## 3 Results and Discussion  
### Interpretation of the Raw Data of the Electronic Nose and Near-Infrared Spectroscopy  
In total, 360 samples were measured by the electronic nose, and the mean data for several samples selected as feature values are plotted in Figure 2(a) to show the variation trends in the data from different sensors (as there were too many concentrations; therefore, only a few concentrations are shown). Additionally, 360 samples were scanned by the NIRS analyzer, and the mean spectra are plotted in Figure 2(b) to show the variation trends in the data for different wavelengths (the selected concentrations are the same as those used in the electronic nose test).  
The average values of 15 samples at different concentrations are shown as the response values for the electronic nose in Figure 2(a) (0%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 4%, 8%, 12%, 16%, and 20%).  
Figure 3: Plots of the mean data at different concentrations: (**a**) representative variation tendency of the electronic nose data; (**b**) representative variation tendency of the spectral data.  
The response values of R1, R3, R4, R5, R8, and R10 showed slight changes, and the response values of R2, R6, R7, and R9 increased gradually; however, the response values of R7 and R9 showed the greatest changes.  
The target substances of the R7 and R9 sensors are aromatics and sulfur- and chlorine-containing organics, as shown in Table 3. Thus, SDPP is rich in sulfur-containing organics, such as sulfur-containing amino acids, which are essential aroma components. For instance, immunoglobulin (IgG) in SDPP contains dimethyl disulfide, which originates from L-cysteine and is an odorous compound. Moreover, the aroma can be a critical ingredient for improving the palatability of creep feed [44]. Thus, the results proved that utilizing an electronic nose for SDPP detection is feasible.  
The mean spectral data are plotted in Figure 3b to show the variation tendencies of different wavelengths (the selected concentrations are the same as those used in the electronic nose test). The major absorbance differences between the samples of different concentrations were surveyed in the second overtone region (1100\(-\)1300 nm), the first overtone region (1600\(-\)1950 nm), and the combination vibrational band region (2050\(-\)2500 nm). The single absorption centered at approximately 1189 nm was the second overtone of the -CH3, -CH2, and -CH functional group vibration bands. The absorption bands at 1506, 1730, and 1939 nm were assigned to the vibrations of the first overtones of symmetric and asymmetric -NH, -CH, -CH2, -CH3, -SH and -OH functional group stretching. Furthermore, the absorption peaks at approximately 2056, 2175, and 2311 nm were attributed to the characteristic bands of the combined vibrational absorption of -OH and -NH2 groups and -CH3, -CH2, and -CH functional groups, respectively [40; 42].  
[PERSON]'s T\({}^{2}\) test (with a confidence level of 95%) was employed as the measure of the variation among samples in the electronic nose and NIR datasets [16]. The results indicated that electronic nose sample 233 was indeed unusual, with a large T\({}^{2}\) value of 159 (95% limit = 8.08) and a small Q residual of 0.000179 (95% limit = 0.0402). The T\({}^{2}\) contributions of the electronic nose data are described in Figure 4a, and the raw data for sample 233 are shown in Figure 4b. Therefore, electronic nose sample 233 was eliminated as an abnormal sample. The iterative random sampling method was adopted to divide the calibration set and prediction set for both detection models and regression models, and the number of iterations was set to 100. Thus, two-thirds of the samples were selected as the calibration set, and one-third of the samples were selected as the prediction set for each detection model and regression model.