FACET: Construction of supporting databases on food consumption. Background. Bottle neck

Transcription

1 FACET: Construction of supporting databases on food consumption Final Workshop: 26 th October 2012 Dr. Áine Hearty UCD Institute of Food & Health, Dublin, Ireland Background Access national food intake data Bottle neck Food categorisation for exposure Ax Knowledge chemical occurrence Limited chemical concentration data Limited data packaging substances FACET has addressed all of these voids to create a sustainable surveillance system

2 Outputs of project AIM: Estimate EU consumer exposure to Food Additives Food Flavourings Food Contact Material Substances 1. Databases Harmonised database on nationally available food intake data Database on occurrence of target food chemicals in foods and on food packaging materials used 2. Models/Tools PC-delivered software for estimating exposure to target food chemicals Migration model for multi-layer packaging QSAR tool to estimate toxicity of food contact substances Why FACET important? First time Single multi-disciplinary consortium to simultaneously estimate exposure assessment to food additives, flavourings and food contact material Industry in conjunction with academia Centralised & harmonised database on food intake suitably categorised for food chemical exposures Validated models and associated software programme for estimation of exposure to target food chemicals

3 Monitoring in practice...estimating EXPOSURE to food chemicals Purpose: to provide a quantitative evaluation of the likely exposure from possible sources Allows risk assessors to characterise the hazard in the context of real life. Required: Information about sources of exposure Levels & duration of exposure EXPOSURE: Scientific Model Chemical Intake = Raw Food Amount Presence Probability Chemical Concentration Actual Intake * Recipe Fraction Correction * Edible Portion Correction * Raw Product Correction Lab data * Literature * Industry Maximum limits (e.g. MPL) * Industry * Analytical measurements

4 Deterministic V Probabilistic Modelling Food Intake Presence Probability Chemical Concentration = Exposure Deterministic approach Fixed 100% Fixed Fixed 0.05 Probabilistic approach Values in 10^1 no yes Values in 10^1 Values in 10^1 Data requirements Food intake: Different regions, countries Sample characteristics: Body weight, Age, Gender etc Food Categories: linked to food chemical uses Occurrence data: Branded foods Concentration Data: MPL s, Industry use levels

5 Sample characteristics: Body weight, Age, Gender etc Extrapolation to other regions based on known data Food intake Harmonised Food Categorisation Occurrence data: Branded foods

6 Work package Food Intake (WP5) Objectives: To compile a database on food intake across 8 geographically representative regions of the EU based on existing databases Harmonised tired categorisation system Targeted FFQ studies Data gaps- relevant to Additives, Flavourings, Food Contact Materials Countries providing food intake data UK Finland Ireland Poland France Hungary Portugal Italy

7 Dietary Assessment Methods Indirect methods: Economically-derived indicators Household budget Food balance sheets Relatively inexpensive No individual data? Limited value estimating chemical exposure Direct methods: Survey based Qualitative: FFQ Retrospective: 24/48-hour recall, diet history Prospective: Food diary Can determine proportion of population who are consumers Survey duration Food consumption data influenced by duration Study: survey duration 14 days, 948 adolescents, 5 EU countries Total population g/day Consumers only g/day (%cons) No. days Apples (26%) Fish (12%) 59 (58%) 24 (49%) 49 (68%) 18 (63%) Lambe J et al., (2000) EJCN 54,

