Gamma irradiation has been paramount for enhancing the quality of grain production, mainly in terms of
distribution and storage. Gamma irradiation classification models for soybeans, however, are still in
development. In this paper, we present a metabonomic model able to distinguish between gamma irradiated and non-irradiated soybeans. The metabonomic model works on 1H NMR spectra of chloroform extracts and makes use of PCA and PLS-DA formalisms to classify the samples and to investigate
which spectral bins are discriminatory. The model has presented an accuracy of 100% in the face of a real
dataset involving 49 samples from diverse cultivars. In turn, the most important chemical shifts ( d) for
discriminating among the samples were d 1.57 and 1.62 ppm, which are assigned to b-carboxyl methylene groups of aliphatic chains of fatty acids. Besides, the gamma-irradiated samples showed an
increasing in the integration areas at d 1.57 ppm (assigned to free fatty acid) whilst non-irradiated
samples showed an increasing in the integration areas at d 1.62 ppm (assigned to fatty acids linked to
glycerol as esters).