外文摘要：This study aimed at using alpha-L-arabinofuranosidase CcABF to improve the clarity and active substances in fermented ginkgo kernel juice by artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. A credible three-layer feedforward ANN model was established to predict the optimal parameters for CcABF clarification. The experiments proved the highest transmittance of 89.40% for fermented ginkgo kernel juice with this understanding, which exhibited a 25.56% increase over the unclarified group. With the clarification of CcABF, the antioxidant capacity in juice was enhanced with the increase of total phenolic and flavone contents, and the maximum DPPH and hydroxyl radical scavenging rates were increased by 89.71% and 26.65%, respectively. The contents of toxic ginkgolic acids declined markedly, while the active ingredients of ginkgetin and ginkgolide B showed a modest increase. Moreover, changes in free amino acids and volatile compounds improved the nutritive value and flavor of clarified fermented ginkgo kernel juice.
外文关键词：Artificial Neural Network (ANN);Clarification;alpha-L-arabinofuranosidase;Ginkgo kernel juice;Active substance
作者：Yang, Jie;Zhou, Jing;Chen, Jinling;Wang, Qiqi;Xu, Linxiang;Huo, Dongming;Wei, Zhen
作者单位：Jiangsu Ocean Univ;Lianyungang Comprehens Inspect & Testing Ctr Qual;Jiangsu Dingweitai Food Joint Stock Ltd Corp
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：450
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台