Pea Protein for Hempseed Oil Nanoemulsion Stabilization
Bibliographic record
Abstract
In this paper, we present the possibility of using pea protein isolates as a stabilizer for hempseed oil (HSO)-based water/oil emulsions in conjunction with lecithin as a co-surfactant. A Box-Behnken design was employed to build polynomial models for optimization of the ultrasonication process to prepare the emulsions. The stability of the system was verified by droplet size measurements using dynamic light scattering (DLS) as well as centrifugation and thermal challenge tests. The z-ave droplet diameters of optimized emulsion were 209 and 207 nm after preparation and 1 week storage, respectively. The concentration of free Linoleic acid (C18:2; n-6) was used for calculation of entrapment efficiency in prepared nanoemulsions. At optimum conditions of the process, up to 98.63% ± 1.95 of entrapment was achieved. FTIR analysis and rheological tests were also performed to evaluate the quality of oil and emulsion, and to verify the close-to-water like behavior of the prepared samples compared to the viscous nature of the original oil. Obtained results confirmed the high impact of lecithin and pea protein concentrations on the emulsion droplet size and homogeneity confirmed by microscopic imaging. The presented results are the first steps towards using hempseed oil-based emulsions as a potential food additive carrier, such as flavor. Furthermore, the good stability of the prepared nanoemulsion gives opportunities for potential use in biomedical and cosmetic applications.
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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".