Brazilian front-of-package nutrition labelling and food additives: an approach to identify ultra-processed food products
Bibliographic record
Abstract
This study aimed to explore combinations of the Brazilian front-of-package nutrition labelling (FoPNL) (high in added sugar, saturated fat or sodium) and/or three specific food additives with cosmetic functions (colourings, flavourings and non-sugar sweeteners) in packaged foods and beverages marketed in Brazil. This approach intends to strengthen the identification of ultra-processed food products (UPFP) by consumers through the information available on their labels. A cross-sectional study was carried out using data from the list of ingredients and the nutrition facts panel on labels of processed foods and UPFP available in Brazilian supermarkets between April and July 2017, totalling 8436 food items assessed, of which 84·0 % were UPFP. Of the total, 62·7 % of the UPFP would have the FoPNL and 65·1 %, 37·9 % and 12·9 % had flavouring, colouring and non-sugar sweeteners, respectively. Combining criteria for the FoPNL with any one of the three cosmetic additives analysed, 45·9 % of the UPFP were identified, and when considering the presence of the FoPNL, flavouring, colouring or non-sugar sweeteners, the identification increased to 89·9 %. Results showed that the current FoPNL in Brazil does not facilitate the identification of UPFP. In this sense, labels that indicate the presence of food additives with cosmetic functions (which are UPFP markers) could be a public health strategy to reduce the consumption of UPFP. Currently, food labelling regulations in Brazil are not aligned with Brazilian Dietary Guidelines recommendations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".