Can the intake of synthetic food colour Amaranth (INS 123) put the health of Brazilian consumers at risk?
Bibliographic record
Abstract
Amaranth is a synthetic red azo dye approved in several countries such as Canada, Australia and Brazil, but banned in the United States. There are few studies evaluating the exposure of the general population to this food colouring substance, in Brazil, specifically, there are virtually no data on its intake. This study aimed to estimate the Theoretical Maximum Daily Intake (TMDI) of the Brazilian population and to quantify Amaranth in foods that contribute the most to its consumption. Data on the presence of Amaranth were correlated with consumption data from National Household Budget Surveys carried out in 2008/2009 and 2017/2018, among people aged ten or older. The results show that the mean TMDI (mg/day) of Amaranth does not exceed the Acceptable Daily Intake (ADI) in any population group, it, however, may get as high as 66% of the ADI among teenagers. For the TMDI balanced by the prevalence of food consumption, that is, considering consumers only (eaters only, rather than the population mean), results show that the amounts can exceed the ADI in all population groups studied. The intake of Amaranth is higher among the younger population (adolescents) reaching up to three times the ADI in the worst-case scenario. The food groups which contribute the most to the intake of Amaranth, are 'juices/artificial juices/reconstituted powdered juice mixes' and 'soft drinks'. Laboratory tests of powdered fruit mixes and soft drinks sold in the city of Porto Alegre (Brazil) show that 17 out of 20 samples tested exceeded the limit set by Brazilian regulations (5 mg/100 mL in the final product). Results show that the intake of Amaranth by the different Brazilian populations may pose a health hazard to several population groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".