Pilot scale manufacturing of black seed oil-loaded alginate beads; process development, and stability of thymoquinone
Bibliographic record
Abstract
The approach of ionic gelation was employed at the pilot scale of the 50 kg batch size to manufacture black seed oil (BSO)-loaded alginate (ALG) beads as a natural source supplementing the main bioactive compound of BSO, i.e., thymoquinone (TQ). The BSO-ALG emulsion was prepared by initially emulsifying BSO with alginate solution at the pilot scale in two stages. The final emulsion was then dripped through 12 units of 3D-printed multi-nozzles into a curing bath containing Ca 2+ . The dripping flow rate was scaled up to 288 mL/min through the 3D-printed multi-nozzles (22-gauge). The characteristics of pilot scale BSO-ALG beads were similar to those produced at the lab scale; the beads were spherical with a size of 1.84–1.94 mm. The mechanical strength and loss on drying ranged from 143.6 to 172 g and 77.85–81.96 %, respectively. The production yield and encapsulation efficiency were 77.53–83.65 % and 95.36–97.9 %, respectively. Furthermore, the emulsification process did not affect TQ stability, while the curing process reduced TQ concentration from 1.51 % to 1.03 % w/w. Additionally, a substantial drop in TQ concentration in the encapsulated BSO was observed after the drying process, where it reached 0.23 % w/w. Finally, the stability of BSO-ALG beads in both wet and dried forms under real-time and accelerated conditions for 3 months revealed that beads were stable in terms of their organoleptic characteristics, size and sphericity, and loss on drying. Findings from this study enable the large-scale manufacturing of encapsulated BSO and similar bioactive compounds in ALG beads for the first time. These findings are valuable for advancing microencapsulation through ionic gelation and enhancing food preservation and safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".