文献类型：期刊文献

英文题名：Physicochemical Changes and Antioxidant Activity Prediction Model of Corn/Ginger-Based Extrudates during a Long Term Storage

作者：Huang, Chengkang[1];Zhang, Jian[1,3];Liu, Shaowei[1];Tang, Xiaozhi[2];Lu, Yanhua[1];Kong, Lina[1]

机构：[1]E China Univ Sci & Technol, Dept Biol Engn, State Key Lab Bioreactor Engn, Shanghai 200030, Peoples R China;[2]Nanjing Univ Finace & Econ, Coll Food Sci & Engn, Nanjing 210046, Jiangsu, Peoples R China;[3]Shandong Marine Resource & Environm Res Inst, Yantai 264006, Shandong, Peoples R China

年份：2015

卷号：21

期号：5

起止页码：715

外文期刊名：FOOD SCIENCE AND TECHNOLOGY RESEARCH

收录：;EI(收录号：20154901645613);WOS:【SCI-EXPANDED(收录号:WOS:000362700400010)】；

基金：This study was sponsored by the National Research Funds for the Central Universities and Baoshan Association for Science & Technology of Shanghai.

语种：英文

外文关键词：antioxidant activity; hardness; crispness; back propagation artificial neural network

摘要：Texture characteristics and antioxidant activities (AOA) of extrudates were stored under different conditions that were detected by texture profile analysis (TPA) and DPPH* method in this study. The physicochemical properties of extrudates were significantly affected by storage time and temperature. The hardness values of extrudates stored at 0 degrees C were the highest, while the crispness values of it were the lowest. The AOA decreased significantly from 20.23% to 14.87% with temperature increasing from -10 degrees C to 25 degrees C. The back propagation Artificial Neural Network (bp-ANN) was used to predict the AOA from hardness and crispness. The optimized model structures had two hidden layers, one with ten neurons per layer (R-2 >= 0.999) and another one with eight neurons per layer (R-2 >= 0.993). The ANN model was a better predictor of AOA from texture characteristics than linear fitting model (AOA vs. hardness: R-2=0.913; AOA vs. crispness: R-2=0.952).