Abstract Developing new techniques to determine fish freshness and quality can enhance nutritional value of the overall household food basket. In this research, digital image analysis was utilized to assess the freshness of rainbow trout fish by tracing the color attributes of its eyes and gills. The image data were collected from left and right eyes and gills in a 10-day ice-storage duration, and color components were extracted in RGB, HSV, and L*a*b* color spaces. Analysis of variance revealed that the RGB components of both eyes and gills had a significant change towards getting brighter during the ice-storage. Feature extraction was fulfilled from the color spaces, and then artificial neural networks (ANNs) and support vector machines (SVMs) were applied for classification of the ice-storage durations. The overall accuracies of the developed models demonstrated that the ANN somewhat outperformed the SVM for both the extracted features from the eyes and gills. Moreover, the gills’ features could describe the variance in the storage durations more efficiently than those extracted from the eyes. Finally, it was concluded that the applied colorimetric system along with the developing models could be employed as a successful non-destructive approach for evaluation of fish freshness.

中文翻译：

---

使用多类人工神经网络和支持向量机根据眼睛和鳃颜色特征进行鱼类新鲜度分类

摘要 开发确定鱼新鲜度和质量的新技术可以提高整个家庭食品篮的营养价值。在这项研究中，通过追踪虹鳟鱼眼睛和鳃的颜色属性，利用数字图像分析来评估虹鳟鱼的新鲜度。在 10 天的冰储存期间从左右眼和鳃中收集图像数据，并在 RGB、HSV 和 L*a*b* 颜色空间中提取颜色分量。方差分析表明，在冰储存期间，眼睛和鳃的RGB分量都有显着的变亮变化。从颜色空间中提取特征，然后应用人工神经网络 (ANN) 和支持向量机 (SVM) 对冰储存持续时间进行分类。所开发模型的整体准确性表明，对于从眼睛和鳃中提取的特征，ANN 在某种程度上优于 SVM。此外，鳃的特征可以比从眼睛中提取的特征更有效地描述存储持续时间的变化。最后，得出的结论是，应用的比色系统和开发的模型可以作为一种成功的非破坏性方法来评估鱼的新鲜度。