文献类型： 外文期刊
作者： Chen, Fangfang 1 ; Chen, Cheng 1 ; Chen, Chen 1 ; Yan, Ziwei 1 ; Gao, Rui 1 ; Han, Huijie 2 ; Li, Wenrong 3 ; Lv, Xiaoyi 1 ;
作者机构： 1.Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830046, Peoples R China
2.Shanghai Jiao Tong Univ, Sch Pharm, Shanghai 200240, Peoples R China
3.Xinjiang Acad Anim Sci, Key Lab Anim Biotechnol Xinjiang, Urumqi, Xinjiang, Peoples R China
4.Xinjiang Univ, Coll Software, Urumqi 830046, Peoples R China
5.Xinjiang Univ, Key Lab Signal Detect & Proc, Urumqi 830046, Peoples R China
关键词： Raman spectroscopy; PLSR; Fast detection; GLU
期刊名称：OPTIK （ 影响因子：2.443； 五年影响因子：1.955 ）
ISSN： 0030-4026
年卷期： 2020 年 224 卷
页码：
收录情况： SCI
摘要： 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(R-train(2)) of training set are 0.1034 and 0.9726 respectively, and root mean square error(RMSEtest) and determination coefficient(R-test(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.
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