Stability and <i>in vitro</i> release behaviour of encapsulated omega fatty acid-rich oils in lentil protein isolate-based microcapsules
Bibliographic record
Abstract
The objective of this study was to investigate the use of a lentil protein isolate-based microcapsule design as a platform for entrapping different types of omega fatty acid-rich oil (e.g. canola, fish and flaxseed oils) and to characterise differences in the physical properties (e.g. moisture content, water activity, colour, wettability, particle size, surface oil and entrapment efficiency), storage stability and in vitro release behaviour of the entrapped oils. All microcapsules displayed similar physical properties regardless of the core material. Free fatty acid content, peroxide value, 2-thiobarbituric acid reactive substances and accelerated oxidation test were investigated between the free and encapsulated oils to determine protective effects from microencapsulation and found the wall material provided the greatest protective effect to the fish oils relative to the others. Based on an in vitro release assay, it was proposed that different intrinsic properties of fatty acids (e.g. polarity, conformation, chain length and number of double bonds) led to different release properties under simulated conditions. For instance, more encapsulated canola oil (∼8.9%) was released within simulated gastric fluid, whereas more encapsulated fish oil (∼73.4%) was released within simulated gastrointestinal fluids. Overall, the capsule design used in this study could be potentially used as a universal platform to deliver more healthy oils.
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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".