Food Science and Technology Research
Online ISSN : 1881-3984
Print ISSN : 1344-6606
ISSN-L : 1344-6606
Original papers
Physicochemical Changes and Antioxidant Activity Prediction Model of Corn/Ginger-Based Extrudates during a Long Term Storage
Chengkang HuangJian ZhangShaowei LiuXiaozhi TangYanhua LuLina Kong
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2015 Volume 21 Issue 5 Pages 715-725

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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).

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© 2015 by Japanese Society for Food Science and Technology
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