Development and characterization of high-performance macadamia oil-based oleogel emulsions
Bibliographic record
Abstract
Macadamia oil is rich in monounsaturated fatty acids and bioactive compounds, making it highly valued in the food industry. However, its high fluidity and susceptibility to oxidation limit its broader applications. This study utilized macadamia oil as a base material to develop highly stable oleogels incorporating a β-sitosterol/γ-oryzanol (BS-GO) composite system. The gelation threshold was identified at 6 %, while oleogels prepared with 7 % -12 % BS-GO exhibited a dense three-dimensional network structure, transitioning the oil into a solid state. The oleogel at the optimized 12 % BS-GO concentration demonstrated a hardness of 6283.96 g, an oil-binding capacity of 99.51 %, and significantly enhanced antioxidant capacity, with DPPH scavenging activity reaching 1922.18 μmol TE/100 g. Emulsions derived from these oleogels revealed that emulsifier type significantly influenced stability. Emulsions stabilized with whey protein isolate achieved the smallest droplet size (256.86 nm) and the highest zeta potential (-32.93 mV). The 7 % BS-GO emulsion exhibited remarkable stability under thermal treatment (30-80 °C), freeze-thaw cycles, and varying ionic strengths (0-100 mmol/L). Furthermore, this emulsion effectively inhibited oxidation, with peroxide values ranging from 7.07 to 12.67 mmol/kg and TBARS values between 2.83 and 4.96 μmol/kg during accelerated storage. This research provides a theoretical foundation and practical guidance for the high-value utilization of macadamia oil and the advancement of oleogel-based emulsions in the food industry.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".