Using Unlabelled Data To Update Classification Rules With Applications In Food Authenticity Studies

A classification method is developed to classify samples when both labelled and unlabelled samples are available. The classification rule is estimated using both the labelled and unlabelled data, in contrast to many classical methods which only use the labelled data for estimation. This methodology models the data as arising from a Gaussian mixture model with parsimonious covariance structure, as is done in model-based clustering (Fraley and Raftery (2002)). A missing-data formulation of the mixture model is used and the models are fitted using the EM and CEM algorithms. A comparison of the performance of model-based discriminant analysis and the proposed method of classification is given. The methods are applied to the analysis of spectra of foodstuffs recorded over the visible and near-infrared wavelength range in food authenticity studies. The aim of this study is to classify the foodstuffs using their spectra. The proposed classification method is shown to yield very good misclassification rates. The correct classification rate was observed to be as much as 15% higher than the correct classification rate for model-based discriminant analysis.