The ability to measure glucose transcutaneously and noninvasively is an exciting prospect. Such a procedure will offer a painless way of glucose self-monitoring improving the lives of people with diabetes by lowering the barriers to optimal glycemic control. The noninvasive measurements involve collecting near-infrared spectra (4000–5000 cm-1; 2.0–2.5 µm) of skin with two optical fibers in a transmission geometry. Previous results indicate that repositioning of the fiber optic interface adversely affects both precision and accuracy of such measurements. Slight movements of the interface increase prediction errors more than 2.5–fold when performed with a stationary rat model. In this dissertation, the chemical heterogeneity of skin tissue is explored as a possible cause for the sensitivity of the measurement to the position of the optical interface. Rat and human skin tissues are mapped by using combination near infrared spectra the to provide distributions of the major components of skin: water, collagen type I protein, fat, keratin protein, and two scattering terms of constant and slope. On the basis of the measured heterogeneity, sets of rat and human skin spectra are simulated to investigate the impact of repositioning the fiber-optic interface. Glucose predictions are analyzed for each location of the interface for a series of partial least squares (PLS) calibration vectors established for different locations on the skin. Significant increases in the measurement errors are observed for the situation where the PLS calibration models are generated from spectra associated with one location of the interface while subsequent measurements are performed at slightly locations of the skin matrix. These increases in prediction errors match the 2.5–fold increase observed in vivo. The impact of replacing the spectrum of bovine fat with spectra of native fat for both rat and human skin samples is established. Principal component analysis (PCA) of the spectral residuals reveals that the magnitude of the spectral residuals and the effects of tissue fat content on the quality of the linear regression were decreased. The key implication of the research detailed in this dissertation is that chemical heterogeneity of skin tissue must be considered in multivariate models intended for noninvasive glucose measurements.