The quality and freshness of eggs deteriorate during storage, impacting their role as a crucial dietary and industrial component. This study utilizes transmittance spectroscopy (200–1000 nm) to predict the S-ovalbumin content and to identify optimal wavelengths for assessing egg freshness levels. Three hundred fresh eggs were evaluated over 21 days of storage at ambient (25 ± 2 °C, 45 ± 5% R.H.) and refrigerated (5 ± 0.5 °C, 75 ± 5% R.H.) conditions by measuring quality indicators (Haugh unit, air cell height, yolk index, S-ovalbumin, and albumin/yolk pH). The samples were divided into several clusters using the fuzzy C-means algorithm based on these parameters. The changes in the S-ovalbumin concentration increased significantly (p < 0.01) across the storage conditions. Employing the first three principal components of preprocessed transmittance spectra as inputs to artificial neural networks enabled the discrimination of fresh from old eggs with 92% accuracy and the prediction of S-ovalbumin content with R = 0.93 and RMSE = 0.09. The light transmittance at wavelengths of 228 and 280 nm, as determined via the RELIEF algorithm, was used to classify the eggs into fresh/old (74.43% accuracy) and fresh/semi-fresh/old (72.85% accuracy) groups, demonstrating the efficacy of spectroscopy for non-destructive egg quality evaluation.
