Analysis of major cannabinoids using Raman microscopy, density functional theory, chemometrics and a novel artificial intelligence approach

Abstract

With a rise in the prominence of cannabis usage, due to its widespread availability and varying legal status, there has been an increased emphasis on the differentiation of cannabinoids present within cannabis using various analytical techniques. The present study aimed to exploit the capability of Raman microscopy to collect high-quality spectra of seven cannabinoid analytical standards, followed by their classification using linear discriminant analysis (LDA) and a novel transfer learning approach. Additionally, the experimental Raman spectra of delta-9-tetrahydrocannabinol (Δ9-THC), cannabidiol (CBD), and cannabichromene (CBC) were compared to simulated spectra from density functional theory calculations (DFT) to connect the spectral features to the underlying vibrational motions. A microscopical approach enabled the determination of the optimal sampling areas to collect Raman spectra for the nonacidic and acidic cannabinoids. An initial visualization of the data using principal component analysis (PCA) confirmed the spectral differences observable by visual comparisons of the spectra of the cannabinoid standards. The application of LDA implemented with a 5-fold cross-validation with 10 repeats, resulted in a classification accuracy of 99.83 %. For the transfer learning approach, the artificial intelligence (AI) model training was conducted in less than 10 min in a graphical processing unit (GPU) environment. All seven cannabinoids were successfully classified into respective classes based on scalograms transformed from Raman spectra, with 100 % classification accuracy. The average prediction probability for correct classification was 99.31 %. The classification outcome provided by the AI model included both prediction labels and probability, which provided a comprehensive evaluation of the samples.