Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds-five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.