Artificial neural networks optimize the establishment of a Brazilian germplasm core collection of winter squash (Cucurbita moschata D.)

Abstract

Abstract With widespread cultivation, Cucurbita moschata stands out for the carotenoid content of its fruits such as β and α-carotene, components with pronounced provitamin A function and antioxidant activity. C. moschata seed oil has a high monounsaturated fatty acid content and vitamin E, constituting a lipid source of high chemical–nutritional quality. The present study evaluates the agronomic and chemical–nutritional aspects of 91 accessions of C. moschata kept at the BGH-UFV and propose the establishment of a core collection based on multivariate approaches and on the implementation of Artificial Neural Networks (ANNs). ANNs was more efficient in identifying similarity patterns and in organizing the distance between the genotypes in the groups. The averages and variances of traits in the CC formed using a 15% sampling of accessions, were closer to those of the complete collection, particularly for accumulated degree days for flowering, the mass of seeds per fruit, and seed and oil productivity. Establishing the 15% CC, based on the broad characterization of this germplasm, will be crucial to optimize the evaluation and use of promising accessions from this collection in C. moschata breeding programs, especially for traits of high chemical–nutritional importance such as the carotenoid content and the fatty acid profile.

Keywords

• Computational intelligence
• Cucurbita
• Fatty acid profile
• Germplasm collection
• Kohonen maps