Tracking the origin of Tempranillo Tinto through whole genome resequencing and high-throughput genotyping  

Grape seeds account for around 5% of the weight of the whole grape berry, representing approximately 40%-50% of the solid by-products that the different wine industries generate during the winemaking process.

Wine polysaccharides (PS) play an important role in balancing mouthfeel and stability of wine and even influence aroma volatility. Despite this, there is limited research into the effect of winemaking additives on the polysaccharide profile and other macromolecules of New Zealand (NZ) Pinot noir wine. In this study the influence of a selection of commercial S. cerevisiae strains on the chemical profile, including polysaccharides, of New Zealand Pinot noir (PN) wine was investigated. Research scale PN fermentations using five strains of commercially available S. cerevisiae (Lalvin EC1118 and RC212, Levuline BRG YSEO, Viallate Ferm R71 and R82) were undertaken. PS were qualified and quantified using HPLC-RID.

Two of the most harmful microbial metabolites for human health that can be present in wines and either fermented or raw foods are biogenic amines (BA) and ochratoxine A (OTA). Winemakers are aware of the need to avoid their presence in wine by using different strategies, one of them is the use of enzymes. Some recombinant laccases have been characterized and revealed as potential tools to degrade these toxic compounds in wine[1], specifically biogenic amines[2].

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.

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).