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. (C) 2015 Elsevier Ltd. All rights reserved.