Comparative study of NMR spectral profiling for the characterization and authentication of cannabis

Abstract

For the authentication of botanical materials, it is difficult to obtain representative reference materials because botanicals vary significantly with respect to cultivation conditions. Chemical profiling of plant extracts or spectral fingerprinting can differentiate botanicals and group them by their chemical profiles. NMR spectroscopy yields a powerful and useful method for profiling plant extracts. Both 500 MHz 1H and 1H-1H correlation NMR spectroscopy coupled with pattern recognition were used to discriminate among Cannabis samples. A rapid method of analysis was achieved by extracting directly into the deuterated solvent. Spectral ranges including or excluding the downfield region were compared to evaluate the effect on classification accuracy by projected difference resolution. Six classification methods-fuzzy rule-building expert system, linear discriminant analysis (LDA), super partial least-squares discriminant analysis, support vector machine (SVM), and SVM classification trees (SVMTrees)-All gave better classification performance for proton NMR spectra than for protonproton correlation NMR spectra for seven Cannabis samples. Among the classification methods for a set of 25 Cannabis samples, the 0.5-7.2 plus 7.4-13.0 ppm ranges gave higher prediction rates of greater than 96% when compared to the reduced range of 0.5-7.2 ppm that excluded the downfield range. The LDA method had the best prediction accuracy of 99.8 ± 0.4%. SVMTree methods provide a robust tool, and classification trees are amenable to interpretation. Hence, NMR spectroscopy combined with chemometrics could be used as a fast screening method for the authentication of Cannabis samples.