A green approach for multivariate consecutive extraction of essential oils and flavonoids from Citrus aurantium L. var. amara Engl.: Process optimization and mechanistic insights based on machine learning methods

Abstract

In this study, a green approach for the multivariate consecutive extraction of essential oils and total flavonoids from Daidaihua was proposed, which combined classical steam distillation with alkali extraction and acid precipitation. The process parameters were optimized through single-factor experiments and Box-Behnken design, and the model for evaluating and predicting the process was constructed by combining artificial neural network and genetic algorithm. The artificial neural network and genetic algorithm model demonstrated satisfactory predictive capabilities, and the following optimal parameters resulted: solid-liquid ratio 1:15.3 (g:mL), distillation time 94.1 h, NaOH concentration 5.4%, extraction temperature 46.7 ℃and extraction time 52.9 min. Under these conditions, the extraction yields of essential oils and total flavonoids reached 0.81 ± 0.07% and 5.52 ± 0.18%, respectively. Significantly, the mass transfer mechanism of the multivariate consecutive extraction process was clearly elucidated using the mass transfer theory model and physical characterization. The essential oils were vaporized from the Daidaihua cells along with water vapor, promoting the destruction of cell structure, reducing the extraction resistance of flavonoids, and speeding up the diffusion of sodium hydroxide solution rapidly into the cell interior and dissolving the flavonoids, improving the efficiency of the mass transfer of flavonoids from the inside to the outside of the cell, consequently the flavonoids extraction yield and efficiency were significantly improved. Obviously, the extraction process dovetailed neatly with environmentally friendly and low-carbon needs. Meanwhile, the composition of the essential oils and total flavonoids was identified by GC-MS and HPLC-MS analysis, respectively. Overall, this research presented a green and clean multivariate consecutive extraction process of Daidaihua, which could contribute to promoting the upgrading and sustainable development of the plant extraction industry.