Conventional morphological descriptors and artificial neural networks for characterizing biofortified lettuce germplasm

Abstract

The classification based on morphological descriptors in lettuce is considered a complex activity and proves to be efficient for studying phenotypic characteristics. Therefore, the objective of this study was to analyze the biofortified lettuce germplasm bank at the Universidade Federal de Uberlândia using both conventional morphological descriptors and artificial neural networks. The experiment was conducted in the field. The experimental design employed was a randomized complete block design, consisting of 14 treatments (11 genotypes of mini lettuce, and the cultivars Purpurita, UDI 10.000, and Pira 72) with four replications. Nine morphological descriptors were evaluated. Following the data acquisition, dissimilarity matrix analyses, principal component analysis, dendrogram construction, and artificial neural network (ANN) analyses were performed. The genotypes exhibited phenotypic variability when compared to the parental strains UDI 10.000 and Pira 72. The purple color of the leaves and anthocyanin presence across the entire leaf surface were predominant among the genotypes. Descriptors such as leaf intensity and color, as well as anthocyanin intensity, coloration, and distribution, were the most influential in assessing genetic variability. The Self-Organizing Map (SOM) demonstrated greater sensitivity in discriminating between genotypes compared to the Unweighted Pair Group Method with Arithmetic Mean (UPGMA). While the UPGMA clustering method grouped genotypes into three clusters, the SOM method grouped into five clusters. The use of genetic distance analyses and SOM dendrogram proved to be effective in selecting individuals UFU 215#1, UFU 215#2, UFU 215#6, UFU 215#10, and UFU 215#13, which are clustered with the cultivar UFU Mini Biofort.