Food Nanotechnology Applications to the Beverage Industry
Bibliographic record
Abstract
Health-conscious consumers are looking for foods that impart benefits beyond simple nutrition. Beverages are seen by consumers as the most active food category for their convenience in usage and distribution, and they have been the most invested functional food category ( Corbo et al., 2014 ). Innovations in food beverages have also benefitted from food nanotechnology applications. Food nanostructures are usually defined as structures of less than 100 nm in size, developed to respond to challenges related to biological or technological functionality. However, often, larger sizes are also utilized in foods for similar purposes, owing to the limitations on the functional ingredients available and permitted for use to build such structures. In this chapter, applications of structures designed to deliver functional molecules are summarized, also with considerations regarding their biological significance demonstrated using in vitro and in vivo reports. The adaptation of nanotechnology to food beverages has increased the opportunities for innovation, and a better understanding of their formation, disruption, and disintegration during consumption and gastrointestinal transit will result in an improved validation of structure function claims. Indeed, a firm validation remains a critical issue, as added value is the driver for the development of such functional beverages.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".