## Results
### E-nose for TSS Detection of Orange  
Another portable detection method (E-nose) was also applied to test the same orange samples. In accordance with previous research [25; 26; 27], 5 commonly used feature extraction methods (75 s value, average value, average of differential value, integral value, and maximum value) and combined feature were applied in this research. The 75 s value is the value of the E-nose response curve at 75 s for each sampling time. The average value is the average of a complete response value. The average of the differential value is the mean value of the differential of the E-nose response curve for each sampling time. The integral value is the area formed by the E-nose response curve and coordinate axis. The combined feature is the combination of the 5 commonly used feature extraction methods previously listed. The PLSR detection results of orange TSS based on E-nose analysis with different features is shown in Table 4. For PLSR results based on single features, the R\({}^{2}\)cals are less than R\({}^{2}\)cals. We can infer that results based on single features cannot provide enough information to guarantee the stability of detection accuracy. Thus, the error ratio of calibration set is higher than validation set, due to the sample number of calibration set being more than the validation set. However, the PLSR detection ability based on combined features is significantly superior to detection ability based on a single feature. The reason could be that the combined features cover more comprehensive information than a single feature. The PLSR detection results of orange TSS based on E-nose with combined features is shown in Figure 6. The R\({}^{2}\), RMSE, and slope of the calibration set of PLSR detection are 0.5345, 0.7071, 0.5345, respectively, of the validation set of PLSR detection are 0.4282, 0.8632, 0.6169, respectively. Thus, certain useful orange TSS detection information was contained in E-nose data, however, the improvement of orange TSS detection accuracy is needed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **F1** & **F2** & **F3** & **F4** & **F5** & **Combined Feature** \\ \hline \multicolumn{2}{c}{**R\({}^{2}\)cal**} & 0.1298 & 0.1438 & 0.1326 & 0.1442 & 0.1868 & 0.5345 \\ \multicolumn{2}{c}{**R\({}^{2}\)cal**} & 0.3333 & 0.2873 & 0.3297 & 0.2868 & 0.4331 & 0.4282 \\ \hline \hline \end{tabular} Note: F1–5 are 75 s value, average value, average of differential value, integral value, and maximum value, respectively.  
\end{table} 
Table 4: PLSR detection results of TSS based on E-nose with different feature extraction methods. Figure 6: PLSR detection results of TSS based on E-nose with combined feature extraction method. To check the necessity of low relation feature elimination for E-nose data, which may further improve the detection ability of orange TSS, MI between E-nose features and TSS were also calculated. The calculation results are shown in Figure 7. Features 1-10 are the 75 s value of the 10 sensors' responses, respectively; features 11-20 are the average value of the 10 sensors' responses, respectively; features 21-30 are the average of the differential values of the 10 sensors' responses, respectively; features 31-40 are the integral value of the 10 sensors' responses, respectively, and features 41-50 are the max values of the 10 sensors' responses, respectively. The MI curves of the 10 sensors are similar for different feature extraction methods. However, for each sensor, its MI value still has a certain difference when feature extraction method is changed. Thus, the correlation between feature and TSS is most influenced by sensor, more so than the feature extraction method. The range of MIs was from 0.6006 to 0.7678.