A hybrid RSM-ANN-GA approach on optimisation of extraction conditions for bioactive component-rich laver (Porphyra dentata) extract

Abstract

This research established the optimal conditions for infusion extraction (IE) and ultrasound-assisted extraction (UAE) of bioactive components from laver (Porphyra dentata) using response surface methodology (RSM) and artificial neural network coupled with genetic algorithm (RSM-ANN-GA). The variables, temperatures (60, 80, and 100 ℃) and times (10, 15, and 20 min) were designed to optimise total phenolic, total flavonoid, total amino acid, a* value, and R-phycoerythrin content of laver extract. The optimised condition for IE and UAE was achieved at 60 ℃ for 18.08 min and 80.66℃ for 14.76 min in RSM while showing 60 ℃ for 19 min and 80℃ for 15 min in the RSM-ANN-GA mode, respectively. Results revealed that RSM-ANN-GA provided better predictability and greater accuracy than the RSM model and laver extract from UAE gave the higher values of responses compared to those from IE. These findings highlight the high-efficient extraction method along with better statistical approach.

Keywords: Artificial neural network; Bioactive components; Genetic algorithm; Response surface methodology.