Assessment of relationships between grape chemical composition and grape allocation grade
A wide range of compounds that contribute to wine’s appearance, aroma, flavour and texture are derived from grapes. Many of these compounds are known and measurable, and can be manipulated through viticultural and/or winemaking practices. Around the world, many wine companies use grape chemical measures to assess grape value or style; however, this approach has not been implemented widely in Australia.
By measuring a range of chemical compounds in multiple grape batches of different grades, this project aims to determine which compounds, independently or in combination, can differentiate between grape grades. The objectives are to determine how variable the chemical measures are across a wide range of fruit grades, if there is a relationship with fruit grade and if fruit can be clustered based on similarity of chemical composition. A primary aim of the project is to assess the practical application of grape grading measurements and to support wine producers who intend to apply these measures in their systems. Potential impacts for industry include the ability for grapegrowers to more efficiently produce grapes to defined specifications, and for winemakers to select fruit with greater confidence that it will be appropriate for a targeted wine style.
Establishing practical markers of grape quality and their impacts on wine style
For Cabernet Sauvignon, Shiraz and Chardonnay grapes from 2014, moderate to good prediction of grape quality grades was possible using multivariate data analysis based on partial least squares (PLS) modelling of grape chemistry measurements and winery quality assessments. The important variables that were higher in higher grades varied between varieties, but included some amino acids, UV-Vis absorbance measures, tannin, precursors to the varietal thiol 3-MH and glycosylglucose. For all three varieties, predictive modelling using discriminant analysis was better than 85% correct in predicting grape quality grade using fruit chemistry data only. Discriminant analysis using UV-Vis spectra from grape extracts, MIR spectra from juice and NIR spectra from homogenates was able to predict grape quality grade with better than 90% accuracy. Clear clustering of grape samples by region was observed for all samples from the Riverland, McLaren Vale and Margaret River when the UV-VIS spectral scans of fruit extracts were used, which indicates spectroscopy may be useful for fruit streaming purposes.
In 2015 fruit batches for each of four style categories for each of Shiraz and Chardonnay were sourced from multiple vineyards and the trial was extended beyond the assessment of grape chemical composition to also include winemaking. The aim of this extension is to establish the relevance and predictive power of the identified grape compounds to forecast grape quality and wine style categories.