## 2 Results and Discussion
### Electronic Nose Analysis  
Electronic nose (E-nose) analysis was performed to evaluate the differences in volatile profiles between samples. A typical E-nose radar plot of the samples is presented in Figure 4. The response value of the S1, S3 and S5 sensors, which are selective toward aromatic compounds (Table 2), increased in the samples of laboratory- and factory-scale fermentation. The results suggested that fermentation processing could result in the formation of aromatic compounds. The decrease in response values of the S2, S6, S7 and S9 sensors indicated that nitrogen oxide, alkanes and sulfides decreased during the fermentation process. Compared with F0, the response values of F63 and L63 exhibited similar trends. Furthermore, the response values of S6, S8 and S9 of F63 were significantly higher than those of L63, which suggested that more alkanes, alcohols and aromatic compounds were produced during factory-scale fermentation. The response values of S7 and S9 sensors were significantly reduced in the laboratory- and factory-scale fermentation processes. These results suggest that fermentation processing could lead to a reduction in sulfides, which is consistent with the results of GC-MS.  
The principal component analysis (PCA) for E-nose analysis is shown in Figure 5. From Figure 5a, it can be observed that two principal components (PC1 81.15% and PC2 11.00%) accounted for 92.15% of the total variance. The fresh noni juice and the samples with different fermentation times and scales were divided into four quadrants, which indicated that there was an obvious difference in the flavor profiles of the samples. In general, these results suggested that the E-nose can characterize the samples produced under different fermentation time and scale conditions. Figure 5b shows the score plot of the 10 E-nose sensors, which indicates the degree of contribution each E-nose sensor made to sample discrimination. PC1 had high contributing factor loadings from S2, S4, S6, S7, S8 and S9 sensors, which helped to discriminate samples F0 from L28, L63 and F63. PC2 had high contributing factor loadings from the S10 sensor. The results of GC-MS analysis showed that F0 had a higher concentration of sulfur compounds (methyl3-(methylthio)propionate, ethyl 3-(methylthio)propionate and amyl 3-(methylthio)propionate) than L28, L63 and F63, which were mainly associated with the response of sensors S7 and S9. F63 had a higher content of 2-heptanone than F0, L28 and L63, which was associated with sensors  
Figure 4: Radar plot of the volatile compounds of noni juice at different fermentation times and scales by E-nose. The data are normalized as a result of LOG2 (mean value \(\times\) 10).  
S8 and S9. L63 had a higher content of 2,6,11-trimethyl-dodecane, indicating that the S6 and S10 may be sensitive to 2,6,11-trimethyl-dodecane in L63. Combined with the results of GC-MS, the results of the PCA showed that the nitrogen oxide, alkanes, alcohols, and aromatic and sulfur compounds, contributed to distinguishing the volatile profiles of different samples. Although the E-nose does not allow for identification of individual volatile compounds, the sensor signals present variable responses for the samples and give volatile fingerprints, which facilitate good discrimination of non juice produced under different conditions [47; 48]. Moreover, the E-nose analysis data contain synthesized information rather than simple qualitative and quantitative results of individual volatile compounds, and the analysis is faster and less expensive than gas GC-MS [48; 49].  
Figure 5: PCA results for E-nose analysis of different samples. Loading plot (**a**); score plot (**b**).