Enhancing vitamin A stability using saponin–chitosan polyelectrolytes coating: Optimization, characterization, and controlled release
Bibliographic record
Abstract
Microencapsulation has the potential to address the stability issues associated with vitamin A. This study examined the effectiveness of emulsifying a saponin-chitosan polyelectrolyte complex to encapsulate vitamin A. Utilizing response surface methodology (RSM), the effects of the chitosan, saponin, and vitamin A contents on various response variables were measured to optimize the formulation. The optimized emulsion was characterized through fluorescence microscopy, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), storage stability, and release profile. Fluorescence microscopy showed that vitamin A was evenly distributed throughout the optimized emulsion. The polyelectrolyte complex and vitamin A were shown to interact hydrophobically and electrostatically by FTIR analysis. The DSC results verified the effective encapsulation and showed that vitamin A heat stability had been enhanced. Study on storage stability demonstrated that during a 2-month storage period, the encapsulated vitamin A remained stable. Moreover, vitamin A was significantly released from the encapsulated form at pH 1.2, based on release assays. In conclusion, saponin-chitosan polyelectrolyte coating proved to be a potentially useful new material for the stability and applications of vitamin A in a range of formulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".