UV-Visible spectroscopy in conjunction with chemometrics, was successfully applied to objectively differentiate sparkling wine press juice fractions of Pinot noir. Two measurements methods were applied: reflectance using a fibre optic probe in-line and transmission using a benchtop spectrophotometer.
Phenolic ripening represents a major interest for quality wine producers. Nevertheless, climatic or genotypical limitations can often prevent optimal maturation process. During winemaking seeds can be easily separated and technologically processed to improve their quality.
Vineyard yield forecasting is a key issue for vintage scheduling and optimization of winemaking operations. High errors in yield forecasting can be found in the wine industry, mainly due to the high spatial variability in vineyards, strong dependency on historical yield data, insufficient use of agroclimatic data and inadequate sampling methods
Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.
Within-field variability can be managed through Precision Viticulture (PV) protocols aiming at identifying homogeneous zones and addressing site-specific operations including selective harvesting (SH). Several authors demonstrated SH profitability in extensive viticulture while few information is available within the Italian context.