Enzymatic Incorporation of Selected Long‐Chain Fatty Acids into Triolein
Bibliographic record
Abstract
Abstract Acidolysis of triolein (tri C18:1) with selected long‐chain fatty acids (LCFA) was carried out using Candida antarctica (Novozym 435), Rhizomucor miehei (Lipozyme RM IM), Pseudomonas sp. (PS‐30), Aspergillus niger (AP‐12), and Candida rugosa (AY‐30). A better incorporation of stearic acid (SA), α‐linolenic acid (ALA), γ‐linolenic acid (GLA), arachidonic acid (AA), and docosapentaenoic acid (DPA) was achieved using lipase from Rhizomucor miehei. Lipase from Pseudomonas sp. catalyzed a better incorporation of linoleic acid (LA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) into triolein. Thus, Rhizomucor miehei and to a lesser extent Pseudomonas sp. might be considered as providing the most effective enzymes for acidolysis of triolein with selected LCFA. In general, incorporation of LCFA into triolein (tri C18:1) may be affected by chain length, number of double bonds, and the location and geometry of the double bonds as well as reaction conditions and reactivity and specificity of lipases used. As the ratio of the number of moles of a mixture of equimole quantities of C18 FA to triolein changed from 1 to 3, incorporation of C18 FA into triolein increased accordingly with Rhizomucor miehei lipase. Similarly, incorporation of n‐3 FA into triolein increased when ALA, DPA, DHA, and EPA were used. The same trend was noticed for a mixture of n‐6 FA (LA + GLA + AA) and triolein.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".