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 d...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.展开更多
基金funding from the National Science Foundation (NSF) CAREER Award (CBET1151154)the National Aeronautics and Space Administration (NASA)Early Career Faculty Grant (NNX12AQ44G)+2 种基金Gulf of Mexico Research Initiative (GoMRI-030)Cullen College of Engineering at the University of Houstonthe MIT Laser Biomedical Research Center supported by the NIH National Center for Research Resources,Grant No.P41-RR02594.
文摘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.
