Phenolic profile of micro- and nano-encapsulated olive leaf extract in biscuits during in vitro gastrointestinal digestion
Bibliographic record
Abstract
Olive leaf was characterized by a high content of phenols and flavonoids (oleuropein, luteolin, and their derivatives), presenting functional and health-related properties. The chemical instability of phenolics through technological processes and their degradation in the digestive system may negatively impact them, leading to lower absorption. This study evaluates the phenolic profile of micro- and nano-encapsulated olive leaf extract in biscuits during the INFOGEST static in vitro digestion, aiming to enhance stability and sensorial properties. Ultrasound-assisted extraction and chromatography characterized the extract, while spray drying (maltodextrin-glucose) and nano-encapsulation (maltodextrin, whey protein isolate, and arabic gum) techniques were used with specific solutions. Encapsulated formulations underwent microscopy (TEM, SEM) and encapsulation efficiency analysis. Micro- and nano-encapsulation improved biscuit functionality by enhancing phenolic stability during digestion. However, the highest concentration adversely affected sensory and textural parameters. These findings contribute to developing functional food products enriched with bioactive compounds, providing improved health benefits while maintaining sensory attributes.
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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".