## 3 Results and Discussion
### Early Discrimination of C. _fimbriata-Infected Sweetpotatoes Combined with Chemometrics_  
First, PCA was used to analyse C. _fimbriata_-infected sweetpotatoes from 0 to 72 h. It can be seen from Figure 8 that the first and second PCs explained 89.5% and 6.1% of the variance in the input variables, respectively, with a total of 95.6%. The first two PCs can be considered as comprehensive information for the representation of entire samples [50]. The samples of the _C. _fimbriata_-infected group and the control group at 0 h showed a large amount of overlap. This indicated that the volatile compound contents of fresh sweetpotatoes and _C. _fimbriata_-infected sweetpotatoes were mostly the same at 0 h, which was consistent with the results of HS-SPME/GC-MS. Based on the results of the two flavor-detection techniques, it can be considered that the _C. _fimbriata_-infected group and the control group had the same freshness at 0 h. The fresh samples at 0 h were well distinguished from the infected samples from 8 to 72 h. However, _C. _fimbriata_-infected sweetpotatoes from 8 to 72 h were indistinguishable. This was probably because the volatile compound contents did not change significantly in the early period of inoculation. Therefore, four distinguishable time points were screened out: 0 h, 48 h, 64 h, and 72 h. Three models were used to discriminate _C. _fimbriata_-infected sweetpotatoes at different infection times: PLS-DA, LDA, and CART. Figure 7: Correlation analysis between sensors and volatile compounds. E-nose data for _C. fimbriata_-infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h were used for discrimination. The division of the data sets referred to the method of [PERSON] et al. [51]; 70% of the data was selected as the calibration set and 30% as the prediction set. The higher the sensitivity, specificity, precision, and accuracy of the models, the better the performance. The results are shown in Figure 9A-C and Table 3. The accuracies of the prediction sets of PLS-DA, LDA, and CART were 100%, 100%, and 97%, respectively. The prediction sets of PLS-DA and LDA had the highest accuracies, both reaching 100%. Among them, the sensitivity, specificity, and precision of LDA at different time points all reached 100%. However, the precision of PLS-DA was lower than that of LDA at 48 h. At the same time, it can be seen from Figure 9A,B that the discrimination result of LDA was better than that of PLS-DA, indicating that LDA can more accurately discriminate _C. fimbriata_ infection at different times, followed by PLS-DA. Figure 9C showed that CART could also distinguish sweetpotatoes at different infection times, but its accuracy was lower than LDA and PLS-DA. Simultaneously, the sensitivity of CART was lower than that of PLS-DA at 48 h; the specificity and precision were also lower than those of PLS-DA at 72 h. Among the above three models, LDA had the best discrimination effect, followed by PLS-DA and finally CART. These results showed that E-nose could not only discriminate fresh sweetpotatoes (0 h) from _C. fimbriata_-infected sweetpotatoes but also accurately discriminate infected sweetpotatoes at 48 h, 64 h, and 72 h. At the same time, it also indicated that E-nose can discriminate _C. fimbriata_-infected sweetpotatoes as early as 48 h in the asymptomatic period. At 48 h, the growth of _C. fimbriata_ entered into the exponential phase. Slight black lesions could be seen in the internal tissue. Volatile compounds changed significantly (\(p<0.05\)), which was consistent with the earliest time point when E-nose could distinguish infected sweetpotatoes from fresh sweetpotatoes. Figure 8: PCA results for the _C. fimbriata_-infected group and the control group from 0 to 72 h.  
Figure 9: Three discriminant models of C. _fimbrata_-infected sweetpotatoes at 0 h, 48 h, 64 h, and 72 h. (**A**) PLS-DA. (**B**) LDA. (**C**) CART. Solid circles in (**A**) and (**B**) represent the calibration set; the symbol \({}^{\star}\) represents the prediction set.