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Volume 61, issue 3
Arch. Anim. Breed., 61, 279-284, 2018
https://doi.org/10.5194/aab-61-279-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Arch. Anim. Breed., 61, 279-284, 2018
https://doi.org/10.5194/aab-61-279-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Original study 16 Jul 2018

Original study | 16 Jul 2018

Prediction of internal egg quality characteristics and variable selection using regularization methods: ridge, LASSO and elastic net

Mehmet Nur Çiftsüren1 and Suna Akkol2 Mehmet Nur Çiftsüren and Suna Akkol
  • 1Van Yuzuncu Yil University, Graduate School of Science Institute, Department of Animal Science, Van, Turkey
  • 2Van Yuzuncu Yil University, Faculty of Agriculture, Department of Animal Science, Biometry and Genetic Unit, Van, Turkey

Abstract. This study was conducted to determine the inner quality characteristics of eggs using external egg quality characteristics. The variables were selected in order to obtain the simplest model using ridge, LASSO and elastic net regularization methods. For this purpose, measurements of the internal and external characteristics of 117 Japanese quail eggs were made. Internal quality characteristics were egg yolk weight and albumen weight; external quality characteristics were egg width, egg length, egg weight, shape index and shell weight. An ordinary least square method was applied to the data. Ridge, LASSO and elastic net regularization methods were performed to remove the multicollinearity of the data. The regression estimating equations of the internal egg quality were significant for all methods (P < 0.01). The goodness of fit of the regression estimating equations for egg yolk weight was 58.34, 59.17 and 59.11% for the ridge, LASSO and elastic net methods, respectively. For egg albumen weight the goodness of fit of the regression estimating equations was 75.60%, 75.94% and 75.81% for the respective ridge, LASSO and elastic net methods. It was revealed that LASSO, including two predictors for both egg yolk weight and egg albumen weight, was the best model with regard to high predictive accuracy.

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This study was conducted to determine inner egg quality characteristics using external egg quality characteristics and to select variables in order to obtain the simplest model using ridge, LASSO and elastic net. The goodness of fit values of the regression estimating equations for egg yolk weight were 58.34 %, 59.17 % and 59.11 % using the ridge, LASSO and elastic net methods, respectively; for egg albumen weight they were 75.60 % for ridge, 75.94 % for LASSO and 75.81 % for elastic net.
This study was conducted to determine inner egg quality characteristics using external egg...
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