Optimization of ultrasound-assisted extraction of polysaccharides from Akebia Fruit using an artificial neural network model: Characteristics and antioxidant activity
Yusang Chen, Meiling Wu, Xiaohong Xu, Shunyao Zhu, M.-H. Herman Shen, Anting Ma, Z. W. She, Senlin Shi, Xi Han, Ting Zhang
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.