Statistical models for food authenticity

Abstract

The authentication of food samples pose a particular problem for regulators. The routine testing of premium food products, most likely to be subject to manipulation for commercial gain, is only feasible if the testing method does not damage the product. Near Infrared (NIR) spectroscopy is one such method that is both fast and non-invasive. However, unlike other spectroscopic methods, peaks in the resulting NIR curves are at imprecise locations, requiring further statistical analysis if it is to be used for the classification of samples. Three NIR datasets are examined in this thesis - two are related to the identification of adulterated samples, the third is a study on the identification of types of meats. Other commonly available, non-NIR, datasets are used for illustrative purposes.