## 4 Conclusions  
In this research, both the electronic nose and NIRS techniques were first applied for SDPP detection in mixed samples. The results demonstrated that both the electronic nose and NIRS were mature enough to provide rapid and accurate techniques for the detection and quantification of the usage of SDPP. The results of the electronic nose experiment indicated that the vital volatile substances of SDPP that enabled the detection of SDPP in mixed samples by electronic nose technology were the sulfur-containing organics, which were odorous compound and sensitive to sensors R7 and R9. The results of the NIRS experiment implied that there exist significantly different components between SDPP and WPC, which may explain the spectral absorption variations. Compared with traditional detection methods, these two electronic technologies could provide faster and more efficient detection methods for SDPP. The details are as follows:  
1. The results of the PCA application indicated that the cluster performance based on electronic nose data was poor. Comparatively, the cluster performance based on NIRS data was better. It revealed that for the electronic nose, the volatile components of SDPP and WPC were similar in large part, but the absorption features of these two types of materials in the near-infrared region existed many differences. 2. The classification result of the PLS-DA model showed that the accuracy of the NIRS discrimination model (the classification accuracy was up to 100%) was higher than that of the electronic nose model (the classification accuracy is 95%). Furthermore, the confidence interval range of the NIRS discrimination model was much smaller, which demonstrate that the discrimination capability of NIRS was superior to that of the electronic nose for SDPP detection. 3. The CR analysis revealed that the optimal regression model based on electronic nose data was MLR, and the optimal regression model based on NIRS data was PLSR. 4. According to the result of multivariate regression analysis, the MLR prediction model with the mean-center preprocessing method was the optimal prediction model for the electronic nose, with \(\text{Rp}^{2}\) and RMSEP values of 0.9247 and 1.7441, respectively. The PLSR prediction model with the Savitzky-Golay first derivative and mean-center preprocessing method was the optimal prediction model for NIRS, with \(\text{Rp}^{2}\) and RMSEP values of 0.999 and 0.1905, respectively. Both results confirmed the accuracy of the CR analysis. Furthermore, compared with the electronic nose, NIRS showed an excellent capacity for qualitative and quantitative analysis, in which both the analytical precision and prediction accuracy were higher than obtained those using the electronic nose. According to the results, when taking detection accuracy and average time consumption into consideration, NIRS, which has superior accuracy and is less time-consuming relative to the electronic nose, should be adopted. When considering the detection cost, an electronic nose that has less cost should be considered. Furthermore, considering that the PEN3 is portable, while the Antaris II must stay within the laboratory, it is much more convenient to use an electronic nose in many outdoor scenes and its analytical result can also be trusted. However, further research is needed to consider more protein raw materials, such as yeast hydrolysate (YH), yeast extract (YE), and other animal protein byproducts, which could be used as substitutes for SDPP to enlarge the sample groups and experimental database. Thus, the robustness and accuracy of the qualitative and quantitative analysis model could be improved. Physical and chemical tests could be performed to reveal the critical components for identifying SDPP in other substitutes. Furthermore, more new advanced techniques, such as the electronic tongue, Raman spectroscopy, and hyperspectral imaging techniques, could be applied. All authors contributed to the conceptualization.