Stability of Cardamom ( <i>Elettaria Cardamomum</i> ) Essential Oil in Microcapsules Made of Whey Protein Isolate, Guar Gum, and Carrageenan
Bibliographic record
Abstract
UNLABELLED: The effects of microencapsulating cardamom essential oil (CEO) in whey protein isolate (WPI) alone and combined with guar gum (GG) and carrageen (CG) on microencapsulation efficiency, oil chemical stability, and microcapsule structure were investigated. Freeze-dried microcapsules were prepared from emulsions containing (w/w): 15% and 30% WPI; 0.1% GG, and 0.2% CG as wall materials with CEO (at 10% of polymer concentration) as core material, and physical properties and chemical stability were compared. Bulk density of microcapsules was highest in WPI without GG or CG and in 30% WPI + GG microcapsules, and was more affected by moisture content (r = -0.6) than by mean particle diameter (d43 ; r = -0.2) and span (r = 0.1). Microcapsules containing only WPI had the highest entrapped oil (7.5%) and microencapsulation efficiency (98.5%). The concentrations of 1,8-cineole and d-limonene were used as indicators for microcapsule chemical stability since they were the main components of CEO. Microcapsules retained higher (P ≤ 0.05) concentrations of both components than non-microencapsulated CEO during 16 wk storage at 20 ºC, but higher loss of both components was noted at 35 ºC. Microencapsulated d-limonene was reduced faster than 1,8-cineole regardless of temperature. The 30% WPI and 30% WPI + GG microcapsules retained CEO best throughout storage at both storage temperatures. Scanning electron micrographs revealed that WPI microcapsules had smooth surfaces, were relatively homogenous and regular in shape, whereas GG and CG addition increased visual surface porosity and reduced shape regularity. It was concluded that the best formulation for encapsulating CEO was 30% WPI. PRACTICAL APPLICATION: Encapsulating cardamom essential oil in whey protein isolate alone or combined with guar gum produced dried powders that effectively retained and chemically stabilized CEO, and therefore enhanced its handling and storability.
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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) | 0.000 | 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".