Michael Kempf

Dr Peter Schreier
email: schreier@pzlc.uni-wuerzburg.de

S Ramm

T Feuerbach

Occurrence of 2,2,4-trimethyl-1,3-pentanediol monoisobutyrate (Texanol ® ) in foods packed in polystyrene and polypropylene cups

Keywords: Texanol, Migration, Packaging -inks Food Types: polysyrene packed foods, polypropylene packed foods contamination, migration, polypropylene, polystyrene, 2, 2, 4-trimethyl-1, 3pentanediol monoisobutyrate, TMPD-MIB, Texanol

Occurrence of 2,2,4-trimethyl-1,3-pentanediol monoisobutyrate (Texanol®) in foods packed in polystyrene and polypropylene cups

Introduction

High-quality food currently offered in a global market requires adequate packaging. There is no doubt that present-day food packaging materials are fulfilling their primary role of ensuring the microbiological safety of our packaged food, and maintaining quality and nutritional value (Barnes et al. 2007). However, concerning chemical migration, especially of printing ink constituents and plasticizers, questions of safety may arise, as in recent years several cases of undesirable migration have been reported. Hence, harmful compounds from plastic gaskets, i.e. material for sealing the metal lids of glass jars, such as epoxidized soybean oil (ESBO) (Hammarling et al. 1998;Fantoni and Simoneau 2003), plasticizers other than ESBO (Petersen and Breindahl 2000), and 2-ethylhexanoic acid (2-EHA) (Elss et al. 2004) were detected in food. The latest event to hit the headlines concerned the notification of the Italian competent authorities in 2005 that liquid baby milk contained 2isopropylthioxanthone (ITX), a known photoinitiator in UV curing inks [START_REF] Cho | UV-Initiated free radical and cationic photopolymerizations of acrylate/epoxide and acrylate/vinyl ether hybrid systems with and without photosensitizer[END_REF]. ITX was present in certain cartons, due to the printing process applied to rolls of packaging. Since then, the contaminant has been found in various food samples (Gallart-Ayala et al. 2008).

The European Commission reacted by the GMP regulation 2023/2006(Commission Regulation 2006). In the 4 th Amendment to the Plastics Directive (Directive 2007/19/EC), one of the significant novelties introduced includes the functional barrier doctrine for plastics. For this a layer reducing the migration of non-listed substances to a "nondetectable" level of <10 µg kg -1 is demanded. This cut-off for the performance of a functional barrier does not remove the need for an individual substance to be listed. Set-off substances are mentioned (almost in passing and only once) in the Directive as an aid to the printing industry. However, printing inks are not regulated by the Commission and Directive 2007/19 is for plastics only. For substances originating from other materials including inks the general provisions of the framework directive apply which requires that producers perform their own risk assessment.

In this paper, we report about the occurrence of a "non-listed" contaminant that has not been described before as a migrant from plastic food contact materials (Skjevrak et al. 2005) ). This ester that consists of two isomers (Figure 1) has been found in many of our routine analyses of food packed in printed plastic cups and was then extensively investigated in various commercial foods packed in polystyrene (n=77) and polypropylene cups (n=42).

Materials and Methods

Chemicals

2,2,4-Trimethyl-1,3-pentanediol monoisobutyrate (TMPD-MIB, Texanol ® ) and solvents were purchased from Sigma-Aldrich (Steinheim, Germany). Solvents were of HPLC grade purity, other solvents were re-distilled before use.

Samples

A large variety of different product groups, liquid as well as solid foodstuffs (in particular, milk products such as yogurt, curd, cream whey, and cheese, as well as, additionally, fruit preparation, jams and marmelades), packed in printed polystyrene (PS) (n = 77) and polypropylene (PP) cups (n = 42) from 61 German suppliers were purchased from local supermarkets. The cups were partly directly printed, partly exhibited printed plastic or paper sleeves. Several "not printed" cups -unprinted, unlabeled, blank cups just sealed with printed aluminium top covers -were also among the samples. Best before dates ranged from June 2007 to April 2009.

Sample preparation

Solid products (350-600g pooled from identical batches, diluted 1+2 with distilled water) and liquids (400-600 ml, thinned 1+1 with distilled water) were subjected to traditional Likens-Nickerson simultaneous distillation-extraction (SDE) over 2h using a pentanediethyl ether mixture (1+1, v/v). The extract was dried over anhydrous sodium sulphate, filtered, carefully concentrated to 1ml using a Vigreux column (45°C) and after adding 2methyl-1-pentanol (100µg) as external standard subsequently analysed by capillary gas chromatography-mass spectrometry (HRGC-MS).

Upon removal of the content one representative cup was cut into small pieces (about 5-6mm 2 surface) and used for subsequent SDE, employing 1000ml distilled water. For checking purposes, a following Soxhlet extraction with diethyl ether and n-pentane (1+1, v/v) under reflux was accomplished for 6h.

Enantioselective multidimensional gas chromatography-mass spectrometry (enantio-MDGC-MS)

A double oven MDGC system (oven I, Fisons GC 8160; oven II, Fisons GC 8130) (Thermo Electron, Dreieich, Germany) equipped with split injection (200°C, 1:20) and a moving column switching system (MCSS) was coupled to a Fisons MD 800 mass spectrometer (Thermo Electron, Dreieich, Germany). Pre-separation was performed using a J&W DB-Wax fused silica capillary column (30m × 0.25mm i.d., d f = 0.25µm), temperature program started at 100°C and raised at 10°C min -1 to 240°C. Enantiomeric separation was performed using a modified cyclodextrin phase: 30% 2,6-O-dimethyl-3-O-pentyl-β-cyclodextrin in OV 1701 (25m × 0.25mm i.d., d f = 0.25µm; Mega, Milano, Italy), temperature program started at 80°C, held for 70min and raised at 0.5°C min -1 to 200°C. Identification of TMPD-MIB was performed by comparison of linear retention indices and mass spectral data of the samples with that of authentic reference compound.

Calibration and expression of results

Texanol was quantified by means of a calibration curve (peak area divided by external standard area versus analyte concentration divided by external standard concentration) constructed with standard solutions in distilled water at eight calibration levels ranging from 0.45 to 52.25 µg l -1 Texanol ® and each of 100 µg 2-methyl-1-pentanol as external standard.

The SDE extracts were concentrated by a Vigreux column (45°C) to a defined volume of 1ml. Calibration equation, y=1.2106x + 0.0206. R 2 = 0.9974. A sample of curd, not containing TMPD-MIB, was spiked with six different concentrations (in the range of 0.1 to 5µg kg -1 ) of Texanol ® and analysed with the above mentioned procedure. The results indicated a detection of overall procedure (DLOP) and the reliable quantification limit (RQL) with signal-to-noise ratios of 3:1 and 7:1 at 0.2 µg kg -1 and 0.9 µg kg -1 , respectively.

Detection limit of overall procedure (DLOP) and reliable quantification limit (RQL)

Results and discussion

As a representative example, figure 2A shows the total ion chromatogram (TIC) for a HRGC-MS separation of a typical TMPD-MIB-containing yogurt extract. In figure 2B the TIC of the extract obtained from the packaging material (PS cup) is shown. As expected, two peaks were observed for the isomers of TMPD-MIB in about 60:40% relation for 2,2,4trimethyl-1,3-pentanediol-1-isobutyrate and 2,2,4-trimethyl-1,3-pentanediol-3-isobutyrate, repectively (Schwenk and Becker 1972). The result shown from figure 2B clearly indicates their origin from the packaging material.

One might conclude a "natural" origin for TMPD-MIB, however, our enantio-MDGC-MS analyses distinctly indicated its synthetic origin, i.e. each a racemic mixture was obtained for both the synthetic reference and the analytes under study. A representative example of the enantio-MDGC-MS analysis is shown in figure 3. HRGC-MS analysis showed, in total, the presence of TMPD-MIB in 55 and 50% of polystyrene and polypropylene packed samples under study, respectively. Amounts ranging from 1.2 to 64.5 µg kg -1 (average 25.1 µg kg -1 ) and 0.9 to 45.7 µg kg -1 (average 10.8 µg kg -1 ) in polystyrene and polypropylene cups, respectively, were found (figure 4A/B). Only 10% of the packaging materials were investigated; in these cases, amounts ranging from 150 to 680 µg kg -1 were found. As no exact quantification was performed for the TMPD-MIB analysis of cups, these amounts cannot be used in a balance study comparing them with the quantities found in food samples under study. The analysis of TMPD-MIB in the cups can therefore only be taken to be indicative of its source in the foods. TMPD-MIB is a common coalescing agent in latex paints and belongs to the group of high production volume chemicals. Assessment and conclusions from previous screening information (Anonymus 1996) indicates low level environmental exposure to a high number of consumers as well as low order of toxicity. More recent data focus on the emission as influence on indoor air quality (Lin et al. 2007). Furthermore, in human sensory experiences it was investigated that TMPD-MIB is one of the compounds responsible for paint odour and its persistence in indoor air (Gallagher et al. 2008). However, since the data are inadequate for assessment, no threshold for TMPD-MIB in indoor air is determined (DIBT 2004).

Considering the above mentioned amounts of TMPD-MIB in the food samples under study, their presence in food could be considered undesirable but it is not likely to present a health risk at the levels reported. As the diester, 2,2,4-trimethyl-1,3-pentandiol diisobutyrate (TXIB), is on the EU list of regulated substances (restricted to be used in single-use gloves only) with a migration limit of 5 mg/kg food (European Commission 2008) and the diester is known to be metabolised rapidly by hydrolysis (EFSA 2006), the observed migration of the monoester Texanol ® in the ppb level can be considered to pose no risk of adverse effects. (polypropylene: 5, 19, 35-37 and polystyrene: 13, 14, 17, 18, 20-23, 27, 39, 41, 60, 62-64) are not listed in the charts.

F

  , i.e. 2,2,4-trimethyl-1,3-pentanediol monoisobutyrate (TMPD-MIB, Texanol ® )(COE 2005;

5

  Capillary gas chromatography-mass spectrometry (HRGC-MS)HRGC-MS was carried out with an Agilent GC 6890 series gas chromatograph with split injection (220°C, 1:20) directly coupled to an HP Agilent 5973 Network mass spectrometer (Agilent Technologies, Inc., Palo Alto, CA, USA). The GC was equipped with a J&W DB-Wax (30m × 0.25mm) fused silica capillary column coated with a 0.25µm film (J&W Scientific Products GmbH, Cologne, Germany), temperature program running 3min isothermal at 50°C, then raised at 4°Cmin -1 to 250°C. The temperature of connecting parts was 220°C; electron ionisation energy was 70eV. Identification was carried out by comparison of linear retention indices and mass spectral data (recorded by full scan in the EI mode) of the samples with that of authentic TMPD-MIB.

