European Food Safety Authority (EFSA), Bruno Dujardin and Laura Kirwan

Transcription

1 TECHNICAL REPORT APPROVED: 21 December 2018 doi: /sp.efsa.2019.en-1532 : strengthening EFSA s capacity to assess dietary exposure at different levels of the food chain, from raw primary commodities to foods as consumed Abstract European Food Safety Authority (EFSA), Bruno Dujardin and Laura Kirwan Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs. European Food Safety Authority, 2019 Key words: raw primary commodities, food consumption, dietary exposure, chemicals Requestor: EFSA Question number: EFSA-Q Correspondence: data.collection@efsa.europa.eu EFSA Supporting publication 2019:EN-1532

2 Acknowledgements: EFSA wishes to thank the following for the support provided to this scientific output: Davide Arcella, Giulio Di Piazza, Fanny Herault, Sofia Ioannidou, Arianna Landini, Evangelia Mavromichali, Ti Kian Seow and Rita Sousa. EFSA wishes to thank the following for reviewing this scientific output: Katleen Baert, Jan Dirk te Biesebeek, Polly Boon, Gerda van Donckersgoed, Mary Gilsenan, Jose Angel Gomez-Ruiz, Christine Horn, Djien Liem and Yi Liu. EFSA wishes to acknowledge all European competent institutions, Member State bodies and other organisations that provided data for this scientific output. Suggested citation: EFSA (European Food Safety Authority), Dujardin B and Kirwan L, Technical report on the raw primary commodity (RPC) model: strengthening EFSA s capacity to assess dietary exposure at different levels of the food chain, from raw primary commodities to foods as consumed. EFSA supporting publication 2019:EN pp. doi: /sp.efsa.2019.en-1532 ISSN: European Food Safety Authority, 2019 Reproduction is authorised provided the source is acknowledged. 2 EFSA Supporting publication 2019:EN-1532

3 Summary Dietary exposure assessment is an essential part of EFSA s risk assessment process for chemicals in food. Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Data reported in this database may either refer to raw primary commodities (RPCs), RPC derivatives (i.e. single-component foods altered by processing) or composite foods (i.e. multicomponent). Consumption data for RPC derivatives and composite foods, however, cannot be used in exposure assessments when the occurrence data are reported for the RPCs. The RPC model aims to bridge this gap by transforming the consumption data for composite foods or RPC derivatives into their equivalent quantities of RPCs. The RPC model functions in two distinctive steps. In the first step, using the available information on recipes and commercial products, consumption data for composite foods (e.g. fruit cake) contained in the Comprehensive Database are disassembled into their single components (e.g. fruit and sponge cake). When a food item or a food component is not considered to be sufficiently specific (e.g. fruit), a more specific item will be assigned on the basis of the probabilities observed in the Comprehensive Database (e.g. apple or raisins). This iterative process is repeated until specific RPC or RPC derivatives have been reached for all components (e.g. apple, wheat flour, eggs, white sugar and butter). In the second step, all RPC derivatives (e.g. wheat flour and butter) are converted into RPCs using reverse yield factors (e.g. wheat grain and milk). Using the RPC model, EFSA successfully managed to convert its Comprehensive Database into a new RPC Consumption Database. The RPC Consumption Database now contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records, which refer to both the quantities of RPC and RPC derivatives instead of foods consumed. The consumption data generated by the RPC model were manually checked and validated through case studies assessing dietary exposure to pesticides and feed additives. These case studies demonstrated that the RPC consumption data are suitable to assess dietary exposure to chemicals where the occurrence data are predominantly available for RPCs. The RPC model thereby contributes to both EFSA s long-term goal of improved data standardisation, and to the harmonisation of EFSA s dietary exposure assessments throughout the various food sector areas. It is therefore recommended that all EFSA s dietary exposure calculators that rely on RPC consumption data are updated (e.g. Pesticide Residues Intake Model, Feed Additives Consumer Exposure calculator). Additional case studies exploring the use of the consumption data for RPC derivatives should also be elaborated as this will enable more refined exposure calculations in the future. The RPC model will need to be updated regularly in order to further improve its accuracy and reliability. 3 EFSA Supporting publication 2019:EN-1532

4 Table of contents Abstract... 1 Summary Introduction Data and methodologies General principles Primary input data Food consumption data Food classification Secondary input data Probability table Disaggregation table Conversion table Algorithm Output validation Balances Outlier analysis Comparison with the Pesticide Residues Intake Model (PRIMo) Results RPC Consumption Database Considerations on dietary exposure assessment Sources of uncertainty Conclusions Recommendations References Abbreviations Appendix A Protocol for creation of intermediate FoodEx codes Appendix B Protocol for the development of the probability table Appendix C Protocol for the development of the disaggregation table Appendix D Protocol for the development of the conversion table Annex A - Input data for the raw primary commodity (RPC) model Annex B - Output data from the raw primary commodity (RPC) model EFSA Supporting publication 2019:EN-1532

