Valorization of red beet peel through green extraction and carrier-driven microencapsulation for improved betalain stability
Bibliographic record
Abstract
The utilization of agri-food by-products and waste is increasingly essential due to sustainability trends and global regulations. Incorporating these nutrient-rich yet underutilized materials into food production enhances both sustainability and economic efficiency. This study evaluated and compared the extraction of betalains from red beet peel (RBP) using ultrasound-, high pressure-, and pulsed electric field-assisted techniques. Sonication yielded the highest levels of total betalains (16.92 ± 0.19 mg/g DM), phenolics (60.36 ± 0.32 mg GAE/g DM), flavonoids (18.49 ± 0.19 mg CTE/g DM), and antioxidant activity (112.10 ± 0.36 μM TE/g DM). Additionally, the stability of sonicated RBP betalains was analyzed using soy protein isolate (SPI) and maltodextrin (MD) as encapsulating agents. Both effectively reduced degradation, with betalain changes fitting zero-order kinetics over 120 days. The predicted shelf-life of encapsulated extracts was 7-12 months, significantly longer than non-encapsulated extracts (5 months). These findings highlight red beet peel as a sustainable source of natural pigments, demonstrating that eco-friendly extraction combined with encapsulation can effectively enhance both the stability and shelf-life of betalains, offering promising applications in functional foods and natural colorant development.
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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".