## 4 Conclusions  
In this study, the NIR hyperspectral imaging system of 866.4-1701.0 nm was chosen to obtain hyperspectral cube data of white shrimps with different refrigeration days. The models were established by combining multivariate methods, and the white shrimps with different refrigeration days were visualized. The main conclusions are as follows:  
1. The raw spectral data were pre-processed by SG1, MSC, and SNV, after which the corresponding data were entered into PLS-DA, LSSVM, RF, and ELM discriminant models for full wavelength modeling analysis. The results demonstrated that the SNV-processed spectral data had the best performance among the four discriminant models (the accuracy of the prediction set was better than the other two pre-processing methods and the raw spectral data). Therefore, the SNV-processed spectral data were chosen for subsequent characteristic wavelength extraction so as to dimensionally downscale the raw spectra and further improve the model performance.
2. The SNV-processed spectral data were extracted by RFA, UVE, and CARS, respectively, and the characteristic wavelengths corresponding to the data were entered into PLS-DA, LSSVM, RF, and ELM discriminant models for modeling and analysis. The comparison with the results of the full wavelength modeling analysis reveals improved modeling results based on characteristic wavelengths, which indicates that the characteristic wavelengths extracted by the three characteristic wavelength extraction algorithms are robust. Additional analyses showed that the combination of CARS and discriminant models had the best performance among the three characteristic wavelength extraction algorithms, while the ELM had the best classification performance among the four discriminant models. Therefore, the SNV-CARS-ELM model was finally chosen as the optimal model.
3. To intuitively judge the white shrimps of different refrigeration days, a total of 56 random original hyperspectral images were input into the SNV-CARS-ELM model and visualized by the object-wise method. The classification visualization results showed that only 1 sample out of the  
Figure 9: Visualization of White Shrimp on Different Refrigeration Days.  
total of 48 white shrimp samples was misclassified with an aggregate classification accuracy of 97.92%, which indicates the reliability of the chosen model.  
The present results indicate that the combination of NIR hyperspectral imaging technology and multivariate methods is feasible to identify the freshness of white shrimp, which could provide a theoretical foundation for the development of a system for online determination of shrimp freshness by using hyperspectral imaging.  
[PERSON] conducted the experiments, analyzed the data, and wrote the paper. [PERSON], [PERSON], Q.D. and [PERSON] prepared the experimental materials and analyzed the data. [PERSON] conceived and designed the experiments. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by China's national key research and development program (Project No.: 2017 YFE0122100).  
We are deeply grateful to the reviewers and the editors for their valuable comments and suggestions, which improved the technical content and presentation of the paper.  
The authors declare no conflicts of interest.