Distribution and patterns of use of food additives in foods and beverages available in Brazilian supermarkets
Bibliographic record
Abstract
The growing consumption of ultra-processed foods and beverages has drawn attention to the use of different food additives in these products. The use of these additives for different purposes in food products is permitted under specific legislation. The objective of the present study was to assess the distribution and patterns of occurrence of the different categories of food additives present in packaged foods and beverages sold in Brazil. A descriptive cross-sectional study was conducted based on data from lists of ingredients used in foods and beverages sold in supermarkets in Brazil, collected by photographing product labels. The number, technological purpose and proportion of food additives in 9856 items (25 groups) were assessed. Exploratory factor analysis was employed to derive the patterns of food additive categories. Linear regression models were used to assess the association between the patterns and food items analyzed. Only 20.6% of the products analyzed contained no food additives, while 24.8% contained ≥6 additives. The use of food additives was high, particularly cosmetic additives, predominantly flavoring agents, colorings and stabilizers. Five patterns of food additive categories were identified and associated with ultra-processed foods and beverages. The results revealed that food additives are highly prevalent in several types of food items sold in the Brazilian market. Also, the same additive category was common to several different food groups, as were specific food additive combinations. This exposure is potentially harmful to human health, given the known deleterious effects associated with the consumption of these substances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".