Harnessing the power of artificial neural networks methodology and multi-objective optimization for enhanced yield and bioactivity of plants polyphenolic compounds

Abstract

The extraction of polyphenolic compounds from plants is crucial in the industrial production of functional nutraceuticals, but traditional methods often yield low and variable results. In this research, an innovative strategy for optimizing polyphenol extraction from two plants Cistus creticus L. and Ephedra alata subsp. alenda (Stapf) Trab., known for their rich composition in polyphenols and their bioactivities, using Ultrasound-Assisted Extraction in conjunction with artificial neural networks (ANNs) and multi-objective optimization is presented. ANNs were trained to model the intricate relationships among UAE parameters, including solvent concentration, temperature, and time, and the outcomes, encompassing polyphenol yield and bioactivity. Multi-objective optimization techniques were subsequently applied to identify extraction conditions that maximize both yield and bioactivity simultaneously. Results validate the accuracy of the ANNs model in predicting polyphenol yields and the significant enhancement in extraction efficiency and bioactivity achieved through multi-objective optimization. The extracts prepared in the optimal conditions have demonstrated superior antioxidant activities, compared to the non-optimized extracts, with the smallest values of IC50 of 242,378 µg/mL, and 146,736 µg/mL for the plants Ephedra alata subsp alenda (Stapf) Trab. and Cistus creticus L. respectively. This study introduces a promising approach for elevating the extraction of plant-derived polyphenols, augmenting their bioactivity with ANNs and multi-objective optimization. In light of the obtained results, it is recommended that further research explore the scalability and applicability of the presented innovative strategy in larger-scale industrial settings. Considering the demonstrated success in optimizing polyphenol extraction from Cistus creticus L. and Ephedra alata subsp. alenda (Stapf) Trab., extending the application of Ultrasound-Assisted Extraction, coupled with artificial neural networks (ANNs) and multi-objective optimization, to other plant species could offer valuable insights. Additionally, investigating the economic feasibility and environmental impact of implementing this strategy on an industrial scale would contribute to its practical viability.