The Australian geographical indication process

This study aims at exploiting an undervalued winemaking by-product, wine yeast lees, by developing efficient and food-grade methods for the extraction of yeast glycoproteins. These extracts were then supplemented to wine and their impact on wine properties assessed.

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.

Full understanding of grapevine responses to variable soil resources requires
assessing the grapevine root system. Grapevine root systems are expansive and examining deep roots (i.e., >40 cm)
is particularly important in conditions where grapevines increase reliance on deep soil resources, such as drought
or plant competition. Traditional methods of assessing roots rely on morphological traits associated specific
functions (e.g., root color, diameter, length), while recent methodological advances allow for estimating root
function more directly (e.g., omics). Yet, the potential of applying refined methods remains underexplored for roots
at deep depths.

Must clarification is an important step occurring just after grape extraction in the elaboration of white wine, consisting in a solid-liquid separation. Traditionally, low must turbidity, around 50-150 NTU, is generally reached in white winemaking in order to prevent reductive aromas and facilitating alcoholic fermentation. Alternatively, a higher turbidity (300 NTU or above) can be sought for reasons such as a better expression of grapes identity (terroir), or for getting a must matrix that could supposedly lead to wines having greater ageing potential.

The professional winegrower usually selects the harvest date considering several elements, such as the vine stem and berry colour, the flavour, appearance and grain elasticity. Nowadays these elements have turned old fashioned.