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

ABSTRACT: 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 fals

SUBMITTER: E A Souza T 

PROVIDER: S-EPMC12019721 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature