Forensic Applications of Sandpaper Spray Ionization Mass Spectrometry (SPS-MS): Detection and Chemometric Profiling of Small Molecules for Prohibited Practices
Bibliographic record
Abstract
The illicit trade of pharmaceuticals and wood presents threats to public health, ecological stability, and global markets. This thesis examines Sandpaper Spray Ionization Mass Spectrometry (SPS-MS) as a rapid, field-deployable technique for direct analysis of solid samples in two distinct forensic applications. (1) SPS-MS was used to detect and identify active pharmaceutical ingredients (APIs) and excipients in pharmaceuticals, assessing product authenticity. (2) SPS-MS was further developed for direct sampling and analysis, obtaining chemical fingerprints for classification and origin tracing of Canadian and Brazilian wood samples, in the context of forestry crimes. Chemometric analysis using PCA revealed clear species differentiation, supporting its use in species-level classification. SPS-MS thus offers a versatile, cost-effective, and operationally simple screening method to supplement current MS techniques. Its portability, minimal reagent use, and possible integration into current analytical workflows make it well-suited for remote or resource-limited settings for frontline testing of fraudulent drugs and trafficked wood.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".