Technology capable of measuring glucose levels noninvasively in the human body would revolutionize the treatment and management of diabetes. The near infrared (NIR) approach passes a selected band of near infrared radiation through a vascular region of living tissue and extracts the concentration of glucose. Albumin is an important component of biological fluids and represents a significant interference from serum for glucose monitoring. The ability to quantify glucose under the interference especially caused by albumin is a critical point to the accuracy of glucose measurement with NIR single-beam and absorbance spectra. A set of standard solutions were prepared with randomized concentrations of glucose, urea, lactate, and albumin in pH 6.86 phosphate buffer. Near infrared spectra were collected with a Thermo-Nicolet Magna 550 Fourier transform spectrometer within combination spectral range of 5000 – 4000 cm-1. All spectra were collected in triplicate when sample solutions were maintained at 37 °C. Partial least square regression analysis was applied to both NIR single-beam and absorbance spectra coupled with optimized spectral range and number of latent variables. Results indicate similar calibration performance for measuring glucose, urea, lactate, and albumin concentrations in these solutions. For example, the standard error of calibration (SEC) and standard error of prediction (SEP) are 0.27 and 0.26 mM, respectively, for the measurement of glucose from single-beam spectra. In comparison, SEC and SEP values are 0.24 and 0.23 mM, respectively, for the measurement of glucose from absorbance spectra. Further investigation confirms the selectivity of calibration models generated from both types of spectra.