Novel fluorescence spectroscopy method coupled with N-PLS-R and PLS-DA models for the quantification of cannabinoids and the classification of cannabis cultivars

Introduction: Cannabis sativa L. inflorescences are rich in secondary metabolites, particularly cannabinoids. The most common techniques for elucidating cannabinoid composition are expensive technologies, such as high-pressure liquid chromatography (HPLC). Objectives: We aimed to develop and evaluate the performance of a novel fluorescence spectroscopy-based method coupled with N-way partial least squares regression (N-PLS-R) and partial least squares discriminant analysis (PLS-DA) models to replace the expensive chromatographic methods for preharvest cannabinoid quantification. Methodology: Fresh medicinal cannabis inflorescences were collected and ethanol extracts were prepared. Their excitation–emission spectra were measured using fluorescence spectroscopy and their cannabinoid contents were determined by HPLC-PDA. Subsequently, N-PLS-R and PLS-DA models were applied to the excitation–emission matrices (EEMs) for cannabinoid concentration prediction and cultivar classification, respectively. Results: The N-PLS-R model was based on a set of EEMs (n = 82) and provided good to excellent quantification of (−)-Δ9-trans-tetrahydrocannabinolic acid, cannabidiolic acid, cannabigerolic acid, cannabichromenic acid, and (−)-Δ9-trans-tetrahydrocannabinol (R²_CV and R²_pred > 0.75; RPD > 2.3 and RPIQ > 3.5; RMSECV/RMSEC ratio < 1.4). The PLS-DA model enabled a clear distinction between the four major classes studied (sensitivity, specificity, and accuracy of the prediction sets were all ≥0.9). Conclusions: The fluorescence spectral region (excitation 220–400 nm, emission 280–550 nm) harbors sufficient information for accurate prediction of cannabinoid contents and accurate classification using a relatively small data set.