Various sensor systems have been developed for detecting sheet properties “on-line,” i.e., on a sheet-making machine while it is operating. Sensors for continuous flat sheet production processes typically employ single or dual-sided packages with on-line sensors that traverse or scan traveling webs of sheet material during manufacture. Near infrared (NIR) spectroscopy is the method of choice for measuring composition or component weight and moisture content in a multitude of products. These include materials produced in sheets such as paper and plastic. The technique is fast, inexpensive, and is compatible with on-line measurement, which allows the process to be controlled in a closed-loop fashion. NIR spectroscopy is accurate if a suitable calibration model can be obtained for the product to be measured. A specific calibration model is required for two main reasons. One reason is that a number of overlapping absorption bands exists in the NIR. Typically, a number of components in the product contribute to the measured absorption bands and a model is required to separate the contributions from the individual components. The second reason is related to light scattering: when light interacts with a sample it gets absorbed and scattered and the amount of scattering depends on the chemical as well as the structural properties of the sample. Paper, in its simplest form, is a mixture of cellulose fibers surrounded by air. Due to index of refraction changes, the cellulose/air interfaces lead to significant light scattering. The scattering power of paper can change dramatically as fillers or even moisture fill the gaps between the cellulose fibers thereby displacing the air. Scattering affects the NIR absorption technique through changes in the average path length through the sample. Scattering, especially in products like paper and powder samples, can significantly reduce the accuracy of absorption-type measurements due to changes in the photon mean free path. As calibrations are not only dependent on a single component but on many components in a non-linear fashion, calibration curves cannot be simply computed. For example, the calibration curves for measuring moisture in paper are multidimensional and depend on cellulose, ash, and furnish contents and concentrations. Simpler calibrations would greatly assist end users by improving the accuracy and robustness of on-line measurements.