Using machine learning techniques for the classification of ultra-low concentrations of cannabis in biological fluids

In this work, the application of three different Machine Learning algorithms, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), to accurately classify ultra-low concentrations of Δ9-tetrahydrocannabinol in biological fluids such as saliva was successfully demonstrated. In doing so, experimental data consisting of the voltammetry signals of 0, 2, and 5 ng/mL of Δ9-tetrahydrocannabinol (THC) in synthetic and …