###### Abstract
The traditional physicochemical detection methods for detecting animal protein sources include feed microscopy, enzyme-linked immunosorbent assays (ELISA), and polymerase chain reaction [6]. Feed microscopy is a method used to identify the morphological characteristics of feed particles from animal sources by optical microscopy. ELISA is an immunological kit method that was developed based on the immunological reaction between protein antibodies and antigens to detect specific proteins in feed samples. Polymerase chain reaction, which is the most common method, is a type of molecular biology technique that can detect protein sources in creep feed by amplifying the DNA from a single cell [7]. The above methods are time-consuming, incur high costs, and involve complex operational processes. Therefore, it is necessary to explore rapid and efficient techniques for qualitative detection and quantitative prediction methods to enforce the legislation of Regulation 64/2018, which limits the use of SDPP in creep feed to prevent further ASF outbreaks. Electronic nose detection [8; 9] and near-infrared spectroscopy (NIRS) [10; 11; 12] are the new and intelligent feed quality detection methods. An electronic nose is able to simulate the biological sense of smell based on an array of gas sensors that have distinct sensitivities to various volatile organic compounds (VOCs) [13]. Thus, an electronic nose is capable of identifying the differences between test samples based on their volatilities. Electronic nose technology was first applied in the analysis of ruminant meat and bone meal (MBM), which caused the spread of bovine spongiform encephalopathy (BSE) in 2004 [14]. In response to feed protein shortages and feed safety requirements, researchers have implemented electronic nose testing of feed materials, such as fish meal, soybean, maize, and wheat for animal nutrition research, and to prevent commercial fraud in the last decade [9; 15; 16; 17]. To the best of our knowledge, no research has been published to date regarding the application of electronic noses for SDPP detection. NIRS, which is a promising technique for nondestructive analysis, can be utilized to correlate chemical concentrations with the absorption intensities in the NIR region at specific wavelengths, which can be attributed to the molecular vibrations of functional groups, such as C-H, O-H, N-H, H-S, and inorganic compounds [18]. In recent years, previous studies have investigated the use of NIRS to detect and predict parameters of nutritional interest for different feeds, including MBM, animal protein byproducts (APBPs), animal fat byproducts (AFBPs), multivitamins, and perennial ryegrass [19; 20; 21; 22]. However, limited studies have investigated the application of NIRS in the qualitative and quantitative detection of SDPP. Although there were a lot of applications for the electronic nose and NIRS in feed detection, very few researchers have focused on compared the detecting features between electronic noses and NIRS. In this study, it was assumed that there were significantly different chemical compositions between SDPP and WPC. According to the related official limitation of SDPP used in feed, pure and impure samples were used to explore the classification model for qualitative detection. A series of different concentration samples were used to identify the predictive capacity for quantitative detection. Hence, both the mixture discrimination experiment and mixture concentration experiment were designed to verify the feasibility of detecting SDPP in mixture samples. Thus, the chemometric analysis was applied to develop a discriminant analysis model and a quantitative prediction model to monitor the usage of SDPP by separately using electronic nose and NIRS techniques. The main objectives of the present study are as follows:  
(1) To explore the feasibility of utilizing electronic noses and NIRS technologies to perform cluster analysis in mixed samples with principal component analysis (PCA) [23].