An Analysis of Sensory Tested Data of Sake by a Neural Network

Sensory tested data of sake were analyzed by a back-propagation method of a neural network, where the input data were trained for the output of sake-categories. To optimize the training condition, effects of the numbers of training iteration and the unit of hidden layer were examined. The accuracy of discrimination by the neural network was better than that by the discriminant analysis under these conditions. The characteristics of the categories were described from the weight coefficient analysis between input and output layers. These results show a new point of view to evaluate the qualities of sake, which is derived from the non-linearity of the neural network.