Measurement of Gliadin and Glutenin Content of Flour by NIR Spectroscopy

Abstract Traditional NIR calibration methods rely on assembling a calibration set of samples and using procedures such as multiple linear regression or partial least squares to develop the calibration. The problem with this methodology is to assemble a calibration set which maximises the diversity of samples represented whilst minimising the intercorrelations between constituents, particularly total protein content and moisture content. The application of NIR measurements of grain has moved beyond simply measuring protein and moisture content. There is now considerable interest in using NIR to measure a range of quality parameters such as Extensograph extensibility and maximum resistance. These parameters are not themselves represented in the NIR spectrum, but are a direct result of the protein composition of the sample. Consequently, a method for predicting the protein composition would be useful. In this paper, we present the results of a comparison of a curve fitting methodology and the more usual partial least squares curve fitting of the component protein spectra, using samples obtained from a wheat breeders» trial. Gliadin and glutenin contents were measured by SE-HPLC and used to develop a partial least squares calibration and the results compared with a curve-fitting methodology. For the situation examined here, the curve fitting methodology did not perform as well as partial least squares calibration. For glutenin, SEP=0·65 for the curve fitting compared to SECV=0·38 for a traditional PLS calibration. However, the results from the curve-fitting are independent of the total protein content and show sufficient discrimination for potential use in sample protein ranking.