Recent Advances in Food Emulsions and Engineering Foodstuffs Using Plant-Based Nanocelluloses
Bibliographic record
Abstract
In this article, the application of nanocelluloses, especially cellulose nanofibrils and cellulose nanocrystals, as functional ingredients in foods is reviewed. These ingredients offer a sustainable and economic source of natural plant-based nanoparticles. Nanocelluloses are particularly suitable for altering the physicochemical, sensory, and nutritional properties of foods because of their ability to create novel structures. For instance, they can adsorb to air-water or oil-water interfaces and stabilize foams or emulsions, self-assemble in aqueous solutions to form gel networks, and act as fillers or fat replacers. The functionality of nanocelluloses can be extended by chemical functionalization of their surfaces or by using them in combination with other natural food ingredients, such as biosurfactants or biopolymers. As a result, it is possible to create stimuli-responsive, tailorable, and/or active functional biomaterials suitable for a range of foodapplications. In this article, we describe the chemistry, structure, and physicochemical properties of cellulose as well as their relevance for the application of nanocelluloses as functional ingredients in foods. Special emphasis is given to their use as particle stabilizers in Pickering emulsions, but we also discuss their potential application for creating innovative biomaterials with novel functional attributes, such as edible films and packaging. Finally, some of the challenges associated with using nanocelluloses in foods are critically evaluated, including their potential safety and consumer acceptance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".