Mannoprotein extracts from wine lees: characterization and impact on wine properties

Annually, around 31.95 million tonnes of grapevine prunings are produced worldwide as agricultural waste.

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.

The development of new generations of multispectral satellites such as Sentinel-2 opens possibilities as to vine water status assessment (Cohen et al., 2019). Based on a three years field campaign, a model of Stem Water Potential (SWP) estimation on vine using four satellite bands in Red, Red-Edge, NIR and SWIR domains was developed (Laroche-Pinel et al., 2021). The model relies on SWP field measures done using a pressure chamber (Scholander et al., 1965), which is a common, robust and precise method to assess vine water status (Acevedo-Opazo et al., 2008). The model was mainly developed from from SWP measures on Syrah N (Laroche Pinel E., 2021).

A large scale monitoring was organized in different vineyards in the Mediterranean region in 2021. 10 varieties amongst the most represented in this area were monitored (Cabernet sauvignon N, Chardonnay B, Cinsault N, Grenache N, Merlot N, Mourvèdre N, Sauvignon B, Syrah N, Vermentino B, Viognier B). The model was used to produce water status maps from Sentinel-2 images, starting from the beginning of June (fruit set) up to September (harvest). The average estimated SWP for each vine was compared to actual field SWP measures done by wine growers or technicians during usual monitoring of irrigation programs. The correlations between mean estimated SWP and mean measured SWP were at the same level than expected by the model. (Laroche Pinel, 2021) The general SWP kinetics were comparable. The estimated SWP would have led to same irrigation decisions concerning the date of first irrigation in comparison with measured SWP.

Acevedo-Opazo, C., Tisseyre, B., Ojeda, H., Ortega-Farias, S., Guillaume, S. (2008). Is it possible to assess the spatial variability of vine water status? OENO One, 42(4), 203.
Cohen, Y., Gogumalla, P., Bahat, I., Netzer, Y., Ben-Gal, A., Lenski, I., … Helman, D. (2019). Can time series of multispectral satellite images be used to estimate stem water potential in vineyards? In Precision agriculture ’19, The Netherlands: Wageningen Academic Publishers, pp. 445–451.
Laroche-Pinel, E., Duthoit, S., Albughdadi, M., Costard, A. D., Rousseau, J., Chéret, V., & Clenet, H. (2021). Towards vine water status monitoring on a large scale using sentinel-2 images. remote sensing, 13(9), 1837.
Laroche-Pinel,E. (2021). Suivi du statut hydrique de la vigne par télédétection hyper et multispectrale. Thèse INP Toulouse, France.
Scholander, P.F., Bradstreet, E.D., Hemmingsen, E.A., & Hammel, H.T. (1965). Sap pressure in vascular plants: Negative hydrostatic pressure can be measured in plants. Science, 148(3668), 339–346.

Maturation of wine on lees (often referred as sur lie) is a common practice applied by many winemakers around the world. In the past this method was applied mainly on white and/or sparkling wine production but recently also to red wine production. In our experiment, we matured red wine on wine lees of two origins: a) Light wine lees, collected after the completion of the alcoholic fermentation, b) Heavy lees, collected after the completion of the malolactic fermentation. The lees were free of off-odors and were added in the red wine in percentage 3% and 8%, simulating common winemaking addition. The maturation lasted in total six months and samples were collected for analysis after one, three and six months. During storage the lees were stirred.

The objective of this study is to review the scientific evidences and to advance into the knowledge of the molecular mechanisms of astringency. Astringency has been described as the drying, roughing and puckering sensation perceived when some food and beverages are tasted (1). The main, but possibly not the only, mechanism for the astringency is the precipitation of salivary proteins (2,3). Between phenolic compounds found in red wines, flavan-3-ols are the group usually related to the development of this sensation. Other compounds, phenolic or not, like anthocyanins, polysaccharides and mannoproteins could act modifying or modulating astringency perception by hindering the interaction between flavanols and salivary proteins either because of their interaction with the flavanols or because of their interaction with the salivary proteins.