Machine Learning-Based Quantification of (−)-<i>trans</i>-Δ-Tetrahydrocannabinol from Human Saliva Samples on a Smartphone-Based Paper Microfluidic Platform
Yan Liang, Avory Zhou, Jeong‐Yeol Yoon
Abstract
-NN), decision tree, and support vector machine (SVM), and the SVM showed the best accuracy of 88% in estimating six different THC concentrations. Additional validation experiments were conducted using independent validation sample sets, successfully identifying positive samples at 100% accuracy and quantifying the THC concentration at 80% accuracy. The platform provided a quick, low-cost, sensitive, and quantitative point-of-care saliva test for cannabis.
Topics & Concepts
SalivaCannabidiolChromatographyDetection limitSupport vector machineDrug detectionChemistryComputer scienceCannabisArtificial intelligenceMedicineBiochemistryPsychiatryCannabis and Cannabinoid ResearchBiochemical Analysis and Sensing TechniquesCoffee research and impacts