## 3 Results and Discussion
### Accuracy Comparison of Single Feature Classification under Different Classification Methods  
To determine which features contribute most to the classification of fish meal quality, 21 fish meal samples of different storage time grades of each group were randomly selected as training sets (total of 126 samples) to train fish meal samples with different qualities, and the remaining 9 samples (total of 54 samples) of each group were employed as prediction sets. The integral value (INV), wavelet energy value (WEV), maximum gradient value (MGV), average differential value (ADV), relative steady-state response average value (RSAV), and variance value (VARV) were employed as characteristic parameters to model. With 10 sensor response data as the input, and fish meal samples at different storage time levels as the output, the classification accuracy of various eigenvalue was tested using a multi-layer perceptron neural network classification method, random forest classification method, K-nearest neighbor algorithm (K = 3), support vector machine algorithm, Naive Bayes classification method, and five recognition modes. The test results were listed in Table 5.  
Figure 4: Linear discriminant analysis (LDA) results for different characteristic values (integral value (**a**); wavelet energy value (**b**); maximum gradient value (**c**); average differential value (**d**); relation steady-state response average value (**e**); and variance value (**f**)). As shown in Table 5, the highest accuracy rate was obtained using the random forest classification algorithm. The lowest classification accuracy rate was obtained using the support vector machine algorithm. This finding may be attributed to the fact that the RF classification method [42] is an integrated learning algorithm that consists of multiple decision trees (DTs). The algorithm is suitable for processing high-dimensional data and runs relatively fast. Its execution process is described as follows: First, the N-group training set is extracted from the original data; second, the N-group training set is used to construct N decision trees. During the growth process of each tree, m (m \(<\) M) characteristic variables are randomly selected from all M characteristic variables to divide the internal nodes. Last, the prediction results of N decision trees are collected and the category of new samples is determined by voting. For the MLPNN (multilayer perceptron neural network) classifier, the highest accuracy of discriminating different quality fish meal samples was obtained using the relation steady-state response average value as a feature vector, and the lowest accuracy of discriminating different quality fish meal samples was obtained using the maximum gradient value as a feature vector. For the RF classifier, the highest accuracy was obtained using the integral value and the wavelet energy value, as features to discriminate the different quality fish meal samples, and the lowest accuracy was obtained using the maximum gradient value as an eigenvector to discriminate different quality fish meal samples. For the KNN classifier, the highest accuracy of discriminating fish meal samples, with different qualities, was obtained using the relation steady-state response average value as a feature vector, and the lowest accuracy of discriminating fish meal samples with different qualities was obtained using the maximum gradient value as a feature vector. For the SVM classifier, the highest accuracy was obtained using the integral value as a feature vector to discriminate different quality fish meal samples, and the lowest accuracy was obtained using the variance value as a feature vector to discriminate different quality fish meal samples.