Plant-Based (Hemp, Pea and Rice) Protein–Maltodextrin Combinations as Wall Material for Spray-Drying Microencapsulation of Hempseed (Cannabis sativa) Oil
Bibliographic record
Abstract
Conscious consumers have created a need for constant development of technologies and food ingredients. This study aimed to examine the properties of emulsions and spray-dried microcapsules prepared from hempseed oil by employing a combination of maltodextrin with hemp, pea, and rice protein as carrier materials. Oil content in the microcapsules was varied at two levels: 10 and 20%. Increasing oil load caused a decrease in viscosity of all samples. Consistency index of prepared emulsions was calculated according to Power Law model, with the lowest (9.2 ± 1.3 mPa·s) and highest values (68.3 ± 1.1 mPa·s) for hemp and rice protein, respectively, both at 10% oil loading. The emulsion stability ranged from 68.2 ± 0.7% to 88.1 ± 0.9%. Color characteristics of the microcapsules were defined by high L* values (from 74.65 ± 0.03 to 83.06 ± 0.03) and low a* values (-1.02 ± 0.015 to 0.12 ± 0.005), suggesting that the materials were able to coat the greenish color of the hemp seed oil acceptably. The highest encapsulation efficiency was observed in samples with rice protein, while the lowest was with hemp protein. Combination of maltodextrin and proteins had a preventive effect on the oxidative stability of hempseed oil. Oil release profile fitted well with the Higuchi model, with hempseed oil microencapsulated with pea protein-maltodextrin combination at 10% oil loading depicting lowest oil release rates and best oxidative stability.
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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".