Preparation and Optimization of Capsaicin-Loaded Chitosan Nanoparticles as Delivery Systems Using Box-Behnken Design
Bibliographic record
Abstract
Capsaicin (CAP) is a pungent alkaloid of chili peppers that is obtained from chili peppers that has a variety of pharmacological activities and can be used in various areas, such as functional foods, nutritional supplements and medical nutrition. Capsaicin has important anticancer, antioxidant and anti-inflammatory properties that allow to be applied as treatment for several diseases. However, its lack of water solubility, as well as its poor oral bioavailability in biological systems, show limiting factors for its successful application. Recently, the formulation of capsaicin for food and pharmaceutical use is limited. Therefore, the present study emphasized on preparation of capsaicin-loaded chitosan nanoparticles (CAP-CSNPs) and design and optimization of the formulation using Box-Behnken experimental design (BBD) and response surface methodology (RSM). The capsaicin-loaded chitosan nanoparticles were prepared by o/w emulsification and ionotropic gelification. The optimized formulation of capsaicin-loaded chitosan nanoparticles had a chitosan concentration of 0.11 (%w/v), a Tween 80® concentration of 1.55 (%w/v) and a CAP concentration of 1 mg/mL and that it should be stored at 4°C. Box-Behnken experimental design and response surface methodology was found to be a powerful technique for design and optimization of the preparation of capsaicin-loaded chitosan nanoparticles using limited number of experimental runs. Our study demonstrated that capsaicin-loaded chitosan nanoparticles can be potentially utilized as dietary supplements, nutraceuticals and functional foods.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".