The food industry makes an effort for development of food products to satisfy various needs of consumers. They must evaluate foods from various viewpoints to satisfy the sensibility of consumers: a physical characteristic, a chemical, nourishment, price, seasoning, temperature, etc. To secure reproducibility by evaluating the features of the food product quantitatively and objectively is useful to establish the manufacturing method of the food product. We pay attention to the food index that the quantitative and objective evaluation with the machinery has not been carried out although the panel test has been carried out, and suggest a new method to mechanize this. We focus on chewy texture that are important property to decide the taste, and suggest a method to check hard/soft distribution in an elastic food. In this article, we explain an application method of the new technique for an example by the design of rice noodle.
Recently there is a growing need for affect-awareness in computer games. In-game emotion recognition, however, requires applying costly feature extraction methods and/or labor-demanding annotation of large datasets. To make emotion recognition cost-efficient, this study proposes (A) a social relations-directing, “emotion-sensitive” dialogue act model consisting of social acts, and (B) an approach for emotion recognition, utilizing the association strength ratio of emotion types and the proposed acts. In the study, five Japanese in-game dialogues were tagged with labels of emotion types and of social acts. Emotion type-social act co-occurrence was analyzed, and the corresponding association strength ratios were computed. In a validation experiment, the proposed approach was tested, enhancing a baseline emotion classifier's recognition accuracy by 8%.