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Abstract
Digital image analysis and multivariate data analysis were used in this study to identify a set of leaf and fruit morphometric traits to discriminate white mulberry (Morus alba L.) cultivars. The trial was conducted using three- to five-year-old potted cuttings of several white mulberry cultivars. 32 leaf morphometric descriptors were recorded in 2011 and 2012 from 11 mulberry cultivars using image analysis of scanned leaves, whereas six fruit descriptors were recorded in 2011 from nine mulberry cultivars. Linear discriminant analysis (LDA) was used to identify a subset of measured variables that could discriminate the cultivars in trial. Biplot analysis, followed by cluster analysis, was performed on the discriminant variables to investigate any possible cultivar grouping based on similar morphometric traits. LDA was able to discriminate the 11 cultivars with a canonical function, which included 13 leaf descriptors. Using those 13 descriptors, the Biplot showed that over 84% of the variability could be explained by the first three factors. Clustering of standardized biplot coordinates recognized three groups: the first including ‘Korinne’ and ‘Miura’ with similar leaf angles and apical tooth size; the second including ‘Cattaneo’, ‘Florio’, ‘Kokusò-21’, ‘Kokusò-27’, and ‘Kokusò Rosso’ with similar leaf size and shape; and the third including ‘Ichinose’, ‘Kayrio’, ‘Morettiana’, and ‘Restelli’, with similar leaf margin. Fruit descriptors were fewer and measured on fewer cultivars, yielding smaller discriminatory power than leaf descriptors. Use of leaf morphometric descriptors, along with image and multivariate analysis, proved to be effective for discriminating mulberry cultivars and showed promise for the implementation of a simple and inexpensive characterization and classification tool.
Highlights
A fair amount of variation across cultivars was detected in all measured leaf and fruit descriptors (Tables 2 and 3), which proved the initial selection of descriptors to be effective for mulberry cultivar identification purposes
Descriptors that were not related to fruit size, such as length of peduncle, total soluble solids (TSS), and width/height ratio (W/H), exhibited a significant amount of variation (c.v. 16%–30%) across cultivars despite the reduced number of cultivars tested in a single year (Table 3)
Linear discriminant analysis (LDA) was able to fully separate (Wilk’s Lambda statistics, p < 0.001) the 11 cultivars with a canonical discriminant function after a backward step analysis, which included L, W, L-P, H-SP, L-V2, L-V3, H-DA, T3, T5, T6, T9, W/L, and BOUNDARY. Using these 13 leaf descriptors selected by LDA, the biplot analysis showed that over 84% of the variability observed was explained by the first three factors, and about 73% was explained by the first two factors (Figure 1). k-means clustering of standardized biplot coordinates from all three factors separated the leaf descriptors into three groups: the first associating vein angles and H-DA; the second associating blade size, vein lengths, L-P, H-SP, and W/L; and the third including BOUNDARY
Summary
Mulberry has been cultivated since the ancient times, and has tremendous economic importance today—especially in Asia—in regard to its use as feed to silk worms, as animal fodder, and in regard to its fruit production. Mulberry species tend to hybridize which has led to its considerable genetic variability. There are more than 68 species of the more widely recognized mulberry [1], of which only a few (e.g., Morus alba L., Morus indica L., Morus bombycis Koidz., Morus latifolia L., Morus multicaulis Perr., and Morus nigra L.) are cultivated for either fruit or leaf production. Mulberry exhibits different ploidy levels, ranging from 2n = 28 all the way up to 22n = 308 [2].
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