Engineered Nanoscale Food Ingredients: Evaluation of Current Knowledge on Material Characteristics Relevant to Uptake from the Gastrointestinal Tract
Bibliographic record
Abstract
The NanoRelease Food Additive project developed a catalog to identify potential engineered nanomaterials (ENMs) used as ingredients, using various food-related databases. To avoid ongoing debate on defining the term nanomaterial, NanoRelease did not use any specific definition other than the ingredient is not naturally part of the food chain, and its dimensions are measured in the nanoscale. Potential nanomaterials were categorized based on physical similarity; analysis indicated that the range of ENMs declared as being in the food chain was limited. Much of the catalog's information was obtained from product labeling, likely resulting in both underreporting (inconsistent or absent requirements for labeling) and/or overreporting (inability to validate entries, or the term nano was used, although no ENM material was present). Three categories of ingredients were identified: emulsions, dispersions, and their water-soluble powdered preparations (including lipid-based structures); solid encapsulates (solid structures containing an active material); and metallic or other inorganic particles. Although much is known regarding the physical/chemical properties for these ingredient categories, it is critical to understand whether these properties undergo changes following their interaction with food matrices during preparation and storage. It is also important to determine whether free ENMs are likely to be present within the gastrointestinal tract and whether uptake of ENMs may occur in their nanoform physical state. A practical decision-making scheme was developed to help manage testing requirements.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".