A Quantitative Method of Analysis for Sterol Glycosides in Biodiesel and FAME Using GC‐FID
Bibliographic record
Abstract
Abstract Sterol glycosides (SG) are known to cause filter blocking problems in biodiesel use. The extraction and quantitative analysis of SG is difficult due to its low problematic concentration and its compatibility with biodiesel. The purpose of this study is to develop a method to quantify SG in FAME and biodiesel using gas chromatography and other equipment found in laboratories performing routine biodiesel analyses. SG was isolated from FAME using n ‐dodecane, acidification and cold soaking, followed by cold centrifugation at −8 to −15 °C. The solids obtained were further separated by phase partition with a Folch wash, followed by a final n ‐dodecane rinse. This solution was analyzed by GC‐FID using the operating conditions outlined in ASTM D6584. A calibration curve for SG was produced and a first order fit gave a value of r 2 = 0.992. Reproducibility tests were performed on soybean FAME and B100 canola biodiesel samples spiked with SG. The recovery of SG by the new method was found to be 99 % for soy FAME with a standard deviation of 0.7 and 100 % for B100 canola with a standard deviation of 3.5 %. The reproducibility based on two standard deviations of the predicted concentration for all 12 spiked samples studied in this work was 2.4 ppm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".