Fast exploration and classification of large hyperspectral image datasets for early bruise detection on apples

Hyperspectral imaging allows to easily acquire tens of thousands of spectra for a single sample in few seconds; though valuable, this data-richness poses many problems due to the difficulty of handling a representative amount of samples altogether. For this reason, we recently proposed an approach based on the idea of reducing each image into a one-dimensional signal, named hyperspectrogram, which accounts both for spatial and for spectral information. In this manner, a dataset of hyperspectral images can be easily and quickly converted into a set of signals (2D data matrix), which in turn can be analyzed using classical chemometric techniques. In this work, the hyperspectrograms obtained from a dataset of 800 NIR-hyperspectral images of two different apple varieties were used to discriminate bruised from sound apples using iPLS-DA as variable selection algorithm, which allowed to efficiently detect the presence of bruises. Moreover, the reconstruction as images of the selected variables confirmed that the automated procedure led to the exact identification of the spatial features related to the onset and to the subsequent evolution with time of the bruise defect.