Characterization of Neutral Lipids of the Oleaginous Alga Micractinum inermum
Bibliographic record
Abstract
An oleaginous microalga Micractinum inermum isolated from Mariana Lake, AB, Canada was cultured in a 1000 L photobioreactor with an f/2 medium to study its lipid content and neutral lipid profile. Algal biomass was collected at the stationary phase contained a significant amount of lipids (44.2%), as determined by Folch’s method. The lipid was fractionated into neutral lipid, glycolipid and phospholipid fractions. The neutral lipid constitutes almost 77.3% of the total lipid species and is mainly composed of triacylglycerols (TAGs) determined by a proton NMR study. UHPLC-HRMS analysis allows us for the first time to identify 81 TAGs in the neutral lipid fraction of M. inermum. The fatty acid acyl side chains were identified based on fragment ions observed in MSMS analysis. TAGs with fatty acid acyl chains 18:1/18:1/18:1, 18:1/18:1/16:0, 18:2/18:1/16:0, and 18:2/18:2/18:0 were the major ones among the identified TAGs. Fatty acid analysis further supports the fact that oleic acid was the major fatty acid present in the neutral lipid fraction of M. inermum constituting 41.7%, followed by linoleic acid at 21.5%, and palmitic acid at 21.2%. The saturated and monounsaturated fatty acids were 67.8% or higher in the lipid fraction. Long-chain fatty acids were only present in a minor quantity. The results clearly demonstrate that M. inermum is an excellent source for TAGs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".