Oats’ geographical origin and internal component contents have great impacts on their flavor, off-flavor,
emulsifying and binding properties. Geographical origin identification was done by classical Soft
Independent Modeling of Class Analogy (SIMCA) and least squares support vector machine (LS-SVM),
respectively. The correct answer rate of the LS-SVM model was 98.3%, better than that of SIMCA (75.0%).
LS-SVM models were also established for the content determination of crude fat and crude protein of
oats. Eleven and thirteen wavelength variables were selected by successive projections algorithm for
crude protein and crude fat analyses, respectively. The correlation coefficients were 0.814 and 0.915,
and the root mean square errors of validation were 0.338 and 0.389 for the crude protein and crude fat
analyses, respectively. Overall Vis-NIRS is a promising technique for the fast and reliable determination
of geographical origins and crude fat content of oats. The determination of crude protein content needs
to be further researched.
Key words: Visible and near-infrared spectroscopy, oats, protein, fat.