Improved Shell Color Index for Chicken Eggs with Blue-green Shells Based on Machine Learning Analysis

Shell color is a commercially valuable trait in eggs, and blue-green eggshells typically exhibit multiple color subtypes. To explore the relationship between the CIELab system and visual color classification and develop simplified discrimination indices, 2274 blue-green eggs across seven batches were selected. The L*, a*, and b* values of each egg were measured, and average visual classification (AveObs) was calculated from four numeric categories (Light = 1, Blue = 2, Green = 3, Olive = 4) separately assigned by four observers. After batch correction using ComBat, four algorithms-linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and neural network (NNET)-were compared. Correction substantially reduced the coefficients of variation of the L*, a*, and b* values. Correlations emerged: L* and b* (-0.722), a* and b* (0.451), and L* and a* (-0.088), while correlations of the L*, a*, and b* values with AveObs were -0.713, 0.218, and 0.771, respectively. The LDA model achieved superior comprehensive performance across all data scenarios, with the highest accuracy and efficiency as compared to the SVM, NNET, and RF models. Among the LDA functions, LD1 explained 78.53% of the variance, with L*, a*, and b* coefficients of -0.134, 0.063, and 0.349, respectively (ratio ≈ 1:0.47:2.60). Simplified formulas based on the L*, a*, and b* values were constructed and compared to the existing indices C* (=a*2+b*2) and SCI (=L* - a* - b*). The correlation between L* - 2b* and AveObs was -0.803, similar to those for C* (0.797) and SCI (-0.782), while the correlation between L* - 4C* and AveObs was -0.810, significantly higher than that for SCI (<i>p</i> < 0.05). In conclusion, the LDA model demonstrated optimal performance in predicting color classification, and L* - 4C* is an ideal index for grading of blue-green eggs.