Potential of NIRS Technology for the Determination of Cannabinoid Content in Industrial Hemp (Cannabis sativa L.)

• Published in: Agronomy (Basel) Vol. 12; no. 4; p. 938
• Main Authors: Jarén, Carmen, Zambrana, Paula C., Pérez-Roncal, Claudia, López-Maestresalas, Ainara, Ábrego, Andrés, Arazuri, Silvia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2022
• Subjects: Cannabidiol; Cannabinoids; Cannabis; Cannabis sativa; CBD; Chemical composition; chemometrics; Chromatography; Data analysis; Flowers & plants; Food sources; Hemp; High performance liquid chromatography; HPLC; Humidity; Infrared spectroscopy; Liquid chromatography; Marijuana; Mathematical models; Methods; Near infrared radiation; near infrared spectroscopy; Principal components analysis; quantification; Regression analysis; Regression models; Software; Spectrum analysis; Tetrahydrocannabinol; Textile fibers; THC
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy12040938

Industrial hemp (Cannabis sativa L.) is a plant native to Asia, and is considered to be a primary source of food, textile fiber, and medicines. It is characterized by containing minimal concentrations of delta-9 tetrahydrocannabidol (THC), which is the main psychoactive chemical component, and cannabidiol (CBD), a non-psychoactive substance. In most European countries, the maximum concentration legally allowed for cultivation is 0.2% of THC, and it is currently under debate whether to increase this level to 0.3%. Moreover, in many countries its production is being regularized and legalized, increasing the need for a rapid analysis method. The present work evaluated the cannabinoid content in hemp (Cannabis sativa L.) using near infrared spectroscopy (NIRS) technology in combination with chemometric techniques. For this, several samples of the Kompolti variety were analyzed. Samples were dried and ground, and the content of total THC (%) and total CBD (%) was determined by high performance liquid chromatography (HPLC) with a diode array detector as reference measurements, and then the spectra were collected by NIRS. Principal component analysis and partial least square regression models were developed. Good coefficients of determination of cross-validation of 0.77 for THC and CBD, and a ratio of prediction to deviation >2 for total THC and CBD, were achieved. The results obtained show that NIRS technology has potential for the quantitative determination of cannabinoids. Therefore, this analytical method would allow a simpler, more robust, precise, and sustainable estimation than the current HPLC approach.