Quantitative method for shape description of almond cultivars (Prunus amygdalus Batsch)
Antonucci, F.; Costa, C.; Pallottino, F.; Paglia, G.; Rimatori, V.; De Giorgio, D.; Menesatti, P.
Food and Bioprocess Technology
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The aim of the present work was to propose a rapid, non-invasive, and quantitative image analysis method based on elliptic Fourier analysis (EFA) and on carpological measurements to discriminate between 18 cultivars and shape groups of almond kernels and in-shell fruit. The shape groups were identified using two clustering techniques: a non-hierarchic method (k-means) and a hierarchical one (Ward’s method). Both methods found the same numbers of groups for in-shell fruit and kernels. The obtained results indicate that such differences can be used to discriminate among shape groups. This method was not efficient in discriminating single cultivars. In order to classify fruit into shape groups, a partial least squares discriminant analysis was applied. This analysis applied on the 18 cultivar groups showed low percentages of correct classification for both in-shell (38.58%) and kernels (31.36%). The same analysis computed on shape groups shows percentages of correct classification higher than 89%. Merging EFA, clustering methods, and modeling techniques set the basis for the implementation of an automated online fruit sorting. A Matlab script was developed to determine the right number of clusters in k-means clustering.