Optimization of Wall Material of Freeze-Dried High-Bioactive Microcapsules with Yellow Onion Rejects Using Simplex Centroid Mixture Design Approach Based on Whey Protein Isolate, Pectin, and Sodium Caseinate as Incorporated Variables
Bibliographic record
Abstract
For the food sector, onion rejects are an appealing source of value-added byproducts. Bioactive compounds were recovered from yellow onion rejects using a pulse electric field process at 6000 v and 60 pulses. The onion extract was encapsulated with whey protein isolate (WPI), pectin (P), and sodium caseinate (SC) with a mass ratio of 1:5 (extract/wall material, w/w). A Simplex lattice with augmented axial points in the mixture design was applied for the optimization of wall material for the encapsulation of onion reject extract by freeze-drying (FD). The optimal wall materials were 47.6 g/100 g (SC), 10.0 g/100 g (P), and 42.4 g/100 g (WPI), with encapsulation yield (EY) of 85.1%, total phenolic content (TPC) of 48.7 mg gallic acid equivalent/g DW, total flavonoid content (TFC) of 92.0 mg quercetin equivalent/g DW, and DPPH capacity of 76.1%, respectively. The morphological properties of the optimal encapsulate demonstrated spherical particles with a rough surface. At optimal conditions, the minimum inhibitory concentration (MIC) of the extract (mean diameter of inhibition zone: 18.8 mm) was shown as antifungal activity against Aspergillus niger.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".