Study of wine-growing land (“terroir”) characteristics in the canton of Vaud (Switzerland): ecophysiological behaviour of the vine (cv. Chasselas)

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Effervescence and foaming properties are the main visual characteristics assessed by the consumer during
sparkling wine tasting.

AIM: To investigate chemical and sensory drivers of regional typicity of Cabernet Sauvignon from different geographical regions of Australia.

AIM: Cross-over applications are an emerging technological approach in food microbiology where a microorganism from one traditional specific fermentation process is used to improve quality and safety in another agri-food production/chain (Dank et al., 2021). A complex microbial diversity is found in association with fermentation in wine, including Saccharomyces, non-Saccharomyces and malolactic bacteria,  all microorganisms versatile in terms of enological utilisation (Tempère et al., 2018). Here, we propose a systematic literature review highlighting the existing trends and possible future applications related to cross-over exploitation of wine-related microbiota. 

Wine quality is defined by sensory and physico-chemical characteristics. In particular, sensory features are very important since they strongly condition wine acceptability by consumers. However, the evaluation of sensory quality can be subjective, unless performed by a tasting panel of experienced tasters. Therefore, it is of great relevance to establish relationships between objective chemical parameters and sensory perceptions, even though the complexity of wine composition makes it difficult. In this sense, more reliable relationships can be found for a particular wine typology or variety. The present study aimed to predict the perceived sensory quality from the physico-chemical parameters of ‘Barbera d’Asti’ DOCG red wines (Italy).