Whole grain products in Brazil: the need for regulation to ensure nutritional benefits and prevent the misuse of marketing strategies
Bibliographic record
Abstract
OBJECTIVE: This study aims to evaluate the use of "whole grains" claims in food products marketed in Brazil and evaluate the nutrient profile of these products. METHODS: Data from 775 grain-based packaged foods collected in Brazil from April to July 2017 were analyzed. Based on the INFORMAS protocol for food labeling, the prevalence of packaged foods with "whole grains" claims was estimated. Information on the list of ingredients was analyzed to evaluate the presence and amount of whole or refined grains in six food groups. The nutrient profiles of the products with and without "whole grains" claims were compared using the Pan American Health Organization (PAHO) nutrient profile model. RESULTS: The packages of about 19% of the evaluated products showed "whole grains" claims in their front panel. Of these, 35% lacked any whole grains among their top three ingredients. Breakfast cereals, granola bars, bread, cakes and other bakery products, cookies, and pasta had higher amounts of refined flour than whole grain ingredients in their compositions.We found 66% of products with "whole grains" claims were high in nutrients of concern according to PAHO's criteria. CONCLUSION: Our results showed that over a third of the products in Brazil with "whole grains" claims lacked whole grains as one of their main ingredients. Most had a high content of nutrients associated with noncommunicable chronic disease risk factors, indicating the overestimation of their health benefits.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".