Development, validation and application of a fast UHPLC-HRMS method for the analysis of amino acids and biogenic amines in wines and musts.

The wine tourism industry has experienced significant transformation over the past years, accelerated by the COVID-19 pandemic.

Vine water status is a crucial determinant of vine growth, productivity, fruit composition and terroir or wine style; therefore, regulating water stress is of great importance. Since vine water status depends on both soil moisture and aerial environment and is very temporally dynamic, direct measurement of vine water potential is highly preferable. Current methods only provide limited data. To regulate vine water status it is critical to monitor vine water status to be able to: (1) measure vine water status to predict the effect of water stress on the overall vineyard performance and fruit quality and optimize harvest management and wine-making (2) properly regulate the water status to impose for a desired fruit quality or style (3) determine if water management has reached the desired stress level.

Wine fraud, encompassing counterfeiting and adulteration, poses a significant threat to the wine industry, resulting in annual losses totalling billions of dollars.

Vineyard exposure to smoke can lead to grapes and wine which exhibit objectionable smoky and ashy aromas and flavours, more commonly known as ‘smoke taint’ [1, 2]. In the last decade, significant bushfires have occurred around the world, including near wine regions in Australia, Canada, South Africa and the USA, as a consequence of the warmer, drier conditions associated with climate change. Considerable research has subsequently been undertaken to determine the chemical, sensory and physiological consequences of grapevine exposure to smoke. The sensory attributes associated with smoke-tainted wine have been linked to the presence of several smoke-derived volatile phenols, such as guaiacols, syringols and cresols [2].

The evaluation of new grapevine genotypes regarding their potential to produce high quality wines is the time limiting factor in the process of grapevine breeding. Hence, the development of quality-related markers useable in marker-assisted selection (MAS) as well as in prediction models for this bottleneck trait will tremendously enhance breeding efficiency. In extensive studies a training set of a segregating white wine F1 population (150 F1 genotypes = POP150; `Calardis Musqué´ x `Villard Blanc´) was deeply phenotyped and genotyped for model development and QTL analysis.