## 3 Results and Discussion
### Visualization of White Shrimp on Different Refrigeration Days  
In order to intuitively judge the white shrimps of different refrigeration days, it is essential to categorize the pixels of hyperspectral images and visualize the categorization effect. In the previous studies, a number of researchers utilized object-wise and pixel-wise methods to implement the visualization of classification effects. The object-wise method treats the whole object as a pixel and employs the average spectral data of all pixels as the spectral data of the sample. The pixel-wise method takes the predicted category of single-point pixel data as the value of each pixel in the sample.  
[PERSON] et al. (2019) utilized variable importance in projection (VIP) to extract characteristic wavelengths and then used PLS-DA to build the model. Viable soybean seeds and the non-viable soybean seeds were categorized and visualized based on the pixel-wise method and the object-wise method, and it was shown that the pixel-wise method of classification cannot detect the non-viable seeds with high precision, but the object-wise method achieved better classification results. In the actual grading of freshness, the degree of degradation of protein and fat varies with the number of days of refrigeration. If the pixel-wise method is employed, some white shrimps with similar spectral characteristics but different refrigeration days may be misjudged, while the use of the object-wise method can avoid the defect. Moreover, the freshness of shrimps is a concept related to the whole shrimp. Therefore, each pixel in the images of white shrimps cannot be considered to represent the freshness of the shrimp. Thus, the present research employed the object-wise method to achieve visualization of white shrimp with different days of refrigeration.  
Every day, eight samples were randomly selected from the 7-day experimental samples, and the original hyperspectral images of a total of 56 samples were input into the SNV-CARS-ELM model and visualized by the object-wise method. Figure 9 shows the visual classification of white shrimp for different days of refrigeration, where each category corresponds to a number in the right-hand column, where the background value is 0. It is clear from the figure that the majority of the classification of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Accuracy of Calibration**} & **Accuracy of Prediction** \\  & & **Set (CCR/\%)** & **Set (CCR/\%)** \\ \hline SNV-RFA-PLS-DA & 7 & 85.08 & 82.86 \\ SNV-UVE-PLS-DA & 26 & 90.16 & 85.71 \\ SNV-CARS-PLS-DA & 3 & 92.06 & 88.57 \\ \hline SNV-RFA-LSSVM & (1, 1) & 93.65 & 90.48 \\ SNV-UVE-LSSVM & (0.6, 7) & 94.60 & 92.38 \\ SNV-CARS-LSSVM & (1.4, 5) & 94.92 & 93.33 \\ \hline SNV-RFA-RF & (550, 14) & 94.60 & 93.33 \\ SNV-UVE-RF & (600, 17) & 96.83 & 95.24 \\ SNV-CARS-RF & (500, 15) & 98.41 & 97.14 \\ \hline SNV-RFA-ELM & 79 & 95.56 & 95.24 \\ SNV-UVE-ELM & 67 & 98.73 & 97.14 \\ SNV-CARS-ELM & 42 & 99.05 & 98.10 \\ \hline \hline \end{tabular}  
* Parameter for partial least squares discrimination analysis (PLS-DA) is number of latent variables (LV), parameters for least squares support vector machine (LSSVM) are penalty coefficient (c) and kernel range (\(\sigma\)), parameters for random forest (RF) are the number of trees (ntree) and the number of features (mtry), and parameter for extreme learning machine (ELM) is the number of hidden layer neurons (L).  
\end{table}
Table 4: Modeling analysis based on characteristic wavelengths.  
white shrimp is correct. One white shrimp category on day 7 was misidentified with an aggregate classification accuracy of 97.92% (47/48). Therefore, the model developed in this study is reliable. In addition, the visualized classification diagram can intuitively and accurately estimate the different refrigeration days of white shrimps, which facilitates quick detection and timely elimination of non-fresh white shrimp, thus ensuring their freshness.