Probabilistic modelling of human exposure to intense sweeteners in Italian teenagers: validation and sensitivity analysis of a probabilistic model including indicators of market share and brand loyalty

Abstract

For the assessment of exposure to food-borne chemicals, the most commonly used methods in the European Union follow a deterministic approach based on conservative assumptions. Over the past few years, to get a more realistic view of exposure to food chemicals, risk managers are getting more interested in the probabilistic approach. Within the EU-funded 'Monte Carlo' project, a stochastic model of exposure to chemical substances from the diet and a computer software program were developed. The aim of this paper was to validate the model with respect to the intake of saccharin from table-top sweeteners and cyclamate from soft drinks by Italian teenagers with the use of the software and to evaluate the impact of the inclusion/exclusion of indicators on market share and brand loyalty through a sensitivity analysis. Data on food consumption and the concentration of sweeteners were collected. A food frequency questionnaire aimed at identifying females who were high consumers of sugar-free soft drinks and/or of table top sweeteners was filled in by 3982 teenagers living in the District of Rome. Moreover, 362 subjects participated in a detailed food survey by recording, at brand level, all foods and beverages ingested over 12 days. Producers were asked to provide the intense sweeteners' concentration of sugar-free products. Results showed that consumer behaviour with respect to brands has an impact on exposure assessments. Only probabilistic models that took into account indicators of market share and brand loyalty met the validation criteria.

Validation analysis of probabilistic models of dietary exposure to food additives

“…Therefore, more sophisticated models of food additive exposure that incorporate information on market share and/or brand loyalty into the modelling algorithms might be required. In the case of intense sweeteners, this approach has been applied by Arcella et al (2003). If lack of information on market share and/or brand loyalty dictates the need to use simpler models of food chemical exposure, exposure estimates from very high percentiles (e.g.…”

Development of databases for use in validation studies of probabilistic models of dietary exposure to food chemicals and nutrients

“…Procedures useful to calculate the best intake estimate of food additives, pesticide residues and nutrients were developed within the Monte Carlo project in order to validate probabilistic modelling (Arcella et al 2003, Gilsenan et al 2003, Lo´pez et al 2003, Rubingh et al 2003). In the present paper, these best estimates were defined as the 'true' intakes.…”

“…The first condition is quite easy to confirm by using one of the conservative methods currently in use. Within the Monte Carlo project different approaches were adopted to obtain conservative estimates, for food additives individual consumption data were combined with MPLs by Arcella et al (2003) and the Step 2 method was used by Gilsenan et al (2003), for pesticide residues Boon et al (2003) used the IESTI method whereas a method which combines the Step 2 and IESTI approaches was used in the validation analyses conducted by Lopez et al (2003).…”

The Total Diet Study: Changes in Food Safety Since the First TDS

“…Consideration of consumer loyalty may be important when assessing high chronic dietary exposure to food chemicals present in processed foods (e.g. food additives, including flavourings, processing aids, or chemicals migrating from packaging) (Arcella et al, 2003). Consumer loyalty may also warrant consideration in the dietary exposure assessments of other chemicals, such as pesticide residues and contaminants.…”