Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis

•PLSR and SVR models were compared to predict egg freshness.•PCA combined with GLCM method was developed to detect egg’s internal bubbles.•An image segment algorithm was developed to identify eggs with scattered yolk.•The eggs internal qualities were inspected comprehensively.

The study develops a nondestructive test based on hyperspectral imaging using a combination of existing analytical techniques to determine the internal quality of eggs, including freshness, bubble formation or scattered yolk. Successive projections algorithm (SPA) combined with support vector regression established a freshness detection model, which achieved a determination coefficient of 0.87, a root mean squared error of 4.01%, and the ratio of prediction to deviation of 2.80 in the validation set. In addition, eggs with internal bubbles and scattered yolk could be discriminated by support vector classification (SVC) model with identification accuracy of 90.0% and 96.3% respectively. Our findings suggest that hyperspectral imaging can be useful to non-destructively and rapidly assess egg internal quality.