This thesis focuses on developing a nondestructive strategy for measuring the quality of food using hyperspectral imaging. The specific focus is to develop a classification methodology for detecting bruised/unbruised areas in hyperspectral images of fruits such as strawberries through the classification of pixels containing the edible portion of the fruit. A multiband segmentation algorithm is formulated to generate a mask for extracting the edible pixels from each band in a hypercube. A key feature of the segmentation algorithm is that it makes no prior assumptions for selecting the bands involved in the segmentation. Consequently, different bands may be selected for different hypercubes to accommodate the intra-hypercube variations. Gaussian univariate classifiers are implemented to classify the bruised-unbruised pixels in each band and it is shown that many band classifiers yield 100% classification accuracies. Furthermore, it is shown that the bands that contain the most useful discriminatory information for classifying bruised-unbruised pixels can be identified from the classification results. The strategy developed in this study will facilitate the design of fruit sorting systems using NIR cameras with selected bands.