Lipase-catalyzed synthesis and characterization of flaxseed oil-based structured lipids
Bibliographic record
Abstract
The biosynthesis of structured lipids (SLs) was carried out by the interesterification of flaxseed oil (FO) and tricaprylin (TC) in an organic solvent medium (OSM), using selected commercial lipases, including Amano DF, Novozym 435, Lipozyme TL-IM and Lipozyme RM-IM. The fatty acyl chains of the synthesized triacylglycerols (TAGs) were identified by atmospheric pressure chemical ionization/mass spectrometric (APCI/MS) analysis, while the fatty acid positional distribution of the MLM- and MML-SLs (M-medium and L-long chain fatty acids) was determined by silver-ion high-performance liquid chromatographic (Ag + /HPLC) analysis. The effects of reaction temperature ( T r , 30–50 °C), enzyme concentration ( E c , 0.5–4%, w/v), initial water activity ( a w , 0.05–0.43) and reaction time ( R t , 0–72 h) on the efficiency of the enzymes, were studied. The bioconversion yield (%) of the synthesized MLM- and MML-SLs was monitored under the established reaction parameters for each lipase. The maximum yield of MLM-SLs was obtained in the order, of Novozym 435 > Lipozyme TL-IM > Lipozyme RM-IM > Amano DF. Moreover, considering the ratio of the MLM- to MML-SLs produced by each enzyme, Novozym 435 and Lipozyme TL-IM were selected as the most effective enzymes for interesterification of FO and TC.
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) | 0.000 | 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".