Oxidative stability of structured lipids produced from borage (<i>Borago officinalis</i> L.) and evening primrose (<i>Oenothera biennis</i> L.) oils with docosahexaenoic acid
Bibliographic record
Abstract
Abstract This study utilized γ‐linolenic acid (18∶3n−6; GLA)‐rich borage oil (BO) and evening primrose oil (EPO) for the synthesis of structured lipids (SL) and compared the oxidative stability of the products with those of unmodified BO and EPO as controls. Immobilized Novozym 435 lipase from Candida antarctica was used as the biocatalyst for SL production. BO or EPO eas enzymatically modified with docosahexaenoic acid (22∶6n−3; DHA), as the acyl donor, to produce SI. The SI were characterized and their oxidative stabilities evaluated. Among the oils examined, SL gave rise to higher quantities (P≤0.05) of conjugated dienes, TBARS, and headspace volatiles as compared to their unmodified counterparts. Results indicated that modified oils were less stable than their unmodified counterparts. The double bond index (DBI) and methylene bridge index (MBI) of oils decreased (P<0.05) during oxidation in the more unsaturated oils. An attempt was made to correlate various parameters of oxidation with DBI and MBI of oils; correlation coefficients (−r) were within the range of 0.574–0.973. This suggests that indicators such as DBI and MBI can reflect oxidative stability of oils.
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".