Improvement of ultrasound-assisted extraction of polyphenolic content of the plant Cytisus triflorus L'Her as a natural resource using artificial neural network modeling and multi-objective optimization

The improvement of environmental performance of production processes is a critical problem right now. Processes that are effective in terms of productivity and environmental effects are the center of interest of researchers. This study established such a method for an ultrasound-assisted extraction procedure. The plant Cytisus triflorus L'Her was used to extract polyphenolic chemicals using ultrasound-assisted extraction as a quick, less energy consuming, high-yield technology. This work offers a new experimental planning approach using Artificial Neural Networks, that enables the extraction process to be optimized using multi-objective optimization in order to identify the best extraction conditions to obtain the maximum polyphenolic compounds possible. Advanced modeling using Artificial intelligence and statistical optimization techniques was used to determine the best combinations of ethanol concentration, extraction temperature and time for the maximum extraction of total phenols, total flavonoids, total flavonols, and total tannins. An artificial neural network (ANN) was used to build the models for each of the four goals, then they were multi-objectively optimized using a genetic algorithm technique. The maximum polyphenolic contents including TPC (4.986 ± 0.006 mg GA/g DE), TFC (14.026 ± 0.070 mg Q/g DE), TFolC (5.225 ± 0.010 mg R/g DE), and TTC (6.240 ± 0.021 mg C/g DE) were obtained using an extraction process under optimal conditions, which included a solvent concentration of 78%, an extraction temperature of 52 °C, and an extraction time of 55 min which were obtained using the MOO Pareto optimum solution selection approach Gray Relational Analysis GRA.