Abstract
Diabetes is one of the most serious metabolic diseases worldwide, and frequent monitoring of blood glucose is an essential part of diabetic management. However, a significant drawback of current monitoring methods was destructive and time-consuming. To meet this need, this study was to develop a method for rapid and noninvasive blood glucose assay in a skin tissue phantom by Near-Infrared spectroscopy (NIRS) and Raman spectroscopy. With partial least-squares (PLS) regression method, the multivariate calibration models of NIRS were generated and optimized individually by considering spectral region, spectral pretreatment methods and latent variables (LVs). The optimal NIR model was established with root mean square error of cross-validation (RMSECV) of 0.114, root mean square error of validation (RMSEP) of 0.061, correlation coefficient (R) of 0.9933, and residual predictive deviation (RPD) of 12.2, respectively. The validation results demonstrated that NIRS could be applied for rapid and noninvasive blood glucose assay.
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