Rapid detection of seven indexes in sheep serum based on Raman spectroscopy combined with DOSC-SPA-PLSR-DS model.

In this research, PLSR, GA-SVR and ELM models combined with sheep serum Raman spectroscopy were used for quantitative detection, the results of PLSR determination are better (a). In order to further improve the performance of the model, the SPA is used for band selection (c). The optimized quantitative model result (b) of DOSC-SPA-PLSR-MN performs well. • It is proposed for the first time to use sheep serum Raman spectroscopy combined with a quantitative model to determine the concentration (/enzyme activity) of seven indicators. • It is the first time to deviation standardization of the model evaluation index for many kinds of substances. • The comparison results of SPA band selection show that there is a correlation among the indicators in the serum. • The optimized DOSC-SPA-PLSR-DS quantitative model shows that the average R p 2 of the seven indicators is 0.9834. Hepatic fascioliasis, ketosis of pregnancy, toxemia of pregnancy and other common sheep diseases will directly affect the concentration (/enzymatic activity) of seven indicators, such as cortisol and high-density lipoprotein cholesterol (HDL-C) in sheep serum. Whether the concentrations (/enzymatic activity) of these indicators can be detected quickly will directly affect the prevention of sheep diseases and the targeted adjustment of breeding methods, thereby affecting the economic benefits of sheep breeding. In this research, we established partial least square regression (PLSR), support vector regression based on genetic algorithm optimization (GA-SVR) and extreme learning machine (ELM) models. Due to the large differences in the content of different substances, it is difficult to directly use the RMSE to evaluate the quantitative effect of the model. This study is the first to propose conducting deviation standardization (DS) for the determination results of various substances. To further improve the performance of the model, we use the successive projections algorithm (SPA) to optimize feature extraction and combine it with the better-performing PLSR model for training. The results show that the optimized DOSC-SPA-PLSR-DS quantitative model has better determination results for 101 sheep serum samples. The average RMSE p* of the concentration of the six substances decreased from 0.0408 to 0.0387, the R p 2 increased from 0.9758 to 0.9846, and the running time was reduced from 0.1659 to 0.0008 s. And the determination performance of lipase (LPS) enzymatic activity has also been improved. The results of this research show that sheep serum Raman spectroscopy combined with DOSC-SPA-PLSR-DS optimization can efficiently monitor the concentration (/enzyme activity) of seven indicators in real time and provide a new strategy for future intelligent supervision of animal husbandry. [ABSTRACT FROM AUTHOR]