title: Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy.
abstract: While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future.
authors: Massachusetts Institute of Technology, G. R. Harrison Spectroscopy Laboratory, Laser Biomedical Research Center, Cambridge, Massachusetts 02139, USA.
authors: Narahara Chari
authors: NC
authors: Dingari
authors: Ishan
authors: I
authors: Barman
authors: Jeon Woong
authors: JW
authors: Kang
authors: Chae-Ryon
authors: CR
authors: Kong
authors: Ramachandra R
authors: RR
authors: Dasari
authors: Michael S
authors: MS
authors: Feld
mesh_terms: D001774:Blood Chemical Analysis / Q000379:methods*; D001786:Blood Glucose / Q000032:analysis*; D006801:Humans; D016018:Least-Squares Analysis; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013059:Spectrum Analysis, Raman / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D001786:Blood Glucose