MOE Key Laboratory of Laser Life Science & Laboratory of Photonic Chinese Medicine, College of Biophotonics, South China Normal University, Guangdong 510631, China.
Molecules. 2019 Apr 17;24(8):1500. doi: 10.3390/molecules24081500.
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 R 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 R 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.
利用激光激发获得的人体皮肤拉曼光谱已被用于无创检测血糖。然而，在之前的报告中，由于激光在皮肤中的穿透深度较浅，因此获得的拉曼光谱主要来源于表皮和间质液。葡萄糖从小血管渗透到间质液的生理过程会引入时间延迟，这不可避免地会给经皮血糖测量带来误差。我们将激光直接聚焦在手指甲襞浅层的微血管上，获得了具有血液多个特征峰的拉曼光谱，这表明所获得的光谱主要来源于血液。我们结合主成分分析（PCA）和反向传播人工神经网络（BP-ANN）的多元方法，分别对 12 名随机选择的志愿者进行了无创血糖测量。12 名志愿者的平均预测性能获得了 0.45mmol/L 的 RMSEP 和 0.95 的 R。在口服葡萄糖耐量试验（OGTT）中，预测血糖与实际血糖之间没有时间延迟。我们还将该程序应用于所有 12 名志愿者的数据，作为一组，总预测性能的 RMSEP 为 0.27mmol/L，R 为 0.98，这优于每个志愿者的个体模型的预测性能。这表明志愿者指甲的解剖差异不会降低预测精度，并且 100%的预测葡萄糖浓度落在 Clarke 误差网格的 A 和 B 区，允许在临床相关范围内进行可接受的预测。从微血管检测血糖的拉曼光谱对拉曼光谱无创血糖检测具有重要意义。这种创新方法也可能有助于无创检测其他血液成分。