  Figure 1

  Council of Europe. Policy statement concerning packaging inks applied to the nonfood contact surface of food packaging materials and articles intended to come into contact with foodstuffs.[Internet]. 2006. [cited: 2008 May 14]. Available: EC relating to plastic materials and articles intended to come into contact with food and Council Directive 85/572/EEC laying down the list of simulants to be used for testing migration of constituents of plastic materials and articles intended to come

	GALLART-AYALA, H., MOYANO, E. and GALCERAN, M.T. 2008, Liquid chromato-graphy/tandem mass spectrometry (highly selective selected reaction monitoring) for 188. Figure 2. HRGC-MS separation (total ion chromatogram, TIC) of an SDE extract from a typical yogurt sample ( A ) and that of the corresponding polystyrene cup ( B ). St, standard; COE. http://www.rotoflex.ch/uploads/media/Resolution_AP-2005-the analysis of isopropylthioxanthone in packaged food. J. Chromatogr. A, 1208, 182-two isomers of Texanol ® ( X ).
	2_ON_PACKAGING_INKS_mit_Tech_Doc_1_und_3.pdf. Accessed: 14. May 2008. HAMMARLING, L., GUSTAVSSON, H., SVENSSON, K., KARLSSON, S. and Figure 3. Enantio-MDGC-MS separation of a TMPD-MIB reference with its two isomers
	COMMISSION REGULATION (EC) No. 2023/2006 on good manufacturing practice for OSKARSSON, A. 1998, Migration of epoxidized soya bean oil from plasticized PVC ( A ) and TMPD-MIB isolated and cut from a yogurt sample out of a polystyrene cup ( B ).
	materials and articles intended to come into contact with food; published in the Official gaskets into baby food. Food Additives and Contaminants, 15, 203-208. Enantio-MDGC-MS conditions (cf. the Materials and methods).
	o r Journal of the European Communities on 29 December 2006 (OJ No. L 384 29.12.2006). DIBT. Deutsches Institut für Bautechnik. Gesundheits-und Umweltkriterien bei der Umsetzung der EG-Bauprodukten-Richtlinie (BPR). [Internet]. 2004. [cited: 2008 May F o r LIN, C.C. and CORSI, R.L. 2007, Texanol ® ester alcohol emissions from latex paints: Temporal variations and multi-component recoveries. Atmospheric Environment, 41, 225-3234. PETERSEN, J.H. and BREINDAHL, T. 2000, Plasticizers in total diet samples, baby food F Figure 4. Amounts of TMPD-MIB in extracts of foods packed in polypropylene cups ( A ) and in polystyrene cups ( B ). The average ( AV ) is given at the end of each row. In ( A ) o r sample numbers 1-26: directly printed; 27-37: printed plastic sleeve and 38-42: printed
	P e e r R e 08]. Available: http://www.dibt.de/de/Data/Aktuelles_Ref_II_4.pdf. Accessed: 08. May 2008. DIRECTIVE 2007/19/EC. Commission Directive of 2 April 2007 amending Directive contact with foodstuffs. R e FJELDAL, P. and HERIKSTAD, H. 2005, Non-targeted multi-component analytical surveillance of plastic food contact materials: Identification of substances not included in EU positive lists and their risk assessment. Food Additives and Contaminants, 22, R e 2002/72/into P e e r and infant formulae. Food Additives and Contaminants, 17, 133-141. SCHWENK, U. and BECKER, A. 1972, Reactions of isobutyraldehyde. IV. 1-and 3-Isobutyrate of 2,2,4-trimethyl-1,3-pentanediol. Liebigs Ann. Chem., 756, 162-169. SKJEVRAK, I., BREDE, C., STEFFENSEN, I.L., MIKALSEN, A., ALEXANDER,J., paper sleeve. In ( B ) sample numbers 1-46: directly printed; 47-54: printed plastic sleeve; P 56-74: printed paper sleeve and 75-77: without printing. Samples containing traces e r e (amount < RQL = 0.9 µg kg -1 ) of TMPD-MIB
	v i e w http://ec.europa.eu/food/food/chemicalsafety/foodcontact/legisl_list_en.htm EFSA. Opinion of the Scientific Panel on food additives, flavourings, processing aids and v i e 1012-1022. v i e materials in contact with food (AFC) on a request related to a 12 th list of substances for food contact materials. 2006, EFSA Journal 395 to 401, 1-21. w w
	O n l ELSS, S., GRÜNEWALD, L., RICHLING, E. and SCHREIER, P. 2004, Occurrence of 2-ethylhexanoic acid in foods packed in glass jars. Food Additives and Contaminants, 21, 811-814. EUROPEAN COMMISSION. Food Contact Materials. Annex I. Substances listed in EU O n l O n l
	y directives on plastics in contact with food. Updated version of 10 th April 2008 y y
	(reference no. PM.95020). Legends
	FANTONI, L. and SIMONEAU, C. 2003, European survey of contamination of
	homogenized baby food by epoxidized soybean oil migration from plasticized PVC Figure 1. Two isomers of TMPD-MIB: esterified with secondary alcohol ( A ) and with
	gaskets. Food Additives and Contaminants, 20, 1087-1096. primary alcohol ( B )
	8 9 10

GALLAGHER, M.,

DALTON, P., SITVARIN, L. and PRETI, G. 2008

, Sensory and analytical evaluations of paints with and without Texanol. Environmental Science & Technology, 42, 243-248.

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


Phil Northing
email: p.northing@csl.gov.uk

Peter Kenneth

Thomas Oldring

Laurence Castle

Philip Alexander

Sebastian Stansfield Mason

P K T Oldring

P A S S Mason

A new hierarchical classification of food items for the assessment of exposure to packaging migrants: Use of Hub Codes for different food groups

Keywords: Food item classification, Hub Codes, Exposure assessment, Probabalistic modelling Additives/Contaminants: Monte-Carlo Simulation, Food contact materials, Migration Food item classification, Probabalistic modelling, Monte-Carlo simulation, 24 Migration, Hub codes, Exposure assessment, Food contact materials

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction 1

Risk assessment involves comparing exposure to a chemical with its 2 toxicological potency. Knowledge of the amounts of different food types that are 3 consumed as part of a diet involves many assumptions and approximations. 4

Determining exposure to substances originating from packaging materials has the added 5 complexity that not only the extent of migration from the packaging needs to be known, 6 but also the food types that are packaged in that material as well as consumption of that 7 food type (ILSI 2007). We have previously described a two-dimensional probabilistic 8 modelling approach for assessing dietary exposure to chemicals migrating from food 9 packaging materials and some of the applications undertaken using the system (Holmes 10 et al, 2005;Castle et al, 2006;Oldring et al, 2006). This earlier work utilised UK 11 consumption data for three age groups; youths (4-18), adults (16-64) and seniors (65+) 12 and has already established the method as a useful tool for the packaging industry. This 13 paper describes the extension of the system in two key areas: 14 1. The reclassification of food items into a hierarchy with greater relevance to 15 packaging. 16 2. The incorporation of new food consumption data from the UK. 17 The modelling approach has been used to assess the exposure to a hypothetical 19 migrant from light metal packaging, most of which is coated, utilising the different 20 levels in the new hierarchy and, for comparison, a model set up using expert judgement 21 on the assumptions for each food item. For the purpose of this exercise we selected 22 metal packaging as it was used in earlier studies (Castle et al, 2006;Oldring et al, 23 2006). This enables us to assess how the hierarchical levels can be used and to ascertain 24 (Hoare et al., 2004). This, like the earlier adult survey in 1986, details all the food and 8 drink consumed by approximately 2000 individuals between the ages of 16 and 64 over 9 seven consecutive days. This survey became generally available recently and so it was 10 incorporated into the CSL Chemical Migrants modelling system. Table 1 lists the 11 NDNS surveys that are within the CSL system and for which exposure assessments 12 have been run. 13 14

Food item reclassification 15

The hierarchical classification of the food items in the UK NDNS (Appendix 1) 16 related to the original nutritional purposes of the surveys and was modified (Holmes et 17 al, 2005) to better reflect the requirements of exposure assessment (Appendix II). There 18 is no standardised food coding across Europe, indeed most countries have different 19 approaches and descriptions (SCOOP 1996). With our long held aspiration to 20 incorporate the dietary data from other countries (which will have their own coding 21 classifications) it was clear that the assessment process could be eased by the 22 development of a hierarchical classification that was much more closely related to the 23 type of food and their simulant designation, bearing in mind their likely packaging. The 24 simulants we have considered are those specified for testing plastic, namely water, 3% 1 acetic acid, 10% ethanol and olive oil (or alternatives), as simulants A, B, C and D for 2 aqueous, acidic, alcoholic and fatty foods respectively and with D/X to reflect reduction 3 factors. Simulant choice is described in (ILSI 2007). 4

As each country's food consumption data is recorded in a different format there 5 are two options. One is to map each of the surveys of the 27 EU Member States onto 6 each other, which equates to 351 mappings (Figure 1a). The alternative is to map onto a 7 central system, which in effect acts as a 'translator' thereby only requiring 27 mappings. 8

We have called this the Hub Code system with the hub being at the centre of a wheel 9 with spokes. 10

The Hub Codes group together food items that are equivalent in terms of their 11 description and simulant designation which is essential for the development of the 12 exposure assessment for migrants from packaging materials. A three-tiered hierarchy 13 was created (Appendix III) and the 4644 food item descriptions from the surveys were 14 each considered and allocated to an appropriate classification. 15

The highest level of the hierarchy (hub 1 in appendix III) contains 19 separate 16 classes, each of which contains a broad group of food items. Examples of these broad 17 groups include meat (15.x.x) and fruit (12.x.x). Each of these 19 classes has been 18

broken down to between 1 and 8 classes at the second level of the hierarchy (hub 2 in 19 Appendix III), making 64 classes altogether. Examples at this level include pizza (8.4.x) 20 and wines (1.2.x). Lastly, each class in the second level has been broken down by 21 simulant type to between 1 and 12 classes at the lowest level (hub 3 in Appendix I), 22 totalling 183. Examples at the lowest level show biscuits (4.3.x) broken down into to 23 three classes; Sweet (4.3.1), Savoury (4.3.2) and Chocolate coated and other fatty 24 Hub.1.2 Hub 1.2.3 3 4 Some of the food items in the system have undergone a considerable change in 5 classification, for example, 'Tomato Juice' in the original classification was classed as 6 "Other vegetables (not raw) includes lentils, dried beans and pulses,mushrooms,onion,7 aubergine,parsnips,sweetcorn,peppers,leeks,courgettes,cauliflower,mixed 8 vegetables,TVP/soya mince,quorn,tofu" (13.6 in Appendix II) but has now been 9 reallocated to "ready to drink juices: acidic" (2.4.1 in Appendix III). This reallocation 10 more clearly reflects the type of packaging of this particular item and shows how 11 individual food items can be grouped more realistically to describe the potential for 12 their packaging to give rise to migrants. 13

Composite foods or recipes require special consideration for their allocation to 14 the hub codes. A detailed description of how recipe items are deconstructed using a 15 recipe database is provided by Holmes et al. (2005). However, a number of non-16 deconstructed recipes remained. These, and all composite items were allocated to the 17 hub code that represented the packaging of either the largest component or that which 18 would contribute the highest concentration data. The model also allows the non-19 deconstructed recipe items to be noted in the database and extracted into a separate 20 basket for individual consideration if desired. 21

The hub codes have been developed and are utilised in such a way as to allow 22 for future amendments to be incorporated with little significant disruption. An example 23 of where this may be required is if in the future new, less detailed, dietary surveys are 24 added to the system. In this situation it is possible that the food item descriptions will 25 7 not allow them to be unequivocally placed in a particular category in Hub 3 or possibly 1 even Hub 2. An example of this would be if the food item description was 'Tuna' as we 2 would not know whether it was raw or processed. In this case, the addition of another 3 code in Hub 3 would capture these uncertain food types for consideration by the user 4 when the baskets were set up. 5

