Prediction of Albumen Height Based on Egg Quality Traits by Principal Components Regression
DOI:
https://doi.org/10.5281/zenodo.18234774Keywords:
Multicollinearity problem, Principal components regression method (PCR), Egg characteristicsAbstract
In this study, multiple linear regression analysis and principal component regression (PCR) method were used to predict the albumen height of Atak-S layer hen eggs. Initially, the effects of independent variables on albumen height were examined through multiple linear regression analysis, revealing that egg weight and Haugh unit had statistically significant impacts. Other factors such as egg size, shape index, and shell thickness did not have a significant effect. The explanatory power of the model was found to be high, as it explained 96 % of the total variation in albumen height. Subsequently, PCR was applied to address issues related to multicollinearity problem, and only three principal components (PC1, PC2, and PC3) were included in the model. The effects of these components on albumen height were found to be significant, with particular emphasis on the importance of morphological parameters (egg size and shell structure) in predicting internal egg quality. The PCR model demonstrated high predictive performance, accurately forecasting albumen height. In conclusion, the PCR method used in this study provided a robust model for predicting albumen height and highlighted the critical role of morphological characteristics in determining egg quality. Future studies could test the generalizability of this model using different hen breeds and larger sample sizes, as well as investigate the effects of environmental factors and feeding strategies.
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