Ultrasound assisted ethanolic gelation of lentil protein to encapsulate β-carotene
Bibliographic record
Abstract
β-Carotene has low stability against temperature, oxygen, and light. Incorporating it into delivery systems enhances its bioavailability and application in the food and beverage industry. Also, lentil protein and pectin offer functionality as wall materials for delivery systems. This study investigated the use of ultrasound-assisted ethanolic gelation of lentil protein and pectin as an effective technique for encapsulating β-carotene. The impact of high-intensity ultrasound (HIUS) nominal power, pH, and ethanol on the physico-chemical and functional properties of the lentil protein-pectin emulsion gels were evaluated. The gelling ability was significantly dependent on the HIUS nominal power, and ethanol content. The β-carotene loading capacity ranged between 78 and 93%. The bioaccessibility of β-carotene in the emulsion gels ranged between 2 and 50% and was significantly dependant on the HIUS nominal power used. Furthermore, the β-carotene loaded emulsion gels were freeze-dried to form aerogels to improve the shelf stability. The aerogels structural conformation revealed the predominance of hydrogen bonding responsible for the interactions between the lentil protein and pectin. In conclusion, the emulsion gels and aerogels obtained in this study had desirable physico-chemical properties such as stability and microstructure, making them suitable for applications as fat replacers, functional foods, and nutraceuticals. • Stable β-carotene loaded emulsion gels of lentil protein and pectin were developed. • High-intensity ultrasound (HIUS) process enhanced lentil protein-pectin interactions. • HIUS process improved encapsulation efficiency and in-vitro digestion of β-carotene. • β-carotene emulsion gels via HIUS are promising for application as oleogels.
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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".