Note that this reclassification of the food items does not affect the underlying 6 exposure model as this utilises every food item consumed per individual without 7 reference to the hierarchy. The classification facilitates the development of the 8 assumptions required to undertake an exposure assessment. 9 10 Creation of four exposure models using the Hub Codes -3 hierarchical levels -and a 11

th using 'expert' judgement 12

A hypothetical migrant from coated metal was used as an example to evaluate 13 the variations in exposure attainable from the model using different levels of detail and 14 different consumption surveys. It was assumed that the substance only migrated into 15 oily foods (two data points of 15 and 30 µg dm -2 ). For migration into aqueous, acidic 16 and alcoholic simulants/foodstuffs it was assumed that it was non-detectable with an 17 LOD (level of detection) of 3 µg dm -2 (scenario 1). In addition, two other scenarios were 18 used, (i) an LOD of zero for these three simulants (scenario 2) and (ii) and an LOD of 19 15 µg dm -2 for these three simulants (scenario 3) in order to evaluate the contribution of 20 the non-detects. 21 Four models (one each at the three hub levels and at the individual item level, all 22 with and without packaging loyalty) were created, each with different assumptions with 23 regards to the food groupings (baskets). Packaging loyalty was applied in the model 24 simulation, for each individual, we looped through all the food items, and a random 2 number generator (Matsumoto & Nishimura, 1998) was used to choose the packaging 3 type and therefore the associated concentration data. When loyalty was applied, the first 4 packaging type chosen in a basket was used for every item consumed by that person 5 from that basket for the whole of that individuals food consumption within that 6 iteration. When loyalty was not applied, the random number generator was used to 7 assign the packaging type for every food item. When the market share was either 100% 8 or 0%, loyalty clearly has no further effect (see Holmes et al, 2005 for further detail). 9

Three models were set up with baskets using the hub codes: Model B1 -High -10 used Hub1, Model B2 -Intermediate -used Hub 2 and Model B3 -Low -used Hub 3 11 (see appendix III for hub details). The fourth model -Model B4 -Individual -was 12 created by examining each item individually and grouping those for which the same 13 assumptions could be made on their parameters (market share, surface area to volume 14 ratio, concentration data and extrapolation factors). These four models increase in 15 refinement from High to Individual because at each stage we only need to deal with 16 baskets that contain food items that are packaged in coated metal. At the High level of 17 the hub codes we have the fewest baskets, with the most food items within each. This 18 makes the estimation of the parameters more difficult, increasing the level of 19 uncertainty. At the Low level we have many more baskets containing fewer items, 20 which allows us to reduce the level of uncertainty surrounding the parameter values. At 21 the individual level we were able to create baskets in which everything was packaged or 22 potentially packaged in coated metal and because there were no constraints as to how 23 the groupings were put together we were able to be much more certain about the 24 into those that could be either in a can or a jar with a metal closure. As the surface area 2 to volume ratios are different for these two possibilities they have been incorporated 3 into the conversion factors for that basket. Secondly, the extrapolation factor represents 4 part of a meal. If relevant, the product of both could be used to represent a D/X and part 5 of meal grouping. 6

The number of baskets in each model varied as follows Model B1 -11, B2 -27, 7 B3 -63 and B4 -29. All four had an additional basket of not relevant for metal 8 packaging. In the case of the low Hub code model, B3, 2 baskets were empty as the 9 food consumption surveys didn't record any of these items being consumed, but they 10 are available for future use. 11

The market share within each of these baskets was set following careful 12 consideration of the amount of food items within each basket, the number of items 13 actually consumed and "expert knowledge". For the 'Individual' model we were able to 14 use the food item descriptions to allocate them to particular baskets. In some cases the 15 packaging was described; e.g. 'canned' meaning 100% market share in packaging of 16 interest, 'raw' or 'fresh' meaning 0% market share in cans, etc. In other cases expert 17 judgement was used to allocate the packaging likely. 18 19

Results and discussion 20

Refinements of basketing: High vs. Intermediate vs. Low vs. Individual 21

The loyalty model runs, for all the surveys, show that as the level of refinement 22 of the baskets increases the assessed exposure of the 97.5 th percentile individuals 23 decrease and the 95% confidence limits around that assessment also decrease (figure 2). 24 This is common across all three scenarios (tables 6, 7 and 8). The results discussed in 1 this paper concentrate on the 97.5 th percentile exposure values as it is the high percentile 2 exposures that the regulators are concerned with. However, we have included the 50 th 3 percentile values in the tables for comparison. 4

This tiered approach suggests that at the cruder level of refinement we are seeing 5 a worst-case scenario and as the refinement progresses the model output approaches a 6 more realistic assessment. This demonstrates that this is a conservative approach and 7 that the high level approach may provide an assessment that is likely to over-estimate 8 the exposure. The question arises; could exposure be higher if loyalty was applied 9 across the baskets giving a greater number of items contributing to exposure. However, 10 we would suggest that that is what we are modelling at the high level of the hierarchy as 11 there are only a few baskets and it is likely that the high percentile values we are 12 reporting on will be from individuals who have had loyalty applied in several or all of 13 their baskets. 14 15

Effects of loyalty by hub code hierarchical level 16

The effect of packaging loyalty is striking. In each of the four surveys the trend 17 along the gradient of refinement for the models is the same (figure 2). For the loyal 18 models, the level of exposure is reduced as the refinement increases, however for the 19 non-loyal models, the exposure increases along the level of refinement. The size of the 20 95% confidence interval in the loyal models also reduces as the level of refinement 21 increases, although for the non-loyal model there is no apparent trend (figure 3). These 22 data can be explained by considering the way the models have been set up and the effect 23 that loyalty has on the output. 24 The data shown in Figure 2 is for the individual at the 97.5 th percentile level of 1 exposure. The high percentile individuals are likely to be high consumers who have had 2 a relatively high proportion of their food consumption from items packaged in materials 3 (as chosen by the random number generator utilising market share values) that contain 4 the example migrant. This high consumption leads to high exposure. 5

In the loyal model runs the functionality works in such a way that ensures 6 exposure from all subsequent items consumed from a basket once the first item in that 7 basket has been chosen as packaged. The individuals with the highest exposure are 8 therefore likely to be those who have had all their baskets linked with the migrant. 9

Hence, at the high percentiles, the number of items has a much greater influence on 10 exposure than the market share values. 11

As the level of refinement increases from High to Individual, the number of 12 items that are potentially linked to exposure decreases and the number of baskets 13 increases (see tables 2 -5). So, for those high percentile individuals, even when they 14 have all of their baskets linked to exposure, there will be fewer items contributing to the 15 exposure levels at each increasing level of refinement. This, coupled with the fact that 16 as the number of baskets increase, there is a greater chance of an individual not having 17 all their baskets linked to exposure, leads to the reduction in exposure at the high level 18 percentile levels as the refinement increases. 19

In contrast, the exposure values in the non-loyal model runs increase as the 20 models are refined. In this model, each food item has the random number generator 21 applied to the market share values to see if its packaging type contains the migrant. 22

Hence, the amount of exposure at all percentiles is influenced by both the number of 23 items and the market share values. 24 Although there are fewer items consumed in baskets that are linked to the exposure 3 data, the high percentile individuals are likely to be those whose consumption contains a 4 larger proportion of food items that are still linked to exposure and hence, the exposure 5 at the high percentile is likely to increase. 6

At the individual level (model B4), the exposure values for the loyal and non-7 loyal runs are converging. This is because we are much more certain about the market 8 share of the baskets at this level (table 5), with half of the baskets having a market share 9 of 100%. Therefore loyalty has no effect on the exposure from those baskets and we 10 would therefore expect the exposure values to be closer than where the uncertainty is 11 greater. 12 13

Effects of Non Detects 14

The importance of 'non detects' was described in Oldring et al. (2005) and using 15 three different scenarios allowed this to be evaluated here too. The increase in LOD 16 value from 0 to 3 to 15 µg dm -2 across the three scenarios causes considerable increases 17 in the exposure estimate in all surveys and in both models (figure 4; table 9). This 18 highlights that, for a migrant with a large amount of food packaged in materials for 19 which there is only concentration data consisting of non-detects, it is important for a 20 The inclusion of a second survey on UK adults provides an opportunity to 2 examine how exposure changes over time (1986 vs 2001), which is an issue raised by 3 the Authorities. Some general conclusions can be made about the data. Foodstuff 4 consumption is higher in 2001 than 1986 by 11%, which correlates with increased body 5 weights up from a mean of 70 kg in 1986 to 76 kg in 2001. The data showed that the 6 consumption of canned food decreased over this period, which is confirmed by a 7 reducing canned food market. However as the packaging for many of the food items 8 consumed in 1986 and indeed in 2001 is unknown, direct comparison of exposure is 9 difficult. This is further complicated due to the relatively small market share of canned 10 food compared to other forms of packaging, hence even a significant change for 11 exposure to migrants from canned food may have minimal effect on overall exposure. 12 An added complication is that the coatings used for canned foodstuffs may (most 13 certainly will) have changed during this period and as a generalisation migration has 14 been reduced. For our purposes we assumed the same market shares and coatings in 15 1986 and 2001, but on an individual level this was facilitated by many of the item 16 descriptions indicating if they were canned or not. 17 -loyal), whilst for Adults and Seniors there is an overestimate of 10 -25%. Therefore 2 whenever practical the actual body weights of persons in any survey should be recorded. 3

Where surveys only have limited data, such as store exit basket surveys, then 4 corrections for over-and under-estimates need to be considered. 5 6 Conclusions 7

The use of the Hub Codes has created a system by which consumption data from 8 different surveys in different countries can be relatively easily related in order to 9 standardise the approach for assessing exposure to migrants from packaging. In future 10 work we plan to use these Hub Codes for modelling exposure using food consumption 11 data from other European countries. 12

The hierarchical structure of the Hub Codes facilitates the approach for 13 assessing exposure by only using a more refined approach (Hub Code level) when the 14 exposure estimate gives rise to concern. As one progresses from the least refined to the 15 most refined, the estimates of exposure at the 97.5 th percentile actually increases for the 16 non-loyal consumer (up to 20% extra depending on age) and decreases (down by 30 to 17 50%) for the loyal consumer. Thus, packaging loyalty is an important factor and as one 18 proceeds from high to low Hub Code levels, as well as at the individual item level, the 19 estimates of exposure obtained from loyal and non-loyal model runs converge. For the 20 critical high-percentile and loyal consumer, the less refined levels indeed give 21 conservative estimates for exposure. 22

The non-detect values used play a major role in any estimate of exposure where 23 there is a significant number of food items linked to concentration data consisting 24 16 mainly or solely of non-detects. For packaging and foodstuffs where the levels of the 1 migrant are non-detectable, reducing the level of detection will legitimately reduce the 2 estimate of exposure. Of course, once detected nothing is gained by lowering the level 3 of detection. Given their importance, the treatment of non-detects could be a fruitful 4 area for further stochastic modelling. 5

Whilst it is possible to estimate changes in exposure over time, provided survey 6 data are available, unless the packaging types and migrant concentrations at those points 7 in time are known any conclusions are questionable. A new form of packaging being 8 introduced between the survey dates would result in different exposures to any migrants 9 from it. The conclusion is that the changes in packaging are more likely to influence 10 exposure over time than differences in food consumption, hence the resolution of how 11 exposure varies over time can be resolved at a high level by the packaging industry 12 periodically reviewing any significant changes in packaging type or composition, as is 13 done in the USA for the FDA 'statistics'. 14

The use of 60 kg as a default body weight instead of the individual's actual body 15 weight underestimates any exposure estimate for youths but overestimates it for adults 16 and seniors. 17

Taken together, we conclude that increased knowledge of the packaging used, 18 the migrant concentration and the body weight of individuals and where relevant using 19 as low a level of detection as possible will benefit exposure assessments more than 20 improving knowledge about the amount of foodstuffs consumed. 21

