We report the first successful study of the use of Raman spectroscopy for quantitative, noninvasive ("transcutaneous") measurement of blood analytes, using glucose as an example. As an initial evaluation of the ability of Raman spectroscopy to measure glucose transcutaneously, we studied 17 healthy human subjects whose blood glucose levels were elevated over a period of 2-3 h using a standard glucose tolerance test protocol. During the test, 461 Raman spectra were collected transcutaneously along with glucose reference values provided by standard capillary blood analysis. A partial least squares calibration was created from the data from each subject and validated using leave-one-out cross validation. The mean absolute errors for each subject were 7.8%+/-1.8% (mean+/-std) with R2 values of 0.83+/-0.10. We provide spectral evidence that the glucose spectrum is an important part of the calibrations by analysis of the calibration regression vectors.
Concentrations of multiple analytes were simultaneously measured in whole blood with clinical accuracy, without sample processing, using near-infrared Raman spectroscopy. Spectra were acquired with an instrument employing nonimaging optics, designed using Monte Carlo simulations of the influence of light-scattering-absorbing blood cells on the excitation and emission of Raman light in turbid medium. Raman spectra were collected from whole blood drawn from 31 individuals. Quantitative predictions of glucose, urea, total protein, albumin, triglycerides, hematocrit, and hemoglobin were made by means of partial least-squares (PLS) analysis with clinically relevant precision (r(2) values >0.93). The similarity of the features of the PLS calibration spectra to those of the respective analyte spectra illustrates that the predictions are based on molecular information carried by the Raman light. This demonstrates the feasibility of using Raman spectroscopy for quantitative measurements of biomolecular contents in highly light-scattering and absorbing media.
Raman spectra measured in the finger are a combination of backscattered signals induced from incident light. They contain Raman scattering, intrinsic tissue fluorescence and noise. Our goal of this study is to find perturbing components which are able to prohibit an accurate prediction of target materials and propose a new terminology for the SNR of regression coefficient vector. We assumed that Raman spectra are consist of fluorescent background spectrum (F), Raman spectrum of glucose (R glucose), Raman spectrum of the skin (R skin), random noise (RN) and unknown spectrum from other components (R unknown). And we used partial least squares regression (PLSR) to evaluate the main perturbing components under the condition that is various combinations of each signal. In simulation, R skin is the main component to cause the error between reference and predicted glucose concentration. F is also able to affect accuracy at the low signal to noise ratio (SNR) of glucose signal. To minimize the perturbing effects, enhancement of R glucose is more important than any other things.
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