## 3 Results and Discussion
### PLS-DA Model  
To distinguish pure samples (0%) from impure samples, the PLS-DA model was adopted as a discrimination model by combining all mixture samples (0.5%\(-\)3%) into one category, the impure category. Before performing PLS-DA calculations, various preprocessing methods were implemented to preprocess both the raw electronic nose data and the raw NIR spectral data. The discrimination results of the PLS-DA models based on different preprocessing methods are summarized in Table 4. The accuracy of all PLS-DA models is greater than 90%, which indicates that there was a distinct difference between the pure and impure samples. According to Table 4, in general, the PLS-DA algorithm performed better on the NIR data than it did on the electronic data for all of the three classification metrics. The high classification rate indicated that both the electronic nose and NIR spectral techniques could be adopted to detect the usage of SDPP. The two types of preprocessing methods containing mean centering performed better than the other methods did, with all metrics reaching absolutely 100%. Although the two techniques had good performances in SDPP detection, compared with the electronic nose technique, the NIRS technique established a more accurate discrimination model with a much smaller confidence interval range.  
The results of the PLS-DA models based on the electronic nose data and NIRS data are described in Figure 6a,b, respectively. The sample number was considered to have X varieties, while the Y variable was associated with sample varieties (a dummy variable was introduced to judge whether a sample belonged to the pure or impure sample group). A threshold value was introduced as the class limit [46]. Furthermore, the appropriate thresholds for the electronic nose data and NIRS data were obtained from the PLS-DA algorithms, as shown in Figure 6c,d, respectively, which demonstrated good sensitivity without losing too much specificity. Figure 6c shows that the threshold value for the electronic nose dataset was Y = 0.53, and Figure 6d shows that the threshold value for the NIR spectral dataset was Y = 0.6. Variable importance in projection (VIP) plots was adopted to inspect the  
Figure 5: The principal component analysis (PCA) scores plot for the (**a**) electronic nose data and (**b**) NIRS data.  
contribution of each variable by describing the relationship between the spectral matrix (X) and the response variable (Y). Two evident peaks from the electronic nose data and five evident peaks from the NIR spectral data (with a score greater than 1) were observed in the VIP score plots in Figure 6e,f. It was confirmed that R7 and R9 were the most sensitive sensors for detecting the usage of SDPP. It was found that 1731, 1939, 2056, 2173, and 2307 nm were the feature wavelengths for detecting the usage of SDPP. Hence, the VIP score can also provide information for analyzing changes in the chemical properties of samples.  
Figure 6: Analysis results of the partial least squares discriminant analysis (PLS–DA) model: Classification results of the validation set for the (**a**) electronic nose data and (**b**) NIRS data. Threshold plots for the PLS–DA models with the (**c**) electronic nose data and (**d**) NIRS data. Variable importance in projection (VIP) plots for the PLS–DA algorithm with the (**e**) electronic nose data and (**f**) NIRS data.