Influence of Raman Spectrometer Collection Efficiency on Performance of Noninvasive Blood Glucose Detection for Device Miniaturization.

Recently the world population with diabetes has increased significantly, and the market demand for noninvasive blood glucose monitoring has increased accordingly. Our previous study demonstrated the capability to detect glucose through the direct observation of glucose Raman fingerprint peaks from in vivo skin but using a benchtop device. From the perspective of commercialization, miniaturized devices are expected to make more impact on the market than bulky benchtop devices. In this study, as an effort for commercialization of noninvasive glucose sensing technology, we investigate the relationship between Raman spectrometer specification, especially collection efficiency, and glucose prediction performance. Raman spectra were synthesized at given spectrometer collection efficiencies in computer simulation, in which spectra are designed to contain glucose signal at specific concentrations. Then, we estimated glucose concentrations back using regression analysis and evaluated prediction performances. Finally, the relationship was analyzed between the collection efficiencies and glucose prediction performances. In order to mimic actual conditions with skin tissue, Monte-Carlo simulations were conducted to count the number of Raman photons escaping from the skin surface in a multi-layered skin model. As the collection efficiency decreased from 3.2 % to 0.2 %, the correlation coefficient between the actual and predicted glucose concentrations dropped from 0.91 to 0.35. The glucose Raman peaks at 1125 cm-1 was identified as the most important wavelength for glucose sensing. This study may help identify optimal Raman spectrometer specifications for transcutaneous blood glucose sensing in miniaturized devices and commercialize noninvasive blood glucose sensors in Raman spectroscopy.

Non-invasive assessments of the advanced glycation end products in human skin using reflectance NIR spectroscopy.

Advanced glycation end products (AGE) are produced by non-enzymatic reaction between glucose and biomolecules including proteins. AGE accumulation is known to cause alternations of structure and function in proteins and to be related with an increased risk of diabetic complications, cardiovascular diseases, and aging processes. Conventionally, AGE accumulation has been estimated by measuring auto fluorescence level using ultraviolet (UV) light excitation. In this study, we investigated an alternative approach to estimate auto fluorescence level and thus AGE accumulation in in vivo human skin using NIR (Near-Infrared) spectroscopy. To examine spectral features attributed to glycation in proteins, we first analyzed in vitro NIR spectra from native and glycated protein. Then, we further examined NIR spectra of in vivo skin from human subjects, and estimated their auto fluorescence level using several multivariate regression approaches. Our analysis in in vitro spectra from native and glycated albumin revealed that glycation may affect -CH and -NH stretching. Furthermore, we elucidated that those bands for -CH and -NH may be responsible for the variation in auto fluorescence level in human skin NIR spectra. Finally, auto fluorescence level was estimated from those NIR spectra using several multivariate regression methods: principal component regression (PCR), partial least square regression (PLS-R) and support vector regression (SVR). Among the three methods, SVR showed the best performance. We demonstrated in this study that NIR spectroscopy can be used as an alternative non-invasive method to estimate AGE accumulation in in vivo human skin tissue without UV radiation on skin tissue.