The rising trend of moderate wine consumption as a part of a healthy lifestyle promotes white wines with higher phenolic content because of their bioactive properties. Duration and temperature of the maceration process have a marked impact on the content and composition of wine phenolics. The aim of this study was to explore the effect of applying maceration processes of different durations and temperature on total phenolic content and flavan-3-ol compounds concentration of Malvazija istarska (Vitis vinifera L.) wines, an autochthonous Croatian white grape variety. Vinification took place at the Institute of Agriculture and Tourism (Poreč) where pre-fermentative two days cryomaceration treatment at 8 °C (CRYO), seven days maceration treatment at 16 °C (M7), and prolonged post-fermentative maceration treatments at 16 °C for 14 days (M14), 21 day (M21), and 42 days (M42) were studied and compared to non-maceration control treatment (C). Total phenolic content was determined by the Folin-Ciocalteu colorimetric method using a UV/VIS spectrophotometer and the results were expressed as gallic acid equivalents (mg/L GAE).

Consumers predominantly use visual, aromatic and texture cues as quality/preference indicators to describe olfactory sensations. In this study, the effect of micro-organism in wine production was investigated using analytical and sensory techniques to achieve relevant analytical characterisation. Selected anthocyanins, flavan-3-ols, flavonols and phenolic acids were quantified in Syrah wines using RP-HPLC-DAD. Standard oenological parameters were also measured. Syrah grape must was fermented with various combinations of Saccharomyces cerevisiae (S. cerevisiae) and non-Saccharomyces (Metschnikowia pulcherrima or Hanseniaspora uvarum) yeasts, which was followed by sequential inoculation of lactic acid bacteria (LAB) (Oenococcus oeni or Lactobacillus plantarum).

Aim: Wine quality and character are defined in part by the terroir in which the grapes are grown. Metabolomic techniques, such as nuclear magnetic resonance (NMR) spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS), are used to characterise wines and to detect wine fraud in other countries but have not been extensively trialled in Australia. This study aimed to investigate the use of ICP-MS and NMR to characterise a selection of Pinot noir wines.

The methodology to viticulture zoning developed and proposed by Gómez-Miguel and Sotés (1992) has been studied in order to validate it. This was the main aim of this work

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65