## 4 Conclusions  
In this paper, the response curves of the fish meal samples, with different storage times, were obtained using a self-made electronic nose. Most VOCS emitted from fish meal malodor  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**MLPNN**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Naive Bayes**} \\ \cline{2-10}  & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** \\ \hline INV & 91 & 91.4 & 100 & 90.7 & 90.5 & 74.1 & 84.09 & 70.83 & 90.5 & 77.8 \\ WEV & 96.7 & 84.5 & 100 & 90.7 & 87.3 & 64.8 & 78.03 & 68.75 & 87.3 & 83.3 \\ MGV & 72.1 & 72.4 & 100 & 83.3 & 83.3 & 61.1 & 62.12 & 54.17 & 81 & 70.4 \\ ADV & 91 & 91.4 & 100 & 85.2 & 92.9 & 70.4 & 78.03 & 56.25 & 89.7 & 88.9 \\ RSAV & 93.4 & 91.4 & 100 & 87 & 92.1 & 81.5 & 78.79 & 62.5 & 88.1 & 87 \\ VARV & 82 & 82.8 & 100 & 88.9 & 90.5 & 88.9 & 56.06 & 35.42 & 84.1 & 77.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy comparison of single feature classification for different classification methods.  
are reducing gases, and the fingerprints of the fish meal with different qualities differed, which enabled the electronic nose to distinguish between different quality fish meal.  
The discriminant result of the linear discriminant analysis was better than those of the principal component analysis. In this paper, the principal component analysis and linear discriminant analysis of fish meal quality were performed by selecting six features: Integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value, and variance value. By PCA and LDA analysis, the sum of the first principal component analysis value and the second principal component analysis value exceeded 85% when all features were considered to be parameters. The effect of using the relation steady-state response average value as the characteristic parameter was better, and the accuracy of linear discriminant analysis was 93.9%. The effect of using the maximum gradient value as the characteristic parameter was worst, and the correct rate of linear discriminant analysis was only 56.7%.  
To further determine the best and worst eigenvalues in fish meal quality detection, the classification methods of a multi-layer perceptron neural network, random forest classification, K-nearest neighbor algorithm, support vector machine algorithm, Bayesian classification method and five recognition modes were used to classify the fish meal quality, and the highest accuracy was obtained using the random forest classification algorithm. The highest accuracy was obtained using the integral value, relation steady-state response average value, and average differential value as eigenvectors, and the lowest accuracy was obtained using the maximum gradient value as eigenvectors.  
The results indicated that the electronic nose could classify the fish meal with different qualities. When selecting an optimal feature, the classification effect was better. Further research shows that the electronic nose has strong sensitivity and selectivity in detecting fish meal samples of different storage times.  
**Author Contributions:** Formal analysis, investigation, methodology and writing-original draft preparation, P.L.; data curation, Z.R. and K.S.; validation, writing-review and editing, H.T. and Z.N.; funding acquisition and project administration, Z.N.  
**Funding:** The work was supported by the Fundamental Research Funds for the Central Universities (2662018 PY081).  
**Conflicts of Interest:** The authors declare no conflict of interest.