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 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 | 0.000 | 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 | 0.001 |
| 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 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".