## 3 Results and Discussion  
### E-Nose Analysis Combined with PCA and LDA  
PCA, as a pattern recognition method, can show the differences in the data [24]. The larger the total variance of PCA, the better the original data reflect [25]. The stable response  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Array No.** & **Sensor Name** & **Performance Description** \\ \hline S1 & W1C & Sensitive to aromatic benzene \\ S2 & W5S & Very sensitive to nitrogen oxides, especially negative to \\  & nitrogen oxides \\ S3 & W3C & Ammonia, sensitive to aromatic components \\ S4 & W6S & Mainly selective to hydrides \\ S5 & W5C & Short-chain alkanes, aromatic compounds sensitive \\ S6 & W1S & Sensitive to methyls \\ S7 & W1W & Sensitive to inorganic sulfides and terpenes \\ S8 & W2S & Sensitive to alcohols, aldehydes, and ketones \\ S9 & W2W & Aromatic ingredients, sensitive to organic sulfur compounds \\ S10 & W3S & Sensitive to long-chain alkanes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance description of E-nose sensors.  
values at 80, 85, and 90 s of the SAEOs extracted by different methods in the E-nose were collected for PCA. The PCA results for the response values at 80, 85, and 90 s showed that the total variance of the sample was 98.51, 98.45, and 98.44%, respectively. The data of 80 s, which best reflected the totality of the data, were selected as feature data, and are shown as a radar map and column chart in Figure 1.  
As shown in Figure 1a, the response values of S6 and S8 of the SAEOs extracted by SCD and ESE were higher than those of SAEOs extracted by the other two methods, whereas the response value of S2 in SAEOs extracted by SE was the highest. This may be attributed to the high concentration of aromatic compounds, which could affect the response values of different sensors [26]. Then, the eigenvalues of SAEOs extracted using different extraction methods were analysed by PCA. In Figure 2a, the principal components PC1 and PC2 represented 94.88% and 3.63% of the total variance, respectively, indicating that they represent the whole sample. The SAEOs were divided into three types by E-nose PCA analysis with ESE, SCD, HD, and SE, which was consistent with the results shown in the E-nose radar diagram (Figure 1b). Interestingly, the SAEOs extracted by ESE and CSD could be distinguished, whereas those extracted by HD and SE had a partial overlap. According to the LDA analysis (Figure 2b), PC1 and PC2 represented 86.81% and 9.45% of the total variance, respectively, representing the entire sample [27]. The SAEOs extracted by the four methods showed good separability and were classified into three types based on the LDA diagram. The first type was HD, the second type was SE, and the third type was ESE and SCD. It could be seen that the E-nose combined with LDA clearly distinguished the SAEOs extracted by HD and SE, which constituted the SAEOs that PCA could not distinguish. Generally, the E-nose combined with PCA and LDA could effectively distinguish the SAEOs extracted by the four extraction methods.  
Figure 1: (a) Column chart and (b) radar map of the E-nose of SAEOs extracted by different methods.  
Figure 2: (a) PCA analysis of the E–nose of SAEOs extracted by different methods. (b) LDA of the E-nose of SAEOs extracted by different methods.