Optimization of Cannabis sativa supercritical CO2 extraction using Design of experiments approach

The high potential of industrial hemp (Cannabis sativa), mainly due to the high-quality fibers and bioactive compounds, coupled with the decriminalization and/or legalization in some regions of the world, led to the steady growth of scientific research over the past couple of decades [1, 2]. From an industrial point of view, cannabinoids, primarily non-psychotropic cannabidiol (CBD), are considered the most valuable compounds, as they possess a broad range of therapeutic properties, such as anxiolytic, neuroprotective, antibiotic, antiinflammatory activity, and anti-oxidant. The hemp industry has already adopted supercritical carbon dioxide scCO2 extraction as a well-established process since it is a promising ‘green’ alternative to conventional extraction methods, selective, with short processing time, and low impact on the environment. In the present study, the preliminary analysis was performed to determine the influence of different scCO2 densities based on the different combinations of process pressure and temperature on the kinetics of supercritical extraction of Cannabis sativa, extract yield and the chemical composition of obtained extracts. Experiments were performed at temperatures ranging from 40 to 60 °C and pressures ranging from 10 to 30 MPa. Prior to extraction, the plant material was grounded and submitted to decarboxylation process at 120 °C for 1 h, in order to transform cannabidiolic acid, which is the precursor present in the plant material, to CBD. Experimental design for optimization plays an important role both in science and industry, because it allows evaluation of the effects of multiple factors and their interactions on one or more response variables. The present work was focused on the further optimization of scCO2 extraction with the aim to maximize the yield of extracts with high recoveries of CBD, by means of response surface methodology (RSM), based on a central composite design (CCD) using Design of Experiment software (Stat-Ease, De