## 4 Conclusions  
In this study, a self-assembled E-nose was used to evaluate the feasibility of early discrimination and prediction for _C. fimbata_-infected sweetpotatoes during the asymptomatic period. The results showed that when the sweetpotatoes were infected by _C. fimbata_,  
Figure 10: The predicted ipomeamarone content and total colony count based on E-nose data. (**A1**,**A2**) KNN model of (**A1**) ipomeamarone and (**A2**) total colony count. (**B1**,**B2**) PLS model of (**B1**) ipomeamarone and (**B2**) total colony count. (**C1**,**C2**) PCR model of (**C1**) ipomeamarone and (**C2**) total colony count. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Set** & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Cross-Validation**} & \multicolumn{2}{c}{**Prediction**} \\ \hline
**Model** & \(\mathbf{R_{c}}^{2}\) & \begin{tabular}{c} **RMSEC** \\ **(log (CFU g\({}^{-1}\)))** \\ \end{tabular} & \(\mathbf{R_{cv}}^{2}\) & \
\begin{tabular}{c} **RMSECV** \\ **(log (CFU g\({}^{-1}\)))** \\ \end{tabular} & \(\mathbf{R_{p}}^{2}\) &
\begin{tabular}{c} **RMSEP** \\ **(log (CFU g\({}^{-1}\)))** \\ \end{tabular} & **RPD** \\ \hline KNN & 0.744 & 0.184 & 0.757 & 0.179 & 0.909 & 0.110 & 3.306 \\ PLS & 0.741 & 0.185 & 0.644 & 0.217 & 0.867 & 0.133 & 2.734 \\ PCR & 0.735 & 0.187 & 0.661 & 0.212 & 0.857 & 0.138 & 2.635 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predicted results for total colony counts for _C. fimbata_-infected sweetpotatoes by KNN, PLS, and PCR. the growth of C. _fimbriata_ was in the lag phase before 40 h and entered the exponential phase at 48 h. The total colony count increased rapidly and lesion growth accelerated from 48 h, resulting in significant differences in volatile compound contents at different times, especially in the increase of ipomeamarone. The response signal of the sensors had a good correlation with volatile compounds detected by HS-SPME/GC-MS. The sensor signals combined with PCA, PLS-DA, LDA, and CART were used to discriminate _C. fimbriata_-infected sweetpotatoes. Among them, LDA achieved the best discrimination effect. It could accurately discriminate _C. fimbriata_-infected sweetpotatoes during the asymptomatic period, as early as 48 h after infection, and the accuracy of the prediction set reached 100%. E-nose combined with KNN, PLS, and PCR was used to predict contents of ipomeamarone and total colony counts. In tests of prediction models, the optimal prediction result for ipomeamarone contents was achieved by KNN (R\({}_{\text{P}}\)2= 0.932, RMSEP = 1.410 ug g\({}^{-1}\) FW, and RPD = 3.852), and the optimal prediction result for total colony counts was also achieved by KNN (R\({}_{\text{P}}\)2 = 0.909, RMSEP = 0.110 log (CFU g\({}^{-1}\)), and RPD = 3.306). To the best of our knowledge, there has been no prior study on the volatile compounds associated with sweetpotatoe disease being detected by HS-SPME/GC-MS and E-nose combined with pathogen growth. This study demonstrated that E-nose could be used as a practical and efficient detection method for _C. fimbriata_-infected sweetpotatoes during the asymptomatic period. In practical applications, ambient gas from different positions in storage warehouses can be collected by gas samplers or pipeline systems and these can be connected to a computer to detect disease in sweetpotatoes online and in real time using E-nose. To sum up, E-nose has great potential application prospects in early disease control and monitoring of agricultural products during storage and transportation. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11131919/s1](https://www.mdpi.com/article/10.3390/foods11131919/s1)]([https://www.mdpi.com/article/10.3390/foods11131919/s1](https://www.mdpi.com/article/10.3390/foods11131919/s1)), Table S1: The sensor array of E-nose; Table S2: The relative humidity in the air chamber during sample testing using E-nose.