Phaleria macrocarpa is historically treasured remedy for treating various allergies, infections and health complications. Commercial availability of this plant extract, however, remains limited as conventional phytochemical extraction techniques require prolong extraction time, high consumption of solvents, in conjunction to being energy intensive. Herein, this study aimed to statistically optimize the ultrasonic-assisted extraction (UAE) of phalerin from P. macrocarpa using the Box–Behnken design (BBD) and the predictive capability of this approach was compared to a model derived from artificial neural network (ANN). In the optimization experiment, for only three relevant UAE parameters viz. solvent ratio, extraction temperature and solid-to-solvent ratio were examined, for the response of the highest extraction of phalerin. Under an optimized condition (R² = 0.98) [71% methanol, 1:45 solid-to-solvent ratio (g/mL) and extraction temperature of 47 °C], a satisfactory amount of 4.26 ± 0.51 mg/g of phalerin was attained. Comparison between the RSM and ANN revealed the latter being a better predictive model and yielded an appreciably higher predictive capability (R² = 0.99) in terms of average absolute deviation, AAD (0.24%) versus RSM (AAD = 1.03%).

鸡蛋花在历史上是用于治疗各种过敏，感染和健康并发症的珍贵药物。然而，由于常规的植物化学提取技术需要延长的提取时间，高的溶剂消耗以及高能量消耗，因此该植物提取物的商业可用性仍然受到限制。在此，本研究旨在统计学优化大果carp中鱼肝素的超声辅助提取（UAE）。使用Box–Behnken设计（BBD），并将该方法的预测能力与源自人工神经网络（ANN）的模型进行了比较。在优化实验中，仅针对三个相关的阿联酋参数。考察了溶剂比，提取温度和固-溶剂比，以求得最大的法呢素提取反应。在优化的条件下（R ²  = 0.98）[71％甲醇，1：45的固溶比（g / mL）和提取温度47°C]，有效量为4.26±0.51 mg / g的phalerin达到了。RSM和ANN之间的比较表明，后者是一个更好的预测模型，并产生了明显更高的预测能力（R ² 就平均绝对偏差而言，AAD（0.24％）对RSM（AAD = 1.03％）= 0.99）。