中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
  4. 中科院安徽光学精密机械研究所
Weighted partial least squares based on the error and variance of the recovery rate in calibration set

文献类型：期刊论文

作者Yu, Shaohui1; Xiao, Xue2; Ding, Hong1; Xu, Ge1; Li, Haixia1; Liu, Jing3
刊名SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
出版日期2017-08-05
卷号183期号:无页码:138-143
关键词Leave-one-out Cross-validation Partial Least Squares Gauss Weight Excitation-emission Fluorescence Spectroscopy
DOI10.1016/j.saa.2017.04.029
文献子类Article
英文摘要The quantitative analysis is very difficult for the emission-excitation fluorescence spectroscopy of multi-component mixtures whose fluorescence peaks are serious overlapping. As an effective method for the quantitative analysis, partial least squares can extract the latent variables from both the independent variables and the dependent variables, so it can model for multiple correlations between variables. However, there are some factors that usually affect the prediction results of partial least"squares, such as the noise, the distribution and amount of the samples in calibration set etc. This work focuses on the problems in the calibration set that are mentioned above. Firstly, the outliers in the calibration set are removed by leave-one-out cross-validation. Then, according to two different prediction requirements, the EWPLS method and the VWPLS method are proposed. The independent variables and dependent variables are weighted in the EWPLS method by the maximum error of the recovery rate and weighted in the VWPLS method by the maximum variance of the recovery rate. Three organic matters with serious overlapping excitation-emission fluorescence spectroscopy are selected for the experiments. The step adjustment parameter, the iteration number and the sample amount in the calibration set are discussed. The results show the EWPLS method and the VWPLS method are superior to the PIS method especially for the case of small samples in the calibration set. (C) 2017 Elsevier B.V. All rights reserved.
WOS关键词NEAR-INFRARED SPECTROSCOPY ; PLS-REGRESSION ; ALGORITHMS ; ROBUST
WOS研究方向Spectroscopy
语种英语
WOS记录号WOS:000403130900019
资助机构National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; National Natural Science Foundation of China(61308063 ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Projects of International Cooperation and Exchanges NSFC(61491240110) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Anhui Provincial Natural Science Foundation(1308085QF111) ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; Student Innovation Training Program(201614098087 ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 61378041) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013) ; 201614098013)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/31907]  
专题合肥物质科学研究院_中科院安徽光学精密机械研究所
作者单位1.Hefei Normal Univ, Sch Math & Stat, Hefei 230061, Peoples R China
2.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt &Technol, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Inst Tech Biol & Agr Engn, Key Lab Ion Beam Bioengn, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Shaohui,Xiao, Xue,Ding, Hong,et al. Weighted partial least squares based on the error and variance of the recovery rate in calibration set[J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,2017,183(无):138-143.
APA Yu, Shaohui,Xiao, Xue,Ding, Hong,Xu, Ge,Li, Haixia,&Liu, Jing.(2017).Weighted partial least squares based on the error and variance of the recovery rate in calibration set.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,183(无),138-143.
MLA Yu, Shaohui,et al."Weighted partial least squares based on the error and variance of the recovery rate in calibration set".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 183.无(2017):138-143.

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。