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Reducing heterogeneity in harvest material would be beneficial for wine quality and this goal may be achieved through advanced berry sorting systems. The general aim was to assess if a relationship could be found between sugar concentration and hyperspectral images. Grapes were picked at different stages of maturity and the berries were sorted according to their size and density. Hyperspectral images of the berry subsamples were obtained in the vis/NIR wavelength range with a complete spectrum from 400 nm to 2500 nm. Our results showed that vis/NIR can be used to improve the segregation of berries from all tested grape varieties based on their sugar concentration. All berries from all 12 grape varieties were used to train the regression model and the predictive power was tested on all each grape variety separately, while later validated on each variety separately, proving the possibility of using a general regression model with constant parameters to predict sugar concentration. Finally, the impact on quality was tested for red wines. Pinot noir berries with higher sugar concentrations presented more color since anthocyanin concentration was higher. Nevertheless, tannin concentration in skins and seeds tended to decrease. Berries with higher sugar concentration resulted in wines with higher anthocyanin and lower tannin concentration.

Non-destructive determination of grape berry sugar concentration using visible/near infrared imaging and possible impact on wine quality