5 1. Introduction Dietary exposure assessment is a key element of EFSA s risk assessment process for chemicals in food. Dietary exposure is typically calculated by combining food consumption data with occurrence data for those chemicals (EFSA, 2011a). The main repository of food consumption data at EFSA is the Comprehensive European Food Consumption Database (Comprehensive Database) (EFSA, 2011b). These data originate from dietary surveys conducted by the competent authorities in the EU Member States and were subsequently provided to EFSA. Within the Comprehensive Database, data may be reported for foods that were subject to varying levels of processing. The following principle food group types can be identified in the database (EFSA, 2015): Raw primary commodities (RPCs): single-component foods which are unprocessed or whose nature has not been changed by processing (e.g. apples) RPC derivatives: single-component foods which have been physically changed by processing (e.g. apple juice) Composite foods: foods consisting of multiple components (e.g. apple strudel) Chemical occurrence data may come from different sources depending on the food sector area and the applicable legal framework (EFSA, 2011a). In certain food sector areas (e.g. food additives and chemical contaminants) the occurrence data are usually reported for the foods as consumed allowing for a direct match with the food consumption data reported in the Comprehensive Database (e.g. pizza). In these areas the Comprehensive Database has therefore already become the cornerstone for most dietary exposure assessments in EFSA. However, in several other food sector areas (e.g. pesticide residues, GMOs and feed additives) chemical occurrence data are predominantly reported for the RPCs (e.g. tomatoes, wheat, milk). In these cases, food consumption data in the Comprehensive Database, reported as composite foods or RPC derivatives, are not compatible with the chemical occurrence data provided. Therefore, not all dietary consumption data in the Comprehensive Database are suitable for estimating dietary exposure in all areas within EFSA s risk assessment remit. In order to address this incompatibility, models converting food consumption data for composite foods or RPC derivatives into their corresponding quantities of RPCs, were previously developed at national level (Boon et al., 2009). At European level, EFSA initiated a project with the Netherlands National Institute for Public Health and the Environment (CFT/EFSA/DATEX/2010/03) which resulted in a first tentative conversion model. In this first step, however, only ingredients of plant origin were addressed. Moreover, the data provided were mainly extracted from the Dutch conversion model, which may lack representativeness for use at the European level. EFSA therefore elaborated on this tentative model with the purpose of addressing these limitations. EFSA s work has resulted in the development of the current RPC model. The RPC model strengthens EFSA s capacity to use the Comprehensive Database to estimate the exposure to chemicals analysed predominantly in RPCs. The model will allow EFSA to match consumption data with occurrence data for RPCs, RPC derivatives, composite foods or a combination of these. The RPC model thus contributes to both EFSA s long-term goal of improved data standardisation, and to the harmonisation of EFSA s dietary exposure assessments throughout the various food sector areas. 2. Data and methodologies 2.1. General principles The RPC model functions in a stepwise approach, where the dietary consumption data contained in the Comprehensive Database are considered to be the primary input data to the model (see Figure 1). Secondary input data employed by the model are contained in three main tables: the disaggregation table, the probability table and the conversion table. 5 EFSA Supporting publication 2019:EN-1532

6 In the first step, consumption data for composite foods are disassembled into their individual components using both the disaggregation table and the probability table. While the probability table assigns specific food codes (e.g. waffles) to the data reported at food group level (e.g. pastries and cakes), the disaggregation table allows for the disassembly of the composite foods (e.g. apple strudel) into their individual components (e.g. puff pastry, apple compote, sugar). This step functions through an iterative process until the RPC (e.g. apple) or RPC derivative (e.g. wheat flour) is achieved for each component. In the second step, consumed quantities of RPC derivatives (e.g. wheat flour) are translated into their equivalent quantities of RPCs (e.g. wheat grain) using the reverse yield factors in the conversion table. Primary input data Food consumption data as consumed by individuals Composite foods, RPC derivatives, RPCs STEP 1 Disaggregation table Disassembles composite foods into single - component foods Probability table A d d s i n f o r m a t i o n t o f o o d s r e p o r t e d a t t h e f o o d g r o u p l e v e l p r o b a b i l i s t i c a l l y b a s e d o n t h e f o o d consumption database Intermediate output data F o o d c o n s u m p t i o n d a t a a s s i n g l e - c o m p o n e n t f o o d s RPC derivatives, RPCs STEP 2 Conversion table C o n v e r t s p r o c e s s e d s i n g l e - c o m p o n e n t f o o d s into unprocessed single - component foods Output data F o o d c o n s u m p t i o n d a t a a s u n p r o c e s s e d s i n g l e c o m p o n e n t f o o d s R P C s Primary Input Data Secondary Input Data Output Data Figure 1: General principles of the raw primary commodity model 2.2. Primary input data Food consumption data The EFSA Comprehensive European Food Consumption Database (Comprehensive Database) provides a compilation of the most recent national dietary surveys at EU level. These data were provided by competent organisations in the EU Member States, at the level of consumption by the individual consumer. The Comprehensive Database was first built in 2010 (EFSA, 2011b; Huybrechts et al., 2011; Merten et al., 2011) and subsequently updated upon reception of new dietary surveys from Member States. Details of how the Comprehensive Database is used have been published by EFSA (EFSA, 2011b). Overall, the food consumption data gathered by EFSA in the Comprehensive Database are the most complete and detailed data currently available at EU level and are already used by EFSA in several food sector areas, e.g. chemical contaminants, food additives and nutrition. Consumption data were collected using single or repeated 24- or 48-h dietary recalls or dietary records covering between EFSA Supporting publication 2019:EN-1532

