Elliptic Fourier analysis and artificial neural networks for the identification of grapevine (Vitis vinifera L.) genotypes

The potential application of the Elliptic Fourier Analysis (EFA) for the objective quantitative description of leaf morphology, combined with the use of a Back-propagation Neural Network (BPNN) for data modelling, was evaluated to characterize and identifiy 12 Sangiovese-related accessions (Vitis vinifera L.). The results enable us to distinguish, with considerable certainty, between 10 accessions. Cluster analysis revealed the existence of a uniform group for the Prugnolo (acerbo, medio and dolce) ecotypes showing a high degree of relatedness. Among all accessions only the so-called Casentino ecotype significantly diverged from all the others, indicating probably a different origin. The application of EFA coupled with the use of artificial neural networks opens interesting prospects for the characterization of varieties, allowing to study differences and/or relationships which can not be detected by standard ampelographic systems.