Nondestructive evaluation of fish meat using ultrasound signals and machine learning methods
2020
Tokunaga, Kazuhiro | Saeki, Chihiro | Taniguchi, Shinichi | Nakano, Shinta | Ohta, Hiromitsu | Nakamura, Makoto
In this study, we propose a new method for the nondestructive measurement of the fat content and texture of fish meat using machine learning and the bag of features approach. We employed two machine learning methods, that is, a self-organizing map (SOM) and radial basis function (RBF) network. The SOM was applied to symbolize the pattern of the frequency spectrum extracted from ultrasound signals and to generate key features for the bag of features technique. The RBF network was applied to estimate the fat content and texture of fish meat from the bag of features histogram. We verified the accuracy of the fat content and texture estimations given by the proposed method through a series of experiments. The results showed that the fat content and texture of fish meat was estimated more accurately using the proposed method than by the conventional approach.
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