Single-beam spectra were collected over the combination region of the near-infrared spectrum for 80 samples collected from 15 people over a two-week period. Partial least-squares (PLS) regression was used to generate an optimized calibration model for urea. PLS calibration models accurately measure urea in the spent dialysate matrix. Prediction errors are on the order of 0.15 mM, which is sufficient for the clinical assessment of the dialysis process. In addition, the feasibility of a global calibration model is demonstrated by generating a calibration model from samples and spectra obtained from 12 people to predict the level of urea in samples collected from 3 different people. In this case, the standard error of prediction is 0.09 mM. Spectra were modified in order to systematically examine the impact of resolution and noise. Little impact is observed by altering the spectral resolution from 4 to 32 cm<sup>-1</sup>. Spectral noise, however, plays an important role in the accuracy of these calibration models. Increasing the magnitude of the spectral noise increases the prediction errors and increases the width of the spectral range necessary for extracting the analytical information. The utility of the method is demonstrated by analyzing dialysate samples collected during actual dialysis treatments. In addition, the necessary resolution and spectral quality necessary for reliable <i>on-line</i> urea monitoring is identified. These findings indicate that a dedicated, <i>on-line</i> urea spectrometer must posses a resolution of 16 cm<sup>-1</sup> coupled with a sample thickness of 1.5 mm and spectral noise levels on the order of 25 micro-absorbance units when measured as the root-mean-square (RMS) noise of 100% lines.

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