Forensic application of sandpaper spray ionization mass spectrometry (<scp>SPS</scp>‐<scp>MS</scp>): Direct analysis of solid pharmaceutical formulations and edible cannabis products
Bibliographic record
Abstract
Abstract In this work, we employed a new ambient ionization mass spectrometry technique, sandpaper spray mass spectrometry (SPS‐MS), as an efficient tool to analyze pills and tablets of pharmaceutical formulations. The following samples were analyzed: regulators of blood pressure, cholesterol, and diabetes, and drugs for the treatment of erectile dysfunction (ED). Additionally, a hard candy of Cannabis sativa containing Δ9‐tetrahydrocannabinol (Δ9‐THC) and its related isomer cannabidiol (CBD) was also evaluated. The surfaces of the samples, without any prior treatment, were sanded onto triangular‐cut sandpaper, and full MS scans (and MS/MS) were acquired in both positive and negative ionization modes. SPS‐MS (and MS/MS) allowed for prompt detection of the active pharmaceutical ingredients (APIs) in each formulation. Other components of the formulations, added as excipients, were also tentatively identified. The results described herein indicate that the SPS‐MS technique can be applied to fast screening of pills and tablets being potentially used as an efficient tool to detect counterfeit pharmaceutical and illicit products, a current issue of increasing concern.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".