Coencapsulation of Polyphenols and Anthocyanins from Blueberry Pomace by Double Emulsion Stabilized by Whey Proteins: Effect of Homogenization Parameters
Bibliographic record
Abstract
Blueberry pomace is a rich source of high-value bioactive polyphenols with presumed health benefits. Their incorporation into functional foods and health-related products benefits from coencapsulation and protection of polyphenol-rich extracts in suitable carriers. This study aimed to create a water-in-oil-in-water (W₁/O/W₂) double emulsion system suitable for the coencapsulation of total phenolics (TP) and anthocyanins (TA) from a polyphenol-rich extract of blueberry pomace (W₁). The effect of critical physical parameters for preparing stable double emulsions, namely homogenization pressure, stirring speed and time, was investigated by measuring the hydrodynamic diameter, size dispersity and zeta potential of the oil droplets, and the encapsulation efficiency of TP and TA. The oil droplets were negatively charged (negative zeta potential values), which was related to the pH and composition of W₂ (whey protein isolate solution) and suggests stabilization by the charged whey proteins. Increasing W₁/O/W₂ microfluidization pressure from 50 to 200 MPa or homogenization speed from 6000 to 12,000 rpm significantly increased droplet diameter and zeta potential and decreased TA and TP encapsulation efficiency. Increasing W₁/O/W₂ homogenization time from 15 to 20 min also increased droplet diameter and zeta potential and lowered TA encapsulation efficiency, while TP encapsulation did not vary significantly. In contrast, increasing W₁/O homogenization time from 5 to 10 min at 10,000 rpm markedly increased TA encapsulation efficiency and reduced droplet diameter and zeta potential. High coencapsulation rates of blueberry polyphenols and anthocyanins around 80% or greater were achieved when the oil droplets were relatively small (mean diameter < 400 nm), with low dispersity (<0.25) and a high negative surface charge (-40 mV or less). These characteristics were obtained by homogenizing for 10 min at 10,000 rpm (W₁/O), then 6000 rpm for 15 min, followed by microfluidization at 50 MPa.
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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".