Microencapsulation of ultrasound-assisted phenolic extracts of sugar maple leaves: Characterization, in vitro gastrointestinal digestion, and storage stability
Bibliographic record
Abstract
Sugar maple leaves (SML), usually considered residue plant biomass and discarded accordingly, contain a considerable amount of phenolic antioxidants. In this study, SML phenolics were extracted employing both advanced (homogenization pretreated ultrasound-assisted extraction) and conventional (maceration) methods followed by their encapsulation by freeze drying and spray drying using a combination of maltodextrin and gum arabic as coating agents. Detailed physicochemical analyses revealed that the encapsulated microparticles had high solubility (>90 %) and encapsulation efficiency (>95 %), acceptable thermal stability with good handling properties. Phenolic compounds were completely released from microparticles during simulated gastric conditions. The microparticles influenced the bioaccessibility of more than 43 % of the phenolic fraction in the intestinal phase. The antioxidant capacity of the microparticles was preserved during storage. These findings suggest the effectiveness of the microencapsulation process for producing high quality microparticles of SML phenolic extracts and the possibility of their use in the food, nutraceutical, bio-pharmaceutical sectors.
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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".