Enzymatic acidolysis of an arachidonic acid single‐cell oil with capric acid
Bibliographic record
Abstract
Abstract Incorporation of capric acid (CA) into arachidonic acid (AA) single‐cell oil, using five commercial lipases, indicated that lipase PS‐30 from Pseudomonas sp. was most effective. The optimal conditions included an oil‐to‐CA mole ratio of 1∶3, a temperature of 45°C, incubation time of 24 h, 4% lipase from Pseudomonas sp., and a 2% (w/w) water content. Examination of positional distribution of FA on the glycerol backbone of modified AA single‐cell oil with CA showed that 89.7% of CA was concentrated in the sn‐1,3 positions of the TAG molecules. AA was mainly located at the sn‐2 position of the modified AA single‐cell oil. Enzymatically modified AA single‐cell oil had a higher conjugated dienes (CD) value than its unmodified counterpart. TBARS values of both modified and unmodified AA single‐cell oils increased progressively during the entire storage period, but no significant difference existed between TBARS values of both oils. Thus, enzymatically modified oil was more susceptible to oxidation than its unmodified counterpart, when considering both CD and TBARS values. Removal of natural antioxidants during oil modification might play a significant role in rapid oxidative deterioration of enzymatically modified oils. This possibility was confirmed when starting materials were subjected to the same reaction process in the absence of any enzyme, as the resultant oil was indeed significantly less stable than the control oil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".