Differentiation of Cannabis seeds employing digital morphological screening and infrared spectroscopy coupled with multivariate modeling

Abstract

Cultivation of Cannabis for medicinal purposes primarily relies on seed propagation with expected variations in yield, cannabinoid content, growth rate and seed material non-uniformity. This study aims to employ digital methods for morphological analysis and infrared spectroscopy, combining them with multivariate analysis to characterize and differentiate Cannabis seeds. Morphological traits of 100 seeds from both commercial Cannabis specimens and wild-growing local varieties were analyzed using the high-throughput phenotyping software in addition to their collected infrared spectra in the mid-IR region. Subsequently, a chemometrics approach by means of Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Partial Least Square-Discriminatory Analysis (PLS-DA) was applied. The statistical indicators of the PLS-DA model (R2X=0.99, R2Y=0.63, Q2=0.64) demonstrate strong predictive capabilities for the differentiation of Cannabis seed specimens based on morphological attributes. The score scatter plot clearly shows a distinct grouping pattern, primarily driven by seed size. Wild-type seeds predominantly cluster into group 1, characterized by smaller diameters, while commercial seeds cluster into group 2. By analysing spectral data, in contrast to the expected differentiation based on secondary metabolites (cannabinoids) in the seeds, differentiation was based on the macronutrient profile with characteristic bands at 3275 cm−1, 2921 cm−1, 2852 cm−1, 1743 cm−1, 1630 cm−1, 1532 cm−1, 1459 cm−1, 1239 cm−1, 1157 cm−1, 1094 cm−1, 1018 cm−1, identified as the most distinctive spectral features. The PCA model (R2X=0.88 and Q2=0.85) was composed of 5 principal components explaining 88% of the spectral variations. The loading plot of the PLS-DA model reveals the distinctive spectral features for both groups (lipid and carbohydrate bands - group 2 samples, protein and water content - group 1 samples). The developed models have the potential for application for rapid screening of quality parameters of Cannabis seeds and their differentiation.