Influence of emulsification methods and use of colloidal silicon dioxide on the microencapsulation by spray drying of turmeric oleoresin in gelatin‐starch matrices
Bibliographic record
Abstract
Abstract Microencapsulated turmeric oleoresin can present improved curcumin stability and be easily applied in hydrophilic systems. Most of the microencapsulation techniques rely on the initial emulsification of the core material in the wall biopolymers and this step affects the encapsulation efficiency and properties of the resulting microcapsules. The objective of this work was to evaluate the effects of different emulsification methods, the use of colloidal silicon dioxide and Tween 80 as additives, and the rheological behaviour of the encapsulating gelatin‐starch dispersions on the emulsion stability, encapsulation efficiency, and yield of turmeric oleoresin microcapsules produced by spray‐drying. The encapsulating matrices were prepared with varied concentrations of modified starch (from 0.22–0.317 g/g (22–31.7 wt%), dry basis) and gelatin (0–0.06 g/g (0–6 wt%), dry basis). The microstructure of the emulsions was evaluated through optical microscopy and small amplitude oscillatory shear rheology. The emulsification of turmeric oleoresin was performed by the following methods: high‐shear mixing, using a rotor‐stator homogenizer, with and without addition of Tween 80 as a surfactant; and by ultrasound homogenizer with and without the colloidal silicon dioxide (Aerosil 200). The homogenization method presented considerable influence on the emulsion stability and on the average droplet sizes in the emulsion. The concentration of gelatin directly affected the emulsion and microcapsule properties. Ultrasound homogenization and the use of colloidal silicon dioxide resulted in the highest encapsulation efficiency of turmeric oleoresin in the low total‐solid formulations.
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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 | 0.001 |
| 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) | 0.001 | 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".