We have previously demonstrated the correlation of continuous-wave near infrared (CW-NIR) tissue measurements, to blood and tissue metabolic parameters using Partial Least Squares (PLS) regression. The practical use of this non-invasive measurement technique depends on the transfer of PLS calibration models from a single calibration unit to multiple secondary units. Variations in the spectral characteristics of the optical components across multiple units result in marked differences in the spectral output, preventing the direct transfer of parameter models from one unit to another. Consequently, we have developed a method for standardizing the spectral output across units that utilizes physical, traceable, reference materials for aligning the wavelength and intensity axes to fixed values, followed by spectral normalization via Standard Normal Variate transformation. The approach employed in this study adjusts the slope and bias differences in the optical spectra across multiple units, without the loss of useful information needed for parameter estimation. In this study, phantoms containing Agar, intralipid and lyophilized human hemoglobin (met-hemoglobin) were used to mimic human tissue. Using PLS regression, a hemoglobin calibration model was developed on the tissue-like phantoms on a prototype of the portable NIR medical monitor. The calibration model was successfully transferred to a second, distinctly different system. The Root Mean Squared Error of Prediction of met-hemoglobin in the phantom samples measured in the second system, improved from 4.94g/dl to 1.15g/dl after the standardization procedure. This compares favorably the PLS model error on the primary instrument (0.94g/dl).