0.5% in total.

[insert figure 2]

The TOF-MS spectra show no detectable impurities of naturally occurring DON. For DON the seven replicate measurements yielded the following relative intensities for the different isotopes, measured as the deprotonated molecular ions: monoisotopic [( 12 (84+16, v+v) [START_REF] Häubl | Short communication: Suitability of a 13 C isotope labeled internal standard for the determination of the mycotoxin deoxynivalenol by LC-MS/MS without clean-up[END_REF]. Therefore, the spiking of the raw extracts can be regarded sufficient to test the suitability of the IS. The measurement results obtained for the spiking experiments were evaluated by plotting the found DON concentrations versus true DON concentrations after fortification of blank maize material. Figure 5 illustrates the linear regression lines which were obtained. When DON was quantified by use of ( 13 C 15 )-DON as IS, a correlation coefficient (R 2 ) of 0.9977 and a slope of 1.01±0.024 (α=0.05, degrees of freedom = 18) were obtained. This corresponds to an estimated recovery of 101±2.4%. In case of no consideration of the IS, a slope of 0.76±0.019 (α=0.05, degrees of freedom = 18) was indicating a recovery of 76±1.9%.

In this case R 2 =0.9974 was obtained. For none of the two evaluation methods there was any evidence of disturbing background signals. This finding is in good agreement with the low values for the Y-intercepts of the recovery functions

(2.94µg/kg (with IS) and 2.74µg/kg (no IS)), none of them being significantly different from zero (α=0.05).

The results of the spiking experiments demonstrate that ( 13 C 15 )-DON can be used to efficiently correct for losses of DON during sample preparation as well as ion suppression effects in the ESI source of the mass spectrometer (resulting in an average recovery of 76% when no lS was used for quantification of DON).

[insert figure 5]

Conclusion

In this paper we analytically characterized ( 13 C 15 )-DON for the use as IS for quantification of DON in cereals using LC-MS/MS. The purity checks of (

  preparation procedure for DON in maize. External and internal calibration was employed to show the suitability of ( 13 C 15 )-DON as IS.ExperimentalChemicals and Reagents: DON and the internal standard ( 13 C 15 )-DON were purchased from biopure Referenzsubstanzen GmbH (Tulln, Austria) as liquid calibrants (25mg/L) in acetonitrile.

:

  An ESI µTOF (Bruker Daltonik, Bremen, Germany) has been used to determine the relative intensities of the different isotopes present in the ( 13 C 15 )-IS and to check for possible contamination with natural DON. The instrument was operated in the negative ion mode and the following MS parameters were applied: ion spray voltage 4500V, nebulizer gas 5L/min, drying gas 0.5bar, 200°C, end plate 500V. The hexapole transfer parameters were: capillary exit voltage 80.0V, hexapole 1: 25.0V, hexapole 2: 22.3V, hexapole RF 60Vpp, skimmer 1: 50.0V, skimmer 2: 24.0V transfer time 48.0µs, pre plus storage 5µs, lens 1 storage: 40.0V, extraction 22.6V, lens 2: 0.2V, lens 3: 22.0V, lens 4: 1.0V, lens 5: 40.0V, detector 1200V. The flight tube and reflector voltage was 1300V. 20000 scans were summarised over a time range of 0.5min.

  both natural DON and ( 13 C 15 )-DON. Additionally, for natural DON the trueness of the determined isotope distribution was estimated by comparing the theoretical isotope ratios (calculated from natural abundances of different C/H/O isotopes) with those obtained for ESI TOF measurements. For the determination of the isotope ratios, standard deviations of the individual isotope intensities were combined according to the rules of error propagation (Eurachem 2000) and expressed as confidence intervals (α=0.05). MS/MS: Stable isotope dilution mass spectrometry and MS/MS characterization of DON and the IS were performed on a QTrap triple quadrupole linear ion trap MS/MS instrument from Applied Biosystems (Foster City, US). The Turbo Ionspray (ESI) interface was used for generation of ions. Enhanced Product Ion (EPI) spectra were recorded both for DON and ( 13 C 15 )-DON. Solutions containing 5mg/L in MeOH/H 2 O (20+80, v+v) were injected in the Turbo Ion Spray (ESI) interface at a flow rate of 10µl/min using a syringe pump. The following MS settings were used: ion spray voltage -4200V, curtain gas 20psi (137.9kPa), source gas 20psi (137.9kPa), collision activated dissociation gas high, declustering potential -40V, collision energy -25eV, Q1 resolution unit, linear ion trap (LIT) fill time 50ms, LIT scan speed 1000amu/s. 100 consecutive (MCA) scans were added for the final spectrum.

