Designing healthier and more sustainable ultraprocessed foods
Bibliographic record
Abstract
The food industry has been extremely successful in creating a broad range of delicious, affordable, convenient, and safe food and beverage products. However, many of these products are considered to be ultraprocessed foods (UPFs) that contain ingredients and are processed in a manner that may cause adverse health effects. This review article introduces the concept of UPFs and briefly discusses food products that fall into this category, including beverages, baked goods, snacks, confectionary, prepared meals, dressings, sauces, spreads, and processed meat and meat analogs. It then discusses correlations between consumption levels of UPFs and diet-related chronic diseases, such as obesity and diabetes. The different reasons for the proposed ability of UPFs to increase the risk of these chronic diseases are then critically assessed, including displacement of whole foods, high energy densities, missing phytochemicals, contamination with packaging chemicals, hyperpalatability, harmful additives, rapid ingestion and digestion, and toxic reaction products. Then, potential strategies to overcome the current problems with UPFs are presented, including reducing energy density, balancing nutritional profile, fortification, increasing satiety response, modulating mastication and digestion, reengineering food structure, and precision processing. The central argument is that it may be possible to reformulate and reengineer many UPFs to improve their healthiness and sustainability, although this still needs to be proved using rigorous scientific studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".