Production of BSA-loaded alginate microcapsules: Influence of spray dryer parameters on the microcapsule characteristics and BSA release
Bibliographic record
Abstract
The aim of this study was to optimize the production of BSA-loaded alginate microcapsules by spray drying and to study the release of bovine serum albumin fraction V (BSA) under gastric simulated conditions. Microcapsule yield, BSA release, microcapsule size and size distribution were characterized following the application of different production parameters including inlet air temperature, inlet air pressure and liquid feed rate. The microcapsules were incubated in 0.1 N HCl and BSA release was quantified over time. The yields were higher with the pressure of 3 bar compared to 4 bar and with a feed rate of 0.45 vs. 0.2 ml s(-1). A high feed rate (0.45 vs. 0.2 ml s(-1)) allows one to obtain microcapsules with a low BSA release (p = 0.0327). The increase of the atomizer inlet temperature leads to microcapsules with a higher BSA release (p = 0.0230). A higher air pressure of 4 bar compared to 3 bar resulted in a lower microcapsule size (2.55 vs. 2.80 microm) and led to a narrower size distribution (0.92 vs. 1.07). In conclusion, the spray dryer parameters influenced the alginate microcapsule characteristics as well as subsequent protein release into a simulated gastric medium.
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".