NondestructivePrediction of Eggshell Thickness UsingNIR Spectroscopy and Machine Learning with Explainable AI

The eggshell protects internal contents, supplies calcium for embryos, and aids embryonic respiration. This study assessed near-infrared (NIR) spectroscopy and machine learning techniques with explainable artificial intelligence for measuring fast, accurate, and real-time eggshell thickness determination. Partial least-squares regression (PLSR), random forest, K-nearest neighbors, and support vector regression calibration models were developed at full wavelength (1300–2525 nm). The PLSR model demonstrated superior and stable predictive performance, achieving a coefficient of determination (<i>R</i>_p²) of 0.867, and root-mean-square error of prediction (RMSEP) of 0.015 mm. A new PLSR model using only five important variables selected by competitive adaptive reweighted sampling showed promising results with an <i>R</i>_p² of 0.910 and an RMSEP of 0.012 mm. The Shapley additive explanation of the CARS-PLSR model revealed that the wavelengths related to protein, moisture, and lipids are crucial for NIR spectroscopic prediction of eggshell thickness.