7 and 7 days per subject. Subjects in the Comprehensive Database are classified according to the following sub-populations: Infants: < 12 months old Toddlers: 12 months to < 36 months old Other children: 36 months to < 10 years old Adolescents: 10 years to < 18 years old Adults: 18 years to < 65 years old Elderly: 65 years to < 75 years old Very elderly: 75 years old. Two additional surveys provided information on specific population groups: Pregnant women ( 15 years to 45 years old; Latvia) and Lactating women ( 28 years to 39 years old; Greece). For the purpose of this exercise, the Comprehensive Database as of 31 March 2018 was used. At that time the Comprehensive Database contained 51 dietary surveys from 23 different countries. These surveys provided a total of 10,470,332 consumption records for 94,532 subjects. An overview of the surveys, countries and population classes is available in Annex A, Table A Food classification At the time of the RPC model development, foods reported in the Comprehensive Database were classified according to the FoodEx classification system (EFSA, 2011c). This FoodEx system was first released in 2011, and contained 1,889 FoodEx codes. The available FoodEx codes, however, were often not detailed enough to be disassembled by the RPC model, e.g. food records classified as chocolate confectionery could not be disaggregated as the records did not include sufficient detail on filling or flavour. The FoodEx classification system was therefore expanded within the framework of this project and additional food codes were created in order to improve the specificity of the food classification system. These additional codes are referred to as intermediate I codes as they follow the same format as existing FoodEx codes, with the initial A in the FoodEx code format (e.g. A Barley, pearled) replaced by an I (e.g. I Barley, cooked). The FoodEx classification was expanded according to the protocol outlined in Appendix A, and data from the Comprehensive Database were manually reclassified (for use in the RPC model only). The expanded FoodEx classification, including the intermediate codes created in this project and the number of consumption records obtained after reclassification, is outlined in Annex A, Table A.2. During reclassification, particular attention was given to foods predicted to have a high impact on consumption values obtained through the RPC model. High-impact foods include foods which have a significant increase or decrease in weight during processing, as inaccurate coding of these foods could result in an over- or underestimation of consumption, depending on the processing type (EFSA, 2011c). For example, the weight of rice is expected to increase by 150% during cooking (due to water absorption) yet the weight of salmon is estimated to decrease by 25% (due to moisture loss). The creation of intermediate I codes (e.g. Rice, cooked) improves the specificity of the food classification and allows the impact of cooking on weight to be considered in the conversion step of the model (see Section 2.4). The FoodEx classification currently applied is considered to be one of the most important limitations of the RPC model as EFSA recently started receiving both food consumption data and occurrence data compliant with the more recent FoodEx2 classification system (EFSA, 2015). Implementation of FoodEx2 will therefore be the main priority when revising the RPC model in the future. Meanwhile, despite input data being classified according to FoodEx, EFSA will ensure that the output of the model is compatible with FoodEx2 (see also Section ). 7 EFSA Supporting publication 2019:EN-1532

8 2.3. Secondary input data Probability table The function of the probability table is to add information to foods coded at the food group level which are not detailed enough to be disaggregated. For example, consumption records for vegetable oil will be assigned to a more specific oil type (e.g. sunflower oil, olive oil). The probability table was generated automatically in accordance with the protocol outlined in Appendix B, and variables contained in the probability table are described in Table 1. The probabilities were calculated based on the data reported in the Comprehensive Database after the reclassification described in Section The probability table thus obtained is provided in Annex A, Table A.3. It contains probabilities for 518 specific foods, addressing the uncertainty of around 212 food categories. Table 1: Description of the variables contained in the probability table Name Age class Category code Category name Description Age classes used for the calculation of probabilities, i.e. subjects < 1 year old, subjects 1 to < 10 years old and subjects 10 years old. This should not be confused with the population groups reported in Section that are normally used for the statistical analysis of food consumption data. For the calculation of probabilities wider age classes were considered to ensure that there would be sufficient consumption records for the calculation of probabilities. FoodEx code of the food group that is not considered sufficiently specific for disaggregation; this code is compliant with the expanded FoodEx classification outlined in Annex A, Table A.2. Description of the category code. Food code Food name FoodEx code of the accurate food description that may be considered for disaggregation; this code is compliant with the expanded FoodEx classification outlined in Annex A, Table A.2. Description of the food code. Probability Source(s) Probability that a consumption record classified at category level will be assigned to that specific food code; this is expressed as a number ranging from 0 to 1. Indicates whether the probability results from a probability calculation within the age class or from an extrapolation Disaggregation table The disaggregation table is used to disassemble composite foods reported in the Comprehensive Database into single-component foods, i.e. RPC derivatives and RPCs. When a composite food might have different flavours (e.g. type of muffin chocolate or plain) or components (e.g. type of meat in a meatball), this table is also used to probabilistically assign such components. This probability should not be confused with the one presented in the probability table, the purpose of which is to assign a more accurate FoodEx code when the FoodEx code is not accurate enough for disaggregation (see Section ). The disaggregation table was developed manually in accordance with the protocol defined in Appendix C, using the available information on recipes and commercial products. Variables contained in the disaggregation table are described in Table 2. In addition to the protocol, percentages were 8 EFSA Supporting publication 2019:EN-1532