Finally the Hub Code approach is capable of further refinement or adjustment 22 should it be necessary as a consequence of incorporating new food consumption surveys 23 or surveys from other countries or to meet the demands of new applications such as the 24 21B Other reduced fat spread spreads containing more than 40% and less than 80% fat, not claiming to be high in polyunsaturated fatty acids; includes spreads made with olive oil, rapeseed oil or fish oil 22 Bacon and ham 22R Bacon and ham including bacon and gammon joints, steaks, chops and rashers; all types of ham, pork shoulder, bacon and cheese grills 23 Beef, veal and dishes 23R Beef, veal and dishes includes beef and veal joints, steaks, minced beef, stewing steak, beef stews, casseroles, meat balls, lasagne, chilli con carne, beef curry, bolognaise sauce, shepherds pie, canned beef 24 Lamb and dishes 24R Lamb and dishes includes lamb joints, chops, cutlets, fillets, lamb curries, Irish stew, lamb casseroles and stews 25 Pork and dishes 25R Pork and dishes includes joints, chops, steaks, belly rashers, pork stews and casseroles, sweet and sour pork, spare ribs, roast roll 26 Coated chicken and turkey 26R Coated chicken and turkey chicken and turkey pieces coated in egg and crumb; drumsticks, nuggets, fingers, burgers etc. Includes Kentucky Fried Chicken, chicken kiev 27 Chicken and turkey dishes 27R Chicken and turkey dishes includes roast chicken and turkey, barbecued, fried (no coating), curries, stews, casseroles, chow mein, tandoori, in sauce, spread, chicken/turkey roll 28 Liver, liver products and dishes 28R Liver, liver products and includes all types of liver -fried, stewed, grilled, dishes braised; liver casserole, liver sausage, liver pate 29 Burgers and kebabs 29R Burgers and kebabs includes beefburgers, hamburgers, cheeseburgers, (with or without roll) doner/shish/kofte kebabs (with or without pitta bread and salad), grillsteaks, steaklets 30 Sausages 30R Sausages includes beef, pork, turkey sausages, polony, sausage in batter, saveloy, frankfurters, sausage dishes 31 Meat pies and pastries 31R Meat pies and pastries any type of meat; includes chicken/turkey pies, vol-au-vents, beef pies, steak and kidney pudding, pork pies, veal and ham pie, pasties, sausage roll, meat samosas, pancake rolls 32 Other meat and meat products 32R Other meat and meat includes game (e.g. venison, grouse, rabbit, products pheasant), duck, goose, all offal (except liver), faggots, black pudding, haggis, haslet, meat paste, tongue, luncheon meats, corned beef, salami, pepperami, meat loaf 33 White fish coated and/or fried including fish fingers 33R White fish coated and/or cod, haddock, plaice, etc. fried without coating, fried including fish fingers or coated in egg and crumb, batter or flour and fried, grilled or baked. Includes fish fingers and fish cakesfried and grilled, fried cartilaginous fish, scampi, filet-o-fish, cod roe fried, prawn balls, fish feasts, fish pancakes 34A Other white fish and fish cod, haddock, plaice etc. poached, steamed, dishes baked, grilled, smoked, dried; includes curried fish, fish in sauce, fish pie, kedgeree 34 Other white fish, shellfish and fish dishes 34B Shellfish all types including mussels, prawns, crabs, shellfish dishes

  18

  Standards Agency undertook a new adult survey in their National 6 Diet and Nutrition Survey (NDNS) programme between July 2000 and June 2001 7

  (4.3.3) with associated simulants of O, O and D/5 respectively. 25

  share assumptions as follows: within each iteration of the Monte Carlo 1

  each food group as those added to each basket could all have the 1 same assumptions. 2 The parameters for which values are required in each basket are (refer to Holmes 3 et al, 2005 for a more detailed explanation of these parameters): 4 1. Market share: what proportion of the items in the basket are packaged in a 5 particular packaging material 6 2. Conversion factor: An estimate of the surface area:volume ratio 7 3. Extrapolation factor: Used if adjustments to the concentration data are 8 required 9 4. Concentration data: results from analysis of migration from packaging into 10 foodstuffs or simulants 11 12At the start of each model build a decision was made as to whether the recipes 13 should be treated as purchased and pre-packaged items (i.e. left within the hub codes) or 14 should be put into a 'recipe' basket enabling them all to be looked at individually, 15 thereby allowing them to be allocated to part of a meal grouping. In models B1-B3, the 16 recipes were left within the hub codes. In model B4 each recipe item was looked at 17 individually and the extrapolation factor was varied between 0 and 1 to represent the 18 content of the meal with the component that could contribute to the exposure to the 19 migrant of interest. 20 See tables 2 -5 for food groupings used in each model (B1 -B4 respectively), 21 the parameters used and the number of items per grouping (basket). In models B1 to B3 22 extrapolation factors below 1 were used to allow for simulant D reduction factors (D/X) 23 where X=2 to 5. It should be noted that in the individual model (B4) two differences 24 to the others. Firstly two groups were further sub-divided 1

  increases, the market share values for each basket increase, 1 making it more likely that individual items are packaged in the packaging of interest. 2

  good exposure assessment to consider the possible effects of what the non-

18

  Comparison with EU 60kg body weight 19The use of the dietary surveys allows us to express the exposure values in terms 20 of the actual body weight of the individuals whose consumption was recorded and 21 hence compare these values with the data from the same model runs using the EU 22 standard approach of 60 kg as the body weight of all of the individuals in the survey. 23The mean weights were Youths 41 (range 15 -150) Kg, Seniors 68 (30.5 -121)Kg, 24 Adults (1986 / 2001) 70 (38 -144) and 76 (39 -200) kg. For Youths the use of 60 kg 25 an underestimate of actual exposure by 33 (individual) to 40% (high 1

  FACET, 2008). The FACET project(Oldring et al., 2009) will further 1 develop and validate the use of stochastic modelling for exposure assessment. , European Council of Paint, Printing Ink and Artists' Colours Industry 3 (CEPE), Confederation of European Paper Industries (CEPI), European Aluminium 4 Association (EAA), European Phenolic Resins Association (EPRA), Ecological and 5 Toxicological Association of Dyes and Organic Pigments (ETAD), European Wax 6 Federation (EWF), Food Contact Additives Panel (FCA), Flexible Packaging Europe 7 (FPE), PlasticsEurope, European Secretariat of Manufacturers of Light Metal Packaging 8 (SEFEL). The statements and conclusions in this paper are the responsibility of the 9 authors alone and they should not be taken to represent the opinion of the sponsors of Hart, A, Holmes, M.J, Oldring, P.K.T. 2006. Approach to stochastic 14 modelling of consumer exposure for any substance from canned foods using simulant 15 migration data. Food Additives and Contaminants, 23: 528-538. 16 FACET 2008. Flavours, Additives and Contact materials and Exposure Task. A new 17 FP7 project for the 7 th Framework DG research programme. (website address to be 18 added once available). 19 Hoare, J., Henderson, L., Bates, C.J., Prentice, A., Birch, M., Swan, G., Farron, M. 20 2004. The National Diet & Nutrition Survey: adults aged 19 to 64 years. .J., Hart, A., Northing, P., Oldring, P. K. T., Castle, L., Stott, D., Smith, G., 1 Wardman, O. 2005. Dietary exposure to chemical migrants from food contact 2 materials: A probabilistic approach. Food Additives and Contaminants, 22: 907-919 3 ILSI 2007. Guidance for exposure assessment of substances migrating from food 4 packaging materials. B. Brands, L. Castle, E. Duffy, R. Franz, L. Garcia, M. Gibney, 5 A. Hart and P. Oldring. ILSI Europe (Brussels, Belgium), 2007. ISBN: 6 9789078637097. 7 Matsumoto M, Nishimura T. 1998. Mersenne Twister: A 623-dimensionally 8 equidistributed uniform pseudorandom number generator. ACM Transactions on 9 Modeling and Computer Simulation 8(1): 3-30. 10 Oldring, P. K. T., Castle, L., Hart, A., Holmes, M.J. 2006. Migrants from food cans 11 revisited -Application of a stochastic model for a more realistic assessment of 12 exposure to Bisphenol A Diglycidyl Ether (BADGE). Packaging Technology and 13 Science, 19: 121 -137. 14 Oldring P.K.T., Castle L., Franz R. 2009). Exposure to substances from food contact 15 materials and an introduction to the FACET project (Food Additives and 16 Contaminants, submitted). 17 SCOOP. 1996 -reports on tasks for scientific cooperation -Report of experts 18 participating in Task 4.1: Improvement of knowledge of food consumption with a 19 view to protection of public health by means of exchanges and collaboration between 20 database managers, EUR 17528 EN. 21 UKDA. 1990. Study No. 2836 Dietary and Nutritional Survey of British Adults, 1986 -22 1987. Dietary and Nutritional Survey of British Adults. UK Data Archive. 23 Study No. 3481 National Diet, Nutrition and Dental Survey of Children 1 Aged 1.5 -4.5 years, 1992 -1993, UK Data Archive. 2 UKDA. 1998. Study No. 4036 National Diet and Nutrition Survey, 1994 -1995, People 3 Aged 65 Years and Over. UK Data Archive. 4UKDA. 2000. Study No. 4243 National Diet and Nutrition Survey, 1997, Young People 5 Aged 4 -18 Years. UK Data Archive.

Figure 1 : 5 Figure 2 :Figure 3 :Figure 4 :Page

 15234 Figure 1: Mapping of member states food consumption survey classifications, A) each 1

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Table 1 :

 1 Dietary Surveys within the Chemical Migrants system 1

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	Named here	Year	Age	Duration Number	Reference:
	UK Adults	1986/7 16 -64	7	2197	UKDA, 1990
	UK Seniors	1994/5	65+	4	1687	UKDA, 1998
	UK Youths	1997	4-18	7	1701	UKDA, 2000
	UK New Adults	2001	19 -64	7	1724	Hoare et al, 2004
	2
	3

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n l y 23Table 3 :

 3 Baskets and associations for Model B2 1

	Model B2 -

Intermediate groupings Conversion Factor Extrapolation Factor * Market Share (in %) Basket Concentration Group Low Mid High Low Mid High Low Mid High No. Items