8 Task: Collate information on food consumption databases from 8 regions Age group Finland France Hungary Ireland Italy Poland Portugal UK d diary (e) 1d recall d diary (e) 1d recall 4d diary (w) 3-5 7d diary (e) 3d diary (e) 1d recall 4d diary (w) d diary (e) 7d diary (w) 3d diary (e) 1d recall 7d diary (w) d diary (e) 7d diary (e) 3d diary (e) 1d recall 7d diary (w) d diary (e) 3d record (e) 7d diary (e) 3d diary (e) 1d recall 7d diary (e) 7d diary (w) d diary (e) 7d diary (e) 3d record (e) 7d diary (e) 3d diary (e) 1d recall 7d diary (e) 7d diary (w) 65+ 3d diary (e) 7d diary (e) 3d record (e) 3d diary (e) 1d recall 7d diary (e) 4d diary (w) e - estimated Nationally representative data apart from Portugal Number of food items range from items w - weighed List of food groups: Common tier 1. Dairy products and analogues 2. Fats and oils and fat emulsions 3. Fruits, nuts and seeds 4. Vegetables, starchy roots, legumes and seaweeds 5. Chocolate products and confectionery 6. Cereals and cereal products 7. Breads and bakery wares 8. Meat and meat products 9. Fish and fish products, molluscs, crustaceans and echinoderms 10. Eggs and egg products 11. Sweeteners and honey 12. Salt, spices, herbs, industrial sauces and soups 13. Foodstuffs intended for particular nutritional uses 14. Non alcoholic beverages (except dairy beverages) 15. Alcoholic beverages 16. Ready to eat savouries 17. Desserts (except bakery and fruit desserts) 18. Composite and take away food

9 Food Categorization Raw data should be as disaggregated as possible Example 1. Additive X in Wafer-biscuits only Level 1: Snacks Level 2: Biscuits Level 3: Wafer-Biscuits Level 4: Brand of Wafer-Biscuits Example 2. Colour Y in Red Lemonade only Level 1: Beverages Level 2: Carbonated Beverages Level 3: Red Lemonade Level 4: Brand of Red Lemonade Additives example: Tier 0, 1,2 Tier 0 Tier 1 Tier 2 A5. Chocolate products & Confectionery A5.1. Cocoa & chocolate products A5.2. Confectionery, including hard & soft candy A5.3. Chewing gum & microsweets A Cocoa & chocolate products A Cocoa-based sandwich spreads

10 Packaging example: Tier 0, 1,2 Tier 0 Tier 1 Tier 2 P5. Chocolate products & Confectionery P5.1. Sugar confectionery P5.2. Chocolate confectionery P5.3. Chocolate spreads & syrups P Countlines P Chocolate tablets P Bagged chocolates P Seasonal chocolates P Chocolate with toys Targeted FFQ to fill data gaps True False 4710 participants, 7 countries Artificially sweetened foods Herbs & Spices cooking at home Takeaway foods & carbonated drinks Not frequently consumed Between country differences

11 Regional Modelling Regional modelling AIM: To create new databases to represent countries with incomplete data for all age groups by extrapolating from donor to recipient countries. OUTPUTS: Clustering approach for grouping European countries with similar diets Novel probabilistic modelling approach to create surrogate food consumption databases for countries with incomplete data by extrapolating from donor countries to recipient countries Extrapolation possible for a 1-day or a 3-day recipient diet, using 1, 3, 4 and 7-day donor survey data

12 Regional modelling Preliminary dataset representing Level 1 in the FACET coding system FUTURE DEVELOPMENTS: Refine model with use of expert elicitation to provide full datasets at Levels 2,3 and 4 in Facet system. Chemical occurrence data

13 Chemical Occurrence Major source of uncertainty = knowledge of the actual occurrence of the chemical MS Access database on occurrence of targeted food additives, flavouring substances and food contact materials in EU regions Based on market share of food categories, branded products bought in 8 EU countries Ingredient and packaging data compiled Occurrence data: Food labels Food additives: Indicated on the label by stating the additive function and its name or E number Flavourings: Indicated on label by: Added flavourings, Vanilla flavouring Natural flavourings or not present by No added flavourings

14 Objectives of chemical occurrence data collection Objectives: 1. To allow for refinement of the exposure assessments for the three chemical groups. The benefit of including occurrence data in exposure assessments will be examined. Provide direction for future data collection for food chemical exposure assessments with regards to the value of inputting occurrence data. 2. Allow for the comparison of food chemical usage in similar products across the 8 countries. Target food categories for data collection 1. Baby food 2. Fruit/vegetables & sauces in cans or jars with metal lids 3. Non alcoholic beverages 4. Desserts 5. Meat (fresh & processed) 6. Fish (frozen & canned) 7. Bakery wares 8. Confectionery 9. Processed cheese 10.Ready meals 11.Takeaway foods

