Supercritical fluid extraction followed by supramolecular solvent microextraction as a fast and efficient preconcentration method for determination of polycyclic aromatic hydrocarbons in apple peels
Why this work is in the frame
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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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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 | 0.002 | 0.001 |
| 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 it