Food additives and PAHO’s nutrient profile model as contributors’ elements to the identification of ultra-processed food products
Bibliographic record
Abstract
The NOVA classification system categorizes foods according to the extent and purpose of industrial processing. Ultra-processed food products (UPF) are frequently composed of excessive amounts of sugars, salt, oils, and fats, and cosmetic additives designed to make them palatable and/or appealing. We aimed to describe the presence of critical nutrients in excess and cosmetic additives in packaged foods and beverages and to evaluate the proportion of UPF that can be correctly identified through the presence of critical nutrients in excess or the presence of cosmetic additives in food products. A total of 9851 items available in Brazilian supermarkets containing lists of ingredients and nutrition facts panels were analyzed. Cosmetic additives and critical nutrients in excess, according to Pan American Health Organization (PAHO)'s nutrient profile model, were assessed. All food items were categorized into the four NOVA classification groups. Relative frequencies of items with at least one critical nutrient in excess and one type of cosmetic additive were estimated. For UPF, 82.1% had some cosmetic additive, and 98.8% had some cosmetic additive or a nutrient in excess. This combined criterion allowed the identification of 100.0% of sweet cookies, salted biscuits, margarine, cakes and sweet pies, chocolate, dairy beverages, and ice cream. Combining the presence of cosmetic additives and the PAHO's nutrient profile model contributes to the identification of UPF.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".