9 aligned with process-specific technical data used in exposure assessments of food enzymes (EFSA CEF Panel, 2016). The disaggregation table thus obtained is provided in Annex A, Table A.4. The table contains compositional data for 930 composite foods, 379 RPC derivatives and 375 RPCs. The table also identifies 79 foods which have been subject to very extensive processing and were considered too complex for accurate disaggregation to RPCs. This category includes foods for specific nutritional use, extracts, food additives, standalone processing agents and foods for infants and young children. These foods were ultimately not converted into RPCs. Table 2: Description of the variables contained in the disaggregation table Food code Food name Name Description FoodEx code of the food to be disaggregated; this code is compliant with the expanded FoodEx classification outlined in Annex A, Table A.2. Description of the food code. Component code Component name FoodEx code of the component obtained after disaggregation; this code is compliant with the expanded FoodEx classification outlined in Annex A, Table A.2. Description of the component code. Facet(s) code FoodEx2 code describing particular aspects of RPC derivatives obtained after disaggregation, such as treatments received, qualitative information, fat content or the nature of the food (EFSA, 2015). When the component obtained after disaggregation is an RPC, the facet code F28.A07XD is used (i.e. PROCESS=Unspecified). When the component obtained requires further disaggregation this is also indicated as To be further disaggregated (d) or To be further disaggregated (p). The letter between brackets indicates whether it requires entry into the disaggregation or probability table, respectively. Percentage Proportion of the component within the food being disaggregated, expressed in %. Probability Probability group Source(s) Reference(s) Reference link (1) Reference link (2) Probability that the component is attributed to the food being disaggregated; this is expressed as a number ranging from 0 to 1. Identifies groups of components that need to be considered together for probability analyses. Probability groups are indicated with ordinal numbers. For example, in the case of cereal-based pudding, cereal types (rice and wheat) are considered in one probability group while the flavours (fruit, caramel or chocolate) are included in a second group. Source of information on the entry; this may be literature, legislation, a recipe, a commercial product, a probability analysis, an assumption, an extrapolation or any combination of those. When the entry refers to an RPC, an RPC derivative or a food that does not require disaggregation, this is also indicated. References to the sources of information. Hyperlink to the first reference (if applicable). Hyperlink to the second reference (if applicable). 9 EFSA Supporting publication 2019:EN-1532

10 Conversion table The conversion table translates quantities of RPC derivatives into their equivalent weight of RPCs before processing. Processing may result in a significant increase or decrease in the weight of a food (e.g. water absorbed during boiling or moisture lost during baking). This change is taken into consideration in the RPC model conversion. The translation of processed single component food quantities (RPC derivatives) into their unprocessed counterparts (RPCs) is based on the reverse yield factors given in the conversion table. The second objective of the conversion table is to ensure that the output generated by the RPC model is compatible with the FoodEx2 classification system (EFSA, 2015). FoodEx2 provides a more exhaustive list of base terms than FoodEx, which enables a more detailed classification of foods. It also provides the possibility to report additional information through the use of facets and facet descriptors such as processing or packaging. While the current version of the RPC model mainly relies on the FoodEx classification, EFSA started receiving occurrence data, which are classified in accordance with FoodEx2 (see Section ). In order to match these occurrence data with the consumption data generated by the RPC model, the conversion table is also used to assign a FoodEx2 code to the RPCs. The conversion table was developed manually in accordance with the protocol outlined in Appendix D, and its variables are described in Table 3. Reverse yield factors were sourced for each combination of RPC and facet descriptor (e.g. wheat grain and milling, wheat grain and distillation) and manually input into the conversion table. The conversion table thus obtained is provided in Annex A, Table A.5. This table contains 396 RPCs and reverse yield factors are reported for 492 RPC derivatives. The table also includes 116 foods or food components that were not considered relevant for conversion to RPCs, i.e. foods for specific nutritional use, extracts, food additives, standalone processing agents and foods for infants and young children EFSA Supporting publication 2019:EN-1532

