Effect of Emulsion Particle Size on the Encapsulation Behavior and Oxidative Stability of Spray Microencapsulated Sweet Orange Oil (Citrus aurantium var. dulcis)
Bibliographic record
Abstract
Three different feed emulsions of different particle sizes were mixed with a modified starch and maltodextrin and spray dried to make a large (LP), small (SP), and nano-size encapsulated powder (NP), respectively. Emulsion size, oil content, loading capacity (LC), encapsulation efficiency (EE), water content, aw, scanning electron microscopy (SEM), glass transition temperature (Tg), as well as d-limonene release characteristic and limonene oxide formation rate during 37 °C and various aw storage were determined. With the increase of the feed emulsion size, the reconstituted emulsion size of the LP tended to increase and change to a bimodal distribution. The surface oil content increased with the increasing size of the reconstituted emulsion, and the opposite was true for EE. The smaller the reconstituted emulsion size, the higher Tg during a low aw condition. The Tg of the LP, SP and NP were 62, 88, and 100 °C, respectively, and NP > SP > LP. The release and the oxidative rate of d-limonene was the lowest for the NP and then increased for the SP and LP. The release and oxidative rates increased with the elevation of aw and peaked at 0.33. The powder surface morphological structure was intact, the spray-dried powder was more stable, and microstructure changed from a glass state to a rubbery state during storage.
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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) | 0.000 | 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) | 0.001 | 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".