Robust statistical analysis to predict and estimate the concentration of the cannabidiolic acid in Cannabis sativa L.: A comparative study.

Growing awareness of the medicinal and therapeutic benefits of cannabis has resulted in extensive research, increasing numbers of commercial growers, and the provision of commercial testing. For commercial production, the importance of monitoring for consistent cannabinoid composition is essential. Gas chromatography and/or liquid chromatography methods are effective methods. Unfortunately, they are not easily scaled up for frequent or large-scale testing. Precision cultivation for medicinal cannabis requires the industry to test large volumes of plants frequently during different stages of the growth cycle. This cannot be achieved with destructive testing alone. A statistical method with 1H nuclear magnetic resonance (NMR) is proposed and investigated to explore its potential for a cost-effective mass-screening method of plant material samples. In addition, hyperspectral imaging (HSI) is employed for frequent non-destructive measurements. This allows increasing the frequency of observations during the growth of the plant. The cannabidiolic acid (CBDA) concentration is determined by the application of both HSI and NMR spectroscopy and validated against liquid chromatography-mass spectrometry (LCMS). The paper proposes a multivariate statistical regression algorithm that automatically determines the CBDA concentration directly from bucket integration of the NMR spectrum. The algorithm successfully predicted the CBDA concentration of 7 unknown samples from data selected from 4 known samples, with an average R2 value of 0.98. The proposed statistical method was applied to the data collected for HSI. It showed that while the hyperspectral dataset can be correlated with CBDA concentration, it was subject to high variance. However, HSI retains the spatial information of the actual plant structure, allowing the CBDA prediction to be mapped back to the original spatial location in the plant while providing visual information on CBDA concentration within a flower or leaf without requiring the plant component to be destroyed in the process. [Display omitted] • Independently LCMS-validated highly predictive multivariate analysis of metabolomic 1H NMR bucketed spectra. • First-in-literature CBDA concentration estimation using 400 – 1700 nm hyperspectral imaging method with machine learning. • Comparative study of LCMS, NMR and HSI techniques that are complementary for application across different cultivation stages. [ABSTRACT FROM AUTHOR]