Abstract 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.

中文翻译：

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使用超声波信号和机器学习方法对鱼肉进行无损评估

摘要 在这项研究中，我们提出了一种使用机器学习和特征袋方法无损测量鱼肉脂肪含量和质地的新方法。我们采用了两种机器学习方法，即自组织映射 (SOM) 和径向基函数 (RBF) 网络。SOM 用于符号化从超声信号中提取的频谱模式，并为特征包技术生成关键特征。应用 RBF 网络从特征直方图袋中估计鱼肉的脂肪含量和质地。我们通过一系列实验验证了所提出的方法给出的脂肪含量和质地估计的准确性。