Encapsulating <i>Salmo salar</i> byproduct-derived protein hydrolysate in chitosan/alginate nanoparticles
Bibliographic record
Abstract
Byproducts-derived protein hydrolysates are known to have different bioactivities such as antioxidative, antihypertensive, antidiabetic, immunomodulatory, and antiproliferative activities that help improve human health. Low bioavailability, stability, heterogenous nature, interaction with food matrix, and hydrophobicity limit their applications. Hence, nanocarriers could be an effective method of delivering these hydrolysates. This study aimed to develop and optimize chitosan/alginate nanoparticles (CS/AL NPs) to deliver Salmo salar by-product-derived protein hydrolysates (SPH). The optimized nanoparticle size, zeta potential, and encapsulation efficiency (EE) were 536.7 nm, −30.2 mV, and 29.8%, respectively. XRD and FTIR results proved the incorporation of SPH into the CS/AL NPs. Moreover, the release of SPH in the salivary phase is higher due to the high amount of free SPH in the nanoparticle suspension. Encapsulated SPH was protected in the gastric phase and showed a controlled release in the intestinal phase. The ultimate goal of utilizing these nanoparticles is to fabricate functional food products, and thereby offer consumers greater health benefits through the bioactive properties of hydrolysates.
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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".