Kono, S., Kawamura, I., Yamagami, S., Araki, T., and Sagara, Y. 2016. ANN modeling for optimum storage condition based on viscoelastic characteristics and sensory evaluation of frozen cooked rice. ⇒ScienceDirect  ⇒ResearchGate International Journal of Refrigeration 65: 218-227.（査読有） Highlights • Optimum frozen conditions of cooked rice have been predicted by ANN. • Tensipresser was a useful tool for predicting the scores for textural evaluation. • Deterioration of cooked rice attributed to the change of viscoelasticity. • Storage conditions affected the measured values of hardness and adhesiveness • Viscoelastic deteriorations also depended on the thawing methods. Abstract The optimum frozen condition of cooked rice has been predicted by artificial neural network (ANN) based on the data obtained from sensory evaluations as well as viscoelastic measurements. Cooked rice was frozen and stored at −5, −15, and −45 °C for 0, 10, 30, 60 and 90 days. Then, after the samples were thawed by natural convection air at room temperature or microwave heating, the viscoelastic parameters were measured with the Tensipresser and sensory scores were evaluated by a 7-point scale. The sensory scores were predicted with high accuracy from the viscoelastic parameters by ANN models. In addition, the ANN model …