Combined Short‐Path Distillation and Solvent‐Assisted Crystallization of Beef Fatty Acid Methyl Esters
Bibliographic record
Abstract
Abstract Solvent‐assisted crystallization has previously been employed to remove long‐chain saturated fatty acids (≥ 18 carbons) from animal fat to improve its cold temperature biofuel properties. The same technology can be used for removing long‐chain saturated fatty acids (SFA) from animal fats for human consumption, but SFA remaining (i.e., 14:0 and 16:0) are more atherogenic than longer chain SFA. In the present study, an easy and efficient method was developed using short‐path distillation prior to solvent‐assisted crystallization for the more complete removal of SFA from beef tallow, and for the first time reports the distillation and crystallization behavior of polyunsaturated fatty acid biohydrogenation products (PUFA‐BHP). Shorter chain SFA methyl esters (i.e., 14:0 and 16:0) were efficiently removed at 90 °C, 9.3 Pa, with a rotor speed of 70 rpm and either two cycles of distillation at 90 drops/min or three cycles at 110 drops/min. Stearic acid (18:0) was then effectively removed by crystallization at −20 °C using a sample to methanol ratio of 1:10. The remaining fraction enriched with PUFA‐BHP (i.e., rumenic acid, c9,t11‐18:2, and its precursor vaccenic acid, t11‐18:1) have potential use in disease model (i.e., cell culture and animal) studies to help further elucidate their bioactivity and mode of action, and may in the future have functional food or nutraceutical potential.
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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) | 0.001 | 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".