Detection of spray-dried porcine plasma (SDPP) based on electronic nose and near-infrared spectroscopy data

Abstract

Recent studies have indicated that spray-dried porcine plasma (SDPP) is a potential transmissionroute forAfricanswine fever (ASF). Therefore, it is essential todeveloprapid, high-efficiency analytical methods to detect SDPP, aiming to both restrict the abuse of SDPP and block the spread of ASF through feed additive. The feasibility of detecting SDPP using an electronic nose and near-infrared spectroscopy (NIRS) is explored and validated by a principal component analysis (PCA). Both discrimination experiments and prediction experiments were implemented to compare the detect feature of the two techniques. On this basis, partial least squares discriminant analysis (PLS-DA) under various preprocessing methods was used to develop a qualitative discriminant model for estimating the prediction performance. Before selecting a specific regression model for the quantitative analysis of SDPP, a continuum regression (CR) model was employed to explore and choose the potential most appropriate regression model for these two different types of datasets. The results showed that the optimal regression model adopted partial least squares regression (PLSR) with the Savitzky-Golay first derivative and mean-center preprocessing for the NIRS dataset (R2p = 0.999, RMSEP = 0.1905). Overall, combining the NIRS technique with multivariate data analysis methods shows more possibilities than an electronic nose for rapidly detecting the usage of SDPP in mixed feed samples, which could provide an effective way to control the spread of ASF.