<i>In vitro</i> digestibility and bioaccessibility of lipid-based delivery systems obtained <i>via</i> enzymatic glycerolysis: a case study of rosemary extract bioaccessibility
Bibliographic record
Abstract
This work studies the effect of enzymatic glycerolysis on digestibility and bioaccessibility of ratfish liver oil (RLO) rich in alkylglycerols (AKGs), as well as the capability of the glycerolysis product (GP) to act as lipid-based delivery system (LBDS) for a supercritical rosemary extract. For comparison purposes, digestibility and bioaccessibility of two additional lipid systems i.e. original RLO and RLO with addition of GRAS monoolein (MO) as emulsifier agent (RLO + MO), have been evaluated. We have studied the efficiency of the GP and RLO + MO lipid systems as LBDS by combining them with a supercritical rosemary extract (RE), i.e. RE lipid-based formulations. In vitro digestibility and bioaccessibility of un-loaded lipid systems, RE lipid-based formulations and un-carried RE have been determined. The results show a higher digestibility and bioaccessibility of the GP as compared to those of original RLO and RLO + MO. Likewise, a substantial improvement of RE bioaccessibility has been observed when GP is used as lipid carrier of RE. The present work demonstrates that enzymatic glycerolysis is an efficient strategy to obtain highly bioaccessible and potentially bioactive alkylglycerol-based delivery systems, which can be used to increase the bioaccessibility of low water-soluble bioactive compounds.
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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".