Benzoates intakes from non-alcoholic beverages in Brazil, Canada, Mexico and the United States Part A Chemistry, analysis, control, exposure & risk assessment
Bibliographic record
Abstract
Food consumption data from national dietary surveys were combined with brand-specific-use levels reported by beverage manufacturers to calculate the exposure to benzoic acid and its salts (INS Nos 210–213) from non-alcoholic beverages in Brazil, Canada, Mexico and the United States. These four jurisdictions were identified as having some of the most prevalent use of benzoates in beverages globally. Use levels were weighted according to the brand’s market volume share in the respective countries. Benzoates were reported to be used primarily in ‘water-based flavoured drinks’ (Codex General Standard for Food Additives (GSFA) category 14.1.4). As such, the assessments focused only on intakes from these beverage types. Two different models were established to determine exposure: probabilistic (representing non-brand loyal consumers) and distributional (representing brand-loyal consumers). All reported-use levels were incorporated into both models, including those above the Codex interim maximum benzoate use level (250 mg kg ⁻¹). The exception to this was in the brand-loyal models for consumers of regular carbonated soft drinks (brand loyal category) which used (1) the interim maximum use level for beverages with a pH ≤ 3.5 and (2) all reported use levels for beverages pH > 3.5 (up to 438 mg kg ⁻¹). The estimated exposure levels using both models were significantly lower than the ADI established for benzoates at the mean level of intake (4–40% ADI) and lower than – or at the ADI only for toddlers/children – at the 95th percentile (23–110% ADI). The results rendered in the models do not indicate a safety concern in these jurisdictions, and as such provide support for maintaining the current Codex interim maximum benzoate level of 250 mg kg ⁻¹ in water-based beverages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".