Last modified: 2018-05-18
Abstract
Food authenticity studies deal with the detection of products that are not
what they claim to be. We introduce a robust semi-supervised classification rule in
which a potential illegal sub-sample is detected by selecting observations with the
lowest contributions to the overall likelihood using impartial trimming. Experiments
on an artificially adulterated dataset are provided to underline the benefits of the
proposed method.
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