Flavour over preservation: a label analysis examining the use of food additives in animal-based foods marketed in Brazil
Bibliographic record
Abstract
Packaging animal-based foods often necessitates the use of additives for preservation. No study has yet focused on the usage of food additives across different animal-based foods. This study aimed to examine food additives declared on labels of animal-based foods (n = 1702) sold in Brazil. Data was collected through a census of food labels conducted in a supermarket in 2020. The number and prevalence of additives were determined by functional class, food group, and food subgroup. Additives were identified in 71% of the analysed labels. Excluding fresh foods, the prevalence of additives was 84%. The food groups with the highest prevalence of additives were dairy products (89%), ready-to-eat dishes (88%), and animal fats (60%). A total of 240 different food additives were identified, with a median of three additives per food. The most reported functional classes across all food groups were flavourings (20%), stabilisers (19%), preservatives (14%), and colourings (9%). These results showed that additives were primarily used for sensory reasons, such as altering taste, smell, colour, and texture, rather than for preservation purposes. These findings may promote discussions and a review of regulations on the use of food additives in animal-based foods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".