Suitability of Soxhlet Extraction to Quantify Microalgal Fatty Acids as Determined by Comparison with In Situ Transesterification
Bibliographic record
Abstract
To assess Soxhlet extraction as a method for quantifying fatty acids (FA) of microalgae, crude lipid, FA content from Soxhlet extracts and FA content from in situ transesterification (ISTE) were compared. In most cases, gravimetric lipid content was considerably greater (up to sevenfold) than the FA content of the crude lipid extract. FA content from Soxhlet lipid extraction and ISTE were similar in 12/18 samples, whereas in 6/18 samples, total FA content from Soxhlet extraction was less than the ISTE procedure. Re-extraction of residual biomass from Soxhlet extraction with ISTE liberated a quantity of FA equivalent to this discrepancy. Employing acid hydrolysis before Soxhlet extraction yielded FA content roughly equivalent to ISTE, indicating that acidic conditions of ISTE are responsible for this observed greater recovery of FA. While crude lipid derived from Soxhlet extraction was not a useful proxy for FA content for the species tested, it is effective in most strains at extracting total saponifiable lipid. Lipid class analysis showed the source of FA was primarily polar lipids in most samples (12/18 lipid extracts contained <5% TAG), even in cases where total FA content was high (>15%). This investigation confirms the usefulness of ISTE, reveals limitations of gravimetric methods for projecting biodiesel potential of microalgae, and reinforces the need for intelligent screening using both FA and lipid class analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".