Microencapsulation of Betacyanin from Dragon Fruit (Hylocereus polyrhizus) Peel by Foam-Mat Drying for Natural Food Colorant Application
Bibliographic record
Abstract
The growing demand for natural food colorants highlights the potential of betacyanin, a red-violet pigment from dragon fruit (Hylocereus polyrhizus) peel, valued for its antioxidant, anti-inflammatory, and anticancer properties.However, its application is limited by sensitivity to heat, light, and pH variations.This study aimed to enhance the stability and usability of betacyanin as a natural food coloring agent through microencapsulation using the foam-mat drying method.The extraction process was optimized using ultrasound-assisted extraction with a Box-Behnken Design (BBD) 3 levels (X1: temperature; X2: solvent concentration; X3: solid to solvent ratio) and a total of 15 experiments to maximize total betacyanin content and color intensity.Foaming agents were screened, with SP exhibiting the highest foam expansion and stability among commercial foaming agents.The foam was subsequently dried at 60℃ under vacuum to produce a stable, bioactive powder.The foam-mat drying resulted in encapsulated betacyanin with favorable properties, including high solubility of around 80% and encapsulation efficiency of up to 85%, making it suitable for applications in the food industry as a natural coloring agent.Moreover, foam-mat microencapsulation is more cost-effective and easier to implement than microencapsulation using spray drying.
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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".