11 Table 3: Description of the variables contained in the conversion table Name Description FoodEx code RPC code as defined by the expanded FoodEx classification outlined in Annex A, Table A.2. FoodEx name Description of the FoodEx code. FoodEx2 code RPC code as defined by the FoodEx2 classification system (EFSA, 2015). FoodEx2 name Description of the FoodEx2 code. Facet(s) code Facet(s) description FoodEx2 code describing particular aspects of RPC derivatives obtained after disaggregation, such as treatments received, qualitative information, fat content or the nature of the food (EFSA, 2015). When the component obtained after disaggregation is an RPC, the facet code F28.A07XD is used (i.e. PROCESS=Unspecified). Description of the facet(s) code. Reverse yield factor Source(s) Reference(s) Reference link Ratio of the quantity of RPC that is required to yield a certain quantity of RPC derivative. For example, a reverse yield factor of 1.54 for apple juice means that 1.54 kg of apples is required to produce 1 kg of apple juice. Source of information on the entry; this may be literature, legislation, a recipe, a commercial product, a probability analysis, an assumption, an extrapolation or any combination of those. When the entry refers to an RPC or a component that is not converted to RPCs, this is also indicated. References to the sources of information. Hyperlink to the reference (if applicable) Algorithm The RPC model functions in two phases. In the first step, consumption data for composite foods contained in the Comprehensive Database are disassembled into RPC and RPC derivatives using both the disaggregation table and the probability table, this step might require more than one iteration. In the second step, quantities of RPC derivatives are converted to their corresponding quantities of RPC using the conversion table. The result is an RPC Consumption Database. A flowchart providing further details on each of these steps is outlined in Figure 2. The first step of the RPC model, where consumption data for composite foods are disassembled to single component foods (i.e. RPC or RPC derivatives), proceeds as follows: 1. Each consumption record reported in the Comprehensive Database has been assigned a food code and the quantity consumed is expressed in grams. These consumption records either enter the process as the original code assigned or as a more specific intermediate I code if one was assigned (see also Section 2.2.2). 2. When the consumption record refers to a food code that is not sufficiently specific (i.e. when the food code is not reported in the disaggregation table), the probability table is used to assign food codes that are more specific in a probabilistic manner EFSA Supporting publication 2019:EN-1532

12 Example: For subjects aged 10 years, 2,215 consumption records were classified as Pastries and cakes in the Comprehensive Database. According to the probability table these consumption records may either be assigned to Buns with a probability of 50% or to Sponge cake with a probability of 50%. This has resulted in 1,100 of these consumption records assigned to Buns and 1,115 of these consumption records assigned to Sponge cake. 3. After assigning specific codes to all relevant consumption records, each consumption record is disassembled to its single components by means of the disaggregation table. The quantity of each component is then calculated by multiplying the initial consumption with the percentage of each component obtained from the disaggregation table. Example: The Comprehensive Database contained a consumption record where 175 g of sponge cake was consumed by a given subject. According to the disaggregation table this sponge cake is made of 26.1% egg, 24.5% wheat flour, 24.5% sugar, 24.5% butter and 0.4% baking powder. This has resulted in the following five consumption records of 46.6 g egg, 42.9 g wheat flour, 42.9 g sugar, 42.9 g butter and 0.7 g baking powder. 4. When component probability applies (i.e. when the original food may have different flavours), only one of these components is retained for each consumption record. The selection of the components is based on the probabilities reported in the disaggregation table. Example: In the Comprehensive Database, 4,022 consumption records for meatballs were reported and 255 consumption records were reported for meat-based meals. All these records were subsequently assigned to meatballs at step 2. Then beef meat and pork meat were assigned as a potential ingredient at step 3 with probabilities of 57 and 43%, respectively. As a result, beef meat was selected for 2,507 consumption records while pork meat was selected for 1,770 consumption records. 5. If the database obtained still contains consumption records for components that require further disaggregation, the process is repeated from step 2 onwards. This iterative process is continued until an RPC or RPC derivative has been reached for all components, i.e. when the Facet(s) code column no longer contains To be further disaggregated. In the second step of the process, food consumption data for all single-food components (including RPC derivatives) are converted to the corresponding quantities of RPCs by following these steps: 6. When consumption data obtained from the first phase of the process already refers to an RPC (i.e. when the facet descriptor F28.A07XD Unspecified was allocated), a reverse yield factor of 1 is assigned. For the RPC derivatives the relevant reverse yield factors are retrieved by matching the food codes and facet codes assigned earlier with those reported in the conversion table. 7. The quantity of RPC consumption is calculated for each consumption record by multiplying the quantity of food obtained after phase 1 with the reverse yield factor. Example: A consumption of 200 g of apple juice was reported for a given subject. According to the conversion the reverse yield factor for apple juice amounts to This consumption is considered equivalent to the consumption of 308 g of raw apples. 8. The conversion process results in an RPC consumption database where consumption records for RPCs are recorded at an individual level. Throughout the transformation process both the information on the consuming subjects (i.e. information on the survey, country, age, bodyweight, etc.) and the information on the original food and intermediate components is retained and incorporated into the database. The transformation from the Comprehensive Database to the RPC Consumption Database was programmed using SAS Data Integration Studio EFSA Supporting publication 2019:EN-1532

