Predicting Bordeaux red wine origins and vintages from raw gas chromatograms

Abstract

Connecting chemical properties to various wine characteristics is of great interest to the science of olfaction as well as the wine industry. We explored whether Bordeaux wine chemical identities and vintages (harvest year) can be inferred from a common and affordable chemical analysis, namely, a combination of gas chromatography (GC) and electron ionization mass spectrometry. Using 12 vintages (within the 1990–2007 range) from 7 estates of the Bordeaux region, we report that, remarkably, nonlinear dimensionality reduction techniques applied to raw gas chromatograms recover the geography of the Bordeaux region. Using machine learning, we found that we can not only recover the estate perfectly from gas chromatograms, but also the vintage with up to 50% accuracy. Interestingly, we observed that the entire chromatogram is informative with respect to geographic location and age, thus suggesting that the chemical identity of a wine is not defined by just a few molecules but is distributed over a large chemical spectrum. This study demonstrates the remarkable potential of GC analysis to explore fundamental questions about the origin and age of wine.

Aromatic Characterization of Graševina Wines from Slavonia and Podunavlje Sub-Regions

“…For instance, this approach has recently been used to characterize the regional aromatic signature of Australian Shiraz wines [15,16]. More recently, Schartner et al [17] managed to predict the origin of Bordeaux red wines from simple GC/MS data with the help of a Machine Learning tool. In the case of Graševina wines from northeastern Croatia, as shown in Figure 6, it seems that the Kutjevo district in the Slavonia sub-region is more prone to favor higher In the case of Graševina wines from northeastern Croatia, as shown in Figure 6, it seems that the Kutjevo district in the Slavonia sub-region is more prone to favor higher levels of terpenoids.…”

Deep reinforcement learning classification of sparkling wines based on ICP-MS and DOSY NMR spectra

“…Deep learning using artificial neural networks (ANN) is increasingly used in the field of NMR with significant growth in the last few years ( Cobas, 2020 ). Different algorithms are applied for classification or regression problems ( Ibrahim et al, 2021 , Schartner et al, 2023 , Wang et al, 2020 ). The aim of this paper was to trace the geographical origin by using the deep learning classification of sparkling wines based on their ICP-MS and DOSY NMR spectra represented in the reduced space.…”

Authenticity Markers of Aged Red Wines from Aglianico, Uva di Troia, Negroamaro and Primitivo Grapes

“…Despite the intense research efforts, the availability of suitable approaches to guarantee wine authenticity is still one of the most important issues in the oenological field [ 1 , 2 , 3 ].…”