Optimization of Wall Materials for Astaxanthin Powder Production from Shrimp Shell Extract Using Simplex Lattice Mixture Design
Bibliographic record
Abstract
Shrimp shell waste is an attractive source of value-added bioactive-rich by-products. Shrimp shell extract containing astaxanthin was recovered by solvent extraction method (petroleum ether/acetone/water with a ratio of 15 : 75 : 10) and ultrasound process (amplitude 20% for 15 min at 35°C). The extract was then encapsulated by freeze-drying using wall materials such as maltodextrin (with the dextrose equivalent (DE) of 7 (MD7) and 20 (MD20)) and modified starch (Hi-Cap 100) incorporated at different ratios. Simplex lattice with augmented axial points in the mixture design was applied for the optimization of wall material. The optimal wall materials were 29.4% (MD7), 34.0% (Hi-Cap 100), and 36.6% (MD20), with encapsulation yield (Y) of 94.6%, encapsulation efficiency (EE) of 91.8%, astaxanthin content (Ast) of 46.1 μg/g DW, and DPPH scavenging capacity of 64.0%, respectively. The optimized microcapsules had spongy morphology and brittle and flaky mass. The degradation kinetics of bioactive astaxanthin in UV light was evaluated and found to follow first-order reaction kinetics. The microcapsules obtained under optimal wall composition exhibited the highest UV light stability with half-life values of 76.8 h, demonstrating a high stability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".