A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels

Abstract

Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R2 of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R2 of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

“…The large Raman shift due to the OH stretching bond is 3400 cm −1 and can be measured to estimate the water content of blood [103]. The blood Raman spectrum includes obvious peaks at 650, 758, 837, 945, 978, 1004, 1130, 1163, 1217, 1332, 1551 and 1660 cm −1 [104]. Glucose makes up a small percentage of the total blood volume and thus makes up a small portion of Raman peak amplitudes.…”

“…Analysis methods include partial least squares regression (PLS), principal component analysis (PCA), support vector machine (SVM) and backpropagation artificial neural network (BP-ANN). The first two methods can be applied as data compression techniques and the two latter methods can be used for finding both linear and non-linear relationships between the glucose concentration and the measured spectrum [102,[104][105][106].…”

“…Signal filtering applied by multivariate analysis of Raman spectra from multiple blood/tissue components (as mentioned in Section 3.3.1) and data calibration applied to a glucose prediction model using a fraction of the total data followed by validation of the rest of the data as independent test data [102,[104][105][106].…”

“…Selection of a test site with a nearly transparent epidermis and a high density of blood vessels. The nail fold or volar side of the fingertip are good examples that minimize signals from tissue components and maximize Raman spectra from blood components [104,107]. Selecting a measurement site with a high density of blood vessels minimizes the time lag between actual blood glucose measurements vs. glucose within the tissue.…”

Label‐free serum detection based on Raman spectroscopy for the diagnosis and classification of glioma

“…This rapid technology with high resolution can offer fingerprint-type molecular and chemical information of biological samples, which will become a promising tool for cancer diagnosis or the monitoring of a prognosis in the future. [15] Some published papers have studied the feasibility of using Raman spectroscopy to diagnose glioma by analyzing tissue specimens, [16][17][18] and invasive surgery is inevitably needed to obtain tissues. In this paper, we preoperatively analyzed healthy and glioma serum samples by Raman spectroscopy, which was combined with principal component analysis (PCA)-linear discriminant analysis (LDA) to distinguish healthy individuals and patients with glioma.…”