This study explored machine learning algorithms to classify single compounds, binary, ternary, and quaternary mixtures by the compound name, and the compound’s class, using seized drugs and common diluents as a model.
The accuracies were ≥ 93% for most pure, binary mixtures, and quaternary mixtures algorithms. Therefore, incorporating machine learning algorithms in portable instruments, can improve the detection of unknown substances with high accuracies. (Publisher abstract provided)
- Combining artificial neural network classification with fully continuous probabilistic genotyping to remove the need for an analytical threshold and electropherogram reading
- Kinetically Modeling Total Ion Chromatograms and Extracted Ion Profiles to Identify Ignitable Liquids for Fire Debris Applications
- STUDIES IN TAPHONOMY: BONE AND SOFT TISSUE MODIFICATIONS BY POSTMORTEM SCAVENGERS