Measurement of gliadin and glutenin content of flour by NIR spectroscopy

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 methodolog y is to assemble a calibration set which maximises the diversity, of sample s represented whilst minimising the intercorrelations between constituents, particularly total protein content and moisture content. The application o f NIR measurements of grain has moved beyond simply measuring protein and m oisture content. There is now considerable interest in using NIR to measure a range of quality parameters such as Extensograph extensibility and maxim um resistance. These parameters arc not themselves represented in the NIR s pectrum, but are a direct result of the protein composition of the sample. Consequently. a method for predicting the protein composition would be usef ul. In this paper. we present the results of a comparison of a curve fittin g methodology and the more usual partial least squares curve fitting of the component protein spectra, using samples obtained from a wheat breeders' t rial. Gliadin and glutenin contents were measured by SE-HPLC and used to de velop a partial least squares calibration and the results compared with a c urve-fitting methodology. For the situation examined here. the curve fining methodology did not perform as well as partial least square, 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 discrimina tion for potential use in sample protein ranking. (C) 2001 Academic Press.