Using Elemental Analysis For Discrimination Of Pinot Noir Wines From Six Different Districts In An Ava
Poster Dec 05, 2017
Helene Hopfer, Courtney Tanabe, Joshua Godshaw, Susan E. Ebeler, Jenny Nelson, Roger B. Boulton.
For U.S. wine consumers place of origin on a region, county and state level are very important decision criteria for wine purchase. Wine consumers associate information about the wine region with higher quality, and they are willing to pay premium prices for wines from well-known regions. The determination of geographical origin of wine is gaining increased interest by researchers and federal agencies around the world, partially due to increased fraud with regards to place of origin labelling. For wine, multi-elemental profiling of macro, micro, and trace elements has been proposed for determination of authenticity.
To successfully determine the geographical authenticity of wine, one needs to
i. understand the variability in elemental concentrations and ratios within and across countries, states, regions and sub-regions.
ii. connect results from controlled studies to commercial real world practices.
iii. study how cultivars and/or wine styles impact the elemental fingerprint.
Past studies looked at elemental differences between countries and wine regions, however, limited information is available for elemental differences of wines made from the same cultivar and coming from within one wine region under commercial practices.
Commercial wines from different wineries in 5 different neighborhoods within one AVA show characteristic elemental fingerprints. Despite different viticultural and enological practices wines group by neighborhood. Macro, micro and trace elements as well as elemental ratios contribute to the observed separation, indicating the involvement of multiple factors and underlying mechanisms, including location and soil composition, elemental uptake by vine and rootstock, viticulture and nutrient management, water sources, and small differences in the different wineries. Ongoing research is looking into soil composition, water sources and scion-rootstock information.