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Nondestructive Detection of Eggshell Thickness Using Near-Infrared Spectroscopy Based on GBDT Feature Selection and

Ziqing Li1,2, Ying Ji1,2, Changheng Zhao3

  • 1College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China.

Foods (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

This study introduces a new non-destructive method using Gradient Boosting Decision Tree (GBDT) and CatBoost for predicting eggshell thickness. The advanced technique improves accuracy for poultry egg quality grading.

Keywords:
CatBoostGBDTeggshell thicknessfeature optimizationnon-destructive testingspectral analysis

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Machine Learning

Background:

  • Eggshell thickness is vital for egg breakage resistance and hatchability.
  • Traditional measurement methods are destructive and inefficient, hindering quality assessment.

Purpose of the Study:

  • To develop a robust, non-destructive prediction approach for eggshell thickness.
  • To integrate Gradient Boosting Decision Tree (GBDT) feature optimization with an improved CatBoost algorithm for enhanced accuracy.

Main Methods:

  • Applied Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) for spectral data preprocessing.
  • Utilized GBDT for nonlinear feature selection, identifying optimal wavelengths.
  • Developed an improved CatBoost regression model with Ordered Boosting and anti-overfitting strategies (10-fold nested cross-validation, Bootstrap resampling).

Main Results:

  • Achieved high prediction accuracy with coefficients of determination (R²) of 0.930 (calibration) and 0.918 (prediction).
  • Obtained a low root mean square error of prediction (RMSEP) of 0.008 mm.
  • Demonstrated superior performance compared to traditional algorithms in prediction accuracy and generalization, with errors following a zero-mean Gaussian distribution.

Conclusions:

  • The proposed GBDT-CatBoost approach offers a reliable, non-destructive method for assessing eggshell thickness.
  • This research provides a strong theoretical and technical basis for intelligent poultry egg quality grading.
  • The method effectively mitigates challenges like the curse of dimensionality and multicollinearity in spectral data.