The early detection of bruises in apples was studied using a system that included hyperspectral cameras equipped with sensors working in the visible and near-infrared (400-1000 nm), short wavelength infrared (1000-2500 nm) and thermal imaging camera in mid-wavelength infrared (3500-5000 nm) ranges. The principal components analysis (PCA) and minimum noise fraction (MNF) analyses of the images that were captured in particular ranges made it possible to distinguish between areas with defects in the tissue and the sound ones. The fast Fourier analysis of the image sequences after pulse heating of the fruit surface provided additional information not only about the position of the area of damaged tissue but also about its depth. The comparison of the results obtained with supervised classification methods, including soft independent modelling of class analogy (SIMCA), linear discriminant analysis (LDA) and support vector machines (SVM) confirmed that broad spectrum range (400-5000 nm) of fruit surface imaging can improve the detection of early bruises. with varying depths. (C) 2012 Elsevier Ltd. All rights reserved.