Sigurðardóttir Andrea Rakel, Sveinsdóttir Hildur Inga, Schultz Nette, Einarsson Hafsteinn, Gudjónsdóttir María
Faculty of Food Science and Nutrition, University of Iceland, Sæmundargata 12, 102 Reykjavík, Iceland.
Matís, Food and Biotech R&D, Vínlandsleið 12, 113 Reykjavík, Iceland.
Foods. 2024 Sep 18;13(18):2952. doi: 10.3390/foods13182952.
Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.
线虫给鱼类加工行业带来了重大挑战，尤其是在白鱼加工方面。尽管技术有所进步，但该行业在检测和去除线虫方面仍依赖人工。本研究探讨了自动检测线虫并将其与鱼片上其他常见缺陷（如残留的鱼皮和血斑）区分开来的初步步骤。使用先进的多光谱成像（MSI）系统VideometerLab 4在受控条件下采集了50片大西洋鳕鱼鱼片的270张图像。总共使用分割一切模型（SAM）标记了173条线虫，该模型经过训练，仅需从少数代表性像素就能自动分割出感兴趣的物体。利用获取的数据集，我们研究了通过线虫的光谱特征识别它们的潜力。我们纳入了归一化典型判别分析（nCDA）来开发分割模型，训练该模型以区分鱼片内的不同成分。通过纳入多个分割模型，我们旨在在假阴性和假阳性之间取得令人满意的平衡。对于我们标注的测试数据，这带来了88%的精度和79%的召回率。这种方法可以通过准确识别有线虫的鱼片来改善过程控制。使用多光谱成像可减少对质量良好的鱼片进行不必要的检查，同时提高产品安全性和质量。
