## 3 Results and Discussion
### Comparison with Other Techniques  
In this part, we compared the proposed method with other state-of-art techniques. In traditional methods, features extraction and classification recognition are usually divided into two separate parts, however, CNN-AFE is an approximate end-to-end model which integrates features extraction and classification recognition into a same architecture. We introduce the comparison methods as follows.  
As for features extraction, techniques such as features of raw response [15], peak-inflection point [10; 16], Discrete wavelet transform (DWT) [17], Discrete cosine transform (DCT) [18], singular value decomposition (SVD) [19] and DCT fused with SVD (DCT + SVD) [20] were utilized as comparison methods. In terms of pattern recognition, several classifiers such as support vector machine (SVM) [9; 12], the \(k\)-nearest neighbor (\(k\)-NN) [13], and random forest (RF) [14] were adopted to compare with the method we proposed. After parameter optimization, we show the best classification accuracy of the comparison algorithm in Table 3, the experimental results (average accuracy \(\pm\) standard deviation) are based on the five-fold cross-validation. The SVD features extraction method performs better (about 98.8%) and the raw response features extraction performs worse (about 80%) relatively.  
It is noteworthy that the test accuracy rate of CNN-AFE with L2 regularization term is over 99.5% regardless of the size of the batch and the loss function, and the highest average recognition rate achieved 99.9%, which demonstrates that CNN-AFE provides a more effective solution to the tea classification problem. Further, we believe that the proposed method would make more prominent contributions in the field of liquid classification and identification.  
Considering all the features obtained are high-dimensional whether the proposed method or traditional methods. Therefore, we applied principal component analysis to compress the features and observe the features distribution image. In detail, as shown in Figure 9, red diamonds, blue tones, blue-green squares, black circles and green plus represent Yuzhu tea, Show bud tea, Biluochun tea, Westlake tea, and Tieguanyin tea, respectively. Figure 9a-f show the two-dimensional features distribution for features of raw response, peak-inflection point, Discrete wavelet transform (DWT),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Batch Size** & **Loss Function** & **L2 Regularization** & **Testing Accuracy** \\ \hline \multirow{4}{*}{16} & & Yes & 99.91\% \\  & C & No & 97.15\% \\  & H & Yes & 99.83\% \\  & H & No & 97.05\% \\ \hline \multirow{4}{*}{32} & & Yes & 99.92\% \\  & C & No & 96.90\% \\  & H & Yes & 99.70\% \\  & H & No & 96.50\% \\ \hline \multirow{4}{*}{64} & & Yes & 99.80\% \\  & C & No & 97.82\% \\ \cline{1-1}  & & Yes & 99.90\% \\ \cline{1-1}  & H & No & 98.35\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing accuracy of different parameters selections in CNN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ClassifierMethods** & **Raw Response** & **Peak-Inflection Point** & **DWT** & **SVD** & **DCT** & **DCT + SVD** \\ \hline \multirow{3}{*}{SVM} & 80.87\% & 95.57\% & 97.80\% & 98.83\% & 94.66\% & 98.96\% \\  & \(\pm\)0.0028 & \(\pm\)0.0034 & \(\pm\)0.0265 & \(\pm\)0.0004 & \(\pm\)0.0340 & \(\pm\)0.0127 \\ \cline{1-1}  & 80.00\% & 95.65  
Discrete cosine transform (DCT), singular value decomposition (SVD) and CNN-AFE, respectively. Compared with other methods in terms of visual effects, in the PCA diagram of the proposed method, different types of tea are more dispersed and the same type of tea is more compactly polymerized. Thus, brings the improvement of classification accuracy.