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		Other (includes deserts)	simulant D	6	8	12	0.2	0.2	0.2	5	10	15	71
		Water	simulant A,B,C	6	8	12	1	1	1	0	5	10	16
		Not relevant for metal packaging	None										2117
		beers/lagers/ciders Total number of items	simulant A,B,C	6	8	12	1	1	1	15	30	45	62 4644
	1 2	ready to drink juices * where extrapolation factor not = 1, then this indicates simulant D reduction factors have been used simulant A,B,C 6 8 carbonated pH <4.5 simulant A,B,C 6 8	12 12	1 1	1 1	1 1	5 15	10 30	15 45	93 65
	3	pasta+D18 & noodles	simulant A,B,C	6	8	12	1	1	1	10	15	30	54
	4	Rice	simulant A,B,C	6	8	12	1	1	1	5	10	15	29
	5 6	milk & milk based drinks wet	simulant A,B,C	6	8	12	1	1	1	0	2.5	5	52
	7	Fish	simulant D	14	17	19	0.33 0.33	0.33	15	20	25	126
	8	fish & shellfish ready meals/pates etc inc. main meal components simulant D	14	17	19	0.2	0.2	0.2	25	35	50	50
		shellfish	simulant A,B,C	14	17	19	1	1	1	1	2	3	36
		processed	simulant A,B,C	6	8	12	1	1	1	10	30	45	170
		meat & game inc. minced and sausages	simulant D	7	11	19	0.25 0.25	0.25	5	10	15	306
		poultry	simulant D	7	11	19	0.25 0.25	0.25	2.5	5	7.5	88
		offal	simulant D	7	11	19	0.25 0.25	0.25	2.5	5	7.5	46
		ready meal and main meal component	simulant D	7	11	19	0.25 0.25	0.25	5	10	15	447
		cream and cream based dishes/ toppings	simulant D	12	14	16	0.5	0.5	0.5	2.5	5	7.5	43
		soups	simulant D	6	8	12	0.2	0.2	0.2	60	65	70	50
		Cooking sauces	simulant A,B,C	6	8	12	1	1	1	10	20	40	31
		Dressings	simulant A,B,C	0.1 0.2 0.8	1	1	1	50	70	100	20
		Condiments, herbs, spices, ketchup, sauces, dips	simulant A,B,C	0.1 0.2 0.8	1	1	1	20	40	60	123
		sugar spreads (jams / honey)	simulant A,B,C	0.4 1.2	4	1	1	1	50	70	100	24
		potatoes/chips	simulant A,B,C	6	8	12	1	1	1	1	2	3	53
		Brassicas/leafy/lettuce	simulant A,B,C	6	8	12	1	1	1	1	2	3	41
		Legumes	simulant A,B,C	6	8	12	1	1	1	25	35	45	71
		salad vegetables/root vegetables (other than potatoes)	simulant A,B,C	6	8	12	1	1	1	5	10	15	149
		milk based product inc powders to which milk is added	simulant A,B,C	6	8	12	1	1	1	0	0.5	1
			24

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Table 4 :

 4 Baskets and associations for Model B3 2

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			Conversion	Extrapolation		Market Share
	Model B3 -Low level groupings		Factor	Factor *		(in %)
		Concentration								No.
	Basket	Group	Low Mid High Low Mid High Low Mid High	Items
	beers/lagers/ciders: alcoholic	simulant A, B, C	6	8 12	1	1	1 15 30 45 51
	beers/lagers/ciders: low alcohol <0.1% A	simulant A, B, C	6	8 12	1	1	1 15 30 45 11
	ready to drink juices: acidic	simulant A, B, C	6	8 12	1	1	1	5 10 15 92
	carbonated pH<4.5 B NOT water: carbonated pH<4.5 B NOT water	simulant A, B, C	6	8 12	1	1	1 15 30 45 65
	Water: Carbonated	simulant A, B, C	6	8 12	1	1	1	0	5 10 8
	pasta+D18 & noodles: processed D/3	simulant D	6	8 12 0.33 0.33 0.33 10 20 30 3
	pasta+D18 & noodles: processed D/4	simulant D	6	8 12 0.25 0.25 0.25 10 20 30 15
	pasta+D18 & noodles: processed A	simulant A, B, C	6	8 12	1	1	1 10 20 30 5
	pasta+D18 & noodles: processed B	simulant A, B, C	6	8 12	1	1	1 10 20 30 17
	rice: processed A	simulant A, B, C	6	8 12	1	1	1 10 25 40 17
	Other (includes deserts): fatty D/5	simulant D	6	8 12 0.2 0.2 0.2	5 10 15 61
	milk & milk based drinks wet: milk & milk based drinks wet	simulant A, B, C	6	8 12	1	1	1	0 2.5	5 52
	fish: processed e.g. smoked D/3	simulant D	14 17 19 0.33 0.33 0.33	0 2.5	5 16
	fish: processed (e.g. breadcrumbs etc) D/5	simulant D	14 17 19 0.2 0.2 0.2 10 20 30 12
	fish: processed oily fish D	simulant D	14 17 19	1	1	1 100 100 100 9
	fish: processed in sauce B,D/3	simulant D	14 17 19 0.33 0.33 0.33 50 70 100 7
	fish: processed A	simulant A, B, C	14 17 19	1	1	1 50 70 100 11
	fish & shellfish ready meals/pates etc inc. main meal components: ready meal D/3	simulant D	14 17 19 0.33 0.33 0.33 10 20 30 6
	fish & shellfish ready meals/pates etc inc. main meal components: ready meal D/5	simulant D	14 17 19 0.2 0.2 0.2 10 20 30 24
	fish & shellfish ready meals/pates etc inc. main meal components: ready meal B,D/3	simulant D	14 17 19 0.33 0.33 0.33 10 20 30 2
	shellfish: processed D/5	simulant D	14 17 19 0.2 0.2 0.2 50 70 100 1
	shellfish: processed A	simulant A, B, C	14 17 19	1	1	1 50 70 100 9
	processed: acidic	simulant A, B, C	6	8 12	1	1	1 10 40 60 134

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uk Food Additives and Contaminants F o r P e e r R e v i e w O n l y 28Table 5 :

 5 Baskets and associations for Model B4 1 , then this indicates simulant D reduction factors have been used, except where it is part of a meal and the range for the extrapolation factor has been adjusted accordingly ** indicates group where the contents could be in either a can or a jar with a metal closure, hence two conversion factors

		sauces as part of meal jar/can simulant B or D/5	simulant A, B, C	0.35		1.2		4	0.3	0.5	0.7	100	100	100	24
		Model B4 -Individual groupings Part of meal simulant A or B simulant A, B, C	Conversion Factor Extrapolation Factor * Market Share (in %) 6 8 12 0.4 0.5 0.7 25 35 50	16
		Basket bottled fruit juices with possible metal closure	Concentration Group Low Mid High simulant A, B, C 0.35 1.2	4	Low	1	Mid	1	High	1	Low Mid 10 20	High No. Items 30 6
		100% canned food including fish simulant D beers possibly canned	simulant D simulant A, B, C	14 6		17 8	19 12		1 1		1 1		1 1	100 20	100 30	100 50	9 19
		beverages possibly canned possible large metal closure simulant A Jar or Can Sim D/5 ** Jar or Can Sim D/5 **	simulant A, B, C simulant A, B, C simulant D simulant D	6 0.35 14 0.35		8 1.2 17 1.2	12 4 19 4	1 1 0.33 0.33	1 1 0.33 0.33	1 1 0.33 0.33	5 2.5 20 20	10 5 35 35	15 50 10 50	85 1 4
		possibly canned D & D/x small MS Not relevant for metal packaging	simulant D None	6	3	8	12	0.25	0.25	0.25	2.5	5	10	60 3882
	Jar or Can Sim A/B ** Total number of items Jar or Can Sim A/B ** sauce bottle small closure simulant D/5 Aerosols * where extrapolation factor not = 11 100% canned alcoholic beverage 2 3	simulant A, B, C simulant A, B, C simulant D simulant D simulant A, B, C	0.35 6 0.08 12 6		1.2 8 0.2 14 8	4 12 0.8 16 12	1 1 0.2 0.5 1	1 1 0.2 0.5 1	1 1 0.2 0.5 1	20 20 30 100 100	35 35 50 100 100	50 50 70 100 100	4644 7 25 5 27
	4	100% canned beverages simulant B	simulant A, B, C	6		8	12		1		1		1	100	100	100	41
		100% food canned simulant B	simulant A, B, C	6		8	12		1		1		1	100	100	100	97
		100% food canned simulant A	simulant A, B, C	6		8	12		1		1		1	100	100	100	51
		100% canned fish simulant A	simulant A, B, C	14		17	19		1		1		1	100	100	100	20
		100% canned food simulant D/5	simulant D	6		8	12	0.2	0.2	0.2	100	100	100	28
		100% food canned D/3	simulant D	6		8	12	0.33	0.33	0.33	100	100	100	7
		100% fish canned simulant D/3	simulant D	14		17	19	0.33	0.33	0.33	100	100	100	10
		100% food canned simulant D/4	simulant D	7		11	19	0.25	0.25	0.25	100	100	100	28
		sauce small closure simulant A B	simulant A, B, C	0.08		0.2	0.8		1		1		1	30	50	70	24
		jars large closure simulant A & B	simulant A, B, C	0.35		1.2		4		1		1		1	30	50	70	40
		jars with large closure simulant D/2, D/3, D/4 & D/5 simulant D	0.35		1.2		4	0.33	0.33	0.33	30	50	70	35
		possibly large metal closure simulant D/2, D/3	simulant D	0.35		1.2		4	0.5	0.5	0.5	2.5	5	10	17
		possibly canned simulant A & B	simulant A, B, C	6		8	12		1		1		1	15	25	35	22
		possibly canned soups (large M/s) simulant D/5	simulant D	6		8	12	0.2	0.2	0.2	20	35	50	22
		part of meal simulant D and D/x large portion	simulant D	7		11	19	0.08	0.12	0.16	100	100	100	8
		100% canned part of meal Simulant A or B	simulant A, B, C	6		8	12	0.2	0.3	0.45	100	100	100
				29

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 6 Exposure estimates for all models under Scenario 1 (the concentration data for simulants A, B & C has one non-detect value and LOD = 3).

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	1								Data as
	2	µgkg -1 body weight day -1 (using actual body weight)
	3
	4
		Percentile Survey		High	Intermediate	Low	Individual
				Loyal	Non-Loyal	Loyal	Non-Loyal	Loyal	Non-Loyal	Loyal	Non-Loyal
			Youth	0.429 (0.186 -0.867) (0.124 -0.336) (0.179 -0.536) (0.133 -0.322) (0.148 -0.480) (0.139 -0.313) (0.143 -0.347) (0.141 -0.325) 0.204 0.31 0.209 0.274 0.199 0.222 0.212
		97.5th	Adult New Adult	0.359 (0.144 -0.708) (0.093 -0.31) (0.139 -0.474) (0.099 -0.296) (0.140 -0.464) (0.133 -0.309) (0.142 -0.481) (0.142 -0.35) 0.176 0.26 0.171 0.245 0.189 0.258 0.207 0.398 0.187 0.267 0.173 0.253 0.195 0.229 (0.166 -0.802) (0.106 -0.32) (0.150 -0.482) (0.103 -0.281) (0.146 -0.423) (0.141 -0.304) (0.143 -0.365) (0.14 -0.352) 0.222
			Seniors	0.32 (0.148 -0.641) (0.115 -0.27) (0.144 -0.307) (0.121 -0.213) (0.129 -0.262) (0.124 -0.218) (0.129 -0.228) (0.128 -0.225) 0.177 0.202 0.159 0.179 0.159 0.172 0.17
			Youth	0.017 (0.007 -0.028) (0.019 -0.12) (0.004 -0.066) (0.018 -0.102) (0.004 -0.062) (0.008 -0.089) (0.004 -0.077) (0.006 -0.079) 0.066 0.039 0.061 0.036 0.047 0.043 0.044
		50th	Adult New Adults	0.024 (0.007 -0.038) (0.018 -0.117) (0.005 -0.044) (0.018 -0.068) (0.005 -0.038) (0.008 -0.054) (0.008 -0.049) (0.009 -0.062) 0.064 0.028 0.045 0.024 0.031 0.031 0.037 0.027 0.07 0.031 0.049 0.024 0.032 0.025 (0.008 -0.041) (0.021 -0.128) (0.004 -0.05) (0.018 -0.077) (0.004 -0.039) (0.007 -0.056) (0.003 -0.043) (0.004 -0.044) 0.025
			Seniors	0.015 (0.003 -0.029) (0.01 -0.099) (0.001 -0.029) (0.01 -0.045) (0.001 -0.021) (0.002 -0.027) (0.001 -0.031) (0.002 -0.031) 0.053 0.017 0.029 0.012 0.015 0.018 0.018
	5
	6

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n l y 31Table 7 :

 7 Exposure estimates for all models under Scenario 2 (the concentration data for simulants A, B & C has one non-detect value and LOD = 0).

