Internal Standard Utilization Strategies for Quantitative Paper Spray Mass Spectrometry
Bibliographic record
Abstract
Premixing internal standards (ISTD) with liquid samples prior to paper spray mass spectrometry (PS-MS) analysis consumes unnecessary amounts of ISTD and is not feasible for all sample types and applications. Depositing ISTD directly on the paper independently from sample has been successfully employed in the literature but can negatively affect quantitative performance. We evaluated different ISTD utilization strategies using drugs of misuse as test analytes to investigate the sources of irreproducibility and bias. Performance was assessed using both pre- and postdeposited ISTD (relative to sample loading) at different volumes with a constant final mass loading of 1 ng of each ISTD compound. Precision and accuracy were lower when using independently deposited ISTD compared to premixed ISTD (average CV = 18% vs 1% and average |bias| = 61% vs 5% for independently deposited ISTD and premixed ISTD, respectively). The use of a robotic liquid sample handling system to deposit ISTD was compared with results obtained via manually pipetting. Predeposited ISTD performed best at lower deposition volumes when a robotic liquid handler was used (average CV = 8% and 11% for 2 and 10 μL, respectively), but manual pipetting of low volume ISTD depositions performed poorly. Postdeposited ISTD was inferior to predeposited ISTD strategies, favoring larger deposition volumes regardless of deposition method (average CV = 22% and 16% for 2 and 10 μL, respectively). Systematic biases associated with each ISTD utilization strategy were effectively corrected for using strategy-matched calibrations, and AAFS (American Academy of Forensic Sciences) accuracy and precision requirements were achieved in almost all cases.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".