Rapid screening procedures for a variety of complex forensic samples using laser diode thermal desorption (LDTD) coupled to different mass spectrometers
Bibliographic record
Abstract
RATIONALE: The applications shared in this paper demonstrate the wide variety of samples that can be analyzed when Laser Diode Thermal Desorption (LDTD) is interfaced with a high-resolution mass spectrometer and show the speed at which high quality data can be generated from complex matrices. METHODS: Samples are solvent extracted and spotted in a 96-well plate. In the case of biological fluids, hydrolysis followed by solid-phase extraction is required. The solvent in the 96-well plate is evaporated followed by mass spectrometric (MS) analysis with atmospheric pressure chemical ionization. Where applicable, the instrument is operated in data-dependent mode, with a full-scan mass spectrum followed by MS/MS spectra of the top 10 ions with a total runtime of 0.4 min. RESULTS: Four applications (MAAQ and Tear Gas, twelve rodenticides, seven explosives, and 40 drugs of abuse) are reported in this paper. MAAQ, tear gas, and rodenticides were identified by full-scan, followed by MS/MS experiments at levels of 125 μg/L, 125 μg/L, and 500 μg/L, respectively. Explosives were all identified at 102 μg/L by full-scan experiments. The drugs of abuse were identified by multiple reaction monitoring (MRM) experiments at defined cutoff levels from 2 to 1000 μg/L. CONCLUSIONS: Interfacing LDTD with a mass spectrometer allows for rapid screening of a wide range of samples, with either minimal or complex sample preparation. Using a high-resolution mass spectrometer with the combination to perform full-scan and MS/MS experiments adds a high level of specificity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 0.002 |
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".