Response Surface Optimization of Enzyme Pretreatment Improves Yield of Ethanol‐Extracted Lipids from <i>Nannochloropsis oceanica</i>
Bibliographic record
Abstract
Abstract Nannochloropsis oceanica are marine microalgae that are rich in eicosapentaenoic acid (EPA); in the future, they may become one of the most effective sources of EPA. The cell walls of N. oceanica contain a three‐layered dense structure called algaenan, which hinders the extraction of intracellular lipids. Here, the main variables for the enzyme pretreatment of N. oceanica are optimized using response surface methodology. Under the optimal variables for enzyme pretreatment, the lipid yield and EPA content are increased to 26.9% and 20.7 g/100 g, respectively. Furthermore, lipidomics analysis is carried out utilizing ultra‐high performance liquid‐quadrupole‐time‐of‐flight mass spectrometry to identify the obtained lipids. After enzyme treatment, the betaine lipids (BL) content significantly increases, probably due to laccase and cellulase promoting the release of BL from membrane structure. In comparison, the hydrolysis of neutral lipids, glycolipids, and phospholipids during the enzyme treatment reduces their respective contents. The results of this study suggest that using compound enzymes to pretreat N. oceanica can effectively increase lipids extraction yields by ethanol and improve the EPA contents in lipids. Practical applications: This study indicates that enzyme pretreatment can promote the extraction of lipids, especially lipids with EPA, from N. oceanica or other marine microalgae.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".