Comparative Evaluation of Ultrasonic, High Pressure, and Pulsed Electric Field Processing on the Extraction and Storage Stability of Betalains From Red Beet By‐Products
Bibliographic record
Abstract
This study explores the potential of red beet peel (RBP) and red beet stalk (RBS), often underutilized by-products, as rich sources of natural pigments and bioactive compounds. Three non-thermal green extraction methods, namely, high-pressure processing, ultrasound, and pulsed electric field treatment, were evaluated for their efficiency in extracting betalains, total phenolics, flavonoids, and antioxidant activity (AOX). Among these, sonication at 300 W for 10 min proved most effective for both RBP and RBS, yielding the highest concentrations of bioactives. Crucially, this work goes beyond conventional yield assessments by addressing the post-extraction storage stability of these compounds over 120 days under refrigeration. Kinetic modeling revealed that betalains and phenolics degraded following first-order kinetics, whereas AOX increased over time and followed a zero-order model. Sonicated samples demonstrated superior pigment stability, with betaxanthins more stable than betacyanins. This study highlights the importance of integrating degradation kinetics with extraction optimization and offers a novel perspective on the valorization of red beet by-products. The findings are particularly relevant for food and nutraceutical industries seeking to develop shelf-stable, functional plant-based ingredients.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".