Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences

Area of Science:

• Analytical Chemistry
• Plant Science
• Spectroscopy

Background:

• Cannabis research requires rapid, non-destructive methods for cannabinoid quantification.
• Existing methods may be time-consuming or destructive, limiting real-time analysis.
• Hand-held near-infrared (NIR) spectroscopy offers a potential solution for in-field analysis.

Purpose of the Study:

• To evaluate a hand-held NIR device for quantifying total cannabidiol (CBD), total delta-9-tetrahydrocannabinol (THC), and total cannabigerol (CBG) in whole cannabis inflorescences.
• To optimize NIR spectral data using pre-processing techniques like Standard Normal Variate (SNV) and Savitzky-Golay (SG) smoothing.
• To assess the accuracy and reliability of the developed models for cannabinoid prediction.

Main Methods:

• Whole cannabis inflorescences were analyzed using a hand-held NIR device.
• Spectral data underwent pre-processing, including SNV and SG smoothing.
• Partial Least-Squares Regression (PLSR) models were built to predict cannabinoid concentrations.
• Model performance was evaluated using Root Mean Square Error of Prediction (RMSEP), coefficient of determination (R²P), and Ratio of Performance to Deviation (RPD).

Main Results:

• SNV pre-processing yielded the best results for total CBD prediction (RMSEP=2.228, R²P=0.792, RPD=2.195).
• Raw spectral data provided the most accurate predictions for total THC (R²P=0.812, RPD=2.306, RMSEP=1.651).
• Raw data also showed the highest R²P (0.806) for total CBG prediction.

Conclusions:

• Hand-held NIR spectroscopy, particularly with SNV pre-processing, is a promising tool for rapid and non-destructive quantification of total CBD in cannabis.
• The study highlights the potential of portable NIR technology for optimizing cannabinoid analysis in cultivation, pharmaceutical, and regulatory settings.
• Further optimization may be needed for THC and CBG, but the results demonstrate the feasibility of NIR for comprehensive cannabinoid profiling.