NONINVASIVE MEASUREMENT OF BLOOD ANALYTES

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The goal of this project is the measurement of clinically-relevant analytes in the blood tissue matrix of human subjects using near-infrared Raman spectroscopy. Such a technology could have great impact on the healthcare practices for the entire population, with the shorter term research directed towards glucose measurements for diabetic patients. We have recently demonstrated detection feasibility of glucose concentrations in vivo from transcutaneous measurements of the forearm by performing individual calibration. From these measurements we have identified the following sources of prediction error when a calibration algorithm is applied on a larger human population: (A) variability in sample turbidity, (B) presence of tissue autofluorescence and photobleaching and (C) physiological lag between blood and interstitial fluid (ISF) glucose. We have previously completed the development of a method (called turbidity corrected Raman spectroscopy, TCRS) for the correction of turbidity-induced distortions in Raman spectra utilizing the photon migration approach. We have demonstrated that, upon application of TCRS, the widely varying Raman spectra observed from a set of tissue phantoms having the same concentration of Raman scatterers but different turbidities tend to show near perfect coalescence onto a single spectral profile. Furthermore, in a prospective study that employs physical tissue models with varying turbidities and randomized concentrations of Raman scatterers and interfering agents, a 20% reduction in prediction error is obtained by applying the turbidity correction procedure to the acquired Raman spectra.