ASSESSMENT OF GRAPE QUALITY THROUGH THE MONITORING OFPHENOLIC RIPENESS AND THE APPLICATION OF A NEW RAPID METHOD BASED ON RAMAN SPECTROSCOPY
The chemical composition of grape berries at harvest is one of the key aspects influencing wine quality and depends mainly on the ripeness level of grapes. Climate change affects this trait, unbalancing technological and phenolic ripeness, and this further raises the need for a fast determination of the grape maturity in order to quickly and efficiently determine the optimal time for harvesting. To this end, the characterization of variety-specific ripening curves and the development of new and rapid methods for determining grape ripeness are of key importance.
As part of this ongoing project, 35 vineyards (26 cv. Nebbiolo, 9 cv. Barbera) from Langhe, Roero, and Monferrato terroirs (Piemonte, Italy) were monitored during two consecutive vintages (2021-2022). The Nebbiolo vineyards were further classified, based on historical data, into ripening classes according to the harvest period estimation (early, medium, and late Nebbiolo). To study the evolution of grape ripening, four grape samples were taken from each vineyard during the ripening period (mid-August – late September), and grape quality assessment was performed by means of parameters commonly used in wine industry: juice technological maturity and phenolic ripeness parameters (total and extractable anthocyanins-EA%, share of tannins from seeds-Mp%). Preliminary results showed differences among cultivars and ripening classes, with a strong influence of the climatic conditions of the vintage, being both hot vintages with a strong water deficit (and decrease in berry weights and anthocyanin accumulation) for the 2022 vintage.
To have a more in-depth insight into the phenolic changes of the grapes during ripening, total extractions of the skins and seeds phenolics were carried out to better characterize the composition of Nebbiolo and Barbera berries. Lastly, this data was used to train a new approach based on Raman spectroscopy (RS), in an attempt to develop a method for the rapid determination of berry quality. At each sampling point, the acquisition of the grape Raman spectra was carried out in parallel with the other chemical analyses, developing a prediction model by correlating technological and phenolic ripening parameters with RS results.
Acknowledgments: The QUALSHELL project is funded by the PSR 2014-2020 Regione Piemonte (Italy), op. 16.1, European Agricultural Fund for Rural Development. We thank Martina Tarditi, Daniele Ronco, Alessandro Bottallo and the wineries supplying grape samples.
Issue: OENO Macrowine 2023
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Grape quality, Phenolic ripeness, Anthocyanins, Red wines