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

Background: Eggshell strength is crucial for ensuring high-quality eggs, reducing breakage during handling, and meeting consumer expectations for freshness and integrity. Conventional methods of eggshell strength measurement are often destructive, time-consuming and unsuitable for large-scale applications. This study evaluated the potential of near-infrared (NIR) spectroscopy combined with explainable artificial intelligence (AI) as a rapid, non-destructive method for determining eggshell strength. Various multivariate analysis techniques were explored to enhance prediction accuracy, including spectral pre-processing and variable selection methods. Results: Principal component analysis and partial least squares discriminant analysis effectively classified eggs based on a threshold shell strength of 30 N. Regression models, including partial least squares regression, random forest (RF), light gradient boosting machine and K-nearest neighbors, were evaluated. Using only 14 selected variables, the RF model achieved a very good prediction performance with (Formula presented.) of 0.83, root mean square error of prediction of 1.49 N and ratio of prediction to deviation of 2.44. The Shapley additive explanation approach provided insights into variable contributions, enhancing the model's interpretability. Conclusion: This study demonstrated that NIR spectroscopy, integrated with explainable AI, is a robust, non-destructive and environmentally sustainable approach for eggshell strength prediction. This innovative method holds significant potential for optimizing resource utilization and enhancing quality control in the egg industry.