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Two different approaches, PLS regression and neural networks, were compared for monitoring the quality of grapes using sugar content predictions based on hyperspectral imaging. The present work expands the result analysis and updates the state-of-the-art published in a conference article of the authors which concern the prediction of sugar content for vintages not used in model creation when the measured...
The potential of hyperspectral imaging combined with machine learning algorithms to measure sugar content of whole grape berries is presented, as a starting point for developing generalized and flexible frameworks to estimate enological parameters in wine grape berries. In this context, to evaluate the generalization ability of the used machine learning procedure, two neural networks were trained...
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