Artificial neural networks reveal genetic diversity in acerola (Malpighia emarginata DC.) based on fruit quality traits

Acerola (Malpighia emarginata DC.), a tropical fruit, is renowned as one of the richest natural sources of vitamin C. Breeding programs are essential for identifying superior genotypes with the desired attributes for various applications. This study aimed to evaluate the genetic diversity of acerola based on fruit quality traits. Fruits from 35 acerola genotypes, sourced from an active germplasm bank, were harvested at the fully expanded—green—and ripe—red—maturity stages. They were assessed in terms of thei diameter, mass, color, firmness, soluble solids (SS) content, titratable acidity (TA), SS/TA ratio, and vitamin C content. Genetic diversity was analyzed using two approaches: (i) a classical hierarchical clustering method, the unweighted pair-group method with arithmetic mean (UPGMA), based on the Mahalanobis distance, and (ii) artificial neural networks via Kohonen self-organizing maps. Significant genetic diversity was observed across all quality traits at both maturity stages. Both clustering methods were consistent in identifying the genetic diversity among the acerola genotypes. The ‘Okinawa’ genotype was the most divergent at the green stage due its higher mass and firmness, as well as its high vitamin C content, making it ideal for industrial vitamin C extraction. At the red maturity stage, 'BRS Rubra' was the most divergent, exhibiting the highest SS content and SS/TA ratio, making it suitable for fresh consumption and processing. The SS/TA ratio was the trait that contributed the most to the genetic diversity of acerola, accounting for 24.8% and 46.4% at the green and red stages, respectively. These results underscore the importance of genetic diversity studies in identifying superior genotypes with desirable quality traits. The considerable genetic variability found offers valuable opportunities for future breeding efforts to improve acerola fruit quality and enhance its market potential.