Optimization of ultrasound-assisted extraction of polysaccharides from Akebia Fruit using an artificial neural network model: Characteristics and antioxidant activity

Yusang Chen; Meiling Wu; Xiao Xu; Shunyao Zhu; Mengdan Shen; Anting Ma; Zhennan She; Senlin Shi; Xi Han; Ting Zhang

doi:10.1016/j.ultsonch.2025.107447

Optimization of ultrasound-assisted extraction of polysaccharides from Akebia Fruit using an artificial neural network model: Characteristics and antioxidant activity

Ultrason Sonochem. 2025 Jun 26:120:107447. doi: 10.1016/j.ultsonch.2025.107447. Online ahead of print.

Authors

Yusang Chen¹, Meiling Wu¹, Xiao Xu², Shunyao Zhu¹, Mengdan Shen¹, Anting Ma¹, Zhennan She³, Senlin Shi¹, Xi Han⁴, Ting Zhang⁵

Affiliations

¹ School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China.
² Asset Management Co., Ltd, Zhejiang Chinese Medical University, Hangzhou 310053, China.
³ School of Pharmaceutical Sciences, Kunming Medical University, Kunming 650500, China.
⁴ School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China. Electronic address: 20241149@zcmu.edu.cn.
⁵ School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, China. Electronic address: zhangting55@zcmu.edu.cn.

PMID: 40580622
DOI: 10.1016/j.ultsonch.2025.107447

Abstract

This study investigated the extraction, structural characterization, and antioxidant activity of polysaccharides derived from Akebia Fruit. The ultrasonic-assisted extraction (UAE) process of polysaccharides was optimized through the application of the Box-Behnken Design (BBD) in conjunction with the genetic algorithm-back propagation (GA-BP) artificial neural network model. The experimental data showed that the GA-BP model performed better than the BBD model, and more polysaccharide components could be extracted under the process parameters predicted by this model. The GA-BP model predicted the optimal extraction parameters as follows: the extraction temperature was 65 ℃, the solid-liquid ratio was 1:50 g/mL, the extraction power was 400 W. Experimental results showed that combining UAE with GA-BP artificial neural network not only enabled efficient extraction of polysaccharides but also optimized the extraction process. After purification, AFP-1 was obtained and its characterization was conducted. Structural analysis results indicated that compound AFP-1 was a homogeneous polysaccharide with a lamellar structure and a molecular weight of 13,775 Da. The polysaccharide contained a network of pyranose rings, which were interconnected to form a complex framework. The polysaccharide was composed of a mixture of monosaccharide units, specifically arranged in a specific configuration that included mannose, ribose, glucose, galactose, and fucose. Finally, the antioxidant activity of AFP-1 was preliminarily verified through in vitro experiments. Subsequent research could systematically explore the biological activities of AFP-1, by employing both in vitro and in vivo models.

Keywords: Akebia Fruit; Artificial neural network; Characterization; Polysaccharide; Ultrasonic-assisted extraction.