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Non-destructive measurement of eggshell strength using NIR spectroscopy and explainable artificial intelligence.

Md Wadud Ahmed1, Sreezan Alam1, Alin Khaliduzzaman1

  • 1The Grainger College of Engineering, College of Agricultural, Consumer and Environmental Sciences, Department of Agricultural and Biological Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.

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PubMed
Summary

Near-infrared (NIR) spectroscopy with explainable artificial intelligence (AI) offers a rapid, non-destructive method for predicting eggshell strength. This approach enhances quality control in the egg industry, improving sustainability and reducing waste.

Keywords:
NIR spectroscopyegg industryeggshell strengthexplainable AIvariable selection

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Area of Science:

  • Agricultural Science
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Eggshell strength is vital for egg quality and consumer satisfaction.
  • Traditional eggshell strength testing is destructive, slow, and impractical for large-scale use.
  • Developing non-destructive methods is crucial for the egg industry.

Purpose of the Study:

  • To evaluate near-infrared (NIR) spectroscopy combined with explainable artificial intelligence (AI) for non-destructive eggshell strength prediction.
  • To explore multivariate analysis techniques for improving prediction accuracy.
  • To assess the interpretability of the AI model for practical application.

Main Methods:

  • Near-infrared (NIR) spectroscopy was used to collect spectral data from eggshells.
  • Various multivariate analysis techniques, including principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA), were applied.
  • Regression models such as random forest (RF) and gradient boosting were developed and validated.
  • Shapley additive explanation (SHAP) was employed for model interpretability.

Main Results:

  • NIR spectroscopy effectively classified eggs based on shell strength using PCA and PLSDA.
  • The random forest (RF) model achieved high prediction accuracy (Rp² = 0.83) using only 14 spectral variables.
  • The model demonstrated a low prediction error (RMSEP = 1.49 N) and a good prediction-to-deviation ratio (RPD = 2.44).
  • SHAP analysis provided insights into the key spectral variables influencing eggshell strength predictions.

Conclusions:

  • NIR spectroscopy integrated with explainable AI provides a robust, non-destructive method for eggshell strength prediction.
  • This innovative approach is environmentally sustainable and suitable for industrial quality control.
  • The findings support optimizing resource use and enhancing quality assurance in the egg industry.