## 3 Results
### Ablation Experiment  
In the RSEM module, we optimized the WGAN by using convolutional layers instead of the original linear layers and using Spectral Normalization instead of Batch Normalization. Therefore, it is necessary to conduct separate ablation experiments on the RSEM module. We designed four sets of experiments: one was set as the baseline, and the other three sets used the control variable method to control the optimized plan. The results of the ablation experiment are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Choice** & **Spectral Norm** & **Convolutional** & **Pahogen** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{1} & & & _Vibrio parahaemolyticus_ & & 0.9091 & 0.9412 & 0.9249 \\  & & & _Escherichia coli_ & 0.9306 & 0.9054 & 0.9178 \\  & & & _Aerononas hydrophphal_ & 0.9324 & 0.9583 & 0.9452 \\  & & & _Aeronomas vermii_ & & 0.9143 & 0.8767 & 0.8951 \\ \hline \multirow{3}{*}{2} & & & _Vibrio parahaemolyticus_ & & 0.9302 & 0.9639 & 0.9467 \\  & & & _Escherichia coli_ & 0.9565 & 0.9296 & 0.9429 \\  & & & _Aerononas hydrophphal_ & 0.9429 & 0.9706 & 0.9565 \\  & & & _Aerononas veronii_ & & 0.9412 & 0.9014 & 0.9209 \\ \hline \multirow{3}{*}{3} & & & _Vibrio parahaemolyticus_ & & 0.9184 & 0.9574 & 0.9375 \\  & & & _Escherichia coli_ & 0.9545 & 0.9333 & 0.9438 \\  & & & _Aerononas hydrophphal_ & & 0.9556 & 0.9773 & 0.9663 \\  & & & _Aerononas veronii_ & & 0.9318 & 0.8913 & 0.9111 \\ \hline \multirow{3}{*}{4} & & & _Vibrio parahaemolyticus_ & & 0.9444 & 0.977 & 0.9605 \\  & & & _Escherichia coli_ & & 0.9636 & 0.9464 & 0.955 \\  & & & _Aerononas hydrophphal_ & & 0.9643 & 0.9818 & 0.973 \\  & & & _Aerononas veronii_ & & 0.9455 & 0.8966 & 0.9204 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of RSEM module ablation experiment.  
Figure 15: ROC curves and average curves of four pathogenic bacteria. The higher AUC values demonstrate that our proposed model is robust.  
The results described in Table 4 show that some of the optimization proofs we have made for the WGAN are successful. Compared to linear layers, convolutional layers have more receptive fields and can efficiently extract features from spectral data. Spectral normalization can solve the Lipschitz constraint problem in the WGAN, making training more stable.  
Furthermore, in order to verify the effectiveness of our overall model, we conducted four ablation experiments to demonstrate the impact of the RSEM and RSDM on the classification results. Those in the first group did not utilize any processing; those in the second group only used RSEM; those in the third group only used RSDM; and those in the fourth group used both RSEM and RSDM. The results of the ablation experiment are shown in Table 5.  
Comparing the first group with the second group, it can be seen that the RSEM can improve the classification results of shrimp pathogenic bacteria, and the improved accuracy reaches 4.65%. This shows that the size of the dataset has a great impact on network training. The larger the amount of data, the better it can avoid overfitting of the network. Comparing the first group and the third group shows that the RSDM can also improve accuracy, and the features of denoised data are easier to extract. Comparing the second group and the third group, it can be seen that the RSEM has a stronger effect on improving performance than does the RSDM. When the RSEM and RSDM are combined, the accuracy rate increases by 6.79%, and the other three indicators also increase significantly.