  For the determination of DON, the MRM transitions from the deprotonated molecular ion [M-H] -m/z = 295.1 to m/z = 265.1 (collision energy (CE) -12eV) and m/z = 138.0 (CE -26 eV) were monitored. For ( 13 C 15 )-DON, two MRM transitions referring to fragments of the molecular ion [M-H] -(m/z = 310.1 to m/z = 279.1 (CE -12 eV)) and m/z = 310.1 to m/z = 145.0 (CE -26eV) were used. The transitions m/z = 295.1 to m/z = 265.1 and m/z = 310.1 to m/z = 279.1 correspond to the loss of formaldehyde from the [M-H] -ions and were used for quantification, while the other two transitions were used as qualifiers to confirm the identity of the analytes.Matrix calibration: 50g of blank maize were extracted with 200mL of acetonitrile/water, 84+16, v+v) on a rotary shaker for 90 minutes. After filtration 4mL aliquots of the raw extract were spiked with ( 13 C 15 )-DON as IS (0.25mL of a 1.0mg/L solution in acetonitrile to each aliquot) and DON (2.0mg/L in acetonitrile). The following spiking levels were achieved: 0, 30, 50, 100, 300, 500, 1000µg DON/kg maize (n=3 per concentration level).Subsequently, the fortified extracts were purified with Mycosep ® 225 clean up columns (Romer Labs, Tulln, Austria) and volumes corresponding to 0.5g of maize were evaporated to dryness under a gentle stream of nitrogen. The dried extracts were re-constituted with 0.5mL of starting mobile phase. Aliquots of 10µL were injected into the HPLC instrument.Calibrants: A blank and 7 standard solutions with DON concentrations of 10, 30, 50, 100, 300, 500 and 1000µg/L in pure water were prepared. Each of these solutions also contained 250µg/L ( 13 C 15 )-DON as IS. Linear regression lines without considering the IS were constructed by plotting the signal intensity (peak area) as a function of the DON concentrations.

  C 3 13 C 12 )-DON-H] -m/z = 307.16: 0.4%±0.16%. These values resulted in a relative amount of 13 C atoms of 98.6%±0.58% (α=0.05) in the isotope substituted ( 13 C 15 spectra of DON and ( 13 C 15 )-DON were recorded (Figure 4) at a collision energy of 25eV and compared to each other. Both the observed m/z values of the MS/MS fragments as well as their relative intensities were identical, except for the mass shift resulting from the 13 C atoms in ( 13 C 15 )-DON. Figure 4 also contains a description of the most abundant fragments with respect to the functional groups cleaved off during CID. Typical for DON is the loss of -30amu (H 2 CO) during fragmentation (Berthiller 2003 and Razzazi-Fazeli 2003). Exact the same cleavage of formaldehyde, but one neutron heavier can be observed in the MS/MS spectra of the fully isotope substituted DON. The comparison of the two MS/MS spectra also demonstrates the great value of isotope labelled compounds for structural characterisation of DON by indicating the number of C-atoms in the cleaved fragments [insert figure 4]To demonstrate the potential of ( 13 C 15 )-DON to be used as IS for the determination of DON, a non-contaminated maize sample was extracted and aliquots of the raw extract were spiked with DON and IS. It has been demonstrated before that spiked DON can be extracted quantitatively from blank maize with acetonitrile / water

FigureFigure 2 :Figure 5 :

 25 Figure 1 : Chemical structure of DON

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

Acknowledgements

The authors thank the Christian Doppler Society for the financial support.