Acidolysis of Tristearin with Selected Long-Chain Fatty Acids
Bibliographic record
Abstract
Five lipases, namely, Candida antarctica (Novozyme-435), Mucor miehei (Lipozyme-IM), Pseudomonas sp. (PS-30), Aspergillus niger (AP-12), and Candida rugosa (AY-30), were screened for their effect on catalyzing the acidolysis of tristearin with selected long-chain fatty acids. Among the lipases tested C. antarctica lipase catalyzed the highest incorporation of oleic acid (OA, 58.2%), gamma-linolenic acid (GLA, 55.9%), eicosapentaenoic acid (EPA, 81.6%), and docosahexaenoic acid (DHA, 47.7%) into tristearin. In comparison with other lipases examined, C. rugosa lipase catalyzed the highest incorporation of linoleic acid (LA, 75.8%), alpha-linolenic acid (ALA, 74.8%), and conjugated linoleic acid (CLA, 53.5%) into tristearin. Thus, these two lipases might be considered promising biocatalysts for acidolysis of tristearin with selected long-chain fatty acids. EPA was better incorporated into tristearin than DHA using the fifth enzymes. LA incorporation was better than CLA. ALA was more reactive than GLA during acidolysis, except for the reaction catalyzed by Pseudomonas sp., possibly due to structural differences (location and geometry of double bonds) between the two fatty acids. In another set of experiments, a combination of equimolar quantities of unsaturated C18 fatty acids (OA + LA + CLA + GLA + ALA) was used for acidolysis of tristearin to C18 fatty acids at ratios of 1:1, 1:2, and 1:3. All lipases tested catalyzed incorporation of OA and LA into tristearin except for M. miehei, which incorportaed only OA. C. rugosa lipase better catalyzed incorporation of OA and LA into tristearin than other lipases tested, whereas the lowest incorporation was obtained using Pseudomonas sp. As the mole ratio of substrates increased from 1 to 3, incorporation of OA and LA increased except for the reaction catalyzed by A. niger and C. rugosa. All lipases tested failed to allow GLA or CLA to participate in the acidolysis reaction, and ALA was only slightly incoporated into tristearin when M. miehei was used.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".