Non-destructive inspection method for egg freshness evaluation via low-field nuclear magnetic resonance technology

Eggs are highly valued for their exceptional nutritional benefits. However, prolonged storage can diminish their freshness. Traditional egg freshness assessment methods are time-consuming and require destructive tests. This study introduced a non-destructive method using Low-Field Nuclear Magnetic Resonance (LF-NMR) technology. Transverse relaxation time (T2) distribution curves revealed three distinct water phases in eggs: bound, immobile, and free water. According to correlation analysis, the peak areas and relaxation times in the T2 distribution curves had a strong relationship with the overall egg freshness indicators. Notably, the relationship between the peak areas and the relaxation periods with the thick albumen height was the most powerful, reaching up to 0.917 and 0.929, respectively. Egg freshness was evaluated using the Back Propagation Neural Network (BPNN), Partial Least Square Regression (PLSR), and Multiple Linear Regression (MLR), with the highest prediction accuracy observed with the Haugh unit. The accuracies were 3.931% for MLR, 3.927% for PLSR, and 3.673% for BPNN. The BPNN model, in particular, showed superior performance in predicting the Haugh unit, with an R² of 0.874. Furthermore, egg grades were classified based on Haugh unit values, and the BPNN model showed the highest identification rate (93.233%) and prediction accuracy (86.466%), outperforming both MLR and PLSR. This study confirmed the reliability of T2 distribution curves obtained from LF-NMR for non-destructive egg freshness detection.