Automation of Solid-Phase Microextraction in High-Throughput Format and Applications to Drug Analysis
Bibliographic record
Abstract
The automation of solid-phase microextraction (SPME) coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) was accomplished using a 96 multiwell plate format, a SPME multifiber device, two orbital shakers, and a three-arm robotic system. Extensive optimization of the proposed setup was performed including coating selection, optimization of the fiber coating procedure, confirmation of uniform agitation in all wells, and the selection of the optimal calibration method. The system allows the use of pre-equilibrium extraction times with no deterioration in method precision due to reproducible timing of extraction and desorption steps and reproducible positioning of all fibers within the wells. The applicability of the system for the extraction of several common drugs is demonstrated. The optimized multifiber SPME-LC-MS/MS was subsequently fully validated for the high-throughput analysis of diazepam, lorazepam, nordiazepam, and oxazepam in human whole blood. The proposed method allowed the automated sample preparation of 96 samples in 100 min, which represents the highest throughput of any SPME technique to date, while achieving excellent accuracy (87−113%), precision (≤20% RSD), and sensitivity (limit of quantitation 4 ng/mL). Automated SPME provides unique advantages over automated solid-phase extraction (SPE) including lower cost, the ability to quantitatively determine free and total drug concentrations in a single biofluid sample, and the ability to directly process whole blood samples with absolutely no sample pretreatment required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".