Fractional Factorial Design Study for the Extraction of Cannabinoids from CBD-Dominant Cannabis Flowers by Supercritical Carbon Dioxide

The optimization of the supercritical fluid extraction (SFE) of cannabinoids, using supercritical carbon dioxide (scCO2), was investigated in a fractional factorial design study. It is hypothesized that four main parameters (temperature, pressure, dry flower weight, and extraction time) play an important role. Therefore, these parameters were screened at predetermined low, medium, and high relative levels. The density of scCO2 was used as a factor for the extraction of cannabinoids by changing the pressure and temperature. The robustness of the mathematical model was also evaluated by regression analysis. The quantification of major (cannabidiol (CBD), cannabidiolic acid (CBDA), delta 9-tetrahydrocannabinol (Δ9-THC), delta 8-tetrahydrocannabinol (Δ8-THC), and delta 9-tetrahydrocannabinol acid (THCA-A)) and minor (cannabidivann (CBDV), tetrahydrocannabivann (THCV), cannabigerolic acid (CBG), cannabigerol (CBGA), cannabinol (CBN), and cannabichomere (CBC)) cannabinoids in the scCO2 extract was performed by RP-HPLC analysis. From the model response, it was identified that long extraction time is a significant parameter to obtain a high yield of cannabinoids in the scCO2 extract. Higher relative concentrations of CBD(A) (0.78 and 2.41% w/w, respectively) and THC(A) (0.084 and 0.048% w/w, respectively) were found when extraction was performed at high relative pressures and temperatures (250 bar and 45 °C). The higher yield of CBD(A) compared to THC(A) can be attributed to the extract being a CBD-dominant cannabis strain. The study revealed that conventional organic solvent extraction, e.g., ethanol gives a marginally higher yield of cannabinoids from the extract compared to scCO2 extraction. However, scCO2 extraction generates a cleaner (chlorophyll-free) and organic solvent-free extract, which requires less downstream processing, such as purification from waxes and chlorophyll.

Optimization by fractional factorial design to initiate L-asparaginase production by Enterobacter xiangfangenesis

L-asparaginase has been used world wide as a potential anticancerous agent as well as an excellent food processing aid. In industrial production processes, process optimization is critical because even minor improvements can make a big difference in terms of commercial success. The adjustment of nutritional and physical parameters in any bioprocess would result in an increase in the productivity of any metabolite. The production of L-asparaginase in the current study with implementation of statistical design (fractional factorial design) increased to about 109.72 IU/ml by Enterobacter xiangfangensis. The significant factors that raised the L-asparaginase production were: Lasparagine (1g%) [Factor: A], maltose (0.25g%) [Factor: B], pH (6.3)[Factor : C] and KH2PO4 (0.2 g%) [Factor : D]. The sequential contributing factors that increased L-asparaginase production according to the model represented by Pareto charts were Lasparagine [Factor: A], maltose [Factor: B] and combination of L-asparagine – maltose [Factors AB] respectively. The normal plot represented the model to be acceptable and considerable. The predicted as well as experimental values appeared closed to each other graphically. The model F-value was 9.62 indicating that the model was statistically significant.