Method development for fingerprinting of biodiesel blends by solid‐phase extraction and gas chromatography–mass spectrometry
Bibliographic record
Abstract
A method based on the combination of solid-phase extraction (SPE) with gas chromatography-mass spectrometry (GC/MS) for detailed chemical fingerprinting of biodiesel/petrodiesel blends was developed in the present study. Forensic identification, commonly referred to as chemical fingerprinting, is based on the relative distributions of individual aliphatic hydrocarbons, aromatic hydrocarbons, fatty acid alkyl esters, and free sterols. Fractionation of fuel samples is optimized for the separation of fatty acid esters and free sterols from petroleum hydrocarbons into four fractions: aliphatic, aromatic, fatty acid ester, and polar components. The final recoveries of aliphatic and aromatic hydrocarbons were determined to be in the range of 65-103%, 73-105% for FAMEs, and 78-103% for free sterols in the polar fraction. Excellent separation with negligible crossover of components with different polarities between fractions was observed. Quantitative analysis of blend levels and individual chemical distribution were achieved. The method has great potential for the identification of biodiesel in diesel fuel blends and could form the basis of a method for characterization of biodiesel-contaminated environmental samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".