Application of PLSR in rapid detection of glucose in sheep serum.

This paper proposes for the first time to establish grid search-support vector regression (GS-SVR), random forest (RF) and partial least squares regression (PLSR) models based on the measured Raman spectral data of sheep serum to find a method that can quickly detect glucose (GLU) concentration. The results show that all performance indexes of PLSR model are optimal, root mean square error(R M S E t r a i n) and determination coefficient(R t r a i n 2) of training set are 0.1034 and 0.9726 respectively, and root mean square error(R M S E t e s t) and determination coefficient(R t e s t 2) of test set are 0.0691 and 0.9632 respectively. Studies have shown that the PLSR model has good predictive performance for the detection of glucose in sheep serum. [ABSTRACT FROM AUTHOR]