Open Port Probe Sampling Interface for the Direct Coupling of Biocompatible Solid-Phase Microextraction to Atmospheric Pressure Ionization Mass Spectrometry
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In recent years, the direct coupling of solid phase microextraction (SPME) and mass spectrometry (MS) has shown its great potential to improve limits of quantitation, accelerate analysis throughput, and diminish potential matrix effects when compared to direct injection to MS. In this study, we introduce the open port probe (OPP) as a robust interface to couple biocompatible SPME (Bio-SPME) fibers to MS systems for direct electrospray ionization. The presented design consisted of minimal alterations to the front-end of the instrument and provided better sensitivity, simplicity, speed, wider compound coverage, and high-throughput in comparison to the LC–MS based approach. Quantitative determination of clenbuterol, fentanyl, and buprenorphine was successfully achieved in human urine. Despite the use of short extraction/desorption times (5 min/5 s), limits of quantitation below the minimum required performance levels (MRPL) set by the world antidoping agency (WADA) were obtained with good accuracy (≥90%) and linearity ( R 2 > 0.99) over the range evaluated for all analytes using sample volumes of 300 μL. In-line technologies such as multiple reaction monitoring with multistage fragmentation (MRM 3 ) and differential mobility spectrometry (DMS) were used to enhance the selectivity of the method without compromising analysis speed. On the basis of calculations, once coupled to high throughput, this method can potentially yield preparation times as low as 15 s per sample based on the 96-well plate format. Our results demonstrated that Bio-SPME-OPP-MS efficiently integrates sampling/sample cleanup and atmospheric pressure ionization, making it an advantageous configuration for several bioanalytical applications, including doping in sports, in vivo tissue sampling, and therapeutic drug monitoring.
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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) | 0.005 | 0.002 |
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".