Encapsulationof lycopene from watermelon in calcium‐alginate microparticles using an optimised inverse‐gelation method by response surface methodology
Bibliographic record
Abstract
Summary Lycopene exhibits strong antioxidant activity due to its unsaturated molecular bonds, which also contributes to its susceptibility for degradation. Encapsulation techniques can reduce lycopene degradation, increasing its potential applications in functional foods and nutraceuticals. The objective of this study was to optimise the encapsulation of lycopene from watermelon in alginate microparticles using the inverse gelation method. Box–Behnken design was used for the optimisation of three variables: concentrations of alginate (w/v %) and CaCl2 (g L−1), and gelation time (min). Two types of alginate were investigated (low viscosity and high viscosity) and optimised separately using encapsulation efficiency and loading capacity as responses. Results indicated that the models had a good fit to the experimental data and the optimal conditions varied depending on the type of alginate. In general, particles prepared with low‐viscosity alginate exhibited higher encapsulation efficiency and loading capacity and could be used for further research.
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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 | 0.001 |
| 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".