Near infrared (NIR) spectroscopy is being developed on living tissue models for noninvasively measuring in vivo glucose concentrations in individuals with diabetes. Multivariate calibration models have been built and the selectivity of each multivariate signature has been evaluated by several means. The primary objective of the research detailed in this dissertation is to practically apply noninvasive NIR glucose measurements on animal models for both short-term and long-term studies and preview future human subject evaluations.

In the animal study, living tissue spectra were collected through a modified optical interface with hyper- and hypo-glycemia control. Selective measurements of glucose molecules are illustrated by the partial lease squares (PLS) algorithm, net analyte signal (NAS) vector, and hybrid linear analysis (HLA). Each model demonstrates the ability to predict prospective glucose concentrations in the short term.

A restraint platform was developed for the long-term study on conscious animals. Conscious animal spectra were collected on multiple days. The anesthetized animal experiment follows on the final day. Principal component analysis (PCA) of spectra collected on different days demonstrates no significant difference between conscious animal spectra and anesthetized animal spectra. Moreover, an NAS vector analysis from conscious animal spectra has the ability predict glucose concentrations which follow the blood glucose transient during the anesthetized animal experiment. This procedure has great potential to be applied in future NIR glucose monitoring device.

Before the application of this noninvasive NIR technology on people with diabetes, the impact of skin difference must be determined. In this human subject study, human skin color and baseline spectra were collected and analyzed to determine differences among individuals and within groups of people. To compare in vivo NIR spectra with different skin characteristics, PCA was performed to obtain principal component (PC) scores. Poor correlation between PC scores and skin characteristics concludes that noninvasive near-infrared technology is insensitive to different types of skin. In addition, glucose prediction was performed by a NAS analysis. The prediction results demonstrate that it is feasible to build a NAS glucose model for noninvasive NIR glucose predictions in human subjects.