Encapsulation of Anthocyanins from Black Rice (Oryza Sativa L.) Bran Extract using Chitosan-Alginate Nanoparticles
Bibliographic record
Abstract
This study was conducted to extract and encapsulate anthocyanins from black rice bran using chitosan-alginate nanoparticles. Ten black rice varieties were screened for the anthocyanin content and the variety with the highest anthocyanins was used for the encapsulation. The anthocyanins were extracted by defatting the bran with n-hexane and soaking it with 85% acidified ethanol. The crude anthocyanin extract (CAE) was freeze-dried at -110°C for 48 h and then encapsulated in chitosan-alginate nanoparticles using two processes: ionic pre-gelation and polyelectrolyte complex formation. The mass ratio of chitosan and alginate polymers used in this study was 100:10. The treatments applied were as follows: T0-0 mg CAE, T1-10 mg CAE, T2-20 mg CAE, and T3-30 mg CAE. The resulting capsules were characterized in terms of chemical properties, surface morphology, particle size, polydispersive index, encapsulation efficiency, and 2, 2-diphenyl-1-picrylhydrazyl radical scavenging activity. Screening of rice samples indicated that Ominio bran had the highest anthocyanin content (36.11 mg/g). Anthocyanins were successfully encapsulated in the matrix as shown by the Scanning Electron Microscopy images and Fourier Transform Infrared spectra of the anthocyanin-loaded chitosan-alginate nanoparticles. Among the different concentrations of CAE, T3 had the highest encapsulation efficiency (68.9%) and antioxidant scavenging activity (38.3%) while T1 and T2 had the lowest. Ascending particle size was observed for T0 (358.5 nm), T3 (467.9 nm), T1 (572.3 nm), and T2 (635.9 nm). All anthocyanin-loaded capsules were found to be of nano-size (<1000 nm). The study concluded that chitosan-alginate nanoparticles can be a good encapsulating material for anthocyanin.
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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) | 0.000 | 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".