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Classification of grain odors by multivariate analyses of chemical and sensory data. L. M. SEITZ (1)
and M. S. Ram (2). (1) USDA, ARS, Grain Marketing and Production Research Center, 1515 College
Avenue, Manhattan, KS 66502 and (2) Department of Grain Science and Industry, Kansas State University,
Manhattan, KS 66506.
To gain information relating to development of an objective method for
classifying grain odors, we used dynamic headspace technology coupled with gas chromatography-mass
spectroscopy to determine volatiles in a set of 745 samples consisting of corn, sorghum, soybeans, and
wheat. Sensory data for each sample was obtained from at least two panels. The chemical and sensory data
was subjected to multivariate analyses such as principal component analysis (PCA) and partial least squares
(PLS) methods to determine what volatiles could be used to classify grain odors. Proper choice of samples
and use of optimized variables (compounds indicating off-odors), as well as preprocessing of raw data,
including scaling, transformation, and normalization, were necessary for obtaining good models. Samples
with discernable mixed odors, i.e. having both musty and sour odors, were avoided in making models. We
found that PCA and PLS methods could classify samples into musty, sour, smoke, insect and other odor
categories from analysis of chemical data concerning relative amounts of specific volatile compounds
purged from each sample. Mixed odors in some samples became apparent only after considering results
from multivariate analyses.
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