No abstract

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.