Determination of polar impurities in biodiesels using solid‐phase extraction and gas chromatography–mass spectrometry
Bibliographic record
Abstract
This paper reports on a method for development and validation for simultaneous characterization and determination of oxygenated polar impurities--free fatty carboxylic acids (FFAs), partial glycerides (monoacylglycerides, MGs), residual glycerol and free sterols--in various biodiesels based on the combination of solid-phase extraction (SPE), silylation and GC/MS technologies. The effects of various SPE and silylation conditions on the method recoveries were evaluated. Using this integrated SPE-GC/MS method, 38 target polar compounds (13 FFAs, 17 glycerides and 8 sterols) in 9 biodiesels derived from 4 different feedstocks were successfully separated and quantified. It was found that the carbon chain length of FFAs was ranged from C(6) to C(24), with C(16) and C(18) being the most abundant in all biodiesels. The total FFAs concentration was consistent with the acid values (AVs) measured by standard method ASTM D974-04. MG congeners with carbon number of 18 (mono-C18) were most abundant in the biodiesel samples, followed by mono-C(16) and free glycerol. β-Sitosterol and campesterol were found to be the prevailing phytosterols in all pure vegetable oil-based biodiesels, while brassicasterol and stigmasterol was only significant in the biodiesel from canola oil and soybean oil, respectively, and abundant cholesterol was only detected in animal fat-based biodiesels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".