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Detection of Spray-Dried Porcine Plasma (SDPP) based on Electronic Nose and Near-Infrared Spectroscopy Data

文献类型： 外文期刊

作者： Han, Xiaoteng 1 ; Lu, Enli 1 ; Lu, Huazhong 2 ; Zeng, Fanguo 1 ; Qiu, Guangjun 1 ; Yu, Qiaodong 1 ; Zhang, Min 1 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510640, Peoples R China

2.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

关键词： continuum regression; discriminant analysis; electronic nose; Fourier transform near-infrared spectroscopy; regression analysis; spray-dried porcine plasma

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.679； 五年影响因子：2.736 ）

ISSN：

年卷期： 2020 年 10 卷 8 期

页码：

收录情况： SCI

摘要： Since the first proposal to use spray-dried porcine plasma (SDPP) as an animal-based protein source feed additive for piglets in the late 1980s, a large number of studies have been published on the promotion effect of SDPP on piglets. SDPP contains biologically active components that support pig health during weaning stress and may be more economical to use compared to similar bovine-milk-derived protein sources. Unfortunately, animal blood proteins have been suspected as a source for African Swine Fever Virus (ASFV) spread in China. Furthermore, there are no officially recognized methods for quantifying SDPP in complex feed mixtures. Therefore, it is essential to develop rapid, high-effciency analytical methods to detect SDPP. The feasibility of detecting SDPP using an electronic nose and near-infrared spectroscopy (NIRS) was 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 (R-p(2) = 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 identify the use of SDPP in feed mixtures.

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