Optimization of the acquisition of NIR spectrum in grape must and wine 

Recently, there has been a shift toward sustainable wine production, according to EU policy (F2F and Green Deal), to reduce pesticide usage, improve workplace health and safety, and prevent the impacts of climate change. These trends have gained the interest of consumers and winemakers. The cultivation of disease resistant hybrid grape cultivars (DRHGC), known as ‘PIWI’ grapes can help with these objectives [1]. This study aimed to profile white and red wines produced from DRHGC in South Tyrol (Italy). Wines produced from DRHGCs were compared with conventional wines produced by the same wineries. The measured parameters were residual sugars, organic acids, alcohol content, pigments and other phenolics by LC-QqQ/MS, colorimetric indexes (CIELab); and volatile profiles (HS-SPME-GCxGC-ToF/MS [2]).

The odors of wines are diverse, complex and dynamic and much research has been devoted to the understanding of their chemical bases. However, while the “basic” chemical part of the problem, namely the identity of the chemicals responsible for the different odor nuances, was satisfactorily solved years ago, there are some relevant questions precluding a clear understanding. These questions are related to the physicochemical interactions determining the effective volatilities of the odorants and, particularly, to the perceptual interactions between different odor molecules affecting in different ways to the final sensory outputs.

The increasingly frequent heat waves during grape ripening pose challenges for premium wine grape production. This makes the development of irrigation and canopy management techniques of great importance to maximize yield and grape quality. A field experiment was carried out during 2021 and 2022 using Manto negro wine grapes to study the effect of two irrigation strategies and different light exposure levels on grape quality.

Enzymatic browning[1] is an oxidation process that occurs in many foods that increases the brown colour[2]. This problem is especially harmful in the wine industry[3]. especially when the grapes are infected by grey rot since this fung release the oxidative enzyme laccase[4]. In the particular case of red wines, the presence of laccase implies the deterioration of the red colour and can even cause the precipitation of the coloring matter (oxidasic haze)[5].

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).