13 Disassembly to RPC and RPC derivatives Comprehensive database Specific FoodEx code? Yes No Specific FoodEx code are probabilistically assigned Probability table No Consumption records are disassembled to single components Disaggregation table Component probability? No Yes Component is probabilistically selected RPC or RPC derivative? Yes Conversion to RPC RPC? No Reverse yield factor is assigned to each RPC derivative Conversion table Yes Corresponding amount of RPC is calculated RPC Consumption Database Figure 2: Process flow for the conversion of dietary consumption data for composite foods to raw primary commodities (RPC) using the RPC model 13 EFSA Supporting publication 2019:EN-1532

14 2.5. Output validation Balances When initiating the RPC conversion, consistency among the input tables is verified, and the following verification steps are programmed in the model. Food codes that are present in the Comprehensive Database but which are missing in both disaggregation and probability tables are identified and reported. Food codes that result from the probability table but which are missing in the disaggregation table are identified and reported. For each food code reported in the disaggregation table the mass balance for n components is calculated as follows: Food codes where the mass balance is not between 99.9 and are identified and reported. Components that are identified in the disaggregation table as To be further disaggregated but which are missing in either the disaggregation table or probability table, are identified and reported. Combinations of RPCs and facet descriptors that result from the disaggregation table but which are missing in the conversion table are identified and reported. After the first phase of the transformation process (see Section 2.4), the quantities of RPC and RPC derivatives that originate from the same consumption record in the Comprehensive Database are summed up. This sum is compared with the quantity of food initially reported for that consumption record. Any consumption record where the sum deviates by more than 1% from the original quantity is identified and reported Outlier analysis High consumption values within population groups were used to identify potential inaccuracies in the RPC model conversion. This analysis was carried out by: calculating for each survey the ratio of the mean RPC consumption over the mean RPC consumption across each population class (total population, expressed in grams per kilogram body weight per day); identifying the highest 5% of the ratios calculated above (i.e. the ratios exceeding 6); analysing the disaggregation path of these high consumption values for irregularities, giving consideration to cultural habits (e.g. higher olive oil consumption in the southern EU). The RPC model was adjusted when high consumptions were not justified Comparison with the Pesticide Residues Intake Model (PRIMo) The Pesticide Residues Intake Model (PRIMo) is currently the standard tool used at EU level to calculate dietary exposure to pesticide residues in the framework of Regulation (EC) No 396/ and Regulation (EU) No 1107/ (EFSA, 2018). This Excel-based calculation spreadsheet was developed by EFSA and uses summary statistics for the consumption of RPCs. These statistics were 1 Regulation (EC) No 396/2005 of the European Parliament and of the Council of 23 February 2005 on maximum residue levels of pesticides in or on food and feed of plant and animal origin and amending Council Directive 91/414/EEC. OJ L 70, , p Regulation (EC) No 1107/2009 of the European Parliament and of the Council of 21 October 2009 concerning the placing of plant protection products on the market and repealing Council Directives 79/117/EEC and 91/414/EEC. OJ L 309, , p EFSA Supporting publication 2019:EN-1532

15 reported directly by the Member States and incorporated in the PRIMo. Considering that this is currently the most comprehensive collection of RPC consumption data available at EU level, these data were selected for comparison with the RPC consumption data generated in this framework. An accurate comparison was, however, not possible because information on how the Member States generated those data is missing. Furthermore, as the PRIMo mainly relies on the highest consumption value reported among Member States (in particular with regard to the acute consumption), this comparison was only used to identify consumption values that may have been overestimated by the RPC model. As a result, potential underestimations generated by the RPC model could not be identified. For this purpose, summary statistics of the consumption data generated by the RPC model were derived for each survey, population class and RPC (expressed in grams per kilogram body weight per day). For chronic consumption, the mean value for the total population was selected. For acute consumption, the 97.5th percentile for consumers only was selected, or the highest reliable percentile when data were not sufficient to derive the 97.5th percentile. These statistics were then compared with the most critical values reported in the PRIMo (revision 3.1). When the summary statistics derived in this framework significantly exceeded the critical value reported in the PRIMo (i.e. at least two times higher), the disaggregation path was retrieved for the relevant surveys, population classes and RPCs. These data were analysed for irregularities and the RPC model was adjusted when necessary. 3. Results 3.1. RPC Consumption Database The RPC Consumption Database covers the same number of surveys, countries and subjects as the Comprehensive Database (see Section ). However, through the disaggregation process, the 10,470,332 consumption records contained in the Comprehensive Database increased to 27,835,390 records in the RPC Consumption Database. This also includes the 1,262,302 records of the 116 foods that were not converted to RPCs. Furthermore, more than one survey may be reported for certain countries and population classes, some of which have been superseded by a more recent survey (see Annex A, Table A.1). Hence an overview of the most recent surveys and the number of records obtained per population class is provided in Table 4. Table 4: Descriptive statistics for the RPC Consumption Database (most recent surveys only) Population class Countries (n) Surveys (n) Subjects (n) RPC records (n) Infants 6 6 3, ,041 Toddlers ,183 1,572,656 Other children ,166 2,668,205 Adolescents ,429 2,332,223 Adults ,873 8,527,162 Elderly ,736 1,363,085 Very elderly , ,022 Lactating women 1 1 1, ,814 Pregnant women , EFSA Supporting publication 2019:EN-1532

