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

Abstract

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°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°C to 25°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 (R² ≥ 0.999) and another one with eight neurons per layer (R² ≥ 0.993). The ANN model was a better predictor of AOA from texture characteristics than linear fitting model (AOA vs. hardness: R² = 0.913; AOA vs. crispness: R² =0.952).