Synthesis of Linoleic Acid Hydroperoxides as Flavor Precursors, Using Selected Substrate Sources
Bibliographic record
Abstract
Abstract The objective of the research was the synthesis of linoleic acid hydroperoxides (HPOD) and their recovery, using selected sources of linoleic acid (LA) as substrate. This is part of on‐going work aimed at the development of an economically viable biotechnological process for the production of natural flavors. The investigated sources included pure (100 %) LA and commercial (67 %) LA as well as safflower oil (SO) and its hydrolyzed product. A model describing commercial LA oxidation by lipoxygenase, based on Michaelis–Menten kinetics, was developed. The conversion of pure LA and commercial LA resulted in insignificant differences in HPOD yield of 69.7 and 68.9 %, respectively. However, there was a significant difference in the HPOD yield, obtained from the SO (2.0 %) and that from the hydrolyzed SO (58.0 %) in comparison to that from pure LA (69.7 %). The ratios of the different 9‐ and 13‐HPOD isomers were insignificantly different for the sources containing free LA, with 13‐(9Z,11E)‐HPOD was the highest relative percentage. Using optimized conditions, HPOD yields were 85.9 and 74.0 % for the commercial LA and the hydrolyzed SO, respectively. Based on experimental findings, commercial (67 %) LA was selected as the most appropriate alternative to pure LA for the production of HPOD. An efficient extraction procedure for the recovery of HPOD was also developed.
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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) | 0.000 | 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".