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Record W2038888375 · doi:10.1007/s11746-014-2543-x

Synthesis of Linoleic Acid Hydroperoxides as Flavor Precursors, Using Selected Substrate Sources

2014· article· en· W2038888375 on OpenAlexaff
Marya Aziz, Najla Ben Akacha, Florence Husson, Sélim Kermasha

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryLinoleic acidFood scienceFlavorYield (engineering)Substrate (aquarium)HydrolysisBiochemistryFatty acidBiology

Abstract

fetched live from OpenAlex

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‐(9 Z ,11 E )‐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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Oil Chemists SocietySame topicEnzyme Catalysis and ImmobilizationFrench-language works237,207