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• W3089833720 abstract "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(RMSEtrain) and determination coefficient(Rtrain2) of training set are 0.1034 and 0.9726 respectively, and root mean square error(RMSEtest) and determination coefficient(Rtest2) 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." @default.
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• W3089833720 date "2020-12-01" @default.
• W3089833720 modified "2023-09-27" @default.
• W3089833720 title "Application of PLSR in rapid detection of glucose in sheep serum" @default.
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• W3089833720 doi "https://doi.org/10.1016/j.ijleo.2020.165734" @default.
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