15 Gathering of Occurrence data Field work Coordinating centre Food category Brand Food label data & food packaging Database MS Access menu screen

16 General Information table Sample Information Product ID Town purchased Country centre Date bought Outlet name Product Identity Bar code Brand Product description Weight/Volume Sale unit weight/volume Individual pack unit weight/volume FACET Code Facet food tier 0 Facet food tier 3 - Additives Facet food tier 3 - Flavourings Facet food tier 3 - Packaging Ingredients and Nutrients table Ingredient Fields* Ingredient name Order on label Ingredient % Composite ingredient Ingredient function Nutrient Fields* Nutrient name Nutrient per 100g *Each ingredient and nutrient on the product label was entered into the database

17 Packaging Components (Direct contact and non-contact) Main and Side components Separator Drip Absorber Insert Pack type Details Appearance Recycling information Measurements (X,Y,Z,D) mm Separator Number in pack Appearance Recycling information Measurements (X,Y) mm Drip Absorber Measurements (X,Y) mm Insert Details Appearance Recycling information Measurements (X,Y,Z,D) mm Packaging Identification of packaging materials Training provided by packaging experts Guidelines/SOP Entry into MS Access database Photograph of each product and different components Packaging materials sent to Innventia and to CEPE In total almost 6000 packaging components sent

18 Data collection: Frequency Portugal Hungary Finland France Ireland UK Italy Poland Total Baby Foods Bakery Wares Canned & Jarred Fruit/Veg Canned & Jarred Sauces Confectionery Desserts Fish (frozen & canned) Meat (fresh & processed) Non-alcoholic Beverages Processed Cheese Ready Meals Take Away Total per country Chemical concentration data

19 Industry involvement Additives: Major role for FoodDrink Europe (formally CIAA) as a partner to collate food additive concentration data Flavourings: Flavour industry contributing expert advice along with data on usage patterns and concentrations Food contact materials: Packaging industry is a full partner but not funded. This industry group are contributing in-kind expert advice Additive Concentration data Specific work-package: FoodDrinkEurope (formally CIAA) Tasks: Determination of the typical technological use of targeted food additives in each targeted food category Characterisation of the individual additives Development of algorithms for exposure modelling Information sense checked by the FACET Expert Group on Additive Concentration Uncertainties Different options for defining how typical is the typical concentration range and define a distribution shape

20 Additive concentration data: Double-range approach Due to the varied and different nature of FoodDrinkEurope s membership, varied feedback received in terms of: Additive usage values and ranges, Food categories and subcategories, representativity of the values, etc. Flavouring Concentration data Involvement of Flavouring Industry Database - Naturally occurring and Added flavours Detailed data collected on 41 target flavourings for project Analytical measurements of flavourings in foodstuffs Linked to food categorisation system in FACET Integrated into software

21 Packaging data supplied by industry FIG supplied pan-european data for: Lists of substances used to make materials Occurrence of substances in each packaging material (e.g. PS, paper,..) Physico-chemical parameters for migration model Concentrations of substances in each material for migration model Measured extraction data for some materials Layer-by-layer construction of packaging materials Market shares for substances in each material Market shares for materials used to make each pack type Market shares including for each pack type in contact with each food group for retail foods Surface area:mass ratio for each food package Summary: FACET datasets Link data from food intake, chemical occurrence and chemical concentration Novel extrapolation modelling approach developed to potentially fill data gaps Harmonised datasets on: Food intake 8 EU countries Food categorisation one database for all 3 chemicals, with different tiers of aggregation Chemical occurrence harmonised database for targeted food categories in 8 countries Chemical concentration data gathered from industry in the 3 areas and linked to the food intake data and integrated into the software