Encapsulation of Formosa Papaya (Carica papaya L.) Seed Extract: Physicochemical Characteristics of Particles, and Study of Stability and Release of Encapsulated Phenolic Compounds
Bibliographic record
Abstract
Papaya seeds are a promising source of phenolic compounds, but these are unstable, and the papaya extract has a bitter taste. This study aimed to encapsulate papaya seed extracts at different maturation levels, and to characterize the obtained microparticles for their physicochemical properties, chemical stability and release of bioactives. Extracts of papaya (unripe and ripe) seeds were spray-dried using concentrations of 0, 15 and 30% of maltodextrin and inlet air temperatures of 130 and 150 °C. The powders were analyzed for yield, moisture, Aw, hygroscopicity, dispersibility, color parameters, morphology, mean diameter, total phenolics, antioxidant activity, stability during storage and release of phenolics in simulated gastrointestinal fluids. Powders produced with maltodextrin showed better results regarding particle diameter, hygroscopicity, dispersibility, and phenolic stability during storage. All powders showed antioxidant action and significant values of total phenolic compounds. Samples without maltodextrin underwent caking. Drying temperatures had little influence on the characteristics of the powders. Encapsulated phenolic compounds were released in large amounts in the intestinal phase (86.6–100%). Powders produced with unripe seeds, 15% of maltodextrin and an inlet air temperature of 130 °C showed the best results. Thus, encapsulation was efficient, and encapsulated papaya seed extract has potential for food application as a natural additive.
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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".