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

Abstract

AbstractIntroductionCannabis 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).ObjectivesWe 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.MethodologyFresh 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.ResultsThe 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 (R2CV and R2pred > 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).ConclusionsThe 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.