Terroir is a holistic concept that relates to both environmental and cultural factors that together influence the grape growing to wine production continuum. The physical factors that influence the process include matching a given grape variety to its ideal climate along with optimum site characteristics of elevation, slope, aspect, and soil

The knowledge of the influence of soil conditions on the effects that different plant densities provoke in the agronomic grapevine behaviour becomes very interesting since it allows to focus the vineyard management on the optimization of the natural, hydric and human resources.

Modern agriculture employs large amounts of plastics, such as mulching and greenhouse films, thermal covers, plant protection tubes and tying tape. The latter two types are very common in viticulture. Guard tubes are employed to protect young vines from mechanic and atmospheric damage, whilst polymeric tying tape has replaced natural-origin materials to hold the canopy of vines. Both materials are made on synthetic polymers, which include a range of additives to improve their environmental stability remaining in the environment of vineyards for years. During this time, they are exposed to the range of pesticides (fungicides, insecticides and in a lesser extend herbicides) applied to vines.

Bottle ageing is a critical period for wine quality, as it undergoes various chemical and sensory changes during storage. Ideally, a phase of qualitative ageing, during which wine sensory quality improves, is followed by a decline of quality. Understanding how different oenological variables influence these phases is a key challenge in modern winemaking. Recent studies highlighted the significant role of oxygen in modulating reactions involving volatile and non-volatile components, impacting aroma evolution during bottle aging. Oxygen exposure of wine during bottle ageing is mediated by closure.

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.