Data mining-based discriminant analysis as a tool for the study of egg quality in native hen breeds

Abstract

Abstract Despite the wide biodiversity of avian species of zootechnical interest in Spain, projects aimed at characterizing these genotypes and their products are necessary. External and internal egg quality traits were measured in 819 eggs laid by hens of 10 different genotypes: White, Franciscan, Black and Partridge varieties of Utrerana, Blue Andalusian, Spanish White-Faced, Andalusian Tufted White and Black varieties, Araucana; and Leghorn Lohmann LSL-Classic lineage (commercial hybrid line) hen breeds. After multicollinearity analysis of egg quality-related traits was performed (VIF ≤ 4), major diameter, minor diameter, egg weight, and albumen height were deemed redundant explanatory variables and discarded. A stepwise discriminant canonical analysis was developed to cluster eggs across hen genotypes considering egg quality attributes. Shell a* and b* variables reported the highest discriminant power (Wilks’ lambda: 0.699 and 0.729, respectively). The first two discriminant functions captured 60.48% of the variance across groups (F1: 39.36%; F2: 21.12%). Clear quality differentiation signs are evidenced for Mediterranean native breeds’ eggs when compared to Leghorn’s eggs. Consequently, this evidence of egg quality differentiation may favor the standardization of breed- and variety-linked distinctive products, which may open new market opportunities based on the existence of a wide spectrum of diet or culinary applications.