1. Stochastic modelling applied to the estimation of intense sweeteners intake. Davide Arcella National Research Institute for Food and Nutrition (INRAN)

2. The EU directive which fixed Maximum Permitted Levels (MPL) for intense sweeteners for all Member States also include the general obligation to establish national systems for monitoring the intake of food additives, in order to evaluate their use safety. In the EU the most commonly used methods for the assessment of exposure to these substances follow a deterministic approach based on conservative assumptions.

3. The food products market changes very rapidly in relation to both product formulation and consumer preferences. It is considered to be neither cost-effective nor necessary to collect detailed data for every food chemical.

4. Present conservative methods serve us well but sometimes they produce estimates of exposure which are biologically improbable. These estimates of potential exposure may obscure the ability of regulators, industry and consumers to determine which scenarios present a risk that is likely to occur and therefore need to be addressed

5. An important activity in the field of food safety is to develop and refine always more efficient statistical methods to periodically estimate the risk of an excessive intake of chemical substances. Over the past years, to get a more realistic view of exposure to hazardous substances, risk managers are getting more interested in probabilistic modelling.

6. A conservative approach tells us that a given intake is possible even though available data show it is improbable. The primary goal of the probabilistic approach is to describe the exposure distribution for the whole population under consideration quantifying the range of exposure and the likelihood of each exposure level.

7. The probabilistic approach: allows the utilisation of all the available information on variability in: the proportion of foods containing the substance, the concentration of the substance present food consumption patterns. allows to take into account all sources of variability and uncertainty in estimates of exposure.

8. 36 104 172241 309 377445 514582 0 0.05 0.1 0.15 0.2 0.25 0.3 F r e q. 36 104 172241 309 377445 514582 g per day 36 104172 241 309 377 445514 582 0 0.05 0.1 0.15 0.2 0.25 0.3 F r e q. 36 104172 241309 377 445 514582 g per day Consumption 0.6 0.91.21.51.82.12.42.73.03.3 3.63.9 0 0.05 0.1 0.15 0.2 F r e q. 0.60.91.21.51.82.12.42.73.03.3 3.63.9 Residue mg/kg 0.6 0.9 1.21.51.82.12.42.73.03.3 3.6 3.9 0 0.05 0.1 0.15 0.2 F r e q. 0.6 0.9 1.2 1.5 1.8 2.1 2.42.73.03.3 3.6 3.9 Residue mg/kg Food chemical residue 8 1012 14 16 1820 22 24 26 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 F r e q. 8 1012 1416 1820 22 24 26 kg 8 10 12 14 16 18 20 22 24 26 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 F r e q. 8 1012 14 16 18 20 22 24 26 kg Result Body weight

9. It must be noticed that simulations can be conducted in a wide variety of different ways using widely different data, assumptions and algorithms.

10. The models are only a simplified representation of real-world system. The structure of mathematical models employed to represent scenarios and phenomena of interest is often a key source of uncertainty.

11. Two different approaches can be used to perform simulations: the parametric method (Monte Carlo) depends on the random samplings from probability distributions describing consumption and occurrence data. the nonparametric method takes into account the consumptions and the levels of chemical occurrence in the simulations using random sampling of raw data. Example - simulation techniques

12. For simplicity and accuracy when using a probabilistic simulation technique, input variables should be as independent as possible. However, the presence of moderate to strong correlations or dependencies between input variables should be included in a model and discussed. Example - Correlations or dependencies

13. Example A: dependency between the amount of food consumed and the body weight are correlated therefore it is better to use food consumption data standardised for the body weight. Example B: dependency between intakes of different food categories for the same individual (e.g. high consumers of pears may be also high consumers of apples, both containing the same pesticide residue). Different methods can be used to generate correlated random variables. Example C: dependency between events of food selection on a given day and on sequential days should be included.

14. Significant approximations are often an inherent part of the assumptions upon which a model is built. But uncertainty arises also from a basic lack of knowledge regarding the input variables. There is therefore the critical need to test and validate probabilistic models against actual exposure data.

15. Validation criteria adopted in the Monte Carlo project Probabilistic models were considered valid when they provided exposure estimates that can be shown not to underestimate the true exposure but at the same time are more realistic than the currently used conservative estimates. Databases of “true” intakes were generated for food additives, based on brand level food consumption and ingredient composition.

16. Food survey INRAN-RM-2001 Adolescents recorded on diaries, at brand level, all foods and beverages ingested over 12 days.

17. FOOD SURVEY DATA ENTRY ANTHROPOMETRICS MEASUREMENT COLLECTION STANDARDIZATIONSTANDARDIZATION INTERVIEWERS

18. Samples 3,982 students completed a screening questionnaire aimed at identifying females high consumers of the main sources of intense sweeteners, table-top sweeteners and sugar-free soft drinks.  The final randomly selected sample comprised 125 males and 108 females.  Female teenagers selected as high consumers of table-top sweeteners were finally 79.  Female teenagers selected as high consumers of sugar-free soft drinks were 75.

19. FOOD DIARY

20. BO Boccale LA Lattina TP Tazza Piccola TM Tazza Media TG Tazza Grande BL Bicchiere da Liquore BP Bicchiere Piccolo BG Bicchiere Grande BO Walky cup FP Fetta Piccola FM Fetta Media FG Fetta Grande PP Porzione Piccola PM Porzione Media PG Porzione Grande UNIT OF MEASURAMENT

21. THE DATA ENTRY SOFTWARE

22. CODES, FOOD PRODUCTS AND PORTIONS FOOD COMPOSITION FOOD RECIPES FOOD LABELS DATABASESDATABASES

23. TERMINALS (interviewers) MASTER

24. Additives presence and concentration a)All the labels of packaged products susceptible to contain intense sweeteners were collected and the food labels database present at INRAN was updated including all the sugar- free products consumed during the survey. b)Producers were asked to declare the intense sweeteners concentration of all the products susceptible to contain intense sweeteners consumed within the survey.

