Oleosome interfacial engineering to enhance their functionality in foods
Bibliographic record
Abstract
This study aimed to increase the physical stability of native sunflower oleosomes to expand their range of applications in food. The first objective was to increase the stability and functionality of oleosomes to lower pH since most food products require a pH of 5.5 or lower for microbial stability. Native sunflower oleosomes had a pI of 6.2. One particularly effective strategy for long-term stabilization, both physical and microbial, was the addition of 40% (w/w) glycerol to the oleosomes plus homogenization, which decreased the pI to 5.3 as well as decreasing oleosome size, narrowing the size distribution and increasing colloidal stability. Interfacial engineering of oleosomes by coating them with lecithin and the polysaccharides xanthan and gellan, effectively increased stability, and lowered their pI to 3.0 for lecithin and lower than 3.0 for xanthan. Coating oleosomes also caused a greater absolute value of the ζ-potential; for example, this amount was shifted to -20 mV at pH 4.0 for xanthan and to -28 mV at pH 4.0 for lecithin, which provides electrostatic stabilization. Polysaccharides also provide steric stabilization, which is superior. A significant increase in the diameter of coated oleosomes was observed with lecithin, xanthan and gellan. The oleosome sample with 40% glycerol showed high storage stability at 4 °C (over three months). The addition of glycerol also decreased the water activity of the oleosome suspension to 0.85, which could prevent microbial growth.
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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".