Extraction of a Docosahexaenoic Acid-Rich Oil from <i>Thraustochytrium</i> sp. Using a Hydrophobic Ionic Liquid
Bibliographic record
Abstract
Thraustochytrium sp. (T18) is a marine heterokont that exhibits a fast growth rate and accumulates a high proportion of polyunsaturated fatty acids (PUFAs), particularly docosahexaenoic acid (DHA), as essential compounds for health. The extraction of DHA-rich oil from marine microorganisms is challenging due to the difficulty in the substantial cost of lyophilization, the incompatibility of solvents with wet biomass, and the large volumes of organic solvents required by traditional extraction techniques. To address these concerns, ionic liquids (ILs) have been used to improve the oil yield from a wet oleaginous microorganism under mild conditions. In this study, a novel hydrophobic IL, 1-decyl-3-methyl-imidazolium bis(2,4,4-trimethylpentyl)phosphinate ([C 10 mim][( i C 8 ) 2 PO 2 ]) was first applied as pretreatment to aid in the extraction of DHA-rich oil from T18 biomass in the aqueous phase. A central composite design and response surface methodology were used to study the effect of different extraction variables on the oil yield (temperature, pH, and the mass ratio of IL to dried T18). The extraction conditions were numerically optimized to maximize the oil yield [69.4 ± 0.5% (w/w)] within the experimental range (the temperature of 76 °C, pH of 5.0, and mass ratio of 4.2). In addition, the extraction efficiency was up to 97.4 ± 0.4% of the total lipid content. The extracted oil contained 44.7 ± 0.2% DHA under 1 h of the pretreatment process. In this work, the proposed approach was found to be efficient and sustainable for the water-compatible recovery of a DHA-rich oil recovery process.
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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".