	1	Data as
	2	µgkg -1 body weight day -1 (using actual body weight)
	3
		4
		5
		6
		7

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n l y 32Table 8 :

 8 Exposure estimates for High (B1) and Individual (B4) models under Scenario 3 (the concentration data for simulants A, B & C has one non-detect 1 value and LOD = 15). Data as µgkg -1 body weight day -1 (using actual body weight) 2 3

	Percentile Survey	High	High	Individual	Individual
			Loyal	Non-Loyal	Loyal	Non-Loyal
			1.832	0.659	0.658	0.680
		Youth	(0.225 -4.28)	(0.144 -1.377)	(0.163 -1.486)	(0.156 -1.340)
			1.575	0.640	0.931	0.714
	97.5th	Adult	(0.174 -3.476) 1.726	(0.11 -1.388) 0.640	(0.163 -2.199) 0.706	(0.151 -1.523) 0.752
		New Adult	(0.202 -3.925)	(0.122 -1.342)	(0.159 -1.645)	(0.152 -1.552)
			1.428	0.520	0.348	0.373
		Seniors	(0.171 -3.254)	(0.132 -1.060)	(0.144 -0.728)	(0.137 -0.711)
			0.021	0.237	0.149	0.173
		Youth	(0.011 -0.035)	(0.030 -0.475)	(0.015 -0.319)	(0.011 -0.335)
			0.034	0.235	0.086	0.119
	50th	Adult	(0.014 -0.056) 0.040	(0.030 -0.476) 0.183	(0.016 -0.168) 0.079	(0.013 -0.218) 0.093
		New Adult	(0.015 -0.069)	(0.033 -0.515)	(0.009 -0.161)	(0.007 -0.174)
			0.025	0.195	0.057	0.063
		Seniors	(0.006 -0.045)	(0.022 -0.401)	(0.006 -0.112)	(0.004 -0.115)
	4
	5

Page 32 of 51 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 33Table 9 :

 9 Exposure estimates for High (B1) and Individual (B4) models under all scenarios. Data as µgkg -1 body weight day -1 (using actual body weight) 1 2

	Page 33 of 51			Food Additives and Contaminants
			1	Appendix I:
					High
				ABC (ND=0) 1A Pasta all types -dried, fresh and canned; including egg noodles, macaroni cheese, ravioli, canned ABC (ND=3) ABC (ND=15)
			Loyal 1 Pasta, rice and	Non-Loyal spaghetti bolognaise Loyal	Non-Loyal	Loyal	Non-Loyal
		0.208 (0.160 -0.276) (0.103 -0.155) (0.186 -0.867) (0.124 -0.336) (0.225 -4.28) (0.144 -1.377) 0.127 0.429 0.204 1.832 0.659 0.154 0.094 0.359 0.176 1.575 other Youth 1B Rice fried and boiled, savoury rice, egg fried rice, rice flakes, rice flour. (Not rice pudding) miscellaneous 1C Pizza all types -thin & crispy, deep pan, French bread cereals 0.64 Adult 1R Other cereals includes flour, bran, oats, dry semolina, papadums, dumplings, Yorkshire pudding
		New	(0.121 -0.201) (0.078 -0.112) (0.144 -0.708) (0.093 -0.31) (0.174 -3.476) (0.11 -1.388) 0.182 0.108 0.398 0.187 1.726 0.64 2R White bread sliced, unsliced, toast, fried; includes French stick, milk loaf, slimmers, pitta bread, rolls, 2 White bread chappatis, soda bread
		Adult Seniors 3 Wholemeal (0.144 -0.237) (0.088 -0.128) (0.166 -0.802) (0.106 -0.32) (0.202 -3.925) (0.122 -1.342) 0.157 0.12 0.32 0.177 1.428 0.52 (0.129 -0.191) (0.099 -0.145) (0.148 -0.641) (0.115 -0.27) (0.171 -3.254) (0.132 -1.060) bread 3R Wholemeal bread sliced, unsliced, toast, fried; includes chappatis, pitta bread, rolls, hi-bran bread, wholemeal soda bread F 4A Softgrain bread sliced, unsliced, toast, fried, rolls, fortified and not fortified o r 4R Other breads sliced, unsliced, toast, fried; includes brown, granary, high fibre white, rye bread, gluten 4 Other breads free, garlic bread, continental breads e.g. ciabatta, oatmeal bread, Vitbe, Hovis, crumpets, English muffins Individual ABC (ND=0) ABC (ND=3) ABC (ND=15) Loyal Non-Loyal Loyal Non-Loyal Loyal (white & wholemeal), pikelets, brown and granary rolls, bagels, brioche, naan, paratha 5 Wholegrain and P high fibre 5R Wholegrain and high fibre all with non-starch polysaccharide (Englyst fibre) breakfast cereals of Non-Loyal 0.148 0.147 0.222 0.212 0.658 0.68 Youth (0.125 -0.174) (0.126 -0.173) (0.143 -0.347) (0.141 -0.325) (0.163 -1.486) (0.156 -1.340) 0.145 0.145 0.258 0.207 0.931 0.714 Adult (0.128 -0.163) (0.128 -0.163) (0.142 -0.481) (0.142 -0.35) (0.163 -2.199) (0.151 -1.523) breakfast cereals 4g/100g or more, e.g. All Bran, muesli, Shredded Wheat. Includes porridge & Ready Brek e 6 Other breakfast cereals 6R Other breakfast cereals all with non-starch polysaccharide (Englyst fibre) of less than 4g/100g, e.g. cornflakes, Coco Pops, Sugar Puffs. Includes Pop Tarts e r 7 Biscuits
	3	New Adult Seniors	0.146 (0.127 -0.167) (0.128 -0.167) (0.143 -0.365) (0.14 -0.352) (0.159 -1.645) (0.152 -1.552) 0.146 0.229 0.222 0.706 0.752 0.137 0.137 0.172 0.17 0.348 0.373 (0.117 -0.158) (0.118 -0.159) (0.129 -0.228) (0.128 -0.225) (0.144 -0.728) (0.137 -0.711) R e v i e w
						O n
						l y
						34

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk 7R Biscuits all types, sweet and savoury; includes cream crackers, flapjacks, breadsticks, crispbread, cereal crunchy bars, ice cream cornet 8A Fruit pies all types, one and two crusts; includes apple strudel, individual fruit pies from takeaways 8

Buns, cakes, pastries and fruit pies 8R

  Buns, cakes and pastries includes Danish pastries, currant bun, doughnuts, eccles cakes, bakewell tarts, jam tarts, scones (sweet and savoury), sponge cakes, fruit cakes, eclairs, currant bread, malt loaf, gateaux, pastry, mince pies, sponge fingers, scotch pancakes, croissants, custard tart, lemon meringue pie 9A Cereal-based milk puddings rice pudding (including canned), custard (not egg custard), Angel Delight, blancmange, confectioners custard, semolina, sweet white sauce

	16 Eggs and egg dishes	16A Eggs includes boiled, fried, scrambled, poached, dried, omelettes (sweet and savoury) 16B Egg dishes includes quiches, flans, souffles, scotch eggs, eggy bread, apple snow, meringue, pavlova, curried eggs
	17 Butter2	17R Butter salted and unsalted, butter ghee, spreadable butter
	18 Polyunsaturated	18A Polyunsaturated margarine margarine claiming to be high in polyunsaturated fatty acids
	margarine and
	oils2	18B Polyunsaturated oils includes corn oil, sunflower oil, solid sunflower oil
		19A Low fat spread spreads containing 40% or less fat, claiming to polyunsaturated be high in
	19 Low fat	polyunsaturated fatty acids
	spread2 20 Margarine and F 19R Other low fat spread spreads containing 40% or less fat, not claiming to be high in polyunsaturated fatty acids o r 20A Block margarine all hard margarine 20B Soft margarine not tub margarine not claiming to be high in polyunsaturated polyunsaturated fatty other cooking fats acids and oils not
	polyunsaturated2 20C Other cooking fats and oils, includes blended vegetable oil, suet, lard, not polyunsaturated compound cooking fat, dripping, olive oil, rapeseed oil P 21A Reduced fat spread, spreads containing more than 40% and less polyunsaturated than 80% fat,
	21 Reduced fat spread2	r e claiming to be high in polyunsaturated fatty acids e
	9 Puddings 10 Whole milk 11 Semi-skimmed milk	9B Sponge puddings steamed, canned, suet pudding, jam roly poly, sponge flan, upside down pudding 9R Other cereal-based puddings includes trifle, fruit fritters, pancakes, crumble, bread pudding, cheesecakes, tiramisu, rum baba, Christmas pudding 10R Whole milk all types of cow's milk including pasteurised, UHT, sterilised, Channel Island 11R Semi-skimmed milk all types of cow's milk including pasteurised, UHT, sterilised, canned, milk with added vitamins R e v i e w
	12 Skimmed milk 13 Other milk and cream 14 Cheese	12R Skimmed milk all types of cow's milk including pasteurised, UHT, sterilised, canned, milk with added vitamins, Vital, Calcia 13A Infant formula1 13B Cream all types, including imitation cream, aerosol, dream topping, Tip Top, creme fraiche O n 13R Other milk3 includes soya alternative to milk, goats, sheeps, evaporated, condensed, dried milk, milk shake, coffee whitener, buttermilk, flavoured milk drink 14A Cottage cheese includes diet and flavoured l y
		14R Other cheese all types, including hard, soft, cream cheese,
	15 Yogurt, fromage frais and other dairy	15A Fromage frais includes fromage frais mousse, Quark 15B Yogurt all types including soya, goats, sheeps, yogurt mousse, yogurt drink, frozen yogurt, custard style yogurt, Greek yogurt
	desserts	15R Other dairy desserts includes chocolate and fruit cream desserts, mousse, milk jelly, junket, egg
		custard, buttermilk desserts, fruit fools, creme caramel
	53 Ice Cream	53R Ice cream all types, including non dairy, choc ices, ice cream desserts, ice cream containing lollies, milk ice lollies, low fat/low calorie ice cream
		35

uk Food Additives and Contaminants

  35R Oily fish includes herrings, kippers, mackerel, sprats, eels, herrings roe (baked, fried, grilled), salmon, tuna, sardines, trout, taramasalata, mackerel pate, fish paste 36A Carrots raw 36B Salad and other vegetables all types of raw vegetables, including coleslaw, (raw) fresh herbs. Not salads made with cooked vegetables or potato salad 37R Other vegetables (not raw) includes lentils, dried beans and pulses, mushrooms, onion, aubergine, parsnips, sweetcorn, peppers, leeks, courgettes, cauliflower, mixed vegetables, TVP/soya mince, quorn, tofu 38A Chips fresh and frozen, including oven and microwave, French fries 38B Fried or roast potatoes roast potato, fried sliced potato with or without and fried potato products batter, fried waffles, croquettes, crunchies, alphabites, fritters, hash browns 40R Other fruit, not canned includes plums, grapes, apricots (raw and stewed) etc. fruit pie fillings, dried fruit, fruit salad 56 Nuts and Seeds 56R Nuts and seeds includes fruit and nut mixes, salted peanuts, peanut butter, tahini, bombay mix 57C Ready to drink soft drinks, all types of still soft drinks, not carbonated not low calorie 58A Concentrated soft drinks, all low calorie, no added sugar, sugar free types low calorie3 58B Carbonated soft drinks, all low calorie, no added sugar, sugar free low calorie types, including slimline tonic water. Not carbonated mineral water Meat pies and pastries any type of meat; includes chicken/turkey pies, vol-au-vents, beef pies, steak and kidney pudding, pork pies, veal and ham pie, pasties, sausage roll, meat samosas,

