Understanding the effect of spatial positioning of coated blade spray devices relative to the mass spectrometry inlet on different instrument platforms and its application to quantitative analysis of fentanyl and related analogs
Bibliographic record
Abstract
RATIONALE: We evaluated the effect that the spatial positioning of coated-blade spray (CBS) devices with respect to the mass spectrometry (MS) inlet has when coupling to diverse MS platforms (i.e., triple quadrupole, linear ion trap and time of flight). Furthermore, as a proof of concept, we evaluated CBS-MS as a tool for quantitation of fentanyl and four analogues on said instruments. METHODS: Custom-made MS interfaces were made to accurately position the blade in front of the MS inlet. CBS devices, coated with hydrophilic-lipophilic balanced particles, were used for both the optimization of the CBS position and the quantitation of fentanyl and analogues in urine and plasma samples on all instruments. RESULTS: The SCIEX triple quadrupole instrument was the most sensitive to the position of the blade due to the presence of a curtain gas flowing laminarly out of the MS inlet. After optimization, the analytical capabilities of CBS on each instrument were assessed and the results obtained on both SCIEX and Waters platforms matched the performance obtained using a more advanced instrument by ThermoFisher Scientific. Furthermore, excellent figures of merit were attained for the quantitation of fentanyl and analogues on both triple quadrupole and linear ion trap platforms. CONCLUSIONS: We demonstrated that optimization of MS parameters on different instrument vendors and front ends, such as the position of the CBS tip regarding the MS inlet, is vital to exploit the full quantitative potential of this technology. Application of the technology to screen and quantify fentanyl and analogues showed great potential when considering its coupling with portable mass spectrometers for therapeutic drug monitoring and point-of-care applications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".