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

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.