	Page 37 of 51 Page 43 of 51 Page 45 of 51	Food Additives and Contaminants Food Additives and Contaminants Food Additives and Contaminants	Page 46 of 51
	F o r P e e r R e v i e w O n l y 36C Tomatoes (raw) 37A Peas (not raw) includes canned, dried, mushy, frozen, mange tout, pease pudding canned 37B Green beans (not raw) includes French, runner, green beans; fresh, canned, frozen 37C Baked beans canned baked beans in sauce. Includes baked beans with additions e.g. sausages, burgers, pasta 37D Leafy green vegetables includes broccoli, spinach, cabbage (all types), (not raw) brussels sprouts; fresh and frozen 37E Carrots (not raw) includes fresh, frozen, canned 37F Tomatoes (not raw) includes fried, grilled, canned, sundried tomatoes 37G Vegetable dishes (not raw) includes curries, pulse dishes, casseroles and stews, pies, vegetable lasagne, cauliflower cheese, vegieburgers, bubble and squeak, vegetable samosas, pancake rolls, ratatouille, vegetable fingers etc. 37 Vegetables (not 35 Oily fish 36 Salad and other raw vegetables raw) 38 Chips, fried and roast potatoes and potato products 38R Potato products not fried croquettes, waffles, fritters, hash browns, alphabites, ketchips, grilled or oven baked 39 Other potatoes, potato salads and dishes 39R Other potatoes, potato includes boiled, mashed, baked (with or without salads and dishes fat), canned, potato salad, instant potato, potato based curries, cheese and potato pie 42 Crisps and savoury snacks 42R Crisps and savoury snacks includes all potato and cereal based savoury snacks, popcorn (not sweet), twiglets 40A Apples and pears includes raw, baked, stewed (with or without not canned sugar), dried, apple sauce 40B Citrus fruit not canned includes oranges, grapefruit, limes, tangerines, ortaniques etc. 40C Bananas includes baked bananas, banana chips 40D Canned fruit in juice includes canned in water 40E Canned fruit in syrup 40 Fruit 41A Sugar all types, including golden syrup, fructose 41B Preserves includes jam, fruit spreads, marmalade, honey, lemon curd 41 Sugars, preserves and sweet spreads 41R Sweet spreads, fillings, includes ice cream topping sauce, chocolate icings spread, mincemeat, glace cherries, mixed peel, icing, brandy/rum butter, marzipan 43 Sugar confectionery 43R Sugar confectionery includes boiled sweets, gums, pastilles, fudge, chews, mints, rock, liquorice, toffees, chewing gum, sweet popcorn, ice lollies (without ice cream) 44 Chocolate confectionery 44R Chocolate confectionery includes chocolate bars, filled bars, assortments 45 Fruit juice 45R Fruit juice includes 100% single or mixed fruit juices, vegetable juices, canned, bottled, cartons; carbonated, still, freshly squeezed 57 Soft drinks, not 57A Concentrated soft drinks, all types including squashes and cordials not low calorie3 F o r P e e r R e v i e w O n l y 57B Carbonated soft drinks, all types, including tonic water. Not carbonated not low calorie mineral water; not alco-pops low calorie 58 Soft drinks, low calorie 58C Ready to drink soft drinks, all low calorie, no added sugar, sugar free low calorie types. Not carbonated. 47A Liqueurs includes cream liqueurs, Pernod, Southern Comfort, Tia Maria, Cherry Brandy 47 Spirits and liqueurs 47B Spirits 70 % proof spirits -brandy, gin, rum, vodka, whisky 48A Wine white, red, sparkling, rose 48B Fortified wine port, sherry, champagne, vermouth 48 Wine 48C Low alcohol and alcohol includes fruit juice and wine drinks free wine 49A Beers and lagers premium and non premium, stout, strong ale (bottled, draft and canned) 49B Low alcohol and alcohol includes shandy free lager and beer 49C Cider and perry includes Babycham 49D Low alcohol and alcohol free cider and perry 49 Beer, lager, cider and perry 49E Alco-pops includes alcoholic lemonade 51A Coffee (made up) includes instant and leaf bean, decaffeinated, vending machine with whitener, coffee essence 51B Tea (made up) infusion, instant, decaffeinated, vending machine with whitener 51C Herbal tea (made up) includes fruit teas 51D Bottled water includes carbonated and still, herbal tonics, (not sweetened drinks or tonic water) 51 Tea, coffee and water 51R Tap water includes tap water as a drink or used as a diluent for powdered beverages only. Includes filtered tap water. Not water as a diluent for concentrated soft drinks, instant coffee or instant tea 50A Beverages (dry weight)5 includes drinking chocolate, cocoa, ovaltine, horlicks, malted drinks etc. 50B Soups includes homemade, dried, condensed, cartons, ca 50 Miscellaneous 50R Savoury sauces, pickles, includes white sauces, cook in sauces, sauce gravies, condiments mixes, tomato ketchup, pickles, chutney, stuffing, gravy, mayonnaise, salad cream, dried herbs, spices 52A Commercial toddlers drinks includes powdered, concentrated and ready to drink beverages specifically manufactured for young children 52 Commercial toddlers foods and drinks1 52R Commercial toddlers foods includes instant and ready to eat foods specifically manufactured for young children 54A Tablets and capsules vitamin and mineral tablets and capsules; includes cod liver oil and other oil based capsules 54B Oils and syrups includes cod liver oil etc (not capsules), malt extract, multivitamin syrups, iron syrups and tonics 54C Drops and powders includes cold relief powders with vitamin C, multivitamin drops 54 Dietary supplements 54R Nutritionally complete liquid or powdered supplement drinks containing supplements protein/fat/carbohydrate plus vitamins/minerals. Includes Complan, Build Up, Fortisip, Ensure, Provide, Fresubin 55 Artificial sweeteners 55R Artificial sweeteners includes granulated table top sweeteners, tablet, liquid or mini cube sweeteners 1 F o r P e e r R e v i e w O n l y F o r P e e r R e v i e w O n l y 5 3 pancake rolls 6 1 Coated chicken and turkey chicken and turkey pieces coated in egg and crumb; drumsticks, nuggets, fingers, burgers etc. Includes Kentucky Fried Chicken, chicken kiev 6 2 Chicken and turkey dishes includes roast chicken and turkey, barbecued, fried (no coating), curries, stews, casseroles, chow mein, tandoori, in sauce, spread, chicken/turkey roll 7 1 White fish coated and/or cod, haddock, plaice, etc. fried without coating, fried including fish fingers or coated in egg and crumb, batter or flour and fried, grilled or baked. Includes fish fingers and fish cakes -fried and grilled, fried cartilaginous fish, scampi, filet-o-fish, cod roe fried, prawn balls, fish feasts, fish pancakes 7 2 Other white fish and fish cod, haddock, plaice etc. poached, steamed, dishes baked, grilled, smoked, dried; includes curried fish, fish in sauce, fish pie, kedgeree 7 3 Shellfish all types including mussels, prawns, crabs, shellfish dishes 7 4 Oily fish includes herrings, kippers, mackerel, sprats, eels, herrings roe (baked, fried, grilled), salmon, tuna, sardines, trout, taramasalata, mackerel pate, fish paste 8 1 Butter salted and unsalted, butter ghee, spreadable butter 8 2 Polyunsaturated margarine margarine claiming to be high in polyunsaturated fatty acids 8 3 Polyunsaturated oils includes corn oil, sunflower oil, solid sunflower oil 8 4 Low fat spread spreads containing 40% or less fat, claiming to polyunsaturated be high in polyunsaturated fatty acids 8 5 Other low fat spread spreads containing 40% or less fat, not claiming to be high in polyunsaturated fatty acids 8 6 Block margarine all hard margarine 8 7 Soft margarine not tub margarine not claiming to be high in polyunsaturated polyunsaturated fatty acids 8 8 Other cooking fats and oils, includes blended vegetable oil, suet, lard, not polyunsaturated compound cooking fat, dripping, olive oil, rapeseed oil 8 9 Block margarine 8 10 Reduced fat spread, spreads containing more than 40% and less polyunsaturated than 80% fat, claiming to be high in polyunsaturated fatty acids 8 11 Other reduced fat spread spreads containing more than 40% and less than 80% fat, not claiming to be high in polyunsaturated fatty acids; includes spreads made with olive oil, rapeseed oil or fish oil 8 12 Other cooking fats and oils 9 1 Eggs includes boiled, fried, scrambled, poached, dried, omelettes (sweet and savoury) 9 2 Egg dishes includes quiches, flans, souffles, scotch eggs, eggy bread, apple snow, meringue, pavlova, curried eggs 9 3 Egg dishes 10 1 Sugar all types, including golden syrup, fructose 10 2 Preserves includes jam, fruit spreads, marmalade, honey, lemon curd 10 3 Sweet spreads, fillings, includes ice cream topping sauce, chocolate icings spread, mincemeat, glace cherries, mixed peel, icing, brandy/rum butter, marzipan 11 1 Peas (not raw) includes canned, dried, mushy, frozen, mange tout, pease pudding canned 11 2 Green beans (not raw) includes French, runner, green beans; fresh, canned, frozen 11 3 Leafy green vegetables includes broccoli, spinach, cabbage (all types), (not raw) brussels sprouts; fresh and frozen 12 1 Chips fresh and frozen, including oven and microwave, French fries 12 2 Fried or roast potatoes roast potato, fried sliced potato with or without and fried potato products batter, fried waffles, croquettes, crunchies, alphabites, fritters, hash browns 12 3 Potato products not fried croquettes, waffles, fritters, hash browns, alphabites, ketchips, grilled or oven baked 12 4 Other potatoes, potato includes boiled, mashed, baked (with or without salads and dishes fat), 15 21 coffee essence canned, potato salad, instant potato, potato based curries, cheese and potato pie F o r P e e r R e v i e w O n l y 12 5 Crisps and savoury snacks includes all potato and cereal based savoury snacks, popcorn (not sweet), twiglets 13 1 Carrots raw 13 2 Salad and other vegetables all types of raw vegetables, including coleslaw, (raw) fresh herbs. Not salads made with cooked vegetables or potato salad 13 3 Baked beans canned baked beans in sauce. Includes baked beans with additions e.g. sausages, burgers, pasta 13 4 Carrots (not raw) includes fresh, frozen, canned 13 5 Vegetable dishes (not raw) includes curries, pulse dishes, casseroles and stews, pies, vegetable lasagne, cauliflower cheese, vegieburgers, bubble and squeak, vegetable samosas, pancake rolls, ratatouille, vegetable fingers etc. 13 6 Other vegetables (not raw) includes lentils, dried beans and pulses, mushrooms, onion, aubergine, parsnips, sweetcorn, peppers, leeks, courgettes, cauliflower, mixed vegetables, TVP/soya mince, quorn, tofu 14 1 Tomatoes (raw) 14 2 Tomatoes (not raw) includes fried, grilled, canned, sundried tomatoes 14 3 Apples and pears includes raw, baked, stewed (with or without not canned sugar), dried, apple sauce 14 4 Citrus fruit not canned includes oranges, grapefruit, limes, tangerines, ortaniques etc. 14 5 Bananas includes baked bananas, banana chips 14 6 Canned fruit in juice includes canned in water 14 7 Canned fruit in syrup 14 8 Other fruit, not canned includes plums, grapes, apricots (raw and stewed) etc. fruit pie fillings, dried fruit, fruit salad 15 1 Fruit juice includes 100% single or mixed fruit juices, vegetable juices, canned, bottled, cartons; carbonated, still, freshly squeezed 15 2 Soft drinks 15 3 Concentrated soft drinks 15 4 Concentrated soft drinks, all types including squashes and cordials not low calorie 3 15 5 Carbonated soft drinks, all types, including tonic water. Not carbonated not low calorie mineral water; not alco-pops 15 6 Ready to drink soft drinks, all types of still soft drinks, not carbonated not low calorie 15 7 Concentrated soft drinks, all low calorie, no added sugar, sugar free types low calorie 3 15 8 Carbonated soft drinks, all low calorie, no added sugar, sugar free low calorie types, including slimline tonic water. Not carbonated mineral water 15 9 Ready to drink soft drinks, all low calorie, no added sugar, sugar free low calorie types. Not carbonated. 