Review of the regulation and safety assessment of food substances in various countries and jurisdictions
Bibliographic record
Abstract
This review compares the regulations, definitions and approval processes for substances intentionally added to or unintentionally present in human food in the following specific countries/jurisdictions: Argentina, Australia, Brazil, Canada, China, the European Union, Japan, Mexico, New Zealand, and the United States. This includes direct food additives, food ingredients, flavouring agents, food enzymes and/or processing aids, food contact materials, novel foods, and nanoscale materials for food applications. The regulatory authority of each target jurisdiction/country uses its own regulatory framework and although the definitions, regulations and approval processes may vary among all target countries, in general there are many similarities. In all cases, the main purpose of each authority is to establish a regulatory framework and maintain/enforce regulations to ensure that food consumed and sold within its respective countries is safe. There is a move towards harmonisation of food regulations, as illustrated by Australia and New Zealand and by Mercosur. The European Union has also established regulations, which are applicable for all member states, to establish a common authorisation procedure for direct food additives, flavourings and enzymes. Although the path for approval of different categories of food additives varies from jurisdiction to jurisdiction, there are many commonalities in terms of the data requirements and considerations for assessment of the safety of use of food additives, including the use of positive lists of approved substances, pre-market approval, and a separation between science and policy decisions. The principles applied are largely reflective of the early work by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) committees and JECFA assessments of the safety of food additives for human and animal foods.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".