Development and characterization of coated-microparticles based on whey protein/alginate using the Encapsulator device
Bibliographic record
Abstract
The aim of this study is to prepare whey protein (WP)-based microparticles (MP) using the Encapsulator(®) device. The viscosity dependence of the extrusion device required to mix WP with a food-grade and less viscous polymer. Mixed WP/ALG MP were obtained with the optimized WP/alginate (ALG) ratio (62/38). These particles were further coated with WP or ALG using non-traumatic and solvent-free coating process developed in this study. Size and morphology of coated and uncoated MP were determined. Then, swelling and degradation (WP release) of formulations were investigated in pH 1.2 and 7.5 buffers and in simulated gastric and intestinal fluids (SGF, SIF) and compared to pure ALG and pure WP particle behaviours. At pH 1.2, pure ALG shrank and pure WP swelled, whereas the sizes of mixed WP/ALG matrix were stable. In SGF, WP/ALG MP resisted to pepsin degradation compare to pure WP particles due to ALG shrinkage which limited pepsin diffusion within particles. Coating addition with WP or ALG slowed down pepsin degradation. At pH 7.5, WP/ALG particles were rapidly degraded due to ALG sensitivity but the addition of a WP coating limited effectively the swelling and the degradation of MP. In SIF, pancreatin accelerated MP degradation but ALG-coated MP exhibited interesting robustness. These results confirmed the interest and the feasibility to produce coated WP-based MP which could be a potential orally controlled release drug delivery system.
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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".