Correlation between Internal and External Egg Quality Indicators in the Early Phase of Hy-Line Brown Laying Hens

Jang, Eunhye

doi:10.5536/kjps.2022.49.2.53

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…This finding is consistent with the results of several previous studies on other poultry species such us quails (Ouaffai et al,2018), Guinea Fowl (Onunkwo and Okoro, 2015), partridge (Alkan et al,2014), and commercial layers (Orhan et al, 2016). The statistically non-significant and negative phenotypic correlation value found in the present study between the egg weight and the egg shape index is consistent with the findings of several previous studies (Olawumi and Ogunlade, 2008;Alkan and Türker, 2021;Jang, 2022).…”

Section: Discussionsupporting

confidence: 94%

Exploring Data Mining Algorithms for Predicting Duck Egg Weight Based on Egg Quality Characteristics

2024

THE JAPS

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The present investigation aimed to compare the performance of twomachine learning algorithms, Artificial Neural Network (ANN), and Classification and Regression Tree (CART), alongside the Automatic Linear Modelling (ALM), and the traditional Multivariate Linear Regression model (MLR) to predict the egg weight (EWT) of Mallard duck from some egg traits including egg length (EL), egg width (EWd), egg shape index (ESI), eggshell weight (ESW), albumen weight (AW), albumen height (AH), yolk weight (YW), yolk height (YH), yolk diameter (YD), and Haugh unit (HU). The Pearson correlation between observed and predicted values (r), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj), Root Mean Squared Error (RMSE), and Relative Approximation Error (RAE) were used to estimate model performance. EWT hada strong correlation with egg dimensions (EL and EWd, r=0.752 and 0.790, respectively), AW (r= 0.815), and YW (r= 0.784). The R 2 adj values were 0.981, 0.970, 0.964 and 0.897, for ANN, ALM, MLR, and CARTmodels, respectively. The lowest RMSE was found for ANN (0.753), while the highest RMSE was observed for CART (1.778). Overall, the ensemble models proposed in this study yielded similar results, with the ANN algorithm showing a marginally superior predictive performance compared to ALM, CART, and MLR models. This finding suggests that ANN could be considered the most suitable for the prediction of egg weight in Mallard duck.

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Section: Discussionsupporting

confidence: 94%

Exploring Data Mining Algorithms for Predicting Duck Egg Weight Based on Egg Quality Characteristics

2024

THE JAPS

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Exploring the physical characteristics of eggs for consumption and industrial use in Kosovo: a comprehensive quality analysis

Mestani,

Zeqiri,

Bytyçi

et al. 2024

International Journal of Food Properties

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Correlation between Internal and External Egg Quality Indicators in the Early Phase of Hy-Line Brown Laying Hens

Cited by 2 publications

References 30 publications

Exploring Data Mining Algorithms for Predicting Duck Egg Weight Based on Egg Quality Characteristics

Exploring Data Mining Algorithms for Predicting Duck Egg Weight Based on Egg Quality Characteristics

Exploring the physical characteristics of eggs for consumption and industrial use in Kosovo: a comprehensive quality analysis

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