Nanoencapsulation of Anthocyanin Extract from Fermented Black Garlic (FBG) Based on Biocompatible Polymeric Materials
Bibliographic record
Abstract
Garlic contains various polyphenolic compounds such as anthocyanin, a bioactive, water-soluble compound generally known for its remarkable health-enhancing properties. However, it is chemically unstable and easily degrades due to various environmental conditions (temperature, pH, presence of oxygen and light, etc.) in addition to its low bioavailability due to fast metabolization and low absorption in the body. Therefore, a nanoencapsulation strategy is essential to address these limitations. In this work, anthocyanin extraction from FBG (Ilocos variety) with 85% acidified ethanol and its encapsulation using the chitosan-alginate nanoparticle system via pre-gelation and polyelectrolyte complex formation were demonstrated. Anthocyanin-loaded chitosan-alginate nanocapsules were characterized in terms of structural features, particle size, morphology, encapsulation efficiency, total phenolic content (TPC), and radical scavenging activity of 2,2-diphenyl-1-picrylhydrazyl (DPPH). The obtained anthocyanin-loaded nanocapsules have small particle sizes ranging from 50.7 nm to 92.0 nm with high encapsulation efficiency (T3: 78.82%, T2: 68.18%, T1: 65.77%). Results showed that a higher initial concentration of anthocyanin extract promotes higher encapsulation efficiency. Antioxidant activity of the nanocapsules showed low phenolic content (0.11 mg GAE/g) but high DPPH scavenging activity (14.02 mg AAE/g). The chitosan-alginate complex has successfully encapsulated the anthocyanin from fermented black garlic.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 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".