The Potential Application of Rice Bran Wax Oleogel to Replace Solid Fat and Enhance Unsaturated Fat Content in Ice Cream
Bibliographic record
Abstract
The development of structure in ice cream, characterized by its smooth texture and resistance to collapse during melting, depends, in part, on the presence of solid fat during the whipping and freezing steps. The objective of this study was to investigate the potential application of 10% rice bran wax (RBW) oleogel, comprised 90% high-oleic sunflower oil and 10% RBW, to replace solid fat in ice cream. A commercial blend of 80% saturated mono- and diglycerides and 20% polysorbate 80 was used as the emulsifier. Standard ice cream measurements, cryo-scanning electron microscopy (cryo-SEM), differential scanning calorimetry (DSC), and transmission electron microscopy (TEM) were used to evaluate the formation of structure in ice cream. RBW oleogel produced higher levels of overrun when compared to a liquid oil ice cream sample, creating a lighter sample with good texture and appearance. However, those results were not associated with higher meltdown resistance. Microscopy revealed larger aggregation of RBW oleogel fat droplets at the air cell interface and distortion of the shape of air cells and fat droplets. Although the RBW oleogel did not develop sufficient structure in ice cream to maintain shape during meltdown when a mono- and diglycerides and polysorbate 80 blend was used as the emulsifier, micro- and ultrastructure investigations suggested that RBW oleogel did induce formation of a fat globule network in ice cream, suggesting that further optimization could lead to an alternative to saturated fat sources for ice cream applications.
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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".