In this study, we investigated the relationship between (D/H)1, (D/H)2 and δ(13)C of ethanol and δ(18)O of water in wine, and variables describing the climate and the geography of the production area, using exploratory visualisation tools, regression analysis and linear modelling. For the first time, a large amount of data (around 4000 wine samples collected over 11 years in Italy) and all the official isotopic parameters, as well as a large number of significant climatic and geographical descriptors (date of harvest, latitude, longitude, elevation, distance from the sea, amount of precipitation, maximum daily temperature, minimum daily temperature, mean daily temperature, δ(18)O and δ(2)H of precipitation) were considered. δ(18)O, followed by (D/H)1, was shown to have the strongest relationship with climate and location. The dominant variables were latitude, with a negative relationship, δ(18)O and δ(2)H of precipitation and temperature, both with positive relationships. The identified correlations and models could be used to predict the isotopic composition of authentic wines, offering increased possibilities for detecting fraud and mislabelling.
Keywords: Climate; Geography; Isotope ratio mass spectrometry; Mislabelling; Prediction; Wine.
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