Fire accelerant classification from GC-MS data of suspected arson cases using machine-learning models.

Using a practical GC-MS dataset containing approximately 4000 suspected arson cases, three machine-learning based classification models were developed and their performances were evaluated. All models trained for classifying the data from fire residue into six categories; no fire accelerants detected or else one of fire accelerants was used within gasoline, kerosene, diesel, solvents, or candle. The classification accuracies of the random forest, supporting vector machine, and convolutional neural network model were 0.

SPME-GC/MS Analysis of Methanol in Biospecimen by Derivatization with Pyran Compound.

Methanol is metabolized in the body to highly toxic formaldehyde and formate when consumed accidentally. Methanol has been typically analyzed with gas chromatography-flame ionization detector (GC-FID). However, its retention time may overlap with other volatile compounds and lead to confusion.