Supercritical fluid extraction followed by supramolecular solvent microextraction as a fast and efficient preconcentration method for determination of polycyclic aromatic hydrocarbons in apple peels
Bibliographic record
Abstract
In this work, reverse micelle-based supramolecular solvent microextraction method coupled with supercritical fluid extraction and used for determining trace amounts of polycyclic aromatic hydrocarbons in apple peels. The extract was analyzed by high-performance liquid chromatography equipped with a fluorescence detector. Coupling supramolecular solvent microextraction with supercritical fluid extraction method, resolve low preconcentration factor of supercritical fluid extraction method, improved limit of detection of polycyclic aromatic hydrocarbons and allow the use of supramolecular solvent microextraction in solid matrices. The effective parameters on the supramolecular solvent microextraction and supercritical fluid extraction efficiency were optimized using one variable at a time and face centered design methods, respectively. Under the optimum condition, the limits of detection and limits of quantifications were in the range of 0.34-1.27 and 1.03-3.82 µg/kg, respectively. Analysis of polycyclic aromatic hydrocarbons in apple peels showed that the supercritical fluid extraction/ supramolecular solvent microextraction method provide great potential for trace analysis of polycyclic aromatic hydrocarbons in fruit samples (RSDs < 7.7%).
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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 | 0.000 | 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 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".