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Discriminant Canonical Analysis as a Validation Tool for Multivariety Native Breed Egg Commercial Quality Classification.

Antonio González ArizaAnder Arando ArbuluFrancisco Javier Navas GonzálezJuan Vicente Delgado BermejoMaria Esperanza Camacho Vallejo
Published in: Foods (Basel, Switzerland) (2021)
This study aimed to develop a tool to validate multivariety breed egg quality classification depending on quality-related internal and external traits using a discriminant canonical analysis approach. A flock of 60 Utrerana hens (Franciscan, White, Black, and Partridge) and a control group of 10 Leghorn hens were placed in individual cages to follow the traceability of the eggs and perform an individual internal and external quality assessment. Egg groups were determined depending on their commercial size (S, M, L, and XL), laying hen breed, and variety. Egg weight, major diameter, minor diameter, shell b*, albumen height, and the presence or absence of visual defects in yolk and/or albumen showed multicollinearity problems (variance inflation factor (VIF) > 5) and were discarded. Albumen weight, eggshell weight, and yolk weight were the most responsible traits for the differences among egg quality categories (Wilks' lambda: 0.335, 0.539, and 0.566 for albumen weight, eggshell weight, and yolk weight, respectively). The combination of traits in the first two dimensions explained 55.02% and 20.62% variability among groups, respectively. Shared properties between Partridge and Franciscan varieties may stem from their eggs presenting heavier yolks and slightly lower weights, while White Utrerana and Leghorn hens' similarities may be ascribed to hybridization reminiscences.
Keyphrases
  • body mass index
  • weight loss
  • physical activity
  • weight gain
  • heat stress
  • body weight
  • machine learning
  • deep learning
  • quality improvement
  • dna methylation
  • single molecule
  • optic nerve