Non-targeted HPLC-UV fingerprinting as chemical descriptors for the classification and authentication of nuts by multivariate chemometric methods

Abstract

Recently, the authenticity of food products have become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption, food adulteration practices are raising as it is in fact much easier to conduct fraud without being easily detected. This is the case of nut fruits processed products such as almond flours that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also having important effects on human health. Non-targeted HPLC-UV chromatographic fingerprints were evaluated as chemical descriptors to achieve nut samples characterization and classification using multivariate chemometric methods. Nut samples were extracted by sonication and centrifugation, and defatted with hexane; extracting procedure and conditions were optimized to maximize the generation of enough discriminant features. The obtained HPLC-UV chromatographic fingerprints were then analyzed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to carry out the classification of nut samples. The proposed methodology allowed the classification of samples not only according to the type of nut but also based on the nut thermal treatment employed (natural, fried or toasted products).