Wine authenticity assessed via trimming

An authentic food is one that is what it claims to be. Consumers and food processors need to be assured they receive exactly the specific product they pay for. To ascertain varietal genuinity and distinguish doctored food, in this paper we propose to employ a robust mixture estimation method. It has been shown to be a valid tool for food authenticity studies, when applied to food data with unobserved heterogeneity, to classify genuine wines and identify low proportions of observations with different origins. Our methodology models the data as arising from a mixture of Gaussian factors and employ a threshold on the multivariate density to bring apart the less plausible data under the fitted model. Simulation results assess the effectiveness of the proposed approach and yield very good misclassification rates when compared to analogous methods