A method is described for measuring clinically relevant levels of glucose in a protein matrix by near-infrared (near-IR) absorption spectroscopy. Results from an initial screening of major blood constituents identify protein as a major potential interference to the near-IR measurement of glucose in blood. The interference by protein is caused by relatively high concentrations coupled with strong near-IR absorption bands between 5000 and 4000 cm-1 (2.0-2.5 microns). Calibration models based on a simple univariate calibration procedure are not capable of providing accurate glucose concentrations from an independent set of prediction spectra. By use of the multivariate technique of partial least squares (PLS) regression, glucose concentrations can be determined with a 0.35 mM (6.3 mg/dL) standard error of prediction. The spectral range for this calibration model extends from 4600 to 4200 cm-1, and the optimum number of PLS factors is 14. In addition, calibration models based on a combination of digital Fourier filtering and PLS regression have been constructed and evaluated. Superior calibration models are obtained by using a preprocessing digital filtering step to remove spectral features not associated with glucose. The best overall calibration model was obtained by using a Gaussian-shaped Fourier filter defined by a mean position of 0.03f and standard deviation of 0.007f coupled with a 12-factor PLS regression computed over the spectral range from 4600 to 4200 cm-1. This model provided a standard error of prediction of 0.24 mM (4.3 mg/dL) for an independent set of prediction spectra.(ABSTRACT TRUNCATED AT 250 WORDS)