Discrimination of white wines by Raman spectroscopy coupled with chemometric methods

Bushfires are a common occurrence throughout Australia and their incidence is predicted to both rise and increase in severity due to climate change. Many of these bushfires occur in areas close to wine regions, which receive different levels of exposure to smoke. Wine produced from smoke-affected grapes are characterised by unpalatable smoky aromas such as “burning rubber”, “smoked meats” and “burnt wood”. These smoke tainted wines are unprofitable and result in significant financial losses for winegrowers.

Cognac is a famous spirit produced in southwest France in the region of the eponymous town from wines mainly from Vitis vinifera cv. Ugni blanc. This variety gives very acidic and poorly aromatic base wines for distillation which are produced according to a very specific procedure. Grapes are picked at low sugar concentrations ranging 13-21 °Brix and musts with high turbidity (>500 NTU) are fermented without sulphite addition [1]. Fermentative aromas, as esters and higher alcohols, are currently the main quality markers considered in Cognac spirits.

the aim of this study was the optimization and validation of a robust and comprehensive method for the determination of volatile carbonyl compounds (VCCs) in wines

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.

Un paysage viticole est une relation entre des formes, dimension objective, et la perception que nous en avons, dimension subjective, émotionnelle. La viticulture n’est pas seulement productrice d’un vin, elle contribue également à façonner le paysage. Pourtant, jusqu’à présent, la connaissance des terroirs était principalement basée sur la caractérisation de leur aptitude à produire des vins de qualité.