Enhancement of ultrasonic-assisted extraction and antioxidant potential of phenolic compounds from Moroccan Cannabis sativa: A green intelligent approach integrating optimal mixture process design and artificial neural networks

Abstract

Environmentally-friendly extraction methods enables both efficient extraction of phenolic compounds from plant sources and safe use of the extracted substances. These characteristics render them as ideal choices for sustainable extraction processes. This study combines a green and sustainable extraction strategy with an intelligent statistical approach to optimize the recovery and the antioxidant activity of phytochemicals derived from Moroccan Cannabis sativa. The optimization of the ultrasound-assisted extraction is achieved using an optimal mixture-process design (OMPD) combined with artificial neural networks (ANNs). The OMPD consists of a mixture of water, glycerol, and ethanol, along with process parameters such as processing time, solvent-to-material (S/M) ratio, and temperature. The objective is to optimize the five responses extraction yield, total phenolic content (TPC), and antioxidant activities measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), total antioxidant capacity (TAC), and Ferric reducing antioxidant power (FRAP) assays. The results of the OMPD indicate that the recovery of phenolic compounds and antioxidant activity can be improved by using a binary mixture of water and ethanol, along with moderate processing time and temperature, and a high S/M ratio. The optimal responses were 26.06 %, 92.6 mg GAE/g, 0.1 mg/ml, 298 mg AAE/g, and 1.3 mg/ml for yield, TPC, TFC, DPPHIC50, TAC, and FRAPEC50, respectively. Subsequently, the OMPD matrix was used as input for the ANNs, employing a 6-6-5 multi-layer perceptron design. The findings of the ANNs closely matched those obtained from the OMPD models. However, the performance parameters of the ANNs models were slightly superior to those of the OMPD.