Solid-Phase Microextraction in Targeted and Nontargeted Analysis: Displacement and Desorption Effects
Bibliographic record
Abstract
An aqueous multicomponent mixture containing a wide range of volatility and polarity compounds (log Kow range 1.26-8.72) was used to clearly define the capabilities and limitations of headspace solid-phase microextraction in quantification of multicomponent complex samples. Commercially available fiber coatings were evaluated by investigating the extraction efficiency and desorption carryover. Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry was selected to map out the differences between the coatings. The investigated components were chosen to represent several homologous groups of metabolites most frequently present in complex food and environmental samples, including straight-chain hydrocarbons, primary alcohols, secondary alcohols, 2-ketones, aldehydes, ethyl esters, and terpenes. Particular emphasis was placed on examination of coating saturation and interanalyte displacements. These effects were assessed by evaluating the linear dynamic range obtained for spiked aqueous samples with divinylbenzene/Carboxen/poly(dimethylsiloxane) fiber. This coating was found to provide the optimum extraction coverage and sensitivity for the widest range of analytes. Displacement investigations were extended to apple homogenate characterized by high chemical diversity. The results indicate that interanalyte displacements are infrequent in the naturally occurring samples considered in this study. When displacements take place, they tend to occur for analytes characterized by small distribution constants, and they can be effectively detected by adding such compounds to the sample and corrected by selecting a shorter extraction time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".