Use of near-infrared (NIR) diffuse reflectance on ground wheat meal for prediction of protein content is a well-accepted practice. Although protein content has a strong bearing on the suitability of wheat (Triticum aestivum L.) for processed foods, wheat quality, as largely influenced by the configuration and conformation of the monomeric and polymeric endosperm storage proteins, is also of great importance to the food industry. The measurement of quality by NIR, however, has been much less successful. The present study examines the effects and trends of applying mathematical transformations (pretreatments) to NIR spectral data before partial least-squares (PLS) regression. Running mean smooths, Savitzky-Golay second derivatives, multiplicative scatter correction, and standard normal variate transformation, with and without detrending, were systematically applied to an extensive set of hard red winter wheat and hard white wheat grown over two seasons. The studied properties were protein content, sodium dodecyl sulfate (SDS) sedimentation volume, number of hours during grain fill at temperature <24 degrees C, and number of hours during grain fill at temperature >32 degrees C. The size of the convolution window used to perform a smooth or second derivative was also examined. The results indicate that for easily modeled properties such as protein content, the importance of pretreatment was lessened, whereas for the more difficult-to-model properties, such as SDS sedimentation volume, wide-window (>20 points) smooth or derivative convolutions were important in maximizing calibration performance. By averaging 30 PLS cross-validation trial statistics (standard error) for each property, we were able to ascertain the inherent modeling ability of each wheat property.