New Analytical Screening Method for Fast Classification of Hemp Oil Based on THC Content

This study presents a novel analytical approach for classifying commercial hemp oil samples according to their Δ9-tetrahydrocannabinol (THC) content, employing mid-infrared (MIR) spectroscopy combined with machine learning algorithms. A total of 204 commercial hemp oil samples, with THC concentrations ranging from 0.0% to 16.6% w/w, were analyzed. Partial least-squares-discriminant analysis (PLS-DA) was employed for classification purposes. Two classification models were developed based on international regulatory thresholds: model A, which classifies samples with THC concentrations exceeding 0.2% w/w, and model B, designed to classify those with THC levels above 0.3% w/w. Both models demonstrated good performance, achieving accuracy values higher than 88.50%. Notably, model B reduced false negatives, improving sensitivity (STR) values from 93.75% to 98.31% for the training set and from 77.27% to 95.00% for the test set, compared to model A. This approach offers a viable alternative to conventional laboratory methods by eliminating complex sample preparation steps and enabling simple and rapid THC screening.