Optimization and Characterization of Naringin‐Loaded Microcapsules Prepared With Caramel Polymer via Anti‐Solvent Precipitation
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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.
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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 | 0.000 |
| 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 it