An updated estimate of benzoate intakes from non-alcoholic beverages in Canada and the United States
Bibliographic record
Abstract
In 2017, the results of a comprehensive assessment of intake for benzoic acid and its salts from non-alcoholic beverages were published for four regions (Brazil, Canada, Mexico, and the United States [U.S.]). These regions were among those identified as having the most prevalent use of benzoates in beverages globally. The results of the 2017 study did not indicate a safety concern relative to the acceptable daily intake (ADI) established for benzoates (5 mg kg body weight-1 day−1, as benzoic acid), and supported maintaining the Codex maximum benzoate level in water-based beverages (250 mg kg−1). Since this time, population-specific food consumption data have been released for public use for Canada, and updated beverage consumption data have become available for the U.S. To ensure estimated intakes remain relevant, these consumption data were incorporated with previously collected brand-specific benzoate use level and market volume data for beverages. Dietary exposure to benzoates from non-alcoholic beverages was assessed using statistical modelling, either probabilistic (non-brand loyal; considering the full distribution of use levels) or deterministic (brand loyal; assuming all regular carbonated soft drinks, the brand loyal beverage type, contain benzoates at the maximum use level, and all other beverage types in which benzoates are used contain benzoates at the market-weighted average use level). In both models, estimated daily intakes at the mean and 95th percentile were below the ADI (≤76% of the ADI) in all Canadian and U.S. population groups with a statistically reliable population size. The findings from updated Canadian and U.S. consumption data continue to support the Codex maximum benzoate level in water-based flavoured drinks at 250 mg kg−1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".