Comparative study on the extraction efficiency, characterization, and bioactivities of Bletilla striata polysaccharides using response surface methodology (RSM) and genetic algorithm-artificial neural network (GA-ANN).

This research established the optimal conditions for alkali-assisted extraction (AAE) of bioactive polysaccharides from Bletilla striata integrated with response surface methodology (RSM) and the genetic algorithm-artificial neural networks (GA-ANN). In comparison with RSM, the ANN model showed a relatively higher determination coefficient in the global output values (RSM: ANN = 0.9270: 0.9742) performing more satisfactorily in the validation. Under the optimum conditions (52 °C; 167 min, and 0.01 mol/L NaOH), the extraction yields, IC ₅₀ of ABTS, and FRAP value were 29.53 ± 0.97 %, 3.41 mg/mL, and 39.11 μmol Fe ²⁺ /g, respectively. The results indicated that BSPs-A was mainly composed of glucose and mannose with small amounts of arabinose, galactose, and galacturonic acid, while possessed a molecular weight of about 305.94 kDa (Mw). The structural characterization of BSPs-A was initially characterized by FT-IR, SEM, and Congo red tests, which indicated that BSPs-A possessed a triple helix conformation of typical Bletilla striata polysaccharides. In addition, BSPs-A exhibited excellent antioxidant activity, which was further confirmed by a series of in vitro antioxidant activity assays including DPPH, ABTS, FRAP, and ORAC. After incubation in the BSA-glucose system for 15 days, BSPs-A showed inhibition of the advanced glycation end products (AGEs) formation for the first time.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 Elsevier B.V. All rights reserved.)