Microencapsulation of Essential Oil from Campomanesia adamantium Residue with Antioxidant Capacity Retention
Bibliographic record
Abstract
The essential oil (EO) extracted from the peels and seeds of guavira (Campomanesia adamantium) was microencapsulated via complex coacervation between gelatin and gum arabic, followed by freeze-drying. This process aimed to reduce the volatility of the essential oil and protect bioactive compounds through the microcapsule wall. For process optimization, the influence of proportions of wall materials (gelatin: gum arabic ratios of 1:1, 1:2, 1:3, 2:1, and 3:1) and EO quantity (20% - 42.8%) on antioxidant capacity, morphology, and microencapsulation yield was measured using a central composite rotational design (CCRD). Additionally, the chemical composition and EO retention rate in the microcapsules were assessed. Gelatin: gum arabic ratio of 1:2 and EO quantity of 40.3% resulted in superior results, with the highest antioxidant capacity, a microencapsulation yield of 68%, and spherical morphology. Notably, the incorporation of a higher amount of EO led to an increase in the antioxidant capacity, with values reaching up to 99% equivalent to pure oil. All formulations maintained the same pure EO’s main constituents, including α-pinene, limonene, β-ocimene, and β-caryophyllene, indicated by gas chromatography coupled to mass spectrometry. Consequently, for the first time, EO microcapsules were successfully obtained from guavira residue, showing high microencapsulation yield and EO retention. This achievement adds sustainable value to residue normally discarded, which enables better use of residue generated by the food industry. Due to the preservation of its antioxidant capacity and enhanced retention of volatile compounds, these microcapsules promise applications in the food, pharmaceutical, and cosmetic industries, aligning with sustainability principles.
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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".