Uses of portable FT-NIR to determine cannabinoids and terpenes in dry-cured cannabis flowers
Authors: Marcal Plans1 ; Adham Hesham ; Ruben Valenzuela2
1. Si-Ware Inc. 101 Jefferson Drive, Menlo Park, CA, USA2. Valenveras, Camí Pla de la Torreta 1 BIS, Sant Andreu de Llavaneres, 08392, Barcelona, Spain
The cannabis industry is growing exponentially worldwide. The crop can engage old and new farmers to adopt it as a novel crop. In that sense, there is a need for fast, on-site, accurate technology to provide the growers, distributions, and producers with a tool to manage the quality control of their sites and improve crop optimization.
NIR infrared has shown the potential to be used as a tool to predict the cannabinols content in dry-cured flowers hemp (1) and cannabis (2).
Handheld portable devices provide good performance to predict quantitative levels of cannabinols in flowers (2). This has opened a lot of opportunities to implement this technology in the field and directly to the quality control; from the crop to the distributor to the medical dispensary. Increasing the traceability of the production and improving the transparency for the final user.
Models showed a good performance predicting THC, CBD, CBG, Total Terpenes, THC acid, and CBD acid with a low error of predictions.
PLS models for THC and CBD show good linearity between predicted levels and measured by HPLC-PDA levels of the cannabinoids.
A Total of 7000 samples were used to calibrate the cannabinols, and 4000 samples to calibrate the total terpenes. The reference analyses were done using ISO certified HPLC-PDA method for cannabinols and GC-FID for the total terpenes.
Partial Least Square regression (PLSR) was used to correlate the spectra obtained from NeoSpectra Scanners (17 scanners (Si-Ware Inc., Menlo Park, CA, USA)) from 1350 – 2550 nm with the reference analysis.
Si-Ware technology coupled with Valenveras as the expert in the cannabis
1L. sector, provides reliable and robust models. The current FT-NIR technology
could be used as an alternative to the classical HPLC and GC analysis for in-
situ analysis of the cannabis flowers.
Moreover, besides the prediction of the Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in cannabinols, total terpenes also can be predicted, giving the final user the tools to discriminate between high and low content of phenotypes.
1. Yao, S., Ball, C., Miyagusuku-Cruzado G., Giusti, M., Aykas, D., Rodriguez-Saona, 2022. A novel handheld FT-NIR spectroscopic approach for real-time screening of major cannabinoids content in hemp. Talanta. Sep 1;247:123559
2. Tran, J., Vassiliadis, S., Elkins, A., Cogan, N., Rochfort, S. 2023. Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa. Sensors (Basel) 2023 Feb 27;23(5):2607.