ANN modeling for optimum storage condition based on viscoelastic characteristics and sensory evaluation of frozen cooked rice

• 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.

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 analysis using the dataset of storage conditions and palatability scores showed that to achieve palatability score greater than the central value of 4.0 after 40 days, storage temperature must be below −25 °C if air thawing by natural convection is used and below −15 °C if microwave thawing and heating are used.