Whey Protein Isolate, Tween 20 and Casein Hydrolysate Stabilized O/W Nano-Vesicular Emulsion Systems with Curcumin Cargo
Bibliographic record
Abstract
Stable oil-in-water nanoemulsions were generated by ultra-high-pressure homogenization (UHPH) at 140 and 210 MPa for use as nano-vesicular vehicles (NVV) to carry hydrophobic generally recognized as safe (GRAS) curcumin (CU) by whey protein isolate (WPI) in aqueous nutraceutical systems. Curcumin was used for its antioxidant activity and participation in the Michael reaction with nucleophiles at pHs above 8.0. Two variables, (1) addition of casein hydrolysate (CH) (2%, w/w of WPI) and, (2) use of UHPH (140 and 210 MPa), were studied for their effect on the stabilization of monodispersed NVV and antioxidant capacity of the CU as cargo in the NVV throughout storage. CH and Tween 20 both were added to increase dispersibility and stability of the NVV. Addition of CH reduced nano-particle size (dvs) by 17% at 210 MPa when compared to140 MPa (P<0.05), and increased the stability with UHPH pressure as reflected by a 63% smaller dvs at 210 MPa as compared to 140 MPa (P<0.05). The nanoparticle distribution was not changed by the addition of CU, with dvs’s of 101 and 93 nm at 140 MPa and 73 and 92 at 210 MPa for NVV and CU-NVV, respectively. The NVV system was stable for 28 days as observed in zeta-potential, contact angle, and surface energy, and can be used to deliver CU and maintain its antioxidant activity.
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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) | 0.001 | 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".