Abstract

The statistical technique of principal components is used to analyze two sets of near-infrared spectra, wheat flour samples for which % moisture and % protein values are included, and milled barley samples for which hot water extract values are included. The methodology and interpretation of this technique are described within the context of NIR data, and its advantages both in providing insight into the variation of the spectra, and as a method of avoiding the problems caused by highly correlated reflectance energy values in the derivation of calibration equations, are highlighted. In each set of samples the first principal component accounts for the vast majority of the variation. These components also have an almost identical shape, which is interpreted as reflecting particle size. The second wheat component and the third barley component are also almost identical, with a shape very similar to that of the spectrum of water. Both fourth components share peaks at points in the spectrum which are used by fixed-filter instruments to measure protein in cereals.

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