Constrained regularization for noninvasive glucose sensing using Raman spectroscopy  

作　　者：Wei-Chuan Shih 

机构地区：[1]Department of Electrical and Computer Engineering Department of Biomedical Engineering,University of Houston 4800 Calhoun Rd.,Houston,TX 77204,USA

出　　处：《Journal of Innovative Optical Health Sciences》2015年第4期46-53,共8页创新光学健康科学杂志（英文）

基　　金：funding from the National Science Foundation (NSF) CAREER Award (CBET1151154);the National Aeronautics and Space Administration (NASA)Early Career Faculty Grant (NNX12AQ44G);Gulf of Mexico Research Initiative (GoMRI-030);Cullen College of Engineering at the University of Houston;the MIT Laser Biomedical Research Center supported by the NIH National Center for Research Resources,Grant No.P41-RR02594.

摘　　要：Multivariate calibration is an important tool for spectroscopic measurermnent of analyte con-centrations.We present a detailed study of a hybrid multivariate calibration technique,con-strained regularization(CR),and demonstrate its utility in noninvasive glucose sensing uasing Raman spectroscopy.Similar to partial least squares(PIS)and principal component regression(PCR),CR builds an implicit model and requires knowledge only of the concentrations of the analyte of interest.Calibration is treated as an inverse problem in which an optimal balance between model complexity and noise rejection is achieved.Prior information is included in the form of a spectroscopic constraint that can be obtained conveniently.When used with an appropriate constraint,CR provides a better calibration model compared to PLS in both numerical and experimental studies.

关 键 词：Gluoose NONINVASIVE multivariate calibration partial least squares principal com-ponent regression Raman spectroscopy constrained regularization 

分 类 号：O17[理学—数学]