Development of Optimized <i>Origanum vulgare</i> L. Essential Oil-Loaded Chitosan/Gum Arabic Nanocapsules by Complex Coacervation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Oregano essential oil (OEO), rich in carvacrol and thymol, has bioactive properties but is prone to degradation due to its volatile nature. Nanoencapsulation by complex coacervation, using chitosan (CHI) and gum arabic acid (GA), emerges as an alternative to increase its stability in food matrices. This study investigated the influence of the CHI/GA mass ratio on the formation of OEO-containing nanocapsules and quantified the encapsulated carvacrol using validated GC–MS. A Box–Behnken experimental design optimized the OEO concentration, CHI/GA ratio, and amount of Tween 80. Physicochemical properties such as the diameter, PdI, and zeta potential were evaluated. Morphology was analyzed by SEM and AFM, and thermal stability by TGA and DSC. Stability was monitored for 120 days at 4 °C, 25 °C, and 40 °C. The optimized formulation (470 mg of OEO, 659 mg of CHI/GA 1:5, and 13 mg of Tween) resulted in nanocapsules with a diameter of 323 ± 22 nm, a PdI of 0.20 ± 0.02, and a zeta potential of +15.8 ± 0.8 mV. FTIR analysis confirmed electrostatic interactions between CHI and GA. GC–MS identified 24 constituents in the OEO, with carvacrol as the main compound (78.8%). The validated method showed an R 2 of 0.9976, proving to be specific, precise, and accurate. The encapsulation efficiency was 95% ± 0.7, indicating that the technique preserved the oil’s composition and concentration. The nanocapsules maintained stability under different temperatures with confirmed structural integrity. It is concluded that nanoencapsulation via complex coacervation, combined with experimental design, allows for the production of stable and effective nanocapsules for the delivery of lipophilic bioactive compounds. The validated analytical method reinforces the system’s applicability in future research and the development of functional products.
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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".