Development and Evaluation of Microcapsules Containing Combined Extracts of Bay, Cherry, and Green Betel Leaves as Natural Antioxidants
Bibliographic record
Abstract
Bay leaf (Syzygium polyanthum), cherry leaf (Muntingia calabura), and green betel leaf (Piper betle) contain phenolic and flavonoid compounds with antioxidant potential, but their utilization is limited by physicochemical instability. This study aimed to develop microcapsules containing a combined extract of these three leaves and to evaluate their physicochemical properties and in vitro antioxidant activity as an initial formulation feasibility study. Each extract was prepared by maceration using 96% ethanol, yielding 11.42–15.86%, and combined in a 1:1:1 (w/w/w) ratio prior to microencapsulation. Microcapsules were produced using a fluidized bed dryer with lactose as the core material and polyvinyl alcohol (PVA) as the coating polymer. Physicochemical characterization included moisture content, flow rate, angle of repose, compressibility index, dissolution time, particle size, and surface morphology. Antioxidant activity was assessed using DPPH and CUPRAC assays, with IC₅₀ values calculated from triplicate measurements. The coating process increased mean particle size from 636.2 µm to 728.0 µm and prolonged dissolution time from 2.14 to 3.55 minutes, indicating coating layer formation. Among the individual extracts, cherry leaf extract showed the strongest antioxidant activity. The microcapsules exhibited antioxidant activity within the same order of magnitude as the combined extract under initial, non-stressed testing conditions. These results demonstrate the feasibility of formulating combined plant extracts into microcapsules with acceptable physical properties, while further stability and comparative studies are required to support antioxidant preservation and potential applications.
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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".