Substance concentrations generation

Both chronic and acute dietary exposure assessments rely on assigning substance concentrations to consumed modelled foods. For chronic exposure assessments, this concentration should be the mean concentration of the food x substance combination, as obtained from the concentration models. For acute, these concentrations are obtained through random sampling. For acute, two approaches are available: sample-based and substance-based.

Sample-based concentrations generation

This approach is based on the analytical samples behind the concentration data. For each modelled food, substance concentration values are generated by taking a random sample from the set of analytical samples. For each analytical sample, the corresponding substance concentration values are kept together, maintaining the correlations between the substance concentrations.

Occasionally, one or more substances have censored or missing concentration values. Then, apply imputation first.

For imputation of missing values there are two methods:

  1. Imputation by zero: all missing values are assumed zero.

  2. Imputation using substance-based concentration models: all missing values are imputed by drawing a concentration value from the substance-based concentration models.

For imputation of censored values, three methods exist:

  1. Replace by zero: Censored values are imputated by a zero concentration value. This is an optimistic approach.

  2. Replace by factor times LOR: Each censored value is replaced by a factor f (e.g., 1 or 1/2) times its LOR.

  3. Replace by factor times LOD LOQ system: Non-detects are replaced by f * LOD; non-quantifications are replaced by LOD + f * (LOQ - LOD) and factor f is e.g., 1 or 1/2. For f = 0, non-detects are replaced by zero, non-quantifications are replaced by LOD; for f = 1, non-detects are replaced by LOD, non-quantifications are replaced by LOQ.

Substance-based concentrations generation

In this approach, substance concentrations for a given food are drawn independently per substance from the food/substance concentration models.