Abstract
The aim of this study was to develop an improved method for detecting internal intrusions (pits and their fragments) in cherries using hyperspectral imaging technique in transmittance mode. Hyperspectral transmission images of pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content (SSC), were acquired in the visible and near-infrared (VNIR) range (450–1000 nm). The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the hyperspectral data were used to construct the supervised classification models. From all the studied classifiers, the best prediction accuracies for whole pit or pit fragment detection were obtained by the backpropagation neural network (BNN) model (94.6% of correctly classified instances for fresh samples and 83.3% for frozen samples). The accuracy of distinguishing between drilled and intact cherries was higher than 87% jointly for fresh and frozen cherries. These results showed that hyperspectral imaging in transmittance mode is an accurate and objective tool for pit detection in fresh and frozen cherries and can be applied in on-line sorting systems.
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