Comparison of different approaches for direct coupling of solid-phase microextraction to mass spectrometry for drugs of abuse analysis in plasma
Bibliographic record
Abstract
The direct coupling of solid-phase microextraction (SPME) to mass spectrometry (MS) (SPME-MS) has proven to be an effective method for the fast screening and quantitative analysis of compounds in complex matrices such as blood and plasma. In recent years, our lab has developed three novel SPME-MS techniques: SPME-microfluidic open interface-MS (SPME-MOI-MS), coated blade spray-MS (CBS-MS), and SPME-probe electrospray ionization-MS (SPME-PESI-MS). The fast and high-throughput nature of these SPME-MS technologies makes them attractive options for point-of-care analysis and anti-doping testing. However, all these three techniques utilize different SPME geometries and were tested with different MS instruments. Lack of comparative data makes it difficult to determine which of these methodologies is the best option for any given application. This work fills this gap by making a comprehensive comparison of these three technologies with different SPME devices including SPME fibers, CBS blades, and SPME-PESI probes and SPME-liquid chromatography-MS (SPME-LC-MS) for the analysis of drugs of abuse using the same MS instrument. Furthermore, for the first time, we developed different desorption chambers for MOI-MS for coupling with SPME fibers, CBS blades, and SPME-PESI probes, thus illustrating the universality of this approach. In total, eight analytical methods were developed, with the experimental data showing that all the SPME-based methods provided good analytical performance with R2 of linearities larger than 0.9925, accuracies between 81% and 118%, and good precision with an RSD% ≤ 13%.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".