Microencapsulation of fish oil rich in EPA and DHA using mixture of Arabic gum and Persian gum through spray‐drying technique
Bibliographic record
Abstract
Abstract The microencapsulation of fish oil by the spray‐drying technique was conducted using Arabic gum (AG) and Persian gum (PG) as wall materials. AG‐to‐PG ratios, including 29:1, 28:2, 27:3, 26:4, and 25:5 (%w/w), wall‐to‐oil ratios, including 5:1, 4:1, 3:1, 2;1, and 1:1, drying temperature (180, 190, 200, 210, and 220°C), and feed flow rate at high and low states were optimized using response surface methodology. Microencapsulation efficiency (MEE), moisture content (MC), peroxide value (PV), and particle size (PS) were determined. Results showed that the highest MEE and the lowest MC, PV, and PS were attained when 26:4, 4:1, 210°C, and high speed were considered, respectively. At this point, the MEE, MC, PV, and PS were 79.49%, 3.39%, 10.98 meq O2/kg oil, and 39.05 µm, respectively. The microstructure of optimum microencapsulated powder exhibited no observable cracks, fissures, or pores while having a typical spherical and smooth surface. Microencapsulation of fish oil using a mixture of AG and PG showed higher oxidative stability associated with high MEE, low MC, and low PV at the final product. Moreover, the optimized emulsion formulation and drying conditions increased the storage 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 | 0.000 | 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".