Support Vector Machine Classification of Adulterated Illicit Opioids Using Paper-Spray Mass Spectrometry
Bibliographic record
Abstract
The complexity and potency of the illicit opioid supply in North America has become increasingly concerning for people who use drugs. Drug checking efforts aim to keep up with the evolving psychoactive components present in the illicit drug supply. While targeted paper-spray mass spectrometry (PS-MS) methods are effective for trace detection and quantitation, they are limited in their ability to detect emerging substances. Here, we demonstrate the use of a support vector machine (SVM) classifier to detect opioid samples containing an additional component outside of a routine targeted analysis method, using ortho -methylfentanyl as proof of concept. This approach allows for the focused selection of samples for additional screening to identify emerging adulterants from full-scan data collected during a 2 min PS-MS analysis. The developed classifier achieved a precision of 0.77 and a recall of 0.94 for the detection of ortho -methylfentanyl in samples containing fentanyl and caffeine. Shapely additive explanations were used to explain and interpret the results of the developed SVM classifier, and enabled a better understanding of the composition of illicit opioids. Our work demonstrates a new and effective approach for identifying adulterants from unit mass resolution mass spectrometry data generated during routine on-site drug analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".