Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements

Abstract

Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood-tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.

Figure 1

Representative Raman spectra acquired from a dog model during a glucose clamping study and corresponding blood glucose concentrations measured over the same time.

Figure 2

Glucose concentration-time profiles used for our tissue phantom and dog model study. The data here is re-drawn from published reports by the Heise (left) [10] [with permission from John Wiley and Sons] and Arnold [25] (right) [with permission from American Chemical Society] groups.

Figure 3

Glucose predictions for the three different calibration models developed for the tissue phantom study using the random (red circle), Heise (blue circle) and Arnold (magenta circle) concentration profiles, respectively, plotted on a single Clarke Error Grid.

Figure 4

Glucose predictions for the four different calibration models developed for the dog subject study using the random (A), Heise (B), Arnold (C) and real (D) concentration profiles plotted on the Clarke Error Grid.