# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 5 Discussion  
### Effect of Sensor Array Optimization on Extracted Gas Features  
In order to verify the effect of sensor array optimization on the gas features extracted by the E-nose, we used the principal component analysis method (PCA) to analyze the experimental gas samples. The gas features for PCA were signal amplitudes collected on the nine types of gas mixtures using the as designed E-nose. Figure 10 shows the PCA analysis results--the contributions of PC1 and PC2 were 87.15% and 1.2%, respectively, and the total contribution of the two principal components equaled 88.35%, which indicates that the initial sensor array had the ability to sense different gases. However, the classification of the nine gas samples could not be discriminated because of the obvious overlap among many gas samples (gases M2, M3, M5, and M6, and the circles around individual groups in the figure were manually added as a guide for the eye). Therefore, the initial sensor array of the E-nose needs to be optimized. Figure 11 shows the PCA analysis results of the gas features extracted by E-nose, with the sensor array optimized according to different single evaluation indicators. After the sensitivity evaluation, the sensors S1, S2, and S3 were suggested to be removed from the initial sensor array, so the gas features were extracted based on the remaining seven sensors. According to the PCA result (Figure 11a), its two components of PC1 and PC2 explained 88.3% and 4.17% of the variance among the gas samples, respectively, and accounted for 92.47% of the total variance. The classification of gas samples was, therefore, improved compared with that of the initial sensor array, and the overlaps only existed between samples M2 and M3, and M8 and M9. Optimizing the sensing array according to the selectivity indicator yielded the same results as the sensitivity, gi  
Figure 10: PCA analysis of gas features extracted by E-nose with the initial sensor array. (S1, S2, and S3) were selected (see Figure 11b). The experimental results after removing sensors S5, S8, and S9 from the initial sensor array, in accordance with the correlation indicator, are shown in Figure 11c. It can be observed that except for samples M1 and M3, which were able to be completely separated, the rest of the samples still had partial overlap. However, compared with the initial sensing array, the discrimination of the rest of the samples was improved, which indicated that the sensors S5, S8, and S9 may have brought some redundant information, and removing them was beneficial for the E-nose to extract gas features. Figure 11d shows the experimental results of the sensor array optimized according to the repeatability indicator. It can be seen that the gas feature extraction ability of the optimized sensor array was also improved compared with the initial sensor array. It is worth noting that the sensor array optimized according to the correlation indicator was not as good as the sensitivity and selectivity indicators for gas feature extraction. According to the calculation results of the EWM, the weight value of the correlation indicator was the lowest among the four indicators. An evaluation indicator with a lower weight value inevitably has a weaker impact on the performance of the E-nose, so the optimization effect of the sensor array based on this indicator was poor. This result also proved the rationality of the weight value assignment based on the EWM proposed in this paper. Figure 11: PCA analysis of gas features extracted by E-nose with sensor array optimized based on a single evaluation indicator: (**a**) sensitivity, (**b**) selectivity, (**c**) correlation, and (**d**) repeatability. Although the ability of E-nose to extract gas features was improved after the sensor array optimization according to the four single evaluation indicators, there were still some samples that overlapped each other (see Figure 11).