16 Summary statistics on the total RPC consumption are reported in Annex B for each RPC, population class and survey. While the chronic statistics are presented for the total population (see Tables B.1 and B.2), the acute statistics are presented for consumers only (see Tables B.3 and B.4). These statistics were only reported for the most recent survey for each country and population class. Following the methodology outlined in Section , the above statistics were compared with the most critical RPC consumption data reported in the PRIMo. Only 3.2% of the statistics derived from the RPC Consumption Database were found to substantially exceed the critical consumption data from the PRIMo (i.e. at least two times higher). The disaggregation paths for these data were retrieved and analysed and no irregularities were identified (see Annex B, Table B.5). For 6.4% of the statistics, however, consumption data are not available in the PRIMo and a comparison was not possible; these statistics mainly refer to products that are usually not taken into consideration for dietary exposure to pesticides (i.e. water, salt and fish). On average, milk and water were the main contributors to the total chronic consumption of RPCs (see Figure 3). Other important contributors to the total RPC consumption are fruits as well as the group of starchy roots and tubers (including sugar plants). When interpreting these data, the following considerations should be made: The quantity of RPC consumption refers to the quantity of RPCs that were required to produce the foods eaten by the end consumer. This is particularly true for dairy products where large quantities of milk are required to produce frequently consumed products such as butter and cheese. Similarly, the relatively high consumption of fruits can be explained by the large quantities of fruit required to produce juices. The group of starchy roots and tubers also includes plants used for sugar production. Hence the consumption data for this group are mainly driven by the conversion of added sugar to their RPCs, sugar beet and sugar cane. The total consumption of water reported in this framework refers to all water that consumers may be exposed to through their food, such as water incorporated in soft drinks or used for cooking. Water consumption of this nature should not be compared to dietary recommendations on the daily consumption of water. Although salt is not a major contributor to the total RPC consumption, it is worth mentioning that the consumption data reported in this framework should be considered as an estimate because the quantity of added salt is strongly dependent on consumer habits. For accurate estimations of salt intake, other methods are considered more appropriate, such as the use of biomarkers EFSA Supporting publication 2019:EN-1532

17 Population Class Infants Toddlers Other children Adolescents Adults Elderly Very elderly Average Consumption (g/kg bw/d) Coffee, cocoa, tea and infusions Eggs and egg products Fruit and fruit products Honey Meat and meat products Salt Vegetables and vegetable products Drinking water Fish, seafood, amphibians, reptiles and invertebrates Grains and grain-based products Legumes, nuts, oilseeds and spices Milk and dairy products Starchy roots or tubers and products thereof, sugar plants Yeast cultures Figure 3: Average chronic consumption of raw primary commodities over the different dietary surveys presented for different population classes 3.2. Considerations on dietary exposure assessment Assessments of dietary exposure to chemicals in food are typically obtained by combining food consumption data with occurrence data for those chemicals (EFSA, 2011a). The RPC Consumption Database has mainly been developed in view of assessing dietary exposure to chemicals where the occurrence data are only available for RPCs (e.g. pesticides, feed additives and the non-allowed veterinary medicinal products), rather than for foods as consumed (e.g. food additives). Exposure estimates may be calculated from summary statistics of the consumption data, such as those provided in Annex B. However, exposure estimates may also be obtained by calculating individual exposures for each subject (or day) reported in the database. The latter approach is often considered to be more informative because it results in a distribution of exposures, rather than single-point estimates. It therefore provides a better view of the variability of exposures within a population EFSA Supporting publication 2019:EN-1532

18 The RPC Consumption Database has been demonstrated to be suitable for both types of dietary exposure assessment. In the process of developing EFSA s guidance on the assessment of the safety of feed additives for the consumer (EFSA FEEDAP Panel, 2017), the database was used to perform several case studies and subsequently incorporated in the Feed Additives Consumer Exposure (FACE) calculator. 3 Case studies assessing the dietary exposure of infants and young children to pesticide residues were also reported (EFSA PPR Panel, 2018). Exposure assessments based on RPCs, however, have the implicit assumption that the entirety of the chemical(s) present in the RPC will reach the end consumer. This assumption may result in a substantial overestimation of the exposure because for the majority of chemicals losses are expected to occur when the RPC is processed prior to consumption. The impact of different processing techniques will strongly depend on the RPC category (see Annex B, Table B.6). For example, the effect of processing on drinking water is expected to be very limited. Likewise, the effect of processing has a larger impact on RPC categories that are exclusively consumed after processing (e.g. cereal grains). The impact of processing will also depend on the population class. While infants and toddlers consume large quantities of unprocessed milk, adults are mainly exposed to milk through the consumption of cheese, butter and cream (see Figure 4). In this specific example, exposure calculations for chemicals that are not fat soluble and that do not concentrate in cheese, butter and cream may be substantially overestimated for adults. Although processing techniques were generally comparable between countries, some differences are observed due to different cultural habits. For example, concentration was found to be the most impactful processing technique for the consumption of milk by children (see Figure 4). This high contribution is actually driven by one specific country (Greece) where 45% of the milk consumption is attributed to concentrated milk, while for all other countries concentrated milk contributed to less than 10% of the total milk consumption. Indeed, the use of concentrated milk was confirmed to be a common practice in Greece. Hence, considering the impact that processing may have on exposure, RPC consumption data are recommended for use in the first tiers of EFSA s risk assessment process, rather than for refined exposure assessments. Should exposures calculated on the basis of the RPC consumption data indicate concerns for the consumers, and if processing is expected to have an impact on the occurrence, it is recommended that a more detailed assessment is performed on the basis of RPC derivatives or on the basis of the foods consumed. Information on the RPC derivatives is also contained in the RPC Consumption Database, and possibilities for making better use of this information will be further explored by EFSA EFSA Supporting publication 2019:EN-1532

