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CONCENTRATION OF DOCOSAHEXAENOIC ACID (DHA) FROM ALGAL OIL VIA UREA COMPLEXATION

2000· article· en· W2014260718 on OpenAlexafffund
S.P. Senanayake, Fereidoon Shahidi

Bibliographic record

VenueJournal of Food Lipids · 2000
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsChemistryDocosahexaenoic acidUreaAlgae fuelYield (engineering)Fatty acidFood sciencePolyunsaturated fatty acidBiochemistryCatalysis

Abstract

fetched live from OpenAlex

ABSTRACT Production of docosahexaenoic acid (DHA; 22:6ω3) from algal oil was achieved using urea complexation technique. The major fatty acids found in algal oil were 22:6, 14:0 and 18:1. As a result of urea complexation, 14:0, 16:0 and 18:1 were eliminated almost completely while DHA was enriched from 47.4 to 97.1% with a process yield of 32.5% of the weight of the original algal oil. The recovery efficiency of DHA was considerably higher and 66.5% of the total DHA present in the original algal oil was found in the DHA concentrate following the urea complexation process.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.292
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2000
Admission routes2
Has abstractyes

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