25. No gross underestimation of intake occurred. The energy intake to basal metabolic rate (EI/BMR) ratio was well above the cut-off point established by Goldberg et al. (1991) to identify energy under-reporting. Results - data quality

26. Percentage of consumers of the sugar- free products in the study samples Sugar-free products Random males and females (%) Females high consumers of sugar-free soft drinks (%) Females high consumers of table-top sweeteners (%) Sugar-free chewing gum and candies 699594 Table-top artificial sweeteners 31339 Sugar-free frizzy drinks, fruit juices and iced teas 62018 Sugar-free mousse and yoghurts 6011

27. Intake of intense sweeteners (mg/kg b.w.) in the sample of randomly selected males and females (n=233). Artificial sweetener mean 95 th percentile ADI Aspartame0.0390.17040 Acesulfame K0.0110.048 9 Saccharin0.0010.0005 Cyclamate0.0140.0497

28. Intake of intense sweeteners (mg/kg b.w.) in the sample of females high consumers of sugar-free soft drinks (n=75). Artificial sweetener mean 95 th percentile ADI Aspartame0.0910.29840 Acesulfame K0.0430.2469 Saccharin0.0010.0005 Cyclamate0.0860.5517

29. Intake of intense sweeteners (mg/kg b.w.) in the sample of females high consumers of table-top sweeteners (n=79). Artificial sweetener mean 95 th percentile ADI Aspartame0.1720.85940 Acesulfame K0.0410.265 9 Saccharin0.0300.2335 Cyclamate0.0490.2927

30.  consumption data are rarely collected at brand level.  food additives that are not strictly necessary for the process are added in some brands and not in others. The nonparametric simulation method can be used to combine eating occasions assessed though food surveys with food additives concentrations values available at brand level. Stochastic modelling applied to the estimation of intense sweeteners intake

31. Market share and brand loyalty are responsible for correlations between intakes on sequential days.  Market share: proportion of the consumption level of a brand with respect to all brands of the same product.  Brand loyalty: consumers’ tendency to repeat the purchase of a brand.

32. Experiment Objective: validate the model for estimating the intake among high consumers. Additive:cyclamate. Source: soft drinks. Sample: 75 pre-screened females who stated to be high consumers of sugar-free beverages. Variable:average daily intake per kg body weight Statistics: 95 th percentile. Number of sets: 10,000. Sensitivity analysis: inclusion\exclusion of information about market share - inclusion\exclusion of information about brand loyalty.

33. B E C D A Day 12 H 13:30 Day 1 H 12:05 Day 1 H 18:05 Day 8 H 21:10 Day 3 H 17:25 A brand is assigned randomly by selecting uniformly from all available brands. No market share and no brand loyalty Brand A Brand B Brand C Brand D Brand E

34. D E C E E Day 12 H 13:30 Day 1 H 12:05 Day 1 H 18:05 Day 8 H 21:10 Day 3 H 17:25 A brand is assigned by selecting from all available brands, with the probability to select a given brand set equal to the Market Share (MS) for that brand. Market share but no brand loyalty

35. Day 12 H 13:30 Day 1 H 12:05 Day 1 H 18:05 Day 8 H 21:10 Day 3 H 17:25 Each subject is first assigned a brand to which he / she is assumed to be loyal. This brand is chosen on the basis of the defined Market Share (MS) values. Brand D His / her probability to select Brand D from MS(D) becomes: {MS(D) + [1 – MS(D)] x LF]} Where LF = Loyalty Factor Market share and brand loyalty

36. D E D D A Day 12 H 13:30 Day 1 H 12:05 Day 1 H 18:05 Day 8 H 21:10 Day 3 H 17:25 Market share and brand loyalty Once the subject is assumed to be loyal to brand D, if the Loyalty Factor (LF) = 0.5, his / her market share becomes: Brand D When LF = 1 the consumer always chooses the brand to which he / she is assumed to be loyal. When LF = 0 the consumer chooses each brand with a probability equal to its market share.

37. Mean of the daily average intake of cyclamate 1)No market share (27 brands) data and no loyalty factor 2)Market share data and no loyalty factor 3)Market share data and Loyalty Factor = 0.5 4)Market share data and Loyalty Factor = 1 This line is the “true” intake

38. 95 th percentile of the daily average intake of cyclamate 1)No market share (27 brands) data and no loyalty factor 2)Market share data and no loyalty factor 3)Market share data and Loyalty Factor (average) 4)Market share data and Loyalty Factor (high) This line is the “true” intake

39. Brand loyalty and market share influence results of a probabilistic model of human exposure to food additives. The probability of high intakes of intense sweeteners was in fact underestimated when they were not taken into account. When no data regarding market share or brand loyalty are available it would be advisable to run the model under different theoretical scenarios and use the worse case scenario to obtain conservative intake distributions. Conclusion

40. The numerical simulation techniques provide powerful tools that will take advantage from all the available knowledge (empirical data, experts judgments, etc.) in order to provide realistic estimates of exposure. The results, however, are only as good as the input data, algorithms and assumption. The impact of the assumptions should always be tested carefully and the results should be fully documented. A modelling tool must be structured so that all algorithms and assumptions inherent to the model can be identified and validated. General conclusion

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42. Research group Food Safety – Exposure Analysis Davide Arcellaarcella@inran.it www.inran.it/Ricerca/rischioalimentare National Research Institute for Food and Nutrition www.inran.it