ABSTRACT
Common hazelnuts are widely present in human diet all over the world, and their beneficial effects on the health have been extensively investigated and demonstrated. Different in-depth researches have highlighted that the harvesting area can define small variations in the chemical composition of the fruits, affecting their quality. As a consequence, it has become relevant to develop methodologies which would allow authenticating and tracing hazelnuts. In the light of this, the present work aims to develop a non-destructive method for the authentication of a specific high-quality Italian hazelnut, "Nocciola Romana," registered with a protected designation of origin (PDO). Thus, different samples of this fruit have been analyzed by near-infrared (NIR) spectroscopy and then classification models have been built, in order to distinguish between the PDO fruits and the hazelnuts not coming from the designated region. In particular, two different classification approaches have been tested, a discriminant one, partial least squares-discriminant analysis, and a class-modeling one, soft independent modeling of class analogies. Both methods led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other, all higher than 92%), suggesting that the proposed methodologies are suitable for a rapid and non-destructive authentication of the product.