15 10 Liqueurs includes cream liqueurs, Pernod, Southern Comfort, Tia Maria, Cherry Brandy 15 11 Spirits 70 % proof spirits -brandy, gin, rum, vodka, whisky 15 12 Wine white, red, sparkling, rose 15 13 Fortified wine port, sherry, champagne, vermouth 15 14 Low alcohol and alcohol includes fruit juice and wine drinks free wine 15 15 Beers and lagers premium and non premium, stout, strong ale (bottled, draft and canned) 15 16 Low alcohol and alcohol includes shandy free lager and beer 15 17 Cider and perry includes Babycham 15 18 Low alcohol and alcohol free cider and perry 15 19 Alco-pops includes alcoholic lemonade 15 20 Ciders Coffee (made up) includes instant and leaf bean, decaffeinated, vending machine with whitener, F o r P e e r R e v i e w O n l y 15 Tea (made up) infusion, instant, decaffeinated, vending machine with whitener Appendix III: Hub code hierarchical classification 1 1 solid fats/oils/spreads D/2 0 D/2 fats & oils, 4 fatty D/5 desserts are in 6 Dried O 22 15 23 Herbal tea (made up) includes fruit teas 15 24 Bottled water includes carbonated and still, herbal tonics, (not sweetened drinks or tonic water) 15 25 Tap water includes tap water as a drink or used as a diluent for powdered beverages only. Includes filtered tap water. Not water as a diluent for concentrated soft drinks, instant coffee or instant tea 16 1 Whole milk 16 2 Semi-skimmed milk all types of cow's milk including pasteurised, UHT, sterilised, canned, milk with added vitamins 16 3 Skimmed milk all types of cow's milk including pasteurised, UHT, sterilised, canned, milk with added vitamins, Vital, Calcia 17 1 Infant formula 1 17 2 Cream all types, including imitation cream, aerosol, dream topping, Tip Top, creme fraiche 17 3 Other milk 3 includes soya alternative to milk, goats, sheeps, evaporated, condensed, dried milk, milk shake, coffee whitener, buttermilk, flavoured milk drink 17 4 Cottage cheese includes diet and flavoured 17 5 Other cheese all types, including hard, soft, cream cheese, 17 6 Fromage frais includes fromage frais mousse, Quark 17 7 Yogurt all types including soya, goats, sheeps, yogurt mousse, yogurt drink, frozen yogurt, custard style yogurt, Greek yogurt 17 8 Other dairy desserts includes chocolate and fruit cream desserts, mousse, milk jelly, junket, egg custard, buttermilk desserts, fruit fools, creme caramel 17 9 Ice cream all types, including non dairy, choc ices, ice cream desserts, ice cream containing lollies, milk ice lollies, low fat/low calorie ice cream 18 1 Nuts and seeds 18 2 Nuts and seeds includes fruit and nut mixes, salted peanuts, peanut butter, tahini, bombay mix 19 1 Sugar confectionery includes boiled sweets, gums, pastilles, fudge, chews, mints, rock, liquorice, toffees, chewing gum, sweet popcorn, ice lollies (without ice cream) 19 2 Chocolate confectionery includes chocolate bars, filled bars, assortments 20 1 Beverages (dry weight)5 includes drinking chocolate, cocoa, ovaltine, horlicks, malted drinks etc. 20 2 Soups 3 includes homemade, dried, condensed, cartons, ca 20 3 Savoury sauces, pickles, includes white sauces, cook in sauces, sauce gravies, condiments mixes, tomato ketchup, pickles, chutney, stuffing, gravy, mayonnaise, salad cream, dried herbs, spices 21 1 Commercial toddlers drinks includes powdered, concentrated and ready to drink beverages specifically manufactured for young children 21 2 Commercial toddlers foods includes instant and ready to eat foods specifically manufactured for young children 21 3 Tablets and capsules vitamin and mineral tablets and capsules; includes cod liver oil and other oil based capsules 21 4 Oils and syrups includes cod liver oil etc (not capsules), malt extract, multivitamin syrups, iron syrups and tonics 21 5 Drops and powders includes cold relief powders with vitamin C, multivitamin drops 21 6 Nutritionally complete liquid or powdered supplement drinks containing supplements protein/fat/carbohydrate plus vitamins/minerals. Includes Complan, Build Up, Fortisip, Ensure, Provide, Fresubin 21 7 Artificial sweeteners includes granulated table top sweeteners, tablet, liquid or mini cube sweeteners 1 Bread 5 bread and rolls 0 Bread 2 Sandwiches 2 fish & shellfish ready meals/pates 1 frozen D/5 5 processed D D/5 8 processed dried O 4 processed B O 7 processed A 3 processed A 1 F o r P e e r R e v i e w O n l y Hub1 Hub1 Hub2 Hub2 Description Hub3 Level 3 Description simulant 6 animal, 5 frozen D/5 fruit section 1 fresh O vegetable & processed 2 liquid fats/oils D 0 D 1 D/2 cream and cream based dishes/ 2 frozen O 4 toppings 2 D/3 3 processed A description 1 alcoholic C 1 beers/lagers/ciders 2 low alcohol <0.1% A 1 alcoholic C* 2 Wines 2 low alcohol <0.1% A 3 spirits/liquors (full strength) 0 C* 1 alcoholic beverages, including beers, 1 confectionary (sugar and other non-fatty) 0 O 7 confectionary -sugar and chocolate based 2 confectionary (chocolate and other fatty) 0 1 dry O 2 wet A 4 processed B 5 processed D 6 Raw, prepared RTE salad vegetables/root vegetables 4 (other than potatoes) A D/5 3 wet B 1 soups 7 Dressed RTE B wines spirits & 4 wet D/5 8 Dressed RTE D low alcohol 4 other alcoholic drinks c* 0 C* 1 dry O 1 dry -tea/ coffee/ chocolate/ horlicks/ bornvita etc 2 dry fatty D/5 1 wet A 2 wet D/5 D/5 3 vended A 2 wet liquid tea/coffees 4 vended D/5 D/5 1 acidic B 3 liquid concentrates for dilution 2 aqueous A 1 acidic B 4 ready to drink juices 2 aqueous A 5 carbonated pH<4.5 B NOT water 0 B 1 Still A 2 Carbonated B 3 Tap (not packaged) O 2 non-alcoholic beverages, non-dairy based, including waters, carbonated soft drinks and concentrates for dilution and non-packaged beverages (i.e. tap water) 6 Water 4 unknown if still/carbonated B 1 fatty D/5 D/5 1 crisps and crisp like 2 non-fatty O 1 fatty e.g. kernel or processed D/5 3 Savoury snacks, crisps, nuts and seeds. Savoury biscuits excluded 2 Nuts 2 non-fatty e.g. in shell O 1 Non-fatty O 1 Pastries inc. fruit pies, tarts, pastries etc 2 Fatty D/5 1 Frozen inc. desserts O 2 Non-fatty A 2 Cakes 3 Fatty D/5 1 Sweet O 2 Savoury O 3 Biscuits 3 Chocolate-coated +other fatty D/5 1 Non-fatty O 4 Bread substitutes eg crispbread, pretzels 2 Fatty D/5 1 Chilled A 4 bakery ware, including, buns, pastries, biscuits (sweet & savoury) cooked pizza bottoms and 5 dough, dry deserts etc 2 Dry or frozen 6 processed in sauce B,D/3 2 frozen O O 5 processed oily fish D 1 fresh O 3 legumes Miscellaneous inc. pizza bottoms, 4 processed (e.g. breadcrumbs etc) 1 Fish 6 Raw, prepared RTE A D/5 5 processed D doughs F o r P e e r R e v i e w O n l y 1 fatty/coated D/5 1 breakfast cereals 2 non-fatty O 2 cereals grain O 0 cereals grain O 1 dry & frozen O 2 processed D/3 3 processed D/4 4 processed A 3 pasta & noodles 5 processed B 1 purchased D/5 2 takeaway D/5 4 Pizza 3 frozen O 1 dry O 5 Rice 2 processed A 6 flour O 0 flour O 1 Cereal snackbars (not choc coated) A 7 Cereal Snackbars 2 Cereal snackbars (choc coated) D/5 1 fatty D/5 2 non-fatty O 8 cereals -breakfast cereals, grains, pasta, pizzas, flours, rice and processed cereals including desserts 8 Other 3 Frozen O 1 eggs 0 O 1 fatty D/5 9 eggs & egg dishes 2 egg dishes purchased 2 non-fatty O 1 milk & milk based drinks wet 0 A 2 milk & milk substitutes, dry, NOT infant formulae 0 O 10 milk & milk drinks 3 Liquid milk substitutes eg soymilk 0 1 raw/wet/chilled D/3 2 frozen D/5 3 processed e.g. smoked D/3 11 fish & seafood l y l y 1 fresh O 2 frozen O 3 processed 4 processed B 2 brassicas/leafy/lettuce A tomatoes y l A F o r P e e r R e v i e w O n F o r P e e r R e v i e w O n 5 acid & oily B,D/5 1 dry O 2 wet A 3 wet B 4 wet 2 Cooking sauces 9 Dried O F 1 o D/5 5 acid & oily B,D/5 1 dry 15 soups, sauces, (not used in this work but listed for completeness) 2 r pickles, herbs O 2 wet A 3 wet B 4 wet D/5 3 dry and wet 1 Dry powdered formular 0 O P 2 Liquid formular RTF 0 A Dressings 5 acid & oily B,D/5 1 dry O 2 wet A 3 wet B 4 wet D/5 e 3 Dry other than formula or RTE 0 O 1 18 infant foods A 2 B 4 RTF foods e 3 D/5 r Condiments, herbs, spices, 4 ketchup, sauces, dips 5 acid & oily B,D/5 1 aqueous (honey) and sugar syrups A 1 sugar spreads (jams / honey) 2 acidic (jams & marmalades) B 2 chocolate spreads & sauces 0 D/3 16 preserves, sweet sauces and sugar dishes, sugar and chocolate spreads 3 sugar & sugar substitutes (dry) 0 O 1 powdered O 2 fresh 3 frozen O 4 frozen (pre-cooked chips) D/5 5 processed A 1 potatoes/chips 6 Raw, prepared RTE A legumes, leafy, root and salad -fresh, frozen, processed, inc. n O potatoes, O 17 vegetables including 19 dietary supplements 0 dietary supplements 0 dietary supplements R 3 e v w e i 4
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Acknowledgements 1

Research funded by: Association of European Producers of Steel for Packaging