19 Population Class Infants Toddlers Other children Adolescents Adults Elderly Very elderly Contribution to RPC Consumption Data (%) Cheesemaking Concentration / evaporation Drying (dehydration) Separation (in liquid phase) Churning Condensing milk (concentration + sugars) Fermentation Unspecified Figure 4: Contribution of different processing techniques to the average consumption of milk presented for different population classes 3.3. Sources of uncertainty It is important to acknowledge that the data obtained in the RPC Consumption Database are subject to several sources of uncertainty. When these data are used for exposure assessment, these sources of uncertainty may lead to the over- or underestimation of the exposure estimates. Within this framework, it is not possible to provide a comprehensive assessment of all the uncertainties, because uncertainty will depend on the food or chemical(s) under assessment. The main objective within this framework is to identify the main sources of uncertainty, which can then be used as a basis for the uncertainty analysis in future exposure assessments. The main sources of uncertainty identified for the RPC model are outlined below EFSA Supporting publication 2019:EN-1532

20 Although the FoodEx classification system has been expanded to include intermediate codes, the specificity of the RPC model is still limited by the FoodEx classification system applied in the Comprehensive Database at the time of the model s development. Food consumption data in the Comprehensive Database have since been updated to include dietary surveys coded with the revised FoodEx2 system (EFSA, 2015). Meanwhile, RPC consumption data resulting from composite foods that could not be assigned with a more accurate classification code may either be over- or underestimated. The assignment of foods and food components using probabilities introduces an element of uncertainty. Although foods are selected based on the reported consumption records in the food consumption database, a food which is not representative of what was actually consumed may be selected. Some sensitivity tests demonstrated that results obtained through the RPC model may be very variable when low probabilities are considered. This instability was addressed by excluding foods and food components that had probabilities below 10%. This approach increased the reliability of the RPC model. However, the exclusion of certain foods also implies that consumption data for frequently consumed RPCs (e.g. apples) may be slightly overestimated. Likewise, RPCs that are not frequently consumed (e.g. cherries) are likely to be underestimated. The probability table and the disaggregation table do not incorporate inter-country variation, consumer habits, personal preferences, and product or recipe variation. Furthermore, differences between commercial products and household prepared foods are not accounted for. This may lead to either over- or underestimations of the RPC consumption data. As there is currently no harmonised list of reverse yield factors available on either an EU or worldwide level, reverse yield factors sourced in the conversion table of the model may not be accurate. Furthermore, the RPC model uses one single factor for each processing technique. In reality, yields will vary among households and industrial manufacturers. This uncertainty is not expected to have a major impact on average consumption/exposure, but it is expected to underestimate upper tail consumption/exposure. As highlighted in Section 3.2, performing exposure assessments on the basis of RPC consumption data implicitly assumes that the entirety of the chemical(s) present in the RPC will reach the end consumer. When losses of the chemical(s) under assessment are expected to occur during processing, this assumption may result in a substantial overestimation of the exposure. Several food items were not converted to RPCs, i.e. foods for specific nutritional use, extracts, food additives, standalone processing agents and foods for infants and young children. Most of these foods were identified on the basis that they are extensively processed and that they are expected to have a minor impact on the consumption of RPCs. Omitting foods for infants and young children, however, may lead to substantial underestimations of exposure in the relevant population groups. Such uncertainties may be ignored when the food category is subject to legislation that is different from the RPCs (e.g. specific legislation applies to pesticides in foods for infants and young children). The current protocol could not identify certain ingredients that are widely used for the industrial manufacturing of composite foods. For example, whey powder is not frequently listed as a component of composite foods (see Annex A, Table A.4) although it is known to be used in a wide range of foods such as biscuits, savoury sauces and confectionery. Such ingredients are normally used in small quantities but underestimation of the RPC consumption cannot be excluded. Reverse yield factors for cheese were derived on the basis of fat content. This approach was found to be reliable for most of the cheeses but the quantity of milk estimated from the consumption of low-fat cheeses is likely to be underestimated. 4. Conclusions Using the RPC model, EFSA successfully managed to convert its Comprehensive Database into a new RPC Consumption Database. The RPC Consumption Database now contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC 20 EFSA Supporting publication 2019:EN-1532