Development and Evaluation of Microcapsules Containing Combined Extracts of Bay, Cherry, and Green Betel Leaves as Natural Antioxidants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it