Development of a solid-phase extraction method for the separation of algal lipid classes
Bibliographic record
Abstract
Algal oils have been extensively used as feedstocks for the production of biodiesel. These can be obtained using one of several extraction techniques, such as Soxhlet extraction. A critical issue is the presence of compounds in the crude extract that lead to lower yields and low-quality biodiesel produced, i.e. phospholipids (PLs). Solid-phase extraction (SPE) is commonly used for the analytical and sample preparation of compounds due to its rapidity, low cost and simplicity; including lipid analysis. The literature presents validated SPE methods for the fractionation of lipids in complex mixtures (i. e. human sources). However, SPE methods applied to algal lipid classification have yet to be fully developed and validated in the literature. \nThe objective of this project is thus to provide the user with a rapid, efficient and reliable standardized analytical method for the determination of useful lipids, triglycerides (TGs) and fatty acids (FAs) from algal biomass to produce biodiesel. \nFirstly, critical factors affecting the performance of the SPE procedure using NH2-Si were identified and evaluated, namely active phase, loading mass, precondition step and elutions. The results indicated that there is a cross-contamination between lipid classes, especially PLs, which were prone to be prematurely eluted when high quantities are present in the algal biomass (>15 mg). SPE conditions (active phase, loading mass, precondition step and elutions) were modified and tested in order to decrease the cross-contamination. The modified SPE method solved the PL elution issue. However, the elution of other type of lipids of interest was negatively affected (i. e. underestimation of fatty acids). \nSecondly, current SPE procedures using -Si and NH2-Si to quantify algal lipids were tested and standardized, and the results were then compared to the composition of standard mixtures of lipids used to perform the SPEs. \nAn accurate reference to determine the total useful lipid content for biofuel production and other applications, using SPE procedures, is recommended in this thesis. SPEs using -Si and NH2-Si columns can be performed consecutively to quantify, firstly, neutral lipids (TGs, and FAs), GLs and PLs; and secondly, TGs and FAs; respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".