Encapsulation: A Promising Technology for Future Food Applications, but What Policies Are Countries Following Today?
Bibliographic record
Abstract
The simple definition of encapsulation is “enclosing something in a capsule”. Encapsulation is applied as micro- and nanotechnology in pharmaceutical and food sciences for varied materials. Moreover, we will see more implementations in the forthcoming years because of the promising nature of this technology. However, the adverse effects of encapsulated tiny materials are unknown, and the health authorities of countries do not follow specific legislations on micro- and nano-encapsulated foods. Indeed, applications of micro- and nanotechnology are observed with different regulations in different countries. For instance, in the USA, there are no regulations for encapsulation studies required by the FDA. Standard food tests are applied for micro and nano food products as well. In the European Union, no strict rules are required by the EFSA for approval requests from authorized institutions regarding the safety of food products utilizing micro- and nanotechnologies [1]. Furthermore, there are no regulations in Argentina, Canada, China, and the Republic of Korea for nanomaterials used in foods. Encapsulated food products are tiny, and the extent of accumulation of materials in the human body is unknown [2]. More importantly, encapsulated foods might create some unpredictable changes in the human body and produce harmful byproducts for the environment as well [3].
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".