Advancement of Protein- and Polysaccharide-Based Biopolymers for Anthocyanin Encapsulation
Bibliographic record
Abstract
Although evidence shows that anthocyanins present promising health benefits, their poor stability still limits their applications in the food industry. Increasing the stability of anthocyanins is necessary to promote their absorption and metabolism and improve their health benefits. Numerous encapsulation approaches have been developed for the targeted release of anthocyanins to retain their bioactivities and ameliorate their unsatisfactory stability. Generally, choosing suitable edible encapsulation materials based on biopolymers is important in achieving the expected goals. This paper presented an ambitious task of summarizing the current understanding and challenges of biopolymer-based anthocyanin encapsulation in detail. The food-grade edible microencapsulation materials, especially for proteins and polysaccharides, should be employed to improve the stability of anthocyanins for effective application in the food industry. The influence factors involved in anthocyanin stability were systematically reviewed and highlighted. Food-grade proteins, especially whey protein, caseinate, gelatin, and soy protein, are attractive in the food industry for encapsulation owing to the improvement of stability and their health benefits. Polysaccharides, such as starch, pectin, chitosan, cellulose, mucilages, and their derivatives, are used as encapsulation materials because of their satisfactory biocompatibility and biodegradability. Moreover, the challenges and perspectives for the application of anthocyanins in food products were presented based on current knowledge. The proposed perspective can provide new insights into the amelioration of anthocyanin bioavailability by edible biopolymer encapsulation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".