Optimization and Characterization of Naringin‐Loaded Microcapsules Prepared With Caramel Polymer via Anti‐Solvent Precipitation
Bibliographic record
Abstract
ABSTRACT Naringin, a flavonoid acknowledged for its antioxidant, anti‐inflammatory, and anti‐carcinogenic properties, faces significant challenges in functional food applications due to its poor solubility, stability, and bioavailability. This research investigates the encapsulation of calcium‐naringin complexes in a caramel matrix, utilizing the structural and functional characteristics of caramel as a carrier to improve the availability of naringin. The polymer precipitation technology was adopted in the encapsulation of naringin with solvents including ethanol, acetone, and isopropanol. Calcium lactate, chloride, carbonate, and sulfate salts were employed as the encapsulation assisting agents. The efficacy of different solvents and calcium sources is analyzed by measuring encapsulation efficiency (EE) by HPLC, structural stability by SEM, and thermal stability by TGA and DSC. Characterization of caramel was analyzed through MALDI‐TOF mass spectrometry and through DSC. SEM images, encapsulation efficiency results demonstrated that different calcium sources as well as precipitants significantly influence the surface morphology of encapsulates and the percentage of retention of naringin in the encapsulates. TGA and DSC results showed improvement in the thermal stability of encapsulates by improving the thermal degradation temperatures, overall underscoring the success of the encapsulation process. This study highlights the potential of caramel‐based encapsulation to improve naringin's stability, bioactivity, and bioavailability, enabling its effective incorporation into confectionery, bakery products, and beverages.
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".