School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China.
Academy of Food Interdisciplinary Science, Dalian Polytechnic University, Qinggongyuan1, Ganjingzi District, Dalian, China.
J Food Sci. 2022 Jun;87(6):2663-2677. doi: 10.1111/1750-3841.16144. Epub 2022 Apr 27.
The surface of carp is easily damaged during the descaling process, which jeopardizes the quality and safety of carp products. Damage recognition realized by manual detection is an important factor restricting the automation in the pretreatment. For the commonly used methods of mechanical and water-jet descaling, damage area recognition according to the hyperspectral data was proposed. Two discrimination models, including decision tree (DT) and self-organizing feature mapping (SOM), were established to recognize the damaged and normal descaling area with the average spectral value. The damage-discrimination model based on DT was determined to be the optimal one, which possessed the best model performance (accuracy = 96.7%, sensitivity = 96.7%, specificity = 96.7%, F1-score = 96.7%). Considering the efficiency and precision of damage-area recognition and visualization, the principal component analysis (PCA) combined with pixel values statistical analysis was used to reduce the dimension of hyperspectral images at the image level. Through statistical analysis, the value 0 was used as the threshold to distinguish the normal area and the damaged area in the PC image to achieve preliminary segmentation. Then, the spectral values of the initially discriminated damage area were input into the DT discrimination model to realize the final discriminant of damaged area. On this basis, the position information of the damaged area could be used to realize the visualization. The final visualization maps for mechanical and water-jet descaling damage were obtained by image morphology processing. The average recognition accuracy can reach 94.9% and 90.3%, respectively. The results revealed that the hyperspectral imaging technique has great potential to recognize the carp damage area nondestructively and accurately under descaling processing. PRACTICAL APPLICATION: This study demonstrated that hyperspectral imaging technique can realize the carp damage area detection nondestructively and accurately under descaling processing. With the advantages of nondestructive and rapid, hyperspectral imaging system and the method can be widely expanded and applied to the quality detection of other freshwater fish pretreatment.
鲤鱼在去皮过程中表面容易受损，这会危及鲤鱼产品的质量和安全。通过人工检测实现的损伤识别是限制预处理自动化的一个重要因素。对于常用的机械去皮和水力喷射去皮方法，提出了基于高光谱数据的损伤区域识别方法。建立了基于决策树（DT）和自组织特征映射（SOM）的两种判别模型，采用平均光谱值来识别去皮区的损伤和正常区域。基于 DT 的损伤判别模型被确定为最优模型，具有最佳的模型性能（准确率=96.7%，灵敏度=96.7%，特异性=96.7%，F1 分数=96.7%）。考虑到损伤区域识别和可视化的效率和精度，在图像层面上采用主成分分析（PCA）结合像素值统计分析来降低高光谱图像的维度。通过统计分析，将 0 值作为阈值，在 PC 图像中区分正常区域和损伤区域，实现初步分割。然后，将初步判别损伤区域的光谱值输入 DT 判别模型，实现损伤区域的最终判别。在此基础上，可利用损伤区域的位置信息实现可视化。通过图像形态学处理，得到机械去皮和水力喷射去皮损伤的最终可视化图谱。平均识别准确率分别可达 94.9%和 90.3%。结果表明，高光谱成像技术在去皮加工过程中具有无损、准确识别鲤鱼损伤区域的潜力。实际应用：本研究表明，高光谱成像技术可在去皮加工过程中实现鲤鱼损伤区域的无损、准确检测。高光谱成像系统具有无损、快速的优点，该方法可广泛扩展应用于其他淡水鱼预处理的质量检测。
