«Aztec» – the new white table grape resistant variety

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.

A novel and efficient Dispersive Liquid-Liquid Microextraction (DLLME) method coupled with gas chromatography–mass spectrometry (GC–MS) was developed to determine 33 key aroma compounds (esters, alcohols, aldehydes, terpenes, norisoprenoids, fatty acids and phenols) present in Pinot noir (PN) wine. Four critical parameters including extraction solvent type, disperse solvent type, extraction solvent volume and disperse solvent volume were optimised with the aid of D-optimal design.

Fossil fuel combustion continues to drive increases in atmospheric carbon dioxide, consequently elevating the global annual mean temperature and specifically increasing the growing season temperatures in many of the world’s most important wine growing regions (IPCC 2014; Jones et al 2005). Grapes are sensitive to changes in growing season temperatures, and past models have shown a direct link between warming temperatures and earlier harvest dates (Cook and Wolkovich 2016). Globally, there have been shifts of 1-2 weeks for wine growing regions (Wolkovich et al 2017 and references within). The phenological shifts resulting from growing season temperature increases are documented internationally, and models predicting phenology using temperature are becoming more precise (Parker et al 2011).

Aim: This study aimed to isolate Oenococcus oeni in red wines from Yinchuan wine region in the East of Helan Mountain, China, and analysis their genetic diversity.

Methods and Results: Oenococcus oeni strains were isolated from Cabernet Sauvignon and Cabernet Gernischt wines of four

Amarone is an Italian red wine produced in the Valpolicella area, in north-eastern Italy. Due to its elaboration with withered grapes, Amarone is a rather unique example of dry red wine. However, there is very limited data so far concerning the volatile composition of commercial Amarone wines, which also undergo a cask aging of 2-4 years before release. The present work aims at characterizing the aroma composition of Amarone and to elucidate the relationships between chemical composition and sensory characters. The analysis of 17 Amarone commercial wines from the same vintage (2015) was carried out by means of Gas Chromatography-Mass Spectrometry (GC-MS) and extracted by Solid Phase Extraction (SPE) and Solid Phase Micro Extraction (SPME). In addition, the sampled wines were subjected to a sensory evaluation in the form of sorting task.RESULTS: 70 volatile compounds were successfully identified and quantified, 30 of which were present in concentrations above their odor thresholds in all the samples. Using the odor activity value (OAV), the compounds that potentially contribute to Amarone perceived aroma are b-damascenone, ethyl and isoamyl acetate, ethyl esters (hexanoate, octanoate, butanoate, 3-methybutanoate), 4-ethyl guaiacol, 3-methylbutanoic acid, dimethyl sulfide (DMS), eugenol, massoia lactone, 1,4-cineol, TDN, cis/trans-whisky lactone. In certain samples, high OAVs were also observed for 4-ethyl phenol and 1,8-cineole.Results from the sorting task sensory analysis showed three clusters formed.