Conventional and alternative pest management strategies: a comparative proteomic study on musts

The wooden barrel is a key factor in enology, since wine chemical composition and sensory properties changes significantly in contact with the barrel[1]. Today’s highly competitive market constantly demands new differentiated products and wineries search innovations continuously.
Wood selection is crucial: barrels stability to keep constant their contribution and the result on products, and additional and differentiated wood contributions to impact their new products. Oak wood selection has traditionally been carried out using parameters such as specie, location and grain, however, it goes one step further nowadays. Large cooperage work with non-destructive techniques that allow classifying oak wood quickly and easily according to their organoleptic contribution[2].

Many works have been carried out to identify the key aroma volatile compounds of oak wood (e.g., whisky-lactone, furfural, maltol, eugenol, guaiacol, vanillin) using conventional gas chromatography coupled with olfactometry and mass spectrometry (GC-O-MS). Inspired by recent untargeted approaches in the field of food “omics”, this work aims to extend our knowledge on the impact of cooperage process on the volatile composition of oak wood using two-dimensional comprehensive gas chromatography coupled with time of flight mass spectrometry (GCxGC-ToFMS).

In Chile there is a dry farming area known as a traditional wine region, where varieties brought by the Spanish conquerors still persist. These varieties, in general, are cultivated under traditional systems, with low use of technical and economic resources, and low profitability for their grapes and wines. In this region, as in other wine grape growing areas, climatic conditions have changed significantly in recent decades. In particular, the occurrence of spring frosts, when bud break has already begun, have generated significant losses for these growers.

The presence of acetic acid bacteria in wine can lead to the appearance of acetic acid at concentrations above the perception threshold, causing the wine rejection by the consumer. During the winemaking process, avoiding the presence of acetic acid bacteria is very difficult, as there is always a residual population accompanying the wine[1], and the problem arises with the significant development of these microorganisms that metabolizes large amounts of acetic acid.
The concern of wineries to control the presence of acetic acid bacteria in wines during their conservation is due to the absence of simple and effective analyses that allow the detection of these microorganisms in the initial stages.

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).