Optimizing the extraction process of total saponins from Panax vietnamensis based on response surface analysis and backpropagation neural network-genetic algorithm

Abstract

Panax vietnamensis (PV), a member of the Araliaceae family, is noted for its saponin-rich roots and rhizomes. Recent pharmacological studies have shown its anti-inflammatory, anti-cancer, anti-myocardial ischemia, and hypoglycemic effects, establishing PV as a highly valuable variety of ginseng worldwide. Herein, we used an ultrasound-assisted method to optimize the extraction of total saponins from PV, and developed a genetic algorithm (GA) to optimize the backpropagation (BP) neural network model and predict the best process conditions. The best process with an ethanol concentration of 65.8 %, extraction times of 4.0, ultrasonic temperature of 40.7°C, and an extraction rate of 4.4103 % was achieved through response surface optimization. Conversely, the best process optimized by the GA-BP neural network model had an ethanol concentration of 70.0 %, extraction times of 4.0, ultrasonic temperature of 41.0°C, and an extraction rate of 4.5492 %. Moreover, 254 terpenoids, consisting of 182 triterpenes, 33 monoterpenes, 19 sesquiterpenes, and 20 diterpenes, along with various types of saponins, were identified through ultra-high performance liquid chromatography-MS/MS (UPLC-MS/MS). Specifically, 87 dammarane-type saponins were detected, including 46 protopanaxadiol types, 41 protopanaxatriol types, 5 octilonol types, and 18 oleanolic acid types. The total saponin extraction rate achieved through the GA-BP neural network model surpassed that of the response surface optimization process, indicating the superiority and stability of the GA-BP neural network optimized process and thus revealing the optimal method for PV total saponin extraction. The comprehensive analysis of PV saponins using UPLC-MS/MS enhanced our understanding of total saponins in PV, laying the foundations for the strategic development and utilization of PV plant resources.