Functional foods: regulations and consumer protection
Bibliographic record
Abstract
The consumer rising attention to health problems and the awareness of importance of relantionship between diet and health have led to a growing consumption of dietary supplements, particular foods and others, as the so-called functional foods, that have appeared for the first time in Japan in the late 1980’s. Later, these products are spread in the USA market, in Canada and in the Europe, where was not only enthusiasm but also skepticism about functional foods. The present framework of regulation in regard to the production and the marketing of functional foods is too weak, showing deep gaps. In this paper the world regulation is examined, with an overview of the regulatory status in Japan, USA, Canada and Europe. Particular attention is addressed to the use of debatable “health claims” on the food labels and to the safety of functional foods. Institutional authorities should ensure consumers that: 1) claims in labelling are true and adequately substantiated, in order to avoid lying expectations about their supposed benefits and/or even negative effects; 2) functional ingredients are safe; 3) they are not added in the foods high in cholesterol , fat, sugar or sodium. Moreover, it’s necessary to harmonize food regulation on the international basis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".