Optimized encapsulation of anthocyanin-rich extract from haskap berries (Lonicera caerulea L.) in calcium-alginate microparticles
Bibliographic record
Abstract
BACKGROUND:The chemical instability of extracted anthocyanins (ACNs) limits their application and broader use as food colorants and health-promoting functional ingredients.Encapsulation technology can improve ACN stability and widen their potential applications.OBJECTIVE: The objective of this study was to optimize the microencapsulation of ACNs from haskap berries (Lonicera caerulea L.) in calcium-alginate particles by the extrusion/gelation method.METHODS: Response Surface Methodology (RSM) by Box-Behnken (BB) design was used for the optimization, followed by the desirability function.Three input variables were evaluated: concentrations of sodium alginate (x 1 , w/w %) and calcium chloride (x 2 , w/v %), and gelation time (x 3 , min).The responses were encapsulation efficiency (y 1 , %) and particle size (y 2 , m).RESULTS: There was a good fit for the model where encapsulation efficiency was used as a separate response (R 2 = 97.98%),however, the model for particle size did not give as good an agreement (R 2 = 63.86%).The desirability function was used to optimize the two responses simultaneously and the optimum conditions were determined as 9.0% (w/w) alginate solution, 2.0% (w/v) CaCl 2 , and 10 min in the gelation solution.CONCLUSIONS: These results illustrate the application of RSM followed by a desirability function to optimize encapsulation parameters for a combined response, where several measures are considered.
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.
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".