Changing the scale of characterization of a wine area: from a single protected designation of origin to a vineyard Loire Valley observatory (viLVO)

The wine spoilage yeast Brettanomyces bruxellensis can be found at several steps in the winemaking process due to its resistance to multiple stress conditions. Among the resistance strategies, one could be the formation of biofilm, a lifestyle known to enhance persistence of microorganisms. In this study, we propose to characterize biofilm of B. bruxellensis in wine, especially through several microscopic analyses.

Visual clarity in wines is crucial for commercial purposes [1]. Potential protein haze in white wines remains a constant concern in wineries, commonly addressed using bentonite [2].

Vineyard and grape variety are two popular ways of classifying wines. Vineyard designation is a traditional practice for European wine labels but is being increasingly replaced by grape variety designation, mainly used for New World and Swiss wine labels.

Synthetic pesticides are widely used in viticulture to ensure steady harvest quality and quantity. Fungicides are primarily used to control grapevine diseases but insecticides and herbicides are likewise used. Pesticide residues in viticultural areas currently represent a strong societal concern, but may also affect different trophic chains in such areas. In this project we wish to analyse honeybees collected from hives placed in different vineyards, their natural predator (the invasive hornet Vespa velutina), as well as the honey, grape berries, and wines produced.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.