Robust Updating Classification Rule with applications in Food Authenticity Studies = Robust Updating Classification Rule con applicazioni a studi di autenticità degli alimenti

In food authenticity studies the central concern is the detection of prod- ucts that are not what they claim to be. Here, we introduce robustness in a semi- supervised classification rule, to identify non-authentic sub-samples. The approach is based on discriminating observations with the lowest contributions to the over- all likelihood, following the impartial trimming established technique. Experiments on real data, artificially adulterated, are provided to underline the benefits of the proposed method.