Near infrared reflectance is now widely used for the rapid analysis of many agricultural and food products for protein, moisture, oil, starch, sucrose, fiber, grain texture and lysine. Research has shown that the technique can also be used for the prediction of malting quality of barley, baking quality of wheat and measurement of the degree of starch damage in flour.



The technique of near infrared reflectance uses very small differences in absorption of NIR radiation at wavelengths corresponding to overtones and combinations of fundamental IR frequencies of chemical functional groups that are characteristic of particular analytes. Complex regression mathematics is used to transform these absorption measurements into an analytical result.

Near infrared reflectance spectroscopy (NIRS) is an accurate and rapid alternative to wet chemistry procedures for determining concentrations of major classes of chemical compounds in organic materials, such as plant foliage. The process utilizes reflectance signals resulting from bending and stretching vibrations in molecular bonds between carbon, nitrogen, hydrogen, and oxygen. Calibration is required to mix the spectral response of each sample at individual wavelengths to known chemical concentrations from laboratory analyses.

In near infrared reflectance, Nitrogen, lignin, and cellulose concentrations for woody plant foliage: green leaf, leaf litter, and decomposing leaf litter can be measured in the laboratory according to procedures. The sample preparation for the calibration equations for woody plant foliage are used for prediction of nitrogen, lignin, and cellulose concentrations in unknown samples. Precision of equation for prediction of unknown samples basically depends on whether the range of variation affecting the chemical and physical properties of the unknown samples is represented in the calibration samples. Universal equations developed for broad, or infinite populations is used for a wide range of samples. The importance of universal equations is to increase the value of NIRS for the study of large-scale ecosystem processes.