Structure and physical properties of oleogels containing peanut oil and saturated fatty alcohols
Bibliographic record
Abstract
This study examined the capability of fatty alcohols with chain lengths from C 14 OH to C 22 OH to gel peanut oil. The gelation was achieved by crystallizing the samples at 5°C/min or 40°C/min. Results showed that minimum gelling concentration decreased as fatty alcohol chain length increased and it was higher for fast cooled samples than for the corresponding slow cooled ones. More than 7% of C 14 OH was necessary to obtain a self‐standing material highlighting its low capacity as oleogelator. Other oleogels were compared at 5% fatty alcohol concentration in peanut oil and oleogels containing C 16 OH yielded the weakest system, with the lowest ability to retain oil. This was attributed to its higher solubility in oil as compared to other fatty alcohols as well as to the formation of larger crystal aggregates. As the fatty alcohol chain length increased, systems became stronger, displaying smaller crystal aggregates. For all cases, an increase in cooling rate lead to the formation of weaker gels with reduced capacity to entrap oil. Practical applications: A novel strategy to reduce saturated/trans fats in food products involves the use of oleogels. Here we report on the use of even saturated fatty alcohols to gel peanut oil, thus preventing oil separation during storage and handling of products such as nut butters. The structure and physical properties of fatty alcohol oleogels greatly depend on fatty alcohols chain length as well as cooling rate applied during crystallization. Fatty alcohols proved to be a very efficient peanut oil stabilizer. Fatty alcohol chain length and cooling rate greatly affected organogel structure and physical properties.
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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".